[Paper Review] Beam Training and Tracking in MmWave Communication: A Survey

Yi Wang, Zhiqing Wei+1 more|arXiv (Cornell University)|May 20, 2022

Millimeter-Wave Propagation and ModelingEngineering18 citations

TL;DR

This survey provides a comprehensive overview of beam training and tracking techniques in millimeter wave (mmWave) communications, focusing on overcoming high overhead and mobility-induced beam misalignment. It proposes advanced methods—including hybrid beamforming, machine learning, and integrated sensing-communication systems—to enable fast, accurate, and robust beam alignment for future 6G networks.

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.

Motivation & Objective

• Address the high training overhead and beam misalignment caused by rapid channel variations and user mobility in mmWave systems.
• Overcome the limitations of traditional beam training, such as excessive feedback and pilot overhead, in large-scale mmWave MIMO systems.
• Explore intelligent beam management techniques using machine learning to reduce search space and improve adaptability to dynamic environments.
• Investigate integrated sensing and communication (ISAC) and THz-band beamforming as future directions for robust mmWave and terahertz communications.
• Identify open research problems to enable fast, accurate, and energy-efficient beam training and tracking for 6G networks.

Proposed method

• Survey and categorize existing beam training and tracking methods, including exhaustive search, codebook-based, and hybrid beamforming techniques.
• Analyze the role of channel state information (CSI) feedback and beam alignment procedures in mmWave systems with large-scale antenna arrays.
• Introduce machine learning-based approaches—particularly deep learning and reinforcement learning—to learn channel dynamics and reduce beam search complexity.
• Propose dual-functional radar-communication (DFRC) systems that eliminate the need for dedicated pilots and feedback by leveraging joint sensing and communication capabilities.
• Examine the challenges of beamforming in terahertz (THz) bands, including high path loss, spatial non-stationarity, and the need for accurate CSI estimation.
• Evaluate hybrid precoding architectures that balance spectral efficiency and hardware cost by combining digital and analog beamforming.

Experimental results

Research questions

• RQ1 How can beam training overhead be minimized in mmWave systems with large-scale antenna arrays?
• RQ2 What are the key challenges in maintaining beam alignment under high user mobility and environmental blockages?
• RQ3 How can machine learning techniques improve the speed and accuracy of beam selection and tracking in dynamic mmWave environments?
• RQ4 In what ways can integrated sensing and communication (ISAC) systems reduce feedback and pilot overhead in beam training?
• RQ5 What are the fundamental differences and challenges in beamforming and beam tracking between mmWave and terahertz (THz) bands?

Key findings

• Highly directional beamforming in mmWave bands can significantly extend communication range and improve spectral efficiency by focusing energy into narrow beams.
• Traditional beam training methods suffer from excessive overhead due to large antenna arrays, especially in high-mobility scenarios with rapidly changing AoD/AoA.
• Machine learning-based beam training reduces the beam search space and enables faster convergence by learning channel dynamics and user mobility patterns.
• Integrated sensing and communication (ISAC) systems eliminate the need for dedicated pilots and uplink feedback, improving spectrum efficiency and reducing latency.
• THz-band beamforming faces even greater challenges than mmWave due to higher path loss and spatial non-stationarity, requiring advanced CSI estimation and beam management.
• Hybrid beamforming architectures reduce hardware cost but introduce constraints that may degrade beamforming gain, necessitating careful design of analog and digital precoders.