为机会网络生成和设计频谱感知模块的框架

Oladiran G. Olaleye, Alaa Ali, Ahmed Aly, M. Iqbal, D. Perkins, M. Bayoumi
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引用次数: 1

摘要

为了提高机会网络(OppN)的频谱利用率,可以通过综合和分析静态和动态网络参数来生成频谱感知(SA)指标。然而,有几种评估网络参数的技术和算法。因此,需要一个标准化的框架来生成SA以改善频谱消耗。据我们所知,目前文献中还没有这样的框架。因此,在这项工作中,我们提出了一种新的方法来构建、验证和改进OppN的SA模块。我们的方法将不同的SA度量映射到网络参数,我们将其定义为SA积分。我们推导了度量和积分之间的比例常数(SA常数)作为可靠性,计算复杂性和延迟的函数。派生的SA常数可用于证明和比较SA技术和算法。基于对一个自组织认知无线电网络的建模和仿真结果,我们利用QualNet说明了SA常数作为一个指标(0.0 < SA常数< 1.0)的适用性,该指标用于量化任意指定的SA模块可衍生的SA水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Framework for generating and designing spectrum awareness modules for opportunistic networking
For improved spectrum utilization in opportunistic network (OppN), spectrum awareness (SA) metrics can be generated from the synthesis and analysis of both static and dynamic network parameters. However, there are several techniques and algorithms for evaluating network parameters. Hence, a standardized framework is needed for generating SA towards improved spectrum consumption. To the best of our knowledge, no such framework currently exist in literature. In this work, therefore, we propose a novel approach for constructing, justifying and improving SA modules for OppN. Our approach maps different SA metrics to network parameters, which we define as SA integrals. We derive the constant of proportionality (SA constant) between the metrics and the integrals as a function of reliability, computational complexity and latency. The derived SA constants can be used to justify and compare SA techniques and algorithms. Based on the results obtained from the modeling and simulation of an adhoc cognitive radio network, using QualNet, we illustrate the applicability of SA constant as an index (0.0 < SA constant < 1.0) for quantifying the level of SA derivable from any specified SA module.
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