A Holistic Approach for Lifetime Prolonging and Femto Base Station’s Sleeping Strategy in HetNets

Abdullah Mohammed Alqasir, A. Kamal
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Abstract

While Femto Base Stations (FBSs) are promising solutions for providing the required throughput for end users, they may impact energy efficiency, especially, as the density of the FBSs increases significantly. In this paper we focus on enhancing energy efficiency in heterogenous networks, where Macro BSs (MBSs) and FBSs co-exist, by presenting a sleeping strategy. In the sleeping strategy FBSs serving few users are turned off and their users and resources are offloaded to neighboring FBSs. However, adapting the sleeping strategy will affect the lifetime of the FBSs, due to the frequent change of power level between turning ON and OFF the FBS. Therefore, in order to maximize energy savings, we formulate an optimization problem that provides an optimal user association and FBSs sleeping strategy for the entire network, while minimizing the total numbers of the power change for every FBS. Since the problem is NP-hard, we propose a clustering heuristic approach, where we divide the network into smaller clusters and solve the optimization for each cluster independently. Simulation results show that the clustering heuristic approach performs close to the optimal approach with the energy consumption being reduced significantly.
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HetNets中延长寿命的整体方法及Femto基站睡眠策略
虽然Femto基站(FBSs)是为最终用户提供所需吞吐量的有前途的解决方案,但它们可能会影响能源效率,特别是当FBSs的密度显著增加时。在本文中,我们重点关注通过提出睡眠策略来提高宏观BSs和FBSs共存的异构网络中的能源效率。在休眠策略中,服务少量用户的FBSs被关闭,其用户和资源被卸载到邻近的FBSs。然而,由于FBS在打开和关闭之间的功率水平频繁变化,采用休眠策略将影响FBS的寿命。因此,为了最大限度地节省能源,我们制定了一个优化问题,为整个网络提供最优的用户关联和FBS睡眠策略,同时最小化每个FBS的功率变化总数。由于问题是np困难的,我们提出了一种聚类启发式方法,我们将网络分成更小的簇,并独立解决每个簇的优化问题。仿真结果表明,聚类启发式算法性能接近最优算法,能耗显著降低。
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