  Compared with the current Shannon's Classical Information Theory (CIT)
paradigm, semantic communication (SemCom) has recently attracted more
attention, since it aims to transmit the meaning of information rather than
bit-by-bit transmission, thus enhancing data transmission efficiency and
supporting future human-centric, data-, and resource-intensive intelligent
services in 6G systems. Nevertheless, channel noises are common and even
serious in 6G-empowered scenarios, limiting the communication performance of
SemCom, especially when Signal-to-Noise (SNR) levels during training and
deployment stages are different, but training multi-networks to cover the
scenario with a broad range of SNRs is computationally inefficient. Hence, we
develop a novel De-Noising SemCom (DNSC) framework, where the designed
de-noiser module can eliminate noise interference from semantic vectors. Upon
the designed DNSC architecture, we further combine adversarial learning,
variational autoencoder, and diffusion model to propose the Latent Diffusion
DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. During
the offline training phase, noises are added to latent semantic vectors in a
forward Markov diffusion manner and then are eliminated in a reverse diffusion
manner through the posterior distribution approximated by the U-shaped Network
(U-Net), where the semantic de-noiser is optimized by maximizing evidence lower
bound (ELBO). Such design can model real noisy channel environments with
various SNRs and enable to adaptively remove noises from noisy semantic vectors
during the online transmission phase. The simulations on open-source image
datasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme in
PSNR and SSIM over different SNRs than the state-of-the-art schemes, including
JPEG, Deep JSCC, and ADJSCC.
