Robust Spectrum Sensing Based on Correlation for Cognitive Radio Networks With Uncalibrated Multiple Antennas

Robust Spectrum Sensing Based on Correlation for Cognitive Radio Networks With Uncalibrated Multiple Antennas
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具有未校准多天线的认知无线电网络中基于相关性的鲁棒频谱感知

DOI:
10.1109/lcomm.2020.3044334
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发表时间:
2021-05
期刊:
IEEE Communications Letters
影响因子:
--
通讯作者:
Guo-Xi Sun
Guo-Xi Sun
中科院分区:
其他
文献类型:
--
作者:
An-Zhi Chen;Zhi-Ping Shi;Gen Liang;Guo-Xi Sun

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在这封信中,我们考虑认知无线电网络中相关信号的多天线频谱感知,其中在次用户处使用未校准的多天线来检测主用户(PU)的存在。在这种情况下,我们证明了接收信号的相关函数矩阵在零假设和替代假设之间存在差异,这可以被用来探测PU的状态。在此基础上,提出了一种基于相关性的局部平均方差(CLAV)检测方法,以利用主信号的相关性。在零假设下,利用中心极限定理导出了它的渐近分布,从而从理论上得到了CLAV方法的决策门限。最后,通过仿真实验验证了该方法比传统方法具有更好的性能。
In this letter, we consider the multiantenna spectrum sensing for correlated signal in a cognitive radio network, where uncalibrated multiple antennas are employed at the secondary user to detect the presence of the primary user (PU). Under such a scenario, we demonstrate that the correlation function matrices of the received signals differ between the null hypothesis and the alternative hypothesis, which can be leveraged to probe the state of PU. Based on this, a correlation-based local average variance (CLAV) detection method is proposed to exploit the correlated property of the primary signals. Also, its asymptotic distribution under the null hypothesis is derived with the aid of the central limit theorem, which enables us to theoretically obtain the decision threshold of the CLAV method. Finally, we carry out the simulation results to illustrate the superior performance of proposed method compared to the conventional methods.
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