Tutorial 2

Tutorial 22023-05-09T11:42:04+00:00

Data-driven modelling and optimization of green future mobile networks

Tuesday, 6 June 2023, 14:00-15:30/16:00-17:30, Room G1
  • Nicola Piovesan (Huawei Technologies, FR)
  • Antonio De Domenico (Huawei Technologies, FR)
  • David Lopez Perez (Universitat Politècnica de València, ES)

Motivation and Context

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.

Structure and Content

The tutorial is structured into three parts

Part I: Energy efficiency in 5G networks (~30min)

  • The energy efficiency challenge in 5G networks
  • Main hardware and software energy efficiency enablers
    • Overview on recent NGMN Green Future Network project guidelines

Part II: Advanced sleep modes and traffic redistribution (~30min)

  • Advanced sleep mode solutions: carrier, channel and symbol shutdown
  • Traffic redistribution/load balancing for network energy savings
  • Overview on recent 5G NR energy efficiency specificationenhancements

Part III: 5G NR network energy efficiency modelling and optimization (~2h)

  • 5G NR energy efficiency optimization problems and their complexity
  • Big data and ML for accurate 5G NR network modeling
  • Traffic demand forecasting
  • ML-based base station power consumption modelling
  • Data-driven load transfer modelling due to cell shutdown and traffic redistribution/mobility load balancing
  • ML- and white box-based large-scale network modelling of energy efficiency and spectral efficiency
  • Optimization of energy consumption and network throughput tradeof
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