Self-Attention for Enhanced OAMP Detection in MIMO Systems
Alexander Fuchs, Christian Knoll, Nima N. Moghadam, Alexey Pak, Jinliang Huang, Erik Leitinger, Franz Pernkopf
Abstract
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Since classical algorithms for symbol detection in MIMO setups require large computational resources or provide poor results, data-driven algorithms are becoming more popular. Most of the proposed algorithms, however, introduce approximations leading to degraded performance for realistic MIMO systems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analytic backbone algorithm with state-of-the-art neural network components. In particular, we introduce a self-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 can outperform existing data-driven approaches for OAMP while having improved generalization to other SNR values at limited computational overhead.
BibTeX
@inproceedings{icassp2023_selfattentionfor,
title = {Self-Attention for Enhanced OAMP Detection in MIMO Systems},
author = {Alexander Fuchs and Christian Knoll and Nima N. Moghadam and Alexey Pak and Jinliang Huang and Erik Leitinger and Franz Pernkopf},
booktitle = {ICASSP 2023},
year = {2023}
}