GSM System Engineering
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Turbo Coding
Rayleigh fading multi-antenna channels
EURASIP Journal on Applied Signal Processing - Space-time coding and its applications - part I
IEEE Transactions on Wireless Communications
Iterative decoding of binary block and convolutional codes
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
On the theory of space-time codes for PSK modulation
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Full rate space-time turbo codes
IEEE Journal on Selected Areas in Communications
On the iterative approximation of optimal joint source-channel decoding
IEEE Journal on Selected Areas in Communications
Joint turbo decoding and estimation of hidden Markov sources
IEEE Journal on Selected Areas in Communications
Joint source-channel turbo decoding of entropy-coded sources
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Joint iterative channel estimation and decoding in flat correlated Rayleigh fading
IEEE Journal on Selected Areas in Communications
Blind and nonblind turbo estimation for fast fading GSM channels
IEEE Journal on Selected Areas in Communications
Iterative decoding of convolutionally encoded signals over multipath Rayleigh fading channels
IEEE Journal on Selected Areas in Communications
Turbo equalization: adaptive equalization and channel decoding jointly optimized
IEEE Journal on Selected Areas in Communications
Turbo equalization for GMSK signaling over multipath channels based on the Gibbs sampler
IEEE Journal on Selected Areas in Communications
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This paper presents a novel channel estimation technique for space-time coded (STC) systems. It is based on applying the maximum likelihood (ML) principle not only over a known pilot sequence but also over the unknown symbols in a data frame. The resulting channel estimator gathers both the deterministic information corresponding to the pilot sequence and the statistical information, in terms of a posteriori probabilities, about the unknown symbols. The method is suitable for Turbo equalization schemes where those probabilities are computed with more and more precision at each iteration. Since the ML channel estimation problem does not have a closed-form solution, we employ the expectation-maximization (EM) algorithm in order to iteratively compute the ML estimate. The proposed channel estimator is first derived for a general time-dispersive MIMO channel and then is particularized to a realistic scenario consisting of a transmission system based on the global system mobile (GSM) standard performing in a subway tunnel. In this latter case, the channel is nondispersive but there exists controlled ISI introduced by the Gaussian minimum shift keying (GMSK) modulation format used in GSM. We demonstrate, using experimentally measured channels, that the training sequence length can be reduced from 26 bits as in the GSM standard to only 5 bits, thus achieving a 14% improvement in system throughput.