Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Alaa M. Mukhtar, R. Saeed, R. Mokhtar, E. Ali, H. Alhumyani
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引用次数: 5

Abstract

Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.
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物联网大流量下下行协调多点联合传输性能评价
新兴的5G蜂窝网络促进了普及物联网的关键赋能技术。不断发展的5G-IoT场景和基础服务,如增强现实、高密度视频流、无人驾驶汽车、电子医疗、智能环境服务等,现在已经无处不在。这些业务负载较大,需要高容量、带宽、数据速率、吞吐量和低延迟。考虑到这些需求,物联网网络在大数据创新背景下提供了全球性的变革,同时也带来了上下行链路连接和流量负载方面的诸多问题。这些包括协调多点处理(CoMP)、载波聚合(CA)、联合传输(jt)、大规模多输入多输出(MIMO)、机器类型通信、集中式无线电接入网(CRAN)等。CoMP是第11版中添加的最重要的技术增强之一,它可以在异构网络实现方法和同构网络拓扑中实现。然而,在具有高流量负载的大规模5G-IoT设备场景中,大多数蜂窝边缘IoT用户都严重受到蜂窝间干扰(ICI)的影响,即用户信号差,数据速率低,QoS有限。这项工作旨在通过在5G-IoT中提出两种符合第18版的DL-JT-CoMP技术来解决这一问题。考虑并评估了两种同构网络CoMP部署场景下的下行JT-CoMP。使用的场景是物联网站点内和站点间的CoMP,使用长期演进(LTE-A)和5G的下行链路系统级模拟器对其性能进行评估。数值模拟结果表明,站点间CoMP的平均UE吞吐量的经验累积分布函数(ECDF)比站点内CoMP的平均UE吞吐量的经验累积分布函数(ECDF)好约4%,站点间CoMP的平均用户实体(UE)频谱效率的ECDF比站点内CoMP的平均UE频谱效率的ECDF好约10%;站间比较的平均信噪比(SINR)的ECDF与站内比较基本相同,站间比较的公平性指数比站内比较高5%。由于用户之间的竞争加剧,公平性指数随着用户数量的增加而下降。
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