Learning-Based Interface for Semantic Communication with Bit Importance Awareness

TL;DR

This paper introduces a learning-based interface for semantic communication that incorporates bit importance awareness, enabling better adaptation and performance in wireless image transmission within existing network architectures.

Contribution

It proposes a trainable interface with bit importance specification and an Importance-Aware Net for dynamic adaptation, enhancing semantic communication performance.

Findings

Improved end-to-end performance over Split DeepJSCC.

Enhanced adaptability to channel conditions and bandwidth ratios.

Effective wireless image transmission results.

Abstract

Joint source-channel coding (JSCC) is an effective approach for semantic communication. However, current JSCC methods are difficult to integrate with existing communication network architectures, where application and network providers are typically different entities. Recently, a novel paradigm termed Split DeepJSCC has been under consideration to address this challenge. Split DeepJSCC employs a bit-level interface that enables separate design of source and channel codes, ensuring compatibility with existing communication networks while preserving the advantages of JSCC in terms of semantic fidelity and channel adaptability. In this paper, we propose a learning-based interface design by treating its parameters as trainable, achieving improved end-to-end performance compared to Split DeepJSCC. In particular, the interface enables specification of bit-level importance at the output of the source code. Furthermore, we propose an Importance-Aware Net that utilizes the interface-derived bit importance information, enabling dynamical adaptation to diverse channel bandwidth ratios and time-varying channel conditions. Experimental results show that our method improves performance in wireless image transmission tasks. This work provides a potential solution for realizing semantic communications in existing wireless networks.