Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks
Enhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7914634/ |
| _version_ | 1818329649580605440 |
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| author | Cheng Qin Wei Ni Hui Tian Ren Ping Liu |
| author_facet | Cheng Qin Wei Ni Hui Tian Ren Ping Liu |
| author_sort | Cheng Qin |
| collection | DOAJ |
| description | Enhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, we design the beamforming jointly for data transmission and energy transfer, under finite fronthaul capacity of C-RAN. A non-convex problem is formulated to balance the fronthaul requirements of different remote radio heads (RRHs). Norm approximations and relaxations are carried out to convexify the problem to second-order cone programming (SOCP). To improve the scalability of the design to large networks, we further decentralize the SOCP problem using the alternating direction multiplier method (ADMM). A series of reformulations and transformations are conducted, such that the resultant problem conforms to the state-of-the-art ADMM solver and can be efficiently solved in real time. Simulation results show that the distributed algorithm can remarkably reduce the time complexity without compromising the fronthaul load balancing of its centralized counterpart. The proposed algorithms can also reduce the fronthaul bandwidth requirements by 25% to 50%, compared with the prior art. |
| first_indexed | 2024-12-13T12:51:25Z |
| format | Article |
| id | doaj.art-672d6697e4534709a3e622527d3495f0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:51:25Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-672d6697e4534709a3e622527d3495f02022-12-21T23:45:19ZengIEEEIEEE Access2169-35362017-01-0157762777510.1109/ACCESS.2017.26991987914634Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access NetworksCheng Qin0https://orcid.org/0000-0002-2710-4884Wei Ni1Hui Tian2Ren Ping Liu3State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaData61, Commonwealth Scientific and Industrial Research Organization, Sydney, NSW, AustraliaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaGlobal Big Data Technologies Centre, University of Technology Sydney, Sydney, NSW, AustraliaEnhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, we design the beamforming jointly for data transmission and energy transfer, under finite fronthaul capacity of C-RAN. A non-convex problem is formulated to balance the fronthaul requirements of different remote radio heads (RRHs). Norm approximations and relaxations are carried out to convexify the problem to second-order cone programming (SOCP). To improve the scalability of the design to large networks, we further decentralize the SOCP problem using the alternating direction multiplier method (ADMM). A series of reformulations and transformations are conducted, such that the resultant problem conforms to the state-of-the-art ADMM solver and can be efficiently solved in real time. Simulation results show that the distributed algorithm can remarkably reduce the time complexity without compromising the fronthaul load balancing of its centralized counterpart. The proposed algorithms can also reduce the fronthaul bandwidth requirements by 25% to 50%, compared with the prior art.https://ieeexplore.ieee.org/document/7914634/Cloud radio access network (C-RAN)energy harvestingfronthauldecentralization |
| spellingShingle | Cheng Qin Wei Ni Hui Tian Ren Ping Liu Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks IEEE Access Cloud radio access network (C-RAN) energy harvesting fronthaul decentralization |
| title | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks |
| title_full | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks |
| title_fullStr | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks |
| title_full_unstemmed | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks |
| title_short | Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks |
| title_sort | fronthaul load balancing in energy harvesting powered cloud radio access networks |
| topic | Cloud radio access network (C-RAN) energy harvesting fronthaul decentralization |
| url | https://ieeexplore.ieee.org/document/7914634/ |
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