稠密有限区域5G mmWave的空间-频谱干扰模型 网络

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摘要翻译：
在亚6 GHz频段拥挤不堪的情况下，毫米波频段为下一代5G无线标准提供了一个很有前途的替代方案。然而，mmWave信号对严重的路径丢失和阴影的敏感性要求使用高定向天线来克服这些不利特性。用定向波束构建网络改变了干扰行为，因为窄波束容易受到阻塞的影响。这种对阻塞的敏感性导致活动干扰节点位置的不确定性。在应用动态信道和频率分配支持5G应用时，配置不确定性也可能体现在频谱域。在本文中，我们首先提出了一个考虑mmWave规范的阻塞模型。随后，利用所提出的阻塞模型，我们推导了一个密集有限区域5G mmWave网络的空间-频谱干扰模型。提出的干扰模型在节点配置上同时考虑了空间和频谱的随机性。最后，根据误码率(BER)和中断概率度量，从任意定位用户的角度计算网络的误码性能。通过Monte-Carlo仿真验证了分析结果的正确性。结果表明，考虑mmWave规范以及频谱和空间节点配置的随机性会导致明显不同的干扰剖面。
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英文标题：
《A Spatial-Spectral Interference Model for Dense Finite-Area 5G mmWave
  Networks》
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作者：
Solmaz Niknam, Balasubramaniam Natarajan and Reza Barazideh
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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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英文摘要：
  With the overcrowded sub-6 GHz bands, millimeter wave (mmWave) bands offer a promising alternative for the next generation wireless standard, i.e., 5G. However, the susceptibility of mmWave signals to severe pathloss and shadowing requires the use of highly directional antennas to overcome such adverse characteristics. Building a network with directional beams changes the interference behavior, since, narrow beams are vulnerable to blockages. Such sensitivity to blockages causes uncertainty in the active interfering node locations. Configuration uncertainty may also manifest in the spectral domain while applying dynamic channel and frequency assignment to support 5G applications. In this paper, we first propose a blockage model considering mmWave specifications. Subsequently, using the proposed blockage model, we derive a spatial-spectral interference model for dense finite-area 5G mmWave networks. The proposed interference model considers both spatial and spectral randomness in node configuration. Finally, the error performance of the network from an arbitrarily located user perspective is calculated in terms of bit error rate (BER) and outage probability metrics. The analytical results are validated via Monte-Carlo simulations. It is shown that considering mmWave specifications and also randomness in both spectral and spatial node configurations leads to a noticeably different interference profile.
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PDF链接：
https://arxiv.org/pdf/1710.04284

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关键词：wave WAV AVE Applications Optimization node 计算 提出 频谱 GHz