One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in tremendousincrease in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network but most of the proposed methods are computationally demanding which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEllswItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed tobe optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that the THESIS produces a significant reduction in the energy consumption of the UDHN and is able to reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.
翻译:5G网络的主要能力增强器之一是部署超常混合网络(UDHNs),但这一部署导致网络的能源消耗因所涉基础站数量众多而大幅增加。除了提高能力外,5G网络还必须具有能源效率,才能使其在经济上可行和环保。动态细胞转换是降低网络总能源消耗的一个非常常见的方法,但大多数拟议方法都是计算上的要求,使得这些网络不适合在使用大量BS的超常网络部署中应用。为解决这一问题,我们建议采用一个轻量细胞转换计划,也称为基于门槛的混合电动转换计划(HEPSIS),以优化UDHNs的能源。发达的方法结合了集群和彻底搜索(ES)算法的好处,以产生一种解决办法,其最佳性接近于ES(保证最佳),但计算上比ES效率更高,而且即使在实际网络的尺寸较大时,也可将其用于转换。我们提议,为了在UDHSIS实现快速的能源消耗量减少,而绩效评估显示,从UDIS的复杂度从接近于UDSA,其快速的解决方案能够使UDIS的能量减少。