基于上下行对偶的CRN下行功率分配和波束赋形算法
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摘要
认知无线网络(CRN)在underlay工作模式下的多用户下行功率分配和波束赋形问题研究中存在通用的SDR算法计算复杂度高、实用性受限以及优化问题中忽视主网络(PN)对认知用户(SU)的干扰等问题。针对这些问题,首先在CRN网络模型中增添PN对SU的干扰,生成优化问题;而后基于上行和下行的对偶特性,采用虚拟功率,将优化问题进行形式变换,成为上行功率分配和波束赋形问题;得到能够简便、快速求解的迭代算法。分析了算法的收敛特性,得到了收敛条件;并进一步计算了算法的复杂度,结果表明优于SDR算法。数值仿真显示,算法收敛很快;而且表明主网络基站(PBS)发送功率的变化影响可行解区域; PBS发送功率的增加会导致CRN下行功率增大,影响较显著。
There are some problems on multi-user downlink power allocation and beamforming in a cognitive radio network (CRN) which works in the underlay mode. They are the high complexity of the conventional SDR algorithm,the limited applicability and ignoring the interferences of the primary network (PN) to the secondary user( SU),etc. Aiming at these problems,this paper firstly added the term of interference of the PN to the SU to the CRN model and formulated the optimization problem. Then based on the duality of uplink-downlink,it obtained the simple and fast iterative algorithm by introducing virtual power,transforming the optimization problem into the problem of uplink power allocation and beamforming. And it analyzed the convergence of the algorithm to obtain the convergent conditions. It also reckoned the algorithm complexity. The result demonstrates that iterative algorithm is superior to the SDR algorithm. Numerical simulation results show that the algorithm converges faster,and that the variation of transmitting power of the primary base station (PBS) could affect the feasibility region. They also show that the increasing of transmitting power of the PBS could lead to the downlink power increasing. The influences are notable.
There are some problems on multi-user downlink power allocation and beamforming in a cognitive radio network (CRN) which works in the underlay mode. They are the high complexity of the conventional SDR algorithm,the limited applicability and ignoring the interferences of the primary network (PN) to the secondary user( SU),etc. Aiming at these problems,this paper firstly added the term of interference of the PN to the SU to the CRN model and formulated the optimization problem. Then based on the duality of uplink-downlink,it obtained the simple and fast iterative algorithm by introducing virtual power,transforming the optimization problem into the problem of uplink power allocation and beamforming. And it analyzed the convergence of the algorithm to obtain the convergent conditions. It also reckoned the algorithm complexity. The result demonstrates that iterative algorithm is superior to the SDR algorithm. Numerical simulation results show that the algorithm converges faster,and that the variation of transmitting power of the primary base station (PBS) could affect the feasibility region. They also show that the increasing of transmitting power of the PBS could lead to the downlink power increasing. The influences are notable.
引文
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[13]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004:491-492.(Zhang Xianda.Matrix analysis and applications[M].Beijing:Tsinghua University Press,2004:491-492.)
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[2]Kibria M G,Yuan Fang,Kojima F.Feedback bits allocation for interference minimization in cognitive radio communications[J].IEEEWireless Communications Letters,2016,5(1):104-107.
[3]Ma Shuai,Sun Dechun.Chance constrained robust beamforming in cognitive radio networks[J].IEEE Communications Letters,2013,17(1):67-70.
[4]Jeong Y,Quek T Q S,Shin H.Downlink beamforming optimization for cognitive underlay networks[C]//Proc of International Symposium on Information Theory&Its Applications.Piscataway,NJ:IEEEPress,2010:934-939.
[5]Huang Yongwei,Palomar D P.Rank-constrained separable semidefinite programming with applications to optimal beamforming[J].IEEETrans on Signal Processing,2010,58(2):664-678.
[6]Le T A,Navaie K.Downlink beamforming in underlay cognitive cellular networks[J].IEEE Trans on Communications,2014,62(7):2212-2223.
[7]Pesavento M,Ciochina D,Gershman A B.Iterative dual downlink beamforming for cognitive radio networks[C]//Proc of the 5th International Conference on Cognitive Radio Oriented Wireless Networks&Communications.Piscataway,NJ:IEEE Press,2010:1-5.
[8]Xu Yongjun,Zhao Xiaohui,Liang Yichang.Robust power control and beamforming in cognitive radio networks:a survey[J].IEEECommunications Surveys&Tutorials,2015,17(4):1834-1857.
[9]Tong Xue,Dong Xiaodai,Shi Yi.Resource allocation strategy for multi-user cognitive radio systems:location-aware spectrum access[J].IEEE Trans on Vehicular Technology,2017,66(1):884-889.
[10]Gaafar M,Amin O,Abediseid W,et al.Underlay spectrum sharing techniques with in-band full-duplex systems using improper gaussian signaling[J].IEEE Trans on Wireless Communications,2017,16(1):235-249.
[11]Schubert M,Boche H.Solution of the multiuser downlink beamforming problem with individual SINR constraints[J].IEEE Trans on Vehicular Technology,2004,53(1):18-28.
[12]Dhifallah O,Dahrouj H,Al-naffouri T,et al.Decentralized.SINRbalancing in cognitive radio networks[J].IEEE Trans on Vehicular Technology,2017,66(4):3491-3496.
[13]张贤达.矩阵分析与应用[M].北京:清华大学出版社,2004:491-492.(Zhang Xianda.Matrix analysis and applications[M].Beijing:Tsinghua University Press,2004:491-492.)
[14]Boyd S,Vandenberghe L.Convex optimization[M].New York:Cambridge University Press,2004:661-664.