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<p>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
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