The importance of spatio-temporal infrastructure assessment: Evidence for 5G from the Oxford–Cambridge Arc

Journal article

The roll-out of 5G infrastructure can provide enhanced high capacity, low latency communications enabling a range of new use cases. However, to deliver the improvements 5G promises, we need to understand how to enhance capacity and coverage, at reasonable cost, across space and over time. In this paper, we take a spatio-temporal simulation modeling approach, using industry-standard engineering models of 5G wireless networks, to test how different infrastructure strategies perform under scenarios of uncertain future demand. We use coupled open-source models to analyze a UK growth corridor, a system-of-cities comprising 7 urban areas, known as the Oxford-Cambridge Arc. We find that population growth has a marginal impact on total demand for 5G (up to 15%), as the main factor driving demand is the increase in per user data consumption resulting mainly from video. Additionally, the results suggest only limited justification for deploying 5G based purely on the need for more capacity. Strategies which reuse existing brownfield Macro Cell sites are enough to meet future demand for Enhanced Mobile Broadband, except in the densest urban areas. While spatio-temporal analysis of infrastructure is common in some sectors (e.g. transport, energy and water), there has been a lack of open analysis of digital infrastructure. This study makes a novel contribution by providing an open and reproducible spatio-temporal assessment of different 5G technologies at a time when 5G is starting to roll-out around the world.

Authors: Oughton, EJ; Russell, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Computers, Environment and Urban Systems
• Volume: 83
• Article number: 101515
• Publication date: 2020-06-15
• Acceptance date: 2020-06-05
• DOI: 10.1016/j.compenvurbsys.2020.101515
• ISSN: 0198-9715
• Language: English
• Keywords: FFR; simulation; 5G; telecommunications; infrastructure; spatio-temporal; assessment