Browsing by Subject "Kalman smoothing"

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    Distributed downlink beamforming with cooperative base stations
    (Institute of Electrical and Electronics Engineers, 2008) Ng, B. L.; Evans, J. S.; Hanly, S. V.; Aktas, D.
    In this paper, we consider multicell processing on the downlink of a cellular network to accomplish "macrodiversity" transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beamforming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-backward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs.
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    Model based computerized ionospheric tomography in space and time
    (Elsevier, 2018) Tuna, H.; Arıkan, Orhan; Arikan, F.
    Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.