基于混合Fourier-Woodward-Lawson-神经网络的辐射方向图合成 可靠MIMO天线系统的网络

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摘要翻译：
本文采用混合Woodward-Lawson-神经网络和加权Fourier方法来合成天线阵。利用神经网络对MIMO天线阵进行相位估计，简化了天线阵的建模。目前的主要问题是寻找最佳的线阵单元权值，使天线的辐射方向图具有最小的副瓣电平(SLL),从而改善天线阵的性能。为了达到这一目的，实现了一个用于频率为2.45GHz的可靠多输入多输出(MIMO)应用的天线阵列。为了验证所提出的方法，在有用信号的方向上放置了许多主波束均匀激励阵列方向图的例子。Woodward-Lawson-Neural Networks综合方法可以为天线阵的综合找到有趣的解析方程，并突出了系统输入参数与输出参数之间的灵活性。这种混合优化的性能强调了系统对无线通信的适应性以及它如何参与减少干扰，以及。
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英文标题：
《Radiation Pattern Synthesis Using Hybrid Fourier- Woodward-Lawson-Neural
  Networks for Reliable MIMO Antenna Systems》
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作者：
Elies Ghayoula, Ridha Ghayoula, Jaouhar Fattahi, Emil Pricop,
  Jean-Yves Chouinard, Ammar Bouallegue
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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：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  In this paper, we implement hybrid Woodward-Lawson-Neural Networks and weighted Fourier method to synthesize antenna arrays. The neural networks (NN) is applied here to simplify the modeling of MIMO antenna arrays by assessing phases. The main problem is obviously to find optimal weights of the linear antenna array elements giving radiation pattern with minimum sidelobe level (SLL) and hence ameliorating the antenna array performance. To attain this purpose, an antenna array for reliable Multiple-Input Multiple-Output (MIMO) applications with frequency at 2.45 GHz is implemented. To validate the suggested method, many examples of uniformly excited array patterns with the main beam are put in the direction of the useful signal. The Woodward-Lawson-Neural Networks synthesis method permits to find out interesting analytical equations for the synthesis of an antenna array and highlights the flexibility between the system parameters in input and those in output. The performance of this hybrid optimization underlines how well the system is suitable for a wireless communication and how it participates in reducing interference, as well.
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PDF链接：
https://arxiv.org/pdf/1710.02633

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关键词：Fourier Lawson four 神经网络 laws antenna 方法 参数 混合 arrays