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Dynamic Spectrum Sharing Models for Cognitive Radio Aided Ad Hoc Networks and Their Performance Analysis

机译:认知无线电辅助Ad Hoc网络的动态频谱共享模型及其性能分析

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In this paper, two dynamic spectrum sharing models are proposed, namely a Markov-chain model and a queue-based model in order, to evaluate the performance of CRAHNs, where the shared Primary Channels (PCs) are complemented by unshared Secondary Channels (SCs). The new contribution of this paper is as follows. Firstly, our Markov-chain model is extendable to any practical number of PCs and SCs and remains accurate for any practical Primary User (PU) and Secondary User (SU) tele-traffic intensity. As a benefit, our technique accurately estimates the maximum number of Secondary Users Per Second (NSUPS) supported by a system, both by queuing analysis and by the Markov-chain model. In addition to our Markov-chain model, we also conceive queue-based models, which generally impose a lower modeling complexity than that of Markov-chain models, although at the cost of being more inaccurate. Our numerical results confirm the reduced evaluation complexity and improved accuracy of the proposed models and analysis.
机译:本文提出了两种动态频谱共享模型,即马尔可夫链模型和基于队列的模型,以评估CRAHN的性能,其中共享的主信道(PC)由非共享的辅助信道(SC)补充)。本文的新贡献如下。首先,我们的马尔可夫链模型可以扩展到任何实际数量的PC和SC,并且对于任何实际的主要用户(PU)和次要用户(SU)远程交通强度都保持准确。作为一项好处,我们的技术可以通过排队分析和马尔可夫链模型来准确估算系统支持的每秒最大辅助用户数(NSUPS)。除了我们的马尔可夫链模型之外,我们还构思了基于队列的模型,该模型通常比马尔可夫链模型具有更低的建模复杂度,但是代价是更加不准确。我们的数值结果证实了所提出的模型和分析的评估复杂度降低,准确性提高。

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