{"title":"Joint Resource Slicing for Uplink in OFDMA-Based Cloud RAN","authors":"Zexu Li, L. Liu, Yong Li, M. Peng","doi":"10.1109/ICCCHINA.2018.8641160","DOIUrl":null,"url":null,"abstract":"A joint resource slicing (JRS) method for the uplink of an OFDMA-based cloud RAN is proposed in this paper. We try to maximize the system utilization by joint subcarrier, power and fronthual rate allocation, while considering different requirements for multiple slices. We define weighted utilization functions which consider the combination of data rate and power consumption. Therefore, the customization can be achieved by adjusting the value of weights for each slice. This method is then formulated as an optimization problem which is solved by using dual decomposition method. Besides, a suboptimal solution which reduces computational complexity is also provided in this paper. Simulation results indicate that our JRS method can improve resource utilization while achieving isolation and customization between slices compared with static reservation method.","PeriodicalId":170216,"journal":{"name":"2018 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/CIC International Conference on Communications in China (ICCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCHINA.2018.8641160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A joint resource slicing (JRS) method for the uplink of an OFDMA-based cloud RAN is proposed in this paper. We try to maximize the system utilization by joint subcarrier, power and fronthual rate allocation, while considering different requirements for multiple slices. We define weighted utilization functions which consider the combination of data rate and power consumption. Therefore, the customization can be achieved by adjusting the value of weights for each slice. This method is then formulated as an optimization problem which is solved by using dual decomposition method. Besides, a suboptimal solution which reduces computational complexity is also provided in this paper. Simulation results indicate that our JRS method can improve resource utilization while achieving isolation and customization between slices compared with static reservation method.