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oai:www.ideals.illinois.edu:2142/14667 2023-07-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Singer, Andrew C. Singer, Andrew C. Riedl, Thomas J. 2010-01-06T16:20:52Z 2010-01-06T16:20:52Z 2010-01-06T16:20:52Z We consider the problem of channel modeling and channel estimation. The widely used wide sense stationary uncorrelated scattering model for the communications channel neglects correlations between different multipath arrivals, but this seems to oversimplify the real channel in many cases. One example is the underwater acoustic channel, whose impulse response is fairly continuous in delay and hence indeed exhibits a certain correlation structure in delay. To address this shortcoming we introduce a novel channel model that is based on a Gaussian Markov random field (MRF) for the complex channel gains. This graphical model is used to capture the local nature of the statistical dependencies (in time and space) of the channel taps. In order for the MRF model to fit the actual physical channel well, its parameters must be adapted appropriately. Our approach is to find the maximum likelihood (ML) estimate of theses parameters based on given observations. Once these parameters are known the MRF model can then either be used for channel estimation directly or it can be embedded into an iterative (turbo) receiver, where it is expected to improve the data estimation performance significantly as the parameterized MRF carries prior knowledge on the channel. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-12-02T14:34:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Riedl_Thomas.pdf: 425429 bytes, checksum: 13e9ab156c84b498f87dc9e50cb2e8c7 (MD5) Made available in DSpace on 2010-01-06T16:20:52Z (GMT). No. of bitstreams: 2 license.txt: 4061 bytes, checksum: a4b3b58bf82a4c741d5cb80634002040 (MD5) Riedl_Thomas.pdf: 425429 bytes, checksum: 13e9ab156c84b498f87dc9e50cb2e8c7 (MD5) http://hdl.handle.net/2142/14667 en Copyright 2009 Thomas J. Riedl communications Markov random field channel estimation channel modeling gaussian message passing A Graphical Model for the Communications Channel 2009-12 Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. PHD:Electr & Computer Eng-UIUC 10KS1200PHD