Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach

Xue, Qing; Liu, Yi-Jing; Sun, Yao; Wang, Jian; Li, Yan; Feng, Gang; Ma, Shaodan

doi:10.1109/tccn.2022.3215527

Cited by 38 publications

(26 citation statements)

References 36 publications

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“…Though FL has already been studied in various aspects of wireless communications [129], [130], [131], [132], the authors in [81] are pioneers to exploit it for BM in ultra-dense mmWave networks. The high density of smaller cells in such a network makes conventional BM methods highly complex and inefficient.…”

Section: Federated Learningmentioning

confidence: 99%

“…To ensure reliable connectivity and to achieve high data rates, a DQN-based user-centric association scheme was proposed in [133]. However, the systematic BM approach in [81] uses FL framework to mitigate the need of centralized data collection and to ensure data privacy. In this approach, a double DQN on each mmWave small cell trains a local BM model on the cleaned data set and then shares the trained model features to the macro BS for aggregation.…”

Section: Federated Learningmentioning

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: Federated Learningmentioning

confidence: 99%

Section: Federated Learningmentioning

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 3 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

View full text Add to dashboard Cite

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URLLC-eMBB hierarchical network slicing for Internet of Vehicles: An AoI-sensitive approach

Cui,

Yang,

et al. 2023

Vehicular Communications

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A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications

Zuo,

Guo,

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

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The research on the sixth-generation (6G) wireless communications for the development of future mobile communication networks has been officially launched around the world. 6G networks face multifarious challenges, such as resourceconstrained mobile devices, difficult wireless resource management, high complexity of heterogeneous network architectures, explosive computing and storage requirements, privacy and security threats. To address these challenges, deploying blockchain and artificial intelligence (AI) in 6G networks may realize new breakthroughs in advancing network performances in terms of security, privacy, efficiency, cost, and more. In this paper, we provide a detailed survey of existing works on the application of blockchain and AI to 6G wireless communications. More specifically, we start with a brief overview of blockchain and AI. Then, we mainly review the recent advances in the fusion of blockchain and AI, and highlight the inevitable trend of deploying both blockchain and AI in wireless communications. Furthermore, we extensively explore integrating blockchain and AI for wireless communication systems, involving secure services and Internet of Things (IoT) smart applications. Particularly, some of the most talked-about key services based on blockchain and AI are introduced, such as spectrum management, computation allocation, content caching, and security and privacy. Moreover, we also focus on some important IoT smart applications supported by blockchain and AI, covering smart healthcare, smart transportation, smart grid, and unmanned aerial vehicles (UAVs). Moreover, we thoroughly discuss operating frequencies, visions, and requirements from the 6G perspective. We also analyze the open issues and research challenges for the joint deployment of blockchain and AI in 6G wireless communications. Lastly, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of blockchain and AI in 6G networks. We hope this survey can shed new light on the research of this newly emerging area and serve as a roadmap for future studies.

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Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach

Cited by 38 publications

References 36 publications

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

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

URLLC-eMBB hierarchical network slicing for Internet of Vehicles: An AoI-sensitive approach

A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications

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