Forward error correction using soft probability estimates is a central component in modern digital communication receivers and impacts end-to-end system performance. In this work, we introduce EQ-Net: a deep learning approach for joint soft bit estimation (E) and quantization (Q) in high-dimensional multiple-input multiple-output (MIMO) systems. We propose a two-stage algorithm that uses soft bit quantization as pretraining for estimation and is motivated by a theoretical analysis of soft bit representation sizes in MIMO channels. Our experiments demonstrate that a single deep learning model achieves competitive results on both tasks when compared to previous methods, with gains in quantization efficiency as high as \(20\%\) and reduced estimation latency by at least \(21\%\) compared to other deep learning approaches that achieve the same end-to-end performance. We also demonstrate that the quantization approach is feasible in single-user MIMO scenarios of up to \(64 \times 64\) and can be used with different soft bit estimation algorithms than the ones during training. We investigate the robustness of the proposed approach and demonstrate that the model is robust to distributional shifts when used for soft bit quantization and is competitive with state-of-the-art deep learning approaches when faced with channel estimation errors in soft bit estimation.

使用软概率估计的前向纠错是现代数字通信接收器的核心组件，会影响端到端系统性能。在这项工作中，我们介绍了 EQ-Net：一种用于高维多输入多输出 (MIMO) 系统中联合软比特估计 (E) 和量化 (Q) 的深度学习方法。我们提出了一种两阶段算法，该算法使用软比特量化作为估计的预训练，并受到对 MIMO 信道中软比特表示大小的理论分析的启发。我们的实验表明，与以前的方法相比，单个深度学习模型在两个任务上都取得了有竞争力的结果，量化效率的增益高达\(20\%\)，估计延迟减少了至少\(21\%\)与实现相同端到端性能的其他深度学习方法相比。我们还证明了量化方法在高达\(64\times 64\)的单用户 MIMO 场景中是可行的，并且可以与训练期间的不同软比特估计算法一起使用。我们研究了所提出方法的鲁棒性，并证明该模型在用于软比特量化时对分布变化具有鲁棒性，并且在面对软比特估计中的信道估计错误时与最先进的深度学习方法具有竞争力。