Energy Efficiency of 5G Radio Access Networks

Abstract: The roll-out of the fifth-generation (5G) wireless networks alongside existing generations and characterized by a dense deployment of base stations (BSs) to serve an ever-increasing number of users and services leads to a drastic increase in the overall network energy consumption (EC). It can lead to an unprecedented rise in operational expenditure (OPEX) for the network operators and an increased global carbon footprint. The present-day networks are dimensioned according to the peak traffic demands, and hence are under-utilized due to the daily traffic variations. Therefore, to save energy, BSs can be put into sleep with different levels following the daily load variations. Selection of the right sleep level at the right instant is important to adapt the availability of the resources to the traffic load to maximize the energy savings without degrading the performance of the network. Previous studies focused on the selection of sleep modes (SMs) to maximize energy saving or the sleep duration given configuration and network resources. However, adaptive BS configuration together with SMs have not been investigated. In this thesis, the goal is to consider the design of the wireless network resources to cover an area with a given traffic demand in combination with sleep mode management. To achieve this, a novel EC model is proposed to capture the activity time of a 5G BS in a multi-cell environment. The activity factor of a BS is defined as the fraction of time the BS is transmitting over a fixed period and is dependent on the amount of BS resources. The new model captures the variation in power consumption by configuring three BS resources: 1) the active array size, 2) the bandwidth, and 3) the spatial multiplexing factor. We then implement a Q-learning algorithm to adapt these resources following the traffic demand and also the selection of sleep levels. Our results show that the difference in the average daily EC of BSs considered can be as high as 60% depending on the deployment area. Furthermore, the EC of a BS can be reduced by 57% during the low traffic hours by having deeper sleep levels as compared to the baseline scenario with no sleep modes. Implementing the resource adaptation algorithm further reduces the average EC of the BS by up to 20% as compared to the case without resource adaptation. However, the EE gain obtained by the algorithm depends on its convergence, which varies with the distribution of the users in the cell, the peak traffic demand, and the BS resources available. Our results show that by combining resource adaptation with deep sleep levels, one can obtain significant energy savings under variable traffic load. However, to ensure the reliability of the results obtained, we emphasize the need to guarantee the convergence of the algorithm before its use for resource adaptation.