基于{alpha}-{mu}组合的大数据集DSRC信道模型 RSSI测量值的

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摘要翻译：
信道建模对于车载网络中众多协议的设计和性能评估至关重要。在本工作中，我们研究了密集车辆网络中通信信道的大尺度和小尺度建模，并提供了结果。我们首先提出了一种方法来消除衰落对大尺度模型确定性部分的影响，并用单个发射机-接收机场景验证了其准确性。然后利用双射线模型来表征路径损耗，并根据新提出的方法从经验数据导出路径损耗参数。在此基础上，首次利用{alpha}-{mu}分布对车载网络的衰落行为进行了建模，并通过Kolmogorov-Smirnov（K-S）拟合优度检验验证了模型的精度。为此，本文研究了{alpha}-{mu}分布在通过K-S检验方面明显优于车辆信道文献中最常用的衰落分布Nakagami-m。一个来自测量活动的大接收信号强度指示(RSSI)数据集被用来评估我们的主张。此外，整个模型在一个可靠的离散事件网络模拟器中实现，该模拟器广泛应用于学术界和工业界的网络分析，即network simulator-3(ns-3),以显示在存在上层网络协议的情况下所提出的模型的结果。
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英文标题：
《Composite {\alpha}-{\mu} Based DSRC Channel Model Using Large Data Set
  of RSSI Measurements》
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作者：
Hossein Nourkhiz Mahjoub, Amin Tahmasbi-Sarvestani, S M Osman Gani,
  and Yaser P. Fallah
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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：
  Channel modeling is essential for design and performance evaluation of numerous protocols in vehicular networks. In this work, we study and provide results for largescale and small-scale modeling of communication channel in dense vehicular networks. We first propose an approach to remove the effect of fading on deterministic part of the large-scale model and verify its accuracy using a single transmitter-receiver scenario. Two-ray model is then utilized for path-loss characterization and its parameters are derived from the empirical data based on a newly proposed method. Afterward, we use {\alpha}-{\mu} distribution to model the fading behavior of vehicular networks for the first time, and validate its precision by Kolmogorov-Smirnov (K-S) goodness-of-fit test. To this end, the significantly better performance of utilizing {\alpha}-{\mu} distribution over the most adopted fading distribution in the vehicular channels literature, i.e. Nakagami-m, in terms of passing K-S test has been investigated and statistically verified in this paper. A large received signal strength indicator (RSSI) dataset from a measurement campaign is used to evaluate our claims. Moreover, the whole model is implemented in a reliable discrete event network simulator which is widely used in the academic and industrial research for network analysis, i.e. network simulator-3 (ns-3), to show the outcome of the proposed model in the presence of upper layer network protocols.
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PDF链接：
https://arxiv.org/pdf/1808.00509

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关键词：Alpha 大数据 测量值 SRC RSS 建模 Channel 信道 数据 网络