Massive MIMO中基于统计信道的波束形成和功率分配
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摘要
为了提高大规模多输入多输出系统(Massive MIMO)的总容量,提出了基于统计信道信息的波束形成和功率分配的优化算法。所提出的波束形成方法以信噪泄漏比为优化标准,而功率分配方案以系统容量为优化目标,同时满足基站总发射功率约束条件。上述优化问题转化为多个变量的等价优化形式,从而可以通过变量的交替优化迭代达到收敛。每次迭代过程中均可以求出闭式解,迭代完成后可得到优化的功率分配方案。仿真结果表明,相比于平均功率分配方案,该算法能有效地提高系统的总容量。
In order to improve the capacity of massive multiple input multiple output(MIMO) systems, this paper proposes an opti-mization method of beamforming and power allocation based on the statistical channel state information. The proposed beamforming aims to optimize the signal-to-leakage-plus-noise ratio(SLNR) and the power allocation is optimized to maximize the system capacity.The former beamforming problem can be solved analytically and the later problem is reformulated to an equivalent problem with multiple variables, which can be iteratively solved. Each iteration of the problem has a closed-form solution. Simulation results show that the proposed algorithm can significantly improve the system capacity.
In order to improve the capacity of massive multiple input multiple output(MIMO) systems, this paper proposes an opti-mization method of beamforming and power allocation based on the statistical channel state information. The proposed beamforming aims to optimize the signal-to-leakage-plus-noise ratio(SLNR) and the power allocation is optimized to maximize the system capacity.The former beamforming problem can be solved analytically and the later problem is reformulated to an equivalent problem with multiple variables, which can be iteratively solved. Each iteration of the problem has a closed-form solution. Simulation results show that the proposed algorithm can significantly improve the system capacity.
引文
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[10]BOYD S,VANDENBERGHE L.Convex optimization[M].Cambridge,U.K.:Cambridge University Press,2004.
[2]MARZETTA T L.Massive MIMO:an introduction[J].Bell Labs Technical Journal,2015,20:11-22.
[3]LI X,JIN S,SURAWEERA H A,et al.Statistical 3-Dbeamforming for large-scale MIMO downlink systems over rician fading channels[J].IEEE Transactions on Communications,2016,64(4):1529-1543.
[4]TAN W Q,JIN S,WANG J,et al.Achievable sum-rate of multiuser massive MIMO downlink in Ricean fading channels[C].Proceedings of IEEE International Conference on Communications(ICC),2015:1453-1458.
[5]RAZAVIZADEH S M,AHN M,LEE I.Three-dimensional beamforming:a new enabling technology for 5G wireless networks[J].IEEE Signal Processing Magazine,2014,31(6):94-101.
[6]LI X,ZHOU L L,JIN S,et al.Multi-user multiple-input single-output downlink transmission systems exploiting statistical channel state information[C].IET Communications,2012,6(18):3114-3121.
[7]SADEK M,TARIGHAT A,SAYED A H.A leakage-based precoding scheme for downlink multi-user MIMO channels[J].IEEE Transactions on Wireless Communications,2007,6(5):1711-1721.
[8]FRITZSCHE R,FETTWEIS G P.Robust sum rate maximization in the multi-cell MU-MIMO downlink[C].2013 IEEEWireless Communications and Networking Conference(WCNC),Shanghai,Shanghai,China,2013:3180-3184.
[9]BOGALE T E,VANDENDORPE L.Weighted sum rate optimization for downlink multiuser MIMO coordinated base station systems:centralized and distributed algorithms[J].IEEETransactions on Signal Processing,2012,60(4):1876-1889.
[10]BOYD S,VANDENBERGHE L.Convex optimization[M].Cambridge,U.K.:Cambridge University Press,2004.