Intelligent Reflecting Surface-Assisted Dual-Function Radar-Communication System

We consider the design of a Dual-Function Radar-Communication (DFRC) system, aided by an Intelligent Reflecting Surface (IRS). The radar precoding matrix and IRS parameters are optimized to maximize the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and the signal-to-interference-and-noise ratio (SINR) at the communication receivers, all while adhering to power constraints, a beampattern error constraint, and constant modulus constraints on the IRS parameters. The primary challenge in this maximization problem stems from the doubly reflected echo, wherein the signal reaches the target after reflection on the IRS and subsequently returns to the radar following reflection upon the IRS. This renders the SNR a non-convex quartic function of the IRS parameters. The problem is decomposed into two sub-problems: waveform design and IRS parameter design, and it is addressed through alternating optimization. The first sub-problem is tackled using semi-definite programming. The second sub-problem, which involves a non-convex quartic polynomial and unit modulus IRS parameter constraints, is resolved through Riemannian Manifold Optimization (RMO). In contrast to prior methods, our approach does not involve bounds or a surrogate function construction, but rather optimizes directly the original objective, thus providing more accurate evaluation of the system performance metrics. Design insights are derived, specifically regarding the selection of IRS size, IRS location, and the comparative analysis of paths involving single or double IRS reflections.

Automated summary

This article presents the design of a Dual-Function Radar-Communication (DFRC) system aided by an Intelligent Reflecting Surface (IRS). The optimized radar precoding matrix and IRS parameters aim to maximize the signal-to-noise ratio (SNR) at the radar receiver and the signal-to-interference-and-noise ratio (SINR) at the communication receivers. The problem is decomposed into waveform and IRS parameter design, and addressed through alternating optimization. The design insights include the selection of IRS size, location, and the comparative analysis of single or double IRS reflections.