Simultaneous Position and Orientation Estimation for Reconfigurable Intelligent Surfaces-Assisted Visible Light Communications

Abstract

This work investigates the problem of simultaneously positioning and orientating (SPAO) the user equipment (UE) with reconfigurable intelligent surface (RIS)-assisted visible light communications (VLCs). In the proposed system model, the utilization of RISs effectively helps to address the SPAO problem of the UE in situations when there are insufficient light-emitting diode (LED) transmitters. Firstly, the channel gain between LED and UE and the one between RIS and UE are obtained through signal separation, followed by formulating an objective optimization function for the SPAO problem. A closed-form Cramer-Rao lower bound (CRLB) is then derived, representing the theoretical lower limit of positioning and orientating errors. Consequently, using CRLB as a benchmark for the SPAO performance, a fuzzy distance-based particle swarm optimization (FDPSO) is proposed to minimize the deviations between SPAO estimation errors and CRLBs. The proposed FDPSO algorithm adaptively adjusts the parameters and mutation operations to balance local exploitation and global exploration. Simulation results demonstrate that the proposed FDPSO algorithm achieves an asymptotic tightness with CRLB and performs better than the other five related iterative algorithms in terms of the cumulative distribution functions (CDFs) of positioning errors (PEs) and orientating errors (OEs).