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The paper introduces EqDeepRx, a deep-learning-aided MIMO receiver that combines linear processing with learned components for improved scaling and generalization. EqDeepRx employs a shared-weight DetectorNN operating on individual spatial streams to achieve near-linear complexity scaling with multiplexing order, and uses a DenoiseNN to enhance channel estimation. End-to-end simulations demonstrate that EqDeepRx achieves improved error rate and spectral efficiency compared to conventional receivers while maintaining low complexity and supporting various MIMO configurations without retraining.
Achieve near-linear scaling in MIMO receivers with deep learning, unlocking practical applications in high-order spatial multiplexing scenarios.
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.