Joint RIS-Assisted Localization and Communication: A Trade-off Among Accuracy, Spectrum Efficiency, and Time Resource

Integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RISs) are viewed as promising technologies for the future 6G wireless networks. ISAC designs assisted by RISs are particularly attractive for tracking and localization problems in internet of everything (IoE) applications. In particular, RISs can be deployed to track the user equipment (UE) in blind spaces, i.e., where the direct line-of-sight (LoS) wireless link is not available. This paper proposes a time resource allocation strategy for integrated RIS-assisted localization and communication in the 60 GHz frequency band. We investigate the number of RISs and of their electronic steering angles required to support both localization and communication processes implemented on shared time resources. The UE localization is obtained through deep learning (DL) algorithms based on convolutional neural networks (CNN) and vision transformers (ViT) structures. The localization system performance is measured in terms of achieved Root Mean Squared Error (RMSE), algorithm complexity, and inference time. A Cramér-Rao bound for estimating the localization error based on the system geometry is also provided. The ISAC simulation results were explored in different setups to evaluate RIS-aided communication performance in terms of throughput as a function of frame efficiency. This analysis highlights an optimal tradeoff between frame efficiency and localization error, leading to maximum network throughput values ranging from 0.75 to 0.98 Gbps, depending on the computational capabilities of the deployed devices and the localization algorithm.