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雷达卡
摘要翻译：
在射频环境中，来自多个源的噪声会降低通信系统的性能。在诸如认知无线电的宽带系统中，接收机处的噪声可能源于RF前端中存在的非线性、接收机无线电系统内的时变热噪声以及来自相邻网络节点的噪声。认知无线电的去噪技术已经被提出，其中一些用于频谱感知，另一些用于通信中接收到的噪声信号。在接收信号中用于噪声消除的这些技术中的一些例子是最小均方(LMS)及其变体。然而，这些算法对非线性信号的性能较差，且不能找到全局最优解。因此，应用进化算法等全局搜索优化技术来消除噪声。本文实现了粒子群优化算法(PSO)和LMS算法，并对它们的性能进行了评价。在发射信号中加入高斯和非线性随机噪声的情况下，进行了广泛的仿真。采用误码率和均方误差两个指标进行性能比较。结果表明，在高斯和非线性随机噪声下，PSO算法的性能优于LMS算法。
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英文标题：
《Denoising Signals in Cognitive Radio Systems Using An Evolutionary
Algorithm Based Adaptive Filter》
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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 originating from several sources in a RF environment degrades the performance of communication systems. In wideband systems, such as cognitive radios, noise at the receiver can originate from non-linearity present in the RF front end, time-varying thermal noise within the receiver radio system, and noise from adjacent network nodes. Several denoising techniques have been proposed for cognitive radios, some of which are applied during spectrum sensing and others to received noisy signal during communication. Examples of some of these techniques used for noise cancellation in received signals are least mean square (LMS) and its variants. However, these algorithms have low performance with non-linear signals and cannot locate a global optimum solution for noise cancellation. Therefore, application of global search optimization techniques, such as evolutionary algorithms, is considered for noise cancellation. In this paper, particle swarm optimization (PSO) and LMS algorithms are implemented and their performances are evaluated. Extensive simulations were performed where Gaussian and non-linear random noise were added to the transmitted signal. The performance comparison was done using two metrics: bit error rate and mean square error. The results show that PSO outperforms LMS under both Gaussian and nonlinear random noise.
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PDF链接：
https://arxiv.org/pdf/1801.09724
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