用扩散模型实现高效MIMO信道压缩，大幅降低传输率。
Generative Diffusion Model-based Compression of MIMO CSI
- 编码用可训练码本固定速率量化，解码用条件扩散过程重建。
- 在部分场景下，数据率减半仍保持相同失真，性能提升超2倍。
- 适合需要侧信息的未来信道预测与低延迟通信系统。
尽管神经有损压缩技术已显著提升多输入多输出（MIMO）通信中信道状态信息（CSI）反馈的压缩与重建效率，但针对更复杂且实际的任务——如基于相关侧信息的未来信道预测与重建——的高效算法仍缺乏研究，现有方法扩展至此类场景时常表现不佳。为此，我们提出一种新型带侧信息的压缩框架：编码阶段采用可训练码本进行固定速率压缩与码字量化，解码过程则建模为以码字和侧信息为条件的反向扩散过程。实验表明，该方法显著优于现有CSI压缩算法，在特定场景下可实现数据率不足一半而失真相当，性能提升超过2倍。结果凸显了基于扩散模型的压缩在通信系统中的实用潜力。
原文摘要 · Abstract (English)
While neural lossy compression techniques have markedly advanced the efficiency of Channel State Information (CSI) compression and reconstruction for feedback in MIMO communications, efficient algorithms for more challenging and practical tasks-such as CSI compression for future channel prediction and reconstruction with relevant side information-remain underexplored, often resulting in suboptimal performance when existing methods are extended to these scenarios. To that end, we propose a novel framework for compression with side information, featuring an encoding process with fixed-rate compression using a trainable codebook for codeword quantization, and a decoding procedure modeled as a backward diffusion process conditioned on both the codeword and the side information. Experimental results show that our method significantly outperforms existing CSI compression algorithms, often yielding over twofold performance improvement by achieving comparable distortion at less than half the data rate of competing methods in certain scenarios. These findings underscore the potential of diffusion-based compression for practical deployment in communication systems.
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