Maximum likelihood approach to DoA estimation using lens antenna array

Abstract

Massive antenna array has been proposed to improve the spectral efficiency and link reliability in wireless communication systems. However, using large antenna arrays incurs additional cost in terms of signal processing and hardware complexity. The electromagnetic (EM) lens-focusing antennas are introduced as a promising technique to reduce the hardware complexity and cost. On the other hand, determining the location of users in terms of their direction-of-arrival (DoA) using these lens array becomes of great interest for different 5G services. This paper addresses the issue of DoA estimation by adopting lens antenna array (LNA). We firstly derive an expression for the received signal with the adoption of LNA, and then a maximum likelihood (ML) estimator for the DoA has been obtained. Depending on the ability of the lens array to focus the signal power on a subset of antennas as a function of DoA. We propose using the antenna selection (AS) technology to select an antenna subset aiming to reduce the number of radio frequency (RF) chains and accordingly reducing the hardware cost. The simulation results show the the capability of the proposed method to avoid the phase ambiguity problem and provide high accurate DoA estimation of signals.

Hybrid Beamforming for Dual Functioning Multi-input Multi-output Radar using Dimension Reduced-baseband Piecewise Successive Approximation

“…To address these issues, a novel mechanism of hybrid beamforming with lens antenna arrays for the dualfunctioning MIMO radar is proposed [13]. The use of lens antennas not only reduces the RF chains but also reduces the interferences while improving the user capacity [14].…”

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

“…The estimation of the direction-of-arrival of sensor arrays is an important research direction in the fields of radar, sonar and mobile communications, which has attracted the interest of many scholars [1][2][3]. At present, the research on uniform linear arrays (ULAs) is relatively mature, and many high-resolution DOA estimation methods have been proposed to improve the accuracy and resolution, such as the subspace method [4,5], maximum likelihood methods [6,7] and compressed sensing methods. Compared with the first two types of methods, the DOA estimation method based on compressed sensing can effectively estimate the incident angle in the case of a few snapshots and low signal-to-noise ratio (SNR).…”