A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

中文翻译：

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贝叶斯方法针对启用5G的IoT应用的FBMC / OQAM系统的信道估计性能分析

具有偏移正交幅度调制（OQAM）（FBMC / OQAM）的滤波器组多载波（FBMC）被认为是未来通信系统中的物理层技术之一，并且它也是支持Internet应用的无线传输技术。物联网（IoT）。但是，有效的信道参数估计是实现高可用性FBMC系统的困难之一。本文研究了用于FBMC / OQAM系统的贝叶斯压缩感知（BCS）信道估计方法，并分析了在多输入多输出（MIMO）情况下的性能。针对高信道估计，提出了一种迭代的快速贝叶斯匹配追踪算法。首先通过探索稀疏信道模型的先验统计信息来提出贝叶斯信道估计。结果表明，BCS信道估计方案可以有效地估计信道冲激响应。然后，通过优化迭代终止条件，提出了一种改进的FBMP算法。仿真结果表明，与传统的压缩感测方法相比，该方法具有更好的均方误差（MSE）和误码率（BER）性能。