实测卫星频谱占用状态拟合与预测方法

Fitting and forecasting method of spectrum occupancy state of measured satellite

摘要:

由于在不同时间、不同空间卫星接收数据底噪是动态起伏的，传统建模固定门限的方法存在缺陷。本文在时间维度上对卫星频谱感知数据的频谱占用模型进行分析，利用自适应阈值法确定噪声门限，对卫星频谱数据进行预处理，得到卫星频谱占用长度序列。为对卫星频谱的态势进行有效的统计分析，利用泊松分布和指数分布方法对频谱占用时间长度序列的概率密度曲线进行拟合，得到了适用于卫星频谱占用时间序列的概率分布模型。基于所得的卫星频谱占用状态模型，通过两状态马尔可夫链计算出卫星信道某一频点的状态转移矩阵，从而预测出信道占用和空闲的概率。利用卫星频谱感知数据构建的数据集进行反向传播(BP)神经网络训练，预测某一频点的占用长度。通过计算BP神经网络与传统的长短期记忆(LSTM)神经网络预测法的均方根误差(RMSE)，得到LSTM神经网络的RMSE为2.208 1，BP神经网络的RMSE为0.172 8。评估结果表明，BP神经网络准确度高。

Abstract:

The spectral occupancy model of satellite spectrum sensing data in the temporal dimension is analyzed. Because the bottom noises in satellite receiving data are undulated in different time and space, the traditional modeling methods with fixed threshold are defective. Therefore, the adaptive threshold method is introduced to determine the noise threshold and preprocess the satellite spectrum data to obtain the satellite spectrum occupancy length sequence. In order to make an effective statistical analysis on the situation of the satellite spectrum, the probability density curve of the spectral occupation time length series is fitted by using the Poisson and exponential distribution methods, and a probability distribution model suitable for the satellite spectrum occupation time series is obtained. Based on the obtained satellite spectral occupancy state model, the state transfer matrix at a certain frequency point of the satellite channel is calculated by two-state Markov chains to predict the probability of outgoing channel occupancy and idle. In addition, the Back-Propagation(BP) neural network is trained through the data set constructed by satellite spectrum sensing data to predict the occupancy length of a certain frequency point. By calculating the Root Mean Square Error(RMSE) of the BP neural network and the conventional Long Short-Term Memory(LSTM) neural network prediction methods, 0.172 8 and 2.208 1 are obtained respectively. The evaluation results show that the BP neural network bears the advantage of high accuracy.