{"title":"多频段小区扩展中基于神经网络的偏移优化性能评价","authors":"Ryuya Sembo, N. Miki","doi":"10.1109/APWCS50173.2021.9548718","DOIUrl":null,"url":null,"abstract":"The amount of data traffic over the mobile network is still increasing. To support such large amount of traffic, additional higher frequency usage is one of the promising techniques. The network needs to carefully associate the users to the base stations and frequency bands, since users can connect multiple BSs and multiple frequency bands in such deployments. In order to offload the users effectively, the cell range expansion (CRE) is effective, and the offset values can be optimized by the neural network (NN). In the paper, we evaluate the performance of the NN-based offset optimization in CRE for multiple frequency bands. Simulation results show the good trade-off between the performance and the amount of feedback from the users.","PeriodicalId":164737,"journal":{"name":"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Evaluation of Neural Network-based Offset Optimization in Cell Range Expansion for Multiple Frequency Bands\",\"authors\":\"Ryuya Sembo, N. Miki\",\"doi\":\"10.1109/APWCS50173.2021.9548718\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The amount of data traffic over the mobile network is still increasing. To support such large amount of traffic, additional higher frequency usage is one of the promising techniques. The network needs to carefully associate the users to the base stations and frequency bands, since users can connect multiple BSs and multiple frequency bands in such deployments. In order to offload the users effectively, the cell range expansion (CRE) is effective, and the offset values can be optimized by the neural network (NN). In the paper, we evaluate the performance of the NN-based offset optimization in CRE for multiple frequency bands. Simulation results show the good trade-off between the performance and the amount of feedback from the users.\",\"PeriodicalId\":164737,\"journal\":{\"name\":\"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APWCS50173.2021.9548718\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCS50173.2021.9548718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance Evaluation of Neural Network-based Offset Optimization in Cell Range Expansion for Multiple Frequency Bands
The amount of data traffic over the mobile network is still increasing. To support such large amount of traffic, additional higher frequency usage is one of the promising techniques. The network needs to carefully associate the users to the base stations and frequency bands, since users can connect multiple BSs and multiple frequency bands in such deployments. In order to offload the users effectively, the cell range expansion (CRE) is effective, and the offset values can be optimized by the neural network (NN). In the paper, we evaluate the performance of the NN-based offset optimization in CRE for multiple frequency bands. Simulation results show the good trade-off between the performance and the amount of feedback from the users.