| 基于神经网络的车联网频谱感知组合算法 |
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| 作者姓名： | 纪玉峰 郑敏 谭冲 刘洪 |
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| 作者单位： | 1. 中国科学院上海微系统与信息技术研究所 中国科学院无线传感网与通信重点实验室, 上海;2. 中国科学院大学, 北京 100049 |
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| 基金项目： | 中国科学院青年创新促进会（2018269）资助 |
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| 摘 要： | 针对车联网环境下无线频谱资源短缺的问题，提出一种基于神经网络的多条件频谱感知组合算法。该算法利用神经网络较强的多分类能力，将信号能量、协方差矩阵的最大特征值、最小特征值、迹和平均特征值融合作为神经网络特征参数实现合作频谱感知，并从理论上分析参数选择方案，算法还充分考虑信道多径衰落和阴影效应导致的信噪比很低的情况以及车辆移动产生的多普勒效应，达到提高频谱感知成功率的目的，从而提高频谱的利用率。仿真结果表明，该算法在低信噪比情况下比已有的频谱感知算法具有更好的检测性能。
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| 关 键 词： | 认知无线电 车联网 频谱感知 神经网络 低信噪比 |
| 收稿时间： | 2019-11-12 |
| 修稿时间： | 2020-01-06 |
Combination algorithm of spectrum sensing in vehicle network based on neural network |
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| Authors: | JI Yufeng ZHENG Min TAN Chong LIU Hong |
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| Institution: | 1. Key Laboratory of Wireless Sensor Networks and Communications of CAS, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai;2. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | In this paper, a multi-conditional spectrum sensing combination algorithm based on neural network is proposed to address the current shortage of spectrum resources in vehicular network. The algorithm combines signal energy, the maximum-minimum of eigenvalues, traces, and the average eigenvalue of the covariance matrix as neural network characteristic parameters, which are achieved through the strong multi-classification ability of neural network. To improve the successful rate of spectrum sensing and the utilization rate of the spectrum, we focus on analyzing the selection of parameter in theory as well as the low signal-to-noise ratio caused by channel fading and shadow effect. Meanwhile, the Doppler effective caused by car moving is also our consideration. Under low signal-to-noise ratio, the simulation results show that the proposed algorithm has better detection performance than existing spectrum sensing algorithms. |
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| Keywords: | cognitive radio vehicular network spectrum sensing neural network low signal-to-noise ratio |
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