Alternate Learning-Based SNR-Adaptive Sparse Semantic Visual Transmission

Semantic Communication (SemCom) demonstrates strong superiority over conventional bit-level accurate transmission, by only attempting to recover the essential semantic information of data. Nevertheless, most SemCom works train the whole system in an End-to-End (E2E) way, with the assumption of a differentiable channel which is rare in reality applications. In this paper, to tackle the non-differentiability of channels, we propose an alternate learning-based sparse SemCom system with an SNR-adaptive capability for visual transmission, named SparseSBC-SADM. Specially, SparseSBC-SADM leverages two separate Deep Neural Network (DNN)-based models at the transmitter (TX) and receiver (RX), respectively. It alternates between learning the encoding and decoding processes, rather than the joint optimization commonly found in existing literature, to solve the non-differentiability in the channel. In particular, a “self-critic” training scheme is leveraged for stable training. Moreover, the DNN-based TX generates a sparse set of bits in deduced “semantic bases”, by further incorporating a binary quantization module by combining Compressive Sensing (CS) and DNN on the basis of minimal detrimental effect to the semantic accuracy. Furthermore, enlightened from the denoising steps in the Denoising Diffusion Model (DDM), a lightweight, SNR-Adaptive Denoising Module (SADM) is provisionally deployed at RX to improve data reconstruction with a gate mechanism to determine the activation under poor channel conditions. Extensive simulation results validate that SparseSBC-SADM shows efficient and effective transmission performance under various channel conditions, and outperforms typical SemCom solutions.