Fast Gaussian Process Regression for Multiuser Detection in DS-CDMA

Recently, Gaussian process has been excellent for proving the effectiveness in solving the multiuser detection issue in code division multiple access system. Even with limited training sequences, Gaussian process-based solutions still surpass other approaches. However, due to the high complexity in terms of computation, the performance of this approach might be degraded. In this letter, we would like to propose an efficient method to reduce the complexity while still maintaining the desired accuracy. Finally, the proposed method is validated via the experiment.

[1]  Hester Bijl,et al.  Speeding up Kriging through fast estimation of the hyperparameters in the frequency-domain , 2013, Comput. Geosci..

[2]  Joachim Denzler,et al.  Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels , 2012, ECCV.

[3]  Larry S. Davis,et al.  Efficient Kernel Machines Using the Improved Fast Gauss Transform , 2004, NIPS.

[4]  Christopher K. I. Williams,et al.  Understanding Gaussian Process Regression Using the Equivalent Kernel , 2004, Deterministic and Statistical Methods in Machine Learning.

[5]  Tengyuan Liang,et al.  Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions , 2013, Journal of Multivariate Analysis.

[6]  Zoran Zvonar,et al.  Multiuser detection for CDMA systems , 1995, IEEE Wirel. Commun..

[7]  Juan José Murillo-Fuentes,et al.  Gaussian process regressors for multiuser detection in DS-CDMA systems , 2009, IEEE Transactions on Communications.

[8]  R. Gallager.  Stochastic Processes , 2014 .

[9]  André Bourdoux,et al.  Gaussian Process Regression for CSI and feedback estimation in LTE , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[10]  Simona Halunga,et al.  Performance evaluation for conventional and MMSE multiuser detection algorithms in imperfect reception conditions , 2010, Digit. Signal Process..

[11]  Iain Murray,et al.  A framework for evaluating approximation methods for Gaussian process regression , 2012, J. Mach. Learn. Res..

[12]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .