{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80001"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80001","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Delay-Optimal MAC Level LTE Scheduling Using Deep Reinforcement Learning for Downlink","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Somayajula Venkata, Someshwar Rao"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Mastronarde, Nicholas","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:11:46Z","date_published":"2019-07-30T15:11:46Z","updated_at":"2026-07-27T19:05:21Z","subjects":["engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. 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University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/80001"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","The topic of this thesis is to introduce a novel method of LTE scheduling and comparing it to previous research on the same topic. The motivation is to introduce deep reinforcement learning techniques to downlink LTE scheduling in order to find a delay optimal method of scheduling users in an LTE network. First, the scope of the problem needs to be defined in order to present a solution for the problem at hand. A single network cell with a limited number of users and Physical Resource Blocks is considered, in order to constrain the complexity of the DeepRL algorithm. From thereon, three popular scheduling algorithms are compared to the the new solution, namely, MaxCI scheduling, Proportional Fair Scheduling and Max Weight Scheduling. It is shown that the Deep RL Scheduler priorities delay minimization by compromising throughput in order to be delay optimal."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Delay-Optimal MAC Level LTE Scheduling Using Deep Reinforcement Learning for Downlink"]}]}],"canonical_facts":{"dc:contributor":["Mastronarde, Nicholas","Electrical Engineering"],"dc:creator":["Somayajula Venkata, Someshwar Rao"],"dc:date":["2019-07-30T15:11:46Z","2019","2019-05-16 23:25:24"],"dc:description":["M.S.","The topic of this thesis is to introduce a novel method of LTE scheduling and comparing it to previous research on the same topic. The motivation is to introduce deep reinforcement learning techniques to downlink LTE scheduling in order to find a delay optimal method of scheduling users in an LTE network. First, the scope of the problem needs to be defined in order to present a solution for the problem at hand. A single network cell with a limited number of users and Physical Resource Blocks is considered, in order to constrain the complexity of the DeepRL algorithm. From thereon, three popular scheduling algorithms are compared to the the new solution, namely, MaxCI scheduling, Proportional Fair Scheduling and Max Weight Scheduling. It is shown that the Deep RL Scheduler priorities delay minimization by compromising throughput in order to be delay optimal."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80001"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. 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