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1	Reduced Complexity Viterbi Decoders for SOQPSK Signals over Multipath Channels Kannappa, Sandeep Mavuduru 10 1900 (has links) ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / High data rate communication between airborne vehicles and ground stations over the bandwidth constrained Aeronautical Telemetry channel is attributed to the development of bandwidth efficient Advanced Range Telemetry (ARTM) waveforms. This communication takes place over a multipath channel consisting of two components - a line of sight and one or more ground reflected paths which result in frequency selective fading. We concentrate on the ARTM SOQPSKTG transmit waveform suite and decode information bits using the reduced complexity Viterbi algorithm. Two different methodologies are proposed to implement reduced complexity Viterbi decoders in multipath channels. The first method jointly equalizes the channel and decodes the information bits using the reduced complexity Viterbi algorithm while the second method utilizes the minimum mean square error equalizer prior to applying the Viterbi decoder. An extensive numerical study is performed in comparing the performance of the above methodologies. We also demonstrate the performance gain offered by our reduced complexity Viterbi decoders over the existing linear receiver. In the numerical study, both perfect and estimated channel state information are considered. Aeronautical Telemetry Channel Equalization Reduced complexity Viterbi decodes Minimum Variance Unbiased Estimator
2	Statistical Inference Chou, Pei-Hsin 26 June 2008 (has links) In this paper, we will investigate the important properties of three major parts of statistical inference: point estimation, interval estimation and hypothesis testing. For point estimation, we consider the two methods of finding estimators: moment estimators and maximum likelihood estimators, and three methods of evaluating estimators: mean squared error, best unbiased estimators and sufficiency and unbiasedness. For interval estimation, we consider the the general confidence interval, confidence interval in one sample, confidence interval in two samples, sample sizes and finite population correction factors. In hypothesis testing, we consider the theory of testing of hypotheses, testing in one sample, testing in two samples, and the three methods of finding tests: uniformly most powerful test, likelihood ratio test and goodness of fit test. Many examples are used to illustrate their applications. finite population correction factor statistical hypothesis type I error composite hypothesis power Central limit theorem Basu theorem critical value goodness of fit test likelihood ratio test Lehmann-Scheffe theorem complete statistic ancillary statistic P-value type II error Rao-Blackwell theorem simple hypothesis one-sided test two-sided test significance level sufficient statistic alternative hypothesis testing hypothesis null hypothesis rejection region confidence interval confidence level minimum variance unbiased estimator interval estimation uniformly most powerful test Cramer-Rao inequality likelihood function mean square error moment estimator error of estimation point estimation maximum likelihood estimator bias consistent estimator minimal sufficient statistic factorization theorem unbiased estimator statistic most powerful test test statistics Neyman-Pearson lemma decision rule

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Reduced Complexity Viterbi Decoders for SOQPSK Signals over Multipath Channels

Statistical Inference