CAJ | 학술논문

为了改善认知无线电系统中频谱感知的检测概率和检测速度，提出了基于极限学习机的频谱感知方法。极限学习机仅需要配置隐层节点个数，参数选择相对较容易，在算法执行过程中可以产生最优的唯一解，且训练速度快。计算出样本信号的能量值和循环谱值，对模型进行训练，使之具有快速判决测试信号的最佳性能。通过仿真结果可以看出，其在检测性能上优于传统的频谱感知算法和支持向量机感知算法，并在训练速度上比支持向量机感知算法有大幅度的性能提升。
위료개선인지무선전계통중빈보감지적검측개솔화검측속도，제출료기우겁한학습궤적빈보감지방법。겁한학습궤부수요배치은층절점개수，삼수선택상대교용역，재산법집행과정중가이산생최우적유일해，차훈련속도쾌。계산출양본신호적능량치화순배보치，대모형진행훈련，사지구유쾌속판결측시신호적최가성능。통과방진결과가이간출，기재검측성능상우우전통적빈보감지산법화지지향량궤감지산법，병재훈련속도상비지지향량궤감지산법유대폭도적성능제승。
To improve the detection accuracy rate and speed of cognitive radio spectrum sensing system,a spectrum sensing method based on Extreme Learning Machine(ELM)is proposed.The ELM only needs to configure the number of nodes in the hidden layer,the parameter selection is relatively easy,and during the implementation of this method,a unique optimal solution and fast training speed can be generated.The energy value and cyclic spectrum value of the sample signal are calculated. These values are used to train the proposed model to make it have optimal performance for fast judgment of test signal. The simulation results show that the proposed method has better performance compared with the traditional methods and SVM?based sensing method, and the training speed is improved significantly compared with SVM?based sensing method.