Efficient Frequency Domain Packet Scheduler for ... - Semantic Scholar

IEEE ICC 2012 - Wireless Communications Symposium

Efficient Frequency Domain Packet Scheduler for Point-to-Multipoint Transmissions in LTE Networks G. Araniti, V. Scordamaglia, M. Condoluci, A. Molinaro, A. Iera DIMET Dep., University Mediterranea of Reggio Calabria, Italy {araniti, valerio.scordamaglia, antonella.molinaro, antonio.iera}@unirc.it

Abstract—Long Term Evolution (LTE) is the emerging 4G wireless technology developed to provide high quality services in mobile environments. It is foreseen that multimedia services and mobile TV will assume an important role for the LTE proliferation in mobile market. However, several issues are still open and meaningful improvements have to be introduced for managing physical resources when group-oriented services should be supplied. To this end, we propose a Frequency Domain Packet Scheduling (FDPS) algorithm for efficient radio resource management of Multimedia Broadcast/Multicast Services (MBMS) in LTE networks. The proposed scheduler exploits an optimization process to organize multicast subscribers in subgroups according to channel quality feedbacks provided by users. Optimization is driven by the aim of minimizing the ”user dissatisfaction” with a consequent improvement in the network capacity. The effectiveness of the proposed scheduling algorithm is evaluated through simulation; obtained results demonstrate significant improvement in the multicast traffic performance. Index Terms—LTE, RRM, FDPS, e-MBMS, Multicast

I. I NTRODUCTION Long Term Evolution (LTE) [1], standardized by the Third Generation Partnership Project (3GPP), is emerging as the wireless technology leading the growth of mobile broadband services in the next years. LTE offers several benefits from a service provider perspective: high data rate (up to 100 Mbps in the downlink direction), low latency (up to 10 ms), low cost per bit, as a result of high spectrum efficiency and a simple network infrastructure. In such a context, grouporiented services are expected to provide the value-added for 4G networks, through advanced multimedia services delivery (e.g., e-press, sport, weather forecasts, video conferencing, mobile TV, multimedia downloading) over mobile terminals, such as cellular phones, smart phones, tablets, netbooks. The growing demand for group-oriented services has led to the specification of the Multimedia Broadcast/Multicast Service (MBMS) [2] standard by 3GPP, which supports integration of multicast and broadcast sessions in 3G networks. This standard has been extended to cope with LTE networks; the evolved-MBMS (e-MBMS) [4] version adds more flexibility and higher spectrum efficiency. The Radio Resource Management (RRM) policy plays a key role in providing high quality multicast sessions in LTE networks; in particular, the Frequency Domain Packet Scheduling (FDPS) algorithm is responsible for managing the set of frequency resources (i.e., the Resource Blocks, RBs) available in LTE systems. A RB is the smallest frequency resource that can be allocated to a given user. Transmission

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parameters for each RB are dynamically set by the FDPS algorithm according to the Channel Quality Indicator (CQI), periodically signaled by mobile terminals to the base station. In this paper, the aim is to design and validate an innovative FDPS algorithm for group-oriented communications in LTE networks. The driving idea is organizing the multicast LTE subscribers into different subgroups on the basis of experienced channel conditions, and differentiating the transmission parameters accordingly. More specifically, we solve a minimization problem aiming at finding the optimal number of subgroups and the consequent optimal distribution of RBs among the subgroups. Optimality is determined by the need to improve at the same time spectrum exploitation and multicast users satisfaction. The remainder of the paper is organized as follows. Section II summarizes the radio resource management features in LTE networks. Section III provides a brief state of the art on FDPS algorithms for LTE multicast environments. In Section IV the proposed algorithm is analytically formulated; simulative performance and comparison with the standard are reported in Section V. Conclusions and future work are addressed in Section VI. II. LTE

OVERVIEW

A complete description of the LTE basic concepts is available in [16]. In short, the LTE air interface supports Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier Frequency Division Multiple Access (SCFDMA) in the downlink and the uplink direction, respectively. Both techniques are based on the Orthogonal Frequency Division Multiplexing (OFDM) as a modulation scheme, where multiple orthogonal sub-carriers spaced 15 kHz apart are used to carry data. A sub-channel of 180 kHz is named Resource Block (RB) and corresponds to 12 consecutive and equally spaced sub-carries. Each sub-channel is the smallest frequency resource which can be assigned to a User Equipment (UE) by the RRM in the frequency domain. The number of RBs depends on the system bandwidth configuration; main standardized values are reported in Table I. In the time domain, every Transmission Time Interval (TTI) (equal to 1 ms) radio resources are allocated on a RB pair basis. A Packet scheduler (see Fig. 1) is implemented at the Medium Access Control (MAC) layer in the evolved NodeB (eNodeB) to efficiently handle resource allocation to mobile users in the time/frequency domain. The main functionalities

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TABLE I N UMBER OF RB S AS FUNCTION OF CHANNEL BANDWIDTH Channel Bandwidth [MHz] 1.4 3 5 10 15 20

TABLE II CQI - DATA R ATES FOR 10 MH Z C HANNEL BANDWIDTH

Number of RBs N 6 15 25 50 75 100

CQI value 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

of the packet scheduler can be summarized in three steps [5], based on the Quality of Service (QoS) parameters and the channel quality experienced by each UE. QoS Parameters

Buffer information

Step 1

Schedulable user set

Schedulability check

Step 2 QoS Time Domain Scheduler

N mux users

Step 3 Frequency Domain Scheduler

subframe builder

Link Adaptation Information Channel Quality Indicator

Fig. 1.

LTE Packet Scheduler block diagram.

During the first step, the scheduler collects information regarding: (i) the number of subscribers requiring LTE services; (ii) QoS parameters which must be guaranteed for each service; (iii) the amount of traffic generated and queued into the buffers. The second step is performed by the Time Domain Packet Scheduler (TDPS). It selects the users which must be served to guarantee QoS constraints. Finally, on the basis of the channel quality experienced by UEs, the Frequency Domain Packet Scheduler (FDPS) executes the Link Adaptation procedure to select the most appropriate: (i) modulation technique (from QPSK to 64-QAM), (ii) coding scheme, (iii) number of RBs. In details, according to the Signal to Interference and Noise Ratio (SINR) and the target Block Error Rate (BLER), each UE estimates and forwards the CQI feedback (associated to the maximum supported Modulation and Coding Scheme, MCS) to the eNodeB. The CQI report cycle can last one or several TTIs [1] and it is selected by the eNodeB depending on the channel variations due to the user mobility. Table II shows CQI values for LTE systems. It is worth remarking that a given MCS is related to each CQI, while the number of RBs can vary between a minimum and maximum value (i.e., 1 and 50 RBs for system bandwidth equal to 10 MHz) that depends on the implemented FDPS algorithm. III. R ELATED W ORK When group-oriented services are supplied, multimedia contents must be delivered to multiple destinations. 3GPP [4] defines two transmission modes: Point-to-Point (PtP) and Pointto-Multipoint (PtM). According to the former, data traffic is conveyed to each MBMS subscriber by using a dedicated channel. In such a case the transmission parameters (i.e., MCS)

Modulation QPSK QPSK QPSK QPSK QPSK QPSK 16-QAM 16-QAM 16-QAM 64-QAM 64-QAM 64-QAM 64-QAM 64-QAM 64-QAM

Code rate 0.076 0.120 0.190 0.300 0.440 0.590 0.370 0.480 0.600 0.450 0.550 0.650 0.750 0.850 0.930

Minimum Rate [kbps] 25.59 39.38 63.34 101.07 147.34 197.53 248.07 321.57 404.26 458.72 558.15 655.59 759.93 859.35 933.19

Maximum Rate [kbps] 1279.32 1968.96 3166.80 5053.44 7366.80 9876.72 12403.44 16078.44 20212.92 22936.20 27907.32 32779.32 37996.56 42967.68 46659.48

and the QoS are optimized on a per-user basis. According to PtM mode, a single transmission feeds the whole multicast group, thus offering theoretically unlimited capacity. Performance evaluation of PtP and PtM modes in LTE MBMS environments can be found in [6] and [7]. Results demonstrated that the PtP approach maximizes the QoS experienced by the single user at the cost of an inefficient use of physical resources and the consequent reduction of the system capacity, especially when the multicast group size increases. PtM exploits system resources better than the PtP mode, and the gain increases with the number of UEs. However, the main disadvantage related to the PtM approach is that QoS is provided on a per-group basis. In more details, the LTE standard algorithm for PtM data delivery [4] selects the transmission parameters (modulation and coding schemes) according to the worst CQI value collected by eNodeB from all the multicast subscribers in its cell. This is performed with the aim of guaranteeing full traffic reception to the overall set of multicast receivers. As a consequence, the multicast session performance will be bounded by the UE(s) with the worst channel conditions (as analyzed in [8]). As PtM is the most promising technique for multicast content delivery, several approaches aiming to overcome the limitations of the standard PtM policy have been proposed in the literature. Authors in [9] propose to set a threshold for the SINR. Terminals experiencing a SINR value lower than the threshold are considered “out of coverage”. This mechanism improves performance in terms of throughput, but at the cost of a reduction in the number of served MBMS users. Analogous results are achieved by algorithms described in [10] and [11]. They are based on the Spectral Efficiency (SE, measured as bit/s/Hz) evaluation where SE is defined as the ratio of the mean throughput experienced by UEs over the channel bandwidth. In particular, in [10] the MCS ensuring the maximum SE is selected for PtM transmission, whereas in [11] the MCS is chosen with the purpose of achieving a predefined SE target value (equal to 1 bit/s/Hz). Simulative analysis pointed out the improvements in terms of SE introduced by these algorithms. Nevertheless, it is worth noting that terminals

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not supporting the selected MCS are affected by increased Block Error Rate (BLER) and packet losses. This aspect is not evaluated in these works. An interesting approach for PtM traffic delivery is the subgrouping: multicast members are split into subgroups, each one including UEs with similar channel conditions. Studies in [12] and [13] demonstrated that this technique can significantly improve session throughput in High Speed Downlink Packet Access (HSDPA) networks without reducing the number of served users. Further enhancements can be achieved with the solution proposed in [14], whose optimization procedure aims at identifying the optimal subgroups configuration that offers the best performance in terms of user satisfaction. To the best of our knowledge, subgrouping-based solutions have not been evaluated for LTE networks yet. IV. T HE P ROPOSED A LGORITHM The proposed FDPS policy foresees three phases: 1) CQI collection, every CQI report cycle eNodeB collects CQI feedbacks from UEs belonging to the multicast group; 2) subgroups creation, the scheduler firstly splits the users into multicast subgroups according to the received CQIs, then selects the MCS and assigns the appropriate RBs for each subgroup (and not for a single user) in order to optimize QoS; 3) radio resource allocation, the multicast service is finally provided. In this work, an algorithm is designed to manage the subgroups creation phase by optimizing a novel QoS parameter, named Group Dissatisfaction Index (GDI). For a given distribution of UEs in the cell, the proposed scheduler selects the optimal subgroup configuration that minimizes the GDI, as described in Section IV-A. A. Group Based - Frequency Domain Packet Scheduler In order to estimate the “user satisfaction” during the multicast session, we first introduce the User Dissatisfaction Index (UDI) ωi : { Bi − di Bi ≥ di ωi = (1) ∞ Bi < di |di = 0 Bi being the maximum data rate supported by the i-th UE, depending on the experienced CQI, and di the data rate assigned by the packet scheduler. UDI represents the “distance” between maximum allowable and assigned data rate. Low UDI value indicates that data rate offered to the UE is close to the maximum allowable. Optimal condition is achieved if Bi = di (ωi = 0). UDI value approaches to infinity when the BLER experienced by UE increases, because it is assigned a MCS that cannot be supported under current channel conditions. Once UDI values are defined, the Group Dissatisfaction Index (GDI) can be introduced: Ω=

C 1 ∑ ωc Uc K c=1

(2)

ωc being the UDI of user with a CQI value equal to c, Uc the number of UEs reporting the c-th CQI, C the overall number of available CQI values (i.e., 15 in LTE network), and K the total number of multicast UEs. GDI represents the mean mismatch value among all multicast receivers. In order to formalize the proposed optimization process, we define: • b = {b1 , b2 , . . . , bC }, the minimum data rate vector. bc is the data rate obtained when just one RB is assigned; • B = {B1 , B2 , . . . , BC } the maximum data rate vector. Bc is the data rate obtained when N RBs are assigned. N is the total number of RBs; • R = {r1 , r2 , . . . , rC }, the resource vector. rc is the amount of RBs assigned to the subgroup related to the c-th CQI. It is worth noting that the sum of elements in R has to be equal to N . The generic data rate dc assigned to the c-th subgroup is: dR c = {max(bp · rp ), p = 1, . . . , c}

(3)

R with dR c bounded 0 < dc ≤ Bc . From (1)-(3), when Bc ≥ dc the GDI can be recast as: C C 1 ∑ 1 ∑ (4) Ω= ωc Uc = [Bc − dR c ]Uc K c=1 K c=1

where [Bc −dR c ] is the UDI related to the c-th subgroup. More generally, the GDI can be rewritten as: Ω=

C 1 ∑ 1 ωc Uc = hR · U K c=1 K

(5)

where U = {U1 , U2 , . . . , UC }T is the user distribution vector and hR is the vector of dissatisfaction indexes experienced by subgroups. The optimization problem, on the basis of the Group BasedFDPS (GB-FDPS) algorithm, aims to minimize the cost function (5) for a given user distribution U and can be modeled as follows:  } { 1 R    Π = arg min K h · U R∈R (6) s.t.   ∑   C r =N c=1 c Being R the set of the allowable subgroup configurations to be assumed by R, the main computational burden of (6) is strictly related to the generation of R set. Since it depends on RB number (i.e., N ) and CQI levels (i.e., C) and does not depend on user distribution (i.e., U ), the R set can be precomputed off-line. This drastically reduced the computation time and allowed to complete the subgroups creation phase within the CQI report cycle. Remark 1. Due to the fact that the number of configurations to be assumed by R is bounded, then the optimization problem defined in (6) admits one solution at least. Remark 2. If (6) admits more than one solution, then a reasonable choice can be any configuration that maximizes the Channel Data Rate.

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V. S IMULATION M ODEL AND R ESULTS In this section we compare the performance of the proposed GB-FDPS algorithm with the one achieved by the standard PtM policy [4]. The standard algorithm puts all multicast users of the cell in a unique group, served with the MCS allowed by the lowest reported CQI value in the group. Channel and system simulators have been implemented in R M ATLAB⃝ . Outputs are achieved by averaging a sufficient number of simulation results to obtain 95% confidence interval. SINR values for each sub-carrier are evaluated according to the following equation [17]: SIN Ri = ∑NBS j=1

P0 × P L0 × h0 (Pj × P Lj × hj ) + No

(b) Central Hot Spot

(c) Edge Hot Spot

(d) Two Hot Spots

(7)

where Pj , P Lj and hj are the transmission power, the path loss and the small scale fast fading of the link between the UE and the j-th base station; No is the noise power. For the effective SINR calculation, the Exponential Effective SIR Mapping (EESM) [17] is adopted. The compression function allowing this mapping is ( ) Nsub SIN R 1 ∑ − β i SIN Ref f = −β ln e (8) Nsub i=1 where Nsub is the total number of sub-carriers and β is a scaling factor used to adjust the mismatch between the actual and the predicted BLER. Estimated SINR is mapped to a CQI level (i.e., MCS) ensuring a BLER target value smaller than 10% [17]. Main simulation assumptions are listed in Table III. TABLE III M AIN S IMULATION A SSUMPTION Parameters Distance attenuation Shadow fading Fast Fading Cell layout Inter site Distance Channel Bandwidth RB size CQI scheme eNodeB transmit power Maximum antenna gain Thermal Noise Multicast group size

(a) Uniform

Value 128.1+37.6*log(d), d [km] Log-normal, σ = 8 [dB] ITU-R PedB (extended for OFDM) Hexagonal, 18 Interfering cells 1732, Macro Case 3 10 MHz, 50 RBs 12 sub-carriers per RB Full Bandwidth 20 W, 13 dB 11.5 dB -100 dBm 100

We consider four scenarios involving different stationary user distributions: (i) Uniform Distribution, UEs are uniformly distributed across the cell; (ii) Central Hot Spot, UEs are close to the base station; (iii) Edge Hot Spot, UEs are located near the cell borders; (iv) Two Hot Spots, UEs are equally distributed among a central and an edge hot-spot. Fig. 2 depicts the user distributions (i.e., vector U) for the above mentioned scenarios. Each configuration is characterized by different CQI values collected by eNodeB in a particular CQI report cycle. As expected, in the Uniform scenario UEs experience about all the possible CQI values (from 1 to 15); in the Central Hot Spot scenario UEs, located close to NodeB, measure high CQI values (between 10 and 15); in the Edge Hot Spot scenario

Fig. 2.

CQI distributions in evaluated scenarios.

UEs, located near the cell border, report low CQI values (between 1 and 6); finally, in the Two Hot Spots scenario UEs, located close to NobeB and at the cell border, signal the combination of CQI values reported in the last two scenarios. Fig. 3(a) shows results in terms of Channel Data Rate. It indicates the amount of data transmitted by the base station over the air interface. In all scenarios the proposed solution is more performing than the standard one. It is worth underling that the performance of the standard policy is strongly affected by cell-edge users with the worst channel conditions. Comparable results are obtained only in the Central Hot Spot case as all users are close to the base station and thus they experience good channel conditions. A Channel Data Rate improvement implies a better radio channel exploitation, hence an enhancement from the provider point of view. By analyzing the performance in terms of GDI (the GDI is normalized to the maximum allowable mean data rate) in Fig. 3(b), it clearly emerges that the standard policy suffers from higher mismatch between maximum allowable and assigned data rate. The gain introduced by the GB-FDPS varies from 24% (i.e., Edge Hot Spot) to 77% (i.e., Two Hot Spots); our policy offers mean GDI gain equal to 47%. Fig. 3(c) shows the results in terms of mean Throughput experienced by multicast members; i.e., the mean rate of successfully received data experienced by all multicast members. In all considered scenarios our policy offers meaningful improvements in terms of Throughput with respect to standard case. This demonstrates that the new QoS indexes (UDI and GDI) fit well the idea of enhancing the performance of multicast users.

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(a) Channel Data Rate

(b) GDI normalized to the maximum allowable mean data rate Fig. 3.

(c) Throughput

Performance evaluation in evaluated scenarios.

VI. C ONCLUSION AND F UTURE W ORK In this paper we proposed the design of an effective Frequency Domain Packet Scheduling algorithm for multicast services in LTE networks. An optimization procedure has allowed to identify the number of multicast subgroups in the cell and the assigned resources to each subgroup, to the aim of maximizing the global user satisfaction and the LTE system capacity. The effectiveness of the proposed approach has been assessed by comparing it with the currently standardized LTE scheduling strategy for multicast transmission. Obtained results showed that the proposed scheduling policy offers significant QoS improvement and guarantees wider network coverage without increasing the amount of control signaling. Even if our policy significantly reduces the negative effects of cell-edge users, further improvements can be introduced in terms of fairness, because multicast users might obtain varying data rates. This issue will be investigated in future works, by defining an appropriate Time Domain Packet Scheduler that couples with the proposed FDPS. Future research will also consider the impact of user mobility with the purpose of assessing the efficiency of the proposed algorithm also in highly dynamic scenarios. ACKNOWLEDGEMENT This research has been conducted within the project “At the roots of TErritorial identity: innovative interdisCipliNary methods for the idEntification, development, dissemination of artistic and cultural heritage (TECNE)” supported by the Calabria Regional Government grant D.D. n. 791-01.02.2010. R EFERENCES [1] 3GPP, Overview of 3GPP release 8, v.0.1.1., Technical report, June 2010. [2] 3GPP, TS 22.146, v.9.0.0, Technical specification group services and system aspects; multimedia broadcast/multicast service, (Release 9), 2008. [3] 3GPP, TS 36.321 Medium Access Control (MAC) protocol specification, v. 10.0.0, Tech. Rep., December 2010 [Online]. Available: http://ftp.3gpp.org/specs/html-info/36321.html

[4] 3GPP, TS 36.440, General aspects and principles for interfaces supporting Multimedia Broadcast Multicast Service (MBMS) within EUTRAN. v. 10.0.0, Tech. Rep., December 2010 [Online]. Available: http://ftp.3gpp.org/specs/html-info/36440.html [5] K. I. Pedersen, T. E. Kolding, F. Frederiksen, I. Z. Kov´acs, D. Laselva, and P. E. Mogensen, An overview of downlink Radio Resource Management for UTRAN Long-Term Evolution, IEEE Communication Magazine, pages 8693, July 2009. [6] M. Phan and J. Huschke, Adaptive point-to-multipoint transmission for Multimedia Broadcast Multicast Services in LTE, Computing Research Repository - CORR, 2009. [7] L. Zhang, Y. Cai, Z. He, C. Wang, and P. Skov, Performance evaluation of LTE MBMS baseline, Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing, pages 289292, 2009. [8] A. Alexious, C. Bouras, V. Kokkinos and G. Tsichritzis, Communication cost analysis of MBSFN in LTE, Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on, pp. 1366-1371, September 2010. [9] L. Zhang, Z. He, K. Niu, B. Zhang, and P. Skov, Optimization of coverage and throughput in single-cell E-MBMS. Vehicular Technology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th, September 2009. [10] A. Alexious, C. Bouras, V. Kokkinos, A. Papazois and G. Tsichritzis, Efficient MCS selection for MBSFN transmissions over LTE networks, Wireless Days (WD), IFIP, pp. 1-5, October 2010. [11] A. Alexious, C. Bouras, V. Kokkinos, A. Papazois and G. Tsichritzis, Spectral efficiency performance of MBSFN-enabled LTE networks, IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications, 2010. [12] A. Chaudhry and J. Y. Khan, An efficient MBMS content delivery scheme over the HSDPA network, Wireless Pervasive Computing, 2009. ISWPC 2009. 4th International Symposium on, February 2009. [13] J. Y. Khan and A. Chaudhry, A group based point-to-multipoint MBMS algorithm over the HSDPA network, Personal, Indoor and Mobile Radio Communications, 2008. PIMRC 2008. IEEE 19th International Symposium on, September 2008. [14] G. Araniti, V. Scordamaglia, A. Molinaro, A. Iera, G. Interdonato, F. Span`o, Optimizing point-to-multipoint transmissions in High Speed Packet Access networks, Broadband Multimedia Systems and Broadcasting (BMSB), 2011 IEEE International Symposium on, June 2011. [15] 3GPP, TS 25.211, Technical Specification Group Radio Access Network; Physical channels and mapping of transport channels onto physical channels (FDD), Rel. 5. [16] E. Dahlman, S. Parkvall, J. Skold, and P. Beming, 3G Evolution HSPA and LTE for Mobile Broadband, Academic Press, 2008. [17] C. Mehlfhrer, M. Wrulich, J. C. Ikuno, D. Bosanska, and M. Rupp, Simulating the Long Term Evolution Physical Layer, 17th European Signal Processing Conference (EUSIPCO 2009), 2009.

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