Talks and Tutorials

As we advance toward the global adoption of 5G networks, it becomes essential not only to boost their speed but also enhance their sustainability. The surge in the number of connected devices and the data explosion they induce has imposed a significant challenge on network operators: how to meet the escalating demand without skyrocketing the energy consumption. Within this context, the energy optimization of base stations emerges as a pivotal aspect of constructing a sustainable and cost-efficient 5G network. Given the myriad factors that impact this energy consumption, having precise models that elucidate how different configurations and parameters affect energy usage is of utmost importance.

The fifth generation (5G) of radio technology is revolutionizing our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. However, while 5G networks offer unprecedented capabilities, there exists a crucial area that demands further enhancement: energy efficiency. Despite achieving around a 4x improvement in energy efficiency compared to 3GPP Long Term Evolution (LTE) deployments, current 5G New Radio (NR) networks still consume up to 3x more energy, leading to heightened carbon emissions and increased operational costs for network operators. In light of the increasing interest in this field, this talk confronts the urgent challenge of 5G energy efficiency from an industrial perspective. We embark on a journey that harnesses the potential of big data and machine learning, exploring how data gleaned from thousands of base stations can be used to construct precise machine learning models to characterize the energy consumption of multi-carrier base stations. The insights gained from this framework is leveraged to create a realistic and analytically tractable power consumption model, which can drive theoretical analyses, standardization, development, and optimization frameworks. Moreover, we peer into the horizon of a future where large language models take center stage, autonomously generating explainable models, thus advancing the pursuit of energy-efficient networks.

As we advance toward the global adoption of 5G networks, it becomes essential not only to boost their speed but also enhance their sustainability. The surge in the number of connected devices and the data explosion they induce has imposed a significant challenge on network operators: how to meet the escalating demand without skyrocketing the energy consumption. Within this context, the energy optimization of base stations emerges as a pivotal aspect of constructing a sustainable and cost-efficient 5G network. Given the myriad factors that impact this energy consumption, having precise models that elucidate how different configurations and parameters affect energy usage is of utmost importance. This is the juncture at which machine learning enters the picture. It potentially offers a robust tool for developing meticulous energy models and optimizing the energy consumption of the network. This challenge is designed to tackle this crucial concern, enabling participants to contribute towards crafting more energy-efficient 5G networks

The talk focuses on the control methods for the management of mobile networks with energy harvesting capabilities. We introduce a two-tier mobile network consisting of macro base stations and renewable energy powered small base stations. Online optimization approaches based on machine learning are proposed as a way to optimize the utilization of the harvested energy by intelligently switching on/off the small base stations. Moreover, Multi-access Edge Computing (MEC) is introduced, where the dynamic placement of functional splits is adopted as a way to further reduce the mobile network energy consumptions.

This project investigates on possible integration architectures between the energy harvesting mobile network and the smart electricity grid. The main scope is to study the capability of 5G mobile networks of intelligently routing energy in a micro grid of interconnected conventional/renewable energy sources and loads, in order to (i) satisfy the demand of communication networks while avoiding energy outages in zones with high user density and/or low ambient energy availability and (ii) to provide ancillary services to the grid.

In this talk we introduce our work in SCAVENGE, an European Training Network. The project is about sustainable design for mobile communication systems by engineering the integration and the control of renewable energy sources within communication network elements, such as base stations and mobile phones. We will describe the main objectives of the Network, detailing the training and research activities that we have been carrying out during our first year of work.

This project investigates on possible integration architectures between the energy harvesting mobile network and the smart electricity grid. The main scope is to study the capability of 5G mobile networks of intelligently routing energy in a micro grid of interconnected conventional/renewable energy sources and loads, in order to (i) satisfy the demand of communication networks while avoiding energy outages in zones with high user density and/or low ambient energy availability and (ii) to provide ancillary services to the grid.

Current trends anticipate that 5G mobile networks will be composed of ultra-dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist to provide the 1000x network capacity increase that is required by 2020. Accordingly, the traditional macro cell layer will be complemented or replaced with multiple overlapping tiers of smaller cells, which extend the system capacity, thanks to a higher spatial reuse and to a better spectral efficiency. We introduce a two-tier cellular network architecture, where self-sustainable small cells (SCs), solely relying on energy harvesting and storage, can offload the grid-connected macro base station. The available energy at the SC must be allocated in an optimal way, in order to jointly minimize the consumption of grid energy and maximize the system performance. The proposed offline optimal algorithm, based on the label correcting algorithm, finds the optimal energy allocation policy, after transforming the problem into a shortest-path problem.

The fifth generation (5G) of radio technology is changing our everyday lives, by enabling a plethora of new use cases, through its better coverage, larger capacity and massive connectivity. Thanks to its ultra-reliable low-latency communications, 5G also allows a high degree of automation, thus helping to expand cellular systems into new ecosystems. Importantly, 5G has already become an integral part of governmental and industrial environmental programs, as it is envisioned that an intelligent exploitation of their resources through 5G will significantly decrease carbon emissions. Despite its unprecedented capabilities, however, 5G networks must further improve in certain key technology areas, particularly in that of network energy efficiency. While current third generation partnership project (3GPP) new radio (NR) deployments provide an improved energy efficiency of around 4x w.r.t. 3GPP long term evolution (LTE) ones, they still consume up to 3x more energy, resulting in increased carbon emissions and electricity bills for operators. Even if the 3GPP NR specification provides a rich set of tools to meet IMT-2020 energy efficiency requirements, it is important to note that one of the main challenges to 5G network energy efficiency is the complexity of their optimization in wide-area deployments: a large-scale, stochastic, non-convex and non-linear optimization problem. In light of the increasing interest in this field, this one-of-a-kind tutorial shares the author’s industrial view on the 5G energy efficiency challenge. This tutorial provides a detailed, up-to-date overview of the most relevant technologies that a 5G radio access network can use to increase its energy efficiency from both a theoretical and practical perspective. Moreover, this tutorial shows how increasing the network energy efficiency by exploiting such technologies in practical scenarios highly depends on the accuracy of the models used to characterize the network. In this line, this tutorial exhaustively surveys and presents machine learning techniques which are being used to create accurate network models for most network components and processes, and optimize a large-scale 5G network.

The fifth generation (5G) of radio technology is revolutionizing our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. Despite its capabilities, however, 5G networks must improve in certain key technology areas, such as that of energy efficiency. While current 3GPP NR deployments provide an improved energy efficiency of around 4x w.r.t. 3GPP LTE ones, they still consume up to 3x more energy. Even if the 3GPP NR specification provides several tools to meet IMT-2020 energy efficiency requirements, one of the main energy consumption challenges of 5G networks is the complexity of their optimization in wide-area deployments: A large-scale, stochastic, non-convex and non-linear optimization problem. In light of the increasing interest in this field, this one-of-a-kind tutorial shares the author's industrial view on this 5G energy efficiency problem. In details, the tutorial provides a fresh look at energy efficiency enabling technologies in 3GPP NR. Moreover, by leveraging on the concepts of big data and machine learning, the tutorial presents practical scenarios in which data collected from thousands of base stations can be used to derive accurate machine learning models for the main building blocks of the energy efficiency optimization problem.