Publication:
Reinforcement Learning Model Selection for Resource Allocation and Subcarrier Spacing Optimization in 5G Sliced Spectrum Networks

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dc.citedby0
dc.contributor.authorSamidi F.S.en_US
dc.contributor.authorRadzi N.A.M.en_US
dc.contributor.authorAripin N.M.en_US
dc.contributor.authorid57215054855en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid35092180800en_US
dc.date.accessioned2025-03-03T07:45:36Z
dc.date.available2025-03-03T07:45:36Z
dc.date.issued2024
dc.description.abstractThe evolution of 5G technology presents unique challenges and opportunities for optimizing network performance through advanced techniques such as Reinforcement Learning (RL). Effective 5G network management requires precise optimization of subcarrier spacing and uplink-downlink (UL-DL) allocation, yet selecting the optimal RL model for this task remains a significant challenge. This paper evaluates the performance of RL models within the RaSSo framework for optimizing 5G networks, aiming to identify the most suitable RL approach. The primary challenge addressed is determining which RL model, in our case, Proximal Policy Optimization (PPO) or Advantage Actor-Critic (A2C), performs better in managing the dynamic and complex 5G environment. The study compares these models in terms of episode reward, entropy loss, value loss, and frames per second (FPS). The results indicate that PPO's stable performance, efficient learning process, and robust adaptation to dynamic conditions make it a more suitable choice for real-time 5G network management. The results indicate that PPO's stable performance, efficient learning process, and robust adaptation to dynamic conditions make it a more suitable choice for real-time 5G network management. Overall, this paper contributes to the field by offering a thorough comparison of RL models tailored for the nuanced demands of 5G network optimization, providing valuable insights for future advancements in this area. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICAEE62924.2024.10667637
dc.identifier.scopus2-s2.0-85204779257
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204779257&doi=10.1109%2fICAEE62924.2024.10667637&partnerID=40&md5=609628a0e22f0de29dfebf4662237cff
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36900
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2024 IEEE International Conference on Applied Electronics and Engineering, ICAEE 2024
dc.subjectNetwork performance
dc.subjectQueueing networks
dc.subjectReinforcement learning
dc.subjectResource allocation
dc.subjectDynamic condition
dc.subjectEfficient learning
dc.subjectLearning process
dc.subjectNetworks management
dc.subjectOptimisations
dc.subjectPerformance
dc.subjectReal- time
dc.subjectReinforcement learning models
dc.subjectStable performance
dc.subjectSubcarrier spacing
dc.subject5G mobile communication systems
dc.titleReinforcement Learning Model Selection for Resource Allocation and Subcarrier Spacing Optimization in 5G Sliced Spectrum Networksen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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