Optimal Deployment of Heterogeneous Wireless Nodes in Integrated LTElWi-Fi Networks

Noha A. Elmosilhy, Ahmed M. Abd El-Haleem, M. M. Elmesalawy
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引用次数: 1

Abstract

Optimal placement of small cells in integrated LTE/Wi-Fi heterogeneous network architecture is considered one of the key approaches that can be used to increase the system capacity and enhance the coverage to meet the unexpected explosion of mobile data traffic. Co-operation and interworking between different Radio Access Technologies (RATs) introduce the LTE/Wi-Fi Aggregation (LWA) which allows the traffic aggregation between Wi-Fi Access Point (WAP) and LTE small cell on the level of Radio Access Network (RAN). In this paper, the effectiveness of the optimal deployment of heterogeneous wireless small nodes in a hotspot zone is considered and explored. The deployment of different wireless small nodes is formulated as an optimization problem with the objective of (i) Maximizing the total system throughput while considering the minimum received Signal-to-Interference-plus-Noise Ratio (SINR)/Signal-to-Noise Ratio (SNR) requirements for LTE/Wi-Fi coverage (ii) Choosing the optimal number of small cells which can guarantee the coverage of the considered hotspot zone (iii) Choosing the optimal formation of WAPs Basic Service Sets (BSSs). The objective function is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem and solved using genetic algorithm. The performance of the proposed optimal deployment approach is compared to the uniform distributed deployment algorithm in terms of system throughput in which a significant improvement is noticed when the proposed approach is adopted.
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综合LTElWi-Fi网络中异构无线节点的优化部署
在集成的LTE/Wi-Fi异构网络架构中,小蜂窝的优化布局被认为是增加系统容量和增强覆盖以满足移动数据流量意外爆炸的关键方法之一。不同无线接入技术(rat)之间的合作和相互作用引入了LTE/Wi-Fi聚合(LWA),它允许Wi-Fi接入点(WAP)和LTE小蜂窝在无线接入网络(RAN)级别上进行流量聚合。本文对热点区域异构无线小节点优化部署的有效性进行了考虑和探讨。不同无线小节点的部署被定义为一个优化问题,其目标是(i)在考虑LTE/Wi-Fi覆盖范围的最小接收信噪比(SINR)/信噪比(SNR)要求的同时最大化系统总吞吐量(ii)选择能够保证所考虑的热点区域覆盖的最优小蜂窝数量(iii)选择最优wap基本服务集(bss)的形成。将目标函数表述为一个混合整数非线性规划(MINLP)问题,并采用遗传算法求解。在系统吞吐量方面,将所提出的最优部署方法与均匀分布式部署算法进行了比较,发现采用所提出的方法可以显著提高系统吞吐量。
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