针对动态信道下的语义通信，提出双路联合编码与个性化联邦学习框架。
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
- 设计双管道联合源信道编码，结合信道感知模型提升鲁棒性。
- 在不同信噪比下验证，模型在多个数据集上表现优异。
- 适合需要个性化通信与高适应性的复杂网络场景使用。
语义通信旨在解决通信系统中带宽受限和高延迟的问题。然而，在多用户复杂网络拓扑中，客户端数据与信道状态信息（CSI）的海量组合给现有语义通信架构带来巨大挑战。为在复杂场景中提升语义通信模型的泛化能力，并满足各用户本地环境的个性化需求，我们提出一种基于信道感知模型的双管道联合源信道编码个性化联邦学习框架（PFL-DPJSCCA）。该框架内提出的方法可实现非凸损失函数下的零优化差距。在不同信噪比分布下的实验验证了该框架在多种数据集上的卓越性能。
原文摘要 · Abstract (English)
Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users, the enormous combinations of client data and channel state information (CSI) pose significant challenges for existing semantic communication architectures. To improve the generalization ability of semantic communication models in complex scenarios while meeting the personalized needs of each user in their local environments, we propose a novel personalized federated learning framework with dual-pipeline joint source-channel coding based on channel awareness model (PFL-DPJSCCA). Within this framework, we present a method that achieves zero optimization gap for non-convex loss functions. Experiments conducted under varying SNR distributions validate the outstanding performance of our framework across diverse datasets.
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