基于支持向量机的认知无线电频谱预测方法

A SVM Based Spectrum Prediction Scheme for Cognitive Radio

频谱预测是认知无线电系统中的关键技术之一,利用该技术可以显著减少认知用户的能量损耗,同时提高系统的频谱利用率。针对现有基于BP神经网络的频谱预测方法预测精度低及失效率高等问题,将建立在统计学习理论和结构风险最小原则上的支持向量机引入认知无线电频谱预测中,利用其对小样本及非线性数据优越的预测性能对信道进行预测。实验结果表明,该方法通过避免无效检测,提高了频谱感知系统的性能,并且比基于BP神经网络算法的模型的预测精度更高,具有良好的实用性与灵活性。

Spectrum prediction is one of the key technologies in cognitive radio （CR） systems. This technology can reduce considerable energy consumed by spectrum sensing, and improve the overall system＇s spectrum utilization. Aiming at the low accuracy and invalid prediction problems of spectrum prediction in cognitive radio, a new prediction method was proposed by integrating support vector machine（SVM） which was based on statistical learning theory （SLT） and structural risk minimization principle （SRM）. The channel status is forecasted by utilizing the excellent forecasting performance of the model in small sample and nonlinear data of SVM. The results show that by avoiding invalid prediction, the spectrum utilization can also be improved, and the forecasting accuracy is better than model based on back propagation （BP）, thus the proposed algorithm is practicable and flexible for spectrum prediction in cognitive radio.