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Urgency-Aware Scheduling Algorithm for Downlink Cognitive Long Term Evolution-Advanced

机译:下行认知长期演进-高级的紧急感知调度算法

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Long Term Evolution-Advanced experiences an increasing demand for more radio spectrums to support the escalating demands of Real-Time (RT) and Non Real-Time (NRT) multimedia contents. However most usable radio spectrums have already been licensed. A number of studies reported that some portions of licensed radio spectrums are underutilized. To support the demand for more radio spectrums, cognitive LTE-Advanced that aggregates the LTE-Advanced current radio spectrums with the underutilized licensed radio spectrums from another system via cognitive radio is introduced. Given that packet scheduling is important in meeting the required Quality of Service (QoS) of multimedia contents, this paper proposes an Urgency-Aware Scheduling (UAS) algorithm for use in the downlink cognitive LTE- Advanced. The UAS algorithm takes the required QoS of a user, urgency of each packet, average achievable data rate and average throughput when selecting users to receive packets. Simulation results demonstrate that the proposed algorithm can significantly optimize the number of cognitive LTE-Advanced users with satisfactory RT QoS whilst having acceptable QoS for the NRT packets.
机译:长期演进高级版对更多无线电频谱的需求不断增长,以支持实时(RT)和非实时(NRT)多媒体内容的不断增长的需求。但是,大多数可用的无线电频谱已获得许可。大量研究报告称,许可无线电频谱的某些部分未得到充分利用。为了支持对更多无线电频谱的需求,引入了认知LTE-高级,该认知LTE-高级通过认知无线电将LTE-高级当前无线电频谱与来自另一系统的未充分利用的许可无线电频谱进行聚合。鉴于数据包调度对于满足所需的多媒体内容服务质量(QoS)很重要,因此本文提出了一种用于下行感知LTE-Advanced的紧急感知调度(UAS)算法。当选择用户接收数据包时,UAS算法会获取用户所需的QoS,每个数据包的紧迫性,可达到的平均数据速率和平均吞吐量。仿真结果表明,所提出的算法可以显着地优化具有令人满意的RT QoS的认知型LTE-Advanced用户数量,同时为NRT数据包提供可接受的QoS。

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