{"id":{"repo_id":"qu-belfast","oai_identifier":"oai:pure.qub.ac.uk/portal:studenttheses/d4dc3bc9-321a-43e3-9849-bc87f4c9b1ce"},"canonical_url":"https://search.dev.ndltd.org/etd/qu-belfast/oai:pure.qub.ac.uk/portal:studenttheses/d4dc3bc9-321a-43e3-9849-bc87f4c9b1ce","repository":{"repo_id":"qu-belfast","name":"Queen's University Belfast","base_url":"https://pureadmin.qub.ac.uk/ws/oai"},"display":{"title":"Reconfigurable intelligent surface and UAV-assisted communications: a deep reinforcement learning approach","abstract":"This thesis proposes novel methods based on the deep reinforcement learning algorithms (DRL) for maximising the energy efficiency (EE), sum-rate in reconfigurable intelligent surface (RIS) and unmanned aerieal vehicles (UAV)-aided wireless communications. 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The thesis carries out comprehensive optimization and evaluation of various DRL algorithms for several real-life applications including UAV&#x27;s trajectory design, power allocation, data collection, wireless power transfer and RIS&#x27;s phase shift matrix adjustment.&lt;br/&gt;&lt;br/&gt;The thesis presents three major contributions. 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