Inter-Cell Interference Suppression Effect Using a Chip Correlation MMSE Receiver with Multipath Interference Correlative Timing

2007 ◽  
Author(s):  
Tsuyoshi Hasegawa ◽  
Masahiko Shimizu
Keyword(s):  
Interference Suppression ◽  
Multipath Interference ◽  
Suppression Effect ◽  
Mmse Receiver ◽  
Inter Cell Interference

scholarly journals Interclass Interference Suppression in Multi-Class Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 450
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Na Jiang ◽  
Ran Cheng ◽  
Xianling Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Validation ◽  
Selection Process ◽  
Interference Suppression ◽  
Generalization Ability ◽  
Suppression Effect ◽  
Binary Classifiers ◽  
The One ◽  
Fold Cross Validation ◽  
Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

Multipath Interference Suppression

2017 ◽  
pp. 201-233 ◽  
Author(s):  
Renbiao Wu ◽  
Wenyi Wang ◽  
Dan Lu ◽  
Lu Wang ◽  
Qiongqiong Jia
Keyword(s):  
Interference Suppression ◽  
Multipath Interference
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