Revealing the Trade-Off in ISAC Systems: The KL Divergence Perspective

Integrated sensing and communication (ISAC) is regarded as a promising technique for sixth-generation (6G) communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler divergence (KLD) performance measure. We firstly use KLD to analyze the detection performance of communication and sensing in the ISAC system. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection (PD) under different parameter settings.