Research Article
Low-complexity timer-based multi-relay selection and sequential power allocation of cooperative cognitive radio networks for future Internet of things
International Journal of Distributed Sensor Networks 2016, Vol. 12(10) Ó The Author(s) 2016 DOI: 10.1177/1550147716671254 ijdsn.sagepub.com
Md Arifur Rahman, Youngdoo Lee and Insoo Koo
Abstract Cooperative cognitive radio networks use cooperative relays to forward signal from the source to the destination. In cooperative cognitive radio networks, the transmission power of each relay is limited by the interference constraint of the primary user receiver. Thus, it is essential to optimize power allocation and multi-relay selection jointly to maximize the secondary system throughput. Optimizing multi-relay selection and power allocation requires an exhaustive search for all possible relay combinations, since this approach uses a large amount of valuable resources and entails high computational complexity. A suboptimal solution for power allocation may reduce the computational complexity but still involves high implementation complexity. Thus, for an efficient utilization of resources to support the applicability of cognitive radio for the Internet of things, we propose a low-complexity timer-based multi-relay selection that determines a forwarding relay set before the source begins to transmit its data. This allows the source to know the instantaneous channel state information of the relays, which helps the source to assign appropriate transmission power to the relays. By simulation, we show that the proposed scheme achieves near-optimal secondary system throughput performance to the optimal multi-relay selection as well as provides a significant secondary system throughput gain when compared to conventional and random relay selection scheme with equal power allocation. Keywords Cooperative communication, cognitive radio, Internet of things, throughput maximization, multi-relay selection, exhaustive search, channel state information, equal power allocation
Date received: 28 April 2016; accepted: 21 August 2016 Academic Editor: George P Efthymoglou
Introduction Cognitive radio (CR) is a wireless technology with promise to resolve the growing scarcity problem of crucial electromagnetic spectrum resources by adaptively changing its transmitter parameters based on the interaction with the environment. In cognitive radio networks (CRNs), while primary users (PUs) use their particular licensed spectrum bands, secondary users (SUs) detect and utilize spectrum holes in the radio environment for their data transmissions.1 Self-adaptation and self-organization are the essential properties
of Internet of things (IoT) that allow communication among nodes, continuous change of context, and join and leave the network spontaneously. Therefore, CRs at physical and link layers, self-organizing network protocols at the application layer are important School of Electrical Engineering, University of Ulsan, Ulsan, South Korea Corresponding author: Insoo Koo, School of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, South Korea. Email:
[email protected]
Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (http://www.uk.sagepub.com/aboutus/ openaccess.htm).
2 candidates for the self-adapting IoT. CR platform is a promising solution for improving the connectivity, and its applicability for future IoT is limited due to high implementation cost and high energy consumption.2 In CRNs, the SUs often pay the cost of opportunism to access the spectrum of PUs in terms of their energy consumption. Moreover, the operational environment awareness is driven from the interference requirement of the PU receiver, which consumes much energy as compared with general radio platform. Nevertheless, energy consumption is an important issue for IoTbased applications. Therefore, cooperative communication scheme (CCS) is a technique in CRNs to reduce the transmission power of the SUs to avoid the interference to the PU receiver. The implementation of cooperative relaying strategy in Xia and Aissa3 reflects that it can effectively reduce the transmission power of the SUs to protect the interference to the PU receiver. In CCS, the source exploits cooperative relays to transmit its signal to the destination. In order to support the applicability of CR for future IoT, cooperative cognitive radio networks (CCRNs) is a promising approach which can reduce the interference of the PU receiver and energy consumption in transmission. CR techniques for spectrum access (SA) are generally classified as underlay SA, overlay SA, and interweave SA.4 Among them, interweave SA is the most conservative and challenging because it strictly prohibits the SU access to a spectrum while a PU is utilizing it. In overlay SA, SUs exclusively and opportunistically use a spectrum when it is not utilized by a PU. Underlay SA allows simultaneous transmission of SUs with PUs, as long as the interference from the SUs to the PUs is kept below a tolerable level. In CCRNs, the cognitive relay can improve the cognitive throughput, extend the coverage, and improve the spectral efficiency in the wireless communication.5,6 Motivated by the promise of these two techniques, CR and the CCS, researchers have been investigating CCRNs as an emerging approach to improve secondary system throughput under interference threshold of the PU system. In underlay CCRNs, the transmission power of secondary source and the secondary relays is limited by the interference requirement of the PU receiver. Transmission power control to avoid the interference to the PU receiver is critical in CCRNs. Moreover, in CCS, participation of all relays may unnecessarily waste valuable spectrum resources. Therefore, an improved CCS for IoT was studied in Lee et al.,7 where a decoding scheme according to the transmission structure was proposed to enhance the communication throughput under a low bit error rate. To prevent unnecessary wastage of valuable spectrum resources, joint relay selection and power allocation in CCRNs were presented in Yu et al.,8 where the SU relays are operated on amplify-and-forward (AF) mode, and optimal
International Journal of Distributed Sensor Networks power allocation (OPA) and suboptimal power allocation schemes were derived for secondary system throughput maximization under a PU receiver interference threshold. Similarly, in Choi et al.,9 a lowcomplexity multi-relay selection (MRS) scheme for CRNs through an exhaustive search (ES) with an OPA and suboptimal MRS was proposed to maximize the secondary system throughput, where the transmission power of each relay was inversely proportional to the interference of that SU relay to the PU receiver. In Niemen et al.,10 a low-complexity interference-aware MRS scheme using ES for CRNs was proposed in which the SU relays were selected to maximize the signal-to-noise ratio (SNR) of their transmission to the destination under the PU receiver interference constraint. To reduce the CC of the ES, a simplified suboptimal power allocation to the SU relays is derived in Choi et al.,11 to achieve secondary system throughput maximization. Although the suboptimal power allocation has advantages in reducing the CC required by the optimal scheme, it still faces high implementation complexity (IC). Therefore, a lower complexity MRS and power allocation schemes are essential for improving the secondary system throughput under the interference constraint of the PU system. Specifically, resources are severely limited for SUs when employing underlay SA. Thus, we propose a scheme that jointly uses a lower complexity timer-based MRS with sequential power allocation to achieve efficient utilization of resource in underlay CCRNs. In this article, we propose a scheme to maximize the secondary system throughput using a timer-based MRS with low IC, which determines a forwarding relay set by the SU source via handshaking mechanism between the SU source and the surrounding SU relays. In our proposed scheme, the SU source sends a request to send (RTS) for the SU destination. After receiving an RTS from the SU source, the SU destination will respond by sending a clear to send (CTS) back to the SU source. Due to the broadcasting nature of wireless communication, multiple SU relays can overhear an RTS and the CTS which are sent by the SU source and the SU destination. Therefore, the SU relays can learn the channel state information (CSI) of the (relay-to-destination) link by overhearing the CTS from the SU destination, which helps the SU relays to get channel quality and further calculate the utility function. Each SU relay can calculate its own offset time based on the utility function. Subsequently, according to its offset time, each SU relay can send CTS sequentially to the SU source. Also, it gets assigned of transmission power by the SU source, and later, it can forward the SU source signal to the SU destination with assigned transmission power. This proposed sequential power assignment algorithm by the SU source maximizes the secondary system throughput under interference threshold level of the PU
Rahman et al. receiver while entailing a much lower IC compared to the optimal MRS. It shows near-optimal throughput performance to the optimal MRS with respect to (a) the maximum allowable transmission power of SU relays when the interference threshold of the PU receiver and the number of the SU relays are fixed, (b) the number of SU relays when the maximum allowable transmission power of the SU relays is fixed, and (c) the interference threshold of the PU receiver when the maximum allowable transmission power of the SU relays is fixed. In the simulations, we used the optimal MRS scheme,9 the conventional relay selection scheme,12,13 and the random relay selection scheme to compare the secondary system throughput performance of the proposed scheme. The main contributions of this article are as follows: 1.
2.
We propose a low-complexity timer-based MRS that avoids an ES over all possible relay combinations. In particular, the SU source determines a forwarding relay set via RTS/CTS handshaking before transmitting its signal to the destination at the beginning of the time slot. We have designed a sequential power assignment algorithm for the SU relays with a low IC approach that offers better secondary system throughput performance, reducing the complexity of optimal MRS that does an ES over all possible relay combinations.
The rest of this article is organized as follows. In section ‘‘Related works,’’ we discuss related for detailed background and motivations of the article. The system model and description are introduced in sections ‘‘System model’’ and ‘‘System description.’’ The proposed timer-based MRS and the sequential power assignment algorithm and the energy consumption analysis are analyzed in sections ‘‘The proposed timerbased MRS,’’‘‘The proposed sequential power assignment,’’ and ‘‘Energy consumption analysis.’’ The simulation results are given in section ‘‘Simulation results,’’ and finally, the article’s conclusions are provided in section ‘‘Conclusion.’’
Related works To maximize the secondary system throughput in CCRNs, the relay selection and power allocation play important roles. We describe the related research literature regarding power allocation to the SU relays having the aim of secondary system throughput maximization and the relay selection strategies of IoT. Therefore, enabling technologies of energy-efficient cooperative machine-to-machine (M2M) networks were studied in AbuAli.14 More specifically, SNR-based selection
3 strategy, distance-based selection strategy, and remaining energy selection–based strategy (RESBS) in M2M communication were studied in Xia et al.,15 where the authors proved that the RESBS can extend the lifetime of the M2M CCS at the expense of transmission power. Energy efficiency–oriented access point (AP) selection for cognitive sensor in IoT was proposed in Ju and Shao,16 where a game model for AP selection in a multi-AP scenario and a distributed learning algorithm were derived for global optimal solution to the problem in a distributed manner. Budhathoki et al.17 utilized maximum ratio combiner (MRC) at the SU destination with a single antenna in an AF-based CCRNs to evaluate the secondary system throughput performance. The SNR at the output of the MRC is equal to the sum of the received SNR from the direct and the relay links. Anghel and Kaveh18 utilized MRC with a single antenna at every radio station to evaluate the system error rate performance for an AF-based cooperative wireless network. They considered that the transmissions of the radios are orthogonal in time and utilized MRC scheme at the destination to combine the SNRs from all relay links to calculate the error rate of the cooperative wireless network. The outage probability of CRNs with cooperation between the SUs based on the underlay SA technique was evaluated in Lee et al.,19 demonstrating that CRNs achieved the full selection diversity order, like conventional relay networks, by employing an ES over the surroundings to maximize the link SNR from the source to the relays and from the relays to the destination. A closed-form solution for the outage probability and the symbol error rate for cognitive amplify-and-forward relay networks (CAFRNs) based on the switch-and-examine diversity relaying was studied in Salhab and Zummo.20 In CAFRNs, the SU relay is being selected based on satisfying a predefined switching threshold to forward the source message to the destination. Similarly, signalto-interference-plus-noise-ratio (SINR) analysis for underlay CRNs with multiple PUs in Nakagami-m fading channel was studied in Bithas et al.21 Moreover, the exact and asymptotic analytical expression for the cumulative distribution function (CDF) of the output SINR at the secondary destination node of the cognitive network was derived for investigating the outage probability. Furthermore, the OPA for CRNs with direct and relay-aided transmissions was studied in Lu et al.,22 where a PA algorithm was derived to maximize the overall data rate of CRNs under a PU receiver interference constraint. Binary particle swarm optimization–based low-complexity relay assignment for multi-user CRNs with discrete power controls was studied in Pareek et al.,23 where optimal power was allocated through ES over all possible relay combinations. The complexity of an ES increases with the number of cooperative SU relays, their discrete power
4 levels, and the number of SUs. Joint relay selection and power allocation with limited interference to the PU receiver in CRNs were investigated in Li et al.,24 where an optimal approach and suboptimal approach were used for maximizing the secondary system throughput. They showed that the suboptimal approach had lower CC than the optimal approach. The OPA for an AF full-duplex relay scheme in CRNs was studied in Shi et al.25 with full interference channel information from the cognitive source to the PU receiver and from the cognitive relay to the PU receiver, with partial knowledge of the interference channel information from the cognitive source to the PU receiver and from the cognitive relay to the PU receiver and without having the interference channel information from the cognitive source to the PU receiver and from the cognitive relay to the PU receiver. In addition, most of the existing studies of cooperative communications have relied on math or computer simulation so far. However, some recent papers have dealt with the implementation of a cooperative communication strategy such as Naito et al.,26 Murphy and Sabharwal,27 and Zhang et al.28 More specifically, Naito et al.26 showed the implementation of cooperative communication strategy for wireless networks in a test bed based on click modular router. They have evaluated the feasibility of CCS on IEEE 802.11a standard devices. The experimental evaluation was performed with universal software radio platform (USRP B200). The results showed that the CCS could work well on IEEE 802.11a standard devices and improve communication performance in practical situations. Murphy and Sabharwal27 presented a design and implementation of a complete cooperative PHY-layer transceiver and the
Figure 1. System model.
International Journal of Distributed Sensor Networks real-time PHY-layer cooperation among distributed nodes for a cooperative communication system. They used distributed Alamouti space-time block code based on the deployment of AF and decode-and-forward relaying which clearly demonstrate significant performance gains provided by physical layer cooperation. Zhang et al.28 showed complete-function test-bed framework based on USRP and GNU Radio to support MRC-based cooperative communication. The implementation was performed to reveal the real system performance of CC, which showed significant throughput gain of cooperative transmission with the direct transmission. Likewise,26,27,28 the proposed scheme can be realized as a feasible CCS with CRNs.
System model As shown in Figure 1, an underlay CCRN is taken into account, which coexists with N SUs and one PU receiver. The PU receiver is labeled as the qth PU (q = 1). The SU utilizes the spectrum of the PU under its tolerable interference. Among N SUs, one SU will be source node, and one SU will be destination node, and the other jth SUs are the SU relay node denoted as rj (j = 1, 2, 3, . . . , N 2). Each SU and PU receiver is equipped with a single antenna, the SU relays forward the signal to the destination by adapting AF protocol, and the SU destination exploits the MRC to combine the signals that are received due to the direct link between the SU source and the SU destination and the relay link between the selected SU relays and the SU destination. Thus, the SU destination receives copies of signal from the SU source as well as the SU relays.
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Figure 2. Time frame structures of the proposed multi-relay selection.
Time frame structures of the proposed MRS are composed of three phases, as shown in Figure 2, which are defined as forwarding relay set determination and transmission power assignment phase, data broadcasting phase, and data forwarding phase. At the first phase, the SU source sends an RTS for the SU destination, and after receiving an RTS from the SU source, the SU destination responds by sending a CTS back to the SU source. Due to the broadcasting nature of wireless communication, multiple SU relays can overhear an RTS and the CTS which are sent by the SU source and the SU destination. Therefore, the jth SU relay can learn the CSI of the (relay-to-destination) link by overhearing the CTS from the SU destination which helps the jth SU relay to get channel quality and further calculate the utility function. Moreover, the jth SU relay also learns the maximum transmission power of the SU source from an RTS which is sent by the SU source. The jth SU relay can calculate its own offset time based on the utility function. Consequently, the jth SU relay can send CTS sequentially to the SU source based on the offset time. For sequential CTS, the jth SU relay can calculate the difference in capacities between the maximum and minimum transmission powers of the jth SU relay based on the interference requirement of the PU receiver and the outage capacity of the SU relay. Therefore, the jth SU relay sends CTS back to the SU source only if the difference in capacities of the jth SU relay is greater than zero; otherwise, the jth SU relay will remain silent for sending CTS back to the SU source. Moreover, the jth SU relay can calculate its offset time based on the utility function. The utility function is composed of the difference in capacities between the maximum and minimum transmission powers of the jth SU relay and the ratio of the channel gain from the jth SU relay to the
SU destination and from the jth SU relay to the qth PU receiver. The SU source receives CTS sequentially from the SU relays based on the offset time. The jth SU relay with having minimum offset time will send CTS first to the SU source, and the SU source assigns transmission power to the SU relay in the time slot Tslot1 . The sequential power assignment will continue until when total interference is greater than the maximum allowable interference level and the improved secondary system throughput is lesser than or equal to zero, and the SU source will broadcast a stop message. Otherwise, the process is sequentially repeated to the next CTS on the time slot Tslot1 . Due to the broadcast nature of wireless communication, the SU relays will receive the stop message and halt sending CTS back to the SU source. Therefore, the SU source can select SU relays before broadcasting a stop message based on the sequential response of the CTS from the SU relays. Once the SU source selects the SU relays on the time slot Tslot1 , the SU source broadcasts the data to the selected SU relays on the time slot Tslot2 , and the selected SU relays forward the data to the SU destination on the time slot Tslot3 . In order to support future IoT, let Tmax be the maximum time threshold of the system for the SU source to determine a forwarding relay set and transmission power assignment. The system model of the proposed MRS is shown in Figure 1, where in the data broadcasting phase, the SU source broadcasts its signal to the SU destination (black line) and the SU relays which are being selected at the first phase will receive the signal due to the broadcasting nature of the wireless communication. In our proposed scheme, the selected SU relays will amplify the received signal and forward the received signal to the SU destination through orthogonal channels as in Nicholas Laneman and colleagues.29,30 Finally, at the
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SU destination, all signals transmitted by the selected SU relays as well as the direct signal by the source node are combined by the maximum ratio combining (MRC) technique.
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 pffiffiffiffiffiffi where n0rj d ¼ ð Prj hrj d nsrj = Ps hsrj þ N0 Þ þ nrj d . Since nsrj and nrj d are independent, then the equivalent noise n0rj d is a zero-mean complex Gaussian random variable, and the variance is expressed as follows 0
1 2 Prj hrj d B C ffi + 1A N 0 N00 = @qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 Ps hsrj + N0
System description Let Ps and Prj be the transmit power of the SU source and the jth SU relay for forwarding the SU source signal to the SU destination, respectively. We consider the following transmit power and interference constraint to maximize the secondary system throughput of CCRNs Ps , Prj pmax SU 2 2 Ps hsq Prj hrj q , IM N0 N0
M where pmax are the maximum allowable transSU and I mit power of the SUs and the maximum allowable interference threshold of the qth PU receiver, respectively. Also hsq and hrj q denote the interference channel gain from the SU source to the qth PU receiver and from the jth SU relay to the qth PU receiver, respectively. In the data broadcasting phase, the SU source broadcasts its data, and the SU destination receives the data. Let x be the transmitted data symbol from the SU source and the received signal at the SU destination due to the direct link between the SU source and the SU destination, which is given by
pffiffiffiffiffi Ps hsd x + nsd
The SNR from the SU source due to the direct link is expressed as follows
ð1Þ ð2Þ
ð5Þ
gsd =
Ps jhsd j2 N0
ð6Þ
The SNR due to the relay link between jth SU relay and the SU destination is defined by Ray Liu et al.31 as follows 2 2 Ps Prj hrj d hsrj g r j d = 2 2 Prj hrj d + Ps hsrj + N0 N0
ð7Þ
By applying Shannon’s channel capacity theorem, the SU system capacity due to the relay link can be expressed as follows
Crj d Prj
0
1 2 2 Ps Prj hrj d hsrj A = log2 @1 + 2 2 Prj hrj d + Ps hsrj + N0 N0
ð3Þ
ð8Þ
where hsd and nsd are the channel gain from the SU source to the SU destination and identical and independent distributed (IID) additive white Gaussian noise (AWGN), respectively. The received signal at the jth SU relay from the pffiffiffiffiffi SU source is ysrj = Ps hsrj x + nsrj , and the jth SU relay retransmits the received signal to the SU destination in orthogonal channels where the corresponding received signal at the SU destination is qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 pffiffiffiffiffiffi yrj d ¼ ð Prj hrj d ysrj = Ps hsrj þ N0 Þ þ nrj d , where hsrj is the channel gain from the SU source to the jth SU relay, and hrj d is the channel gain from the jth SU relay to the SU destination, respectively, which are modeled as Rayleigh fading channel. We assume that, nsd , nsrj , and nrj d are IID AWGN with zero mean and variance N0 . Moreover, we can qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 pffiffiffiffiffiffi rewrite the yrj d ¼ ð Prj hrj d ysrj = Ps hsrj þ N0 Þ þ nrj d by replacing ysrj as follows
It is known that MRC at the destination in an AFbased CCS maximizes the link SNR from the source. Therefore, we utilize MRC to combine the received signals from the SU source and from the jth SU relays. As the instantaneous SNR at the output of the MRC equals to the sum of the SNRs of the incoming signals,31 the instantaneous SNR g after applying the MRC is expressed as follows
ysd =
pffiffiffiffiffiffiffiffiffiffi Ps Prj hrj d hsrj x ffi + n0rj d yrj d = qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 Ps hsrj + N0
ð4Þ
0
1 2 2 2 Ps Prj hrj d hsrj P h j j s sd A ð9Þ g=@ + 2 2 N0 Prj hrj d + Ps hsrj + N0 N0
The instantaneous secondary system throughput of the CCRNs by employing MRC is expressed as follows Tj (Ps , Prj ) = log2 (1 + g)
ð10Þ
With a fixed transmit power of the SU source, the secondary system throughput depends on the relay transmit power. Therefore, in the next section, we propose a relay selection and power assignment scheme to maximize the instantaneous secondary system throughput of the CCRNs, represented in equation (10).
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7 Crmin = Crj d (Pm rj ) j
The proposed timer-based MRS At the beginning of a time slot, the SU source sends an RTS to the neighboring SU relays. Due to the broadcasting nature of wireless communication, the surrounding SU relays overhear the RTS and can detect the maximum allowable transmission power of the SU source. Therefore, the maximum allowable transmission power of the SU source under the PU receiver interference requirement can be given as follows IM PM s = hsq 2
ð11Þ
The maximum transmission power of each SU relay is calculated in Mietzner et al.32 as follows PM rj
= min
Pmax SU ,
IM
!
2 hr q j
subject to :
PM rj
ð12Þ
Pmax SU
As the SU relays know the maximum transmission power of the SU source by an RTS, and the outage capacity C0 is given to the system, then the minimum transmission power of the SU relays can be expressed as follows 0
1 2 2 m hsr PM P h r d j j s r A ð13Þ C0 = log2 @1 + 2 j 2 m Prj hrj d + Ps hsrj + N0 N0 2 Let us put that li = jhrj d j2 and bi = PM s jhsrj j . Then the minimum transmission power of the jth SU relay can be extracted from equation (13) as follows
Pm rj
bi N0 2C0 1 + N0 2 2C0 1 = ðli bi Þ ðli N0 ð2C0 1ÞÞ
ð14Þ
By substituting the value of li and bi in equation (14), the minimum transmission power jth SU relay can be expressed as follows 2 C hsr N0 2 0 1 + N 2 2C0 1 PM j s 0 Pm rj = hr d 2 hsr 2 N0 hr d 2 ð2C0 1Þ PM j j j s
ð17Þ
The capacity difference of the jth SU relay can be expressed as follows DCrj = Crmax Crmin j j subject to : DCrj .0
ð18Þ
The jth SU relay sends CTS back to the SU source only, if the capacity difference of the jth SU relay will satisfy the condition of equation (18); otherwise, the jth SU relay remains silent for transmission. As the CSI from the jth SU relay to the SU destination is obtained through the CTS of the SU destination, by applying Shannon’s channel capacity form, the ratio of the channel gain from the jth SU relay to the SU destination and from the jth SU relay to the qth PU receiver is mrj = log2 (1 + jhrj d j2 =jhrj q j2 ). The utility function of the jth SU relay, Urj = DCrj mrj , is composed of the difference in capacities between the maximum and minimum transmission powers of the SU relays, DCrj = Crmin , and the ratio of the channel gain between Crmax j j the jth SU relay and the SU destination and the jth SU relay and the qth PU receiver, mrj = log2 (1 + jhrj d j2 =jhrj q j2 ). In the proposed timer-based MRS scheme, the jth SU relay sends CTS according to its offset time. The offset time of the jth SU relay can be calculated as follows Trj =
a +b Urj
ð19Þ
where a and b are the scaling parameter and the duration of the CTS that sent by the SU destination to the SU source, respectively. In sequential CTS, the jth SU relay having the minimum offset time sends CTS first to the SU source and a forwarding relay set is determined on the time slot Tslot1 and the SU source assigns transmission power to the SU relays. The proposed sequential power assignment scheme will be discussed in the next section.
ð15Þ
By considering the maximum allowable transmission power of each SU relay, the maximum system capacity of the jth SU relay is given by Crmax = Crj d (PM rj ) j
ð16Þ
The minimum system capacity of the jth SU relay by considering the minimum allowable transmission power each relay is given by
The proposed sequential power assignment As the CTS from the jth SU relay is received at the SU source, the SU source assigns transmission power to the jth SU relay. The assigned transmission power to the jth SU relay is formulated as follows DPrj d = Pm rj + nrj dp
ð20Þ
where dp is the small given value, and nrj = (1, 2, 3, . . . ) is the weighting value for the CTS of the jth SU relay.
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Figure 3. An operation example of the proposed timer-based multi-relay selection.
The total interference to the qth PU receiver caused by power assignment can be expressed as follows 2 ) DPrj d hrj q Itotal (nrj ) = N0
power to the SU relays on the forwarding relay set until satisfying the constraints of equations (21) and (22).
(
ð21Þ
subject to : Itotal (nrj ) I M
Example of the proposed timer-based MRS Figure 3 shows an operation example of the proposed timer-based MRS, where the SU source selects SU relays from its surroundings based on the interference and system capacity criteria. For example, after the b duration, the SU relay 1 will send CTS first to the SU source based on the offset time of the SU relay. After receiving the CTS from the SU relay 1, the SU source assigns transmission power by checking the interference and the capacity criteria. During transmission power
After the power assignment to the jth SU relay, the SU source broadcasts data and the selected SU relays receive the data on time slot Tslot2 , and then, forward it to the SU destination on time slot Tslot3 , and the SU destination receives copies of original signal that are transmitted by the SU source. Thus, the instantaneous secondary system throughput of the CCRNs by transmission power assignment is expressed as follows 8 9 2 2 2 < = M X P DP h h PM j h j r d r d sr sd s j j j Ctotal (nrj ) = log2 1 + s + : ; N0 2 2 rj 2ðr1 , r2 , r3 , ..., rN 2 Þ Prj hrj d + Ps hsrj + N0 N0 subject to : Ctotal (nrj ) Ctotal (nrj 1) 0 The stop message will be sent by the SU source to the SU relays, if the total interference by transmission power assignment is greater than the maximum interference threshold of the qth PU receiver, and the total instantaneous secondary system throughput by transmission power assignment is less than or equal to zero; otherwise, the weighting value will be increased as nrj = (nrj + 1) and repeatedly assigns transmission
ð22Þ
assignment, if the total interference by transmission power assignment is less than the interference threshold of the PU receiver as well as the secondary system throughput improvement is greater than zero, this process will repeat for the next CTS from the SU relay 2. Otherwise, the stop message will be broadcasted by the SU source. After receiving the stop message, the SU relay 3 and SU relay 4 will stop sending CTS back to the source. Consequently, the SU source will select the
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SU relay 1 and the SU relay 2 before broadcasting the stop message for the SU relays. At the data broadcasting phase, the SU source will broadcast the data for the SU destination. Moreover, the selected SU relays in the first phase will receive the data, and then, by the aid of AF relaying protocol, the SU relays will forward the data in time division multiple access (TDMA) orthogonal channels to the SU destination in the data forwarding phase. Finally, at the SU destination, all signals transmitted by the selected SU relays as well as the direct signal by source node are combined by the MRC technique.
Energy consumption analysis As well known, the optimal MRS, which was proposed in Choi et al.,9 requires an ES over all possible relay combinations for secondary system throughput maximization. Moreover, it requires all SU relays’ information to allocate the optimal power to the SU relays, which may increase the energy consumption in transmission. The energy consumption of CCRN can be expressed as follows EC = E D +
X
ERj
ð23Þ
Figure 4. Energy consumption per transmission according to the number of secondary users.
contrast, optimal MRS requires high energy consumption per transmission because it must wait for the information of all relays to allocate the transmission power. Therefore, the proposed scheme spends lower energy cost for MRS and power allocation than optimal MRS, which can be applicable for the future IoT.
j2N
where ED is the energy used to run the circuitry at SU source and SU destination, ERj is the amount of energy consumed for collecting information from the jth SU relay, and N is the number of SU relays participated in forwarding. For power allocation of SU relays, PM s , M pmax rj , I , and N0 are commonly required to both the optimal MRS and the proposed scheme. However, the optimal MRS scheme requires the additional information on hsrj , hrj d , and hrj q from all the SU relays in the transmission range of the SU source in order to get the optimal transmission powers of the SU relays. On the other hand, the proposed scheme only requires the information on hrj d , hrj q , and Pm rj only from the selected SU relays. Subsequently, the proposed scheme required lower energy consumption than the optimal MRS scheme. Figure 4 shows the energy consumption per transmission for the optimal MRS and the proposed schemes, according to the number of SUs. Our proposed scheme has lower energy consumption than the optimal MRS, since it does not require the full information of the relays for power assignment. In contrast, the optimal MRS requires high energy consumption per transmission because it must wait for all the SU relays’ information for power assignment. Our proposed scheme has lower energy consumption than the optimal MRS because it does not require the information from all the relays for power allocation. In
Simulation results Throughout the simulations, all the channels are assumed to be Rayleigh fading. We also consider that the frequency for the large-scale propagation model is 700 MHz, the average path loss exponent 4, the maximum time threshold Tmax = 200 ms, the scaling parameters a = 0:02, b = 20 ms, dp = 0:05 dBm, and the outage capacity C0 = 0:26 bps=Hz. The considered CCRN consists of one PU receiver, one SU source, one SU destination, and 18 SU relays, and the distance between the SU source and the SU destination is near to the maximum transmission range of the SUs. For simulation comparison, we use the proposed scheme (denoted as Proposed scheme) with other schemes: optimal MRS (denoted as Optimal MRS),9 conventional relay selection (denoted as ConventionRS scheme),12,13 and the random relay selection (denoted as RandomRS scheme). In simulation, Proposed scheme and Optimal MRS are evaluated by considering MRC at the SU destination and without MRC at the SU destination. When MRC is utilized at the SU destination, the secondary system throughput increases because it combines the signals from the direct link and from the relay link. In Optimal MRS, the relays are selected through an ES over the surroundings based on maximum SNR level of the SU relays by satisfying the interference threshold of the PU receiver. In RandomRS scheme, the SU source selects an SU relay randomly
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International Journal of Distributed Sensor Networks
Figure 5. Secondary system throughput performance according to the maximum transmission power of SU relays.
Figure 6. Secondary system throughput performance according to the number of candidate SU relays.
from the surroundings. ConventionRS scheme is a single best relay selection scheme, where the SU source selects one of the surrounding relay based on maximizing the minimum SNRs in the source-to-relay and the relay-todestination links. Moreover, we compare the performance of RandomRS scheme and ConventionRS scheme with equal power allocation in Lu et al.,22 as follows
we can observe from Figure 4 that Proposed scheme achieves a significant secondary system throughput gain over ConventionRS scheme and RandomRS scheme with equal power allocation (EPA). RandomRS scheme shows very low secondary system throughput performance due to the randomness in relay selection. Figure 6 shows the secondary system throughput performance of Proposed scheme according to the number of candidate SU relays when the interference threshold is 10 dBm and the maximum transmit power of the SU relays is fixed at 15 dBm and compares the secondary system throughput with Optimal MRS, ConventionalRS scheme, and RandomRS scheme. We observe that the secondary system throughput increases as the number of SU relays increases. Proposed scheme with its much lower IC shows near-optimal secondary system throughput performance to Optimal MRS when compared with MRC at the SU destination and without MRC at the SU destination. Furthermore, Proposed scheme outperforms ConventionalRS scheme which selects the best relay based on the criterion in equation (25). RandomRS scheme with EPA does not provide any throughput improvement because the relay is selected randomly from the surroundings. Figure 7 shows the secondary system throughput according to the interference threshold of the PU receiver when the maximum allowable transmission power of SU relays is 10 dBm. We observe that the secondary system throughput performance increases as the interference threshold increases. Proposed scheme achieves near-optimal secondary system throughput performance to Optimal MRS and outperforms ConventionalRS scheme and RandomRS scheme. This is reasonable because a larger interference threshold allows more transmission power to the SU relays, which can improve the secondary system throughput.
( Ps = Prj = min
Pmax SU ,
!)
IM
2 2 hsq + hr q
ð24Þ
j
The relay selection criterion in conventional relay networks is defined in Bletsas et al.12 and Muller and Speidel13 as follows j = arg max min
n o hsr 2 , hr d 2 j
j
ð25Þ
rj = fj1 , j2 , j3 , ..., jn g
Figure 5 shows the secondary system throughput performance of Proposed scheme in comparison with Optimal MRS, ConventionRS scheme, and RandomRS scheme for different values of the maximum transmission power of the SU relays when the interference threshold is 15 dBm. The secondary system throughput performance of Proposed scheme and Optimal MRS is evaluated with MRC and without MRC at the SU destination. It is observed that Proposed scheme with its much lower IC shows near-optimal throughput performance to the Optimal MRS in both situations. The secondary system throughput increases as the maximum allowable transmission power of the SU relays increases. In the high maximum transmission power region, the secondary system throughput is restricted by the interference threshold of the PU receiver. As a result, the secondary system throughput is not restrained to the maximum transmission power. Also,
Rahman et al.
11 Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding This work was supported by the National Research Foundation (NRF) of Korea funded by the MEST (nos. NRF-2015R1A2A1A15053452, 2015R1D1A1A09057077). Dr Youngdoo Lee was also supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2013R1A1A2063779).
References Figure 7. Secondary system throughput performance according to the interference threshold of the PU receiver.
RandomRS scheme shows lower secondary system throughput, whereas Proposed scheme shows nearoptimal secondary system throughput to Optimal MRS in both low and high regions of interference threshold of PU receiver and outperforms ConventionalRS scheme and RandomRS scheme with EPA.
Conclusion In this article, we have proposed a relay selection and a sequential power allocation scheme to improve the secondary system throughput of CCRNs for future IoT under PU interference and SU power limitations. To maximize the secondary system throughput, we first developed timer-based MRS to create a relays’ order before the SU source starts transmitting its signal, and we then developed a sequential power assignment algorithm considering only the relays’ order for forwarding the signal to the SU destination. The optimal MRS requires ES over the surroundings, whereas our proposed scheme avoids the ES and efficiently assigns transmission power to the SU relays with lower energy consumption. By simulation, it was shown that the proposed scheme provides a near-optimal secondary system throughput performance to the optimal MRS and also provides a significant gain over conventional and random relay selection scheme with EPA. Authors’ contributions I.K. provided the guideline to focus on issues, requiring solutions, and reviewed the overall manuscript. M.A.R. and Y.L. conceived the study, drafting the article, revising it critically for intellectual content of the whole manuscript. They reviewed the technical contribution of the work and approved the final. All authors read the full manuscript and approved for final submission.
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