Filtreler

2015 - 20162
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Yazar: Turan, Merve × Dil: en ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    Joint modulation classification and antenna number detection for MIMO systems
    (IEEE, 2016-01-07) Turan, Merve; Öner, Mustafa Mengüç; Çırpan, Hakan Ali
    Noncooperative 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.
  • Küçük Resim
    Yayın
    Space time block code classification for MIMO signals exploiting cyclostationarity
    (Institute of Electrical and Electronics Engineers Inc, 2015) Turan, Merve; Öner, Mustafa Mengüç; Çırpan, Hakan Ali
    Blind and noncooperative identification of the transmission parameters of unknown communication signals has been employed both in military and civilian applications. Multiple-Input-Multiple-Output (MIMO) transmission systems emerging in the last decade pose new challenges to the signal identification systems, one of which is the identification of the Space-Time Block Code (STBC) used in the transmission. In this work, we present a novel STBC classification algorithm that exploits the joint wide sense cyclostationary characteristics of the coded transmit signals as discriminating features. Compared to existing algorithms, the proposed method can discriminate between a large number of different STBCs.