Learning-Based Interface for Semantic Communication with Bit Importance Awareness

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.