Reconfigurable Intelligent Surface for Near Field Communications: Beamforming and Sensing

TL;DR

This paper explores reconfigurable intelligent surfaces (RIS) designed for near-field communications, optimizing wave conversion based on receiver type and proposing sensing methods to accurately locate receivers, thereby enhancing channel capacity.

Contribution

It introduces novel RIS coefficient designs for near-field scenarios and develops ML and FS sensing methods with derived position error bounds.

Findings

RIS schemes reduce energy leakage and increase channel capacity.

ML method accurately senses receiver location with high complexity.

FS method provides rough localization with low complexity.

Abstract

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.