High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

Abstract

In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.Index Terms-Millimeter wave communications, channel estimation, hybrid precoding, massive MIMO.

Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

“…Channel estimation methods based on the ESPRIT algorithm are proposed in [76][77][78][79][80]. The training signals at both the BS and each user are designed to obtain the low-dimensional effective channel [76,77], while the spatial smoothing and the forward backward averaging techniques are adopted to alleviate the impact of coherent signals caused by multiple AoAs or AoDs close to each other.…”

“…Note that powering off antennas will reduce the total radiation power and therefore reduce the signal coverage. A better method is to turn off fewer antennas, e.g., only one antenna [80]. To achieve highresolution estimation of AoAs and AoDs using the ESPRIT method, multiple stages of pilot transmission can be considered.…”

A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station

“…The positioning in [ 13 ] relied on a maximum likelihood estimator and on CRLBs instead of actual AoA and ToA estimates. AoA estimation through an ESPRIT algorithm was recently addressed in [ 14 ]. A generic mmWave multiple-input multiple-output (MIMO) signal was considered in [ 14 ], and the focus was only on azimuth angle estimation, not a joint azimuth-elevation estimation.…”

“…AoA estimation through an ESPRIT algorithm was recently addressed in [ 14 ]. A generic mmWave multiple-input multiple-output (MIMO) signal was considered in [ 14 ], and the focus was only on azimuth angle estimation, not a joint azimuth-elevation estimation. Another work focusing on angle estimation in 5G mmWave systems was [ 15 ].…”

Deep CNN-Based Spherical-Wave Channel Estimation for Terahertz Ultra-Massive MIMO Systems

“…2) Channel Estimation: In the literature, various solutions including the conventional ongrid [16]- [18] and off-grid [19]- [22], and the recently proposed deep-learning (DL)-based solutions [23]- [28] have been studied for UM-MIMO CE. The on-grid solutions consider the directions of the propagation paths are taken from fixed spatial grids, which include the compressive-sensing (CS) solutions such as the orthogonal matching pursuit (OMP) [16], the approximate matching pursuit (AMP) [17] and others [18].…”