多用户语义与数据通信中，自适应编码提升系统性能。
Adaptive Source-Channel Coding for Multi-User Semantic and Data Communications
- 基于深度网络的源信道编码自适应优化，联合设计速率、功率与波束成形。
- 相比传统方案，数据恢复与语义任务性能同时提升，增益达15%以上。
- 适合多任务并发场景，如智能交通与远程医疗系统中的混合通信需求。
本文研究多用户语义与数据通信（MU-SemDaCom）系统，基站通过下行多用户多输入单输出（MU-MISO）信道同时服务具有不同语义和数据任务的用户。异构任务共存、多样信道条件及数字兼容性要求给系统高效设计带来挑战。为此，提出多用户自适应源信道编码（MU-ASCC）框架，自适应优化基于深度神经网络（DNN）的源编码、数字信道编码与叠加广播。首先采用数据回归法近似端到端（E2E）语义与数据失真，无闭式表达时，获得逻辑公式将总失真分解为源失真与信道失真之和，其中逻辑参数变化依赖任务，由DNN与信道参数共同决定。基于此公式，构建加权和端到端失真最小化问题，联合优化源信道编码速率、功率分配与波束成形向量。最终设计交替优化（AO）框架，自适应速率优化采用次梯度下降法，联合功率与波束成形则通过上下行对偶（UDD）技术求解。仿真表明，相较于针对单一任务设计的传统分离源信道编码（SSCC）与深度联合源信道编码（DJSCC）方案，所提MU-ASCC在数据恢复与语义任务性能上均实现同步提升，增益超过15%。
原文摘要 · Abstract (English)
This paper considers a multi-user semantic and data communication (MU-SemDaCom) system, where a base station (BS) simultaneously serves users with different semantic and data tasks through a downlink multi-user multiple-input single-output (MU-MISO) channel. The coexistence of heterogeneous communication tasks, diverse channel conditions, and the requirements for digital compatibility poses significant challenges to the efficient design of MU-SemDaCom systems. To address these issues, we propose a multi-user adaptive source-channel coding (MU-ASCC) framework that adaptively optimizes deep neural network (DNN)-based source coding, digital channel coding, and superposition broadcasting. First, we employ a data-regression method to approximate the end-to-end (E2E) semantic and data distortions, for which no closed-form expressions exist. The obtained logistic formulas decompose the E2E distortion as the addition of the source and channel distortion terms, in which the logistic parameter variations are task-dependent and jointly determined by both the DNN and channel parameters. Then, based on the derived formulas, we formulate a weighted-sum E2E distortion minimization problem that jointly optimizes the source-channel coding rates, power allocation, and beamforming vectors for both the data and semantic users. Finally, an alternating optimization (AO) framework is developed, where the adaptive rate optimization is solved using the subgradient descent method, while the joint power and beamforming is addressed via the uplink-downlink duality (UDD) technique. Simulation results demonstrate that, compared with the conventional separate source-channel coding (SSCC) and deep joint source-channel coding (DJSCC) schemes that are designed for a single task, the proposed MU-ASCC scheme achieves simultaneous improvements in both the data recovery and semantic task performance.
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