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Yayın A sequential Monte Carlo method for blind phase noise estimation and data detection(IEEE, 2005) Panayırcı, Erdal; Çırpan, Hakan Ali; Moeneclaey, MarcIn this paper, a computationally efficient algorithm is presented for blind phase noise estimation and data detection jointly, based on a sequential Monte Carlo method. The basic idea is to treat the transmitted symbols as " missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data are obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.Yayın Parçacık süzgeçleme ile hedef izleme uygulamasında topak çizelgeleme(IEEE, 2007) Özfidan, Özgür; Bayazıt, Uluğ; Çırpan, Hakan AliBu çalışmada, uzaklık ölçer algılayıcılarla hedef takibi uygulamasında algılayıcı çizelgeleme problemi ele alınmıştır. Çok algılayıcılı uygulamalarda algılayıcıların yönetimi ürettikleri verilerin sınıflandırılması için olduğu kadar algılayıcıların verimli kullanımı için de gereklidir. Algılayıcı yönetimindeki önemli hususlardan biri algılayıcı çizelgelemesidir. Algılayıcıları çizelgeleyerek bant genişliği, güç, ve hesaplamada ciddi ölçüde kazanımlar sağlanabilir.Yayın Automatic modulation classification for mimo systems using fourth-order cumulants(IEEE, 2012) Mühlhaus, Michael S.; Öner, Mustafa Mengüç; Dobre, Octavia Adina; Jkel, Holger U.; Jondral, Friedrich K.Automatic classification of the modulation type of an unknown communication signal is a challenging task, with applications in both commercial and military contexts, such as spectrum surveillance, cognitive radio, and electronic warfare systems. Most of the automatic modulation classification (AMC) algorithms found in the literature assume that the signal of interest has been transmitted using a single antenna. In this paper, a novel AMC algorithm for multiple input multiple output (MIMO) signals is proposed, which employs fourth-order cumulants as features for classification. First, perfect channel state information (CSI) is assumed. Subsequently, a case of more practical relevance is considered, where the channel matrix is unknown and has to be estimated blindly by employing independent component analysis (ICA). The performance of the proposed classification algorithm is investigated through simulations and compared with an average likelihood ratio test (ALRT) which can be considered as optimum in the Bayesian sense, but has a very high computational complexity.Yayın Frequency selective fading channel estimation in OFDM systems using KL expansion(IEEE, 2005) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, ErdalThis paper proposes a computationally efficient, linear minimum mean square error (MMSE) channel estimation algorithm based on KL series expansion for OFDM systems. Based on such expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, truncation in the linear expansion of channel is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We provide performance analysis results studying the influence of the effect of SNR and correlation mismatch on the estimator performance. Simulation results confirm our theoretical results and illustrate that the proposed algorithm is capable of tracking fast fading and improving performance.Yayın Pilot-aided bayesian MMSE channel estimation for OFDM systems: Algorithm and performance analysis(IEEE, 2004) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, ErdalThis paper proposes a computationally efficient, pilot-aided minimum mean square error (MMSE) channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover, optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE.
