Large‐scale Wireless Spectrum Monitoring

Automated monitoring of wireless spectrum over frequency, time, and space is still a difficult research problem. This chapter discusses the main challenges of large‐scale wireless spectrum monitoring. It concentrates on two very concrete spectrum analysis examples where the use of machine learning is promising. To emphasize the power of such a framework, the chapter concentrates on two major applications: anomaly detection and wireless signal classification. A crowdsourced spectrum monitoring framework, which leverages state‐of‐the‐art software‐defined radio and big data architectures, can help to address these challenges and democratize spectrum awareness. A semi‐supervised deep learning setup based on the latest deep learning research which achieves performance close to fully supervised models with only 20% of the labeled samples is discussed. The chapter analyzes the performance of quantized models and shows that, in principle, considerable computational performance can be achieved at a cost of 10% classification accuracy loss.