Wireless Channel Identification via Conditional Diffusion Model

TL;DR

This paper introduces a novel wireless channel scenario identification method using a conditional diffusion model, significantly improving accuracy over traditional statistical and neural network approaches by capturing implicit dynamic features.

Contribution

It formulates channel identification as a MAP estimation problem and employs a transformer-based conditional diffusion model to extract hidden features for enhanced accuracy.

Findings

Outperforms CNN, BPNN, and random forest classifiers by over 10% in accuracy.

Utilizes a transformer within a diffusion model to capture implicit channel features.

Demonstrates superior performance in differentiating similar channel scenarios.

Abstract

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%.