We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramér-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

中文翻译：

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用于毫米波混合 MIMO-FBMC-OQAM 系统的基于贝叶斯学习的双选择性稀疏信道估计

我们设计并分析了基于滤波器组多载波 (FBMC) 偏移正交幅度调制 (OQAM) 的毫米波 (mmWave) 混合多输入多输出 (MIMO) 系统。此外，为准静态毫米波混合 MIMO-FBMC-OQAM (mmH-MFO) 系统构想了一种新颖的信道估计模型，该系统在零符号传输期间重新配置射频 (RF) 电路。随后，提出了一种用于稀疏信道估计的贝叶斯学习 (BL) 技术，该技术依赖于多个测量向量结合选择性子载波分组进行增强估计。此外，基于在线 BL 的卡尔曼滤波器 (OBL-KF) 设计用于双选择性 mmH-MFO 系统中的稀疏信道跟踪。然后导出贝叶斯克拉默-拉奥下限 (BCRLB) 以表征所提出的频率选择性和双选择性信道估计技术的性能。最后，针对混合预编码器/组合器设计，提出了一种基于波束空间信道估计的基于有限反馈的算法。我们的模拟结果证实了我们分析结果的准确性。