Integrated Sensing and Communication for RIS-Assisted Backscatter Systems

Abstract

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this paper, we investigate the system sum rate maximization problem in an integrated sensing and RIS backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts and receive beamforming design problem under the Cramér-Rao bound (CRB) constraint for target angle estimation. To solve the non-convex problem, we then propose a fractional programming based alternating optimization algorithm. In particular, the fractional programming technique is firstly employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrate the trade-off between the communication and sensing performance.