Research Article
Iterative Receiver with Gaussian and Mean-Field Approximation in Massive MIMO Systems
@INPROCEEDINGS{10.1007/978-3-319-72823-0_29, author={Sheng Wu and Linling Kuang and Xincong Lin and Baosheng Sun}, title={Iterative Receiver with Gaussian and Mean-Field Approximation in Massive MIMO Systems}, proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings}, proceedings_a={5GWN}, year={2018}, month={1}, keywords={Belief propagation Channel estimation Decoding Massive MIMO Message passing Mean-field approximation Orthogonal frequency-division multiplexing (OFDM)}, doi={10.1007/978-3-319-72823-0_29} }- Sheng Wu
Linling Kuang
Xincong Lin
Baosheng Sun
Year: 2018
Iterative Receiver with Gaussian and Mean-Field Approximation in Massive MIMO Systems
5GWN
Springer
DOI: 10.1007/978-3-319-72823-0_29
Abstract
In this paper, a computationally efficient message-passing receiver that performs joint channel estimation and decoding is proposed for massive multiple-input multiple-output (MIMO) systems with OFDM modulation. We combine the loopy belief propagation (LBP) with the mean-field approximation and Gaussian approximation to decouple frequency-domain channel taps and data symbols from noisy observations. Specifically, pair-wise joint belief of frequency-domain channel tap and symbol is obtained by soft interference cancellation, after which the marginal belief of frequency-domain channel tap and symbol are estimated from the pair-wise joint belief by the mean-field approximation. To estimate time-domain channel taps between each pair of antennas, a Gaussian message passing based estimator is applied. The whole scheme of joint channel estimation and decoding is assessed by Monte Carlo simulations, and the numerical results corroborate the superior performance of the proposed scheme and its superiority to the state of art.