发帖
雷达卡
摘要翻译：
信道估计对于未来的无线移动通信系统至关重要。本文主要研究在加性高斯白噪声存在下，多路径信道参数估计问题的求解。我们假设多径环境中的路径数是已知的，传输信号由已知瞬态信号的衰减和延迟复制品组成。为了确定最大似然估计，人们必须解决一个复杂的优化问题。遗传算法(GA)以其在求解复杂优化问题时的鲁棒性而闻名。采用遗传算法提取信道参数，使导出的误差函数最小。该方法基于信道参数的最大似然估计。仿真结果也证明了遗传算法对信道参数估计误差的鲁棒性。
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英文标题：
《Estimation of Channel Parameters in a Multipath Environment via
Optimizing Highly Oscillatory Error-Functions Using a Genetic Algorithm》
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作者：
Amir Ebrahimi, Ardavan Rahimian
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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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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：
Channel estimation is of crucial importance for tomorrow's wireless mobile communication systems. This paper focuses on the solution of channel parameters estimation problem in a scenario involving multiple paths in the presence of additive white Gaussian noise. We assumed that number of paths in the multipath environment is known and the transmitted signal consists of attenuated and delayed replicas of a known transient signal. In order to determine the maximum likelihood estimates one has to solve a complicated optimization problem. Genetic Algorithms (GA) are well known for their robustness in solving complex optimization problems. A GA is considered to extract channel parameters to minimize the derived error-function. The solution is based on the maximum-likelihood estimation of the channel parameters. Simulation results also demonstrate GA's robustness to channel parameters estimation errors.
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PDF链接：
https://arxiv.org/pdf/1804.01455
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