Localization and Discrete Beamforming with a Large Reconfigurable Intelligent Surface

TL;DR

This paper introduces a scalable protocol and algorithms for near-field localization and discrete beamforming with large reconfigurable intelligent surfaces, achieving centimeter-level accuracy in mmWave systems.

Contribution

It proposes a partitioned-far-field protocol and a fast passive beamforming algorithm to address near-field effects and discrete phase constraints efficiently.

Findings

Achieves centimeter-level localization accuracy at medium/high SNR.

Reduces beamforming complexity from exponential to linear.

Demonstrates the effectiveness of RIS for high-precision localization.

Abstract

In millimeter-wave (mmWave) cellular systems, reconfigurable intelligent surfaces (RISs) are foreseeably deployed with a large number of reflecting elements to achieve high beamforming gains. The large-sized RIS will make radio links fall in the near-field localization regime with spatial non-stationarity issues. Moreover, the discrete phase restriction on the RIS reflection coefficient incurs exponential complexity for discrete beamforming. It remains an open problem to find the optimal RIS reflection coefficient design in polynomial time. To address these issues, we propose a scalable partitioned-far-field protocol that considers both the near-filed non-stationarity and discrete beamforming. The protocol approximates near-field signal propagation using a partitioned-far-field representation to inherit the sparsity from the sophisticated far-field and facilitate the near-field localization scheme. To improve the theoretical localization performance, we propose a fast passive beamforming (FPB) algorithm that optimally solves the discrete RIS beamforming problem, reducing the search complexity from exponential order to linear order. Furthermore, by exploiting the partitioned structure of RIS, we introduce a two-stage coarse-to-fine localization algorithm that leverages both the time delay and angle information. Numerical results demonstrate that centimeter-level localization precision is achieved under medium and high signal-to-noise ratios (SNR), revealing that RISs can provide support for low-cost and high-precision localization in future cellular systems.