Modelling and minimization of energy use in 5G networks

与4G长期演进(LTE)相比，5G网络设计为每单位区域提供100倍以上的数据，尽管比特/焦耳的能效总体上有所提高，但5G基站(BS)的能耗仍是4G基站的两倍以上。考虑到这一点，网络能效正成为通信网络的一项重要指标。这项与英国电信合作进行的研究项目旨在使用模型和数据驱动的方法来优化通信网络中的能源效率。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，以在保持服务质量要求的同时最大限度地提高能源效率。然而，传统的BSS通常不会收集这些方法在实践中运行所需的培训或输入数据。解决这个问题的一种有希望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仿真和转移学习的元素，以减少对大量训练数据的需求。目前，最大似然方法通常并不比局部优化技术好，特别是考虑到它们对计算的要求。为了使最大似然方法更具可行性，本研究可以探索最大似然方法的各个方面，如选择合适的最大似然体系结构，确定算法应该以集中式还是分布式的方式部署；另一个研究方向可能是调查BS参数如何影响相邻网络组件。BS参数最常被优化，目的是改善小区内操作。一个BS选择的策略(休眠模式、发射功率等)还会影响相邻节点，例如通过改变业务或干扰级别。人们对这些关系知之甚少，因为很难对这些依赖关系进行建模。随机几何是分析大型无线网络的最先进的数学工具。这类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在空间中均匀分布的一组随机点。该模型已被证明为各种网络变量(中断概率、干扰等)提供了合理易处理的表达式。然而，为了了解各种网络参数之间的关系，我们需要引入一些空间相关性，但这往往会违反PPP中存在的同质性，导致失去数学上的可操作性。考虑到这一点，我们的目标是开发一些方法来对可单独配置的网络组件进行建模，这种方法足够简单，可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.