Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement

To solve the problem that some imbalanced small sample datasets only contain a few labeled samples, a semi-supervised gaussian processes active learning model based on improved tri-training with enhanced data is proposed. Firstly, the label samples are balanced and enhanced, and we present a quantitative enhanced data evaluation criteria based on the JS distance and the similarity of information entropy between enhanced data and original data to select the best enhanced data. Secondly, an improved semi-supervised learning method based on tri-training is proposed to find the unlabeled samples which have high confidence, so the certainty of the labeled samples group can be increased, in order to ensure that the three classifiers of tri-training have both difference and robustness, random forest is introduced to divide the features of the dataset into three groups with equal contribution, and each classifier trains different combinations of two feature groups. Thirdly, in order to query and classify the most informative unlabeled samples more precisely, active learning based on the Gaussian process and JS distribution range is structured, because of the high uncertainty of the unlabeled samples predicted by active learning, the similarity distribution range of JS distance is introduced to compare the similarity of unlabeled samples and labeled samples in active learning‘s classifier, so the model can classify more diverse samples. The final experimental results show that compared with several traditional models, the proposed model performs better on artificial datasets and imbalanced small-size UCI datasets

An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion

Recently, a large-scale fading precoding (LSFP) for the wireless massive multiple-input, multiple-output (MIMO) systems has been proposed. In this precoding, the channel information of all the cells using re-use pilot sequences is processed jointly, and pilot contamination and interference due to a certain number of antennas are effectively eliminated. Additionally, recent studies have found that research in the asymptotic field can be applied to the wireless large-scale MIMO systems. In the LSFP, pilot contamination and signal interference will be completely eliminated when a number of antennas at a base station tend to be unlimited. In this research found that the LSFP method can eliminate most pilot contamination and interference in practical applications only when the number of antennas of the base station reaches hundreds of orders, which greatly increases the equipment construction cost. On the other hand, channel inversion denotes a multi-user channel modulation technology, where a vector signal generated between a user and a base station is used to form an inverse channel matrix so that the channels of each user are balanced during the transmission. In this paper, the channel inversion technology is used in the LSFP. The improved LSFP can effectively reduce the number of antennas required by the base station without affecting the performance of eliminating the pilot contamination and interference. It is shown that when the number of antennas of a base station tends to be unlimited, the improved LSFP can eliminate pilot contamination and signal interference. The simulation results show that in the same practical scenario, when the base station is equipped with the same number of antennas, the improved method can more effectively improve the anti-contamination and anti-interference performance over conventional LSFP