Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications
This addresses the challenge of reliable data transmission in wireless scenarios like multimedia under extreme conditions, offering a novel alternative to traditional channel coding.
The paper tackles the problem of enhancing data reconstruction in wireless communications by proposing conditional denoising diffusion probabilistic models (DDPMs) to transform noisy signals into clean ones, achieving over 10 dB improvement in reconstruction performance in low SNR regimes without reducing information rates.
In this paper, conditional denoising diffusion probabilistic models (DDPMs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of DDPM is to decompose the data generation process over the so-called "denoising" steps. Inspired by this, the key idea is to leverage the generative prior of diffusion models in learning a "noisy-to-clean" transformation of the information signal to help enhance data reconstruction. The proposed scheme could be beneficial for communication scenarios in which a prior knowledge of the information content is available, e.g., in multimedia transmission. Hence, instead of employing complicated channel codes that reduce the information rate, one can exploit diffusion priors for reliable data reconstruction, especially under extreme channel conditions due to low signal-to-noise ratio (SNR), or hardware-impaired communications. The proposed DDPM-assisted receiver is tailored for the scenario of wireless image transmission using MNIST dataset. Our numerical results highlight the reconstruction performance of our scheme compared to the conventional digital communication, as well as the deep neural network (DNN)-based benchmark. It is also shown that more than 10 dB improvement in the reconstruction could be achieved in low SNR regimes, without the need to reduce the information rate for error correction.