Adaptive Source-Channel Coding for Semantic Communications

2025-08-11

AI Summary

Existing semantic communication systems suffer from limited system compatibility and environmental adaptability in joint source-channel coding (JSCC), while separate source and channel coding (SSCC) exhibits suboptimal performance under short blocklengths. Method: We propose an adaptive source–channel collaborative coding framework that, for the first time, decouples deep semantic source encoding from conventional digital channel coding. Leveraging differentiable approximation, we jointly optimize rate, transmit power, and distortion. The method integrates deep semantic feature extraction, end-to-end distortion–bit-error-rate mapping via logistic regression, and continuous convex approximation to solve the multi-variable optimization problem. Results: Experiments under single- and multi-path Gaussian channels demonstrate that our approach significantly outperforms representative JSCC and SSCC baselines, achieving superior semantic fidelity, data reliability, and backward compatibility with legacy communication infrastructure.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions, combining the demands for both the bit-accurate delivery and end-to-end (E2E) distortion minimization. However, current joint source-channel coding (JSCC) in SemComs is not compatible with the existing communication systems and cannot adapt to the variations of the sources or the channels, while separate source-channel coding (SSCC) is suboptimal in the finite blocklength regime. To address these issues, we propose an adaptive source-channel coding (ASCC) scheme for SemComs over parallel Gaussian channels, where the deep neural network (DNN)-based semantic source coding and conventional digital channel coding are separately deployed and adaptively designed. To enable efficient adaptation between the source and channel coding, we first approximate the E2E data and semantic distortions as functions of source coding rate and bit error ratio (BER) via logistic regression, where BER is further modeled as functions of signal-to-noise ratio (SNR) and channel coding rate. Then, we formulate the weighted sum E2E distortion minimization problem for joint source-channel coding rate and power allocation over parallel channels, which is solved by the successive convex approximation. Finally, simulation results demonstrate that the proposed ASCC scheme outperforms typical deep JSCC and SSCC schemes for both the single- and parallel-channel scenarios while maintaining full compatibility with practical digital systems.

Problem

• Adaptive coding for semantic communication compatibility issues
• Minimizing distortion in finite blocklength regime
• Optimizing rate and power allocation over parallel channels

Innovation

• DNN-based semantic source coding
• Adaptive source-channel coding design
• Successive convex approximation optimization