摘要翻译：
噪声对消是任何通信系统的重要信号处理功能之一，因为噪声影响数据的完整性。在现有的系统中，传统的滤波器用于消除接收信号中的噪声。这些滤波器使用能够过滤特定频率或频率范围的固定硬件。然而，下一代通信技术，如认知无线电，将需要使用自适应滤波器，可以动态地为任何频率重新配置其滤波参数。为此，人们提出了几种噪声消除技术，包括最小均方(LMS)及其变体。然而，这些算法容易受到非线性噪声的影响，无法找到全局最优解。本文研究了基于全局搜索优化的遗传算法和粒子群算法在认知无线电系统噪声消除中的有效性。以误码率和均方误差作为性能评价指标，实现了这些算法，并将其性能与LMS进行了比较。对加性高斯白噪声和随机非线性噪声进行了仿真。结果表明，对于AWGN信号的破坏，遗传算法和粒子群算法的性能优于LMS，而对于非线性随机噪声，PSO算法的性能优于其他两种算法。
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英文标题：
《Noise Cancellation in Cognitive Radio Systems: A Performance Comparison
  of Evolutionary Algorithms》
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作者：
Adnan Quadri, Mohsen Riahi Manesh, Naima Kaabouch
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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：
  Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
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PDF链接：
https://arxiv.org/pdf/1801.09725