📝 SummarXiv

Abstract

This letter introduces a dual application of denoising diffusion probabilistic model (DDPM)-based channel estimation algorithm integrating data denoising and augmentation. Denoising addresses the severe noise in raw signals at pilot locations, which can impair channel estimation accuracy. An unsupervised structure is proposed to clean field data without prior knowledge of pure channel information. Data augmentation is crucial due to the data-intensive nature of training deep learning (DL) networks for channel state information (CSI) estimation. The network generates new channel data by adjusting reverse steps, enriching the training dataset. To manage varying signal-to-noise ratios (SNRs) in communication data, a piecewise forward strategy is proposed to enhance the DDPM convergence precision. The link-level simulations indicate that the proposed scheme achieves a superior tradeoff between precision and computational cost compared to existing benchmarks.