用信息论正则化向量量化自编码器，实现高效固定长度信道反馈。
Precoding-Oriented CSI Feedback Design with Mutual Information Regularized VQ-VAE
- 基于向量量化变分自编码器设计信道状态反馈框架
- 在固定反馈长度下达到可与可变长度方案媲美的速率
- 学习到的码字使用更均匀，结构可解释性强
用户设备上的信道状态信息（CSI）压缩效率对大规模多输入多输出系统中的精确信道重建和预编码设计至关重要。核心挑战在于平衡反馈开销与下行链路速率之间的关系，即在有限反馈预算下最大化系统性能。本文提出一种面向预编码的CSI反馈框架，基于向量量化变分自编码器，并引入信息论正则化。为此，我们设计了一个可微分的互信息下界估计器作为训练正则项，以促进在固定反馈预算下对学习码本的有效利用。数值结果表明，所提方法在保持固定长度反馈的同时，实现了与可变长度神经压缩方案相当的速率。此外，学习到的码字表现出显著更均匀的使用分布，并捕捉到与底层信道状态信息强相关的可解释结构。
原文摘要 · Abstract (English)
Efficient channel state information (CSI) compression at the user equipment plays a key role in enabling accurate channel reconstruction and precoder design in massive multiple-input multiple-output systems. A key challenge lies in balancing the CSI feedback overhead with the achievable downlink rate, i.e., maximizing the utility of limited feedback to maintain high system performance. In this work, we propose a precoding-oriented CSI feedback framework based on a vector quantized variational autoencoder, augmented with an information-theoretic regularization. To achieve this, we introduce a differentiable mutual information lower-bound estimator as a training regularizer to promote effective utilization of the learned codebook under a fixed feedback budget. Numerical results demonstrate that the proposed method achieves rates comparable to variable-length neural compression schemes, while operating with fixed-length feedback. Furthermore, the learned codewords exhibit significantly more uniform usage and capture interpretable structures that are strongly correlated with the underlying channel state information.
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