Abd, B. (2025). Improvement of signal detection based on using machine learning. , 43(2), 149-158. doi: 10.30684/etj.2024.152557.1796
Bassam H. Abd. "Improvement of signal detection based on using machine learning". , 43, 2, 2025, 149-158. doi: 10.30684/etj.2024.152557.1796
Abd, B. (2025). 'Improvement of signal detection based on using machine learning', , 43(2), pp. 149-158. doi: 10.30684/etj.2024.152557.1796
Abd, B. Improvement of signal detection based on using machine learning. , 2025; 43(2): 149-158. doi: 10.30684/etj.2024.152557.1796

Improvement of signal detection based on using machine learning

Engineering and Technology Journal
Volume 43, Issue 2, February 2025, Pages 149-158    PDF (560.57 K)
Document Type: Research Paper
DOI: 10.30684/etj.2024.152557.1796
Author
Bassam H. Abd*
Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
During the communication system development over the years, detecting and identifying the signal from the affected noise had the major role and the most attention from the researchers. The deep learning algorithm has become a very attractive tool for distinguishing between signal and noise. Learning and training are the two important steps in designing any deep learning system. The proposed system depends on the support vector machine (SVM). The SVM is one of the most popular learning algorithms in different fields, such as signal processing, image processing, communication, and pattern recognition. The SVM classifier is a supervised learning algorithm that uses the closest data points as "support vectors" to build a hyperplane that divides classes. SVM is used to identify the large RADIOML 2018.01A dataset with various signal schemes. The paper strongly emphasized extracting the dataset's most essential features, which improved Support Vector Machines’ capacity to detect signals in noisy and complicated situations. The measured accuracy for the SVM classier for QPSK, 8PSK, and 16 QAM equals 99.7%, 99%, and 99.6%, respectively. The final measured results show the proposed detection system's correctness, robustness, and flexibility based on utilizing the Support Vector Machines (SVM) classifier; this classifier approves its efficacy in signal detection.

Highlights
  • This study performed signal detection for various types of modulated signals
  • Simulations used pre-processing steps like fading noise, Gaussian noise, low-pass filtering for real-world scenarios
  • Features like energy, power, skewness, kurtosis, zero-crossing rates, phase differences were statistically extracted
  • Applying features to an SVM classifier achieved 98.72% detection accuracy across various noisy signal types
Keywords
Signal detection; Deep learning; SVM classifier; Feature extraction; QPSK; 8PSK and QAM
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