Self-attention for enhanced OAMP Detection in MIMO Systems

Open Webpage

Alexander Fuchs, Christian Knoll, Erik Leitinger, Franz Pernkopf

Published: 01 Jan 2023, Last Modified: 04 Nov 2025ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ProceedingsEveryoneRevisionsBibTeXCC BY-SA 4.0
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.