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Virual Oral Question and Answer
IEEE/ICACT20260241 Question.3
Questioner: tranthithanhbg1995@gmail.com    2026-02-09 오전 10:36:52 		IEEE/ICACT20260241 Answer.3
Answer by Auhor md.t.a.812@ms.saitama-u.ac.jp   2026-02-09 오전 10:36:52
How does the performance of machine learning–based spectrum sensing compare with traditional energy detection at low SNR? At low SNR, machine learning–based spectrum sensing significantly outperforms traditional energy detection (ED). While ED relies on a fixed threshold and suffers from poor detection reliability and high false alarm rates in noisy conditions, ML models such as neural networks (NN), support vector machines (SVM), and logistic-based classifiers adapt to the non-linear characteristics of signals and noise. They achieve higher accuracy, better probability of detection, and lower probability of false alarm without needing a predefined threshold. This makes ML-based approaches more robust and reliable for spectrum sensing in challenging low-SNR environments.

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