Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in graphical models
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Multiuser Detection
CDMA for Wireless Personal Communications
CDMA for Wireless Personal Communications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Sparse on-line Gaussian processes
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
Probability of error in MMSE multiuser detection
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Mutual information and minimum mean-square error in Gaussian channels
IEEE Transactions on Information Theory
Recent advances in cellular wireless communications
IEEE Communications Magazine
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
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In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-of-the-art solutions.