Machine-Learning-Based Joint Resource Scheduling and Link Adaptation for Massive MIMO Networks in 5G.

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dc.contributor.advisorDoost-Mohammady, Rahmanen_US
dc.creatorAn, Qingen_US
dc.date.accessioned2024-05-20T19:32:30Zen_US
dc.date.available2024-05-20T19:32:30Zen_US
dc.date.created2024-05en_US
dc.date.issued2024-04-15en_US
dc.date.submittedMay 2024en_US
dc.date.updated2024-05-20T19:32:30Zen_US
dc.description.abstractThe large number of antennas in 5G massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. Additionally, in dynamic wireless environments characterized by rapidly changing channel conditions, selecting the appropriate transmission link parameters, such as modulation and coding scheme (MCS), presents another formidable task for maximizing spectral efficiency. Inspired by recent achievements in machine learning to solve problems in wireless communication, we propose SMART, a dynamic scheduler for massive MIMO combining the state-of-the-art Soft Actor-Critic (SAC) DRL model with the K-Nearest Neighbors (KNN) algorithm, and a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) based adaptive MCS technique. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments on the RENEW platform, we demonstrate the effectiveness of our proposed models in various channel conditions.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAn, Qing. Machine-Learning-Based Joint Resource Scheduling and Link Adaptation for Massive MIMO Networks in 5G. (2024). Masters thesis, Rice University. https://hdl.handle.net/1911/115908en_US
dc.identifier.urihttps://hdl.handle.net/1911/115908en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectMassive MIMOen_US
dc.subjectMachine Learningen_US
dc.subject5G Wireless Communicationen_US
dc.titleMachine-Learning-Based Joint Resource Scheduling and Link Adaptation for Massive MIMO Networks in 5G.en_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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