Multiuser Detection
Bayesian turbo multiuser detection for nonlinearly modulated CDMA
Signal Processing
Adaptive Bayesian multiuser detection for synchronous CDMA withGaussian and impulsive noise
IEEE Transactions on Signal Processing
Guest editorial special issue on monte carlo methods for statistical signal processing
IEEE Transactions on Signal Processing
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
Bayesian Monte Carlo multiuser receiver for space-time coded multicarrier CDMA systems
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
IEEE Journal on Selected Areas in Communications
Blind decoding of multiple description codes over OFDM systems via sequential Monte Carlo
EURASIP Journal on Wireless Communications and Networking - Special issue on advanced signal processing algorithms for wireless communications
Adaptive mobile positioning in WCDMA networks
EURASIP Journal on Wireless Communications and Networking
Towards applications of particle filters in wildfire spread simulation
Proceedings of the 40th Conference on Winter Simulation
An introduction to Bayesian techniques for sensor networks
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
OFDM channel equalization based on radial basis function networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Many statistical signal processing problems found in wireless communications involves making inference about the transmitted information data based on the received signals in the presence of various unknown channel distortions. The optimal solutions to these problems are often too computationally complex to implement by conventional signal processing methods. The recently emerged Bayesian Monte Carlo signal processing methods, the relatively simple yet extremely powerful numerical techniques for Bayesian computation, offer a novel paradigm for tackling wireless signal processing problems. These methods fall into two categories, namely, Markov chain Monte Carlo (MCMC) methods for batch signal processing and sequential Monte Carlo (SMC) methods for adaptive signal processing. We provide an overview of the theories underlying both the MCMC and the SMC. Two signal processing examples in wireless communications, the blind turbo multiuser detection in CDMA systems and the adaptive detection in fading channels, are provided to illustrate the applications of MCMC and SMC respectively.