{"title":"小型网络中协调多点传输的上界计算","authors":"W. Utschick, A. Brack","doi":"10.1109/WSA.2011.5741933","DOIUrl":null,"url":null,"abstract":"Coordinated beamforming for intercell interference management in multicell networks is considered. The base stations are equipped with multiple antennas, while the mobile terminals only have a single antenna. The base stations perform beamforming, user group selection, and scheduling operations. A mobile terminal can be served from multiple base stations, which requires advanced receiver techniques, whereas remaining intercell interference is treated as noise. The corresponding resource allocation problem is cast as a utility maximization problem. After a suitable reformulation, the problem can be solved to global optimality using a monotonic optimization method. The proposed framework allows computing benchmarks for certain scenarios, utility functions, and cooperation strategies.","PeriodicalId":307097,"journal":{"name":"2011 International ITG Workshop on Smart Antennas","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Computing upper bounds for coordinated multipoint transmission in small networks\",\"authors\":\"W. Utschick, A. Brack\",\"doi\":\"10.1109/WSA.2011.5741933\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coordinated beamforming for intercell interference management in multicell networks is considered. The base stations are equipped with multiple antennas, while the mobile terminals only have a single antenna. The base stations perform beamforming, user group selection, and scheduling operations. A mobile terminal can be served from multiple base stations, which requires advanced receiver techniques, whereas remaining intercell interference is treated as noise. The corresponding resource allocation problem is cast as a utility maximization problem. After a suitable reformulation, the problem can be solved to global optimality using a monotonic optimization method. The proposed framework allows computing benchmarks for certain scenarios, utility functions, and cooperation strategies.\",\"PeriodicalId\":307097,\"journal\":{\"name\":\"2011 International ITG Workshop on Smart Antennas\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International ITG Workshop on Smart Antennas\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WSA.2011.5741933\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International ITG Workshop on Smart Antennas","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WSA.2011.5741933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing upper bounds for coordinated multipoint transmission in small networks
Coordinated beamforming for intercell interference management in multicell networks is considered. The base stations are equipped with multiple antennas, while the mobile terminals only have a single antenna. The base stations perform beamforming, user group selection, and scheduling operations. A mobile terminal can be served from multiple base stations, which requires advanced receiver techniques, whereas remaining intercell interference is treated as noise. The corresponding resource allocation problem is cast as a utility maximization problem. After a suitable reformulation, the problem can be solved to global optimality using a monotonic optimization method. The proposed framework allows computing benchmarks for certain scenarios, utility functions, and cooperation strategies.