Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms
Spurred by both economic and environmental concerns, energy efficiency (EE) has now become one of the key pillars for the fifth generation (5G) mobile communication networks. To maximize the downlink EE of the 5G ultra dense network (UDN), we formulate a constrained EE maximization problem and trans...
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| Veröffentlicht in: | Computer communications Jg. 130; S. 10 - 19 |
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01.10.2018
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| ISSN: | 0140-3664, 1873-703X |
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| Abstract | Spurred by both economic and environmental concerns, energy efficiency (EE) has now become one of the key pillars for the fifth generation (5G) mobile communication networks. To maximize the downlink EE of the 5G ultra dense network (UDN), we formulate a constrained EE maximization problem and translate it into a convex representation based on the fractional programming theory. To solve this problem, we first adopt a centralized algorithm to reach the optimum based on Dinkelbach’s procedure. To improve the efficiency and reduce the computational complexity, we further propose a distributed iteration resource allocation algorithm based on alternating direction method of multipliers (ADMM). For the proposed distributed algorithm, the local and dual variables are updated by each base station (BS) in parallel and independently, and the global variables are updated through the coordination and information exchange among BSs. Moreover, as the noise may lead to imperfect information exchange among BSs, the global variables update may be subject to failure. To cope with this problem, we propose a robust distributed algorithm, for which the global variable only updates as the information exchange is successful. We prove that this modified robust distributed algorithm converges to the optimal solution of the primal problem almost surely. Simulation results validate our proposed centralized and distributed algorithms. Especially, the proposed robust distributed algorithm can effectively eliminate the impact of noise and converge to the optimal value at the cost of a little increase of computational complexity. |
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| AbstractList | Spurred by both economic and environmental concerns, energy efficiency (EE) has now become one of the key pillars for the fifth generation (5G) mobile communication networks. To maximize the downlink EE of the 5G ultra dense network (UDN), we formulate a constrained EE maximization problem and translate it into a convex representation based on the fractional programming theory. To solve this problem, we first adopt a centralized algorithm to reach the optimum based on Dinkelbach’s procedure. To improve the efficiency and reduce the computational complexity, we further propose a distributed iteration resource allocation algorithm based on alternating direction method of multipliers (ADMM). For the proposed distributed algorithm, the local and dual variables are updated by each base station (BS) in parallel and independently, and the global variables are updated through the coordination and information exchange among BSs. Moreover, as the noise may lead to imperfect information exchange among BSs, the global variables update may be subject to failure. To cope with this problem, we propose a robust distributed algorithm, for which the global variable only updates as the information exchange is successful. We prove that this modified robust distributed algorithm converges to the optimal solution of the primal problem almost surely. Simulation results validate our proposed centralized and distributed algorithms. Especially, the proposed robust distributed algorithm can effectively eliminate the impact of noise and converge to the optimal value at the cost of a little increase of computational complexity. |
| Author | Li, Wei Li, Shaoqian Zuo, Yue Shao, Qijia Wang, Jun Yang, Guosheng |
| Author_xml | – sequence: 1 givenname: Wei surname: Li fullname: Li, Wei email: WeiLee@std.uestc.edu.cn – sequence: 2 givenname: Jun surname: Wang fullname: Wang, Jun – sequence: 3 givenname: Guosheng surname: Yang fullname: Yang, Guosheng – sequence: 4 givenname: Yue surname: Zuo fullname: Zuo, Yue – sequence: 5 givenname: Qijia surname: Shao fullname: Shao, Qijia – sequence: 6 givenname: Shaoqian surname: Li fullname: Li, Shaoqian |
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| Cites_doi | 10.1016/B978-0-12-408080-5.00005-5 10.1186/1687-1499-2014-1 10.1109/TSP.2014.2331611 10.1016/j.comcom.2015.05.012 10.1109/TMC.2016.2622263 10.1287/opre.26.4.681 10.1109/TSP.2016.2544743 10.1109/JSAC.2016.2550338 10.1137/07070111X 10.1137/S1052623496306620 10.1109/ICC.2006.255311 10.1006/jmaa.2001.7650 10.1109/ICTEL.2013.6632074 10.1109/JSAC.2016.2544498 10.1109/TWC.2014.2323971 10.1109/COMST.2016.2571730 10.1109/26.983324 10.1109/JSAC.2014.2328098 10.1109/TSP.2014.2304432 10.1109/TSP.2017.2673813 10.1109/CAMAD.2013.6708098 10.1007/s10957-007-9199-8 10.1109/JSAC.2015.2435374 |
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| Keywords | 5G Energy efficiency Robust distributed algorithm Ultra-dense network Resource allocation |
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| SubjectTerms | Energy efficiency Resource allocation Robust distributed algorithm Ultra-dense network |
| Title | Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms |
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