{"title":"不完美CSIT下的多小区MIMO用户速率平衡:SESIP与RESIP","authors":"Imène Ghamnia, D. Slock, Y. Yuan-Wu","doi":"10.1109/spawc51304.2022.9833920","DOIUrl":null,"url":null,"abstract":"In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Cell MIMO User Rate Balancing with Imperfect CSIT: SESIP vs. RESIP\",\"authors\":\"Imène Ghamnia, D. Slock, Y. Yuan-Wu\",\"doi\":\"10.1109/spawc51304.2022.9833920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.\",\"PeriodicalId\":423807,\"journal\":{\"name\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/spawc51304.2022.9833920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/spawc51304.2022.9833920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Cell MIMO User Rate Balancing with Imperfect CSIT: SESIP vs. RESIP
In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.