We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal Neyman-Pearson detector from first principles. The optimal detector exploits the inherent correlation of the OFDM signal incurred by the repetition of data in the cyclic prefix, using knowledge of the length of the cyclic prefix and the length of the OFDM symbol. We compare the optimal detector to the energy detector numerically. We show that the energy detector is near-optimal (within 1 dB SNR) when the noise variance is known. Thus, when the noise power is known, no substantial gain can be achieved by using any other detector than the energy detector. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based onempirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise power or the signal power. The GLRT detector is compared to state-of-the-art OFDM signal detectors, and shown to improve the detection performance with 5 dB SNR in relevant cases.
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机译:我们考虑在AWGN信道中对OFDM信号进行频谱感测。对于完全已知的噪声和信号功率,我们为具有循环前缀的OFDM信号建立了矢量矩阵模型,并从第一原理中得出了最佳的Neyman-Pearson检测器。最佳检测器利用循环前缀的长度和OFDM符号的长度的知识来利用由于循环前缀中的数据重复而产生的OFDM信号的固有相关性。我们在数值上比较最佳探测器和能量探测器。我们表明,当已知噪声方差时,能量检测器接近最佳(SNR小于1 dB)。因此,当知道噪声功率时,使用能量检测器以外的任何其他检测器都无法获得实质性的增益。对于完全未知的噪声和信号功率,我们基于接收到的数据的经验二阶统计量得出广义似然比测试(GLRT)。提出的GLRT检测器利用了OFDM信号的非平稳相关结构,并且不需要任何噪声功率或信号功率知识。 GLRT检测器与最新的OFDM信号检测器进行了比较,并显示在相关情况下以5 dB SNR改善了检测性能。
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