 Research
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Resource allocation in twoway OFDMbased cognitive radio networks with QoE and power consumption guarantees
 Weiwei Yang^{1}View ORCID ID profile and
 Xiaohui Zhao^{1}Email authorView ORCID ID profile
https://doi.org/10.1186/s1363801710006
© The Author(s) 2017
 Received: 9 May 2017
 Accepted: 5 November 2017
 Published: 19 December 2017
Abstract
In this paper, a resource allocation algorithm in twoway orthogonal frequency division multiplexing (OFDM) based cognitive radio networks with quality of experience (QoE) and power consumption guarantees is proposed. We define the overall QoE perceived by secondary users (SUs) per power consumption as QoEW. The power consumption model consists of fixed circuit power, dynamic circuit power, and transmit power which depends on the efficiency of the power amplifiers at different terminals. Under the constraint of total maximum transmit power, the optimization objective is to maximize QoEW while meeting the minimum QoE demands of SUs and maintaining interference threshold limitations of multiple primary users. The resource allocation problem is formulated into a nonlinear fractional programming and transformed into an equivalent convex optimization problem via its hypograph form. Based on the Lagrange dual decomposition method and crosslayer (CL) optimization architecture, this convex optimization problem is separately solved in the physical layer and the application layer. The optimal QoEW is achieved through the proposed CL alternate iteration algorithm. Numerical simulation results demonstrate the impacts of system parameters on QoEW and the effectiveness and superiority of the proposed algorithm.
Keywords
 Cognitive radio network
 Twoway relay
 Resource allocation
 Crosslayer
 QoE and power consumption
1 Introduction
Cognitive radio (CR), as a promising technique to solve spectrum scarcity and improve spectrum utilization by means of dynamic spectrum access, has drawn intensive interests in recent years [1]. Orthogonal frequency division multiplexing (OFDM) is an effective technique to combat channel fading and multipath loss. And it has been widely accepted in CR networks (CRNs) owing to its advantages such as spectrum efficiency improvement and dynamic resource allocation. In an OFDMbased CRN, secondary users (SUs) are allowed to access the spectrum of primary users (PUs) as long as the interference to PUs below their thresholds, so that the transmission power of SUs is always limited and the communication quality of SUs cannot be guaranteed well [2, 3].
Recently, cooperative relay technique has been introduced into CRNs for throughput enhancement and coverage extension without large energy consumption [4]. Traditional oneway relay transmission has a 1/2 spectral efficiency loss than direct transmission, which is induced by halfduplex relay nodes [5]. In other words, since halfduplex relay nodes cannot simultaneously transmit and receive signals, oneway relay transmission needs four time slots to accomplish information exchange when two users communicate with each other. In order to overcome the inherent spectrum loss, twoway relaying transmission with physicallayer network coding (PNC) is proposed [6], in which only two time slots are required to finish information exchange. According to the difference of signal processing functions at relay nodes, PNC has several subprotocols, such as decodeandforward (DF) and amplifyandforward (AF). Many previous works focus on PNCAF protocol since it is easily realized in practical systems [7, 8]. Therefore, we focus on twoway OFDMbased CRN with PNCAF protocol in this paper.
Radio resource allocation is very significant to performance enhancement for wireless networks. Most of the existing studies are carried out on radio resource allocation with quality of service (QoS) optimization target [9–11]. However, with the wide proliferation of mobile devices as well as the ubiquitous availability of multimedia services, traditional optimization metric (e.g., date rate and spectrum efficiency) cannot directly reflect end users’ satisfaction, which may cause a waste of valuable radio resource. Quality of experience (QoE) is a widely used metric which can indicate not only multimedia service performance but also end users’ subjective satisfaction of the multimedia service directly. Therefore, both academic studies and industries have turned their concentrations from network QoS parameters to QoE conception [12, 13]. Generally, an end user’s QoE is affected by both physical layer and application layer parameters. There have been some researches that depend on crosslayer (CL) optimization architecture to solve QoEoriented optimization problems [14–16]. In [14], a joint multiuser scheduling and multiuser rate adaptation strategy is proposed to provide an appropriate tradeoff between efficiency and fairness, while ensuring QoE. In [15], a near optimal power allocation scheme for transmitting scalable video coding based videos is proposed with the target to maximize QoE over multiinput multioutput systems. In [16], novel and practical CL QoEaware radio resource allocation algorithms for the downlink of a heterogeneous OFDM access system are proposed. However, in [14–16], the energy consumption is not taken into consideration.
In recent years, rapid development of information and communications technology significantly contributes to the energy consumption and global warming, which is very crucial to the performance of wireless networks [17]. A power consumption model in wireless networks generally consists of the transmit power which depends on efficiency of power amplifier (PA) at different terminals, fixed circuit power, and dynamic circuit power related to data transmission rate [18]. How to maximize QoE perceived by end users while minimizing the power consumption is a challenging problem. Recently, some research works have been conducted on this topic [19, 20]. In [19], a QoEdriven resource allocation algorithm in the OFDM system is proposed to address the system energy efficiency and guarantee userperceived QoE for different multimedia services. The power consumption model in [19] only consists of the transmit power. In [20], a joint optimization scheme of the fairness of users’ QoE and power consumption for the OFDM access multicell networks is proposed. The power consumption model in [20] only has the fixed power and the transmit power with the assumption of identical efficiency values of PA at different terminals. However, this assumption is not practical since the efficiency value of PA varies with the design and the implementation of the terminals. Moreover, none of [19, 20] consider the dynamic power consumption. To our best knowledge, most of the existing resource allocation algorithms adopt the power consumption model ignoring dynamic circuit power and assuming identical efficiency values of PA at different terminals. In addition, there are barely resource allocation algorithms taking both QoE and power consumptionrelated issues into consideration for a twoway OFDMbased CRN.

We adopt the power consumption model incorporates fixed circuit power, dynamic circuit power, and transmit power which depends on the efficiency of the PAs at different terminals. We define the tradeoff between overall QoE perceived by SUs and power consumption as QoEW.

The resource allocation problem is formulated as a nonlinear fractional programming problem and converted it into an equivalent convex optimization problem via its hypograph form. Based on the Lagrange dual decomposition method and CL optimization architecture, the convex optimization problem is separately solved in the physical layer and the application layer.

The optimal QoEW is achieved through the proposed CL alternate iteration algorithm. Numerical simulation results show the impact of system parameters on QoEW and the effectiveness and outperformance of the proposed algorithm through comparisons with other algorithms.
The rest of this paper is organized as follows. The system model and the formulation of QoE and power consumption oriented resource allocation problem is described in Section 2. A CL alternate iteration algorithm is proposed in Section 3. Simulation results and performance analysis are presented in Section 4. Finally, conclusions are drawn and future works are given in Section 5.
2 System model and problem formulation
2.1 System model
where \(g^{n}_{i,l},i\in \) [1:2] denotes the channel gains on the n _{ th } subcarrier transmitted from S _{ i } to the l _{ th } PURX, respectively. \(\rho _{k}^{n}\) is a binary decision variable to indicate whether the n _{ th } subcarrier selects the k _{ th } relay node. If \(\rho _{k}^{n} = 1\), it means the k _{ th } relay node is allocated to the n _{ th } subcarrier, otherwise not. We adopt the assumption that the interference from the primary network to secondary network is neglected according to the features of CRN [21, 22].
where \({\tilde {g}_{k,l}^{n}} \) denotes the channel gain of the n _{ th } subcarrier transmitted between the k _{ th } relay node and the l _{ th } PURX.
2.2 Power consumption model
where the first term and the second term in (11) denote the transmit power of SUs and the relay nodes, respectively. The factors ε _{ i }>1,∀i and ξ _{ k }>1,∀k denote the reciprocal of the drain efficiency of PAs at SUs and the relay nodes, respectively. The third term P _{ C } denotes the total fixed circuit power consumption usually consumed by electronics devices. The forth term denotes the dynamic circuit power consumption which is rate dependent, and the factor α denotes power consumption per unit data rate.
2.3 Utilitybased QoE model
Traditional QoS assessment provides an objective metric rather than a subjective opinion for end users, but it cannot directly reflect the perceived quality of end users and make full use of the radio resource. Currently, there are a growing number of studies on the assessment models of QoE instead of QoS, in which the mean opinion score (MOS) is the most widely used measure metric [23]. The MOS is the reflection of user data rate in application layer \(\tilde R\) and modeled by utility function \(U({\tilde R}) \in \, [ {1,{Q_{\max }}} ]\), where Q _{max} is a positive upper bound of MOS. Generally, MOS from 1 to 4.2 can continuously describe the perceived quality of user from poor to excellent. The expression of \(U({\tilde R})\) varies with different multimedia traffic. Assurance of the appreciate level of QoE for heterogeneous services is an important consideration for future wireless communication system. Therefore, we consider two typical heterogeneous multimedia services, i.e. video application and best effort application in this work.
2.3.1 Video application
where FR and PER denote the frame rate and packet error rate, respectively. The metric coefficients a _{1} to a _{5} are obtained by a nonlinear regression of the prediction model with training sets and they vary with different content. This model is strictly concave.
2.3.2 Best effort application
where \({\tilde R_{\min }}\) and \({\tilde R_{\max }}\) denote the lower bound and upper bound of user data rate in the application layer.
2.4 Problem formulation
where \({MOS}_{i}^{\min }\) represents the minimum MOS of S _{ i } required in the application layer. C1 guarantees the minimum perceived quality demand of S _{ i }. If C1 is not satisfied, communication outage may happen, since terminating the multimedia service with poor satisfaction level can avoid power consumption and is very important to improve energy efficiency for green communications. R _{ i } and \({\tilde R_{i}}\) are the user data rate in the physical layer and the application layer, respectively. C2 decouples the CL optimization problem and establishes the relationship between the physical layer and the application layer. R _{ i } and \({\tilde R_{i}}\) will converge to the same value when a feasible solution to P1 is achieved. \(I^{th}_{l}\) is the interference threshold of the l _{ th } PURX. C3 and C4 are the interference threshold constraints of the l _{ th } PURX in both two time slots. P _{max} is the maximum total power value of SUs and the relay nodes. C5 and C6 are the peak transmit power constraints of SUs and the relay nodes. C7 and C8 are the subcarrier assignment constraints to ensure that each subcarrier can select only one relay for itself.
3 Resource allocation algorithm with QoE and power consumption guarantees
where \({\left [x \right ]^ +} \buildrel \Delta \over = \max \left ({0,x} \right)\). \(s_{1}^{l}\), \(s_{2}^{l}\), and s _{3} are the small positive step sizes. t _{1} is the number of iteration in the physical layer. Finally, pseudo code of the physical layer algorithm also called inner loop algorithm is listed in Algorithm 1.
where \(s_{4}^{i}\) and \(s_{5}^{i}\) are the small positive step sizes. t _{2} is the number of iteration in the application layer. Then, the pseudo code of the application layer algorithm also called an outer loop algorithm is listed in Algorithm 2. We alternate iterations of the physical layer algorithm and the application layer algorithm, which is defined as the CL alternate iteration algorithm until the convergence of the optimal z ^{∗} is obtained.
The computational complexity of the proposed CL alternate iteration algorithm can be estimated roughly as follows. In the physical layer (i.e., inner loop), we first perform the power allocation under the given subcarrier assignment scheme. Then the power allocation problem is decomposed into N parallel power allocation subproblems. Thus, the power allocation algorithm requires N evaluations for all subcarriers. In every evaluation, we assume I _{ S } is the number of iterations to obtain the optimal power solution \({{P_{m}^{n}}^{*}}\) with the search method. The subcarrier assignment scheme is carried out after we obtain \({{P_{m}^{n}}^{*}}\) with the computational complexity K. After N evaluations, the computational complexity of the power allocation and subcarrier assignment procedure is N(I _{ S }+K). The iteration number of subgradient method for maximizing \({\mathcal {L}_{PHY}}\) is I _{ phy }. Then, the computational complexity required in the physical layer is \(\mathcal {O}({{I_{phy}}N({{I_{S}} + K})})\). In the application layer (i.e., outer loop), the number of iterations of subgradient method for z ^{∗} is I _{ app }. To sum up, the overall computational complexity is \(\mathcal {O}({I_{app}} {I_{phy}} N ({I_{S}}+K))\) when optimal z ^{∗} is obtained.
4 Simulation results
In this section, we use computer simulation to validate the effectiveness of our proposed resource allocation algorithm and show its outperformance than the fixed relay selection with equal power allocation (FRSEPA) scheme, random relay selection with equal power allocation (RRSEPA) scheme, fixed relay selection with optimal power allocation (FRSOPA) scheme, random relay selection with optimal power allocation (RRSOPA) scheme, and QoE maximization algorithm. Simulation parameters are assumed as follows unless specified otherwise. We assume L=2, K=2, and N=16. The interference thresholds of PU1 and PU2 are \(I_{1}^{th} = 4\times {10^{ 10}}W\) and \(I_{2}^{th} = 6 \times {10^{ 10}}W\), respectively. The maximum total transmit power, the fixed circuit power, and the dynamic circuit power consumption factor are P _{max}=10W, P _{ C }=0.05W, and α=0.01, respectively. The reciprocal of PAs’ drain efficiency at SUs and the relay nodes are ε _{1}=4, ε _{2}=4, ξ _{1}=2, and ξ _{2}=2, respectively. The channel gains are assumed to be the frequency flat Rayleigh fading channels. They are independent and identically distributed (i.i.d.) circularly symmetric complex Gaussian (CSCG) random variables (RVs) and distributed as \(h\sim \mathcal {C}\mathcal {N}\left (0,\,\,\frac {1}{(1+d)^{\tau }}\right) \), where τ=4 is the path loss coefficient and d is the distance among different nodes in the system. We adopt rapid movement video application, thus the coefficients a _{1} to a _{5} are set to be −0.0228, −0.0065, 0.6582, 10.0437, and 0.6865. We assume there are no packet loss and F R=10. The minimum required MOS for S _{1} and S _{2} are 3.6 and 4, respectively.
5 Conclusions
In this paper, a QoE and power consumptiondriven resource allocation problem in a twoway OFDMbased CRN is studied. The tradeoff between the sum of QoE perceived by SUs and power consumption is defined as QoEW and adopted as a new performance metric. A CL alternate iteration algorithm is proposed to solve this resource allocation problem. Numerical simulation results show the outperformance of the proposed algorithm through comparisons with other algorithms and validate the effectiveness of the proposed algorithm for the satisfaction of the minimum QoE demands of SUs and the guarantee of the interference thresholds of multiple PUs. In addition, the impacts of the fixed power, the dynamic circuit power consumption factor and the drain efficiency of PAs on QoEW are also given. In our future work, we will extend this framework for multiple SUs with various multimedia services and different QoE requirements under green communications considerations.
Declarations
Acknowledgements
The work of this paper is supported by the National Natural Science Foundation of China under grant No.61171079.
Authors’ contributions
WY contributed in the conception of the study and design of the study and wrote the manuscript. Furthermore, WY carried out the simulation and revised the manuscript. XH helped to perform the analysis with constructive discussions and helped to draft the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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References
 YC Liang, KC Chen, GY Li, P Mahonen, Cognitive radio networking and communication: an overview. IEEE Trans. Veh. Technol. 60(7), 3386–3407 (2011).View ArticleGoogle Scholar
 X Kang, HK Garg, YC Liang, R Zhang, Optimal power allocation for OFDMbased cognitive radio with new primary transmission protection criteria. IEEE Trans. Wirel. Commun. 9(6), 2066–2075 (2010).View ArticleGoogle Scholar
 N Zhao, FR Yu, HJ Sun, M Li, Adaptive power allocation schemes for spectrum sharing in interferencealignmentbased cognitive radio networks. IEEE Trans. Veh. Tech. 65(5), 3700–3714 (2016).View ArticleGoogle Scholar
 E Hossain, DI Kim, VK Bhargava, Cooperative cellular wireless communications (Cambridge Univ Press, Cambridge, 2011).Google Scholar
 M Naeem, A Anpalagan, M Jaseemuddin, DC Lee, Resource allocation techniques in cooperative cognitive radio networks. IEEE Commun. Surv. Tutorials. 16(2), 729–744 (2014).View ArticleGoogle Scholar
 SL Zhang, SC Liew, PP Lam, in 12th MobiCom. Hot topic: physicallayer network coding, (2006), pp. 358–365.Google Scholar
 K Xiong, PY Fan, KB Letaief, S Yi, M Lei, in Proc. IEEE Globecom’12. Joint subcarrierpairing and resource allocation for twoway multirelay OFDM networks (Anaheim, 2012), pp. 4874–4879.Google Scholar
 Z Chang, QQ Zhang, XJ Guo, T Ristaniemi, Energyefficiency resource allocation for OFDMA twoway relay networks with imperfect CSI. EURASIP. J. Wirel. Commun. Netw. 2015(1), 1–11 (2015).View ArticleGoogle Scholar
 MG Adian, H Aghaeinia, Optimal resource allocation for opportunistic spectrum access in multipleinput multipleoutputorthogonal frequency division multiplexing based cooperative cognitive radio networks. IET Signal Process. 7(7), 549–557 (2013).MathSciNetView ArticleGoogle Scholar
 S Mohammadkhani, MH Kahaei, SM Razavizadeh, Robust beamforming and power allocation in cognitive radio relay networks with imperfect channel state information. IET Commun. 8(9), 1560–1569 (2014).View ArticleGoogle Scholar
 YJ Xu, XH Zhao, YC Liang, Robust power control and beam forming in cognitive radio networks: a survey. IEEE Commun. Surv. Tutorials. 17(4), 1834–1857 (2015).View ArticleGoogle Scholar
 H Zhu, Y Cao, W Wang, BX Liu, T Jiang, QoEaware resource allocation for adaptive devicetodevice video streaming. IEEE Netw.29(6), 6–12 (2015).View ArticleGoogle Scholar
 C Singhal, S De, Energyefficient and QoEaware TV broadcast in nextgeneration heterogeneous networks. IEEE Commun. Mag. 54(12), 142–150 (2016).View ArticleGoogle Scholar
 N Khan, MG Martini, QoEdriven multiuser scheduling and rate adaptation with reduced crosslayer signaling for scalable video streaming over LTE wireless systems. EURASIP J. Wirel. Commun. Netw. 2016(1), 1–23 (2016).View ArticleGoogle Scholar
 X Chen, JN Hwang, CN Lee, SI Chen, A near optimal QoEdriven power allocation scheme for scalable video transmissions over MIMO systems. IEEE J. Sel. Top. Signal Process. 9(1), 76–88 (2015).View ArticleGoogle Scholar
 M Rugelj, U Sedlar, M Volk, J Sterle, M Hajdinjak, A Kos, Novel crosslayer QoEaware radio resource allocation algorithms in multiuser. IEEE Trans. Commun. 62(9), 3196–3208 (2014).View ArticleGoogle Scholar
 R Mahapatra, Y Nijsure, G Kaddoum, NU Hassan, C Yuen, Energy efficiency tradeoff mechanism towards wireless green communication: a Survey. IEEE Commun. Surv. Tutorials. 18(1), 686–705 (2016).View ArticleGoogle Scholar
 S Kim, YH Lee, Energyefficient power allocation for OFDM signaling over a twoway AF relay. IEEE Trans. Veh. Technol. 64(10), 4856–4863 (2015).MathSciNetView ArticleGoogle Scholar
 BP Li, S Li, CW Xing, ZS Fei, JM Kuang, A QoEbased OFDM resource allocation scheme for energy efficiency and quality guarantee in multiusermultiservice system, (Anaheim, 2012).Google Scholar
 H Shao, WP Jing, XG Wen, ZM Lu, HJ Zhang, YW Chen, DB Ling, Joint optimization of quality of experience and power consumption in OFDMA multicell networks. IEEE Commun. Lett. 20(2), 380–383 (2016).View ArticleGoogle Scholar
 DM Jiang, HX Zhang, DF Yuan, Multiuser twoway relay processing and power control methods for cognitive radio networks. Wirel. Commun. Mob. Comput. 13(15), 1353–1368 (2013).Google Scholar
 DC Yang, L Xiao, JS Xu, WG Li, Joint power control and relay selection scheme for cognitive twoway relay networks. J. Syst. Eng. Electronics. 24(4), 571–578 (2013).View ArticleGoogle Scholar
 ZF He, SW Mao, T Jiang, A survey of QoEdriven video streaming over cognitive radio networks. IEEE Netw.29(6), 20–25 (2015).View ArticleGoogle Scholar
 A Khan, L Sun, E Jammeh, E Ifeachor, Quality of experiencedriven adaptation scheme for video applications over wireless networks. IET Commun.4(11), 1337–1347 (2010).View ArticleGoogle Scholar
 GC Song, Y Li, Crosslayer optimization for OFDM wireless networkspart I: theoretical framework. IEEE Trans. Wirel. Commun. 4(2), 614–624 (2005).View ArticleGoogle Scholar
 S Boyd, L Vandenberghe, Convex optimization (Cambridge University Press, Cambridge UK, 2004).View ArticleMATHGoogle Scholar
 W Yu, R Liu, Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Trans. Commun. 54(7), 1310–1322 (2016).View ArticleGoogle Scholar
 YU Jang, ER Jeong, YH Lee, A twostep approach to power allocation for OFDM signals over twoway amplifyandforward relay. IEEE Trans. Signal Process. 58(4), 2426–2430 (2010).MathSciNetView ArticleGoogle Scholar
 GAS Sidhu, F Gao, W Chen, A Nallanathan, A joint resource allocation scheme for multiuser twoway relay networks. IEEE Trans. Commun. 59(11), 2970–2975 (2011).View ArticleGoogle Scholar