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Joint Connection Level and Packet Level Analysis of Cognitive Radio Networks with VoIP Traffic

机译:具有VoIP流量的认知无线电网络的联合连接级别和数据包级别分析

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摘要

In this paper, a novel joint connection-level and packet-level analytical model for the performance evaluation of cognitive radio networks (CRNs) under VoIP traffic is proposed and developed. The proposed teletraffic model captures the most relevant features of both CRNs (i.e., unreliable spectrum sensing, primary users' activity detection) and VoIP traffic (i.e., ON/OFF activity processes for individual VoIP sessions, periodic packet generation of individual VoIP sessions in the ON state). In addition, our mathematical model captures relevant aspects of multiuser VoIP-based networks such as the use of packet buffering, adaptive modulation coding, proportional fair sharing scheduling for secondary users with different link qualities, and call admission control (CAC). Our mathematical analysis is based on the time-scale decomposition technique and employs conventional discrete time Markov chain tools. However, the most important feature of our mathematical formulation is the introduction of the so called cross-level analysis methodology used here to link packet-level and connection-level analyses. This proposed methodology allows to use time-scale decomposition technique when it is no longer possible to perform the connection level analysis in an independent manner from the packet level analysis (i.e., when connection level statistics depend on packet level parameters).
机译:本文提出并开发了一种新的联合连接级和分组级分析模型,用于在VoIP流量下评估认知无线电网络(CRN)的性能。拟议的电信业务模型捕获了CRN(即不可靠的频谱感知,主要用户的活动检测)和VoIP流量(即,各个VoIP会话的ON / OFF活动过程,在VoIP会话中各个VoIP会话的周期性数据包生成)的最相关特征。开启状态)。此外,我们的数学模型还捕获了基于多用户VoIP的网络的相关方面,例如分组缓冲的使用,自适应调制编码,针对具有不同链路质量的辅助用户的比例公平共享调度以及呼叫允许控制(CAC)。我们的数学分析基于时标分解技术,并使用传统的离散时间马尔可夫链工具。但是,我们数学公式的最重要特征是引入了所谓的跨级别分析方法,该方法用于链接数据包级别和连接级别的分析。当不再可能以与分组级别分析无关的方式执行连接级别分析时(即,当连接级别统计信息取决于分组级别参数时),该提出的方法允许使用时标分解技术。

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