Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications

Booth, Matthew Benjamin; Suresh, Vinayak; Michelusi, Nicolò; Love, David J.

doi:10.1109/lcomm.2019.2919016

Cited by 59 publications

(27 citation statements)

References 18 publications

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“…In this regard, different (deep) RL algorithms such as multi-armed bandit (MAB), Q-learning, deep Q-learning approaches have been proposed. A novel ML-based beam tracking and alignment framework for a sparse and time-varying mm-wave channel was proposed in [157]. The channel tracking was performed using Bayesian learning and Kalman filtering after which the optimal beam selection strategy was obtained using MAB.…”

Section: ) Wireless Data Aided Handover Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: ) Wireless Data Aided Handover Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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“…In 52 , a compressed sensing-based algorithm robust to frequency offsets and phase noise is presented. The sparsity of the mmWave channel is exploited in 13 , where a novel algorithm based on multiple-armed bandit beam selection for both initial beam alignment and beam tracking is proposed. In 28 , Hashemi et al exploit the channel correlation to reduce the searching space and subsequently, the delay of beam discovery.…”

Section: Related Workmentioning

confidence: 99%

Energy and Latency of Beamforming Architectures for Initial Access in mmWave Wireless Networks

Barati

Dutta²,

Rangan³

et al. 2020

J Indian Inst Sci

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Future millimeter-wave systems, 5G cellular or WiFi, must rely on highly directional links to overcome severe pathloss in these frequency bands. Establishing such links requires the mutual discovery of the transmitter and the receiver, potentially leading to a large latency and high energy consumption. In this work, we show that both the discovery latency and energy consumption can be significantly reduced using fully digital front-ends. In fact, we establish that by reducing the resolution of the fully digital front-ends we can achieve lower energy consumption compared to both analog and high-resolution digital beamforming. Since beamforming through analog front-ends allows sampling in only one direction at a time, the mobile device is “on” for a longer time compared to a digital beamformer, which can get spatial samples from all directions in one shot. We show that the energy consumed by the analog front-end can be four to six times more than that of the digital front-ends, depending on the size of the employed antenna arrays. We recognize, however, that using fully digital beamforming post beam discovery, i.e., for data transmission, is not viable from a power consumption standpoint. To address this issue, we propose the use of digital beamformers with low-resolution analog to digital converters (4 bits). This reduction in resolution brings the power consumption to the same level as analog beamforming for data transmissions while benefiting from the spatial multiplexing capabilities of fully digital beamforming, thus reducing initial discovery latency and improving energy efficiency.

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“…To reduce the training overhead, the core approach is to reduce the beam search space for the training process via extracting and exploiting the information from the training history, which leads to machine learning (ML) based beam training algorithms [12]- [21]. According to the underlying ML approaches or principles, these beam training solutions roughly fall into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…The key of the supervised learning based beam training solutions is to prepare for a sufficiently large database of training samples. As for the second category, it is designed based on reinforcement learning, or more generally, Markov decision process [18]- [21]. An important benefit of the second category is that the burden of collecting training samples can be relieved to a great extent.…”

Section: Introductionmentioning

confidence: 99%

Beam Drift in Millimeter Wave Links: Beamwidth Tradeoffs and Learning Based Optimization

Zhang

Masouros

2021

IEEE Trans. Commun.

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Millimeter wave (mmwave) communications, envisaged for the next generation wireless networks, rely on large antenna arrays and very narrow, high-gain beams. This poses significant challenges to beam alignment between transmitter and receiver, which has attracted considerable research attention. Even when alignment is achieved, the link is subject to beam drift (BD). BD, caused by non-ideal features inherent in practical beams and rapidly changing environments, is referred to as the phenomenon that the center of main-lobe of the used beam deviates from the real dominant channel direction, which further deteriorates the system's performance. To mitigate the BD effect, in this paper we first theoretically analyze the BD effect on the performance of outage probability as well as effective achievable rate, which takes practical factors (e.g., the rate of change of the environment, beam width, transmit power) into account. Then, different from conventional practice, we propose a novel design philosophy where multi-resolution beams with varying beam widths are used for data transmission while narrow beams are employed for beam training. Finally, we design an efficient learning based algorithm which can adaptively choose an appropriate beam width according to the environment. Simulation results demonstrate the effectiveness and superiority of our proposals.

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Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications

Cited by 59 publications

References 18 publications

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

Energy and Latency of Beamforming Architectures for Initial Access in mmWave Wireless Networks

Beam Drift in Millimeter Wave Links: Beamwidth Tradeoffs and Learning Based Optimization

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