{"title":"基于自适应滤波框架的毫米波混合MIMO系统低信噪比空间稀疏信道估计","authors":"B. K. Das","doi":"10.1109/ASPCON49795.2020.9276659","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an adaptive filtering framework for spatially sparse channel estimation technique applicable to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless communication with hybrid precoding. The proposed algorithm is claimed to be a viable alternative to the recently proposed compressed sensing based framework, especially the celebrated orthogonal matching pursuit (OMP) algorithm. The proposed algorithm helps to attain better performance than the existing algorithms in low SNR as we demonstrate by numerical simulations. The paper is an attempt to justify the use of sparse adaptive filtering framework for these particular application, and we choose a simple narrow-band, single user scenario to do that. In future, it can be extended to include frequency selective and multi user cases.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low SNR Spatially Sparse Channel Estimation in Millimeterwave Hybrid MIMO Systems based on Adaptive Filtering Framework\",\"authors\":\"B. K. Das\",\"doi\":\"10.1109/ASPCON49795.2020.9276659\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an adaptive filtering framework for spatially sparse channel estimation technique applicable to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless communication with hybrid precoding. The proposed algorithm is claimed to be a viable alternative to the recently proposed compressed sensing based framework, especially the celebrated orthogonal matching pursuit (OMP) algorithm. The proposed algorithm helps to attain better performance than the existing algorithms in low SNR as we demonstrate by numerical simulations. The paper is an attempt to justify the use of sparse adaptive filtering framework for these particular application, and we choose a simple narrow-band, single user scenario to do that. In future, it can be extended to include frequency selective and multi user cases.\",\"PeriodicalId\":193814,\"journal\":{\"name\":\"2020 IEEE Applied Signal Processing Conference (ASPCON)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Applied Signal Processing Conference (ASPCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASPCON49795.2020.9276659\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Applied Signal Processing Conference (ASPCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASPCON49795.2020.9276659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low SNR Spatially Sparse Channel Estimation in Millimeterwave Hybrid MIMO Systems based on Adaptive Filtering Framework
In this paper, we propose an adaptive filtering framework for spatially sparse channel estimation technique applicable to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) wireless communication with hybrid precoding. The proposed algorithm is claimed to be a viable alternative to the recently proposed compressed sensing based framework, especially the celebrated orthogonal matching pursuit (OMP) algorithm. The proposed algorithm helps to attain better performance than the existing algorithms in low SNR as we demonstrate by numerical simulations. The paper is an attempt to justify the use of sparse adaptive filtering framework for these particular application, and we choose a simple narrow-band, single user scenario to do that. In future, it can be extended to include frequency selective and multi user cases.