{"title":"分数阶多普勒HMM先验下基于uamp的OTFS信道估计","authors":"Zhongjie Li, W. Yuan, Lin Zhou","doi":"10.1109/ICCCWorkshops55477.2022.9896709","DOIUrl":null,"url":null,"abstract":"Orthogonal time frequency space (OTFS) modulation is a promising candidate to support reliable information transmission in high-mobility wireless communications. In this paper, we consider the channel estimation problem for OTFS in the presence of fractional Doppler. We first propose a statistical channel model based on the hidden Markov model (HMM) to characterize the structured sparsity of the effective delay-Doppler (DD) domain channel. The HMM prior is then incorporated with the unitary approximate message passing (UAMP) algorithm to solve the structured sparse channel estimation problem. Finally, simulation results verify that the proposed algorithm can achieve a significant gain over various state-of-art algorithms.","PeriodicalId":148869,"journal":{"name":"2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"UAMP-Based Channel Estimation for OTFS in the Presence of the Fractional Doppler with HMM Prior\",\"authors\":\"Zhongjie Li, W. Yuan, Lin Zhou\",\"doi\":\"10.1109/ICCCWorkshops55477.2022.9896709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Orthogonal time frequency space (OTFS) modulation is a promising candidate to support reliable information transmission in high-mobility wireless communications. In this paper, we consider the channel estimation problem for OTFS in the presence of fractional Doppler. We first propose a statistical channel model based on the hidden Markov model (HMM) to characterize the structured sparsity of the effective delay-Doppler (DD) domain channel. The HMM prior is then incorporated with the unitary approximate message passing (UAMP) algorithm to solve the structured sparse channel estimation problem. Finally, simulation results verify that the proposed algorithm can achieve a significant gain over various state-of-art algorithms.\",\"PeriodicalId\":148869,\"journal\":{\"name\":\"2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCWorkshops55477.2022.9896709\",\"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/CIC International Conference on Communications in China (ICCC Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCWorkshops55477.2022.9896709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
UAMP-Based Channel Estimation for OTFS in the Presence of the Fractional Doppler with HMM Prior
Orthogonal time frequency space (OTFS) modulation is a promising candidate to support reliable information transmission in high-mobility wireless communications. In this paper, we consider the channel estimation problem for OTFS in the presence of fractional Doppler. We first propose a statistical channel model based on the hidden Markov model (HMM) to characterize the structured sparsity of the effective delay-Doppler (DD) domain channel. The HMM prior is then incorporated with the unitary approximate message passing (UAMP) algorithm to solve the structured sparse channel estimation problem. Finally, simulation results verify that the proposed algorithm can achieve a significant gain over various state-of-art algorithms.