Radio spectrum awareness, including understanding radio signal activities, is crucial for improving spectrum utilization, detecting security vulnerabilities, and supporting adaptive transmissions. Related tasks include spectrum sensing, identifying systems and terminals, and understanding various protocol layers. In this paper, we investigate various identification and classification tasks related to fading channel parameters, signal distortions, Medium Access Control (MAC) protocols, radio signal types, and cellular systems. Specifically, we utilize deep learning methods in those identification and classification tasks. Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

The Extreme Learning Machine (ELM) and its variants are effective in many machine learning applications such as Imbalanced Learning (IL) or Big Data (BD) learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM (DW-ELM) algorithm is proposed, which is based on the MapReduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated. Then, to further improve the computational efficiency, an Improved DW-ELM algorithm (IDW-ELM) is developed using only one MapReduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.