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Yayın Joint modulation classification and antenna number detection for MIMO systems(IEEE, 2016-01-07) Turan, Merve; Öner, Mustafa Mengüç; Çırpan, Hakan AliNoncooperative classification of the modulation type of communication signals finds application in both civilian and military contexts. Existing modulation classification methods for multiple-input multiple-output (MIMO) communication systems commonly require a priori information on the number of transmit antennas employed by the multiantenna transmitter, which, in most of the noncooperative scenarios involving modulation classification, is unknown and needs to be blindly extracted from the received signal. Since the problems of MIMO modulation classification and detection of the number of transmit antennas are highly coupled, we propose a decision theoretic approach for spatial multiplexing MIMO systems that considers these two tasks as a joint multiple hypothesis testing problem. The proposed method exhibits a high performance even in moderate to low SNR regimes while requiring no a priori knowledge of the channel state information and the noise variance.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 A novel algorithm for MIMO signal classification using higher-order cumulants(IEEE, 2013) Muehlhaus, Michael S.; Öner, Mustafa Mengüç; Dobre, Octavia Adina; Jaekel, Holger U.; Jondral, Friedrich K.Automatic modulation classification (AMC) of unknown communications signals is employed in both commercial and military applications, such as cognitive radio, spectrum surveillance, and electronic warfare. Most of the AMC methods proposed in the literature are developed for systems with a single transmit antenna. In this paper, an AMC algorithm for multiple-input multiple-output (MIMO) signals is proposed, which is based on higher-order cumulants. The use of cumulants with different orders, as well as their combinations as feature vectors are investigated. The ideal case of a priori knowledge of the channel state information (CSI) is considered, along with a setting of practical relevance, where the channel matrix is blindly estimated through independent component analysis. The performance of the proposed algorithm with different features is evaluated through simulations and compared with that of the average likelihood ratio test (ALRT).
