An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR...

Area of Science:

• Electrical Engineering
• Wireless Communication
• Signal Processing

Background:

• The proliferation of wireless technologies has led to significant frequency spectrum scarcity.
• Cognitive radio (CR) offers a solution by enabling secondary users to opportunistically access licensed spectrum bands.
• Spectrum sensing is a critical function in CR systems for detecting vacant channels.

Purpose of the Study:

• To address the challenge of selecting an optimal decision threshold for spectrum sensing in low signal-to-noise ratio (SNR) environments with noise uncertainty.
• To minimize the probability of error in spectrum sensing for cognitive radio systems.
• To enhance the detection probability for protecting licensed primary users.

Main Methods:

• Analysis of sensing failure issues in cognitive radio systems.
• Development of an adaptive double threshold concept for robust spectrum sensing.
• Derivation of a closed-form equation for an optimal threshold to minimize error probability.
• Simulation-based evaluation of the proposed method.

Main Results:

• The proposed adaptive double threshold method improves the probability of detection.
• A reduction in the probability of error is achieved, particularly at low SNR.
• The method demonstrates robustness in the presence of noise uncertainty.
• The derived optimal threshold effectively minimizes the overall error rate.

Conclusions:

• The adaptive double threshold strategy is effective for robust spectrum sensing in cognitive radio.
• The derived optimal threshold provides a significant performance improvement in low SNR conditions with noise uncertainty.
• This research contributes to more efficient and reliable spectrum utilization in wireless communication.