BEM-based EKF-RTSS Channel Estimation for Non-stationary Doubly-selective Channel

Xuanfan Shen, Yong Liao, Xuewu Dai, Daotong Li, Kai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

An extended Kalman filter and Rauch-Tung-Striebel Smoother (EKF-RTSS) based on Basis Expansion Model (BEM) is proposed in this paper to cope with the challenges of doubly-selective and non-stationary channel in high-speed environments. For doubly-selective channel, the BEM is adopted to reduce the estimation complexity. For non-stationary channel, a channel estimation based on EKF which is able to jointly estimate the time-varying time correlation coefficients and channel impulse response (CIR) is proposed. For further improving the channel estimation accuracy, a 'filtering and smoothing' channel estimator structure is designed by introducing the RTSS into channel estimation and interpolation. Simulation results illustrate that the proposed BEM-based EKF-RTSS method show better estimation accuracy, robustness and bit error rate (BER) performance than the traditional methods in high-speed scenarios.

Original languageEnglish
Title of host publication2018 IEEE/CIC International Conference on Communications in China, ICCC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages536-541
Number of pages6
ISBN (Electronic)9781538670057
ISBN (Print)9781538670064
DOIs
Publication statusPublished - 14 Feb 2019
Event2018 IEEE/CIC International Conference on Communications in China, ICCC 2018 - Beijing, China
Duration: 16 Aug 201818 Aug 2018

Conference

Conference2018 IEEE/CIC International Conference on Communications in China, ICCC 2018
CountryChina
CityBeijing
Period16/08/1818/08/18

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