快变信道环境下基于支持向量回归的频谱预测

频谱预测是一种通过分析历史频谱数据获得频谱使用规律，从而预测未来频谱使用状态的技术。为了实现快变信道（本文指信道占用状态快速变化）环境下频谱状态的可靠预测，提出了一种基于支持向量回归的频谱预测算法。比较了在不同训练样本数时，该算法与一个典型的BP神经网络频谱预测算法的性能差异，结果表明所提算法在小样本学习时，预测效果更为理想。并在此基础上，加入正确检测概率和虚警概率，验证了当频谱检测不理想条件下，支持向量回归算法预测的可行性。 

Support Vector Regression-based Spectrum Prediction Under Quick-Changing Channel Occupancy

Spectrum prediction is a promising technique to infer future spectrum state from historical spectrum data by exploiting the correlation of spectrum states in time domain. To realize reliable prediction of spectrum state under quick-changing channel occupancy, this article proposes a support vector regression (SVR)-based spectrum prediction algorithm. By adopting three typical distribution models of spectrum state evolution, this article compares the prediction accuracy performance of the proposed algorithm and the existing back propagation (BP) neural network-based spectrum prediction algorithm. Simulation results show that the proposed algorithm has the capability of small sample learning and obtains better prediction accuracy with less training samples. On the basis of the previous study, research on the imperfect training spectrum sample which considering the probability of detection and the probability of false alarm are analyzed. The analysis shows that the prediction accuracy performance of the SVR-based spectrum prediction algorithm still achieves satisfactory result with the imperfect training samples.