摘要翻译：
本文研究了宽带频谱感知的三种不同场景。在假定信号和噪声统计量不确定的情况下，利用随机矩阵估计噪声方差。这些场景是：1-指定了子带的数量，并且对于每个子带都有足够的关于正在使用或未使用的信息。2-子带的数目是已知的，但没有关于它们之间使用分布的信息。3-子带数未知。仿真结果表明了该方案的优越性能。在样本数方面，与循环平稳频谱感知算法相比，该方法需要更少的样本数，而与基于能量检测的方法相比，该方法需要更多的样本数。但是，在检测概率方面，该方法优于其他两种频谱感知方法。
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英文标题：
《A Random Matrix Approach to Wide Band Spectrum Sensing: Unknown Noise
  Variance Case》
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作者：
Sajjad Imani, Amin Banitalebi-Dehkordi, and Mehdi Cheraghi
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  In this paper three different scenarios in wide band spectrum sensing have been studied. While the signal and noise statistics are supposed to be unspecified, random matrixes have been utilized in order to estimate the noise variance. These scenarios are: 1- Number of subbands is specified and there is enough information regarding being used or being unused for each of them. 2- Number of subbands is known but there is no information about usage distribution among them. 3- Number of subbands is unknown. Simulation results showed the superior performance of the proposed scheme. Regarding the number of samples, the proposed method requires less number of samples compared to the cyclo-stationary spectrum sensing algorithms and more samples compared to the energy detection based methods. But, regarding the detection probability, the proposed method is superior compared to both other spectrum sensing methods.
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PDF链接：
https://arxiv.org/pdf/1803.04627