{"title":"Joint AoA and Channel Estimation for Pulse-shpaed SIMO-OFDM systems","authors":"C. Horng, Wen-Rong Wu, Din-Hwa Huang","doi":"10.1109/ICDSP.2018.8631818","DOIUrl":null,"url":null,"abstract":"Beamforming has been considered a crucial technique in 5G era. To perform received beamforming, one must know the information of angle-of-arrival (AoA). This paper considers AoA estimation in single-input-multiple-output (SIMO) pilot-assisted OFDM systems. Exploiting the sparsity of the wireless channel, we can first efficiently estimate channel impulse response with a compressive-sensing based technique. However, existing works often do not take the pulse-shaping effect into account, resulting in poor performance in real-world systems. We propose a basisadaptive block sparse Bayesian learning framework to solve the problem. Once the CIRs corresponding to received antennas are obtained, AoAs can then be estimated accordingly.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDSP.2018.8631818","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Beamforming has been considered a crucial technique in 5G era. To perform received beamforming, one must know the information of angle-of-arrival (AoA). This paper considers AoA estimation in single-input-multiple-output (SIMO) pilot-assisted OFDM systems. Exploiting the sparsity of the wireless channel, we can first efficiently estimate channel impulse response with a compressive-sensing based technique. However, existing works often do not take the pulse-shaping effect into account, resulting in poor performance in real-world systems. We propose a basisadaptive block sparse Bayesian learning framework to solve the problem. Once the CIRs corresponding to received antennas are obtained, AoAs can then be estimated accordingly.