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

TL;DR

BitSemCom introduces a lightweight, learnable bit-level semantic communication framework that enables robust, end-to-end digital transmission compatible with various modulation schemes, outperforming traditional methods in image transmission tasks.

Contribution

The paper presents a novel modular learnable bit mapper using Gumbel-Softmax for differentiable bit generation, enabling true joint source-channel coding at the bit level.

Findings

Achieves competitive performance in image transmission.

Demonstrates superior robustness over traditional schemes.

Adds minimal parameters and computational complexity.

Abstract

Most existing semantic communication systems employ analog modulation, which is incompatible with modern digital communication systems. Although several digital transmission approaches have been proposed to address this issue, an end-to-end bit-level method that is compatible with arbitrary modulation formats, robust to channel noise, and free from quantization errors remains lacking. To this end, we propose BitSemCom, a novel bit-level semantic communication framework that realizes true joint source-channel coding (JSCC) at the bit level. Specifically, we introduce a modular learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bits, utilizing the Gumbel-Softmax trick to enable differentiable bit generation. Simulation results on image transmission demonstrate that BitSemCom achieves both competitive performance and superior robustness compared to traditional separate source-channel coding (SSCC) schemes, and outperforms deep learning based JSCC with uniform 1-bit quantization, validating the effectiveness of the learnable bit mapper. Despite these improvements, the bit mapper adds only 0.42% parameters and 0.09% computational complexity, making BitSemCom a lightweight and practical solution for real-world semantic communication.