| 摘要: |
| 针对传统信噪比加权频谱感知方法在车联网（Internet of Vehicles,IoV）环境中受噪声影响较大、感知准确率较低的问题，提出了一种基于反向传播（Back Propagation,BP）神经网络的IoV协同频谱感知（Cooperative Spectrum Sensing based on BP Neural Network,BPCSS）算法。该算法首先将本地次用户能量检测结果进行协方差处理，然后通过BP神经网络对次用户信噪比进行权值优化，使用训练好的模型进行协同频谱感知。仿真结果表明，在信噪比0~25 dB范围内、10个次用户协同感知时，该算法在噪声干扰较大的环境中的平均检测准确率为90%，比基于信噪比加权频谱感知方法提升20%，比基于门限值频谱感知方法提升30%。 |
| 关键词: 车联网 BP神经网络 协同频谱感知 |
| DOI： |
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| 基金项目:国家重点研发计划子课题（2020YFC1511704）；国家自然科学基金资助项目（61971048）；北京市科技计划课题（Z191100001419012）；北京信息科技大学2020年促进高校内涵发展科研水平提高项目（2020KYNH212） |
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| An intelligent cooperative spectrum sensing algorithm based on back propagation neural network in IoV |
| ZHANG Yuexia,ZHAO Yifei |
| (a.School of Information Communication Engineering;b.Key Laboratory of Modern Observation and Control Technology of Ministry of Education,Beijing Information Science and Technology University，Beijing 100101,China) |
| Abstract: |
| In the Internet of vehicles(IoV) environment,the traditional spectrum sensing method based on the weighted signal-to-noise ratio(SNR) is greatly affected by noise,resulting in low sensing accuracy.To solve this problem,this paper proposes a cooperative spectrum sensing algorithm based on Back Propagation neural network(BP-CSS).The algorithm first processes the covariance of the local secondary user(SU) energy detection results,then optimizes the SNR of the SU through a BP neural network,and uses the trained model for cooperative spectrum sensing.The simulation results show that when the SNR is in 0~25 dB and 10 times of users cooperative perception,the average detection accuracy of the algorithm is 90% in the noisy environment,which is 20% higher than that of the algorithm based on SNR and 30% higher than that of the algorithm based on threshold. |
| Key words: Internet of vehicles(IoV) BP neural network cooperative spectrum sensing |
