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17-06-2024 | Research

Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks

Authors: Aruna Valasa, Anjaneyulu Lokam, Chayan Bhar

Published in: Wireless Personal Communications | Issue 1/2024

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Abstract

Heterogeneous networks (HetNets) are essential in contemporary wireless communication networks as they help operators address challenges related to coverage, capacity, and quality. HetNets also enable the efficient use of resources and preparedness for future wireless technologies. The efficient allocation of limited resources to user equipments (UEs) is a critical problem that can be tackled using joint resource allocation and user association (JRAUA) techniques. However, this problem is challenging due to its non-convex and combinatorial nature, which is further complicated by the hybrid action space that involves both continuous actions and discrete actions. Consequently, determining the most efficient approach for JRAUA is a challenging endeavor. The objective of this study is to address the above challenge and improve the energy efficiency of networks by examining the JRAUA problem in downlink HetNets using orthogonal frequency division multiple access (OFDMA). This work presents a new strategy, called the Variable Hybrid Action Space—Deep Q-Network (VHAS-DQN), to optimize the learning policy and improve the average cumulative reward by incorporating constraints like wireless backhaul capacity of each UE and quality-of-service (QoS) into the learning process. To demonstrate the advantages of the JRAUA model in terms of energy efficiency while guaranteeing that the backhaul capacity constraints and quality of service (QoS) criteria are met, the simulation results from the VHAS-DQN were compared with other experimental case studies.

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Literature
14.
go back to reference Hasan, M. K., Shahjalal, M., Islam, M. M., Alam, M. M., Ahmed, M. F., & Jang, Y. M. (2020). The role of deep learning in NOMA for 5G and beyond communications. 2020 international conference on artificial intelligence in information and communication, ICAIIC 2020 (pp. 303–307). https://doi.org/10.1109/ICAIIC48513.2020.9065219 Hasan, M. K., Shahjalal, M., Islam, M. M., Alam, M. M., Ahmed, M. F., & Jang, Y. M. (2020). The role of deep learning in NOMA for 5G and beyond communications. 2020 international conference on artificial intelligence in information and communication, ICAIIC 2020 (pp. 303–307). https://​doi.​org/​10.​1109/​ICAIIC48513.​2020.​9065219
19.
go back to reference Amiri, R., Mehrpouyan, H., Fridman, L., Mallik, R. K., Nallanathan, A., & Matolak, D. (2018). A machine learning approach for power allocation in HetNets considering QoS. In IEEE international conference on communications (Vol. 2018-May, pp. 1–7). IEEE. https://doi.org/10.1109/ICC.2018.8422864 Amiri, R., Mehrpouyan, H., Fridman, L., Mallik, R. K., Nallanathan, A., & Matolak, D. (2018). A machine learning approach for power allocation in HetNets considering QoS. In IEEE international conference on communications (Vol. 2018-May, pp. 1–7). IEEE. https://​doi.​org/​10.​1109/​ICC.​2018.​8422864
21.
37.
go back to reference Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947. https://doi.org/10.1109/JSAC.2017.2720898CrossRef Zhang, H., Huang, S., Jiang, C., Long, K., Leung, V. C. M., & Poor, H. V. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947. https://​doi.​org/​10.​1109/​JSAC.​2017.​2720898CrossRef
47.
go back to reference Mohajer, A., Daliri, M. S., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2022). Heterogeneous computational resource allocation for NOMA: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing, 16(2), 1225–1238.CrossRef Mohajer, A., Daliri, M. S., Mirzaei, A., Ziaeddini, A., Nabipour, M., & Bavaghar, M. (2022). Heterogeneous computational resource allocation for NOMA: Toward green mobile edge-computing systems. IEEE Transactions on Services Computing, 16(2), 1225–1238.CrossRef
48.
go back to reference Dong, S., Zhan, J., Hu, W., Mohajer, A., Bavaghar, M., & Mirzaei, A. (2023). Energy-efficient hierarchical resource allocation in uplink-downlink decoupled NOMA HetNets. IEEE Transactions on Network and Service Management, 20(3), 3380–3395. Dong, S., Zhan, J., Hu, W., Mohajer, A., Bavaghar, M., & Mirzaei, A. (2023). Energy-efficient hierarchical resource allocation in uplink-downlink decoupled NOMA HetNets. IEEE Transactions on Network and Service Management, 20(3), 3380–3395.
49.
go back to reference Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16(4), 5188–5199.CrossRef Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16(4), 5188–5199.CrossRef
50.
go back to reference Hausknecht, M., Stone, P., & Mc, O. P. (2016, July). On-policy vs. off-policy updates for deep reinforcement learning. In Deep reinforcement learning: Frontiers and challenges, IJCAI 2016 Workshop. New York, NY, USA: AAAI Press. Hausknecht, M., Stone, P., & Mc, O. P. (2016, July). On-policy vs. off-policy updates for deep reinforcement learning. In Deep reinforcement learning: Frontiers and challenges, IJCAI 2016 Workshop. New York, NY, USA: AAAI Press.
53.
go back to reference Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2016). Prioritized experience replay. In 4th International conference on learning representations, ICLR 2016—conference track proceedings (pp. 1–21). Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2016). Prioritized experience replay. In 4th International conference on learning representations, ICLR 2016—conference track proceedings (pp. 1–21).
Metadata
Title
Variable Hybrid Action Space Deep Q-Networks for Optimal Power Allocation and User Association in Heterogeneous Networks
Authors
Aruna Valasa
Anjaneyulu Lokam
Chayan Bhar
Publication date
17-06-2024
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2024
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-024-11255-4