{"title":"3.52 GHz室内办公场景下基于测量的大规模MIMO天线选择","authors":"Xiaonan Wang, Limin Xiao, Yan Zhang, Zunwen He","doi":"10.1109/ICCT.2018.8600120","DOIUrl":null,"url":null,"abstract":"Massive multiple-input-multiple-output (MIMO) offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability and has been considered as a leading 5G technology candidate. However, high cost and energy consumption are major challenges in massive MIMO systems. Antenna selection at the base station side by reducing the number of radio-frequency (RF) chains is a practical and effective technology to solve high cost and energy consumption problems. In this paper, we investigate indoor massive MIMO antenna selection based on the measured data in a indoor office scenario at 3.52 GHz by using a uniform linear array (ULA) with 128 transmit (Tx) antennas and 7 receiving (Rx) users. A convex optimization algorithm is applied to select optimal Tx antenna subset by maximizing dirty-paper coding (DPC) capacity. Then, the antenna selection system performance is researched. The investigation shows that indoor antenna selection based on measured data can both improve the system performance and reduce the cost.","PeriodicalId":244952,"journal":{"name":"2018 IEEE 18th International Conference on Communication Technology (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Measurement-Based Massive MIMO Antenna Selection in Indoor Office Scenario at 3.52 GHz\",\"authors\":\"Xiaonan Wang, Limin Xiao, Yan Zhang, Zunwen He\",\"doi\":\"10.1109/ICCT.2018.8600120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Massive multiple-input-multiple-output (MIMO) offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability and has been considered as a leading 5G technology candidate. However, high cost and energy consumption are major challenges in massive MIMO systems. Antenna selection at the base station side by reducing the number of radio-frequency (RF) chains is a practical and effective technology to solve high cost and energy consumption problems. In this paper, we investigate indoor massive MIMO antenna selection based on the measured data in a indoor office scenario at 3.52 GHz by using a uniform linear array (ULA) with 128 transmit (Tx) antennas and 7 receiving (Rx) users. A convex optimization algorithm is applied to select optimal Tx antenna subset by maximizing dirty-paper coding (DPC) capacity. Then, the antenna selection system performance is researched. The investigation shows that indoor antenna selection based on measured data can both improve the system performance and reduce the cost.\",\"PeriodicalId\":244952,\"journal\":{\"name\":\"2018 IEEE 18th International Conference on Communication Technology (ICCT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 18th International Conference on Communication Technology (ICCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCT.2018.8600120\",\"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 IEEE 18th International Conference on Communication Technology (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT.2018.8600120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Measurement-Based Massive MIMO Antenna Selection in Indoor Office Scenario at 3.52 GHz
Massive multiple-input-multiple-output (MIMO) offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability and has been considered as a leading 5G technology candidate. However, high cost and energy consumption are major challenges in massive MIMO systems. Antenna selection at the base station side by reducing the number of radio-frequency (RF) chains is a practical and effective technology to solve high cost and energy consumption problems. In this paper, we investigate indoor massive MIMO antenna selection based on the measured data in a indoor office scenario at 3.52 GHz by using a uniform linear array (ULA) with 128 transmit (Tx) antennas and 7 receiving (Rx) users. A convex optimization algorithm is applied to select optimal Tx antenna subset by maximizing dirty-paper coding (DPC) capacity. Then, the antenna selection system performance is researched. The investigation shows that indoor antenna selection based on measured data can both improve the system performance and reduce the cost.
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