BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping

Abstract

Most existing semantic communication systems based on joint source-channel coding (JSCC) employ analog modulation and are thus inherently incompatible with modern digital communication systems and impose stringent hardware design challenges. Although several digital transmission approaches have been proposed to address this issue, they often suffer from high sensitivity to bit errors, limited adaptability to varying source distributions, or re-training overhead under different modulation schemes. This letter proposes BitSemCom, a novel end-to-end bit-level JSCC framework that is robust to channel noise and modulation-agnostic. The core component is a learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bit sequences. By leveraging a sampling-based bit generation method based on the Gumbel-Softmax trick, the framework enables differentiable bit-level optimization while maintaining robustness to channel errors. Simulation results on image transmission demonstrate that BitSemCom achieves consistent peak signal-to-noise ratio (PSNR) gains of 2-3 dB over codebook-based digital semantic transmission methods and competitive performance with stronger robustness compared to separate source-channel coding (SSCC) benchmarks. Ablation studies further validate the effectiveness of the learnable bit mapper.