Joint Supervised and Unsupervised Machine Learning for Spectrum Sensing
Bisma Manzoor, Akram Al‐Hourani · 2024
As the Internet of Things (IoT) applications continue to experience exponential growth, the resulting congestion in frequency spectrum highlights the necessity for innovative spectrum monitoring techniques. Spectrum sensing plays a crucial role in cognitive radio systems by facilitating the efficient utilization of spectrum through the detection and utilization of unused frequency bands. This work presents a novel methodology for detecting radio frames from a noisy spectrum. We implement joint machine learning comprising a supervised Semantic Segmentation neural network and unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect IoT packets from the spectrogram images. The proposed method achieves 95.4% mean accuracy and 93% mean intersection-overunion (IoU) at low Signal-to-Noise Ratio (SNR) level. Moreover, the proposed method outperforms the traditional threshold detection methodologies as demonstrated by the Receiver Operating Characteristics (ROC).