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Polarized Intelligent Reflecting Surface Aided 2D-DOA Estimation for NLoS Sources | IEEE Journals & Magazine | IEEE Xplore

Polarized Intelligent Reflecting Surface Aided 2D-DOA Estimation for NLoS Sources


Abstract:

Intelligent Reflecting Surface (IRS) represents a significant breakthrough in wireless communications, allowing the reconstruction of wireless channels even for occluded ...Show More

Abstract:

Intelligent Reflecting Surface (IRS) represents a significant breakthrough in wireless communications, allowing the reconstruction of wireless channels even for occluded users to the base station (BS). Estimating the Direction-of-Arrival (DOA) of a source oriented toward Non-Line-of-Sight (NLOS) propagation is an intriguing topic in an IRS-aided wireless communication scenario. However, the existing optimization-based approaches are overly complex to be practically implemented. In this paper, we propose a polarized IRS architecture, in which both IRS and BS are equipped with arbitrarily placed Electromagnetic Vector Sensor (EMVS) arrays. A Normalized Vector-Cross Product (NVCP) estimator is developed for DOA estimation, which avoids the need for complicated data recovery or exhaustive grid search. The proposed framework enables Two-Dimensional (2D) DOA estimation for NLOS signals without requiring prior knowledge of the BS-IRS channel. Numerical simulations have been conducted to verify its effectiveness.
Published in: IEEE Transactions on Wireless Communications ( Volume: 23, Issue: 7, July 2024)
Page(s): 8085 - 8098
Date of Publication: 09 January 2024

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Funding Agency:

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I. Introduction

Direction-of-Arrival (DOA) estimation using a colocated antenna array has been a prominent topic in wireless communications, garnering continuous attention over the past decades. Typically, multiple antennas are strategically placed at different positions to sense the time delay/phase shift of incident sources. Numerous strategies, including Multiple Signal Classification (MUSIC) [1], maximum likelihood estimator [2], matrix pencil method [3], Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [4], sparsity-aware algorithms, cumulant-based approaches [5], and tensor-aware algorithms [6], have been established for DOA estimation from time delay/phase shift measurements. In practical applications, the appeal lies in Two-Dimensional (2D) DOA estimation rather than One-Dimensional (1D) approaches. Consequently, several efforts have been dedicated to nonlinear scalar array geometries, such as L-shaped, circular, and rectangular arrays [7], [8]. Additionally, the use of vector sensors has shown promise as a flexible alternative for 2D-DOA estimation [9], [10], [11], offering advantages in array architecture and computational complexity compared to scalar arrays.

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References

References is not available for this document.