Adaptive double threshold energy detection based on Markov model for cognitive radio

Area of Science:

• Cognitive Radio Technology
• Spectrum Sensing
• Signal Processing

Background:

• Cognitive radio demands enhanced spectrum sensing, especially in low signal-to-noise ratio (SNR) conditions.
• Existing double-threshold energy sensing methods face challenges with time-varying channel occupancy and noise uncertainty.

Purpose of the Study:

• To propose an adaptive double-threshold energy sensing method based on a Markov model (ADEMM).
• To enhance spectrum sensing performance in low SNR environments.

Main Methods:

• Developed a modified Markov model to address time-varying channel occupancy.
• Introduced an adaptive double-threshold mechanism adjusting thresholds based on maximal detection probability.
• Evaluated performance through numerical simulations.

Main Results:

• The proposed ADEMM demonstrates superior detection performance compared to conventional double-threshold energy sensing schemes.
• ADEMM shows significant improvements in very low SNR regions.

Conclusions:

• The ADEMM offers a robust solution for spectrum sensing in challenging low SNR cognitive radio environments.
• The adaptive thresholding and modified Markov model effectively improve detection accuracy and reliability.