基于信号能量分布拟合优度的长短时记忆网络频谱感知算法研究

传统频谱感知算法性能在低信噪比下不够理想，在高信噪比下较好，算法性能随信噪比降低逐渐变差。本文提出了基于信号能量分布拟合优度的长短时记忆网络频谱感知算法，利用授权用户信号存在时的接收信号为基础，计算接收信号的能量分布，并将通过拟合优度算法得到的距离值作为特征构造特征向量，然后将特征向量输入长短时记忆网络训练得到模型，最后将测试数据输入训练模型进行预测，从而实现频谱感知。仿真结果表明，本文提出的新算法在信噪比为-13 dB，采样点数为28时，检测概率达到96.21%，明显优于传统能量检测算法和传统拟合优度算法。 

Research on Spectrum Sensing Algorithm Based on Long Short-Term Memory Network and Goodness of Fit of Distribution of Signal Energy

The performance of traditional spectrum sensing is not ideal at low signal to noise ratio(SNR) and good at high SNR. As the SNR decreases, the performance of algorithms gradually deteriorates. This paper proposes a spectrum sensing algorithm based on long short-term memory(LSTM) network and the goodness of fit of the distribution of signal energy. This method calculates the distance value form the energy distribution of the received signal when primary user signals exist. Then the feature vector consists of the distance value is input into a LSTM network training to obtain a model. Finally, test data is input into the model for prediction to achieve spectrum sensing. Results show that the new method proposed in this paper has a detection probability of 96.21% when the SNR is -13dB and the number of sampling points is 28, which is significantly better than the traditional energy detection algorithm and the traditional goodness-of-fit algorithm.