Leveraging Location Information for RIS-Aided mmWave MIMO Communications

Markku Juntti,Henk Wymeersch,Jiguang He

doi:10.1109/lwc.2021.3067474

Abstract

Location information offered by external positioning systems, e.g., satellite navigation, can be used as prior information in the process of beam alignment and channel parameter estimation for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output networks. Benefiting from the availability of such prior information, albeit imperfect, the beam alignment and channel parameter estimation processes can be significantly accelerated with less candidate beams explored at all the terminals. We propose a practical channel parameter estimation method via atomic norm minimization, which outperforms the standard beam alignment in terms of both the mean square error and the effective spectrum efficiency for the same training overhead.

Highlights

Reconfigurable intelligent surfaces (RISs) are expected to play a pivotal role in the millimeter wave multipleinput multiple-output (MIMO) systems with very-low cost and near-zero power consumption [1], [2]
We study the effect of prior location information on channel parameter estimation in RIS-aided mmWave MIMO systems and evaluate the performance in terms of the mean square error (MSE) and the effective spectrum efficiency (SE)
We model the prior information on mobile station (MS) location as m = [mx, my]T = m+e with m = [mx, my]T being the true coordinate of the MS, where the estimation error e is upper bounded as e 2 ≤

Summary

INTRODUCTION

Reconfigurable intelligent surfaces (RISs) are expected to play a pivotal role in the millimeter wave (mmWave) multipleinput multiple-output (MIMO) systems with very-low cost and near-zero power consumption [1], [2]. In [9], [10], the authors considered the beam alignment in mmWave vehicle-to-infrastructure (V2I) communications based on position information. Both supervised offline learning and unsupervised online learning were considered therein. We study the effect of prior location information on channel parameter estimation (in particular angular parameters) in RIS-aided mmWave MIMO systems and evaluate the performance in terms of the mean square error (MSE) and the effective spectrum efficiency (SE). Different benchmark schemes, including beam alignment approach, are evaluated to verify the superiority brought by leveraging the prior location information

SYSTEM MODEL

LOCATION-BASED TRAINING BEAMS

CHANNEL PARAMETER ESTIMATION VIA ANM

Case 1

SIMULATION RESULTS

CONCLUSIONS