  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.
