基于低秩矩阵完备的大规模MIMO系统信道估计研究
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摘要
随着大规模MIMO系统中天线数的增长,获取信道状态信息(channel state information at the transmitter,CSIT)所需的下行信道训练开销和上行反馈开销变得非常巨大。针对信道估计开销过大的问题,提出了一种新的CSIT估计方案和基于低秩矩阵完备的信道估计算法。在所提方案中,基站发送训练信号给各个用户之后,用户直接将其观测信号反馈给基站,并在基站端进行统一的CSIT估计。然后,利用大规模MIMO信道矩阵的特点,将信道估计问题转换为低秩矩阵完备问题,从而可以利用软阈值算法恢复出所有用户的信道状态信息。仿真结果表明,该算法可以获得精确的信道状态信息,并有效地减少了信道估计开销和复杂度。
With the number of transmit antennas increases largely in massive MIMO system,the training and feedback overhead for the acquisition of CSIT becomes rather overwhelming. In order to solve the problem of huge overhead of channel estimation,this paper proposed a new CSIT estimation scheme and a channel estimation algorithm based on low-rank complete matrix. In this scenario,after the base station transmits training signals to each user,the users directly send back the observed signals to the base station and then the joint CSIT acquisition problem could be realized at the base station. Then,by using the property of massive MIMO channel matrix,the recovery of the CSIT of all users could be transformed into a low-rank matrix completion problem,which could be efficiently solved by using the proposed soft threshold operator( STO) algorithm. Simulation results show that the proposed algorithm can achieve accurate CSIT with lower overhead and complexity.
With the number of transmit antennas increases largely in massive MIMO system,the training and feedback overhead for the acquisition of CSIT becomes rather overwhelming. In order to solve the problem of huge overhead of channel estimation,this paper proposed a new CSIT estimation scheme and a channel estimation algorithm based on low-rank complete matrix. In this scenario,after the base station transmits training signals to each user,the users directly send back the observed signals to the base station and then the joint CSIT acquisition problem could be realized at the base station. Then,by using the property of massive MIMO channel matrix,the recovery of the CSIT of all users could be transformed into a low-rank matrix completion problem,which could be efficiently solved by using the proposed soft threshold operator( STO) algorithm. Simulation results show that the proposed algorithm can achieve accurate CSIT with lower overhead and complexity.
引文
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[14]Lin Zhouchen,Chen Minming,Ma Yi.The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL].(2013-10-18).https://ar Xiv.org/abs/1009.5055v3.
[15]Cai Jianfeng,Candes E J,Shen Zuowei.A singular value thresholding algorithm for matrix completion[J].SIAM Journal on Optimization,2010,20(4):1956-1982.
[16]Meka R,Jain P,Dhillon I S.Guaranteed rank minimization via singular value projection[C]//Advances in Neural Information Processing Systems 23.Vancouver:NIPS,2009:937-945.
[17]Rusek F,Persson D,Lau B K,et al.Scaling up MIMO:opportunities and challenges with very large arrays[J].IEEE Signal Processing Magazine,2012,30(1):40-60.
[2]Lu Lu,Li G Y,Swindlehurst A L,et al.An overview of massive MIMO:benefits and challenges[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):742-758.
[3]Zhu Fengchao,Gao Feifei,Yao Minli,et al.Joint information-and jamming-beamforming for physical layer security with full duplex base station[J].IEEE Trans on Signal Processing,2014,62(24):6391-6401.
[4]Gao Zhen,Dai Linglong,Wang Zhaocheng.Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems[J].Electronics Letters,2015,50(12):896-898.
[5]Wang Xuesi,Wang Jintao,Ma Xu,et al.Pilot allocation for MIMOOFDM systems:a structured compressive sensing perspective[C]//Proc of International Wireless Communications and Mobile Computing Conference.Piscataway,NJ:IEEE Press,2016:227-232.
[6]Rao Xiongbin,Lau V K N.Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems[J].IEEE Trans on Signal Processing,2014,62(12):3261-3271.
[7]Shen Wenqian,Dai Linglong,Shim B,et al.Joint CSIT acquisition based on low-rank matrix completion for FDD massive MIMO systems[J].IEEE Communications Letters,2015,19(12):2178-2181.
[8]彭义刚,索津莉,戴琼海,等.从压缩传感到低秩矩阵恢复:理论与应用[J].自动化学报,2013,39(7):981-994.
[9]Bell R M,Koren Y.Lessons from the Netflix prize challenge[J].ACM SIGKDD Explorations Newsletters,2007,9(2):75-79.
[10]闫慧辰,彭石宝,张旭东.矩阵完备理论在雷达成像中的应用[J].清华大学学报:自然科学版,2013,53(4):546-549.
[11]Candes E J,Recht B.Exact matrix completion via convex optimization[J].Foundations of Computational Mathematics,2009,9(6):7-17.
[12]Grant M,Boyd S.CVX user’s guide for CVX version 1.21[EB/OL].(2011).http://cvxr.com/cvx/cvx_userguide.pdf.
[13]Toh K C,Yun S.An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems[J].Pacific Journal of Optimization,2010,6(3):615-640.
[14]Lin Zhouchen,Chen Minming,Ma Yi.The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL].(2013-10-18).https://ar Xiv.org/abs/1009.5055v3.
[15]Cai Jianfeng,Candes E J,Shen Zuowei.A singular value thresholding algorithm for matrix completion[J].SIAM Journal on Optimization,2010,20(4):1956-1982.
[16]Meka R,Jain P,Dhillon I S.Guaranteed rank minimization via singular value projection[C]//Advances in Neural Information Processing Systems 23.Vancouver:NIPS,2009:937-945.
[17]Rusek F,Persson D,Lau B K,et al.Scaling up MIMO:opportunities and challenges with very large arrays[J].IEEE Signal Processing Magazine,2012,30(1):40-60.