提出自适应语义编解码方案，提升通信效率与系统兼容性。
Adaptive Source-Channel Coding for Semantic Communications
- 分离设计语义源编码与数字信道编码，通过模型适配动态变化的信道和源数据。
- 在并行高斯信道上实现端到端失真最小化，性能优于传统深度联合与分离编码。
- 保留现有数字系统兼容性，适合实际部署的智能通信场景。
语义通信（SemComs）作为兼顾数据准确传输与端到端失真最小化的新兴范式，当前联合源信道编码（JSCC）难以适配现有通信系统且无法应对源或信道变化；而分离源信道编码（SSCC）在有限块长下性能不佳。为此，本文提出一种面向并行高斯信道的自适应源信道编码（ASCC）方案，采用基于深度神经网络（DNN）的语义源编码与传统数字信道编码分离部署，并实现自适应设计。为实现源与信道编码间的高效协同，首先通过逻辑回归将端到端数据与语义失真近似为源编码速率与误比特率（BER）的函数，其中BER进一步建模为信噪比（SNR）与信道编码速率的函数。随后，构建加权总端到端失真最小化问题，联合优化源信道编码速率与功率分配，采用逐次凸逼近求解。仿真结果表明，所提ASCC方案在单通道与并行通道场景下均显著优于典型深度JSCC与SSCC方案，同时保持与现有数字系统的完全兼容性。
原文摘要 · Abstract (English)
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.
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