用扩散模型识别无线信道，精准区分相似场景。
Wireless Channel Identification via Conditional Diffusion Model
- 将信道识别转为后验概率估计，用条件生成扩散模型求解。
- 在真实场景中识别准确率比传统方法高10%以上。
- 适合需要高精度信道辨识的定位与通信系统设计。
无线系统中的信道场景识别在信道建模、无线电指纹定位和收发机设计中至关重要。传统方法依赖信道的典型统计特征（如K因子、路径损耗、时延扩展等），但难以区分动态散射体带来的隐含特征，导致相似信道场景识别性能极差。本文提出一种新方法，将识别任务建模为最大后验估计（MAP），并进一步转化为最大似然估计（MLE），通过条件生成扩散模型近似求解。具体地，利用Transformer网络在扩散模型逆过程的多个潜在噪声空间中捕捉信道的深层特征，这些特征直接影响MLE中的似然函数，实现高精度场景识别。实验结果表明，所提方法优于卷积神经网络（CNN）、反向传播神经网络（BPNN）和随机森林分类器，识别准确率提升超过10%。
原文摘要 · Abstract (English)
The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.
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