Relay Probing for Millimeter Wave Multi-Hop D2D Networks

“…Different technology companies like QUALCOMM [ 13 ], Intel [ 14 ], and TP-Link [ 15 ] have created 2.4/5/60 GHz tri-band Wi-Fi demo chipsets products. Moreover, academically, many related research works investigated Wi-Fi/mmWave integrations [ 3 , 16 , 17 , 18 , 19 ]. In this paper, the mmWave D2D NDS is devised using CMAB schemes where the arms are mmWave D2D links constructed by the nearby devices.…”

Section: Introductionmentioning

confidence: 99%

“…The motivation behind taking Wi-Fi information as a context of the mmWave D2D links is its ease of accessibility in multiband devices [ 3 , 16 , 17 ], which does not require extra processes. Furthermore, the authors in [ 3 , 16 , 17 , 18 , 19 ] proved the direct relationship between Wi-Fi and mmWave link statistics for multiband devices. The Wi-Fi signal statistics can predict mmWave link blockage’s probability and the likelihood of the mmWave RSS [ 3 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the authors in [ 3 , 16 , 17 , 18 , 19 ] proved the direct relationship between Wi-Fi and mmWave link statistics for multiband devices. The Wi-Fi signal statistics can predict mmWave link blockage’s probability and the likelihood of the mmWave RSS [ 3 , 16 , 17 , 18 , 19 ]. The main target of the modeled CMAB problem is to optimize the average throughput while considering the devices’ remaining energy.…”

Section: Introductionmentioning

confidence: 99%

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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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“…This can be overwhelmed by using directional communication through antenna beamforming, thanks to the high number of packed antenna elements [ 11 ]. Accordingly, mmWave coverage is limited to be within a few meters around the mmWave transmitter, which mandates the use of relaying to extend its coverage range [ 13 ]. Integrating millimeter wave (mmWave) band, 30~300 GHz, communications with UAV BSs can sustain 5G/B5G requirements due to the large available bandwidth [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

Mohamed

Hashima

Aldosary

et al. 2020

Sensors

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Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information. In this paper, the gateway UAV selection problem is addressed. The main aim is to maximize the long-term average data rates of the UAVs relays while minimizing the flights’ battery cost, where millimeter wave links, i.e., using 30~300 GHz band, employing antenna beamforming, are used for backhauling. A tool of machine learning (ML) is exploited to address the problem as a budget-constrained multi-player multi-armed bandit (MAB) problem. In this setup, access UAVs act as the players, and the arms are the gateway UAVs, while the rewards are the average data rates of the constructed relays constrained by the battery cost of the access UAV flights. In this decentralized setting, where information is neither prior available nor exchanged among UAVs, a selfish and concurrent multi-player MAB strategy is suggested. Towards this end, three battery-aware MAB (BA-MAB) algorithms, namely upper confidence bound (UCB), Thompson sampling (TS), and the exponential weight algorithm for exploration and exploitation (EXP3), are proposed to realize gateways selection efficiently. The proposed BA-MAB-based gateway UAV selection algorithms show superior performance over approaches based on near and random selections in terms of total system rate and energy efficiency.

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Relay Probing for Millimeter Wave Multi-Hop D2D Networks

Cited by 36 publications

References 41 publications

D2D Multi-hop Multi-path Communications in B5G Networks: A Survey on Models, Techniques, and Applications

D2D Multi-hop Multi-path Communications in B5G Networks: A Survey on Models, Techniques, and Applications

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

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

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