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Yayın Signal identification for multiple-antenna wireless systems: achievements and challenges(IEEE, 2016-01-18) Eldemerdash, Yahia A.; Dobre, Octavia Adina; Öner, Mustafa MengüçSignal identification is an umbrella term for signal processing techniques designed for the identification of the transmission parameters of unknown or partially known communication signals. Initially a key technology for military applications such as signal interception, radio surveillance and electronic warfare, signal identification techniques recently found applications in commercial wireless communications as an enabling technology for cognitive receivers. With the advance and rapid adoption of multiple-input multiple-output (MIMO) communication systems in the last decade, extension of signal identification methods to include this transmission paradigm has become a priority and focus of intensive research efforts. The aim of this work is to provide a comprehensive state-of-the-art survey on algorithms proposed for the new and challenging signal identification problems specific to MIMO systems, including space-time block code (STBC) identification, MIMO modulation identification, and detection of the number of transmit antennas. Finally, concluding remarks on MIMO signal identification are provided along with an outline of the open problems and future research directions.Yayın A low complexity modulation classification algorithm for MIMO systems(IEEE-INST Electrical Electronics Engineers Inc, 2013-10) Mühlhaus, Michael S.; Öner, Mustafa Mengüç; Dobre, Octavia Adina; Jondral, Friedrich K.A novel algorithm is proposed for automatic modulation classification in multiple-input multiple-output spatial multiplexing systems, which employs fourth-order cumulants of the estimated transmit signal streams as discriminating features and a likelihood ratio test (LRT) for decision making. The asymptotic likelihood function of the estimated feature vector is analytically derived and used with the LRT. Hence, the algorithm can be considered as asymptotically optimal for the employed feature vector when the channel matrix and noise variance are known. Both the case with perfect channel knowledge and the practically more relevant case with blind channel estimation are considered. The results show that the proposed algorithm provides a good classification performance while exhibiting a significantly lower computational complexity when compared with conventional algorithms.Yayın Cyclostationarity-based robust algorithms for QAM signal identification(IEEE-INST Electrical Electronics Engineers Inc, 2012-01) Dobre, Octavia Adina; Öner, Mustafa Mengüç; Rajan, Sreeraman; Inkol, RobertThis letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the identification performance of the proposed algorithms compares favorably with that of alternative approaches.Yayın On the second-order cyclic statistics of signals in the presence of receiver impairments(IEEE-INST Electrical Electronics Engineers Inc, 2011-12) Öner, Mustafa Mengüç; Dobre, Octavia AdinaCyclostationary characteristics of communication signals can be exploited for performing various signal processing tasks. Receiver impairments that affect the cyclic statistics of signals may lead to a degradation in the performance of cyclostationarity-based signal processing algorithms. Inphase/quadrature (I/Q) imbalance, oscillator phase noise, and random sampling jitter can be counted amongst the typical receiver impairments encountered in wireless communication systems. In this work, we investigate the effects of these nonidealities on the second-order cyclic statistics of baseband communication signals. General results are derived for arbitrary complex-valued (and possibly improper) signals, and examples are provided for cases of practical interest.
