Abstract
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.
Highlights
To date, the application of mobile communication systems in the field of Internet of Things (IoT) is not in-depth [1, 2]
Conventional least square (LS), orthogonal matching pursuit (OMP), and regularized OMP (ROMP) methods are used to compare with the proposed method, where the OMP and ROMP are the two wellknown CS recovery algorithms
A modified fast Bayesian matching pursuit (FBMP) algorithm was proposed by optimizing the iterative termination conditions for filter bank multicarrier (FBMC) sparse channel estimation
Summary
The application of mobile communication systems in the field of Internet of Things (IoT) is not in-depth [1, 2]. They reduce the imaginary interference or exploit this interference to improve CE performance Combined with these methods, a novel preamble structure for FBMC systems was proposed in [22]. A scattered pilot CE method based on CS for FBMC was proposed in [30] by utilizing the wireless channel sparsity. (1) Based on Bayesian CS approach, we propose an iterative fast Bayesian matching pursuit approach for high channel estimation in FBMC and its MIMO scenario. As far as we know, the Bayesian approach for high CE in FBMC systems has not yet been investigated (2) To evaluate the performance of the proposed Bayesian approach, well-known CS methods and the least square (LS) method are utilized for comparison.
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