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.

翻译：多输入多输出（MIMO）系统是无线通信的重要组成部分。由于MIMO系统中符号检测的传统算法要么需要大量计算资源，要么性能较差，数据驱动算法正日益普及。然而，大多数现有算法引入近似处理，导致在实际MIMO系统中性能下降。本文提出了一种神经增强混合模型，通过整合最先进的神经网络组件增强分析骨干算法。具体而言，我们引入了一种自注意力机制来增强基于迭代正交近似消息传递（OAMP）的解码算法。实验表明，所提模型在有限计算开销下，不仅优于现有的OAMP数据驱动方法，还能在其他信噪比（SNR）条件下展现出更优的泛化能力。