Abstract
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.
Highlights
The use of unmanned aerial vehicles (UAVs), commonly known as drones, gained a lot of consideration in recent years from both academia and industry [1,2]
This comes from the low interference and time-sharing scheduling experienced by the small number of access UAVs
We considered the problem of gateway selection in a fully decentralized UAV
Summary
The use of unmanned aerial vehicles (UAVs), commonly known as drones, gained a lot of consideration in recent years from both academia and industry [1,2]. The motivation behind using online learning comes from its ability to deal with both complex and dynamic environments effectively [26], without any prior information, where an agent learns to enhance its future actions based only on its past actions/observations Towards this end, the gateway UAV selection problem is formulated as a budget-constrained multi-player multi-armed bandit (MAB) problem [27,28,29]. A budget-constrained multi-player MAB model is formulated and introduced In this model, the access UAVs act as the agents, the gateway UAVs act as the arms of the bandit, and the rewards are the long-term achievable data rates constrained by the limited budget of the battery capacity of the access UAVs; Three BA-MAB algorithms, i.e., BA-UCB, BA-TS, and BA-EXP3, are proposed to be exploited by each access UAV to selfishly interact with the environment and select the proper gateway.
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