摘要翻译：
认知无线电(CR)是一种很有前途的提高频谱利用率的方案。频谱感知(SS)是CR的主要任务之一。协同频谱感知(CSS)被用于CR以提高检测能力。基于能量检测的传感技术由于其简单性和低复杂度而被广泛采用，称为传统能量检测(CED)。CED可以通过改变接收样本振幅的平方运算以任意正幂p来推广，这称为广义能量检测器(GED)。当存在噪声不确定性(NU)时，GED的性能下降。本文研究了当所有次用户（SUs）都使用GED时，考虑噪声NU的情况下CSS的性能。我们推导了CSS的信噪比墙，用于硬判决和软判决合并。用蒙特卡罗(MC)模拟验证了所有推导的表达式。
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英文标题：
《SNR Wall for Cooperative Spectrum Sensing Using Generalized Energy
  Detector》
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作者：
Kamal Captain and Manjunath Joshi
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最新提交年份：
2017
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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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Cognitive radio (CR) is a promising scheme to improve the spectrum utilization. Spectrum sensing (SS) is one of the main tasks of CR. Cooperative spectrum sensing (CSS) is used in CR to improve detection capability. Due to its simplicity and low complexity, sensing based on energy detection known as conventional energy detection (CED) is widely adopted. CED can be generalized by changing the squaring operation of the amplitude of received samples by an arbitrary positive power p which is referred to as the generalized energy detector (GED). The performance of GED degrades when there exists noise uncertainty (NU). In this paper, we investigate the performance of CSS by considering the noise NU when all the secondary users (SUs) employ GED. We derive the signal to noise ratio (SNR) wall for CSS for both hard and soft decision combining. All the derived expressions are validated using Monte Carlo (MC) simulations.
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PDF链接：
https://arxiv.org/pdf/1712.06905