Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Soft-decision equalizers for in-service error rate monitoring
Signal Processing
Multiuser Detection
CDMA Systems Engineering Handbook
CDMA Systems Engineering Handbook
Bayesian turbo multiuser detection for nonlinearly modulated CDMA
Signal Processing
Robust joint channel and noise estimation in Bayesian blind equalizers
Signal Processing
Adaptive Bayesian multiuser detection for synchronous CDMA withGaussian and impulsive noise
IEEE Transactions on Signal Processing
Multiuser detection of synchronous code-division multiple-accesssignals by perfect sampling
IEEE Transactions on Signal Processing
Convergence analyses and comparisons of Markov chain Monte Carloalgorithms in digital communications
IEEE Transactions on Signal Processing
Blind restoration of linearly degraded discrete signals by Gibbssampling
IEEE Transactions on Signal Processing
Linear multiuser detectors for synchronous code-division multiple-access channels
IEEE Transactions on Information Theory
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An asynchronous multiuser CDMA detector based on the Kalman filter
IEEE Journal on Selected Areas in Communications
Iterative multiuser detection for coded CDMA signals in AWGN and fading channels
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Analysis of a simple successive interference cancellation scheme in a DS/CDMA system
IEEE Journal on Selected Areas in Communications
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In this paper, a new family of near-optimum blind Bayesian multiuser detectors (MUDs) for digital mobile communications is developed. First, a general framework for multiuser Bayesian detection is briefly presented. The extension to the blind case as well as both the study of the capability of blind bit error rate (BER) estimation and some implementation aspects are examined with further detail. Next, the core of the paper focuses on the development of several techniques that are applied to limit the number of hypotheses (possible sets of symbols transmitted by the users of the system) which are considered in the Bayesian approach. Special care must be taken so as to maintain a high diversity within the symbol hypotheses' set related to the user of interest. Finally, the performance is evaluated and compared to that of some traditional and recent MUDs. Simulations show how the different proposed algorithms offer, each of them, a different balance between computational complexity and performance. The proposed methods exhibit good near-far resistance and estimation accuracy, constituting an attractive and feasible alternative to previous algorithms.