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Joint optimization of energy efficiency and spectrum efficiency in 5G ultra-dense networks

机译:5G超密集网络中能源效率和频谱效率的联合优化

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The heterogeneous deployment of ultra dense small cells such as femtocells in the coverage area of the traditional macrocells is seen as a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the fifth generation wireless network. However, the unplanned and ultra-dense deployment of femtocells will lead to increase in total energy consumption, cross-tier interference (interference between macrocells and femtocells), co-tier interference (interference between neighbouring femtocells) and inadequate QoS provisioning. Therefore, there is a need to develop a radio resource allocation algorithm that will jointly maximize the energy efficiency (EE) and spectrum efficiency (SE) of the overall networks. Unfortunately, maximizing the EE results in low performance of the SE and vice versa. This paper investigates how to balance the trade-off that arises when maximizing both the EE and the SE simultaneously. The joint EE and SE maximization problem is formulated as a multi-objective optimization problem, which is later converted into a single-objective optimization problem using the weighted sum method. An iterative algorithm based on the Lagrangian dual decomposition method is proposed. Simulation results show that the proposed algorithm achieves an optimal trade-off between the EE and the SE with fast convergence.
机译:在传统宏蜂窝的覆盖区域中,超密集型小型蜂窝小区(如毫微微小区)的异构部署被视为一种经济高效的解决方案,可为第五代无线网络中的可持续环境提供网络容量,室内覆盖范围和绿色通信。然而,毫微微小区的无计划和超密集部署将导致总能耗增加,跨层干扰(宏小区与毫微微小区之间的干扰),同层干扰(相邻毫微微小区之间的干扰)以及QoS设置不足。因此,需要开发一种无线电资源分配算法,该算法将共同最大化整个网络的能量效率(EE)和频谱效率(SE)。不幸的是,最大化EE会导致SE的性能下降,反之亦然。本文研究如何平衡同时最大化EE和SE时出现的权衡。将EE和SE联合最大化问题公式化为多目标优化问题,然后使用加权和方法将其转化为单目标优化问题。提出了一种基于拉格朗日对偶分解法的迭代算法。仿真结果表明,该算法在收敛速度快的前提下,实现了EE与SE之间的最优折衷。

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