Signal Classification for Spectrum Sharing with Machine Learning Using a Low-Cost SDR
Akimun Jannat Alvina, Vijay Harid, Yao Ma, Mark Gołkowski · 2024
In the dynamic and ever-growing landscape of wireless networks, the intelligent detection and classification of signals are crucial for efficient spectrum sharing. A system that shares spectrum with signal knowledge can effectively allocate spectrum for various purposes such as dynamic channel access, computation, and interference mitigation. This study showcases the effectiveness and potential of machine learning algorithms in software defined radio (SDR) based spectrum sharing and classification, specifically focusing on LTE, WLAN, and 5G-new radio (NR) signals.