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On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio
Sunghwan Bae1, Jaewoo So2, Hongseok Kim3
1Department of Electronic Engineering, Sogang University, Seoul 04107, Korea. usher731@sogang.ac.kr.
Sensors (Basel, Switzerland)
|September 16, 2017
Summary
This study introduces an optimal cooperative sensing technique for cognitive radio (CR) that maximizes spectrum sensing performance. It addresses diverse signal-to-noise ratios (SNRs) by optimizing sensing thresholds for improved detection probability.
Area of Science:
- Wireless Communications
- Signal Processing
- Cognitive Radio Networks
Background:
- Energy detection is a common spectrum sensing method in cognitive radio (CR).
- Existing cooperative sensing methods often assume identical signal-to-noise ratios (SNRs) among nodes, limiting effectiveness.
- Diverse SNRs among cooperative sensing nodes can degrade performance with standard energy detection.
Purpose of the Study:
- To propose an optimal cooperative sensing technique for cognitive radio (CR) to maximize spectrum sensing performance.
- To address the performance degradation caused by diverse SNRs in cooperative sensing.
- To develop a method that ensures effective cooperation regardless of individual node SNRs.
Main Methods:
- Formulated an optimization problem based on the Neyman-Pearson criterion to maximize sensing performance using optimized thresholds.
- Identified a condition that convexifies the non-convex optimization problem, providing a sufficient optimality condition.
- Validated the technique through extensive simulations under practical operating parameters.
Main Results:
- Demonstrated that non-optimized, equal thresholds severely degrade sensing performance when SNRs vary.
- Showcased that the proposed technique achieves globally optimal solutions for spectrum sensing.
- Confirmed maximization of the probability of detection under varying SNRs and number of nodes.
Conclusions:
- Optimized sensing thresholds are crucial for effective cooperative spectrum sensing in cognitive radio (CR) networks.
- The proposed technique provides a practical and globally optimal solution for maximizing sensing performance.
- This method enhances spectral efficiency and reliability in dynamic radio environments.
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