Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network
In next-generation centralized or cloud radio access networks (C-RANs), time and wavelength division multiplexed passive optical network (TWDM-PON) has been well recognized as a promising candidate to build the mobile fronthaul. Considering the stringent bandwidth efficiency, latency, and cost requi...
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10499798/ |
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| author | Yuansen Cheng Yingjie Shao Shifeng Ding Chun-Kit Chan |
| author_facet | Yuansen Cheng Yingjie Shao Shifeng Ding Chun-Kit Chan |
| author_sort | Yuansen Cheng |
| collection | DOAJ |
| description | In next-generation centralized or cloud radio access networks (C-RANs), time and wavelength division multiplexed passive optical network (TWDM-PON) has been well recognized as a promising candidate to build the mobile fronthaul. Considering the stringent bandwidth efficiency, latency, and cost requirements in C-RAN, an efficient bandwidth and wavelength allocation scheme is highly desirable for TWDM-PON-based fronthaul. Especially for the massive multiple input multiple outputs (mMIMO) enabled beamforming scenario, the additional radio resource is required to be jointly allocated with bandwidth and wavelength resources in TWDM-PON. In this paper, we formulate the joint allocation problem into an integer linear programming mathematical model and propose a deep reinforcement learning (RL)-based joint allocation scheme with an energy-efficient architecture for the TWDM-PON-based mMIMO fronthaul network. The proposed scheme couples the heuristic radio resource allocation algorithm with the RL-based wavelength allocation model to optimize the fronthaul bandwidth, radio resource, and wavelength utilization efficiencies jointly in the downstream direction. Simulation results show that the proposed scheme achieves a high bandwidth efficiency and high radio resource block utilization simultaneously across different traffic loads and, meanwhile, reduces the wavelength usage compared with the benchmark. |
| format | Article |
| id | doaj-art-50c2e7f008e448f9a9290c0f1109bece |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-50c2e7f008e448f9a9290c0f1109bece2025-08-20T03:33:21ZengIEEEIEEE Photonics Journal1943-06552024-01-0116311110.1109/JPHOT.2024.338857110499798Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul NetworkYuansen Cheng0https://orcid.org/0000-0001-8684-8894Yingjie Shao1https://orcid.org/0000-0003-2308-5943Shifeng Ding2https://orcid.org/0000-0003-2810-2212Chun-Kit Chan3https://orcid.org/0000-0002-7046-5335Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, ChinaCentre for Applied Photonics, Fraunhofer U.K. Research Ltd., Glasgow, U.K.Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, ChinaIn next-generation centralized or cloud radio access networks (C-RANs), time and wavelength division multiplexed passive optical network (TWDM-PON) has been well recognized as a promising candidate to build the mobile fronthaul. Considering the stringent bandwidth efficiency, latency, and cost requirements in C-RAN, an efficient bandwidth and wavelength allocation scheme is highly desirable for TWDM-PON-based fronthaul. Especially for the massive multiple input multiple outputs (mMIMO) enabled beamforming scenario, the additional radio resource is required to be jointly allocated with bandwidth and wavelength resources in TWDM-PON. In this paper, we formulate the joint allocation problem into an integer linear programming mathematical model and propose a deep reinforcement learning (RL)-based joint allocation scheme with an energy-efficient architecture for the TWDM-PON-based mMIMO fronthaul network. The proposed scheme couples the heuristic radio resource allocation algorithm with the RL-based wavelength allocation model to optimize the fronthaul bandwidth, radio resource, and wavelength utilization efficiencies jointly in the downstream direction. Simulation results show that the proposed scheme achieves a high bandwidth efficiency and high radio resource block utilization simultaneously across different traffic loads and, meanwhile, reduces the wavelength usage compared with the benchmark.https://ieeexplore.ieee.org/document/10499798/BeamformingC-RANpointer networkresource allocationreinforcement learningTWDM-PON |
| spellingShingle | Yuansen Cheng Yingjie Shao Shifeng Ding Chun-Kit Chan Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network IEEE Photonics Journal Beamforming C-RAN pointer network resource allocation reinforcement learning TWDM-PON |
| title | Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network |
| title_full | Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network |
| title_fullStr | Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network |
| title_full_unstemmed | Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network |
| title_short | Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network |
| title_sort | deep reinforcement learning based joint allocation scheme in a twdm pon based mmimo fronthaul network |
| topic | Beamforming C-RAN pointer network resource allocation reinforcement learning TWDM-PON |
| url | https://ieeexplore.ieee.org/document/10499798/ |
| work_keys_str_mv | AT yuansencheng deepreinforcementlearningbasedjointallocationschemeinatwdmponbasedmmimofronthaulnetwork AT yingjieshao deepreinforcementlearningbasedjointallocationschemeinatwdmponbasedmmimofronthaulnetwork AT shifengding deepreinforcementlearningbasedjointallocationschemeinatwdmponbasedmmimofronthaulnetwork AT chunkitchan deepreinforcementlearningbasedjointallocationschemeinatwdmponbasedmmimofronthaulnetwork |