用扩散模型同时细化环境信息和信道指纹，生成更精准的环境感知信道图谱
EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model
- 设计条件生成扩散模型，联合优化粗粒度环境信息与信道指纹
- 重建出融合环境细节的细粒度信道指纹（EnvCF），性能显著优于基线
- 适合无线通信系统设计者、智能环境感知研究者使用
从无环境感知的通信转向智能环境感知通信的范式转变，有望为未来无线通信获取信道状态信息。信道指纹（CF）作为环境感知通信的新兴使能技术，可为通信区域内的潜在位置提供信道相关知识。然而，由于实际传感设备有限且难以测量信道相关知识，当前获取的环境信息与信道指纹多为粗粒度，不足以指导无线传输设计。为此，本文提出一种深度条件生成学习方法——定制化的条件生成扩散模型（CDiff）。该模型同时精炼环境信息与信道指纹，从其粗粒度版本中重构出融合环境信息的细粒度信道指纹（称为EnvCF）。实验结果表明，相比基线方法，所提方法在构建EnvCF方面性能显著提升。
原文摘要 · Abstract (English)
The paradigm shift from environment-unaware communication to intelligent environment-aware communication is expected to facilitate the acquisition of channel state information for future wireless communications. Channel Fingerprint (CF), as an emerging enabling technology for environment-aware communication, provides channel-related knowledge for potential locations within the target communication area. However, due to the limited availability of practical devices for sensing environmental information and measuring channel-related knowledge, most of the acquired environmental information and CF are coarse-grained, insufficient to guide the design of wireless transmissions. To address this, this paper proposes a deep conditional generative learning approach, namely a customized conditional generative diffusion model (CDiff). The proposed CDiff simultaneously refines environmental information and CF, reconstructing a fine-grained CF that incorporates environmental information, referred to as EnvCF, from its coarse-grained counterpart. Experimental results show that the proposed approach significantly improves the performance of EnvCF construction compared to the baselines.
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