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。
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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