{"title":"利用单元开/关的分布式资源分配实现超密集网络的最佳能源效率","authors":"Paul James Wambi, O. Falowo","doi":"10.1109/ICSPCS.2018.8631735","DOIUrl":null,"url":null,"abstract":"This paper addresses the energy usage concerns in Ultra-Dense Networks (UDNs) with focus on the energy consumption of the base stations (BSs), given that base stations consume 80% of the total network energy. It models the Network Energy Efficiency (NEE) optimization problem using stochastic geometry. The NEE optimization problem takes into consideration the user's Quality-of-service (QoS), inter-cell interference as well as each user's spectral efficiency. The formulated NEE optimization problem is of type NP-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This combined with Lagrangian dual decomposition is used to create a distributed solution. After cell-association and resource allocation phases, the proposed solution performs a Cell On/Off to further reduce power consumption. Then finally, the performance of the proposed scheme is examined in comparison to four other UDN NEE management schemes under different network scenarios. The results obtained show that the proposed scheme's performance is the closest to the Exhaustive Search algorithm, which demonstrates the superiority of the proposed scheme to the other sub-optimal schemes.","PeriodicalId":179948,"journal":{"name":"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards Optimum Energy Efficiency in Ultra-Dense Networks Using Distributed Resource Allocation with Cell On & Off\",\"authors\":\"Paul James Wambi, O. Falowo\",\"doi\":\"10.1109/ICSPCS.2018.8631735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses the energy usage concerns in Ultra-Dense Networks (UDNs) with focus on the energy consumption of the base stations (BSs), given that base stations consume 80% of the total network energy. It models the Network Energy Efficiency (NEE) optimization problem using stochastic geometry. The NEE optimization problem takes into consideration the user's Quality-of-service (QoS), inter-cell interference as well as each user's spectral efficiency. The formulated NEE optimization problem is of type NP-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This combined with Lagrangian dual decomposition is used to create a distributed solution. After cell-association and resource allocation phases, the proposed solution performs a Cell On/Off to further reduce power consumption. Then finally, the performance of the proposed scheme is examined in comparison to four other UDN NEE management schemes under different network scenarios. The results obtained show that the proposed scheme's performance is the closest to the Exhaustive Search algorithm, which demonstrates the superiority of the proposed scheme to the other sub-optimal schemes.\",\"PeriodicalId\":179948,\"journal\":{\"name\":\"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSPCS.2018.8631735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPCS.2018.8631735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards Optimum Energy Efficiency in Ultra-Dense Networks Using Distributed Resource Allocation with Cell On & Off
This paper addresses the energy usage concerns in Ultra-Dense Networks (UDNs) with focus on the energy consumption of the base stations (BSs), given that base stations consume 80% of the total network energy. It models the Network Energy Efficiency (NEE) optimization problem using stochastic geometry. The NEE optimization problem takes into consideration the user's Quality-of-service (QoS), inter-cell interference as well as each user's spectral efficiency. The formulated NEE optimization problem is of type NP-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This combined with Lagrangian dual decomposition is used to create a distributed solution. After cell-association and resource allocation phases, the proposed solution performs a Cell On/Off to further reduce power consumption. Then finally, the performance of the proposed scheme is examined in comparison to four other UDN NEE management schemes under different network scenarios. The results obtained show that the proposed scheme's performance is the closest to the Exhaustive Search algorithm, which demonstrates the superiority of the proposed scheme to the other sub-optimal schemes.