This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources. The main challenge lies in obtaining a compact and efficient representation of the CSI given that it exhibits strong temporal correlation across successive snapshots. Existing memoryless compression models do not exploit this property, while simple temporal extensions often incorporate multiple observations without explicitly modeling the latent dynamics. We propose a context-aware compression framework based on a k-memory Markov variational autoencoder (k-MMVAE), which uses a finite temporal window to capture the evolution of CSI in the latent space. The model introduces Markov-structured latent dynamics with finite memory, enabling efficient use of temporal dependencies for compression. Simulation results show that the proposed approach improves target CSI reconstruction performance compared to memoryless and weakly sequential baselines, particularly at low and moderate compression rates. These results suggest that explicit latent temporal modeling can provide an effective mechanism for CSI compression under limited feedback constraints.

翻译：本文研究了在频分双工（FDD）系统中，面向时变大规模多输入多输出（MIMO）信道且受限于有限反馈资源的神经信道状态信息（CSI）压缩问题。主要挑战在于，考虑到连续快照间呈现的强时间相关性，如何获取紧凑且高效的CSI表示。现有无记忆压缩模型未利用该特性，而简单的时间扩展方法往往融合多次观测却未显式建模潜在动态过程。本文提出一种基于k-记忆马尔可夫变分自编码器（k-MMVAE）的上下文感知压缩框架，该框架利用有限时间窗口捕捉CSI在潜在空间中的演化过程。该模型引入了具有有限记忆的马尔可夫结构潜在动态过程，从而能够有效利用时间依赖性进行压缩。仿真结果表明，相较于无记忆及弱时序基线方法，所提方法在中低压缩率下能显著提升目标CSI的重建性能。这些结果表明，在有限反馈约束下，显式潜在时序建模可为CSI压缩提供有效机制。