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Compared with the current Shannon's Classical Information Theory (CIT)paradigm, semantic communication (SemCom) has recently attracted moreattention, since it aims to transmit the meaning of information rather thanbit-by-bit transmission, thus enhancing data transmission efficiency andsupporting future human-centric, data-, and resource-intensive intelligentservices in 6G systems. Nevertheless, channel noises are common and evenserious in 6G-empowered scenarios, limiting the communication performance ofSemCom, especially when Signal-to-Noise (SNR) levels during training anddeployment stages are different, but training multi-networks to cover thescenario with a broad range of SNRs is computationally inefficient. Hence, wedevelop a novel De-Noising SemCom (DNSC) framework, where the designedde-noiser module can eliminate noise interference from semantic vectors. Uponthe designed DNSC architecture, we further combine adversarial learning,variational autoencoder, and diffusion model to propose the Latent DiffusionDNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. Duringthe offline training phase, noises are added to latent semantic vectors in aforward Markov diffusion manner and then are eliminated in a reverse diffusionmanner through the posterior distribution approximated by the U-shaped Network(U-Net), where the semantic de-noiser is optimized by maximizing evidence lowerbound (ELBO). Such design can model real noisy channel environments withvarious SNRs and enable to adaptively remove noises from noisy semantic vectorsduring the online transmission phase. The simulations on open-source imagedatasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme inPSNR and SSIM over different SNRs than the state-of-the-art schemes, includingJPEG, Deep JSCC, and ADJSCC.

Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems