One-bit analog-to-digital converter (ADC), performing signal sampling as an extreme simple comparator, is an overwhelming technology for spectrum sensing due to its low-cost, low-power consumptions and high sampling rate. In this letter, we propose a novel one-bit sensing approach based on the eigenvalue moment ratio (EMR), which has been proved to be highly efficient for conventional multi-antenna spectrum sensing in $\infty $ -bit situation. The statistical covariances of the one-bit samples inherent the statistical correlation properties, thus allow us to detect the existence of primary user (PU) though the test for sphericity. Particularly, we verify the element-by-element independence of one-bit sample covariance matrix (SCM) in the absence of signal, which allows us to asymptotically determine the null distributions of statistical covariance based spectrum sensing techniques. On this basis, we formulate the asymptotic distribution of one-bit EMR under null hypothesis via the central limited theorem (CLT) and perform spectrum sensing with one-bit samples directly. Theoretical and simulation results show the new approach can provide superior sensing performance at a low hardware cost.

中文翻译：

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基于统计协方差的一位频谱感知：特征值矩比方法

一位模数转换器 (ADC) 作为一个极其简单的比较器执行信号采样，由于其低成本、低功耗和高采样率，是一种压倒性的频谱感知技术。在这封信中，我们提出了一种基于特征值矩比 (EMR) 的新型单比特感知方法，该方法已被证明对传统多天线频谱感知非常有效。 $\infty $ -位情况。一位样本的统计协方差固有的统计相关性，因此允许我们通过球形测试来检测主用户（PU）的存在。特别是，我们在没有信号的情况下验证了一位样本协方差矩阵 (SCM) 的逐元素独立性，这使我们能够渐近地确定基于统计协方差的频谱感知技术的零分布。在此基础上，我们通过中心有限定理（CLT）制定了零假设下一位EMR的渐近分布，并直接用一位样本进行频谱感知。理论和仿真结果表明，新方法可以以较低的硬件成本提供卓越的传感性能。