In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system with high accuracy and efficient resource utilization. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

翻译：本文提出一种基于数字孪生的用户中心型方法，用于在通感一体化（ISAC）系统中以高精度和高效资源利用率处理感知数据。所考虑的场景包含一个搭载轻量级深度神经网络（DNN）的ISAC设备，以及一个配备大型DNN的移动边缘计算（MEC）服务器。在收集感知数据后，ISAC设备可选择本地处理数据，或将其上传至服务器以获得更高精度的数据处理结果。为应对数据漂移，服务器会在必要时更新轻量级DNN，该过程称为持续学习。本文目标是通过优化两个决策——即感知数据卸载与用于DNN更新的感知数据选择——来最小化MEC服务器的长期平均计算成本。为预测潜在决策对服务器长期计算成本的影响，我们构建了ISAC设备的数字孪生模型，并基于该模型以闭式公式形式做出决策。通过执行基于DNN的人体运动识别任务实验，验证了所提数字孪生方法在计算成本最小化方面的卓越性能。