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arXiv · 2303.07821

Self-attention for Enhanced OAMP Detection in MIMO Systems

Alexander Fuchs·Christian Knoll·Nima N. Moghadam·Alexey Pak Jinliang Huang·Erik Leitinger·Franz Pernkopf
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Abstract

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.

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Alexander Fuchs, Christian Knoll, Nima N. Moghadam, Alexey Pak Jinliang Huang, Erik Leitinger, Franz Pernkopf. 2023-03-14. Self-attention for Enhanced OAMP Detection in MIMO Systems. https://arxiv.org/abs/2303.07821

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