Deep learning-based AMP for massive MIMO detection

Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce...

Full description

Saved in:
Bibliographic Details
Published in:China communications 2022-10, Vol.19 (10), p.69-77
Main Authors: Yang, Yang, Chen, Shaoping, Gao, Xiqi
Format: Article
Language:English
Subjects:
approximate message passing
Approximation algorithms
Convergence
Detectors
Frequency-domain analysis
machine learning
Manganese
Massive MIMO
Message passing
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learn-able coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.
ISSN:1673-5447
DOI:10.23919/JCC.2022.10.005