Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication.
CoRR(2023)
摘要
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.
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