IRS-Enhanced Integrated Sensing, Communication, and Powering Systems: Beamforming and Reflecting Optimization

Abstract

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station (BS) transmits signals for simultaneous radar sensing, as well as multiuser information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multitarget sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly nonconvex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the BS, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the BS, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further design the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.