Blind digital modulation classification for STBC-OFDM system in presence of CFO and channels estimation errors

作     者：Dehri, Brahim Besseghier, Mokhtar Djebbar, A. B. Dayoub, Iyad 

作者机构：Univ Djillalil Liabes Sidi Bel Abbes Lab TTNS Sidi Bel Abbes Algeria Univ Mustapha Stambouli Mascara Mascara Algeria Univ Lille Univ Polytech Hautsde De France CNRS ISENCent LilleUMR 8520IEMNDOAE Valenciennes France 

出 版 物：《IET COMMUNICATIONS》 (IET通信)

年 卷 期：2019年第13卷第17期

页      面：2827-2833页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：higher order statistics channel estimation feature extraction OFDM modulation signal classification MIMO communication space-time block codes impulse noise frequency selective channel modulation identification problem STBC-OFDM system channel estimation errors single-carrier systems frequency-flat channels carrier frequency offset robust blind digital modulation classification algorithm space time block coding MIMO-OFDM system semiblind CFO estimation BDMC algorithm higher order statistics HOS feature extraction impulsive noise pattern recognition methods 

摘      要：Here, the authors propose a robust blind digital modulation classification (BDMC) algorithm for space time block coding (STBC)-based MIMO-OFDM system in the presence of carrier frequency offset (CFO) and channel estimation errors. Previous papers published on the topic of modulation identification were limited to single-carrier systems operating over frequency-flat channels. The problem of joint channel and CFO estimation in conjunction with blind digital modulation classification for STBC-OFDM in frequency selective channel and in the presence of the impulsive noise has not been addressed before to the best of their knowledge. To cope with performance degradation of BDMC due to CFO and channels errors, they propose joint semi-blind CFO and channels estimation methods. Higher order statistics (HOS), used for feature extraction, are combined with pattern recognition methods to solve the modulation identification problem. The main contribution of their work is the development of estimators, the study of their impacts on the blind classification capability, and the use of simulations to demonstrate the superior performance of the proposed algorithms.