Optimal Wireless Rate and Power Control Using Reinforcement Learning

dc.contributor.advisor Miao, Lei
dc.contributor.author Raji, Fadlullah Adeola
dc.contributor.committeemember Sbenaty, Saleh
dc.contributor.committeemember Faezipour, Misagh
dc.date.accessioned 2021-12-09T17:05:21Z
dc.date.available 2021-12-09T17:05:21Z
dc.date.issued 2021
dc.date.updated 2021-12-09T17:05:27Z
dc.description.abstract Increased throughput and energy efficiency of wireless devices are achievable when factors such as interference reduction, power management, and data transmission rate selection are considered. This thesis proposes an algorithm for optimizing the performance of wireless networks using reinforcement learning. The algorithm, a.k.a. the agent, observes the state of a wireless device’s battery, packet queue, transmission medium, etc. and establishes the optimal policy for joint control of transmission power and speed. Using the NS3 network simulation software, we implement agents focusing on three different reward functions: throughput-critical, energy-critical, and throughput and energy balanced. We compare their performance to the conventional Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and roughly the same energy when using the throughput and energy balanced reward function.
dc.description.degree M.S.
dc.identifier.uri https://jewlscholar.mtsu.edu/handle/mtsu/6586
dc.language.rfc3066 en
dc.publisher Middle Tennessee State University
dc.source.uri http://dissertations.umi.com/mtsu:11529
dc.subject Deep Learning
dc.subject Machine Learning
dc.subject Rate Control
dc.subject Reinforcement Learning
dc.subject Wireless Communication
dc.subject Wireless Network
dc.subject Computer science
dc.subject Electrical engineering
dc.subject Computer engineering
dc.thesis.degreelevel masters
dc.title Optimal Wireless Rate and Power Control Using Reinforcement Learning
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