Modelling and minimization of energy use in 5G networks

与4G长期演进（LTE）相比，5G网络的设计目标是每单位面积提供100倍以上的数据，尽管以比特/焦耳为单位的能源效率总体上有所提高，但5G基站（BS）的能耗仍然是4G基站的两倍多。考虑到这一点，网络能源效率正在成为通信网络的主要优点。该研究项目是与英国电信合作进行的，旨在利用模型和数据驱动的方法来优化通信网络的能源效率。例如，机器学习（ML）方法已被用于预测网络流量和/或优化BS参数，以最大限度地提高能源效率，同时保持服务质量要求。然而，传统的BSs通常不收集这些方法在实践中工作所需的训练或输入数据。对于处理这个问题，一个很有前途的方法是将分析模型与机器学习结合使用。这种方法可能会结合模拟和迁移学习的元素，以减少对大量训练数据的需求。目前，机器学习方法通常并不比局部优化技术更好，特别是考虑到它们苛刻的计算需求。为了使机器学习方法更加可行，本研究可以探索机器学习方法的各个方面，例如选择合适的机器学习架构，以及确定算法应该以集中还是分布式的方式部署。另一个研究方向可能是研究BS参数如何影响相邻网络组件。BS参数通常是为了改善细胞内操作而优化的。一个BS选择的策略（睡眠模式、发射功率等）也会影响相邻节点，例如通过改变流量或干扰水平。由于很难对这些依赖关系进行建模，因此很难理解这些关系。随机几何是分析大型无线网络的最先进的数学工具。这种网络最常见的模型是所谓的泊松点过程（PPP）模型，该模型将网络组件视为均匀分布在空间中的随机点集。该模型已被证明可以对各种网络变量（中断概率、干扰等）产生合理的易于处理的表达式。然而，为了理解各种网络参数之间的关系，我们需要引入一些空间依赖性，但这往往会违反购买力平价中存在的同质性，从而导致数学可追溯性的丧失。考虑到这一点，我们可以致力于开发一些方法，以一种足够简单的方式为单独可配置的网络组件建模，以提供一些设计洞察力。

Compared to 4G long-term evolution (LTE), 5G networks are designed to provide 100 times more data per unit area and despite an overall increase in energy efficiency in bits/joule, 5G base stations (BS) still consume more than twice as much energy as their 4G counterparts. With this in mind, network energy efficiency is becoming a major figure of merit in communication networks. This research project, which is conducted in collaboration with BT, will be aimed at using models and data-driven methods to optimize energy efficiency in communication networks.Machine learning (ML) methods, for example, have been used in order to forecast network traffic and/or optimize BS parameters to maximize energy efficiency while maintaining quality of service requirements. Traditional BSs, however, don't usually collect the training or input data required for these methods to work in practice. A promising approach which has been suggested for dealing with this problem is the use of analytic models in conjunction with machine learning. This approach would possibly incorporate simulations and elements of transfer learning to reduce the demand for extensive training data.Currently, ML methods are usually no better than local optimization techniques, especially given their demanding computational requirements. In hopes of making ML methods more viable, this research could explore various aspects of the ML approach, such as selecting the appropriate ML architecture and determining whether the algorithms should be deployed in a centralized or distributed manner.Another research direction could be to investigate how BS parameters affect adjacent network components. BS parameters are most often optimized with the intent of improving intra-cell operation. The policies chosen by one BS (sleep modes, transmit power, etc.) also affect adjacent nodes for example by shifting traffic or interference levels. These relationships are poorly understood as it is quite difficult to model these dependencies. Stochastic geometry is the state-of-the-art mathematical tool for analyzing large wireless networks. The most common model for such networks is the so-called Poisson point process (PPP) model which considers network components as a random set of points uniformly distributed in space. This model has been shown to yield reasonably tractable expressions for various network variables (outage probability, interference, etc.). However, to understand the relationships between various network parameters we would need to introduce some spatial dependence, but this tends to violate the homogeneity present in PPPs resulting in the loss of mathematical tractability. Considering this, we could aim to develop some method of modeling individually configurable network components in a way which is simple enough to provide some design insight.