Modelling and minimization of energy use in 5G networks

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.