{"title":"基于二值多步合成的分散海量MIMO上行信号估计","authors":"Pascal Seidel, S. Paul, Jochen Rust","doi":"10.1109/IEEECONF44664.2019.9048772","DOIUrl":null,"url":null,"abstract":"While linear equalization schemes like zero forcing or minimum mean-square error achieve a near optimal uplink signal estimation performance in large-scale multi-user multiple-input multiple-output systems, the corresponding algorithms lean on centralized processing. To avoid disproportionate interconnect data rates due to the centralized signal estimation, performing a decentralized equalization can mitigate these effects. In this paper, we present a decentralized signal estimation architecture, which combines the ideas of existing decentralized architectures to (i) reduce the overall latency of the signal estimation and (ii) maintain a high data detection performance.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"10 1","pages":"1967-1971"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Decentralized Massive MIMO Uplink Signal Estimation by Binary Multistep Synthesis\",\"authors\":\"Pascal Seidel, S. Paul, Jochen Rust\",\"doi\":\"10.1109/IEEECONF44664.2019.9048772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While linear equalization schemes like zero forcing or minimum mean-square error achieve a near optimal uplink signal estimation performance in large-scale multi-user multiple-input multiple-output systems, the corresponding algorithms lean on centralized processing. To avoid disproportionate interconnect data rates due to the centralized signal estimation, performing a decentralized equalization can mitigate these effects. In this paper, we present a decentralized signal estimation architecture, which combines the ideas of existing decentralized architectures to (i) reduce the overall latency of the signal estimation and (ii) maintain a high data detection performance.\",\"PeriodicalId\":6684,\"journal\":{\"name\":\"2019 53rd Asilomar Conference on Signals, Systems, and Computers\",\"volume\":\"10 1\",\"pages\":\"1967-1971\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 53rd Asilomar Conference on Signals, Systems, and Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEECONF44664.2019.9048772\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEECONF44664.2019.9048772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Decentralized Massive MIMO Uplink Signal Estimation by Binary Multistep Synthesis
While linear equalization schemes like zero forcing or minimum mean-square error achieve a near optimal uplink signal estimation performance in large-scale multi-user multiple-input multiple-output systems, the corresponding algorithms lean on centralized processing. To avoid disproportionate interconnect data rates due to the centralized signal estimation, performing a decentralized equalization can mitigate these effects. In this paper, we present a decentralized signal estimation architecture, which combines the ideas of existing decentralized architectures to (i) reduce the overall latency of the signal estimation and (ii) maintain a high data detection performance.
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