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摘要翻译：
本文提出了一个分析大规模ad hoc网络在干扰信道中频谱共享的自组织分布式联盟形成过程的框架。在这种方法中，我们在网络中使用联盟簇的概念，其中不同簇之间的相互依赖是由空间网络相关性的概念来描述的。然后，利用过程的随机模型，我们放弃了联盟博弈的一些细节特征，以便能够包含一些额外的网络规模参数。该模型的应用包括：a）通过闭式方程估计到达大联盟的平均时间及其方差，这些参数在动态环境中设计过程中非常重要；b）对网络中的联盟簇进行维数化；c）对网络空间相关性进行建模，表征干扰链路的相互可见性；d）对新链路激活/失活对联盟形成过程的影响进行建模；e）对链路移动性对联盟形成过程的影响进行建模。
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英文标题：
《Stochastic Models of Coalition Games for Spectrum Sharing in Large Scale
Interference Channels》
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作者：
Ebrahim Karami and Savo Glisic
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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：
In this paper, we present a framework for the analysis of self-organized distributed coalition formation process for spectrum sharing in interference channel for large-scale ad hoc networks. In this approach, we use the concept of coalition clusters within the network where mutual interdependency between different clusters is characterized by the concept of spatial network correlation. Then by using stochastic models of the process we give up some details characteristic for coalition game theory in order to be able to include some additional parameters for network scaling. Applications of this model are a) Estimation of average time to reach grand coalition and its variance through closed-form equations. These parameters are important in designing the process in a dynamic environment. b) Dimensioning the coalition cluster within the network c) Modelling the network spatial correlation characterizing mutual visibility of the interfering links. d) Modeling of the effect of the new link activation/inactivation on the coalition forming process. e) Modeling the effect of link mobility on the coalition-forming process.
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PDF链接：
https://arxiv.org/pdf/1803.03738
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