Machine Learning for Robust Beam Tracking in Mobile Millimeter-Wave Systems

Sarkar, Sopan; Krunz, Marwan; Aykin, Irmak; Manzi, David

doi:10.1109/globecom46510.2021.9685716

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The beam tracking approach in [79] was further explored by Sarkar et al in [80]. To better track mmWave channel variations, the impact of the forgetting and boosting factor was studied on beam tracking performance.…”

Section: ) Multi-armed Banditsmentioning

confidence: 99%

“…However, FIGURE 8. Comparison of beam measurements overhead of the multi-codebook-based [39], [44], [45], [71] and subspace-reduction-based [40], [41], [43], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [68], [69], [73], [74], [75], [76], [77], [79], [80], [81] surveyed works against exhaustive beam sweeping. The number of UE beams is fixed to N = 16.…”

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

“…However, increasing the number of BS beams results in fine narrower beams M W M . Furthermore, the subset of candidate beams for [40], [41], [43], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [68], [69], [73], [74], [75], [76], [77], [79], [80], [81] at the BS is selected as S M = M 8 . Consequently, the subset of candidate beams increases with increased number of beams at the BS which ensures that the beam prediction accuracy stays similar even with increasing the number of BS beams.…”

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

“…This results in a higher number of wider beams (W M = M 4 ) by increasing the number of BS beams. Furthermore, the subset of candidate beams for [40], [41], [43], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [68], [69], [73], [74], [75], [76], [77], [79], [80], [81] at the BS is selected as S M = 8, which means that the ML model always predicts the eight best beams irrespective of number of the BS beams. Fig.…”

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

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Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Khan¹,

Gaber²,

Schulz

et al. 2023

IEEE Access

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Next-generation wireless communication networks will benefit from beamforming gain to utilize higher bandwidths at millimeter wave (mmWave) and terahertz (THz) bands. For high directional gain, a beam management (BM) framework acquires and tracks optimal downlink and uplink beam pairs through exhaustive beam scan. However, for narrower beams at higher carrier frequencies this leads to a huge beam measurement overhead that negatively impacts the beam acquisition and tracking. Moreover, volatility of mmWave and THz channels, user random mobility patterns, and environmental changes further complicate the BM process. Consequently, machine learning (ML) algorithms that can identify and learn complex mobility patterns and track environmental dynamics have been identified as a remedy. In this article, we provide an overview of the existing ML-based mmWave/THz BM and beam tracking techniques. Especially, we highlight key characteristics of an optimal BM and tracking framework. By surveying the recent studies, we identify some open research challenges and provide our recommendations that can serve as a future direction for researchers in this area.INDEX TERMS 6G, beam management (BM), beam tracking, federated learning (FL), machine learning (ML), millimeter wave (mmWave), reinforcement learning (RL), supervised learning (SL), terahertz (THz). I. INTRODUCTION5G New Radio is being deployed across the world, but in order to meet the requirements of extremely demanding applications like holographic video conferencing and extended reality, the industry and academia have already shifted their attention to the sixth generation (6G) of mobile communications systems. In order to support the multiplicity of use cases, 6G will have to provide much higher data rates, e.g., Tbps [1]. To enable high data rate communications, 5G benefits from higher bandwidths in the range of the millimeter wave (mmWave) spectrum, i.e., from 24.25 GHz to 52.6 GHz. It is now anticipated that 6G will stretch theseThe associate editor coordinating the review of this manuscript and approving it for publication was Olutayo O. Oyerinde . limits to terahertz (THz) bands (0.1 THz to 10 THz) [2], [3]. However, communication at higher frequency bands suffers from severe propagation losses. Consequently, directional communication with high beamforming gain is utilized to ensure coverage in mmWave bands. This necessitates the need of a sophisticated beam management (BM) framework at the transmitter (Tx) and the receiver (Rx) to carefully select an ideal beam pair for data transmission. A. PRELIMINARIES OF TRADITIONAL BEAM MANAGEMENTThe mmWave BM framework specified by the 3rd generation partnership project (3GPP) comprises three operations: initial beam establishment including beam sweeping, beam

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Section: ) Multi-armed Banditsmentioning

confidence: 99%

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Khan¹,

Gaber²,

Schulz

et al. 2023

IEEE Access

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show abstract

“…Since the pioneer reseach in DL for modulation recognition [48], DL has been used as the effective solution for other critical problems such as RF technology identification [66], or wireless localization [152]. In [153], DL is also used to reinforce the beam-tracking decisions for Base Station in mmWave communications system. DL-based receiver beamforming has also attracted some attention from researchers.…”

Section: Universal Rf Beamforming-relaymentioning

confidence: 99%

Robust and secure wireless communications: a deep learning approach

Nguyen

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First and foremost, I want to express my sincere gratitude to my Ph.D. advisor, Professor Guevara Noubir, who has continuously supported me with his kindness, meticulousness and patience, and inspired me with his passion for research and commitment for his students.Our discussions and conversations about scientific research and life in general have helped me to continuously improve myself over these years. He is the kindest teacher I have ever had, and I consider myself extremely lucky to have been advised by him.I would like to thank Professor Aanjhan Ranganathan, Professor Alina Oprea, and Professor Paul Patras, for serving on my doctoral thesis committee. Their insightful advice has been very helpful in this thesis. I am also grateful for my colleagues and friends in the Khoury College of Computer Sciences for the valuable research discussions that helped me complete this thesis.I would like to extend my appreciation to my mentors, Hao Zhong, Sri Bala, Bob Kamma, Ying Zhang, and Siyang Xie, for their valuable advice and continuous support when I was doing internships at Facebook. Their guidance and encouragement helped me to reclaim myself when I was at rock bottom. I am also thankful to all the friends I met in California during my internships. The good times when we traveled around San Francisco, or parasailing in Lake Tahoe are some of the cherished memories I will never forget.Finally, and most importantly, I would like to express my deepest gratitude to my parents, Nguyễn Hữu Teo -Lê Thị Yên, my sister and her husband, Nguyễn Lê Minh Nguyệt -Nguyễn Hoàng Hải, and my lovely twin nephews, Nguyễn Hải Long -Nguyễn Hải Lân, for being my greatest motivation, helping me overcome many challenges throughout my academic journey. Without their unconditional love, unwavering support, and constant encouragement, I would never have become who I am today.

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