We consider the application of the sequential Monte Carlo (SMC) methodology to the problem of joint mobility tracking and hard handoff detection in cellular wireless communication networks based on the pilot signal strength measurements. The dynamics of the system under consideration are described by a nonlinear state-space model. Mobility tracking involves an on-line estimation of the location and velocity of the mobile, whereas handoff detection involves an on-line prediction of the pilot signal strength at some future time instants. The optimal solution to both problems is prohibitively complex due to the nonlinear nature of the system. The sequential Monte Carlo (SMC) methods are therefore employed to track the probabilistic dynamics of the system and to make the corresponding estimates and predictions. Hard handoff initiation is considered and two novel locally optimal handoff schemes are developed based on different criteria. It is seen that under the SMC framework, optimal mobility tracking and handoff detection can be implemented naturally in a joint fashion, and significant improvement is achieved over existing methods, in terms of both the tracking accuracy and the trade-off between service quality and resource utilization during hard handoff.

We consider the problem of demodulating and decoding multiuser information symbols in an uplink asynchronous coded code-division multiple-access (CDMA) system employing long (aperiodic) spreading sequences, in the presence of unknown multipath channels, out-cell multiple-access interference (OMAI), and narrow-band interference (NBI). A blind turbo multiuser receiver, consisting of a novel blind Bayesian multiuser detector and a bank of MAP decoders, is developed for such a system. The effect of OMAI and NBI is modeled as colored Gaussian noise with some unknown covariance matrix. The main contribution of this paper is to develop blind Bayesian multiuser detectors for long-code multipath CDMA systems under both white and colored Gaussian noise. Such detectors are based on the Bayesian inference of all unknown quantities. The Gibbs sampler, a Markov chain Monte Carlo procedure, is then used to calculate the Bayesian estimates of the unknowns. The blind Bayesian multiuser detector computes the a posteriori probabilities of the channel coded symbols, which are differentially encoded before being sent to the channel. Being soft-input soft-output in nature, the proposed blind Bayesian multiuser detectors and the MAP decoders can iteratively exchange the extrinsic information to successively refine the performance, leading to the so-called blind turbo multiuser receiver.

We consider the design of a blind optimal multiuser receiver for space-time block coded (STBC) multi-carrier code-division multiple-access (MC-CDMA) systems in unknown frequency-selective fading channels. A novel blind Bayesian multiuser detector is derived for joint estimation of unknown fading channel and symbols. Such a detector is based on the Bayesian inference of all unknown quantities. The Gibbs sampler, a Markov chain Monte Carlo (MCMC) method, is then used for Bayesian computation. Moreover, being soft-input and soft-output, the proposed Bayesian multiuser detector fits well into the turbo receiver framework and it exchanges the extrinsic information with the MAP channel decoder to successively refine its performance. Finally, its performance over random generated slow and fast frequency-selective fading channel is demonstrated through simulation examples.

A novel receiver is derived for Gaussian minimum shift keying (GMSK) signals through a multipath channel. A nonlinear signal model is derived which avoids the linear approximation in the conventional finite impulse response (FIR) system model. A Bayesian equalizer based on the Gibbs sampler, a Markov chain Monte Carlo (MCMC) procedure, is developed for joint channel estimation and symbol detection, and finally, a turbo equalizer structure is proposed for a coded GMSK system, in which the Bayesian equalizer successively refines its processing based on the information from the decoding stage, and vice versa.

We consider the problem of demodulating the multiuser symbols in an uplink long-code CDMA system in the presence of unknown out-cell multiple-access interference and narrowband interference. A novel blind Bayesian multiuser detector is derived based on the Bayesian inference of all unknown quantities. The Gibbs sampler, a Markov chain Monte Carlo (MCMC) procedure, is then used for Bayesian computation. Being soft-input and soft-output, the blind Bayesian multiuser detector is designed to be a part of a turbo multiuser receiver, which refines its processing based on the information from the decoding stage.

We consider the design of optimal multiuser receivers for space-time block coded (STBC) multicarrier code-division multiple-access (MC-CDMA) systems in unknown frequency-selective fading channels. Under a Bayesian framework, the proposed multiuser receiver is based on the Gibbs sampler, a Markov chain Monte Carlo (MCMC) method for numerically computing the marginal a posteriori probabilities of different users' data symbols. By exploiting the orthogonality property of the STBC and the multicarrier modulation, the computational complexity of the receiver is significantly reduced. Furthermore, being a soft-input soft-output algorithm, the Bayesian Monte Carlo multiuser detector is capable of exchanging the so-called extrinsic information with the maximum a posteriori (MAP) outer channel code decoders of all users, and successively improving the overall receiver performance. Several practical issues, such as testing the convergence of the Gibbs sampler in fading channel applications, resolving the phase ambiguity as well as the antenna ambiguity, and adapting the proposed receiver to multirate MC-CDMA systems, are also discussed. Finally, the performance of the Bayesian Monte Carlo multiuser receiver is demonstrated through computer simulations.

We consider the problem of Bayesian data restoration for Gaussian minimum shift keying (GMSK) signals over unknown multipath channels. As an alternative to the linear approximation method employed in the conventional finite impulse response (FIR) model, we develop a nonlinear signal model for this system. A Bayesian equalizer based on the Gibbs sampler, a Markov chain Monte Carlo (MCMC) procedure, is developed for estimating the a posteriori symbol probability in the GMSK system without explicit channel estimation. The basic idea of this technique is to generate ergodic random samples from the joint posterior distribution of all unknowns, and then to average the appropriate samples to obtain the estimates of the unknown quantities. Being soft-input soft-output in nature, the proposed Bayesian equalization technique is well suited for iterative processing in a coded system, which allows the Bayesian equalizer to successively refine its processing based on the information from the decoding stage, and vice versa.