Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver
Abstract
In this paper, we propose a new algorithm of iterative least squared (LS) channel estimation for 64 antennas Massive Multiple Input, Multiple Output (MIMO) turbo-receiver. The algorithm employs log-likelihood ratios (LLR) of low-density parity-check (LDPC) decoder and minimum mean square error (MMSE) estimator to achieve soft data symbols. These soft data symbols are further MMSE-weighted again and combined with pilot symbols to achieve a modified LS channel estimate. The modified LS estimate is employed by the same channel estimation unit to enhance turbo-receiver performance via channel re-estimation, as a result, the proposed approach has low complexity and fits any channel estimation solution, which is quite valuable in practice. We analyze both hard and soft algorithm versions and present simulation results of 5G turbo-receiver in the 3D-UMa model of the QuaDRiGa 2.0 channel. Simulation results demonstrate up to 0.3dB performance gain compared to the unweighted hard data symbols utilization in the LS channel re-calculation.
Cite
@article{arxiv.2003.09317,
title = {Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver},
author = {Alexander Osinsky and Andrey Ivanov and Dmitry Lakontsev and Roman Bychkov and Dmitry Yarotsky},
journal= {arXiv preprint arXiv:2003.09317},
year = {2020}
}
Comments
Accepted for presentation at the VTC2020-Spring conference