Adaptive Source-Channel Coding for Semantic Communications
Dongxu Li, Kai Yuan, Jianhao Huang, Chuan Huang, Xiaoqi Qin, Shuguang Cui, and Ping Zhang

TL;DR
This paper introduces an adaptive source-channel coding scheme for semantic communications that optimally balances data fidelity and task performance, adapting to source and channel variations using neural networks and convex optimization.
Contribution
It proposes a novel adaptive coding framework combining neural source coding with traditional channel coding, enabling compatibility and improved performance in variable channel conditions.
Findings
Outperforms deep JSCC and SSCC schemes in simulations.
Achieves better end-to-end distortion minimization.
Maintains compatibility with practical digital systems.
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…
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Taxonomy
TopicsWireless Signal Modulation Classification · Wireless Communication Security Techniques · Advanced Wireless Communication Techniques
