Blind digital modulation classification for STBC‐OFDM system in presence of CFO and channels estimation errors. Issue 17 (1st October 2019)

• Record Type: Journal Article
• Authors: Dehri, Brahim; Besseghier, Mokhtar; Djebbar, A.B.; Dayoub, Iyad
• Is Part Of: IET communications. Volume 13:Issue 17(2019)
• Page Start: 2827
• Page End: 2833
• Publication Date: 2019-10-01
• Subjects: 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; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0362
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : 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.