{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80830"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80830","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Noncoherent Communication Over Fading Channels","abstract":"Finally, we consider signaling designs for a high SNR, high mobility communication scenario. For a Gauss-Markov Rayleigh fading channel, we derive upper and lower bounds for the mutual information generated by the Gaussian input distribution. The upper bound shows that the mutual information remains bounded at high SNR. We obtain necessary and sufficient conditions that characterize good input distributions, using the criterion that they generate unbounded mutual information in the high SNR limit. Examples of distributions that satisfy these conditions are given. These distributions approach a doubly logarithmic growth rate of the mutual information as a function of SNR.","abstract_html":"Finally, we consider signaling designs for a high SNR, high mobility communication scenario. For a Gauss-Markov Rayleigh fading channel, we derive upper and lower bounds for the mutual information generated by the Gaussian input distribution. The upper bound shows that the mutual information remains bounded at high SNR. We obtain necessary and sufficient conditions that characterize good input distributions, using the criterion that they generate unbounded mutual information in the high SNR limit. Examples of distributions that satisfy these conditions are given. 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For a Gauss-Markov Rayleigh fading channel, we derive upper and lower bounds for the mutual information generated by the Gaussian input distribution. The upper bound shows that the mutual information remains bounded at high SNR. We obtain necessary and sufficient conditions that characterize good input distributions, using the criterion that they generate unbounded mutual information in the high SNR limit. Examples of distributions that satisfy these conditions are given. These distributions approach a doubly logarithmic growth rate of the mutual information as a function of SNR.","Made available in DSpace on 2015-09-25T20:08:22Z (GMT). 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