Efficient DOA Estimation in Hybrid Analog-Digital Structures: Mitigating the Impact of Mutual Coupling
Journal article, 2025

Hybrid analog-digital structures (HADS) have emerged as an efficient solution for mitigating transmission loss and reducing power consumption in multiple-input multiple-output (MIMO) systems. However, the limited number of radio frequency (RF) chains and the presence of array mutual coupling (MC) pose significant challenges to achieving high-performance direction-of-arrival (DOA) estimation, thereby hindering effective downlink beamforming. To address these challenges, an efficient DOA estimation method specifically designed for HADS is proposed, effectively mitigating the impact of MC by employing a two-stage framework. The first stage reconstructs the spatial covariance matrix (SCM) by adjusting switch states and leveraging a middle subarray, combined with the real-valued subspace technique for initial DOA estimation. Using these initial estimates, MC is modeled and compensated by adjusting amplifiers and phase shifters. In the second stage, enhanced DOA estimation is achieved by fully exploiting the data from the entire array. Simulation results validate the effectiveness of the proposed method, demonstrating its capability to mitigate the MC effect and deliver accurate DOA estimation under practical conditions.

hybrid analog-digital structure

real-valued subspace technique

Direction-of-arrival (DOA) estimation

mutual coupling

Author

Ye Tian

Ningbo University

Hongyun Zhao

Ningbo University

Tuo Wu

School of Electrical and Electronic Engineering

Wei Liu

Hong Kong Polytechnic University

M. Elkashlan

Queen Mary University of London

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Fumiyuki Adachi

Tohoku University

G. K. Karagiannidis

Lebanese American University

Aristotle University of Thessaloniki

Chau Yuen

School of Electrical and Electronic Engineering

IEEE Transactions on Vehicular Technology

0018-9545 (ISSN) 1939-9359 (eISSN)

Vol. In Press

Areas of Advance

Transport

Subject Categories (SSIF 2025)

Probability Theory and Statistics

Signal Processing

DOI

10.1109/TVT.2025.3594548

More information

Latest update

8/15/2025