Computer Science ›› 2020, Vol. 47 ›› Issue (1): 252-257.doi: 10.11896/jsjkx.181202352

• Computer Network • Previous Articles     Next Articles

Channel Allocation and Power Control Algorithm for Cognitive Satellite Networks Based on Cooperative Game Theory

ZHONG Xu-dong1,2,HE Yuan-zhi2,REN Bao-quan2,DONG Fei-hong2   

  1. (College of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China)1;
    (Academic of Military Sciences,Beijing 100141,China)2
  • Received:2018-12-18 Published:2020-01-19
  • About author:ZHONG Xu-dong,born in 1991.He is now a doctoral candidate and an engineer.His research concerns resource management for satellite networks;HE Yuan-zhi,born in 1974.She is now a Research Fellow with research concerns satellite communications and cognitive satellite networks.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61231011,91338021).

Abstract: With continuous increasing of communication service requirements,satellite networks and terrestrial networks are both facing a serious spectrum crisis because of the limitation of spectrum resource.Cognitive radio technology makes it possible torea-lize the resource sharing for network utility improvement between satellite networks and terrestrial networks.This paper investigated the power control and channel allocation problem for cognitive satellite networks,where satellite users cognitively access the same spectrum resource allocated to terrestrial networks as primary users.A reasonable system model is constructed based on the characteristics of satellite networks and terrestrial networks,and the outage probability threshold is used to represent the effect on system capacity of channel estimation error.To protect the communication performance of primary base station,the optimization function is designed based on bargaining game theory by taking into account with channel estimation errors,constrain of channel resource,maximum transmit power of cognitive satellite users and interference constrains of primary base stations.In this paper,the closed from solutions of optimal transmit power and channel allocation for the problem are derived based on convex optimization theory,and a dual iteration algorithm is designed to find the solutions.Finally,the system parameters are set based on characteristics of satellite networks,and several simulations are obtained for the proposed algorithm with Matlab simulation platform based on the parameters.The simulation results show that the proposed algorithm has a proper convergence performance under different arrival rates.It also shows that the channel estimation error can decrease the capacity performance of the network.Compared with existing methods,the proposed algorithm can improve the capacity performance with more than 50bps/Hz than the proportional fairness method when the number of beams is more than 15,and the fairness performance is more than double of the capacity maximizing method under the same condition.Therefore,the proposed algorithm can find a reasonable trade-off between system capacity and fairness among users.

Key words: Cognitive satellite network, Resource allocation, Power control, Cooperative game

CLC Number: 

  • TN915.81
[1]WU W W.Satellite Communications [J].Proceeding of IEEE,1997,85(6):998-1010.
[2]BEM J D,WIECKOWSKI T,ZIELINSKI R J.Broadband Satellite Systems [J].IEEE Communication Surveys & Tutorials,2000,3(1):2-15.
[3]MITOLA J,MAGUIRE G Q.Cognitive Radio:Making Software Radios More Personal [J].IEEE Personal Communications,1999,6(4):13-18.
[4]JIA M,GU X,GUO Q,et al.Broadband Hybrid Satellite-Terrestrial Communication Systems Based on Cognitive Radio toward 5G [J].IEEE Wireless Communications,2016,23(6):96-106.
[5]ABIDEL-RAHMAN M J,KRUNZ M,ERWIN R.Exploiting Cognitive Radios for Reliable Satellite Communications [J].International Journal of Satellite Communication Networks,2015,33(3):197-216.
[6]MALEKI S,CHATZINOTAS S,KRAUSE J,et al.Cognitive Zone for Broadband Satellite Communication in 17.3-17.7GHz [J].IEEE Wireless Communication Letter,2015,4(3):305-308.
[7]LIN Z,LIN M,OUYANG J,et al.Beamforming for Secure Wireless Information and Power Transfer in Terrestrial Networks Coexisting with Satellite Networks [J].IEEE Signal Process Letters,2018,25(8):1166-1170.
[8]WANG L,LI F,LIU X,et al.Spectrum Optimization for Cogni-tive Satellite Communications with Cournot Game Model [J].IEEE Access,2018,6:1624-1634.
[9]CHAE S H,JEONG C,LEE K.Cooperative Communication for Cognitive Satellite Networks [J].IEEE Transactions on Communications,2018,66(11):5140-5154.
[10]KOLAWOLE O Y,VOPPALA S,SELLATHURAI M,et al.On the Performance of Cognitive Satellite-Terrestrial Networks [J].IEEE Transactions on Cognitive Communication Networks,2017,3(4):668-683.
[11]AN K,LIN M,ZHU W,et al.Outage Performance of cognitive hybrid satellite-terrestrial networks with interference constraint [J].IEEE Transactions on Vehicle Technology,2016,65(11):9397-9404.
[12]VASSAKI S,POULAKIS M I,PANAGOPOULOS A D,et al.Power Allocation in Cognitive Satellite Terrestrial Networks with QoS Constrains [J].IEEE Communication Letter,2013,17(7):1344-1347.
[13]GAO B,LIN M,AN K,et al.ADMM-Based Optimal Power Control for Cognitive Satellite Terrestrial Uplink Networks,[J].IEEE Access,2018,PP(99):1-1.
[14]SHI S,AN K,LI G,et al.Optimal Power Control in Cognitive Satellite Terrestrial Networks with Imperfect Channel State Information [J].IEEE Wireless Communication Letter,2018,7(1):34-37.
[15]ZUO P,PENG T,LINGHU W,et al.Optimal Resource Allocation for Hybrid Interwave-Underlay Cognitive SatCom Uplink [C]∥Proceedings of IEEE Wireless Communication Networks.Conference (WCNC).Barcelona:IEEE Press,2018:1-6.
[16]LAGUNAS E,MALEKI S,CHATZINOTAS S,et al.Power and Rate Allocation in Cognitive Satellite Uplink Networks[C]∥Proceedings of 2016 IEEE International Conference on Communications (ICC).Kuala Lumpur:IEEE Press,2016:1-6.
[17]LAGUNAS E,SHARMA S,MALEKI S,et al.Resource Allocation for Cognitive Satellite Communications with Incumbent Terrestrial Networks [J].IEEE Transactions on Cognitive Communication Networks,2015,1(3):305-317.
[18]ZHANG H,JIANG C,BEAULIEU N C,et al.,Resource Allocation for Cognitive Small Cell Networks:A Cooperative Bargaining Approach [J].IEEE Transactions on Wireless Communications,2015,14(6):3481-3493.
[19]HEW S,WHITE L B.Cooperative Resource Allocation Games in Shared Networks:Symmetric and Asymmetric Fair Bargaining Models [J].IEEE Transactions on Wireless Communications,2008,7(11):4166-4175.
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