摘要翻译：
影响通信系统性能的一个基本挑战是噪声对信号的不期望的影响。噪声会使信号失真，其产生是由于多种原因，包括系统非线性和来自邻近环境的噪声干扰。传统的通信系统使用滤波器来消除接收信号中的噪声。在认知无线电系统中，信号去噪在频谱感知期间以及与其他网络节点的通信期间都很重要。基于我们的发现，很少有调查只回顾认知无线电通信频谱感知阶段使用的特定去噪技术。本文对认知无线电系统中各个阶段的去噪技术进行了综述，并讨论了几种去噪技术在认知无线电系统中的应用。为了建立全面的概述，还提供了所讨论的去噪技术的性能比较。
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英文标题：
《A Review of Noise Cancellation Techniques for Cognitive Radio》
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作者：
Adnan Quadri
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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：
  One of the fundamental challenges affecting the performance of communication systems is the undesired impact of noise on a signal. Noise distorts the signal and originates due to several sources including, system non-linearity and noise interference from adjacent environment. Conventional communication systems use filters to cancel noise in a received signal. In the case of cognitive radio systems, denoising a signal is important during the spectrum sensing period, and also during communication with other network nodes. Based on our findings, few surveys are found that only review particular denoising techniques employed for the spectrum sensing phase of cognitive radio communication. This paper aims to provide a collective review of denoising techniques that can be applied to a cognitive radio system during all the phases of cognitive communication and discusses several works where the denoising techniques are employed. To establish comprehensive overview, a performance comparison of the discussed denoising techniques are also provided.
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PDF链接：
https://arxiv.org/pdf/1801.01111