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Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks
Yuqing Feng1, Wenjun Xu2, Zhi Zhang1
1The State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|April 12, 2022
Summary
This study introduces a cooperative spectrum sensing method for cognitive unmanned aerial vehicle networks using a continuous hidden Markov model (CHMM). The novel approach improves spectrum utilization and sensing accuracy in dynamic wireless environments.
Area of Science:
- Wireless Communication
- Network Engineering
- Signal Processing
Background:
- Cognitive unmanned aerial vehicle networks (CUAVNs) face challenges in efficient spectrum utilization.
- Cooperative spectrum sensing is crucial for dynamic spectrum access in CUAVNs.
Purpose of the Study:
- To enhance spectrum utilization in CUAVNs.
- To propose a novel cooperative spectrum sensing scheme using a continuous hidden Markov model (CHMM) and an improved signal-to-noise ratio (SNR) estimation method.
Main Methods:
- Modeling spectrum states and fusion values using a CHMM to exploit Markov properties.
- Integrating CHMM parameters with preliminary sensing results for spectrum prediction.
- Developing a novel SNR estimation scheme based on signal reconstruction on graphs for practical applications.
Main Results:
- The proposed CHMM-based cooperative spectrum sensing scheme demonstrates superior performance compared to schemes without CHMM.
- The CHMM-based scheme with the novel SNR estimator significantly outperforms existing algorithms.
Conclusions:
- The developed CHMM-based cooperative spectrum sensing scheme effectively enhances spectrum utilization in CUAVNs.
- The integration of a practical SNR estimation method improves the robustness and performance of the proposed scheme.
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