Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge on edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS.

翻译：语义通信（SemCom）系统以深度学习（DL）为驱动，代表了数据传输领域的范式转变。这类系统优先考虑内容的重要性而非单纯的数据量。然而，现有语义通信设计在应对多样化计算能力与网络条件时面临挑战，尤其是在时间敏感型应用中。其关键问题在于：语义通信系统默认不同设备能够统一受益于标准的大型深度学习模型。这一假设在工业自动化或关键医疗等高速高可靠性应用中愈发不切实际。为此，本文提出一种面向异构资源受限边缘设备与计算密集型服务器的新型语义通信框架。该方法通过动态知识蒸馏（KD）为每台设备定制语义模型，在平衡计算与通信约束的同时保障服务质量（QoS）。我们建模了一个优化问题，并开发出自适应算法，该算法在边缘设备上迭代优化语义知识，最终生成适配其资源特性的更优模型。该算法可策略性调整蒸馏知识的粒度，使设备即使在不稳定的网络条件下也能保持高语义精度以完成精准推理任务。大量仿真结果表明，该方法显著降低了边缘设备的模型复杂度，实现了更优的语义提取并达成了预期服务质量目标。