基于PARAFAC分解的大规模MU-MIMO稀疏信道估计
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摘要
针对大规模MU-MIMO(多用户多输入多输出)系统中上行链路的信道估计问题,提出了一种基于平行因子(PARAFAC)分解的稀疏信道估计算法.该算法利用稀疏数学模型构造稀疏信道模型,将稀疏理论与张量分解相结合,对基站端的接收信号进行PARAFAC建模.在满足唯一性分解条件下,利用双线性交替最小二乘(BALS)拟合算法联合估计出多个用户的信号矩阵与信道矩阵.仿真结果表明:所提算法的估计性能优于经典的正交匹配跟踪算法等稀疏信道估计算法,与基于导频序列的估计方法相比,其信道估计的精度大幅提高;仅需少量导频,降低了导频开销,实现了高频谱效率的通信传输.
For the channel estimation problem of the uplink in a large-scale MU-MIMO systems,a sparse channel estimation algorithm based on parallel factor(PARAFAC) decomposition was proposed.In this paper,a sparse mathematical model was used to construct a sparse channel model,and sparse theory was combined with tensor decomposition to perform PARAFAC modeling of the received signal at the base station.Under the condition of uniqueness decomposition,a bilinear alternating least squares(BALS) fitting algorithm was used to jointly estimate the signal matrix and the channel matrix of multiple users.The simulation results showed that the proposed algorithm had better estimation performance than the classical orthogonal matching tracking algorithm and other sparse channel estimation algorithms.Compared with the pilot sequence based estimation method,the accuracy of channel estimation was greatly improved.Only a small amount of pilot was needed.The pilot overhead was reduced,and high spectral efficiency communication transmission was realized.
For the channel estimation problem of the uplink in a large-scale MU-MIMO systems,a sparse channel estimation algorithm based on parallel factor(PARAFAC) decomposition was proposed.In this paper,a sparse mathematical model was used to construct a sparse channel model,and sparse theory was combined with tensor decomposition to perform PARAFAC modeling of the received signal at the base station.Under the condition of uniqueness decomposition,a bilinear alternating least squares(BALS) fitting algorithm was used to jointly estimate the signal matrix and the channel matrix of multiple users.The simulation results showed that the proposed algorithm had better estimation performance than the classical orthogonal matching tracking algorithm and other sparse channel estimation algorithms.Compared with the pilot sequence based estimation method,the accuracy of channel estimation was greatly improved.Only a small amount of pilot was needed.The pilot overhead was reduced,and high spectral efficiency communication transmission was realized.
引文
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[2]MASOOD M,AFIFY L H,Al-NAFFOURI T Y.Efficient coordinated recovery of sparse channels in massive MIMO[J].IEEE transactions on signal processing,2014,63(1):104-118.
[3]WU S,NI Z,MENG X,et al.Block expectation propagation for downlink channel estimation in massive MIMO Systems[J].IEEE communications letters,2016,20(11):2225-2228.
[4]XIE H X,GAO F F,JIN S.An overview of lowrank channel estimation for massive MIMO systems[J].IEEE access,2016,4:7313-7321.
[5]ENDRA O.Joint optimization of measurement matrix and sparse dictionary in compressive sensing[C]//2012 International Conference on Computer and Communication Engineering.Kuala Lumdur:IEEE,2012:420-425.
[6]方昕,刘云驹,曹海燕,等.大规模MIMO系统中低复杂度的稀疏信道估计[J].电信科学,2016(5):89-95.
[7]YANG N,XIN S,LI Z.Decision aided uplink compressive channel estimation for massive MIMO systems[J].Wireless personal communications,2017,96(1):153-162.
[8]DAI L L,WANG Z C,YANG Z X.Spectrally effcient time-frequency training OFDM for mobile-scale MI-MO systems[J].IEEE journal on selected areas in communications,2013,31(2):251-263.
[9]MANSOOR B,NAWAZ S J,GULFAM S M.Massive-MIMO sparse uplink channel estimation using implicit training and compressed sensing[J].Applied sciences,2017,7(1):63-68.
[10]WANG A,WANG Y,JIANG L.Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing[C]//International Conference on Wireless Communications&Signal Processing.Nanjing:IEEE,2015:1-5.
[11]穆晓敏,刘越,李双志,等.基于张量分解的MIMO多中继系统半盲信道估计方法[J].郑州大学学报(工学版),2016,37(6):83-86.
[12]RAIMONDI F E D,COMON P.Tensor Do A estimation with directional elements[J].IEEE signal processing society,2017,24(5):648-652.
[13]王忠勇,郭秋歌,王法松,等.基于分层模型的SC-FDE系统低复杂度稀疏信道估计[J].信号处理,2015,31(9):1106-1111.
[14]RONG Y,KHANDAKER M R A,XIANG Y.Channel estimation of dual-hop MIMO relay system via parallel factor analysis[J].IEEE transactions on wireless communications,2012,11(6):2224-2233.
[15]SIDROPOULOS N D,BUDAMPTI R S.Khatri-Rao space-time codes[J].IEEE transactions on signal processing,2002,50(10):2396-2407.
[16]KRUSKAL J B.Three-way arrays:rank and uniqueness of trilinear decompositions,with application to arithmetic complexity and statistics[J].Linear algebra&its applications,1977,18(2):95-138.
[17]张日升.平行因子技术在DOA估计中的应用研究[D].吉林大学通信工程学院,2007.
[18]RAJIH M,COMON P,HARSHMAN R.Enhanced line search:a novel method to accelerate parafac[J].SIAM journal on matrix analysis and applications,2008,30(3):1128-1147.