Spectrum Sensor Based on a Self-Organizing Feature Map

Abstract

The paper focuses on the problem of burst signal detection in noisy radio environment. The ability to detect a burst signal in environment with low signal-to-noise level is crucial for cognitive radio solutions for which the detection of primary user transmission should be performed quickly and in energy efficient way. While the currently used spectrum sensing techniques based on the analysis of signal spectrum energy are simple to implement and are efficient, the sensitivity of these sensors in particular environment highly depends on the threshold estimation technique used in implementation. The classifier based on the self-organizing feature map proposed in this paper, takes the decision of the primary user signal presence in the measured environment. An experimental investigation was performed in 25 MHz wide frequency band on 949 MHz central frequency. The proposed spectrum sensing technique was compared with the alternatively proposed semi-adaptive threshold setting techniques for energy based spectrum sensors.