Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Sinceclassical algorithms for symbol detection in MIMO setups require large computational resourcesor provide poor results, data-driven algorithms are becoming more popular. Most of the proposedalgorithms, however, introduce approximations leading to degraded performance for realistic MIMOsystems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analyticbackbone algorithm with state-of-the-art neural network components. In particular, we introduce aself-attention model for the enhancement of the iterative Orthogonal Approximate Message Passing(OAMP)-based decoding algorithm. In our experiments, we show that the proposed model canoutperform existing data-driven approaches for OAMP while having improved generalization to otherSNR values at limited computational overhead.
@article{arxiv.2303.07821,
title = {Self-attention for Enhanced OAMP Detection in MIMO Systems},
author = {Alexander Fuchs and Christian Knoll and Nima N. Moghadam and Alexey Pak Jinliang Huang and Erik Leitinger and Franz Pernkopf},
journal= {arXiv preprint arXiv:2303.07821},
year = {2023}
}