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.

翻译：在基于学习的人体运动识别应用中，刻画通感一体化（ISAC）系统的感知与通信性能权衡关系极具挑战性，这是因为需要大规模实验数据集且深度神经网络具有黑箱特性。本文提出SDP3——一种仿真驱动的性能预测与优化器，包含SDP3数据模拟器、SDP3性能预测器和SDP3性能优化器三个模块。具体而言，SDP3数据模拟器能够在虚拟环境中生成逼真的无线感知数据集，SDP3性能预测器基于函数回归方法预测感知性能，SDP3性能优化器则通过解析方法研究感知与通信性能的权衡关系。研究表明，在运动识别准确率方面，仿真感知数据集与实验数据集具有高度一致性。通过利用SDP3，我们发现识别准确率与通信吞吐量的可达区域由通信饱和区、感知饱和区以及通信-感知对抗区构成，而ISAC系统期望的均衡性能恰位于第三区域。