基于特征值拟合优度的频谱感知算法研究  

Research of spectrum sensing algorithm based on goodness-of-fit test with eigenvalues

作　　者：李贺 赵文静[1] 罗雪松 刘畅[1] 邹德岳 金明录[1] LI He;ZHAO Wenjing;LUO Xuesong;LIU Chang;ZOU Deyue;JIN Minglu(School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China)

机构地区：[1]大连理工大学信息与通信工程学院,辽宁大连116024

出　　处：《大连理工大学学报》2020年第6期635-641,共7页Journal of Dalian University of Technology

基　　金：国家自然科学基金资助项目(61701072).

摘　　要：现代拟合优度频谱感知算法直接采用信号的样本或能量作为拟合统计量,对独立的接收信号表现出良好的检测性能,对相关信号则表现不出令人满意的效果.基于最大特征值的拟合优度频谱感知算法可表现出更好的检测性能,但是基于最大特征值的拟合优度算法是半盲检测算法,需要已知噪声的功率,这在实际应用中是难以实现的.为此,提出了新的基于最大最小特征值的全盲拟合优度频谱感知算法.同时基于随机矩阵理论成果,推导分析了新算法的检测概率、虚警概率和判决门限.实验结果表明,新算法有效克服了噪声不确定性问题,相对于其他拟合优度检测算法性能有所提升.The advanced goodness-of-fit test algorithm for spectrum sensing directly adopts signal samples or energy as fitting statistics,and shows good detection performance for independent signal,while the satisfactory performance is not achieved for correlated signal.The maximum eigenvalue based goodness-of-fit test algorithm for spectrum sensing has better detection performance.However,the maximum eigenvalue based goodness-of-fit test algorithm is a semi-blind detection algorithm that needs to know the power of the noise,which is difficult to realize in practical application.A new totally-blind spectrum sensing algorithm based on goodness-of-fit test using maximum and minimum eigenvalues is proposed.In addition,based on the results of random matrix theory,the detection probability,false alarm probability and decision threshold of the new algorithm are deduced and analyzed.The experimental results show that the new algorithm overcomes the problem of noise uncertainty effectively and has better performance than other goodness-of-fit detection algorithms.

关 键 词：认知无线电 频谱感知 拟合优度检验 特征值 

分 类 号：TP914[自动化与计算机技术]