1
Random Access Schemes for Satellite Networks: from VSAT to M2M - A Survey Riccardo De Gaudenzi, Oscar del R´ıo Herrero, Gennaro Gallinaro, Stefano Cioni, Pantelis-Daniel Arapoglou
Abstract In this survey paper we review the Random Access (RA) techniques with particular emphasis on the issues and the possible solutions applicable to satellite networks. RA dates back to the 1970’s when the ALOHA protocol was developed to solve the problem of interconnecting university computers located in different Hawaiian islands. Since then, several evolutions of the ALOHA protocol have been developed. In particular, solutions were devised to mitigate the problem of packet collisions severely degrading the RA protocols performance. The approach followed for many years has been to avoid the occurrence of collisions rather than solving them. More recently, techniques tackling the RA packet collision problem have appeared triggered by the need of improving RA performance in satellite and terrestrial wireless networks. In particular, satellite networks large propagation delay does not allow the adoption of enhanced terrestrial RA techniques based on channel sensing. Adopting conventional demand assignment multiple access protocols is not suitable for supporting a large number of sensors or devices transmitting small size low duty cycle packets as required for Machine-to-Machine (M2M) communications. This provided the stimulus to exploit Successive Interference Cancellation (SIC) schemes to solve packet collision issues. The use of SIC in RA is relatively new and has opened up a promising research area. We provide an extensive review of recent high performance RA techniques achieving more than three orders of magnitude throughput increase compared to the original ALOHA at low packet loss rate. In this survey we cover both slotted and unslotted techniques. Finally, we review the use of RA in satellite systems and related standards including recent proposals for M2M applications.
Index Terms Satellite Communications, Random Access, Multiple Access, Machine-to-Machine Communications, Multi-User Detection
Corresponding Author: R. De Gaudenzi, European Space Agency, Keplerlaan 1, 2200 AG Noordwijk, The Netherlands. e-mail: {
[email protected]}
2
I. I NTRODUCTION This survey aims to review recent advances in the field of RA for satellite networks. Until recently, the use of RA in satellite networks was limited to initial logon or to low-efficiency messaging systems. This is because the large propagation delay of the satellite channel as well as its noise limited nature was making the adoption of more efficient RA schemes not viable. These known satellite features have recently triggered the development of some unconventional solutions to make RA protocols able to efficiently cope with collisions. M2M is defined as data communication among devices without the need for human interaction. This may be data communication between devices and a server, or device-to-device either directly or over a network. Examples of M2M services include security, tracking, payment, smart grid and remote devices maintenance/monitoring [1], [2]. The support to M2M applications by satellite networks is important for complementing the terrestrial infrastructure to provide ubiquitous coverage. However, this requires the exploitation of access techniques at physical layer providing high spectral, power and energy efficiency with minimum overhead in terms of signalling and network synchronization. A similar collision resolution protocols trend is also emerging in terrestrial wireless applications. We believe that some of the new RA techniques devised for satellite networks may also find application in the growing market of terrestrial M2M networks. In the early years of satellite communications key applications were point-to-point trunk connections aggregating traffic from large geographical areas (mostly voice and television). As a consequence, multiple access to the satellite transponder was implemented by fixed Frequency Division Multiple Access (FDMA) assignments to each Earth station [3]. Very Small Aperture Terminals (VSAT) professional satellite networks appeared in the 1980s. VSAT networks used bidirectional communications and were typically characterized by a star topology, where VSATs communicate to a larger master Earth station (called the hub). In some circumstances, a VSAT can also communicate directly to other VSAT when operating in mesh configuration [4]–[6]. The inbound channel (multipoint-to-point) typically consists of several lower bit rate carriers operating in FDMA or Multi-Frequency Time Division Multiple Access (MF-TDMA) with digitally modulated signals [7]. Only few satellite networks were implemented using Code Division Multiple Access (CDMA). The most popular examples of CDMA technology adoption are represented by the OmniTRACSr messaging system [8] and the Globalstar global personal
3
mobile satellite communication system [9]. With a medium to low level of traffic aggregation on each VSAT terminal, traffic was no longer deterministic and constant like in the point-to-point trunk connections, but rather bursty and with a low duty cycle. As a result, it was no longer efficient to use fixed resource assignments such as FDMA or TDMA in the inbound channel. The arrival of the VSAT and mobile satellite networks, triggered the need for new multiple access protocols able to efficiently share the satellite communication resources among all terminals, while maintaining acceptable throughout, loss and delay performances. The satellite environment is characterized by radio link power limitations and a large propagation delay, amongst other inherent attributes (e.g. non-linearities). As discussed in Sect. II, the large propagation delay, in the range of 250 ms for a geostationary satellite, represents a very peculiar property of this type of network, which conditions the applicability of terrestrial multiple access schemes to the satellite environment. In this channel, the propagation delay is much larger than the time taken to transmit a packet, and a sender may have sent several packets before the receiver starts receiving (or not in case of collisions) the first packet. Satellite multiple access schemes must be able to deal with all these aspects. The services initially supported by VSAT networks were mainly voice calls or data circuitswitched communications, thus the network resources were allocated for a relatively long time compared to the physical layer frame duration. As a consequence, the system was designed to dynamically allocate the available resources (e.g. frequency sub-bands, frame time slots or spreading codes) according to the request occurring on a contention channel. The RA used for the contention channel was just representing a tiny part of the overall system traffic. Thus, its efficiency was not a major concern for satellite network designers. In recent years, the traffic evolution towards packet oriented Internet Protocol (IP) and the emergence of M2M applications, has been generating new challenges. In this new landscape, both satellite and terrestrial networks share the same need to efficiently cope with the low-duty cycle traffic generated by a much larger population of small size, low-power and low-cost terminals. Classical VSAT solutions for resource allocation based on Demand Assignment (DA) are clearly not adequate for this type of traffic. This issue has triggered research for more efficient satellite RA schemes. In parallel, terrestrial wireless networks are also facing the challenge of efficiently supporting M2M type of traffic with several orders of magnitude increase for the population of supported terminals by each base station [10]. We can conclude that both application domains
4
are looking for reliable, highly spectral, power and energy efficient RA schemes. The following survey will cover past and recent RA schemes with emphasis on physical and Medium Access Control (MAC) aspects. The more conventional schemes reviewed are still in use and thus, relevant for the survey. However, while reviewing them their main limitations will be outlined to justify the search for more suitable solutions described in the following sections. The review is also covering terrestrial RA schemes to illustrate their possible issues when applied to satellite. The survey of RA schemes research is split according to their key physical/MAC layer protocol features, i.e. slotted vs. unslotted, interference cancellation vs. non-interference cancellation, and spread-spectrum vs. non spread spectrum multiple access schemes (see Fig. 1). Slotted protocols require that the physical layer packet emitted by each terminal is aligned to a common network frame and slot timing structure. Instead, in case of unslotted RA, the packet can be asynchronously transmitted by each terminal. In the following, we will use both terms unslotted and asynchronous terms interchangeably. The rest of the paper is organized as follows: Section II deals with multiple access in satellite networks and provides a review of the key terrestrial RA techniques and their applicability to satellite networks; Section III illustrates the more recent advances in RA for slotted access systems both spread and non-spread spectrum variants; Section IV summarizes advanced unslotted spread-spectrum RA techniques; Section V is dealing with unslotted non spread-spectrum high performance RA solutions; Section VI provides an overview of satellite systems exploiting RA techniques and the corresponding standards. Finally in Section VII conclusions are provided. II. T HE S ATELLITE M ULTIPLE ACCESS P ROBLEM AND I NITIAL R ANDOM ACCESS S OLUTIONS A. The Satellite Multiple Access Problem 1) Initial Solutions: As mentioned in the introduction, during the 1980’s and 1990’s numerous multiple access protocols were developed [5], [6], [11]–[16]. They are all based on combinations of RA (unslotted or slotted) with fixed-allocation TDMA, demand-assigned TDMA or CDMA. The available inbound bandwidth resources are shared employing FDMA, TDMA, MF-TDMA or CDMA [7]. As opposed to the purely fixed assignment multiple access techniques, the protocols in [5], [6], [11]–[16] combined DA, RA and fixed assignment to dynamically control the access to the shared resources by the large population of contending users. These protocols typically
5
Random Access (RA) Techniques for Satellite Networks
Slotted Random Access
Conventional RA Protocols (Sect. II): · Slotted ALOHA · Diversity Slotted ALOHA
IC-based RA Protocols (Sect. III): · Contention Resolution Diversity Slotted ALOHA (CRDSA) · Sliding-Window-Based CRDSA (SW-CRDSA) · Multi-Frequency CRDSA (MF-CRDSA) · Multi-Frequency Spread-Spectrum CRDSA (MF-SS-CRDSA) · Irregular Repetition Slotted ALOHA (IRSA) · Multi-Slots Coded ALOHA (MuSCA) · Coded Slotted ALOHA (CSA) · Network-Coded Diversity Protocol (NCDP)
Unslotted Random Access
Conventional RA Protocols (Sect. II): · ALOHA · Spread Spectrum ALOHA
Spread-Spectrum IC-based RA Protocols (Sect. IV): · Enhanced Spread-Spectrum ALOHA (E-SSA) · Minimum Mean Square Error plus E-SSA (ME-SSA)
Non Spread-Spectrum IC-based RA Protocols (Sect. V): · Contention Resolution ALOHA (CRA) · Enhanced Contention Resolution ALOHA (ECRA) · Asynchronous Contention Resolution Diversity ALOHA (ACRDA) · Multi-Frequency Asynchronous Contention Resolution Diversity ALOHA (MF-ACRDA)
Fig. 1.
Classification of Satellite RA Techniques considered in this review. IC stands for Interference Cancellation
have a centralized control located in the hub, which manages the access to the satellite resources, as it allows a more efficient multiplexing of services with different priorities (e.g. data, voice, video). Oriented initially to the telephone service, a portion of the satellite resources (e.g. frequency channel in FDMA or timeslots in TDMA) was assigned dynamically on a call basis [17] and the grade of service was determined through the well-known call blocking formulas, such as the Poisson and Erlang B formulas used for terrestrial links [18]. 2) Demand Assignment Protocols: With the increase of data services (e.g. corporate networks interconnection, retail point-of-sale transactions, Supervisory Control and Data Acquisition (SCADA)), it was necessary to assign satellite resources in a more dynamic way, i.e. on a packet basis rather than call (or circuit) basis. Demand Assignment Multiple Access (DAMA)
6
schemes evolved introducing faster capacity reservation mechanisms in order to reduce end-toend transmission time. In the Contention-based Priority Oriented Demand Assignment protocol (CPODA) protocol [19] capacity reservations can be implemented via contention mini slots and piggybacking them in the header of the scheduled packet transmissions. The Combined Free/Demand-Assignment Piggy-Backed (CFDAMA-PB) multiple-access protocol [20] behaves like the CPODA protocol, but in addition it randomly assigns the unused traffic slots to inactive users. At low traffic loads and with relatively small terminal population, the chance that an Earth station obtains free-assigned slots is high, thus reducing its end-to-end transmission time to a minimum of one satellite hop propagation delay (i.e. 250 ms for GEO satellites). At high loads, the end-to-end transmission delay is not reduced as no spare satellite resources are left, but the system remains stable. However, the performance of DAMA protocols is highly dependent on the traffic characteristics and the number of Earth stations sharing the satellite resources. Internet data traffic is known to be statistically self-similar [21], [22]. Satellite terminals generate heavy tailed bursts of packets with a large variance in the inter-arrival times between bursts. Besides, by aggregating streams of such traffic from different Earth stations we intensify the self-similarity (burstiness) instead of smoothing it (fractal-like behavior). In [23], the performance of the CFDAMA-PB protocol has been analyzed with traffic following a Poisson regime, which is more appropriate for voice, facsimile or SCADA type of services, but not for Internet data traffic as described above. Ref. [24] investigates the sensitivity of the CFDAMA-PB protocol under different types of Internet data traffic and a large number of Earth stations (i.e. greater than the number of traffic slots in one satellite hop propagation delay). It is shown that the performance of CFDAMA-PB is equivalent to a pure DAMA scheme, i.e. the majority of the free capacity assignments are wasted. Figure 2 shows that the throughput component for Free-Assigned slots with bursty traffic is very low at all loads, while for Poisson traffic is quite high at loads up to 80%. Under these conditions, every packet transmission undergoes first a reservation cycle (two satellite hops) and then another satellite hop for the packet transmission (i.e. a latency larger than 750 ms for a GEO scenario) like in a pure DAMA scheme. This poses some limitations on the use of DAMA for consumer broadband access to support real-time or interactive applications.
7
(a)
(b) Fig. 2. CFDAMA-PB throughput decomposition in Demand and Free Assigned slots from [24]: a) Poisson traffic, b) Self-similar traffic.
8
To enhance the performance of DAMA protocols for broadband satellite networks, predictive DAMA protocols have been developed, such as PRDAMA [27]. PRDAMA estimates the positive varying trend of the traffic on each Earth station to allocate the free capacity assignments. Nonlinear prediction methods are used to predict the traffic burstiness [28]. This results in a more efficient assignment of the free bandwidth to those terminals that are predicted to need the resource, thus minimizing the wastage of free bandwidth assignments. However, the effectiveness of the predictive methods cannot be guaranteed for all traffic data [28]. Besides, the improvement in the end-to-end delay with respect to CFDAMA-PB is rather limited (i.e. ∼ 100 ms delay reduction) with an average end-to-end delay over a bent pipe GEO satellite still in the order of 750 ms. A final consideration for DAMA schemes is related to their overhead. All DAMA schemes introduce control sub-frames that are needed by Earth stations to make capacity requests and to remain synchronized with the satellite network (e.g. for TDMA and MF-TDMA). This represents a sizeable overhead in the inbound channel of large size networks. Ref. [24] has also shown that, as opposed to DAMA schemes, RA schemes are much less sensitive to the traffic characteristics and the number of Earth stations. This is demonstrated by analyzing the performance of Spread-Spectrum ALOHA (SSA) [25] for different number of Earth stations and for different traffic types (voice, web client, web server). Furthermore, [26] illustrates how the common smoothing methods in RA such as random blocking and random delays force Poisson characteristics on the traffic for heavy loads. For these reasons, in the following, RA schemes will be analyzed in the presence of Poisson type of traffic. 3) Hybrid RA and DAMA Protocols: In [29] and [30] two combined random/reservation multiple access schemes have been proposed dubbed Combined Random/Reservation Access (CRRMA) and Random Access with Notification (RAN) respectively. The basic principle is to perform the first transmission attempt in a RA slot, thus avoiding the initial two hops satellite delay for capacity reservation, and in case it is unsuccessful (i.e. due to collision), perform a second transmission in a reserved slot. RA performs well under bursty traffic, but, due to the high probability of packet collisions at high loads, the improvement in delay of the CRRMA and RAN schemes is limited to low load conditions. 4) Emerging Satellite Broadband and M2M Networks Needs: The emergence of broadband access and M2M communications has triggered the need for further performance improvements of satellite multiple access protocols. The M2M multiple access protocols shall provide fast and
9
reliable access for a very large population of Earth stations (tens of thousands) generating bursty traffic with a very low duty cycle. RA protocols represent a good candidate solution, as they are quite insensitive to the network population size and traffic characteristics, as well providing low access delays and reduced terminal complexity. RA protocols used in combination with DAMA are also a good alternative to the free capacity assignment scheme for the less predictive, low duty cycle, time sensitive traffic encountered in broadband access networks. B. Main terrestrial random access techniques and their applicability to satellite RA protocols originated in the 1970s from the need for terminal-computer and computercomputer communication. In this kind of communication, data traffic is bursty. This is the result of the high degree of randomness seen in the message generation time and size, and of the relatively low-delay constraint required by the user. Users generate traffic with a low duty cycle, but when they do, they require a fast response. As a result, there is a large peak-to-average ratio in the required data transmission rate. In this context, it is not efficient to use fixed channel allocation schemes, as they would result in a low channel utilization, that is the percentage of the channel capacity that goes into the throughput.. A more advantageous approach is to provide a single shared high-speed channel to a large number of users. However, when dealing with shared media conflicts arise, i.e. more than one user want to access the shared resources simultaneously. Therefore, the challenge with shared media is to control the access to the common channel while providing good level of performance and maintaining reduced implementation complexity. RA protocols were developed to address this multiple access scenario, and today are extensively used in terrestrial networks over wired and wireless shared media [11], [31], [32]. In this section, we review the main terrestrial RA techniques and analyze their applicability to the satellite environment. 1) Carrier Sensing Protocols: One of the most widely used distributed packet access schemes is the Carrier Sense Multiple Access (CSMA) and its variants. In CSMA, a station senses the medium before transmitting only starting transmission if the medium is not being used by other transmitters [33]. CSMA/Collision Detection (CSMA/CD) operates similarly to CSMA, but once the transmission has started, if the sender detects a collision it stops transmitting to reduce the overhead. When collisions occur, each station willing to transmit backs off for a random time period [34]. CSMA/CD cannot be efficiently implemented over a terrestrial wireless network due
10
to the hidden terminal problem, where some stations are out of the transmission and detection range of each other [35]. However, CSMA/CD mechanism performs well in wired networks and the IEEE has standardized CSMA/CD in the IEEE 802.3 standard [36]. Another CSMA variant used in wireless networks to avoid collisions is the CSMA/Collision Avoidance (CSMA/CA) scheme. In CSMA/CA, the sender tries to avoid a collision after the channel becomes idle, by waiting for an inter frame spacing time before contending for the channel. The IEEE has standardized CSMA/CA in the IEEE 802.11 standard [37]. The back-off algorithm in CSMA/CA tries to avoid collisions, but does not remove them completely. Small values of the random back-off time cause many of them while very large values can cause unnecessarily long delays. Secondly, CSMA/CA fails to solve the hidden terminal problem, and cannot always detect that the medium is busy, thus creating a collision in the channel. All the previous multiple access protocols employ carrier sensing to avoid collisions and offer a good channel utilization, low latency and good stability over channels where packet transmission times are larger than propagation delays. But unfortunately, they cannot operate over satellite channels where propagation delays are very large. 2) Distributed Reservation Protocols: Another family of schemes are distributed reservation schemes, such as the Multiple Access Collision Avoidance (MACA) [38], also adopted in wireless networks. In MACA, a sender transmits a Request To Send (RTS) message to its intended receiver before the data transmission. The data is transmitted only after reception of a Clear To Send (CTS) message from the receiver, which is sent after reception of a successful RTS. A different distributed reservation scheme is represented by the Random Access Channel (RACH) adopted in the third generation (3G) cellular mobile networks [39]. In case of RACH, terminals randomly transmit first short packet preambles, and then wait for a positive acquisition indicator from the base station prior to the transmission of the complete message (i.e. after successful reservation of the channel). Distributed reservation schemes solve the hidden terminal problem typically present in terrestrial radio links, but also rely on short propagation delays, i.e. the reservation delay only represents a small overhead of the total packet transmission time. In the satellite environment, centralized reservation schemes are used instead [20] to avoid several failed attempts prior to the packet transmission, which could represent a very large overhead to the total end-to-end packet transmission delay.
11
3) ALOHA Protocols: We review now protocols not exploiting any form of channel sensing or reservation. ALOHA, Slotted ALOHA and Diversity Slotted ALOHA - The pure ALOHA protocol, first proposed by N. Abramson in [40], is one of the oldest and simplest multiple access protocols. In ALOHA, a terminal transmits a packet in an asynchronous fashion without checking if any other terminal is active. Within an appropriate timeout period, it receives an acknowledgment from the destination, confirming that no conflict has occurred. Otherwise, it assumes that a collision has occurred and must retransmit. To avoid repeated collisions, the re-transmission time is randomized across the terminals, thus spreading the retry packets over time. A slotted version, referred to as Slotted ALOHA (S-ALOHA) is obtained by dividing time into slots of duration equal to the duration of a fixed-length packet [41]. Users are required to synchronize the start of transmission of their packets to the slot boundaries. When two packets collide, they will overlap completely rather than partially, providing an increase on channel utilization over pure ALOHA. The S-ALOHA has the advantage of higher efficiency, but requires time slot synchronization. Both schemes are applicable to the satellite environment, as they have no dependency on the propagation delay. Unfortunately, these schemes are subject to a high collision probability due to the lack of carrier sensing. As a consequence, their operation in the high load region is not practical in the satellite environment due to the high number of retransmissions required yielding very large latencies. S-ALOHA stability analysis has been investigated in [42]. The Diversity Slotted Aloha (DSA) [43] slightly improves the S-ALOHA performance at low channel loads by sending twice the same packet at random slot locations in the frame in favor of increasing the time diversity and thus reducing the Packet Loss Ratio (PLR). As for S-ALOHA, operation in the high packet collision probability region is not practical in satellite environment. It is generally assumed that whenever two packet transmissions overlap in time, they cancel each other. This assumption is pessimistic as it neglects capture or near-far effects in radio channels. Capture occurs when a terminal receives messages simultaneously from two terminals, but the signal from one of them drowns out the other, so that no collision occurs. The terminal with the higher received power is said to have captured the receiver. Some of these effects have been addressed in [41], [44]–[46]. Capture is good in the sense that it reduces the time needed in resolving collisions, but may also drive weak terminals completely out of the medium.
12
Fig. 3 from [48] presents the performance results for S-ALOHA and DSA in the presence of packets power imbalance following independent identically distributed lognormal distributions with equal mean µ and standard deviation σ, where both parameters are expressed in dB in the logarithmic domain. The corresponding packet amplitude a distribution is given by [47] 20 (20 log10 a − µ)2 . pA (a) = √ exp − 2σ 2 2π ln 10σ a
(1)
The results have been obtained by detailed simulations (see the figure caption for details on the physical layer assumptions) and by using the analytical model derived in [48]. The x-axis represents the normalized average channel MAC load (G) expressed in information bits/symbol for non spread-spectrum RA schemes and in information bits/chip for RA schemes employing spread spectrum. In this way we avoid any dependence on the modulation cardinality or coding rate used. A similar approach has been used for measuring the RA throughput shown in y-axis. The figure shows that in both schemes the throughput improves with increasing power imbalance as collisions become easier to resolve (power capture effect). However, as expected, the PLR rises quickly as the load on the channel increases. Although for equi-powered packets the maximum channel utilization available is 18% for pure ALOHA, and 36% for S-ALOHA, low packet collision probabilities (e.g. < 10−3 ) typically required by satellite networks1 are achieved at very low loads, i.e. < 10−3 bits/symbol. This results in very low channel utilization (see Fig. 3). The low PLR operating point is justified by the need to avoid excessive and highly variable retransmission latencies in the satellite environment which is characterized by very long propagation delays, and to maximize the throughput of applications using the TCP protocol that is also highly sensitive to the packet losses [51], [52]. It is important to remark the fact that often in the literature RA schemes are compared looking at their peak throughput rather than the value achieving the required MAC PLR for a given service or application. In fact, for low loads (e.g. G < 0.2 bits/symbol), DSA outperforms S-ALOHA although its peak throughput is lower than S-ALOHA. This can be better appreciated in Fig. 4, where S-ALOHA and DSA PLR curves are combined in one figure for the case of no power 1
The low PLR operating point is justified by the need to avoid excessive and highly variable retransmission latencies in the
satellite environment which is characterized by very long propagation delays, and to maximize the throughput of applications using upper-layer protocols such as the Transmission Control Protocol (TCP) that is also highly sensitive to the packet losses [49], [50]
13
imbalance and the low load region is expanded. For instance, for a target PLR= 10−2 , DSA can achieve a throughput T = 0.05 bits/symbol while for S-ALOHA the maximum achievable throughput is T = 0.01 bits/symbol. This is justified by the fact that under light traffic multiple transmission, hence time diversity, gives better PLR performance.
14
0.6
Throughput [bits/symbol]
0.5
0.4
0.3
0.2 S−ALOHA, σ=0 dB DSA, σ=0 dB S−ALOHA, σ=3 dB DSA, σ=3 dB
0.1
0
0
0.5 1 1.5 Average MAC Channel Load [bits/symbol]
2
0
Packet Loss Ratio [PLR]
10
−1
10
−2
10
DSA, σ=3 dB S−ALOHA, σ=3 dB DSA, σ=0 dB S−ALOHA, σ=0 dB
−3
10
0
0.5 1 1.5 Average MAC Channel Load [bits/symbol]
2
Fig. 3. S-ALOHA and DSA performance from [48] for Quaternary Phase Shift Keying (QPSK) modulation, 3GPP turbo code FEC r = 1/2, packet block size 100 bits, Energy per Symbol to Noise Power Spectral Density ratio Es /N0 = 7 dB in the presence of lognormal packets power imbalance with mean µ = 0 dB, standard deviation σ and Poisson traffic.
15
0
10
−1
Packet Loss Ratio [PLR]
10
−2
10
−3
10
DSA Simulation σ=0 dB S−ALOHA Simulation σ=0 dB DSA Analytical σ=0 dB S−ALOHA Analytical σ=0 dB
−4
10
−5
10
Fig. 4.
0
0.05 0.1 0.15 Average MAC Channel Load [bits/symbol]
0.2
Analytical vs. simulation S-ALOHA and DSA performance comparison in the low load region for QPSK modulation,
3GPP turbo code FEC r = 1/2, packet block size 100 bits, Es /N0 = 7 dB in the presence of no power imbalance and Poisson traffic.
Spread Spectrum ALOHA - Slotted RA systems require terminals to keep time slot synchronization. For satellite applications the resulting network synchronization overhead greatly reduces system efficiency, in particular for networks characterized by large number of terminals with very low transmission duty cycle (e.g. M2M applications). Moreover, a requirement of accurate network-level time synchronization increases the complexity of the terminal. Thus, slotted RA is penalizing low-cost terminal solutions. To mitigate this limitation, a pure ALOHA scheme can be employed, but its performance is worse than for S-ALOHA increasing by a factor two the packet collision probabilities [41]. Direct-sequence spread spectrum (DS-SS) multiple access is the most common form of CDMA, whereby each user is assigned a particular code sequence which is modulated on the carrier with the digital data modulated on top of that [7]. Users can transmit asynchronously and even when the same spreading code is used, data can be received [25], [53]. The SSA protocol proposed in [25] has potentially attractive features as it provides a much higher throughput capability
16
than S-ALOHA for the same PLR target under equal power multiple access conditions when adopting powerful physical layer Forward Error Correction (FEC) (e.g. coding rates ≤ 1/2) and low order modulations (e.g. Binay Phase Shift Keying (BPSK), QPSK). Ref. [24] shows that the SSA throughput is critically dependent on the demodulator Signal-to-Noise plus Interference Ratio (SNIR) threshold for packet decoding. Results reported in [24] indicate that, differently from S-ALOHA, SSA shows a steep PLR increase with MAC load. Thus, SSA can be operated with low PLR close to the peak of the throughput characteristic. As an example, using turbo codes and relatively small packets, SSA can achieve a much higher throughput than ALOHA or S-ALOHA, in the order of T ' 0.5 bits/chip for a PLR of 10−3 (see Fig. 5 with σ = 0 dB). SSA represents a very interesting RA scheme for the satellite environment, in particular when asynchronous access is required. The main reason for performance improvement of SSA techniques with regards to pure ALOHA is that they can take advantage of a higher traffic aggregation. The average number of packet arrivals over one packet duration λ can be computed as follows: λ = Nrep G Gp ,
(2)
where Nrep is the number of replicas transmitted for each packet, G is the MAC load expressed in information bits/symbol in non-spread systems and bits/chip in spread systems. The processing gain is given by Gp = SF/(r log2 M ) where r is the FEC code rate, M is the modulation cardinality and SF is the spreading factor. It can be observed from (2) that large processing gain will proportionally increase the value of λ. Typical values for non-spread spectrum systems are λ ≤ 5, while for SS systems assuming SF = 32 and r = 1/3, λ ≈ 100. Fig. 6 demonstrates that the Poisson density normalized to the mean value, approaches a Dirac delta function as λ increases. This means that the instantaneous number of interfering packets fluctuation will reduce with increasing λ. This is a favorable consequence for RA, because the system operational average MAC load can be chosen to be close to its maximum operational value (i.e. when the PLR is around 10−3 − 10−4 ). Furthermore, when operating with large λ values, the co-channel interference can be accurately approximated as Additive White Gaussian Noise (AWGN) thanks to the large number of interfering packets thus easing its analysis. Despite the attractive features listed above, SSA Achilles’ heel resides in its high sensitivity to multiple access carrier power imbalance. This phenomenon is disrupting the SSA scheme
17
throughput because of the well known CDMA near-far problem. As shown in Fig. 5, for PLR of 10−3 or less, the SSA throughput is diminished by several orders of magnitude when the received packets power is lognormally distributed with standard deviation of 2-3 dB, as opposed to S-ALOHA where power imbalance results in improved performance due to the power capture effect.
18
0.7 σ=0dB, semi−ana σ=1dB, semi−ana σ=2dB, semi−ana σ=3dB, semi−ana σ=0dB, simulated σ=1dB, simulated σ=2dB, simulated σ=3dB, simulated
0.6
Throughput [bits/chip]
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6 0.8 Average MAC load (bits/chip)
1
1.2
1.4
0
10
−1
10
−2
Packet Loss Ratio (PLR)
10
−3
10
−4
10
−5
10
σ=0dB, semi−ana σ=1dB, semi−ana σ=2dB, semi−ana σ=3dB, semi−ana σ=0dB, simulated σ=1dB, simulated σ=2dB, simulated σ=3dB, simulated
−6
10
−7
10
−8
10
0
0.1
0.2
0.3
0.4 0.5 0.6 0.7 Average MAC load (bits/chip)
0.8
0.9
1
Fig. 5. Simulated vs. Analytical SSA performance with and without power unbalance from [54]: 3GPP turbo code FEC r = 1/3 with block size 100 bits, BPSK modulation, spreading factor 256, Es /N0 = 8.9 dB.
19
0.4 λ=1 λ=2 λ=3 λ=5 λ=6 λ=10 λ=100
0.35 0.3
PDF
0.25 0.2 0.15 0.1 0.05 0 0
2
4
6
8
10
x/λ
Fig. 6.
Traffic probability distribution normalized to the mean value.
4) Tree-based Contention Resolution Protocols: Another class of RA algorithms is represented by the Tree Algorithms. The most basic collision resolution protocol is called the Standard (binary) Tree Algorithm (STA) protocol and was proposed by Hayes [57] and Capetanakis [58]. According to this protocol when a collision occurs, say in slot k, all users that are not involved in the collision wait until the collision is resolved. The users involved in the collision split randomly into two subsets, by (for instance) each flipping a coin. The users in the first subset, those that flipped 0, retransmit in slot k + 1 while those that flipped 1 wait until all those that flipped 0 transmit successfully their packets. If slot k + 1 is either idle or contains a successful transmission, the users of the second subset (those that flipped 1) retransmit in slot k + 2. If slot k + 1 contains another collision, then the procedure is repeated, i.e., the users whose packets collided in slot k + 1 (the colliding users) flip a coin again and operate according to the outcome of the coin flipping, and so on. Massey’s Modified Tree Algorithm (MTA) [59] is eliminating avoidable collisions caused by multiple user retransmissions in the same slot of the STA protocol. Except for eliminating that
20
issue, the MTA protocol evolves exactly as the STA protocol. The Successive Interference Cancellation Tree Algorithm (SICTA) has been introduced in [60] as an evolution of the STA and MTA. Colliding packets are discarded in STA and MTA with no attempt to extract pertinent packet information. In contrast, SICTA retains them for future reuse. The SICTA algorithm relies on SIC to take advantage of collided packets in a conventional STA. With a cross-layer design approach, SICTA combines SIC with Tree Algorithm, and thus permeates SIC benefits to the MAC layer. In addition to detailing the SICTA algorithm, Ref. [60] provides analytical derivation of the Collision Resolution Interval (CRI) length, delay and throughput statistics. The CRI represents the time interval from the slot where initial collision occurs up to and including the slot in which all senders recognize that all packets involved in this collision have been successfully received. The main advantages of SICTA are its high throughput (up to 0.693) and low complexity. The high throughput renders SICTA suitable for medium to high traffic load, and widens the applicability of RA. However, SICTA as STA and MTA protocols packet retransmission is requiring quick feedback sent by the receiver. This mechanism is not suitable for satellite networks characterized by long propagation delays. 5) Summary of the Section: The above review of well-known terrestrial and satellite RA techniques reveals that none of them is fully satisfactory for satellite M2M applications. Table I provides a summary of the various RA techniques analyzed in this section. In general, conventional RA schemes provide low channel utilization over the satellite environment due to the long propagation delays. Among them, the ALOHA-based techniques adapt better to the satellite environment, as they do not have any dependency on the propagation delay, but their performance is constrained by their high collision probability. Subsequently, today’s satellite systems only use ALOHA-based RA for initial network login, capacity request and short packet transmissions [55], [56].
21
Slotted / unslotted
Channel utilization
Sensitivity to power imbalance
Peak power requirement
Implementation complexity
Carrier Sense Multiple Access (CSMA) [33]
Sensitivity to delay
Technique
High
Slotted
High
Medium
High
Low
& unslotted CSMA Collision Detection (CSMA/CD) [34]
High
Slotted
High
Medium
High
Medium
Medium
Medium
High
Low
& unslotted CSMA Collision Avoidance (CSMA/CA) [37]
High
Slotted & unslotted
Multiple Access Collision Avoidance (MACA) [38]
High
Unslotted
Medium
Low
High
Low
3G Random Access Channel (RACH) [39]
High
Slotted
Medium
High
Low
Medium
ALOHA [40]
Low
Unslotted
Low
Low
High
Low
Slotted ALOHA (S-ALOHA) [41]
Low
Slotted
Low
Low
High
Low
Diversity Slotted ALOHA (DSA) [43]
Low
Slotted
Low
Low
High
Low
Spread Spectrum ALOHA (SSA) [25], [53]
Low
Unslotted
Medium
High
Low
Medium
Successive Interference Cancellation
High
Slotted
Medium
Medium
High
Medium
Tree Algorithm (SICTA) [60] TABLE I S UMMARY OF C ONVENTIONAL R ANDOM ACCESS T ECHNIQUES .
III. A DVANCED S LOTTED RA T ECHNIQUES In this section we review recently proposed RA schemes suitable for satellite applications which are sharing the following features: slotted access, open loop RA, collision resolution through interference cancellation by using no (or light) spreading techniques. A. Contention Resolution Diversity Slotted ALOHA To address the shortcomings of conventional RA techniques over satellite, the Contention Resolution Diversity Slotted ALOHA (CRDSA) technique was introduced in [61]. Similarly to
22
DSA, CRDSA is transmitting every packet two or more times (number of replicas2 , Nrep ) in randomly selected slots within a TDMA frame of Nslots . The novelty is that, to help contention resolution, each physical layer packet contains in the header information about the location of the replicas within the frame (see Fig. 7). 6 1
3
2
2
3
5
5
1
4
4
6
RA slot TDMA RA frame with Nslots
Fig. 7.
Example of CRDSA frame with two replicas per packet.
The complete TDMA frame is sampled and stored in a digital memory at the receiver side. By using simple, yet efficient interference cancellation techniques, clean bursts are recovered (e.g. packet 3 in slot 5 in Fig. 7) and the interference generated by their replicas on other slots is cancelled (e.g. packet 3 in slot 4). By performing iterative processing of the TDMA frame, it is proven that most of the initial collisions can be resolved. In Fig. 8, it is shown that CRDSA largely outperforms classical S-ALOHA techniques in terms of throughput and PLR. For a MAC packet loss probability of 1%, channel utilization of 25% can be achieved with the CRDSA technique with two replicas (see Fig. 8(b)), while for S-ALOHA a channel utilization of only 1% can be achieved for the same packet loss probability (see Fig. 4). This represents a 25-fold throughput improvement compared to S-ALOHA. With 3 replicas, the performance improvement is even larger. As shown in Fig. 8(b), a channel utilization of 58% can be achieved, which represents a 58-fold throughput improvement compared to S-ALOHA. Ref. [61] presents detailed CRDSA delay performance results. For a target PLR=10−3 , CRDSA with 2 and 3 replicas achieves channel utilization of 0.07 and 0.45 respectively compared to a modest 0.001 for S-ALOHA. It is important to highlight the relevance of PLR improvement in the satellite environment, as it has a direct benefit on the end-to-end delay due to the reduced need for packet 2
The use of number of replicas in the context of CRDSA shall be understood as the number of same packet instances physically
repeated in the same frame.
23
retransmissions. The performance boost of the CRDSA protocol is achieved thanks to the implementation in the hub receiver of a collision resolution capability that exploits Interference Cancellation (IC) techniques. IC techniques have been extensively investigated for CDMA [62], but before CRDSA have never been proposed in a TDMA S-ALOHA context. CRDSA is basically a TDMA access scheme that uses the information from the successfully decoded packets to cancel the interference their replicas may generate on other slots. It is possible to cancel the interference because the interfering data symbols are known from the successfully decoded packets. One of the main issues in applying IC techniques to TDMA S-ALOHA is related to the need for accurate channel estimation for the burst replicas removal where collision(s) occur. In fact, collisions in a satellite TDMA multiple access channel are typically destructive, as the near-far effect is rather limited. While carrier frequency, symbol timing, and signal amplitude estimation for IC can be derived from the clean replica (e.g. packet 3 in slot 5 in the example of Fig. 7), carrier phase has to be estimated on the slot(s) where collision(s) occurs (e.g. in slot 4). This is because in practical broadband systems the carrier phase is time variant also from slot to slot. This key problem was initially solved in [61] by exploiting the burst preamble which is individually “signed” by a pseudo-random binary sequence randomly selected among the available code family by each active Earth station for each burst in each frame. All replicas of the same burst use the same preamble code. This approach does not require a centralized preamble code assignment, thus allowing to maintain the RA nature of the proposed scheme. In this way, the preamble can be used for carrier phase estimation also in case of multiple collisions which normally are destructive for channel estimation and payload decoding. With the above channel estimation implementation, the CRDSA performance with real detector realization is very close to the one obtained with ideal channel estimation [61]. An alternative, yet simpler way to perform channel estimation described in [63], is to use a common Unique Word (UW) (or preamble) for all Earth stations, and in addition all or a sub-set of the payload symbols, which are known from the clean replicas. Payload symbols are uncorrelated among the colliding packets, even though without optimized cross-correlation properties. It is found that by employing payload symbols for channel estimation, performance results are comparable to those obtained by using multiple pseudo-random binary sequences for
24
1 S−ALOHA DSA CRDSA 2 replicas CRDSA 3 replicas
0.9
0.8
Throughput [bits/symbol]
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3 0.4 0.5 0.6 0.7 Average MAC Channel Load [bits/symbol]
0.8
0.9
1
(a) Throughput versus load 0
10
−1
Packet Loss Ratio (PLR)
10
−2
10 −2
10
−3
10
−4
10
−3
10
0
0.02
0.04
0.08
0.1
S−ALOHA DSA CRDSA 2 replicas CRDSA 3 replicas
−4
10
0.06
0
0.1
0.2
0.3
0.4 0.5 0.6 0.7 Average MAC Channel Load [bits/symbol]
0.8
0.9
1
(b) Packet Loss Ratio versus load Fig. 8.
Simulation S-ALOHA, DSA and CRDSA performance for Nrep = 2 and 3, Nslots = 100, QPSK modulation, 3GPP
FEC r = 1/2, packet block size 100 bits, Es /N0 = 7 dB in the presence of no power imbalance and Poisson traffic.
25
the burst preambles. This approach reduces complexity on both the earth stations and hub as a single preamble is used. Besides, the knowledge of the payload data allows to estimate the carrier phase along the burst by using a conventional carrier phase estimator for unmodulated carrier. This turns out particularly advantageous when low cost, low bit rate terminals are employed (e.g. for interactive TV), since the RF front-end introduces a non-negligible amount of phase noise [63]. CRDSA has a number of parameters that can greatly influence its behaviors. The most important ones are the FEC coding rate r, the number of replicas Nrep and the packets power imbalance. With the aim of optimizing its performance, in [48] an analytical framework has been developed jointly with a detailed simulator for the performance assessment of slotted RA protocols. The proposed analytical framework assumes arbitrary power and traffic distributions, and accurately models the RA system behavior. It has to be remarked that using a powerful FEC code (e.g. r = 1/3) and an adequate Es /N0 value (e.g. Es /N0 = 10 dB), there is a non negligible probability of correctly detecting the packet even in presence of a collision and equi-powered packets (capture effect). Besides, the FEC code is important to help the collision resolution process in the presence of strong co-channel interference from colliding packet(s), thus not under pure AWGN-like conditions. Therefore low FEC coding rates, which apparently reduce the individual packet information bit rate, end up playing an important role in enhancing the overall RA scheme throughput. The main reason limiting the PLR performance of CRDSA with two replicas is the so called loop phenomenon described and analyzed in [48]. A loop occurs when all replicas of a set of packets are causing an unrecoverable collision with one or more replicas. A loop phenomenon occurs when all replicas of a set of packets are in unrecoverable collision with one or more replicas. Irresolvable loops are the loops which can not be resolved with further demodulator processing and lead to packet losses. In the example of Fig. 7, packets 4 and 5 have formed a loop as their replicas have been transmitted in the same slots. The probability of occurrence is linked to the length of the frame, the MAC load and, more importantly, the number of replicas from each packet. Longer frames and larger number of replicas will significantly reduce the occurrence of loops at the expenses of latency and energy efficiency. Its probability is responsible for the mild slope of the PLR versus MAC load characteristic reported in Fig. 8(b) for CRDSA with two replicas. As observed in Fig. 8(b) and in [64], three packet replicas gives better CRDSA
26
performance than two, because it significantly reduces the loop occurrence probability. This issue has been extensively analyzed in [48], and with three packet replicas the probability of occurrence is well below the PLR target for our applications (PLR< 10−3 ). CRDSA stability has been investigated in [65]–[67]. The latter reference also contains a very short review of conventional RA protocols such as SA, DSA and the more advanced ones such as CRDSA, IRSA. As for ALOHA and S-ALOHA, packets power imbalance boosts the performance of CRDSA. Stronger packets are decoded first and weaker packets are successively decoded following the iterative IC process. Power imbalance results from the combined effect of the time variant atmospheric propagation, open loop power control errors (if applicable), Earth station equivalent isotropically radiated power (EIRP) and satellite receive antenna gain variations. It is shown in Fig. 9 that CRDSA with FEC r = 1/3, Nrep = 3 and power imbalance that follows a lognormal power distribution with standard deviation σ = 3 dB, can achieve a throughput T = 1.4 bits/symbol for a PLR< 10−3 , which represents a 1400-fold improvement with respect to S-ALOHA. As shown in [54], the PLR floor appearing in Fig. 9 for σ = 3 dB is due to presence of lognormal packet power variations which causes a number of packets to be received at the gateway below the detector threshold even assuming perfect interference cancellation.
27
1.5
Normalized Throughput (bits/symbol)
Simulation σ=3 dB Simulation σ=0 dB Analytical σ=3 dB Analytical σ=0 dB
1
0.5
0
0
0.2
0.4
0.6 0.8 1 1.2 1.4 Average MAC Channel Load (bits/symbol)
1.6
1.8
2
1.6
1.8
2
(a) Throughput 0
10
Simulation σ=3 dB Simulation σ=0 dB Analytical σ=3 dB Analytical σ=0 dB
−1
10
−2
Packet Loss Ratio [PLR]
10
−3
10
−4
10
−5
10
−6
10
−7
10
0
0.2
0.4
0.6 0.8 1 1.2 1.4 Average MAC channel load [bits/symbol]
(b) PLR iter Fig. 9. Analytical vs. Simulation CRDSA performance from [48] for Nrep = 3, Nmax = 15, Nslots =1000, QPSK modulation,
3GPP FEC r = 1/3, packet block size 100 bits, Es /N0 = 10 dB in the presence of lognormal packets power imbalance with mean µ = 0 dB, standard deviation σ and Poisson traffic.
28
Further CRDSA performance improvement can be achieved optimizing the power distribution of the gateway demodulator incoming packets. In fact, [68] has demonstrated that a uniform in dB power distribution provides close to optimum performance. Selecting the uniform instead of the lognormal distribution will also remove the PLR floor appearing in Fig. 9. In [69] a reservation scheme for CRDSA, called R-CRDSA, is proposed. The results reported are only related to the throughput and compared to the very early 2 replicas CRDSA scheme results reported in [61]. It is therefore difficult to compare the scheme with the more CRDSA optimized versions operating at a practical PLR value. In [70] the authors propose a slotted frameless evolution of CRDSA dubbed Sliding WindowBased CRDSA (SW-CRDSA). In SW-CRDSA, as soon as a user has a packet available for transmission, the first instance is sent in the next available slot while the other Nrep − 1 copies for the same packet are placed in the next Nsw − 1 slots with equally distributed probability. Nsw − 1 represents the number of successive slots in which a certain user can place its packet instances and corresponds to the number of CRDSA frame slots. The SW-CRDSA is claimed to have a higher throughput and a lower latency than CRDSA at the expense of increased central detector memory requirements. Unfortunately, the results reported in [70] are based on a simple collision-based physical layer model (no FEC is modelled) and only throughput (no PLR) performance are reported. A recent interesting enhancement of the CRDSA detector, dubbed Multi-replicA decoding using corRelation baSed locALisAtion (MARSALA), has been recently proposed in [71]. MARSALA proposes a new decoding technique for CRDSA based on replicas localisation exploiting packet autocorrelation. In particular, MARSALA takes advantage of correlation procedures to locate replicas of packets even when all of them are undergoing a collision and their replica pointers are not decodable. Furthermore, samples of slots containing the same packets are combined, enhancing the SNIR and enhancing packet decoding probability. The transmitter side in MARSALA is the same as in CRDSA, modifications made only regard the receiver side. In particular, the modulator side of MARSALA can be exactly the same as the one described for CRDSA in the DVB-RCS2 standard [105]. The results reported in [72] show an appreciable CRDSA throughput performance increase when using MARSALA enhanced detection scheme. At PLR=10−3 for CRDSA 3 replicas with r = 1/3 FEC and Es /N0 = 7 dB, the MARSALA may provide a 40 % throughput improvement compared to the conventional CRDSA detector. The channel estimation
29
algorithm analyzed in [72] shows a degradation of about 10 % in throughput compared to ideal MARSALA channel estimation. However, the MARSALA processing results reported so far assumes that the carrier phase remains constant over the packet duration which may not be the case due to phase noise in practical systems. B. Multi-Frequency CRDSA A major drawback related to the exploitation of TDMA for RA is the high-peak transmit power requirement resulting from the fact that each terminal is transmitting during a small fraction (1/Nslots ) of the TDMA frame duration. Reducing the number of frame slots Nslots will have a negative impact on the RA performance in particular if Nslots < 60 [74]. Since the PLR of a system is directly related to the ratio between the symbol energy and the noise floor level (i.e. the Es /N0 ), TDMA needs a (peak) power transmission Nslots times higher than FDMA or CDMA. In thin route satellite communications networks, MF-TDMA was introduced to mitigate this problem (see Sect. 9.2.6.1 of [73]). The multi-frequency concept can be easily applied to CRDSA leading to MF-CRDSA RA [68], [75]. In this scheme the packets are randomly located in a two-dimensional space composed by NTM F time slots and NFM F frequency sub-bands [68]. To minimize the MF-CRDSA peak power requirement, one should minimize the number of available time slots in a frame, while avoiding the need to transmit the packet replicas at the same time slot. In [68], it was found that for optimum MF-CRDSA performance NTM F = Nrep and Nslots = NFM F NTM F . MF-TDMA allows to reduce the required terminal peak transmit power by a factor Nslots /NTM F which is typically 1. It is remarked that MF-CRDSA is affected by a slight increase of the loop probability. This is because by imposing the transmission of the packet replicas in different time slots results in fewer possible choices of slot combinations compared to standard CRDSA. Simulation results reported in [68], indicate that MF-CRDSA MAC performance is just slightly worse than CRDSA, but with a huge reduction in the terminal peak power requirement. C. Spread Spectrum and Multi-Frequency Spread Spectrum CRDSA We have seen in Sect. III-A that CRDSA PLR performance with two replicas is largely degraded by the occurrence of loops. In the following we see how this effect can be mitigated in a slotted system without increasing the number of replicas. Sect. V will illustrate solutions
30
based on unslotted RA. Spread-Spectrum CRDSA (SS-CRDSA) [68] represents an evolution of the standard CRDSA that adopts a light direct-sequence spreading on top of the CRDSA physical layer packets. The short spreading sequence to be used by each terminal is randomly selected from a codebook family known to the gateway. The minimum number of different spreading sequences required depends on the amount of loop probability reduction required. SS-CRDSA reduces the number of irresolvable loops thanks to the cross-correlation properties of the spreading sequence that reduce the colliding packets cross-talk. SS-CRDSA entails some complexity increase in the packet demodulator because more preambles have to be searched. The minimum number of different spreading sequences required depends on the amount of loop probability reduction required. In [68] it is shown that combining MF- with the SS- variants results in MF-SS-CRDSA, the simulation results of which for 2 replicas leads to MAC performance slightly better than CRDSA with 3 replicas. This is combined with a huge reduction in the transmitter peak power requirement. D. Irregular Repetition Slotted ALOHA The key idea behind Irregular Repetition Slotted ALOHA (IRSA) [76] is to have a non constant, yet random, number of packet replicas transmitted in the frame. To derive the optimized irregular packet repetition scheme probabilities the author exploits the bipartite graphs techniques typically used for the design of FEC Low Density Parity Check (LDPC) codes. When IRSA is used, each burst is transmitted l times within the MAC frame, where the repetition rate l varies from burst to burst according to a given mass probability distribution. CRDSA can be seen as a special case of IRSA, where the repetition rate is fixed (e.g. l = 2 or l = 3). Fixing the IRSA maximum number of burst repetitions lmax , the graph-based methodology described in [76] allows to derive the mass probability distribution pl for the probability to have l repetitions in each frame with 1 ≤ l ≤ lmax . The paper also derives the asymptotic IRSA PLR vs. MAC load performance. Simulation results reported [76] refer to balanced packet power with constant traffic (no Poisson) and simplified collision-based physical layer model. The IRSA RA scheme shows some advantages compared with two replicas CRDSA in terms of a higher peak throughput. However, for PLR < 10−3 the IRSA throughput is lower or comparable to CRDSA with 3 replicas. It is noted that the IRSA randomized and variable number of packet replicas in each
31
frame makes the scheme implementation and the associated signalling mechanism more complex than CRDSA. This is because the random location generation represents a problem as the terminal aims to transmit multiple unique payloads in the same CRDSA/IRSA frame. The complexity is due to the necessity that the replicas of these multiple unique payloads must be randomly located in non-overlapping timeslots. The number of trial-and-errors attempts required increases with IRSA compared to CRDSA because it may randomly use a higher number of replicas per unique payload. With IRSA, a 4-bit field is typically needed in each replica packet to indicate the total number of replicas that correspond to the unique payload. This is not required with CRDSA, because the number of replicas can be fixed at the time of channel setup. A comparison between IRSA and CRDSA TCP performance in a satellite study case has been reported in [77]. It is concluded that there is no appreciable difference between the two techniques when incoming packets power is balanced. Ref. [78], extended the original IRSA graph-based model to cope with the packet capture effect. However, the model assumes that a packet is captured in a slot if the corresponding SNIR is above a given fixed threshold. This approach was found to be inaccurate for predicting the PLR performance of the RA scheme. For this reason in [48] a more accurate physical layer modelling was introduced. To make a more realistic and fair comparison of CRDSA with IRSA with the real FEC and Poisson traffic simulations have been performed in [68] and results are reported in Fig. 10. We can see that, even more realistic IRSA simulations with optimized IRSA FEC code rate 1/3 as CRDSA, show no advantages compared to the simpler and more energy efficient CRDSA with 3 replicas. Reducing the Es /N0 from 10.2 dB down to 1.2 dB IRSA throughput performance at PLR=10−3 gets almost identical to CRDSA. Appendix B of [76] also provides an approach for improving the channel estimation during the cancellation step which is very similar to the one independently proposed in [63].
32
0.9
0.8
Throughput, T (bits/symbol)
0.7
0.6
0.5
0.4
0.3
0.2 CRDSA, Nrep=3, Constant Power, Es/N0=10.2 dB IRSA Γ3(x) from [66], Constant Power, Es/N0=10.2 dB
0.1
IRSA Γ4(x) from [66], Constant Power, Es/N0=10.2 dB 0
0
0.5
1
1.5
0.5 1 Average MAC Load, G [bits/symbol]
1.5
Average Load, G [bits/symbol]
(a) Throughput 0
10
CRDSA, Nrep=3, Constant Power, Es/N0=10.2 dB IRSA Γ3(x) from [66], Constant Power, Es/N0=10.2 dB IRSA Γ4(x) from [66], Constant Power, Es/N0=10.2 dB
−1
10
−2
Packet Loss Ratio
10
−3
10
−4
10
−5
10
−6
10
0
(b) PLR Fig. 10. Simulated IRSA performance from [68] with Λ3 (x) = 0.5x2 + 0.28x3 + 0.22x8 , Λ4 (x) = 0.25x2 + 0.6x3 + 0.15x8 iter from [76] and CRDSA performance for Nrep = 3, Nmax = 15, Nslots =1000, QPSK modulation, real 3GPP FEC r = 1/3,
packet block size 100 bits, Es /N0 = 10.2 dB in the presence of no packets power imbalance and Poisson traffic.
33
E. Multi-Slots Coded ALOHA In Multi-Slots Coded ALOHA (MuSCA) [79], differently from CRDSA, the different Np slots randomly assigned in a given frame to a specific terminal packet do not contain the same payload information. Instead, the coded packet symbols embedding FEC redundancy are partitioned in sub-packets spread across two or more slots randomly located in the frame slots. This means that, instead of using repetition coding as in CRDSA, in MuSCA it is possible to exploit lower coding rate FEC for the same MAC redundancy. This approach has the potential to enhance the collision resolution capability of the protocol provided that channel estimation quality at low SNIR ratios is good. Similarly to CRDSA, each sub-packet contains some signalling information indicating the location of the other packet slots in the frame. In the MuSCA scheme the coded symbols are partitioned in Np distinct sub-packets each assigned to a different frame slot. Thus, the subpackets can not be decoded at slot level as it was the case for CRDSA. Consequently, the subpacket location signalling information needs to be independently coded from the payload. For this purpose, [79] suggests the introduction of a Reed Muller block code. This represents a drawback compared to CRDSA since the associated signalling overhead is higher, particularly for small size packets. Simulation results reported in [79] show that MuSCA outperforms CRDSA when disregarding the signalling overhead. More specifically, it is shown that for PLR=10−3 MuSCA with 2 replicas and r = 1/6 FEC can achieve 100 % throughput increase compared to CRDSA with 3 replicas and r = 1/2 FEC. For the more optimized r = 1/3 CRDSA configuration, the gain is close to 60 % (again without accounting for the extra signalling overhead). F. Coded Slotted ALOHA The Coded Slotted ALOHA (CSA) protocol [80] represents a generalization of the IRSA and MuSCA RA schemes. User packets are encoded prior to transmission in the MAC frame, instead of being simply repeated as in IRSA. It follows that CSA is more power efficient than IRSA but less than MuSCA, which puts all the extra FEC redundancy at physical layer level. In CSA, prior to the transmission, the packet of an active user is divided into k information (or data) segments, all of the same length in bits. The k segments are then encoded by the user via a packet-oriented linear block code. Different redundancy levels are possible and can be selected in a finite code-book family C. In CSA, the level of block code protection for each segment (instead of the number of repetitions such as in IRSA) Ch is then randomly picked-up
34
(in alternative of being fixed as in MuSCA) according to a probability mass function which is the same for all users. Encoded segments are further encoded through a physical layer code before being transmitted over the multiple access channel (frame slices). In CSA the physical layer protection is applied at individual (packet) slice level instead of at the packet level. On the receiver side, decoding is performed as follows: segments that are received in clean slices (i.e., segments not experiencing collisions) are first decoded at physical layer. Then information about the relevant user, i.e. the code Ch ∈ C adopted by the user, and the positions of the other segments in the MAC frame, are extracted. For each active user the receiver becomes aware of, maximum-a-posteriori (MAP) erasure decoding of the code Ch is performed in order to recover as many encoded segments as possible for the user. Recovered segments may now be exploited in order to subtract their contribution of interference in those slices where collisions occurred. This procedure is iterated until the maximum number of iterations is reached. Simulated throughput results for optimized CSA reported in [80] appears slightly superior to IRSA for coding rates greater than 1/2. Unfortunately, no PLR results are reported thus a fair comparison with other RA schemes is not possible. Ref. [81] present a framework for the analysis of the error floor of CSA for finite frame lengths over the packet erasure channel. From the implementation point of view, CSA is a more complex RA scheme due to its associated signalling mechanism. G. Frameless ALOHA Protocols Ref. [82], [83] have been investigating the potential of applying rateless codes [84] to the SA framework (FSA). In FSA the random access is based on common network time slots but frame length is not a priori set and becomes time variant. New slots are added until a sufficiently high fraction of users has been resolved. This implies that the time instant at which feedback arrives also adapts to the contention process. More specifically, a downlink base station beacon marks the start of the contention round. The second beacon target is to ensure that the starting instants of the slots become aligned across. Finally the beacon broacast the numbering sequence of the slots to the users. In each slot every user attempts transmission with a predefined probability, denoted as slot access probability. This parameter is received via the beacon at the start of the round and is the same for all users in the given slot. In general, the access probability is a function of the slot number. The round is terminated when the fraction of resolved users reaches a predefined
35
threshold, which is signaled by the next beacon that acknowledges resolved users and initializes the next round. The results reported in [82] shows that the FSA schemes provides a higher peak throughput compared to recent literature with relatively modest complexity. However, no comparative throughput results at typical PLR values with realistic physical layer modelling have been reported. In [85] the impact of capture effect in Rayleigh fading scenario for FSA are reported. Through asymptotic analytical and (simplified) simulation results it shown that the capture effect can be very beneficial in terms of achieved throughput. The class of FSA protocols are based on feedback sent by the receiver thus not considered suitable for a satellite environment characterized by long propagation delays. H. Physical Layer Network Coding RA Ref. [86] presents an approach to random access that is based on Physical-layer Network Coding [87], [88]. The gist of this strategy is that whenever packets collide, the receiver decodes a linear combination of these packets. The throughput that is achieved by this approach is significantly better than that of other approaches. In [89] Network-Coded Diversity Protocol (NCDP) has been devised. The key idea of NCDP is to apply PNC on top of the CRDSA packet repetition(s) at random locations within the frame composed by S slots. NCDP exploits extended Galois fields PNC for recovering collisions in symbol synchronous S-ALOHA systems. As for CRDSA, several replicas of the same packet are transmitted in the same frame. Once the channel has been estimated, the gateway demodulator applies PNC decoding to calculate the bit-wise XOR of the transmitted messages. After the PNC is applied to decode the collided bursts, the receiver uses common matrix manipulation techniques over finite fields to recover the original messages. See Ref. [89] for more details on the NCDP demodulator operations. To operate with high level of MAC load, the NCDP scheme requires the use of orthogonal preambles. This implies a more complex gateway demodulator preamble acquisition unit compared to CRDSA single preamble approach. In [89], it was found that the NCDP scheme performs best with BPSK modulation and its performance is negatively impacted by packet power unbalance. With perfectly power balanced packets, the NCDP performance is found to be inferior to CRDSA with 3 replicas.
36
IV. A DVANCED U NSLOTTED RA S PREAD -S PECTRUM T ECHNIQUES In this section we review recently proposed RA schemes suitable for satellite applications which are sharing the following features: unslotted access, open loop RA, collision resolution through interference cancellation using spreading techniques. A. Enhanced Spread Spectrum ALOHA Enhanced Spread Spectrum ALOHA (E-SSA) design [54] is the same as SSA on the transmitter side (see Sect. II-B). The E-SSA novelty lies on the packet detector design exploiting iterative SIC (iSIC) approach customized to the asynchronous RA DS-SS scheme [91]. The detector located at the hub is the heart of the system, as it has to provide reliable detection of the incoming packets even under heavy MAC channel load conditions and with arbitrary packets’ power distribution. In Sect. II-B it is shown how the conventional SSA demodulator is very sensitive to packets’ power imbalance. Thanks to a more suitable gateway digital signal processing, compared to conventional SSA, E-SSA provides remarkable enhancements in terms of robustness and absolute throughput. The principle of the E-SSA detector is illustrated in Fig. 11. The received signal at the gateway is band-pass filtered, sampled, digitally down-converted to baseband with I-Q components stored in a digital memory of 2W Nsc real samples, where Nsc corresponds to the number of chips per physical layer channel symbol, and W corresponds to the memory window size in symbols for the iSIC process at the hub. The window size W shall be optimized to be the smallest possible value providing good detector performance. Typically, W should be three times the physical layer packet length in symbols. The receiver window memory is shifted in time in discrete steps allowing some overlap of packets on each window step (sliding window process). To reduce the detector complexity the window step ∆W , in symbols, shall be the largest possible compatible with a negligible performance loss. Normally, the window step ∆W shall be between 1/3 to 1/2 of the window length W .
37
Iterative IC process within window
Sliding Window 7 4
11
17
9
3
14
8
2
6
1
5
18
13
19
12
16
10
15
Time
W W+(n-1) W
Fig. 11.
W+n W
Enhanced Spread Spectrum ALOHA algorithm description from [54].
At each window step, the following iSIC process takes place: 1) Store in the detector memory the new baseband signal samples corresponding to the current window step (n); 2) Perform packets preamble detection and select the packet with highest SNIR value; 3) Perform data-aided channel estimation for the selected packet over the preamble; 4) Perform FEC decoding of the selected packet; 5) If the decoded FEC frame is considered correct after CRC check then: a) Perform enhanced data-aided channel estimation over the whole recovered packet (carrier frequency, phase, amplitude, timing) [63]; b) Reconstruct at baseband the detected packet for following cancellation step; c) Perform interference cancellation; 6) Repeat from step 2 until the maximum number of SIC iterations are performed. When the limit is reached, advance the observation window by ∆W . Two important steps for an optimal E-SSA detection performance are the packet preamble detection (step 2) and the IC process (step 5-c). The hub demodulator starts searching for the presence of packet preambles by means of a conventional preamble correlator. Because of the incoming packets carrier frequency uncertainty (due to the oscillator instabilities), preamble parallel search in the frequency domain is typically required. The preamble miss detection and
38
false alarm probability needs to be lower than the target PLR (e.g. 10−3 ) (see Sect. IV.E in [54]). In practice, in [92] it has been shown that E-SSA can operate with a single spreading sequence common to all terminals thus greatly simplifying the gateway demodulator implementation. The E-SSA performance has been investigated in-depth both, by analysis and simulation, in [54]. The analytical framework reported in this reference, precisely models the performance of spread-spectrum RA techniques, such as SSA and E-SSA, and assumes arbitrary power and traffic distributions, and accurately models the FEC and RA IC behavior (see Section IV-A from [54]). Fig. 12 reports some key conclusions about E- SSA performance compared to the SSA ones previously reported in Fig. 5. First of all, assuming a target PLR of 10−3 and no power imbalance, the E-SSA throughput is 1.12 bits/chip, i.e. 2.4 times higher than conventional SSA. When lognormal distributed packet power is assumed (with standard deviation σ = 3 dB), then the E-SSA throughput exceeds 1.9 bits/chip, i.e. more than 110 times larger than SSA. This striking result is due to the iSIC superior performance compared to conventional SSA burst demodulator in case of imbalanced power.
39
2 E−SSA σ=0 dB, analytical E−SSA σ=3 dB, analytical E−SSA σ=0 dB, simulated E−SSA σ=3 dB, simulated SSA σ=0dB, analytical SSA σ=0dB, simulated SSA σ=3dB, simulated SSA σ=3dB, analytical
1.8
1.6
Throughput (bits/chip)
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
0.5
1 1.5 Average MAC channel load − G [bits/chip]
2
2.5
(a) E-SSA and SSA throughput with and without power unbalance. 0
10
−1
10
−2
Packet Loss Ratio (PLR)
10
−3
10
−4
10
E−SSA σ=0 dB, analytical E−SSA σ=3 dB, analytical E−SSA σ=0 dB, simulated E−SSA σ=3 dB, simulated SSA σ=0dB, analytical SSA σ=0dB, simulated SSA σ=3dB, analytical SSA σ=3dB, simulated
−5
10
−6
10
0
0.5
1 1.5 Average MAC channel load − G [bits/chip]
2
2.5
(b) E-SSA and SSA PLR with and without power unbalance. Fig. 12.
Simulated vs. analytical SSA and E-SSA throughput and PLR performance with and without power unbalance from
[54], 3GPP FEC r = 1/3 with block size 100 bits, BPSK modulation, spreading factor 256, Es /N0 = 6 dB.
40
Initial E-SSA performance results reported in [54] assumed a lognormal incoming packet power distribution because this represented a close approximation for the case of a mobile satellite interactive system exploiting the open loop packet power control algorithm described in the same reference. Similarly to CRDSA, also E-SSA is affected by a PLR floor in the presence of large lognormal packet power standard deviations. As explained in Sect. III-A, the PLR floor appearing in Fig. 12 for σ = 3 dB, is simply due to the probability that a packet Es /N0 is below the FEC decoding threshold given to the lognormal power distribution, and has nothing to do with the E-SSA iSIC process. More recently, some in-depth investigation on the optimum packets power distribution for the E-SSA detector was reported in [93]. The analysis and simulation findings show that for E-SSA, a uniform in dB incoming hub demodulator packets power distribution is very close to the optimum and avoids the appearance of PLR floor effects as in the lognormal case. In the same reference, semi-analytical formulation is provided for computing the optimum minimum packet Es /N0 as a function of the key RA system parameters and the amount of residual power after interference cancellation in the iSIC demodulator. The open loop power control algorithm described in the paper is able to achieve the quasi optimum power distribution in a multi-beam satellite network. More recently a theoretical analysis to calculate the capacity optimizing user SNIR profile for a SSA RA system adopting SIC and FEC has been reported in [94]. B. Minimum Mean Square Error plus E-SSA Ref. [95] describes an extension of the E-SSA detector dubbed Minimum mean square plus Enhanced Spread Spectrum ALOHA (ME-SSA) which further improves the spectral efficiency in particular when the packets power imbalance is modest. The main changes introduced by ME-SSA are: a) the use of QPSK modulation before spreading instead of BPSK used in E-SSA; b) the introduction of a multi-stage approximation of the Minium Mean Square Error (MMSE) filter at the gateway demodulator before the iSIC. As discussed in [95], incorporating the MMSE detector in an E-SSA like scheme is not straightforward. E-SSA is in fact a totally asynchronous system with short time packets being randomly transmitted in time. Thus, active transmitters are continuously changing making the co-channel interference non stationary. This fact, jointly with the use of long spreading code sequences and relatively short packets, makes infeasible the use of a conventional adaptive MMSE detector. Since the implementation of MMSE through a direct
41
matrix inversion is too cumbersome, the adopted solution is the use of a multistage detector approximating the MMSE one following the approach described in [96]–[99]. The multi-stage approximation of the MMSE optimum detector allows to make the detector complexity linear with the number of users. As shown in Fig. 13, the MMSE detector is now approximated by S linear stages with each stage performing despreading (with a single user matched filter detector) and then re-spreading of the input signal. The weighting factors required for combining the S detector stages can be computed off-line following [98], [99]. The adoption of QPSK instead of BPSK modulation before spreading is justified by the need to exploit the maximum number of signal dimensions available to maximize the MMSE performance [100]. Simulation results reported in [95] for SF = 16 (see Fig. 14), show that in case of balanced packets power MESSA gains about 50 % over E-SSA. In case of uniformly distributed in dB power imbalance, the throughput gain can go up to 80%. These gains are obtained at the cost of some affordable gateway complexity increase.
42
Matched Filter User #k
Respread User #k
Matched Filter User #k
Matched Filter User #2
Respread User #2
Matched Filter User #2
Matched Filter User #1
Respread User #1
Matched Filter User #1
A
AH
Ts
A· AH
A Ts
Delay (M-1)Ts
Weighting
Fig. 13.
M-th Stage
1st Stage: Each stage computes A· AH
Delay (M-2)Ts
Ts
Weighting
Weighting
Sum
Sum
ME-SSA MMSE multistage detector functional block diagram from [95].
V. A DVANCED U NSLOTTED N ON S PREAD -S PECTRUM T ECHNIQUES In this section we review recently proposed RA schemes suitable for satellite applications which are sharing the following features: unslotted access, open loop RA, collision resolution through interference cancellation without using spreading techniques. A. Contention Resolution ALOHA Contention Resolution ALOHA (CRA) [101] represents a first step in evolving CRDSA towards an unslotted RA scheme. Differently from CRDSA, the burst transmissions happen
43
2 1.8
E−SSA ME−SSA
Throughput [bits/chip]
1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
0
0.5
1 1.5 Average MAC load [bits/chip]
2
0
10
E−SSA ME−SSA −1
Packet Loss Ratio (PLR)
10
−2
10
−3
10
−4
10
Fig. 14.
0
0.5
1 1.5 Average MAC load [bits/chip]
2
ME-SSA and E-SSA performance comparison from [95]: Packet Length 1200 bits, Code rate 1/3, Preamble Length
1536 chips. ME-SSA uses QPSK modulation. E-SSA uses BPSK modulation.
44
at random times within the frame thus there is no longer a notion of time slots within the frame as for the RA schemes investigated in Sect. III. Similarly to IRSA, the number of packet replicas max can be randomized up to a given maximum number of replicas Nrep . The CRA main advantages
reside in the reduction of the loop probability and the flexible physical layer packet size. The loop probability is reduced in CRA because of the asynchronous packet transmission within the frame. The abolition of the slots within the frame also gives much more freedom in selecting the most appropriate physical layer packet size matching the information packet nature. The main drawbacks of the protocol are the fact that time synchronization at network level is still required and the higher signalling overhead necessary for identifying the replicas with non integer offset and variable packet size. Simulation results reported in [101] show a steeper PLR curve with respect to CRDSA with two replicas due to the reduced loop probability. CRA performance are comparable to the ones of CRDSA with 3 replicas. B. Enhanced Contention Resolution ALOHA Enhanced Contention Resolution ALOHA (ECRA) [102] represents an extension of the CRA concept improving further the loop phenomenon resolution. Initial detection steps are the same as CRA. For each remaining user in the frame, the packet replica(s) chunks without interference are taken and used for creating a new combined packet for the considered user. If some portions of the user packets encounter interference in all the replicas, the replica symbols with the lowest interference level are taken and exploited for creating the combined packet. Similarly to MuSCA, ECRA requires a very robust FEC applied to the headers to allow retrieving the information about replica locations although the packet itself is not decodable due to collisions. The corresponding overhead increase has not been considered in [102]. Some other technical aspects not reported are: a) the way to identify the packet chunks with the highest SNIR; b) the channel estimation techniques and required accuracy for combining complementary packet chunks. C. Asynchronous Contention Resolution Diversity ALOHA The Asynchronous Contention Resolution Diversity ALOHA (ACRDA) protocol has been proposed and analyzed in detail in [103]. Unlike S-ALOHA, DSA, CRDSA or CRA, ACRDA completely eliminates the need to maintain slot (S-ALOHA, DSA, CRDSA) or frame (CRA) synchronization among all transmitters. Differently to SSA and E-SSA, ACRDA does not require
45
the use of spread spectrum techniques. As previously discussed, the need for transmitter synchronization is a major drawback for very large networks (e.g. M2M), as the signaling overhead scales up with the number of transmitters independently from their traffic activity factor. In ACRDA, Earth stations behave like in CRDSA, exploiting packet replicas and the associated location signaling, but do not need to maintain slot synchronization with the hub. The term Virtual Frame (VF) is introduced here to refer to the concept of frame which is only locally valid to each transmitter. In ACRDA, for all transmitters, each VF is composed of a number of slots Nslots and each slot has a duration Tslot with an overall frame duration Tframe = Nslots · Tslot . Fig. 15 shows an example of the Virtual Frame compositions for the ACRDA scheme. The different transmitters are not time synchronized, and hence, the time offset between VF(i), VF(i − 1), VF(i + 1) is arbitrary. In case of mobile applications, the Doppler effect may have an appreciable impact in terms of incoming packets clock frequency offset. In this situation, the VFs will have slightly different duration. However, the localization process of the replica packets within each VF will remain accurate, as the hub burst demodulator will extract for each VF its own clock reference. Since the demodulator has no knowledge of the start of VFs, the signaling of the replicas location within the VF has to be relative to each packet position and not absolute to the start of the VF. Two options exist for the ACRDA modulator, which consist in transmitting all packet replicas randomized within the VF, or transmitting the first packet replica in the first slot of the VF and the remaining replicas randomized within the VF [103]. The latter option improves the end-to-end delay performance, since the first packet replica is transmitted without any added delay.
46
9
9
9
Virtual Frame (i+4) 8
8
8 Virtual Frame (i+3)
7
7
7
Virtual Frame (i+2) Offset between VF(i) and VF(i+1)
6
6
6 Virtual Frame (i+1)
Offset between VF(i-1) and VF(i)
5
5
5
Virtual Frame (i) 4
4
4
Virtual Frame (i-1) 3
3
3
Virtual Frame (i-2) 2
2
2 Virtual Frame (i-3)
1
1
1
Virtual Frame (i-4)
W (s)
∆W W (s + 1)
Fig. 15.
Virtual frame compositions in ACRDA from [103].
At the receiver side, ACRDA combines features of CRDSA, and E-SSA. On one hand, the same E-SSA sliding window-based memory processing described in Sect. IV-A is adopted to handle the packet replicas arriving asynchronously. On the other hand, for a given receiver memory position, the replica packets cancellation scheme described in Sect. III is borrowed from the CRDSA detector processing. The received signal is sampled at baseband, and complex signal samples are stored in a sliding window memory of W virtual frames (see Fig. 15). Typically a value W = 3 is recommended for optimal ACRDA performance (like for E-SSA). For a given window position, the demodulator performs the same iterative processing as for CRDSA to decode the clean packets and perform IC of the replica packets. The detailed ACRDA demodulator operation is described in Sect. II.B in [103]. ACRDA provides better PLR and throughput performance than CRDSA. Fig. 16 compares the performance of the ACRDA vs. CRDSA and E-SSA, evaluated via mathematical analysis and computer simulations. Ref. [103] reports an extension to ACRDA of the analytical framework presented in [48]. For a target PLR= 10−3 and balanced packet power, the ACRDA channel can be loaded up to 1 bits/symbol, while CRDSA not more than 0.75 bits/symbol and E-SSA up to 1.2 bits/chip. It is important to note that the PLR floor present at low loads (i.e. G < 1 bits/symbol) is lower for ACRDA than for CRDSA. The reason for this is that the probability of
47
loops occurrence, described in Sect. III, is significantly lower in ACRDA than in CRDSA due to the asynchronous nature of the access3 . ACRDA with Nrep = 2 is capable to achieve slightly better performance than the optimum CRDSA configuration with Nrep = 3 shown in Fig. 9, thus reducing the demodulator complexity which is proportional to the number of replicas used [68]. It shall be recalled that ACRDA also reduces Earth station complexity, as it does not require any control loop to keep time slot synchronization with the hub. Similarly to CRDSA, ACRDA performance improves under power imbalance. Fig. 8 from [103] shows that ACRDA with FEC r = 1/3, Nrep = 2 and power imbalance following a lognormal power distribution with standard deviation σ = 3 dB, can achieve a throughput T = 1.5 bits/symbol for a target PLR< 10−3 . Figure 17 shows the ACRDA analytical performance as a function of Es /N0 following the methodology developed in [103]. There is a sizeable reduction in performance reducing Es /N0 from 7 to 3 dB. In [103] Sect. IV analyzes and compares the delay performance between ACRDA and CRDSA. Results show that ACRDA reduces the latency by a factor of 10 compared to CRDSA for low loads (e.g. G = 0.3 bits/symbol) and by a factor of 2 at high loads (e.g. G = 0.9 bits/symbol). 3
The effect of loops in performance is analyzed in detail in Sect. III from [103] for ACRDA and in Appendix D from [48]
for CRDSA.
48
1.5
Throughput − T [bits/symbol − bits/chip]
ACRDA, simulated ACRDA, analytical CRDSA, analytical CRDSA, simulated E−SSA, analytical E−SSA, simulated
1
0.5
0
0
0.2
0.4
0.6 0.8 1 1.2 1.4 Average MAC Channel Load − G [bits/symbol − bits/chip]
1.6
1.8
2
(a) Throughput 0
10
−1
10
−2
Packet Loss Ratio [PLR]
10
−3
10
−4
10
CRDSA, simulated ACRDA, simulated ACRDA, analytical CRDSA, analytical E−SSA, analytical E−SSA, simulated
−5
10
−6
10
0
0.2
0.4
0.6 0.8 1 1.2 1.4 Average MAC Channel Load − G [bits/symbol − bits/chipl]
1.6
1.8
2
(b) PLR Fig. 16. Simulation and analytical ACRDA, CRDSA and E-SSA performance from [103]. CRDSA and ACRDA with Nrep = 2, Nslots =100 (simulations), QPSK modulation, 3GPP FEC r = 1/3, packet block size 100 bits, Es /N0 = 10 dB in the presence of no packets power imbalance and Poisson traffic. For ACRDA window size W = 3 virtual frames and a window step ∆W = 0.15.
49
1.1 1
Es/N0=3 dB Es/N0=7 dB
Throughput − T [bit/symbol]
0.9
Es/N0=10 dB
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Normalized Load − G [bits/symbol]
(a) Throughput 0
10
−1
Packet Loss Ratio [PLR]
10
−2
10
−3
10
−4
10
Es/N0=3 dB Es/N0=7 dB Es/N0=10 dB
−5
10
−6
10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Normalized Load − G [bits/symbol]
(b) PLR Fig. 17.
Analytical ACRDA performance versus for different Es /N0 values with Nrep = 2, Nslots =100 (simulations), QPSK
modulation, 3GPP FEC r = 1/3, packet block size 100 bits, window size W = 3 virtual frames and a window step ∆W = 0.15. The results are obtained in the presence of no packets power imbalance and Poisson traffic.
50
D. Multi-Frequency ACRDA The ACRDA concept can be easily extended to Multi-Frequency ACRDA (MF-ACRDA) following the same approach used for MF-CRDSA. MF-ACRDA allows to reduce the terminal power requirements the same way MF-CRDSA does to better compete with schemes such as E-SSA or ME-SSA. . VI. S ATELLITE S YSTEMS AND S TANDARDS A. Congestion Control in RA Satellite Networks Congestion control mechanisms are needed in RA schemes in order to operate around the desired channel load range, e.g. around the maximum throughput region or below a target PLR. Satellite networks typically operate below a target PLR, as retransmissions introduce a large delay penalty due to the long propagation delays (e.g. PLR < 10−3 ). Typical techniques employed for congestion control are a p-persistent algorithm, exponential backoff or a combination of the two. These techniques are widely used in Ethernet networks [104]. In the p-persistent algorithm the sender transmits with a probability p or defers its transmission for a random interval [0, TBO ] with a probability 1 − p. In exponential backoff, a terminal checks whether a packet transmission has been successful or not (e.g. through acknowledgement from the receiver). In case of failure the sender backs-off for a random time chosen between [0, 2 · TBO ] before retransmitting its packet. If the retransmission also fails, the sender backs off for a random time in the interval between [0, 4 · TBO ], and tries again. Each retransmission doubles the interval until the transmission is successful (i.e. increases exponentially). On a successful transmission the backoff interval is reset to its initial value. The values of p and TBO must be chosen to balance the transmission delay and RA channel performance under heavy loads. Small values of p offer good behavior at high channel loads but also increase transmission delay. In satellite systems, the values for these parameters are typically broadcasted on the forward channel (from ground Earth station to all network terminals) [105] and are adapted dynamically as a function of the average channel load. Some examples of implementation of congestion control for CRDSA in satellite systems are provided in [106], [107], [108].
51
B. Random Access Based Satellite Systems Based on RA Differently from common belief, the adoption of RA in satellite networks for messaging service support dates back from 1980s, when the European Space Agency (ESA) developed the PRODAT messaging system applying SSA as the return link access technique and the Lband MARISAT satellite capacity [109]. It featured state-of-the art technology such as DS-SS CDMA access (both SSA RA and DAMA) coupled with convolutional coding, interleaving, Reed-Solomon block codes, and Automatic Repeat reQuest (ARQ) protocols. The very first commercial worldwide deployment of a mobile (trucks) messaging system corresponds to the Qualcomm OmniTRACSr [8] in 1989. OmniTRACSr , which is still operational, is providing messaging and polling capabilities for trucks through geostationary satellites operating at Ku-band. The low-rate return link access scheme is based on SSA. To minimize the power spectral density, OmniTRACSr adopts DS-SS along with frequency hopping schemes. Orbcomm is an American company [110] that since 1998 offers machine to machine (M2M) communications solutions designed to track, monitor, and control fixed and mobile assets in markets including transportation, heavy equipment, maritime, oil and gas, utilities and government. Orbcomm operates in the VHF-band using TDMA multiple access. As of May 2016, Orbcomm has more than 1.6 million billable subscriber communicators, serving Original Equipment Manufacturers (OEMs). Since March 2016 the second generation Orbcomm satellites are operational providing higher throughput per satellite than the first generation ones. A more recent satellite communication system based on SSA access technology is the Viasat ArcLight One [111]. This system exploits constant envelope Gaussian Minimum Shift Keying (GMSK) chip pulse shaping to maximize the user terminal High Power Amplifier (HPA) efficiency. It also reuses the same spreading sequence among different users (like in E-SSA) [112]. Furthermore, inbound packets are transmitted in the same outbound carrier band to reduce the spectrum occupancy. The gateway cancels the outbound carrier from the incoming signals using the Viasat Paired Carrier Multiple Access technology [113]. In the last decade Iridium Low Earth Orbiting (LEO) constellation of 64 satellites has been successfully introducing the M2M service [114]. The air interface is based on the TDMA/FDMA and supports small (up to 350 bytes) sparse packet transmission in a 41.77 kHz channel. The system operates in L-band in time division diplexing with a very small transceiver (31x29 mm).
52
In the same period, Globalstar deployed the Simplex data network [115], [116], for vehicle and asset tracking as well remote data reporting having limited size requirement. The Simplex transmitter sends one-way short data messages in L-band without the need of a forward link carrier. The selected RA protocol is based on SSA along with the concept of sending multiple replicas of the same burst to increase reception probability. The Newtec Sat3Play technology [117] allows the economic provision of triple play services over Ku- and Ka-band geostationary satellites. The return link allows a mixed S-ALOHA RA/DAMA scheme to support different types of traffic. The return link physical layer is based on coded Continuous Phase Modulation (CPM) to reduce the outdoor unit cost and power consumption. The Sat3Play solution has been adopted by SES for their Astra Connect service exploiting Ka-band capacity [118]. The last, in chronological order, realization of an advanced RA protocol in a real system is the Eutelsat Smart-LNB [119]. The Smart-LNB concept integrates the conventional receive only technology of Direct-To-Home (DTH) Low Noise Block (LNB) downconverters [120] with a return link capability based on an adaptation of the S-band Mobile Interactive Multimedia (S-MIM) standard [121], which achieves high spectral efficiency thanks to the E-SSA concept. The return link can be allocated either in Ka- or in Ku-band. The pre-commercial deployment of the Smart-LNB technologies started in 2014 and commercial deployments have been started in 2016. As far as newly proposed satellite M2M systems, MUSTANG [122] is a LEO constellation conceived by Airbus operating at L-band and exploiting the terrestrial M2M SigFox ultranarrowband air interface [123]. The idea is to complement the terrestrial SigFox coverage using the same air interface and very low-cost M2M modules. The LEO satellites can also use existing GEO mobile satellites for relaying the received packets to the ground stations. C. Satellite M2M Key Requirements To design an effective satellite system aiming at provisioning M2M/IoT services, it is important to bear in mind some key design drivers. Firstly, being a business case dominated by the Capital Expenditures (CAPEX), it is fundamental to minimize the terminal manufacturing costs, its size and weight, without neglecting the overall ruggedness and reliability. Then, the efficient usage of the spectrum resources (both forward and return link) along with light network synchronization
53
protocols and resource allocation procedures are essential as well to reduce the Operational Costs (OPEX). Motivated by these key system design aspects, the following requirements shall be taken into account: •
efficient support of bursty traffic: minimizing transmission time in favor of terminal idle state;
•
low terminal data rates: from few hundreds to tenths of thousands bits/s;
•
terminal energy efficiency: minimization of energy required to deliver the information packet;
•
terminal costs: very low (e.g., target less than 50 $);
•
mobility: support of fixed, nomadic and mobile terminals;
•
scalability: from thousands to several hundred thousands connected devices sharing the same bandwidth without degrading the overall system performance;
•
minimization of satellite spectrum requirements: to minimize the OPEX;
•
return link only capability: to operate completely asynchronously not requiring the support of a forward link;
•
minimum overhead: to ease system scalability and maximize spectrum resource exploitation;
•
data security and integrity: encryption techniques (at the link layer and/or at the application layer) and data verification methods;
•
robustness: communication guaranteed in both directions regardless the channel quality;
•
reliability: long life and trustiness to minimize the maintenance/replacement costs;
•
flexibility: air interface adaptable to the different operational frequencies (e.g., UHF, L, S, Ku, Ka bands).
The use of state-of-the-art RA protocols previously illustrated, is a key enabler to satisfy a number of the above listed requirements. In particular RA is effective in terms of supporting bursty traffic, ,low data rates, network scalability, return link only capability, overhead minimization, terminal energy efficiency, mobility support, reliability of data transfer, air interface adaptability to the operational frequency band. D. Standards with RA features RA has been traditionally adopted in satellite communication standards for the initial network login and the transmission of short control or data packets, such as in the Digital Video Broadcast-
54
ing Return Channel via Satellite (DVB-RCS) standard [55], or in the IP over Satellite standard [56]. In the last years, a number of satellite communication standards have been developed integrating more recent RA techniques. As far as slotted RA solutions are concerned, the second generation of the DVB-RCS standard [105] has added CRDSA and IRSA techniques as an optional feature particularly advantageous for SCADA and consumer profiles. These techniques can be easily integrated in the MF-TDMA nature of this standard, and they can provide very large throughput improvement (about 1400 times as presented in Sec. III-A) compared to classical S-ALOHA solutions. The technology readiness is relatively high, since a complete hardware test-bed has been developed in 2011 [124] for the laboratory validation of such techniques. The fully RA asynchronous low transmit power solution offered by E-SSA has been considered in two mobile standardization contexts. First, the inclusion of the low-latency profile in the DVBSatellite services to Handhelds (DVB-SH) standard has opened up the possibility to add a return link which is complementing the DVB-SH for interactive services. Consequently, the European Telecommunications Standards Institute (ETSI) S-MIM standard [125] has adopted the E-SSA protocol for the asynchronous access to support satellite mobile messaging and low-data rate services [126]. The technology maturity of E-SSA is very high, since several joint ESA/Eutelsat activities related to the design [126] and implementation of the DVB-SH/S-MIM standards have fostered the deployment of prototypes [127] and supported extensive validation campaigns in the field [128]. The second E-SSA application in standards is in the frame of the Single European Sky ATM Research (SESAR) programme, where new data-links are being developed to support 4D Air Traffic Management (ATM). A draft proposal for the satellite data-link standard has been published by ESA in 2013 [129] and the return link communications (both data and voice services) are provided through the E-SSA protocol. The full standardization process started in 2015 within the European Organisation for Civil Aviation Equipment Working Group 82, dealing with new terrestrial and satellite data link technologies for ATM. E. Example of M2M Satellite System Dimensioning Hereafter, we present a realistic study case in which we show how low power and low data-rate services can be efficiently served via existing satellites by implementing the E-SSA protocol. The focus is on fixed M2M applications through a geostationary satellite. In the example, the
55
satellite terminal acts as a collector of information packets from a small network of sensors. This assumption fits perfectly the case of delivery of M2M services in areas not sufficiently covered by terrestrial communications infrastructures. Inspired by the Smart-LNB solution, and to reduce both space and ground segment costs, the satellite is operating in Ku and C-band, whereas the M2M terminal is reachable via existing Fixed Satellite Service (FSS) carriers (e.g., DTH technologies) in the forward link. In the return link, the M2M system is occupying about 300 kHz of bandwidth for sending the sensors information. Fig. 18 shows a typical example of link budget for such type of services. Only the up-link parameters have been reported, because it is the one driving the overall return link performance. The physical layer waveform has been designed to offer a data rate of 5 kbps per satellite terminal. In addition, two different hypothesis on the reverse link satellite coverage have been considered i.e. global and regional beams. This choice is typically a trade-off between satellite antenna gain, service coverage region and space segment costs. When looking at the link budget, it is noted that the maximum terminal transmission power is very low, less than 100 mW in both cases. Nonetheless, this small transmitter power provides enough link margin for both coverage, i.e. about 6 or 11 dB for a global or regional beam, respectively. As discussed in Sect. IV, this is beneficial for the E-SSA waveform, because the larger is the range for the power randomization algorithm, the higher is the achievable RA scheme spectral efficiency. A small part of the available link margin can be used for counteracting the atmospheric fading while the rest can be used for randomizing the transmit packet power by means of the open loop power control algorithm described in [93].
56
Fig. 18. Example of link budget in Ku-band for low data rate M2M services using E-SSA, and assuming different reverse link satellite coverage: global vs. regional beam.
The offered traffic and the relative PLR curves for this type of E-SSA waveform are summarized in Fig. 19, as a function of two different values for the terminal transmission power randomization range. As expected, the largest packet power randomization value of 9 dB, compatible with the 11 dB link margin, provides the best performance. The corresponding overall system throughput is close to T = 1.8 bits/chip for the regional beam and to T = 1.4 bits/chip for the global coverage case.
57
Fig. 19.
E-SSA offered throughput and PLR performance as a function of different power transmission ranges in the context
of the M2M example.
In practice, for the system designer it is interesting to derive the maximum number of satellite terminals Nu that could co-exist in the network with still acceptable quality of service (e.g., PLR < 10−3 ) without the introduction of sophisticated congestion control mechanisms. An effective rule of thumb is Nu '
T · Rc T (Gp ) = · Gp , Rb · da da
(3)
where T (Gp ) is the achievable throughput at the desired PLR value for a given Gp value4 , Rc is the E-SSA chip rate, Rb represents the single M2M terminal transmitted packet data rate, and finally 0 ≤ da ≤ 1 stands for the average activity factor of the M2M terminal. Looking at (3), we notice that as expected, by increasing the processing gain Gp , i.e. increasing the occupied bandwidth for given user bit rate, the number of supported users will scale up almost linearly. In our example, assuming that each satellite terminal is transmitting a burst every hour, this solution is allowing to support Nu ' 311 000 devices. 4
Typically, the RA throughput has a mild, yet not negligible for SF < 64, dependence on Gp .
58
VII. C ONCLUSIONS AND O UTLOOK The extensive review of high performance RA techniques suitable to satellite networks contained in this paper shows that the combination of advanced signal processing at physical layer with smart MAC design can provide outstanding performance improvements well in excess of 3 order of magnitudes compared to the original ALOHA protocol. Thanks to the collision resolution techniques adopted, a properly designed high performance RA scheme can achieve a steep PLR transition from 0 to 1 with no packet errors up to a critical MAC load level. This greatly simplifies the implementation of congestion control algorithms. This feature also makes it possible to have a fire and forget type of operation from the transmitter side at the advantage of the device energy efficiency. Furthermore, the adoption of RA techniques for M2M type of networks has the advantage of simplicity and scalability. We have shown that even when operating a large quantity of terminals in asynchronous open loop fashion, state-of-the-art RA protocols can achieve performance comparable to fixed resource assignment without the large inefficiencies typically associated with infrequent packet transmission. The review of high performance RA schemes has shown that the best performance is not always coupled with the most complex scheme. The complexity on the transmitter side of the new satellite RA schemes is generally kept to a minimum and comparable to the traditional S-ALOHA or SSA protocols. It is very important to stress the criticality of channel estimation, which is usually neglected in the analysis of RA techniques. Contention resolution RA techniques are typically operating in a highly interfered conditions due to colliding packets and require effective detection, synchronization and cancellation techniques to work in practice with acceptable overhead. It has been shown that SSA RA with SIC achieves the best throughput performance, thanks to the inherent collision resolution capability and the high level of traffic aggregation which is smoothing the traffic burstiness. Also from the overhead, power and energy efficiency point of view, SSA turns out to be superior to slotted techniques. Among non spreadspectrum unslotted techniques, MF-ACRDA can approach the E-SSA performance, but with lower spectral and energy efficient. Exploiting high aggregated throughput, high reliability RA techniques enable achieving low energy, reliable and scalable transmission of small size scattered transmission of packets as required to support low-cost ubiquitous M2M applications via satellite. Several of the new satellite RA techniques have been developed and validated by means
59
of real time hardware testbeds or even developed at pre-commercial level, thus confirming their viability in the field, at least for satellite applications. Some of the high performance RA techniques reviewed already entered in recent satellite telecommunication standards. Some more recent and attractive schemes such as (MF-)ACRDA, MARSALA and ME-ESSA are yet to be demonstrated in practical implementations. Another area requiring further investigation is related to the application of the surveyed advanced RA schemes to satellites in Low Earth Orbit. This will require enhanced gateway demodulator processing to cope with the increased Doppler shift and Doppler rate compared to Geostationary satellites. The challenge now is to exploit these kind of new collision resolution RA paradigms in the frame of terrestrial wireless networks for supporting Internet of Things (IoT) M2M-type of communications. The need for reliable RA schemes supporting a large number of devices with minimum overhead and maximum spectral and energy efficiency is in common between RA techniques for satellite and terrestrial applications. We hope that this survey material will help bridging the two research communities. The accumulated research on RA collision resolution techniques we reviewed in this paper, although initially stimulated by satellite applications, can be such a technology enabler for future M2M networks. More specifically, what remains to be investigated are the advanced RA schemes performance with channel estimation techniques representative of mobile terrestrial channels. The need to minimize the cost of the M2M/IoT sensor also requires to devise robust detection techniques at the base station which can cope with low quality oscillators in the transmitter in terms of frequency stability and phase noise. This represents an interesting subject of further research.
60
A BBREVIATIONS 3GPP:
Third Generation Partnership Project
ACRDA:
Asynchronous Contention Resolution Diversity ALOHA
ATM:
Air Traffic Management
BPSK:
Binary Phase Shift Keying
CDMA:
Code Division Multiple Access
CFDAMA-PB:
Combined Free Demand Assignment Multiple Access Piggy Backed
CPM:
Continuous Phase Modulation
CPODA:
Contention-Based Demand Assignment
CRDSA:
Contention Resolution Diversity Slotted ALOHA
CRRMA:
Combined Random Reservation Multiple Access
CSMA/CA:
Carrier Sense Multiple Access/Colision Avoidance
CSMA/CD:
Carrier Sense Multiple Access/Colision Detection
CRI:
Collision Resolution Interval
CRA:
Contention Resolution ALOHA
CSA:
Coded Slotted ALOHA
CTS:
Clear to Send
DAMA:
Demand Assignment Multiple Access
DSA:
Diversity Slotted ALOHA
DS-SS:
Direct-Sequence Spread Spectrum
DTH:
Direct-To-Home
DVB-RCS:
Digital Video Broadcasting Return Channel via Satellite
DVB-SH:
Digital Video Broadcasting Satellite services to Handhelds
ECRA:
Enhanced Contention Resolution ALOHA
EIRP:
Effective Isotropically Radiated Power
ESA:
European Space Agency
E-SSA:
Enhanced Spread Spectrum ALOHA
ETSI:
European Telecommunications Standards Institute
FEC:
Forward Error Correction
FDMA:
Frequency Division Multiple Access
FSS:
Fixed Satellite Service
GEO:
Geostationary
GMSK:
Gaussian Minimum Shift Keying
HPA:
High Power Amplifier
IC:
Interference Cancelation
IP:
Internet Protocol
IRSA:
Irregular Repetition Slotted ALOHA
SIC:
Successive Interference Cancellation
iSIC:
iterative Successive Interference Cancellation
LDPC:
Low Density Parity Check
LNB:
Low Noise Block
M2M:
Machine-to-Machine
61
MARSALA:
Multi-ReplicA Decoding using corRelation baSed LocAlisAtion
MAC:
Medium Access Control
MACA:
Multiple Access Collision Avoidance
ME-SSA:
Minimum mean square plus Enhanced Spread Spectrum ALOHA
MF-ACRDA:
Multi-Frequency Asynchronous Contention Resolution Diversity ALOHA
MF-CRDSA:
Multi-Frequency Contention Resolution Diversity Slotted ALOHA
MF-TDMA:
Multi-Frequency Time Division Multiple Access
MMSE:
Minimum Mean Square Error
MTA:
Massey’s Modified Tree Algorithm
MuSCA:
Multi-Slots Coded ALOHA
NCDP:
Network-Coded Diversity Protocol
PLR:
Packet Loss Ratio
PNC:
Physical Network Coding
PRDAMA:
Predictive Demand Assignment Multiple Access
QPSK:
Quadrature Phase Shift Keying
RA:
Random Access
RACH:
Random Access Channel
RAN:
Random Access with Notification
R-CRDSA:
Reservation CRDSA
RFID:
Radio Frequency Identification
RTS:
Request to Send
S-ALOHA:
Slotted ALOHA
SCADA:
Supervisory Control and Data Acquisition
SESAR:
Single European Sky ATM Research
SIC:
Successive Interference Cancelation
SICTA:
Successive Interference Cancellation Tree Algorithm
S-MIM:
S-band Mobile Interactive Multimedia
SNIR:
Signal-to-Noise and Interference Ratio
SSA:
Spread Spectrum ALOHA
SS-CRDSA:
Spread Spectrum Contention Resolution Diversity Slotted ALOHA
STA:
Standard Tree Algorithm
SUMF:
Single User Matched Filter
TCP:
Transmission Control Protocol
UW:
Unique Word
VF:
Virtual Frame
VSAT:
Very Small Aperture Terminal
62
L IST OF S YMBOLS a
random variable representing the packet amplitude
C/(N + I)
carrier-to-noise and intereference
∆W
memory window step size in symbols
da
activity factor of M2M terminal
Es /N0
symbol energy over noise spectral density
G
normalized average channel MAC load
Gp
processing gain
G/T
antenna gain over noise temperature
l
burst repetition rate of IRSA scheme
lmax
maximum number of burst repetition
λ
average number of packet arrivals over one packet duration
M
modulation cardinality
NFM F
number of frequency sub-bands in the MF-CDRSA frame
iter Nmax
maximum number of iterations of CRDSA detector
Nrep
number of replicas transmitted for each packet
Nsc
number of chips per physical layer channel symbol in E-SSA scheme
Nslots
number of slots within a TDMA frame
NTM F
number of time slots in the MF-CDRSA frame
NSW
number of slots within sliding window
Nu
maximum number of satellite terminals
pA (.)
probability density function of lognormal distribution
Rb
single M2M terminal transmitted packet data rate
Rc
E-SSA chip rate
r
FEC code rate
µ, σ
the mean and standard deviation of the random variable natural logarithm
SF
spreading factor
T
throughput
Tframe
frame duration
Tslot
slot duration
V F (i)
i-th virtual frame
W
memory window size in symbols
63
R EFERENCES [1] J. Kim, J. Lee, J. Kim, and J. Yun, “M2M Service Platforms: Survey, Issues, and Enabling Technologies”, IEEE Commun. Surveys Tutorials, Vol. 16, No. 1, pp. 61-76, 2014. [2] Nokia LTE M2M, “Optimizing LTE for the Internet of Things”, White Paper, 2014. [3] T. Nguyen, T. Suda, ”Survey and evaluation of multiple access protocols in multimedia satellite networks,” in IEEE Proceed. of the Southeastcon ’90, vol.2, pp.408-413, 1-4 Apr 1990. [4] G. Maral, “VSAT Networks”, Second Edition, John Wiley & Sons Ltd, 2003, ISBN: 978-0-470-86684-9. [5] A.H. Rana, J.S. McCoskey, and W.A. Check, “VSAT technology, trends, and applications”, Proceedings of the IEEE, Vol. 78, Issue 7, pp. 1087-1095, July 1990. [6] D. Chakraborty, “VSAT Communications Networks - An overview”, IEEE Communications Magazine, Vol. 26, No. 5, pp. 10-24, May 1988. [7] G. Maral, and M. Bousquet, “Satellite Communications Systems”, Fifth Edition, John Wiley & Sons Ltd, 2009, ISBN: 978-0-470-71458-4. [8] I.M. Jacobs, A. Salmasi, K.S. Gilhousen, L.A. Weaver, A. Lindsay, T.J. Bernard, and J.Thomas, “A second anniversary operational review of the OmniTRACSr : The first two-way mobile Ku-band satellite communications system”, Proc. of the 2nd International Mobile Satellite Conference (IMSC), Ottawa, Canada, 17-20 June 1990, pp 13-18. [9] R. A. Wiedeman, and A. J. Viterbi. “The Globalstar Mobile Satellite System for Worldwide Personal Communications”, Proc. 3rd Int. Mobile Sat. Conf., (IMSC), vol. 93, pp. 291-296. 1993. [10] A. Laya, L. Alonso, and J. Alonso-Zarate, “Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives”, IEEE Commun. Surveys and Tutorials, vol. 16, No. 1, pp. 4-16, Jan. 2014. [11] F.A. Tobagi, “Multiaccess Protocols in Packet Communication Systems”, IEEE Transactions on Communications, Vol. COM-28, No. 4, pp. 468-488, April 1980. [12] C. Retnadhas, “Satellite Multiple Access Protocols”, IEEE Communications Magazine, Vol. 18, No. 5, pp. 16-20, September 1980. [13] C.J. Wolejsza, D. Taylor, M. Grossman, and W.P. Osborne, “Multiple Access Protocols for Data Communications via VSAT Networks”, IEEE Communications Magazine, Vol. 25, No. 7, pp. 30-39, July 1987. [14] D. Raychaudhuri, and K. Joseph, “Channel Access Protocols for Ku-band VSAT Networks: a comparative evaluation”, IEEE Communications Magazine, Vol. 26, No. 5, pp. 34-44, May 1988. [15] H. Peyravi, “Medium Access Control Protocol Performance in Satellite Communications”, IEEE Communications Magazine, Vol. 37, No. 3, pp. 62-71, March 1999. [16] P. M. Feldman, “An Overview and Comparison of Demand Assignment Multiple Access (DAMA) Concepts for Satellite Communications Networks”, RAND corporation, 1996. [17] J.G. Puente, “A PCM-FDMA demand assignment satellite multiple access experiment”, Proc. of the AIAA 2nd Communications Satellite Systems Conference, paper No. 68-451, 8-10 April 1968, San Francisco (USA). [18] G. Frenkel, “The grade of service in multiple-access satellite communications systems with demand assignments”, IEEE Transactions on Communications, Vol. 22, No. 10, pp. 1681-1685, October 1974. [19] I. Jacobs, L.N. Lee, A. Viterbi, R. Binder, R. Bressler, N.T. Hsu, and R. Weissler, “CPODA - A demand Assignment protocol for Satnet”, Proc. of the 5th Symposium on Data Communications (SIGCOMM’77), pp. 2.5-2.9, 1977. [20] T. Le-Ngoc, and S.V. Krishnamurthy, “Performance of Combined Free/Demand Assignment Multiple-Access Schemes in
64
Satellite Communications”, Wiley International Journal of Satellite Communications, Vol. 14, No. 1, pp. 11-21, January 1996. [21] W.E. Leland, “On the self-similar nature of Ethernet traffic (Extended version)”, IEEE ACM Transactions on Networking, Vol. 2, No. 1, pp. 1-15, February 1994. [22] V. Paxson, S. Floyd, “Wide Area Traffic: the failure of Poisson modeling”, IEEE ACM Transactions on Networking, Vol. 3, No. 3, pp. 1-21, June 1995. [23] T. Le-Ngoc and I.M. Jahangir, “Performance Analysis of CFDAMA-PB Protocol for Packet Satellite Communications”, IEEE Trans. on Communications, Vol. 46, No. 9, pp. 1206-1214, September 1998. [24] O. del R´ıo Herrero, G. Foti and G. Gallinaro, “Spread-Spectrum Techniques for the Provision of Packet Access on the Reverse Link of Next-generation Broadband Multimedia Satellite Systems”, IEEE Journal on Selected Areas in Communications, Vol. 22, Issue 3, pp. 574-583, April 2004. [25] N. Abramson, “Multiple Access in Wireless Digital Networks”, IEEE Proceedings, Vol. 82, No. 9, pp. 1360-1370, September 1994. [26] R. R. Tyagi, F. Aurzada, K.-D. Lee, M, Reisslein, ”Connection Establishment in LTE-A Networks: Justification of Poisson Process Modeling,” in IEEE Systems Journal, vol. PP, No. 99, pp. 1-12. [27] Z. Jiang, Y. Li, and V.C.M. Leung, “Predictive Demand Assignment Multiple Access Protocol for Broadband Satellite Networks Supporting Internet Applications”, Proc. of the IEEE International Conference on Communications (ICC), pp. 2973-2977, Vol. 5, 28 April - 2 May 2002, New York (USA). [28] M. Hasegawa, G. Wu, and M. Mizuno, “Applications of Nonlinear Prediction Methods to the Internet Traffic”, Proc. of International Symposium on Circuits and Systems, Vol. 3, pp. 169-172, May 2001, Sydney (Australia). [29] H.W. Lee and J. W.Mark, “Combined Random/reservation Access for Packet Switched Transmission over a Satellite with On-board Processing: Part I Global Beam Satellite”, IEEE Transactions on Communications, Vol. 31, No. 10, pp. 1161-1171, October 1983. [30] D. M. Chitre, “VSAT Networks: Architectures, Protocols, and Management”, IEEE Communications Magazine, July 1988, Vol. 26, No. 7, pp. 28-38, July 1988. [31] D. Bertsekas and R. Gallager, “Data Networks”, Second Edition, Prentice Hall, 1992, ISBN 978-0132009164. [32] S. Keshav, “An Engineering Approach to Computer Networking”, Addison-Wesley Professional, 1997, ISBN 9780201634426. [33] L. Kleinrock and F.A. Tobagi, “Packet Switching in Radio Channels: Part I - Carrier Sense Multiple Access Modes and their Throughput-delay Characteristics”, IEEE Trans. on Communications, Vol. 23, No. 12, pp. 1400-1416, December 1975. [34] R. M. Metcalfe and D.R. Boggs, “ETHERNET: Distributed Packet Switching for Local Computer networks”, Communications of the ACM Magazine, Vol. 19, No. 7, pp. 395-404, July 1976. [35] F.A. Tobagi, and L. Kleinrock, “Packet Switching in Radio Channels: Part II - The Hidden Terminal Problem in Carrier Sense Multiple-Access and the Busy-Tone Solution”, IEEE Trans. on Communications, Vol. 23, No. 12, December 1975. [36] IEEE Std 802.3-2005, “IEEE Standard for Information Technology; Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks; Specific Requirements Part 3: Carrier Sense Multiple Access with Collision Detection (CSMA/CD) Access Method and Physical Layer Specifications”, December 2005. [37] IEEE Std 802.11-2012, “IEEE Standard for Information Technology; Telecommunications and Information Exchange
65
Between Systems Local and Metropolitan Area Networks; Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications”, March 2012. [38] P. Karn, “MACA - A New Channel Access Method for Packet Radio”, Proc. of the ARRL/CRRL Amateur Radio and 9th Computer Networking Conference, pp. 134-140, 22 September 1990, London and Ontario (Canada). [39] 3GPP TS 25.214 v3.12.0, “Physical Layer Procedures (FDD); Release 1999”, March 2003. [40] N. Abramson, “The ALOHA system - Another Alternative for Computer Communications”, Proc. of 1970 Fall Joint Computer Conference AFIPS, vol. 37, pp. 281-285, AFIPS Press, Montvale (NJ), 1970. [41] N. Abramson, “The Throughput of Packet Broadcasting Channels”, IEEE Trans. on Communications, Vol. 25, No. 1, pp. 117-128, January 1977. [42] L. Kleinrock and S. Lam, “Packet switching in a multiaccess broadcast channel: Performance evaluation, IEEE on Transactions, vol. 23, no. 4, pp. 410-423, April 1975. [43] G.L. Choudhury and S.S. Rappaport, “Diversity ALOHA - A Random Access Scheme for Satellite Communications”, IEEE Trans. on Communications, Vol. 31, pp. 450-457, March 1983. [44] L. G. Roberts, “ALOHA Packet Systems with and Without Slots and Capture”, Arpanet Satellite System Note 8 (NIC Document #11290), 26 Jun. 1972. [45] M. Zorzi and R. Rao, “Capture and retransmission control in mobile radio, IEEE Journ. on Selected Areas in Communications, pp. 1289-1298, October 1994. [46] A. Zanella and M. Zorzi,“Theoretical Analysis of the Capture Probability in Wireless Systems with Multiple Packet Reception Capabilities,” IEEE Trans. Commun., vol. 60, no. 4, pp. 1058-1071, Apr. 2012. [47] Proakis, J. G. Digital Communications, 4th ed., New York, McGraw-Hill, 2001. [48] O. del R´ıo Herrero and R. De Gaudenzi, “Generalized Analytical Framework for the Performance Assessment of Slotted Random Access Protocols”, IEEE Trans. on Wireless Communications, Vol. 13, No. 2, pp. 809-821, February 2014. [49] M. Mathis, J. Semke, J. Mahdavi, “The Macroscopic Behavior of the TCP Congestion Avoidance Algorithm”, in Computer Communication Review, a publication of ACM SIGCOMM, vol. 27, no. 3, July 1997, pp. 67-82. [50] J. Padhye, V. Firoiu, D. Townsley and J. Kurose, “Modelling TCP Throughput: A Simple Model and its Empirical Validation”, in Proc. SIGCOMM Symp. Communications Architectures and Protocols, August 1998, pp. 304-314. [51] M. Mathis, J. Semke, J. Mahdavi, “The Macroscopic Behavior of the TCP Congestion Avoidance Algorithm”, in Computer Communication Review, a publication of ACM SIGCOMM, vol. 27, no. 3, July 1997, pp. 67-82. [52] J. Padhye, V. Firoiu, D. Townsley and J. Kurose, “Modelling TCP Throughput: A Simple Model and its Empirical Validation”, in Proc. SIGCOMM Symp. Communications Architectures and Protocols, August 1998, pp. 304-314. [53] C. Pateros, “Novel direct sequence spread spectrum multiple access technique,” Proc. MILCOM 2000, 21st Century Military Communications Conference Proceedings, Vol. 2, pp. 564-568, Los Angeles (CA) Oct. 22-25, 2000. [54] O. del R´ıo Herrero and R. De Gaudenzi, “High Efficiency Satellite Multiple Access Scheme for Machine-to-Machine Communications”, IEEE Trans. on Aerospace and Electronic Systems, Vol. 48, No. 4, pp. 2961-2989, October 2012. [55] ETSI EN 301 790 v1.2.2, “Digital Video Broadcasting (DVB); Interaction Channel for Satellite Distribution Systems”, December 2000. [56] Telecommunication Industry Association TIA-1008, “IP Over Satellite”, October 2003. [57] J.F. Hayes, “An Adaptive Technique for Local Distribution,” IEEE Trans. on Communications, vol. 26, No. 8, pp. 11781186, August 1978.
66
[58] J.I. Capetanakis, “Tree Algorithm for Packet Broadcast Channels, IEEE Trans. on Information Theory, Vol. IT-255, pp. 505-515, September 1979. [59] J. L. Massey, “Collision Resolution Algorithm and Random Access Communications”, Multiuser Communication Systems (CISM Course and Lecture Notes), G. Longo, Ed. New York: Spriger-Verlag, 1981, vol. 265. [60] Y. Yu and G.B. Giannakis, “High-Throughput Random Access Using Successive Interference Cancellation in a Tree Algorithm”, IEEE Trans. on Information Theory, Vol. 53, No. 12, December 2007, pp. 4628-4639. [61] E. Casini, R. De Gaudenzi, and O. del R´ıo Herrero, “Contention Resolution Diversity Slotted Aloha (CRDSA): an Enhanced Random Access Scheme for Satellite Access Packet Networks,” IEEE Trans. on Wireless Communications, Vol. 6, No. 4, pp. 1408-1419, April 2007. [62] S. Verdu, “Multiuser detection”, Cambridge Univ. Press, 1998, ISBN 9780521203951. [63] E. Casini, O. del R´ıo Herrero, R. De Gaudenzi, D.M.E. Delaruelle, and J.P. Choffray, “Packet data transmission over a shared transmission channel”, US Patent No. 8,094,672 January 2, 2012. [64] R. De Gaudenzi and O. Del R´ıo Herrero, “Advances in Random Access protocols for satellite networks”, Proc. of International Workshop on Satellite and Space Communications (IWSSC), September 10-12 2009, Siena (Italy). [65] C. Kissling, “On the Stability of Contention Resolution Diversity Slotted ALOHA (CRDSA)”, In the Proc. of IEEE 2011 Global Telecommunications Conference (Globecom 2011), Houston, Texas, USA, Dec. 5-9, 2011, pp. 1-6. [66] A. Meloni and M. Murroni, “CRDSA, CRDSA++ and IRSA: Stability and Performance Evaluation”, In the Proc. of 2012 6th Advanced Satellite Multimedia Systems Conference (ASMS) and 12th Signal Processing for Space Communications Workshop (SPSC), Baiona, Spain, September 5-7, 2012, pp. 220-225. [67] A. Meloni,”Advanced Random Access Techniques for Satellite Communications”, PhD Thesis in Electronic and Computer Engineering, Dept. of Electrical and Electronic Engineering University of Cagliari, March 2014. [68] A. Mengali, R. De Gaudenzi, and P.D. Arapoglou, “Optimizing Contention Resolution Diversity Slotted ALOHA to Support Massive Access Protocols”, submitted to IEEE Trans. on Comm, January 2016. [69] M. Lee, J.-K. Lee, J.-J. Lee, and J. Lim, “R-CRDSA: Reservation-Contention Resolution Diversity Slotted ALOHA for Satellite Networks”, IEEE Communications Letters, Vol. 16, No. 10, pp. 1576-1579, August 2012. [70] A. Meloni, M. Murroni, C. Kissling, M. Berioli, “Sliding window-based Contention Resolution Diversity Slotted ALOHA”, IEEE Global Communications Conference (GLOBECOM) 2012, Anaheim, CA, USA, pp. 3305 - 3310, December 3-7, 2012. [71] H.-C. Buiy, K. Zidane, J. Lacan, M. L. Boucheret, “A Multi-Replica Decoding Technique for Contention Resolution Diversity Slotted Aloha”, VTC Fall, 2015, Boston, MA, 6-9 Sept. 2015. [72] K. Zidane, J. Lacan, M. Ginestex, C. Bes, A. Deramecourt and M. Dervin, “Performance Evaluation of MARSALA with Synchronisation Errors in Satellite Communications”, arXiv:1511.05359v1 [cs.IT] 17 Nov 2015. [73] G.E. Corazza, “Digital Satellite Communications”, ISBN 9780387256344, Springer 2007. [74] E.
Garrido
Master
Barrab´es,
Thesis,
“ACRDA
Universitat
Advanced
Ramon
Llull,
Random La
Salle
Access
Scheme
Barcelona
-
for
Satellite
European
Space
Communications”, Agency,
2013:
https://esecretary.salle.url.edu/zonaa utentica/memoriesTFC/7836G K2 012.pdf. [75] G. Liva, “Method for Contention Resolution in Time Hopping or Frequency Hopping”, US Patent Application No. US/2011/0096795 A1, April 2011. [76] G. Liva, “Graph-Based Analysis and Optimization of Contention Resolution Diversity Slotted ALOHA,” IEEE Trans. on Communications, Vol. 59, Issue 2, February 2011, pp. 477-487.
67
[77] N. Celandroni, E. Ferro, and A. Gotta, “RA and DA Satellite Access Schemes: a Survey and Some Research Results and Challenges,” International Journal of Communication Systems, Vol. 27, No. 11, pages 2670-2690, November 2014. ˇ Stefanovi´c, M. Momoda, and P. Popovski, “Exploiting Capture Effect in Frameless ALOHA for Massive Wireless [78] C. Random Access,” in Proc. of IEEE WCNC 2014, Istanbul, Turkey, Apr. 2014, pp. 1-6. [79] H.C. Bui, J. Lacan, and M.-L. Boucheret, “An enhanced multiple random access scheme for satellite communications”, Proc. of the 2012 Wireless Telecommunications Symposium (WTS), 18-20 April 2012. [80] E. Paolini, G. Liva and M. Chiani, “High Throughput Random Access via Codes on Graphs: Coded Slotted ALOHA”, Proc. of the IEEE International Conference on Communications (ICC), 5-9 June 2011, Kyoto (Japan), pp. 1-6. [81] M. Ivanov, F. Br¨ annstr¨ om, A. Graell i Amat, P. Popovski, “Error Floor Analysis of Coded Slotted ALOHA over Packet Erasure Channels”, IEEE Communications Letters, Vol. 19, No. 3, pp. 419-422, March 2015. ˇ Stefanovi´c, P. Popovski, D. Vukobratovic, “Frameless ALOHA Protocol for Wireless Networks”, IEEE Communications [82] C. Letters, Vol. 1, No. 12, pp. 2087-2090, December 2012. ˇ Stefanovi´c, P. Popovski, “ALOHA Random Access that Operates as a Rateless Code”, IEEE Transactions on [83] C. Communications, Vol. 61, No. 11, pp. 4653-4662, October 2013. [84] M. Luby, “LT Codes,” in IEEE FOCS 02, Vancouver, Canada, 2002. ˇ Stefanovi´c, M. Momoda, P. Popovski, “Exploiting Capture Effect in Frameless ALOHA for Massive Wireless Random [85] C. Access”, IEEE Wireless Communications and Networking Conference 2014 (WCNC), pp. 1762-1767, Istanbul, Turkey, April 6-9 2014. [86] J. Goseling, M. Gastpar, J. H. Weber, “Physical-layer network coding on the random-access channel”, IEEE International Symposium on Information Theory Proceedings (ISIT) 2013, Istanbul, Turkey, July 2013, pp. 2339-2343. [87] S. C. Liew, S. Zhangb, L. Lu, “Physical-layer network coding: Tutorial, survey, and beyond”, Elsevier, Physical Communication, Vol. 6, March 2013, pp. 442. [88] P. Popovski and H. Yomo, “Physical network coding in two-way wireless relay channels, IEEE International Conference on Communications 2007 (ICC), 24-28 June 2007, Glasgow, Scotland, pp. 707-712. [89] G. Cocco, N. Alagha, C. Ibars, and S. Cioni, “Network-coded Diversity Protocol for Collision Recovery in Slotted ALOHA Networks”, International Journal of Satellite Communications and Networking, Vol. 32, No. 3, pages 225-241, May/June 2014. [90] P. Castiglione, F. Ricciato, and P. Popovski, “Pseudo-random ALOHA for inter-frame soft combining in RFID systems,” Proc. 18th Int. Conf. Digital Signal Processing, 2013, pp. 1-6. [91] O. del R´ıo Herrero and R. De Gaudenzi, “Methods, Apparatuses and System for Asynchronous Spread-Spectrum Communications,” U.S. Patent 7,990,874 B2, August 2, 2011. [92] R. De Gaudenzi, O. del Rio Herrero, G. Gallinaro, “Enhanced Spread Aloha Physical Layer Design and Performance”, Wiley Int. Journal of Satellite Communications and Networking, Volume 32, Issue 6, pages 457-473, November/December 2014. [93] F. Collard and R. De Gaudenzi, “On the Optimum Packet Power Distribution for Spread Aloha Packet Detectors with Iterative Successive Interference Cancellation”, IEEE Trans. Wireless Comm., Vol. 13, No. 12, pp. 6783-6794, December 2014. [94] J. Sala-Alvarez, J. Vilares, and F. Rey, “SINR Profile for Spectral Efficiency Optimization SIC Receivers in the Many-User Regime”, Proc. of IEEE International Communication Conference (ICC), London, United Kingdom, June 8-12 2015, pp. 1-6.
68
[95] G. Gallinaro, N. Alagha, R. De Gaudenzi, K. Kansanen, R. M¨ uller, and P. Salvo Rossi, “ME-SSA: an Advanced Random Access for the Satellite Return Channel,” Proc. of the IEEE International Communication Conference (ICC), London, UK, June 2015, pp. 1-6. [96] S. Moshavi, E. G. Kanterakis, and D. L. Schilling, “Multistage Linear Receivers for DS-CDMA Systems, Int. J. Wireless Inform. Networks, vol. 3, No. 1, pp. 1-17, January 1996. [97] R. M¨ uller and S. Verdu, ˙ “Design and Analysis of Low-Complexity Interference Mitigation on Vector Channels,” IEEE Journal on Selected Areas in Communications, vol. 19, Issue 8, pp. 1429-1441, August 2001. [98] L. Cottatelluci, M. Debbah and R. M¨ uller, “Asymptotic Design and Analysis of Multistage Detectors for Asynchronous CDMA Systems,” IEEE International Symposium on Information Theory, 27 June - 2 July 2004, Chicago, USA. [99] L. Cottatellucci and R. Muller, ˙ “A Systematic Approach to Multistage Detectors in Multipath Fading Channels”, IEEE Trans. on Information Theory, Vol. 51, No. 9, pp. 3146-3158, September 2005. [100] D. Boudreau, G. Caire, G. E. Corazza, R. De Gaudenzi, G. Gallinaro, M. Luglio, R. Lyons, J. Romero-Garcia, A. Vernucci, and H. Widmer, “Wideband CDMA for the Satellite Component of UMTS/IMT-2000”, IEEE Trans. on Vehicular Technology, Vol. 51, No. 2, March 2002, pp. 306-330. [101] C. Kissling, “Performance Enhancements for Asynchronous Random Access Protocols over Satellite”, Proc. of International Communication Conference (ICC), June 5-9 2011, pp. 1-6, Kyoto, Japan. [102] F. Clazzer and C. Kissling, “Enhanced Contention Resolution ALOHA - ECRA”, Proc. of the 9th International ITG Conference on Systems, Communications and Coding (SCC), January 2013, Munich, Germany. [103] R. De Gaudenzi, O. del R´ıo Herrero, G. Acar, and E. Garrido Barrab´es, “Asynchronous Contention Resolution Diversity Aloha: Making CRDSA Truly Asynchronous”, IEEE Transactions on Wireless Communications, Vol. 13, No. 11, pp. 6193-6206, November 2014. [104] IEEE Std 802.3-2005, “IEEE Standard for Information technology; Telecommunications and information exchange between systems local and metropolitan area networks; Specific requirements Part 3: Carrier sense multiple access with collision detection (CSMA/CD) access method and physical layer specifications”, December 2005. [105] ETSI EN 301 542-2 v1.1.1, “Digital Video Broadcasting (DVB); Second Generation DVB Interactive Satellite System (DVB-RCS2); Part 2: Lower Layers for Satellite Standard”, January 2012. [106] O. del R´ıo Herrero, E. Casini and R. De Gaudenzi, “Contention Resolution Diversity Slotted Aloha Plus Demand Assignment (CRDSA-DA): an Enhanced MAC Protocol for Satellite Access Packet Networks”, 23rd AIAA International Communications Satellite Systems Conference, 25-28 September 2005, Rome, Italy. [107] C. Kissling and A. Munari, “On the Integration of Random Access and DAMA Channels for the Return Link of Satellite Networks”, IEEE International Conference on Communications (ICC), pp. 4282 - 4287, 9-13 June 2013, Budapest, Hungary. [108] A. Meloni and M. Murroni, “Random Access in DVB-RCS2: Design and Dynamic Control for Congestion Avoidance”, IEEE Transactions on Broadcasting, Vol. 60, Issue 1, pp. 16-28, March 2014. [109] C. Miguel, A. Fernandez, L. Vidaller, and V. Burillo, “PRODAT: a multi-functional satellite mobile communication system”, Proceedings of the Mediterranean Electrotechnical Conference (MELECON), Lisbon, Portugal, 11-13 April 1989, pp. 489-492. [110] “Orbcomm”, http://www.orbcomm.com/ [111] Viasat, “A breakthrough in satellite communications: the story of the technology behind ArcLight”, Online J. Space Commun., Fall 2004.
69
[112] M.J. Miller, M.D. Dankberg, C.N. Pateros, “Method and Apparatus for Multiple Access Commumnication Over a Channel”, US patent No. US 7,065,125 B1, June 2006. [113] Viasat, “PCMA Satellite Bandwidth Optimization”, https://www.viasat.com/technologies/paired-carrier-multiple-accesspcma. [114] “Iridium M2M”, https://www.iridium.com/solutions/m2m. [115] S. Vaddiparty, P. Monte, and B. Shah, “Simplex remote telemetry via a satellite system”, US Patent No. 20040147220 A1, July 29, 2004. [116] S.A. McDermott and T.W. Vaneck, “Transmit-only data exfiltration: the Sensor Enabled Notification System (SENS)”, Proceedings of SPIE Vol. 5417 (SPIE, Bellingham, WA 2004), pp.307-315. [117] Newtec, “Sat3Play IP Broadband System”, http://www.newtec.eu/product/sat3play-ipbroadband-hub . [118] SES Broadband Services, “Astra Connect Service”, http://www.ses-broadband.com/10338323/about-astra-connect . [119] ViaSatellite, “Eutelsat Unveils New Direct-to-Home Antenna”, July 2013, http://www.satellitetoday.com/broadcasting/ 2013/07/25/eutelsat-unveils-new-direct-to-home-antenna/. [120] A. Arcidiacono, D. Finocchiaro, F. Collard, S. Scalise, F. Lazaro Blasco, R. De Gaudenzi, S. Cioni, N. Alagha, and M. Andrenacci, “From S-MIM to F-SIM: Making Satellite Interactivity Affordable at Ku and Ka-band”, Int. Journal of Satellite Communications and Networking, http://onlinelibrary.wiley.com/doi/10.1002/sat.1158/abstract, November 2015. [121] S. Scalise, C. Parraga Niebla, R. De Gaudenzi, O. Del Rio Herrero, D. Finocchiaro, A. Arcidiacono, “S-MIM: a Novel Radio Interface for Efficient Messaging Services over Satellite”, IEEE Communications Magazine, Vol. 51 , Issue 3, pp. 119-125, March 2013. [122] V. Deslandes, “MUSTANG: The Ultimate Integrate System for IoT/M2M Communications”, In the Proc. of the 8th Advanced Satellite Multimedia Systems Conference - 14th Signal Processing for Space Communications Workshop, 2016 September 5-7, Palma de Mallorca, Spain. [123] M. Anteur, V. Deslandes, N. Thomas, “Ultra Narrow Band Technique for Low Power Wide Area Communications”, In the Proc. of IEEE 2015 Global Communications Conference (GLOBECOM), San Diego (CA), USA, Dec. 6-10, 2015, pp. 1-6. [124] R. Romanato, E. Rossini, G. Chiassarini, A. Masci, D. Silvi, P. Burzigotti, and D. Giancristofaro, “AMPIST: an Advanced Modem Prototype for Interactive Satellite Terminals with Contention Resolution Diversity Slotted ALOHA (CRDSA) capabilities”, Proc. of 17th Ka and Broadband Communications, Navigation and Earth Observation Conference, October 3-5, 2011, Palermo (Italy). [125] ETSI TS 102 721-3 v1.1.1, “Satellite Earth Stations and Systems; Air Interface for S-band Mobile Interactive Multimedia (S-MIM); Part 3: Physical Layer Specification, Return Link Asynchronous Access”, December 2011. [126] G. Gallinaro, F. Di Cecca, M. Marchitti, R. De Gaudenzi and O. del R´ıo Herrero, “Enhanced spread spectrum ALOHA system level performance assessment”, Wiley International Journal of Satellite Communications and Networking, Special Issue on S-band Mobile Interactive Multimedia, Vol. 32, Issue 6, pp. 485-503, November/December 2014. [127] M. Andrenacci, G. Mendola, F. Collard, D. Finocchiaro, and A. Recchia, “Enhanced spread spectrum aloha demodulator implementation, laboratory tests and satellite validation”, Int. Journal of Satellite Communications and Networking, pp. 521-533, November 2014. [128] R. Hermenier, A. Del Bianco, M.A. Marchitti, T. Heyn, A. Recchia, F. Collard, M. Andrenacci, G. Mendola, and A. Vaccaro, “S-MIM field trials results”, Int. Journal of Satellite Communications and Networking, pp. 535-548, November 2014.
70
[129] ANTARES Communication Standard Technical Specifications, http://artes.esa.int/news/ iris-antares-study-updates-draftsatellite-communications-standard.
