摘要翻译：
本文利用随机几何的工具，刻画了有限区域电视空白异构网络的最坏情况干扰。我们导出了概率分布函数(PDF)的闭式表达式，并给出了在不同路径损耗指数下总干扰的平均值。所提出的干扰特性简单，可用于改进频谱接入技术。利用导出的PDF函数，我们证明了一种基于特征值的认知无线电网络频谱检测器在频谱检测中的性能增益。
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英文标题：
《Stochastic-Geometry Based Characterization of Aggregate Interference in
  TVWS Cognitive Radio Networks》
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作者：
Madhukar Deshmukh, S.M. Zafaruddin, Albena Mihovska, Ramjee Prasad
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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, we characterize the worst-case interference for a finite-area TV white space heterogeneous network using the tools of stochastic geometry. We derive closed-form expressions on the probability distribution function (PDF) and an average value of the aggregate interference for various values of path loss exponent. The proposed characterization of the interference is simple and can be used in improving the spectrum access techniques. Using the derived PDF, we demonstrate the performance gain in the spectrum detection of an eigenvalue-based detector for cognitive radio networks.
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PDF链接：
https://arxiv.org/pdf/1806.1045