Communication systems engineering
Communication systems engineering
Recurrent radial basis function networks for optimal symbol-by-symbol equalization
Proceedings of the COST #229 international workshop on Adaptive methods and emergent techniques for signal processing and communications
Practical loss-resilient codes
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Sparse on-line Gaussian processes
Neural Computation
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
The evidence framework applied to classification networks
Neural Computation
Digital communication receivers using gaussian processes for machine learning
EURASIP Journal on Advances in Signal Processing
Modern Coding Theory
Nonlinear Channel Equalization With Gaussian Processes for Regression
IEEE Transactions on Signal Processing - Part II
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Efficient encoding of low-density parity-check codes
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
| Hi-index | 35.68 |
In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.