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

TL;DR

This paper explores the fundamental trade-offs in integrated sensing and communication (ISAC) systems using KL divergence, analyzing how design choices affect performance metrics like BER and detection probability.

Contribution

It introduces a unified KL divergence framework to quantify ISAC performance trade-offs and employs deep learning and SDR for system design optimization.

Findings

Identifies the relationship between KL divergence and ISAC performance metrics.

Demonstrates the impact of constellation and beamforming design on the Pareto bound.

Shows the trade-off between sensing and communication performance through simulations.

Abstract

Integrated sensing and communication (ISAC) is regarded as a promising technique for 6G communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler (KL) divergence performance metric. We firstly present the relationship between KL divergence and explicit ISAC performance metric, i.e., demodulation error and probability of detection. 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 under different parameter set-ups.