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Journal Articles IEEE Transactions on Network and Service Management Year : 2021

Harnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learning

Abstract

Terrestrial wireless infrastructure-based networks do not always guarantee that their resources will be shared uniformly by nodes in vehicular networks mostly due to the uneven and dynamic distribution of vehicles in the network as well as path loss effects. In this paper, we leverage multiple fifth-generation (5G) unmanned aerial vehicles (UAVs) to enhance network resource allocation among vehicles by positioning UAVs on-demand as "flying communication infrastructure". We propose a deep reinforcement learning (DRL) approach to determine the position of UAVs in order to improve the fairness and efficiency of network resource allocation while considering the UAVs' flying range, communication range, and limited energy resources. We use a parametric fairness function for resource allocation that can be tuned to reach different allocation objectives ranging from maximizing the total throughput of vehicles, maximizing minimum throughput, as well as achieving proportional band-width allocation. Simulation results show that the proposed DRL approach to UAV positioning can help improve network resource allocation according to the targeted fairness objective.
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Dates and versions

hal-03001383 , version 1 (12-11-2020)
hal-03001383 , version 2 (25-10-2021)

Identifiers

Cite

Tingting Yuan, Christian Esteve Rothenberg, Katia Obraczka, Chadi Barakat, Thierry Turletti. Harnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learning. IEEE Transactions on Network and Service Management, In press, ⟨10.1109/TNSM.2021.3122505⟩. ⟨hal-03001383v2⟩
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