Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Area of Science:

• Wireless communication
• Cognitive radio networks
• Spectrum sensing

Background:

• Conventional spectrum sensing relies on signal energy, but auxiliary information can improve performance.
• Cognitive radio networks (CRNs) require efficient spectrum sensing for opportunistic access to licensed frequency bands.
• Geolocation information can serve as valuable auxiliary data for spectrum sensing.

Purpose of the Study:

• To propose a novel spectrum sensing scheme for cellular cognitive radio networks (CCRNs) that utilizes geolocation information.
• To enhance the accuracy and efficiency of spectrum sensing for secondary user equipments (SUEs).
• To reduce the energy consumption associated with spectrum sensing.

Main Methods:

• Secondary user equipments (SUEs) acquire wireless fingerprints (WFPs) using received signal strength (RSS) or time of arrival (TOA) from base stations (BSs).
• SUEs determine their geographical locations via WFP matching in a wireless fingerprint database (WFPD).
• The WFPD and support vector machine (SVM) algorithm are used to identify available licensed frequency bands (LFBs).
• A cooperative joint prediction mechanism involving multiple nearby SUEs is proposed to overcome single-SUE limitations.

Main Results:

• The proposed geolocation-aided spectrum sensing scheme achieves higher detection probability.
• The novel scheme demonstrates reduced energy consumption compared to conventional methods.
• Cooperative sensing among geographically proximate SUEs further improves sensing performance.

Conclusions:

• Geolocation information significantly enhances spectrum sensing in CCRNs.
• The proposed WFP matching and SVM-based approach effectively identifies available spectrum.
• Cooperative sensing is a viable strategy to improve the reliability and efficiency of spectrum sensing in cognitive radio environments.