14.Joint symbol detection and channel estimation based on variational Bayesian in MIMO relay system

Based on the variational Bayesian inference and tensor decomposition, a joint symbol detection and channel estimation of MIMO relay system under Rayleigh fading channel is proposed. In this algorithm, the symbol matrix and the channel matrix are modeled as unknown matrices by introducing the implicit parameter variables, and recursive formula of the unknown matrix is deduced by the principle of maximizing the model evidence. Compared with the traditional estimation model based on tensor model, this algorithm introduces the a priori information of the channel into symbol estimation and channel estimation process to improve the estimation performance. The simulation results show that the proposed algorithm has high estimation accuracy compared with the existing joint estimation algorithm based on ALS (Alternating Least Square) and PLS (Partial Least Squares).

[1]  Mischa Dohler,et al.  Cooperative Communications: Hardware, Channel and PHY , 2010 .

[2]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[3]  Yuri C. B. Silva,et al.  PARAFAC-PARATUCK Semi-Blind Receivers for Two-Hop Cooperative MIMO Relay Systems , 2014, IEEE Transactions on Signal Processing.

[4]  Mu Yu Ting Definite Integral Automatic Analysis Mechanism Research and Development Using the "Find the Area by Integration" Unit as an Example. , 2017 .

[5]  Guangsheng Shi,et al.  Quality Control of a Complex Lean Construction Project Based on KanBIM Technology , 2017 .

[6]  André Lima Férrer de Almeida,et al.  Nested Tucker tensor decomposition with application to MIMO relay systems using tensor space-time coding (TSTC) , 2016, Signal Process..

[7]  Lieven De Lathauwer,et al.  A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization , 2006, SIAM J. Matrix Anal. Appl..

[8]  André Lima Férrer de Almeida,et al.  Semi-Blind Receivers for Non-Regenerative Cooperative MIMO Communications Based on Nested PARAFAC Modeling , 2015, IEEE Transactions on Signal Processing.

[9]  Mats Viberg,et al.  Least-Squares based channel estimation for MIMO relays , 2008, 2008 International ITG Workshop on Smart Antennas.

[10]  Peter Strobach,et al.  Bi-iteration SVD subspace tracking algorithms , 1997, IEEE Trans. Signal Process..

[11]  Nikos D. Sidiropoulos,et al.  Khatri-Rao space-time codes , 2002, IEEE Trans. Signal Process..

[12]  J. Romano,et al.  Tensor space-time coding for MIMO wireless communication systems = : Codificação tensorial espaço-temporal para sistemas de comunicação sem fio MIMO , 2014 .

[13]  Yong Xiang,et al.  Channel Estimation of Dual-Hop MIMO Relay System via Parallel Factor Analysis , 2012, IEEE Transactions on Wireless Communications.

[14]  Hengchang Jing COVERAGE HOLES RECOVERY ALGORITHM BASED ON NODES BALANCE DISTANCE OF UNDERWATER WIRELESS SENSOR NETWORK , 2014 .

[15]  Stephen J. Roberts,et al.  A tutorial on variational Bayesian inference , 2012, Artificial Intelligence Review.

[16]  Lei Cao,et al.  Multi-User Cooperative Communications with Relay-Coding for Uplink IMT-Advanced 4G Systems , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[17]  André Lima Férrer de Almeida,et al.  Closed-Form Semi-Blind Receiver For MIMO Relay Systems Using Double Khatri–Rao Space-Time Coding , 2016, IEEE Signal Processing Letters.

[18]  Lieven De Lathauwer,et al.  Exact line and plane search for tensor optimization , 2013, Computational optimization and applications.