Browsing by Subject "Channel identification"

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    Detection of sparse targets with structurally perturbed echo dictionaries
    (Elsevier, 2013) Guldogan, M. B.; Arıkan, Orhan
    In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels.
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    Multipath channel identification by using global optimization in ambiguity function domain
    (ELSEVIER, 2011-06-15) Guldogan, M. B.; Arıkan, Orhan
    A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delayDoppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delayDoppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delayDoppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.
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    Particle swarm optimization based channel identification in cross-ambiguity domain
    (IEEE, 2010) Güldoğan, Mehmet Burak; Arıkan, Orhan
    In this paper, a new array signal processing technique by using particle swarm optimization (PSO) is proposed to identify multipath channel parameters. The proposed technique provides estimates to the channel parameters by finding a global minimum of an optimization problem. Since the optimization problem is formulated in the cross-ambiguity function (CAF) domain of the transmitted signal and the received array outputs, the proposed technique is called as PSO-CAF. The performance of the PSO-CAF is compared with the space alternating generalized expectation maximization (SAGE) technique and with another recently proposed PSO based technique for various SNR values. Simulation results indicate the superior performance of the PSO-CAF technique over mentioned techniques for all SNR values. ©2010 IEEE.