While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization. This paper presents EqDeepRx, a practical deep-learning-aided multiple-input multiple-output (MIMO) receiver, which is built by augmenting linear receiver processing with carefully engineered ML blocks. At the core of the receiver model is a shared-weight DetectorNN that operates independently on each spatial stream or layer, enabling near-linear complexity scaling with respect to multiplexing order. To ensure better explainability and generalization, EqDeepRx retains conventional channel estimation and augments it with a lightweight DenoiseNN that learns frequency-domain smoothing. To reduce the dimensionality of the DetectorNN inputs, the receiver utilizes two linear equalizers in parallel: a linear minimum mean-square error (LMMSE) equalizer with interference-plus-noise covariance estimation and a regularized zero-forcing (RZF) equalizer. The parallel equalized streams are jointly consumed by the DetectorNN, after which a compact DemapperNN produces bit log-likelihood ratios for channel decoding. 5G/6G-compliant end-to-end simulations across multiple channel scenarios, pilot patterns, and inter-cell interference conditions show improved error rate and spectral efficiency over a conventional baseline, while maintaining low-complexity inference and support for different MIMO configurations without retraining.

翻译：尽管基于机器学习（ML）的接收机算法在近期文献中受到了广泛关注，但它们通常存在随空间复用阶数增加而扩展性差、可解释性不足以及泛化能力有限的问题。本文提出EqDeepRx，一种实用的深度学习辅助多输入多输出（MIMO）接收机，其通过在传统线性接收机处理中精心设计并嵌入ML模块构建而成。该接收机模型的核心是一个共享权重的DetectorNN，它独立作用于每个空间流或层，从而实现了相对于复用阶数的近似线性复杂度扩展。为确保更好的可解释性和泛化能力，EqDeepRx保留了传统信道估计模块，并引入一个轻量级的DenoiseNN来学习频域平滑处理。为降低DetectorNN输入的维度，接收机并行使用两个线性均衡器：一个采用干扰加噪声协方差估计的线性最小均方误差（LMMSE）均衡器，以及一个正则化迫零（RZF）均衡器。经过并行均衡后的数据流由DetectorNN联合处理，随后一个紧凑的DemapperNN生成用于信道解码的比特对数似然比。在多种信道场景、导频模式及小区间干扰条件下进行的5G/6G兼容端到端仿真表明，相较于传统基线方法，该接收机在误码率和频谱效率方面均有提升，同时保持了低复杂度的推理过程，并支持不同的MIMO配置而无需重新训练。