MAMBA: A Multi-armed Bandit Framework for Beam Tracking in Millimeter-wave Systems

Aykin, Irmak; Akgun, Berk; Feng, Mingjie; Krunz, Marwan

doi:10.1109/infocom41043.2020.9155408

Cited by 50 publications

(43 citation statements)

References 26 publications

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“…The proposed model considers the traffic pattern and different types of blockages in order to select the optimal beam in real-time without prior training of the model. In [160], a beam tracking approach based on MAB is proposed to determine the optimal beams and data rates of the beams in a mm-wave communication system. The proposed model uses the beam quality information, and the feedback obtained from users during initial access to determine the optimal beam and transmission rate during the next transmission.…”

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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“…We will prove the potency of the proposed contextual-based algorithms over the noncontextual ones due to the valuable Wi-Fi contextual information. The authors of [ 39 ] proposed an adaptive TS (ATS) algorithm for beam alignment of mmWaves. ATS can precisely evaluate the best beam/rate pair without assuming any channel settings and user mobility.…”

Section: Literature Reviewmentioning

confidence: 99%

Wi-Fi Assisted Contextual Multi-Armed Bandit for Neighbor Discovery and Selection in Millimeter Wave Device to Device Communications

Hashima

Hatano

Kasban

et al. 2021

Sensors

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The unique features of millimeter waves (mmWaves) motivate its leveraging to future, beyond-fifth-generation/sixth-generation (B5G/6G)-based device-to-device (D2D) communications. However, the neighborhood discovery and selection (NDS) problem still needs intelligent solutions due to the trade-off of investigating adjacent devices for the optimum device choice against the crucial beamform training (BT) overhead. In this paper, by making use of multiband (μW/mmWave) standard devices, the mmWave NDS problem is addressed using machine-learning-based contextual multi-armed bandit (CMAB) algorithms. This is done by leveraging the context information of Wi-Fi signal characteristics, i.e., received signal strength (RSS), mean, and variance, to further improve the NDS method. In this setup, the transmitting device acts as the player, the arms are the candidate mmWave D2D links between that device and its neighbors, while the reward is the average throughput. We examine the NDS’s primary trade-off and the impacts of the contextual information on the total performance. Furthermore, modified energy-aware linear upper confidence bound (EA-LinUCB) and contextual Thomson sampling (EA-CTS) algorithms are proposed to handle the problem through reflecting the nearby devices’ withstanding battery levels, which simulate real scenarios. Simulation results ensure the superior efficiency of the proposed algorithms over the single band (mmWave) energy-aware noncontextual MAB algorithms (EA-UCB and EA-TS) and traditional schemes regarding energy efficiency and average throughput with a reasonable convergence rate.

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“…However, without adapting it in RIS-assisted networks, the accuracy of the algorithm decreases with larger number of RIS elements. Learning-based beam tracking algorithms are emerging these days thanks to their ability in beam prediction [15]- [17]. The machine-learningbased algorithms will require additional information, such as multiple base stations in [15] and situational awareness information in [16], to train the learning model and execute beam prediction.…”

Section: A Beam Tracking Algorithms In Mmwave Systemsmentioning

confidence: 99%

“…Although adding a RIS into the mmWave systems creates an additional path and overcome the blockage issue, the support for mobile users is also essential, especially with the rapid developments of autonomous vehicle techniques [15], [16]. One solution to user mobility in mmWave systems is adopting beam tracking algorithms [11]- [17], which automatically switch the paired beams in order to maintain the quality of communication link above a certain threshold. However, existing beam tracking algorithms in mmWave systems can not be used directly with RIS-assisted systems.…”

Section: Introductionmentioning

confidence: 99%

Fast Beam Tracking for Reconfigurable Intelligent Surface Assisted Mobile mmWave Networks

Xiao¹,

Sun²

2021

Preprint

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Millimeter wave (mmWave) communications are vulnerable to blockages and node mobility due to the highly directional signal beams. The emerging Reconfigurable Intelligent Surfaces (RISs) technique can effectively mitigate the blockage problem by exploring the non-line-of-sight (NLOS) path, where the beam switching is realized by digitally configuring the phases of RIS elements. To date, most efforts have been made in the stationary scenario. However, when considering node mobility, beam tracking algorithms designed specifically for RIS are needed in order to maintain the NLOS link. In this paper, a fast RIS-based beam tracking algorithm is developed by partly transforming the large amount of signaling time into the calculation happens at base station in a mmWave system with mobile users. Specifically, the differential form of optimal RIS configuration is exploited as the updating beam tracking parameter to avoid complex channel estimation procedure. The RIS-based beam tracking problem is then transformed into an optimization problem whose solution is found by a calculationbased search. Finally, by training on a small set candidate, RISbased beam tracking is realized. The effectiveness and efficiency of the proposed RIS-based beam tracking algorithm is evaluated by simulations. It shows that the proposed algorithm has nearoptimal performance with dramatic savings in terms of signaling time.

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MAMBA: A Multi-armed Bandit Framework for Beam Tracking in Millimeter-wave Systems

Cited by 50 publications

References 26 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

Wi-Fi Assisted Contextual Multi-Armed Bandit for Neighbor Discovery and Selection in Millimeter Wave Device to Device Communications

Fast Beam Tracking for Reconfigurable Intelligent Surface Assisted Mobile mmWave Networks

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