Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments

Abstract

The efficient use of spectrum has become an active research area due to its scarcity and underutilization. In a spectrum sharing scenario as cognitive radio (CR), the vacancy of licensed frequency bands could be detected by a secondary user through spectrum sensing techniques. Usually, this sensing approaches are performed with prior knowledge of the channel features. In the present work, a blind spectrum sensing approach based on independent component analysis (ICA) and singular spectrum analysis (SSA) is proposed. The approach is tested and compared with other outcomes. Results show that the proposed scheme is capable of detecting most of the sources with low time consumption, which is a remarkable aspect for online applications with demanding time issues.