Rigorous dynamics of expectation-propagation signal detection via the conjugate gradient method

This paper investigates iterative detection based on expectation propagation (EP) in overloaded multiple-input multiple-output systems. The conjugate gradient (CG) method is utilized to reduce the per-iteration complexity of EP detection. Under the assumption of unitarily invariant channel matrices, the dynamics of EP detection based on the CG method (EP-CG) is analyzed in the large system limit, where the transmit and receive dimensions tend to infinity at an identical rate. The main result is a rigorous derivation of state evolution for EP-CG.

[1]  Markku J. Juntti,et al.  Iterative implementation of linear multiuser detection for dynamic asynchronous CDMA systems , 1998, IEEE Trans. Commun..

[2]  Xiaojun Yuan,et al.  Iterative equalization for MIMO systems: Algorithm design and evolution analysis , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[3]  B. Rajan,et al.  Improved large-MIMO detection based on damped belief propagation , 2010, 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo).

[4]  Florent Krzakala,et al.  On convergence of approximate message passing , 2014, 2014 IEEE International Symposium on Information Theory.

[5]  Sundeep Rangan,et al.  Expectation consistent approximate inference: Generalizations and convergence , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).

[6]  Andrea Montanari,et al.  Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.

[7]  Pablo M. Olmos,et al.  Expectation Propagation Detection for High-Order High-Dimensional MIMO Systems , 2014, IEEE Transactions on Communications.

[8]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[9]  Sundeep Rangan,et al.  On the Convergence of Approximate Message Passing With Arbitrary Matrices , 2014, IEEE Transactions on Information Theory.

[10]  Y. Kabashima.  A CDMA multiuser detection algorithm on the basis of belief propagation , 2003 .

[11]  Keigo Takeuchi,et al.  Rigorous Dynamics of Expectation-Propagation-Based Signal Recovery from Unitarily Invariant Measurements , 2020, IEEE Transactions on Information Theory.

[12]  Andrea Montanari,et al.  The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, ISIT.

[13]  Mikko Vehkaperä,et al.  Signal recovery using expectation consistent approximation for linear observations , 2014, 2014 IEEE International Symposium on Information Theory.

[14]  Koujin Takeda,et al.  Analysis of CDMA systems that are characterized by eigenvalue spectrum , 2006, ArXiv.

[15]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[16]  Ole Winther,et al.  Expectation Consistent Approximate Inference , 2005, J. Mach. Learn. Res..

[17]  Sundeep Rangan,et al.  Vector Approximate Message Passing , 2019, IEEE Transactions on Information Theory.

[18]  Li Ping,et al.  Orthogonal AMP , 2016, IEEE Access.