Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning

被引：0
|
作者：
Wang Y. [1 ]
Yu Y.-H. [1 ]
Zheng Y. [1 ]
Lin B. [1 ]
机构：
[1] College of Information Science and Technology, Dalian Maritime University, Liaoning, Dalian
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 05期
关键词：
expectation-maximization (EM); generalized frequency division multiplexing (GFDM); Kalman filtering; smoothing; sparse Bayesian learning; time-varying channel;
D O I：
10.12263/DZXB.20220480
中图分类号：
学科分类号：
摘要：
In order to improve the accuracy of time-varying channel estimation in generalized frequency division multiplexing (GFDM) systems, a joint iterative channel estimation and symbol detection algorithm for GFDM systems using sparse Bayesian learning is proposed. Specifically, we use a GFDM multi-response signal model with non-interfering pilot insertion. Under the sparse Bayesian learning framework, we combine the expectation-maximization (EM) algorithm and the Kalman filter and smoothing algorithm to realize the maximum likelihood estimation of the block time-varying channel. Consequently, GFDM symbols are detected based on the estimated channel state information (CSI), and the accuracy of the channel estimation and symbol detection is progressively improved through the iterative processing of the channel estimation and symbol detection. Simulation results demonstrate that the proposed algorithm can achieve better bit error rate (BER) performance close to that under perfect CSI conditions, and it has the advantages of fast convergence speed and high robustness to Doppler frequency shift. © 2024 Chinese Institute of Electronics. All rights reserved.
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页码：1496 / 1505
页数：9
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