Beam Scanning Design and Performance Analysis for Near-Field IRS-Aided Integrated Sensing and Communication Systems

A near-field based on a semi-passive intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) framework is proposed. Our objective is to identify the optimal beam direction from the base station to both the communicating user and the sensing target through downlink beam scanning signals. Especially, in the communication module, the beam scanning process in the downlink by IRS’s passive reflecting elements (REs) searches for the optimal reflected beam and sends the signal from the base station to the communicating user. Meanwhile, in the sensing module, the IRS can estimate the location of nearby targets by analyzing the echo signals received by the active sensing elements (SEs) on the IRS. To achieve the above objectives, in this paper, we design the near-field codebook by considering the near-field cascaded array steering vectors of the IRS and match the optimal codewords of the IRS through an exhaustive training process, so as to efficiently complete the near-field beam training under the matched near-field channel model. We have done a performance analysis of this ISAC system for the Cramer-Rao bound (CRB), and CRB provides performance constraints for near-field target sensing. Specifically, the CRB curve shows a monotonic decrease with increasing beam scan duration, while the achievable rate initially increases and then decreases. Therefore, we need to set the size of the model codebook space reasonably to balance the perception and communication performance. Simulation results show that the perceptual performance of our proposed scheme in the ISAC system is close to the derived CRB.