Title :
Semi-blind block channel estimation and signal detection using hidden Markov models
Author :
Chen, Pei ; Kobayashi, Hisashi
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
Abstract :
We propose two maximum likelihood based semi-blind block channel estimation and signal detection algorithms for multipath channels with additive Gaussian noise. The algorithms are based on the Baum-Welch (1972) algorithm and the segmental k-means algorithm for hidden Markov models (HMMs). By making use of a training signal, the algorithms are applied block-wise to sequential disjoint subintervals of the whole observation interval. We study the effects of block length in terms of the bit error rate (BER), the mean square error (MSE) of the estimated channel impulse response, and its Cramer-Rao lower bound. Our simulation results show that the BER performance does not suffer even for a short block length when a good initial estimate is available
Keywords :
Gaussian noise; error statistics; hidden Markov models; maximum likelihood estimation; mean square error methods; multipath channels; signal detection; transient response; BER performance; Baum-Welch algorithm; Cramer-Rao lower bound; HMM; MLE; MSE; additive Gaussian noise; bit error rate; estimated channel impulse response; hidden Markov models; initial estimate; maximum likelihood estimation; mean square error; multipath channels; observation interval; segmental k-means algorithm; semi-blind block channel estimation; signal detection algorithms; simulation results; time-varying radio channel; training signal; Additive noise; Bit error rate; Channel estimation; Gaussian noise; Hidden Markov models; Maximum likelihood detection; Maximum likelihood estimation; Mean square error methods; Multipath channels; Signal detection;
Conference_Titel :
Global Telecommunications Conference, 2000. GLOBECOM '00. IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-6451-1
DOI :
10.1109/GLOCOM.2000.891298