Energy Efficient joint user association and power allocation using Parameterized Deep DQN
Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption i...
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my.iium.irep.1078202023-11-01T03:21:58Z http://irep.iium.edu.my/107820/ Energy Efficient joint user association and power allocation using Parameterized Deep DQN Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul TK5101 Telecommunication. Including telegraphy, radio, radar, television Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. IEEE 2023-08-15 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/107820/7/107820_Energy%20Efficient%20joint%20user%20association%20and%20power.pdf application/pdf en http://irep.iium.edu.my/107820/8/107820_Energy%20Efficient%20joint%20user%20association%20and%20power_Scopus.pdf Mughees, Amna and Tahir, Mohammad and Sheikh, Muhammad Aman and Amphawan, Angela and Yap, Kian Meng and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul (2023) Energy Efficient joint user association and power allocation using Parameterized Deep DQN. In: 9th International Conference on Computer and Communication Engineering (ICCCE 2023), 15-16 August 2023, Kuala Lumpur, Malaysia. https://ieeexplore.ieee.org/document/10246069 doi:10.1109/ICCCE58854.2023.10246069 |
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TK5101 Telecommunication. Including telegraphy, radio, radar, television Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
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Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. |
format |
Proceeding Paper |
author |
Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul |
author_facet |
Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul |
author_sort |
Mughees, Amna |
title |
Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
title_short |
Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
title_full |
Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
title_fullStr |
Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
title_full_unstemmed |
Energy Efficient joint user association and power allocation using Parameterized Deep DQN |
title_sort |
energy efficient joint user association and power allocation using parameterized deep dqn |
publisher |
IEEE |
publishDate |
2023 |
url |
http://irep.iium.edu.my/107820/7/107820_Energy%20Efficient%20joint%20user%20association%20and%20power.pdf http://irep.iium.edu.my/107820/8/107820_Energy%20Efficient%20joint%20user%20association%20and%20power_Scopus.pdf http://irep.iium.edu.my/107820/ https://ieeexplore.ieee.org/document/10246069 |
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1781777403056488448 |
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13.18916 |