Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

中文翻译：

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滤波器组多载波系统双选 MIMO 信道的稀疏贝叶斯学习辅助估计


基于稀疏贝叶斯学习 (SBL) 的信道状态信息 (CSI) 估计方案是为使用偏移正交幅度调制 (OQAM) 的滤波器组多载波 (FBMC) 系统开发的。最初，基于干扰近似法（IAM）的基于SBL的信道估计方案被设计用于频率选择性准静态单输入单输出（SISO）-FBMC系统。 IAM技术虽然复杂度较低，但仅适用于表现出温和频率选择性的信道。因此，针对高频率选择性信道开发了一种基于稀疏信道估计框架的替代时域（TD）模型。随后，基于卡尔曼滤波（KF）的 IAM 及其 TD 对应物被开发用于 SISO-FBMC 系统中的稀疏双选择 CSI 估计。在分别演示了 IAM 和基于 TD 模型的特殊块和组稀疏结构后，这些方案还扩展到基于 FBMC 的多输入多输出 (MIMO) 系统，适用于准静态和双选信道，这是此类渠道的特征。分别针对所提出的准静态和双选择性稀疏 CSI 估计模型推导了贝叶斯 Cramér-Rao 下界 (BCRLB) 和时间递归 BCRLB。我们的数值结果与分析结果非常吻合，证明了所提出的方案相对于现有技术的性能增强。