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QoS Based Independent Carrier Scheduling Scheme for Heterogeneous Services in 5G LTE-Advanced Networks

机译:基于QoS的独立运营商调度方案,用于5G LTE-Advanced Network的异构服务

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Efficient user-level QoS provisioning for heterogeneous services is of vital importance in future 5G Long-Term Evolution-Advanced (LTE-A) with Carrier Aggregation (CA) mobile system. The CA is very vital technique which invented by 3GPP for creating the huge virtual carrier bandwidths to support the 5G LTE-A network with unprecedented speed of data transmission and minimal latency. To the best of our knowledge, in order to support and fulfill the user level QoS obligations for diversified services "Real-Time (RT) and Non-Real-Time (NRT)", one of the main challenges in the presence of CA is to offer robust and suitable resource scheduling scheme. In this paper, the authors propose an efficient scheduling scheme, termed as "QoS based Independent Carrier (QoS-IC)" by designing Round-Robin with Service (RR-S) method for ensuring the load balancing based on the user's service in 5G LTE-A system. In addition, a user-level Quality of Service aware packet scheduling for OFDMA resource blocks suitable to multi-services system is proposed by introducing the service utility factor. The design objective is to guarantee the user quality of service (QoS) and equitable allocation of radio resources among users. Extensive simulation outcomes demonstrate that our scheduling algorithm offers significantly better QoS performance in terms of packet drop rate (PDR), variance of packet drop rate, and the average packet delay for RT traffic as well as satisfying the minimum transmission rate demand for "NRT traffic.
机译:有效的用户级QoS供应在未来的5G长期演进 - 高级(LTE-A)中具有至关重要的重要性,具有载波聚合(CA)移动系统。 CA是非常重要的技术,它由3GPP发明,用于创建庞大的虚拟载波带宽,以支持5G LTE-A网络,其具有前所未有的数据传输速度和最小延迟。据我们所知,为了支持和满足用户级别的多元化服务义务“实时(RT)和非实时(NRT)”,所以CA存在的主要挑战之一是提供强大和合适的资源调度方案。在本文中,作者提出了一种有效的调度方案,通过设计具有服务(RR-S)方法的循环方法来提出称为“基于QoS的独立载波(QOS-IC)”,以确保基于用户服务在5G中的服务LTE-A系统。此外,通过引入服务实用程序因子,提出了适合于多服务系统的OFDMA资源块的服务级别的用户级质量。设计目标是保证用户服务质量(QoS)和用户之间的无线电资源的公平分配。广泛的仿真结果表明,我们的调度算法在数据包下降速率(PDR)方面提供了明显更好的QoS性能,以及数据包跌落率的方差,RT流量的平均数据包延迟以及满足“NRT流量的最小传输速率需求。

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