用扩散模型提升6G接收机的信道估计效率，实现低导频开销与智能信号处理。
Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers
- 将信道估计建模为生成式任务，利用扩散模型从粗略初始估计中恢复高精度信道。
- 在大规模OFDM系统中验证了低导频开销下仍能保持良好性能，逼近传统方法极限。
- 为6G智能接收机设计提供新范式，适合对高效能无线通信感兴趣的科研与工程人员。
随着人工智能技术的发展，基于AI的无线收发器设计成为新兴研究方向。其中，信道表征与估计因传统方法效果不佳，已成为大规模正交频分复用（OFDM）系统中制约收发器效率的瓶颈。本文将信道估计问题转化为生成式人工智能任务，利用扩散模型（DMs）有效处理粗糙初始估计，展现出与传统信号处理方法协同的巨大潜力。论文聚焦于基于扩散模型的OFDM系统收发器设计，展示了其在无线收发器中的应用前景，并指出了相关研究方向。此外，还提供了进一步优化扩散模型以提升无线接收机性能的可行性案例研究。
原文摘要 · Abstract (English)
With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.
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