In order to enhance communication reliability of differential frequency hopping system, a receiver implemented with the concatenation of an optimal subblock-by-subblock maximum a posteriori probability (OBB-MAP) detec...In order to enhance communication reliability of differential frequency hopping system, a receiver implemented with the concatenation of an optimal subblock-by-subblock maximum a posteriori probability (OBB-MAP) detector and a soft-decision Turbo decoder is proposed and validated in both AWGN and Rayleigh flat fading channels. It is shown that the OBB-MAP decoder can iteratively decode a cyclic trellis, and back-search the trellis for any state to obtain estimates for the prior information bits which can be employed by soft-decision Turbo decoder. The proposed receiver achieves a better bit error rate(BER) performance than maximum likelihood sequence estimation(MLSE) detector employing Viterbi algorithm. The simulation results demonstrate that the combined signal detection method improves communication quality.展开更多
Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studie...Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation ofpiecewise linear regression models. The method used to estimate the parameters ofpicewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC （Marcov Chain Monte Carlo） algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters ofpicewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.展开更多
文摘In order to enhance communication reliability of differential frequency hopping system, a receiver implemented with the concatenation of an optimal subblock-by-subblock maximum a posteriori probability (OBB-MAP) detector and a soft-decision Turbo decoder is proposed and validated in both AWGN and Rayleigh flat fading channels. It is shown that the OBB-MAP decoder can iteratively decode a cyclic trellis, and back-search the trellis for any state to obtain estimates for the prior information bits which can be employed by soft-decision Turbo decoder. The proposed receiver achieves a better bit error rate(BER) performance than maximum likelihood sequence estimation(MLSE) detector employing Viterbi algorithm. The simulation results demonstrate that the combined signal detection method improves communication quality.
文摘Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation ofpiecewise linear regression models. The method used to estimate the parameters ofpicewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC （Marcov Chain Monte Carlo） algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters ofpicewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.
