Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-10-09 DOI:10.1109/LSP.2024.3477254
Zhuo Chen;Xiaoming Niu;Jian Ding;Hong Wu;Zhiyang Liu
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Abstract

The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.
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自适应模式耦合稀疏贝叶斯学习用于 OTFS 系统中的信道估计
正交时频空间(OTFS)因其抗多普勒效应的强大优势,已成为高移动性无线通信领域一种前景广阔的调制波形。然而,由于帧持续时间的限制,会出现小数多普勒频移,这对 OTFS 系统的信道估计是一个挑战。在这封信中,我们将信道估计问题表述为块稀疏信号恢复问题,并提出了一种自适应模式耦合稀疏贝叶斯学习(APCSBL)方法。具体来说,我们引入了一个模式耦合分层高斯先验模型来描述相邻信道系数之间的依赖关系。在此基础上,我们提出了一种自适应超参数策略,即进一步适当利用各种耦合参数来描述相邻元素之间的相关性强度。然后,我们利用期望最大化(EM)算法来更新隐藏变量和信道向量。仿真结果表明,所提出的算法优于现有方法,并适用于各种环境。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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