计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 252-257.doi: 10.11896/jsjkx.181202352
钟旭东1,2,何元智2,任保全2,董飞鸿2
ZHONG Xu-dong1,2,HE Yuan-zhi2,REN Bao-quan2,DONG Fei-hong2
摘要: 随着通信业务需求的不断增长,频谱资源的有限性使得卫星通信网络和地面网络都面临着严重的频谱危机。认知无线电技术的出现,使得卫星网络与地面网络共用频率资源以提升网络效用成为可能。文中对认知接入分配给地面网络作为主用户的同一频谱资源的认知卫星网络的功率控制和信道分配问题进行了研究。根据卫星网络和地面网络的特性构建了合理的系统模型,并利用中断概率门限表征了信道估计误差对系统容量的影响。为了保护主基站的通信性能,在考虑信道估计误差、信道资源约束、认知卫星用户最大发射功率和微波基站干扰约束的条件下,根据议价博弈理论设计了优化函数。其次,根据凸优化理论推导了最优发射功率和信道分配的闭式解,并在此基础上设计了一种对偶迭代算法来求解该优化问题。最后,根据卫星网络的特性设置了合理的网络参数,并根据参数利用Matlab仿真平台对提出的算法进行了仿真实验。仿真结果表明:所提方法在不同到达速率的条件下均具备良好的收敛性;信道估计误差会降低网络的总容量;所提方法在波束数多于15个时,相比比例公平性算法容量提升超过50bps/Hz,相比最大容量法公平性能提升超过一倍,因此,相较于这两种方法,该方法能在系统容量和用户间公平性之间获得较好的折中。
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[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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