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Title:Reinforcement learning for dynamic aerial base station positioning
Author(s):Lee, Isabella
Advisor(s):Caesar, Matthew
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):UAV relay network
reinforcement learning
IoT network
emergency relief
disaster response
aerial base station
Abstract:Reliable communication infrastructure plays an extremely important role in peoples' everyday lives and the lack of sufficient communication means could have severe negative consequences. In the case of a post-disaster situation, it could be the difference in an emergency responder's ability to rescue a trapped victim. In the case of a state-wide stay-at-home order, it could be the difference in a parent's ability to continue their work remotely and in a student's ability to receive a proper education. In the case of a remote region, it could be the difference in someone's ability to connect with the outside world. Unfortunately, in many of these cases, the number of functioning communication network infrastructure is actually limited. In such scenarios, unmanned aerial vehicles (UAVs) can be used as aerial base stations or relays to help form a connected network amongst users. Since users are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other baseline methods across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid in communication efforts.
Issue Date:2020-05-12
Type:Thesis
URI:http://hdl.handle.net/2142/108184
Rights Information:Copyright 2020 Isabella Lee
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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