  Diffusion models (DM) can gradually learn to remove noise, which have been
widely used in artificial intelligence generated content (AIGC) in recent
years. The property of DM for removing noise leads us to wonder whether DM can
be applied to wireless communications to help the receiver eliminate the
channel noise. To address this, we propose channel denoising diffusion models
(CDDM) for wireless communications in this paper. CDDM can be applied as a new
physical layer module after the channel equalization to learn the distribution
of the channel input signal, and then utilizes this learned knowledge to remove
the channel noise. We design corresponding training and sampling algorithms for
the forward diffusion process and the reverse sampling process of CDDM.
Moreover, we apply CDDM to a semantic communications system based on joint
source-channel coding (JSCC). Experimental results demonstrate that CDDM can
further reduce the mean square error (MSE) after minimum mean square error
(MMSE) equalizer, and the joint CDDM and JSCC system achieves better
performance than the JSCC system and the traditional JPEG2000 with low-density
parity-check (LDPC) code approach.
