Computer Science ›› 2017, Vol. 44 ›› Issue (5): 48-52.doi: 10.11896/j.issn.1002-137X.2017.05.009

Previous Articles     Next Articles

Suboptimal Dynamic Resource Allocation Algorithm in OFDM Based Cognitive Radio Network

HAN Jie, SONG Xiao-qin, DONG Li and JIN hui   

  • Online:2018-11-13 Published:2018-11-13

Abstract: This paper investigated the multi-user resource allocation in OFDM-based cognitive radio(CR-OFDM) including subcarrier allocation and power allocation.In CR system,not only the interference between primary uers(PUs) and second users(SUs) is considered,but also the interference caused by SUs need to be controlled under the threshold.Thus the system model is more complicated.Because of the integer constraints,the complexity of the algorithm which can obtain optimal solution is too high to suit for real-time systems.Therefore,this paper proposed a distributed algorithm to obtain the suboptimal solution with low complexity.First,a subcarrier allocation algorithm considering power constraint and interference constraint was proposed and then a modified linear water-filling algorithm was put forward to allocate power.Simulation results show that the proposed algorithm can obtain the satisfactory system capacity and reduce the complexity in comparison with the optimal Lagrange dual method,which is more suitable for real-time systems.

Key words: Cognitive radio network,OFDM,Resource allocation,Linear water-filling algorithm

[1] HAYKIN S.Cognitive radio:brain-empowered wireless communications[J].IEEE J.Sel.Areas Commun,2005,3(2):201-220.
[2] WEISS T,JONDRAL F K.Spectrum pooling:an innovative stra-tegy for the enhancement of spectrum efficiency[J].IEEE Commun.Mag,2004,3(3):S8-S14.
[3] WEISS T,HILLENBRAND J,KROHN A,et al.Mutual interference in OFDM-based spectrum pooling systems[J].Proc.IEEE Vehicular Technol.Conf.(VTC’04 Spring),2004,4(4):1873-1877.
[4] WANG S,HUANG F,et al.Fast power allocation algorithm for cognitive radio networks[J].IEEE Communications Letters,2011,15(8):845-847.
[5] BANSAL G,HOSSAIN M,BHARGAVA V.Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems[J].IEEE Transactions on Wireless Communications,2008,7(11):4710-4718.
[6] TANG L,WANG H,CHEN Q,et al.Subcarrier and power allocation for OFDM-based cognitive radio networks[C]∥ IEEE International Conference on Communication and Technology and Applications.IEEE,2009:457-461.
[7] XU L,LV T M,LI Q M,et al.Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network[J].Wireless Personal Communications,2014,9(2):1591-1607.
[8] ZHANG D M,XU Y Y,CAI Y M.Linear water filling power allocation algorithm in OFDMA system[J].Journal of Electro-nics & Information Technology,2007,9(6):1286-1289.(in Chinese) 张冬梅,徐友云,蔡跃明.OFDMA系统中线性注水功率分配算法[J].电子与信息学报,2007,29(16):1286-1289.
[9] WU J,YANG L X,LIU X.Subcarrier and Power allocation in OFDM Based Cognitive Radio Systems[C]∥International Conference on Intelligent Computation Technology & Automation.2011:728-731.
[10] YAN S C,REN P Y,LV F S.Power allocation algorithms forOFDM-based cognitive radio system[C]∥Proc.WiCOM 2010.2010:1-4.
[11] ZHAO Q,SADLER B M.A Survey of Dynamic Spectrum Access[J].IEEE Signal Processing Magazine,2007,24(3):79-89.
[12] ALMALFOUH S M,STUBER G L.Interference-aware radioresource allocation in OFDMA-based cognitive radio networks[J].IEEE Trans.Veh.Technol.,2011,0(4):1699-1713.
[13] LI W,ZHANG Y,SO A,et al.Slow adaptive OFDMA systems through chance constrained programming[J].IEEE Trans.Signal Process.,2010,8(7):3858-3869.
[14] DANIELS R.Approximation methods for electronic filter design[M].New York:McGraw-Hill,1974.
[15] GOLDSMITH A,CHUA S.Variable-rate variable-power MQAMfor fading channels[J].IEEE Transactions on Communications,1997,5(10):1218-1230.
[16] BANSAL G,Hossain M J,BHARGAVA V K.Adaptive Power Loading for OFDM-Based Cognitive Radio Systems with Statistical Interference Constraint[J].IEEE Transactions on Wireless Communications,2011,0(9):2786-2791.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .