基于压缩感知的大规模MIMO下行信道状态信息获取
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摘要
信道状态信息(Channel State Information,CSI)对于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)发挥高性能至关重要。但在上下行传输信道不存在互易性的频分双工(Frequency Division Duplex,FDD)制式下,若采用传统的信道估计方法会给CSI的获取带来巨大的导频开销和计算量。考虑利用大规模MIMO信道的虚角域稀疏性来减少获取CSI所需开销,在此基础上进一步研究了大规模MIMO正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中各子载波信道在虚角域的共同稀疏特性和稀疏支撑集的时间相关特性,达到降低信道维度的目的,则大大减少了基站对CSI获取所需的资源开销。同时,为了降低信道稀疏支撑集信息获取所需的导频开销和提高信息的时效性,利用压缩感知技术对支撑集进行估计。仿真结果验证了所提方案性能的优越性。
Channel state information( CSI) is essential for massive multiple-input multiple-output( MIMO).However,under the frequency division duplex( FDD) system in which the upstream and downstream transmission channels are not mutually reciprocal,the conventional channel estimation method will bring a huge pilot overhead and computational cost to the CSI acquisition.In order to reduce the cost of CSI acquisition,the virtual angular domain sparsity of massive MIMO channel is considered. On this basis,the common sparse characteristics of each subcarrier channel in a massive MIMO orthogonal frequency division multiplexing( OFDM) system and the temporal correlation of sparse support set are further studied.so as to reduce the channel dimension,thus greatly reducing the overhead of the base station for CSI acquisition.In addition,compressed sensing technology is used to reduce the pilot overhead required for acquisition of sparse support set information and improve the timeliness of information.Simulation results verify the superiority of the proposed scheme.
Channel state information( CSI) is essential for massive multiple-input multiple-output( MIMO).However,under the frequency division duplex( FDD) system in which the upstream and downstream transmission channels are not mutually reciprocal,the conventional channel estimation method will bring a huge pilot overhead and computational cost to the CSI acquisition.In order to reduce the cost of CSI acquisition,the virtual angular domain sparsity of massive MIMO channel is considered. On this basis,the common sparse characteristics of each subcarrier channel in a massive MIMO orthogonal frequency division multiplexing( OFDM) system and the temporal correlation of sparse support set are further studied.so as to reduce the channel dimension,thus greatly reducing the overhead of the base station for CSI acquisition.In addition,compressed sensing technology is used to reduce the pilot overhead required for acquisition of sparse support set information and improve the timeliness of information.Simulation results verify the superiority of the proposed scheme.
引文
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[2] LIANG H W,CHUNG W H,KUO S Y.FDD RT:a simple CSI acquisition technique via channel reciprocity for FDD massive MIMO downlink[J]. IEEE Systems Journal,2016,49(39):3441-3463.
[3] DING W,YANG F,LIU S,et al.Spectrally efficient CSI acquisition approach for large-scale MIMO systems[C]//Proceedings of 2017 IEEE GLOBECOM Workshops.San Diego:IEEE,2017:1-6.
[4] GAO Z,DAI L,DAI W,et al.Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO[J].IEEE Transactions on Communications,2017,64(2):601-617.
[5] GAO Z,DAI L,WANG Z H,et al.Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO[J]. IEEE Transactions on Signal Processing,2016,63(23):6169-6183.
[6] YANG N,LI Z,XIN S.An efficient downlink channel estimation approach for TDD massive MIMO systems[C]//Proceedings of 2016 IEEE Vehicular Technology Conference.Nanjing:IEEE,2016:1-5.
[7] NAM J,AHN J Y,ADHIKARY A,et al.Joint spatial division and multiplexing:realizing massive MIMO gains with limited channel state information[C]//Proceedings of Information Sciences and Systems. Princeton:IEEE,2012:1-6.
[8] TSE D,VISWANATH P.Fundamentals of wireless communication[M].Cambridge:Cambridge University Press,2009.
[9] CANDES E J,ROMBERG J K,TAO T.Stable signal recovery from incomplete and inaccurate measurements[J].Communications on Pure and Applied Mathematics,2006,59(8):1207-1223.
[10] GAO Z,DAI L,WANG Z,et al.Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO[J]. IEEE Transactions on Signal Processing,2015,63(23):6169-6183.
[11] FANG X,LIU Y,CAO H,et al.Low-complexity sparse channel estimation for massive MIMO systems[J]. Telecommunications Science,2016,23(12):5121-5211.
[12]周恩.下一代宽带无线通信OFDM与MIMO技术[M].2版.北京:人民邮电出版社,2008.
[13] WANG A,WANG Y,JIANG L.Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing[C]//Proceedings of 2015 International Conference on Wireless Communications and Signal Processing.Nanjing:IEEE,2015:1-5.
[14] MASOOD M,AFIFYL H. Efficient collaborative sparse channel estimation in massive MIMO[C]//Proceedings of IEEE International Conference on Acoustics,Speech and Signal Processing.Brisbane:IEEE 2015:2924-2928.
[15] DAI J,LIU A,LAU V K N.FDD massive MIMO channel estimation with arbitrary 2D-array geometry[J].IEEE Transactions on Signaln Processing,2018,26(10):2441-3463.