Fast Beam Training Architecture for Hybrid mmWave Transceivers

Abstract

Millimeter-wave (mmWave) communications attract considerable interest due to the massive available spectrum. However, to establish communication links, a beam training procedure is indispensable. How to accelerate the beam training process is one of the key challenges towards realizing mmWave communications in practice. In this study, we first propose a novel low-cost digital beamforming (DBF) module assisted hybrid (DA-hybrid) architecture, by exploiting both the capabilities of analog and digital modules. To make this topology practical, we deploy coarse radio frequency (RF) chains and low-resolution analog-to-digital converters in the low-cost DBF module to reduce cost and power consumption. Second, we design a fast beam training method (named DAH-BT) by utilizing the proposed DAhybrid architecture and leveraging the sparse nature of mmWave channels, in which an internal calibration method is adapted to obtain the parameters of the RF impairments and the orthogonal matching pursuit algorithm is utilized to estimate beams. We also prove that the developed measurement matrices satisfy the restricted isometry property. Extensive simulation results show that the DA-hybrid architecture can not only provide close to 100% beam matching accuracy, but also dramatically reduce the system power consumption and cost. In addition, the proposed DAH-BT scheme consumes the shortest time for beam training over the state-of-art methods with comparable spectral efficiency.

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

“…To accelerate the beam training process, the authors of [30] proposed a low-cost hybrid architecture assisted by a digital beamforming module and designed a fast beam training method by utilizing the proposed architecture and using the sparsity of mmWave channels. To improve the latency performance of mmWave networks for industrial automation, the authors of [31] proposed an adaptive beam selection strategy to select the best set of beams among multiple users to reduce the overall latency for all users.…”

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

“…In (13), and are the speed of light and the distance of the path from the vehicle i to the mmSBS, respectively.…”

“…By analyzing the average search delay of two different mmWave network models, the authors in [12] found that the average number of searches is related to the number of search sectors. The authors in [13] designed a low-cost beamforming module-assisted hybrid architecture and proposed a fast beam training method. The authors in [14] studied the sensitivity of the beam stability selected by the base station.…”

Efficient Multi-Beam Training For Terahertz Wireless communications

“…By contrast. The auxiliary array and the data transmission array used the same frequency band in [5]. The auxiliary array of the base station (BS) was used to transmit omnidirectional wave, and the data transmission array of the mobile station (MS) estimated AoDs of multiple paths by orthogonal matching pursuit (OMP) algorithm.…”

“…In this way, the reciprocity of the two channels is stronger than that between sub-6G and millimeter wave [4]. Compared with the auxiliary array of fully digital architecture adopted in [5], the auxiliary array of our scheme employs a small-scale hybrid structure for less cost consumption.…”