Mitigating RF Front-End Nonlinearity of Sensor Nodes to Enhance Spectrum Sensing

Area of Science:

• Electrical Engineering
• Wireless Communications
• Signal Processing

Background:

• Cognitive radio wireless sensor networks (CR-WSNs) are crucial for efficient spectrum utilization.
• Spectrum sensing performance is degraded by radio frequency (RF) front-end nonlinearities, leading to false alarms and reduced detection probability.
• Existing adaptive interference cancellation (AIC) algorithms have limitations in mitigating these nonlinear distortions.

Purpose of the Study:

• To develop a novel mitigation architecture for nonlinear distortions in CR-WSNs.
• To overcome the limitations of current adaptive interference cancellation (AIC) algorithms.
• To enhance the reliability of spectrum sensing in CR-WSNs.

Main Methods:

• Development of a new mitigation architecture to alleviate nonlinear distortions.
• Implementation of the proposed algorithm and verification through simulations.
• Validation using actual radio frequency (RF) front-end measurements.

Main Results:

• Significant suppression of nonlinear distortions was achieved.
• The proposed algorithm demonstrated improved spectrum sensing reliability.
• Superior performance was observed compared to the adaptive interference cancellation (AIC) algorithm, particularly at interferer signal band edges.

Conclusions:

• The developed mitigation architecture effectively alleviates nonlinear distortions in CR-WSNs.
• The proposed algorithm offers enhanced reliability for spectrum sensing.
• This work provides a significant improvement over traditional AIC methods for nonlinear distortion mitigation.