  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.
