Variable-Length Joint Source-Channel Coding for Semantic Communication

TL;DR

This paper introduces a novel end-to-end coding framework for semantic communication that effectively manages variable-length codewords, optimizing the tradeoff between bit rate and semantic fidelity, and outperforms existing methods.

Contribution

It extends the information bottleneck theory to noisy channels and develops a trainable encoder for finite codebook compression in digital SemCom systems.

Findings

Achieves high inference quality at low bit rates.

Outperforms baseline methods in semantic transmission tasks.

Supports gradient-based optimization for non-differentiable operations.

Abstract

This paper investigates a key challenge faced by joint source-channel coding (JSCC) in digital semantic communication (SemCom): the incompatibility between existing JSCC schemes that yield continuous encoded representations and digital systems that employ discrete variable-length codewords. It further results in feasibility issues in achieving physical bit-level rate control via such JSCC approaches for efficient semantic transmission. In this paper, we propose a novel end-to-end coding (E2EC) framework to tackle it. The semantic coding problem is formed by extending the information bottleneck (IB) theory over noisy channels, which is a tradeoff between bit-level communication rate and semantic distortion. With a structural decomposition of encoding to handle code length and content respectively, we can construct an end-to-end trainable encoder that supports the direct compression of a data source into a finite codebook. To optimize our E2EC across non-differentiable operations, e.g., sampling, we use the powerful policy gradient to support gradient-based updates. Experimental results illustrate that E2EC achieves high inference quality with low bit rates, outperforming representative baselines compatible with digital SemCom systems.