Beam Training and Tracking in MmWave Communication: A Survey

Abstract

Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

Beamwidth Design Tradeoffs in Radar-Aided Millimeter-Wave Cellular Networks: A Stochastic Geometry Approach

“…• The average total system throughput in bits/s depends on the total detection rate by the radar sub-system as given in (30). 4 The communication sub-system can be extended to serve multiple MUs simultaneously in a single time slot with a single antenna beam using some user grouping techniques such as Non-Orthogonal Multiple Access (NOMA). This is a stand-alone problem that requires power allocation strategy and successive interference cancellation (SIC) mechanism, and hence is left as future work to not dilute the paper's main focus.…”

“…However, it incurs significant overhead. To reduce this overhead, alternative approaches like hierarchical search and two-stage search are now employed [4]. • Side information-aided beam training: involves gathering information about the user, such as its position, through sensors.…”

“…These sensors encompass automotive sensors, cameras, or radar-assisted beam training, leveraging information derived from radar to configure mm-wave communication beams. • Machine learning-based beam training: utilizes information gained from previous training to restrict the search area and predict the next training phase [4]. Nevertheless, blind beam training techniques remain the most widely used in practical systems.…”

Evaluation Methodology for 6G Sensing-Assisted Communication System Performance

“…As the number of possible beams increases, the complexity of the problem grows exponentially, and the effective performance drops. Therefore, multiple solutions are being proposed in the literature to reduce the beam pair searching time, including algorithms that simplify the beam pair search, using prior information to aid the beam training, machine learning approaches [20], and beam tracking.…”