Signal Pattern Recognition Based on Fractal Features and Machine Learning
Source abstract
Abstract
As a typical pattern recognition method, communication signal modulation involves many complicated factors. Fractal theory can be used for signal modulation feature extraction and recognition because of its good ability to express complex information. In this paper, we conduct a systematic research study by using the fractal dimension as the feature of modulation signals. Box fractal dimension, Katz fractal dimension, Higuchi fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension are extracted from eight different modulation signals for signal pattern recognition. Meanwhile, the anti-noise function, box-diagram, and running time are used to evaluate the noise robustness, separability, and computational complexity of five different fractal features. Finally, Bback-Propagation (BP) neural network, grey relation analysis, random forest, and K-nearest neighbor are proposed to classify the different modulation signals based on these fractal features. The confusion matrices and recognition results are provided in the experimental section. They indicate that random forest had a better recognition performance, which could reach 96% in 10 dB.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wireless Signal Modulation Classification · AI and Multimedia in Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
References (35)
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