边端智能在无线感知中实现跨域持续学习，解决资源受限下的遗忘问题。
Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks
- 设计基于Transformer的判别器，处理噪声大且非等距的信道状态信息序列。
- 采用压缩核心集方法保留历史域知识，内存仅需累计训练的3%。
- 实测性能达累积训练的89%，遗忘率降低79%，适合边缘设备部署。
在集成感知与通信（ISAC）的无线网络中，边端智能（EI）需在边端设备（ED）上利用信道状态信息（CSI）感知用户活动。然而，由于CSI高度依赖用户特征，其与活动的关系具有强域依赖性，要求EI从多个域获取足够数据以具备跨域感知能力。这带来严峻挑战：边端设备资源有限，存储所有域数据将造成巨大负担。本文提出EdgeCL框架，使边端智能能持续学习并丢弃每个新数据集，同时避免灾难性遗忘。设计基于Transformer的判别器，处理噪声大且非等距的CSI序列；提出基于压缩核心集的知识保留方法，结合增强鲁棒性的优化策略，在保留先前域性能的同时防止未来遗忘。实验表明，EdgeCL达到累积训练89%的性能，内存仅消耗其3%，遗忘率降低79%。
原文摘要 · Abstract (English)
In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on channel state information (CSI). However, due to the CSI being highly specific to users' characteristics, the CSI-activity relationship is notoriously domain dependent, essentially demanding EI to learn sufficient datasets from various domains in order to gain cross-domain sensing capability. This poses a crucial challenge owing to the EDs' limited resources, for which storing datasets across all domains will be a significant burden. In this paper, we propose the EdgeCL framework, enabling the EI to continually learn-then-discard each incoming dataset, while remaining resilient to catastrophic forgetting. We design a transformer-based discriminator for handling sequences of noisy and nonequispaced CSI samples. Besides, we propose a distilled core-set based knowledge retention method with robustness-enhanced optimization to train the discriminator, preserving its performance for previous domains while preventing future forgetting. Experimental evaluations show that EdgeCL achieves 89% of performance compared to cumulative training while consuming only 3% of its memory, mitigating forgetting by 79%.
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