A Survey of Beam Management for mmWave and THz Communications Towards 6G

TL;DR

This survey addresses the critical problem of beam management overhead and robustness in mmWave and THz communications for 6G. It articulates a comprehensive framework around three enabling technologies—AI-powered beam management, ISAC sensing, and RIS-assisted propagation—and extrapolates their roles in both conventional BM and RIS-enabled two-hop systems. The authors classify AI-based BM into independent and collaborative training, review predictive ISAC-enabled BM, and examine RIS-specific BM strategies, including near-field codebooks and multi-beam training. They also discuss open challenges such as collaboration across edge AI, sensing accuracy, privacy, and deployment life-cycle management, and extend the discussion to THz BM where near-field effects are pronounced. Overall, the paper provides a broad, multi-technology roadmap for advancing BM in 6G mmWave/THz networks, intended to guide researchers and practitioners in designing low-latency, high-reliability links in dynamic environments.

Abstract

Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is ushering in a new era of wireless communications. Beam management, namely initial access and beam tracking, has been recognized as an essential technique to ensure robust mmWave/THz communications, especially for mobile scenarios. However, narrow beams at higher carrier frequency lead to huge beam measurement overhead, which has a negative impact on beam acquisition and tracking. In addition, the beam management process is further complicated by the fluctuation of mmWave/THz channels, the random movement patterns of users, and the dynamic changes in the environment. For mmWave and THz communications toward 6G, we have witnessed a substantial increase in research and industrial attention on artificial intelligence (AI), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). The introduction of these enabling technologies presents both open opportunities and unique challenges for beam management. In this paper, we present a comprehensive survey on mmWave and THz beam management. Further, we give some insights on technical challenges and future research directions in this promising area.

Figures (11)

• Figure 1: The outline of this survey paper.
• Figure 2: 3GPP beam management procedure.
• Figure 3: Classification of existing algorithms for beam management.
• Figure 4: Roadmap to edge AI. In particular, 3GPP R17 introduced AI's FL technology in the global 5G communication field for the first time, and IEEE Std 3652.1$^{\text{TM}}$-2020 IEEE-Guide-FL approved the first FL standard.
• Figure 5: The agent-environment interaction in an MDP.