Integrated Sensing and Communication for RIS-Assisted Backscatter Systems

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 article, we investigate the system sum-rate maximization problem in an integrated sensing and reconfigurable intelligent surface (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 Cramer-Rao bound (CRB) constraint for target angle estimation. To solve the nonconvex problem, we then propose a fractional programming (FP)-based alternating optimization algorithm. In particular, the FP technique is first 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 illustrates the tradeoff between the communication and sensing performance.

Automated summary

In this article, the system sum-rate maximization problem in an integrated sensing and reconfigurable intelligent surface (RIS) backscatter communication system is investigated. The proposed design significantly improves the system sum rate and illustrates the tradeoff between the communication and sensing performance.