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\begin{document}

\title{A Low-Complexity Double EP-based Detector for Iterative Detection and
Decoding in MIMO}



\author[1]{Juan José Murillo Fuentes }%
\author[2]{irene santos }%
\author[2]{José Carlos Aradillas }%
\author[2]{Matilde Sánchez-Fernández }%
\affil[1]{Universidad de Sevilla}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
We propose a new iterative detection and decoding algorithm for
multiple-input multiple-output (MIMO) based on expectation propagation
(EP) with application to massive MIMO scenarios. Two main results are
presented. We first introduce EP to iteratively improve the Gaussian
approximations of both the estimation of the posterior by the MIMO
detector and the soft output of the channel decoder. With this novel
approach, denoted by double-EP (DEP), the convergence is very much
improved with a computational complexity just two times the one of the
linear minimum mean square error (LMMSE), as illustrated by the included
experiments. Besides, as in the LMMSE MIMO detector, when the number of
antennas increases, the computational cost of the matrix inversion
operation required by the DEP becomes unaffordable. In this work we also
develop approaches of DEP where the mean and the covariance matrix of
the posterior are approximated by using the Gauss-Seidel and Neumann
series methods, respectively. This low-complexity DEP detector has
quadratic complexity in the number of antennas, i.e., the same as the
low-complexity LMMSE techniques. Experimental results show that the new
low-complexity DEP achieves the performance of the DEP as the ratio
between the number of transmitting and receiving antennas decreases%
\end{abstract}%



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