Compressive Sampling Framework for 2D-DOA and Polarization Estimation in mmWave Polarized Massive MIMO Systems

The polarized massive multiple-input multiple-output (MIMO) technique has been regarded as a promising solution to millimeter wave (mmWave) communication systems, because it experiences more degrees-of-freedom than the scalar configuration, and it represents a significant opportunity for secure communication. To deliver smart service to terminals, it is essential to provide base stations (BS) with the capability of terminal’s direction-of-arrival (DOA) awareness. In this paper, a compressive sampling (CS) framework is proposed for two-dimensional (2D) DOA and polarization estimation in mmWave polarized massive MIMO systems. The proposed approach first reduces the data volume via a reduced-dimension matrix. Then it computes the signal subspace via the eigendecomposition of the compressed array measurement. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization steering vector. Finally, 2D-DOA and polarization are estimated by incorporating the Poynting vector and the least squares (LS) techniques. The proposed architecture is computationally much more economical than existing algorithms. Besides, it allows a mmWave BS to provide comparable estimation performance with arbitrary sensor geometry, which is more flexible than most of the existing architectures. Furthermore, it is robust to the sensor position error. Numerical simulations verify the advantages of the proposed framework.

I. Introduction

Benefiting from the availability of large frequency bands and the use of massive multiple-input multiple-output (MIMO) technology [1], the next generation millimeter wave (mmWave) communication systems, named B5G/6G, aims at providing super-high experienced data rate, extremely low latency and high reliability services [2], [3], [4], [5], thus constructing a ubiquitous communication network. Actually, B5G/6G is envisioned as a multi-function system that integrates control, computing, communications, localization as well as sensing (3CLS) [6]. Thus, a sensor array is one of the most important components in wireless base stations (BS), and it is the basic component for 3CLS. In order to deliver multiple 3CLS services to the terminals efficiently, it is crucial to enable the BS with the capability of terminal direction awareness to accurately estimate the direction-of-arrival (DOA) using the data collected by the sensor array. In fact, DOA estimation is also useful for channel estimation in wireless communications [7], it is the preliminary task for beamforming [8], [9].