Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
This work addresses a domain-specific problem in wireless sensing and communication systems, offering incremental improvements through a novel simulation and optimization approach.
The paper tackles the challenge of characterizing the tradeoff between sensing and communication performance in integrated sensing and communication systems for learning-based human motion recognition by introducing SDP3, a simulation-driven framework that generates realistic datasets and predicts performance, finding that the achievable region includes a balanced communication-sensing adversarial zone.
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental datasets and the black-box nature of deep neural networks. This paper presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing datasets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the function regression method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing dataset matches the experimental dataset very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one.