Digital communications are an essential part of a national infrastructure system. Hence, the UK Government is committed to becoming a leader in the deployment of 5G, and also has ambitions to rollout Fibre-To-The-Premises (FTTP) to the majority of households by the mid 2020s. Consequently, the Infrastructure Transitions Research Consortium has developed an open-source software tool for assessing both mobile and fixed networks that can be used to evaluate different digital infrastructure strategies.
The simulation-based model uses data from a range of sources, including population forecasts, system capacities, coverage and data demands, and budget constraints, to examine how different strategies for implementation could be designed. The outcomes can be used to inform government decision-making, as well as for businesses looking towards the future.
Illustration of the estimation process: Population, imagery, terrain and OpenStreetMap layers are combined to develop demand inputs which can be fed into a simulation model based on least-cost network design to produce high resolution digital infrastructure analytics.
Full spatio-temporal results can be obtained from the model to help inform local, national or international decisions. Spatially the results can be very granular, for example in the UK postcode sectors are used (~9000). The temporal resolution of the simulation model is annual.
So far, the model has been applied to many countries globally including the UK, the Netherlands, Mexico, Peru, Albania, Pakistan, Senegal, Côte d’Ivoire, Mali, Malawi, Tanzania, Uganda and Kenya.
Figure 1 provides an example of the 5G strategy results for the Netherlands, demonstrating the potential cost per urban, suburban and rural user for different scenarios.
Figure 2: Example of the cost composition of national broadband strategies.
Additional features of the digital communications model include:
The high-resolution digital infrastructure assessment results can be used to provide both analytical insight and support for a range of operational decisions. The capability developed is best suited to evaluate three different areas of decision making, which include:
- Technology options relating to the different types of technologies that could be used to design and deploy a fixed or wireless network, given different capacity, coverage and cost implications
- Business model options helping to inform the degree to which infrastructure sharing strategies can reduce cost and improve coverage to unviable rural and remote locations
- Regulatory/policy options quantifying the coverage impacts of universal service obligations, spectrum auction prices and the level of taxation imposed on the telecommunications sector.
The analytics produced for each of these areas can be of great value to infrastructure operators, governments and other digital ecosystem actors, as well as international aid organisations working to reduce the digital divide.
Figure 3 provides an example of the square kilometre cost for rolling out universal service broadband across the African continent using 4G and a wireless backhaul connection.
Figure 3: Square kilometer cost for 4G universal broadband using wireless backhaul (n=6395).
The Cambridge Digital Communications Assessment Model (cdcam) is a decision support tool to quantify the performance of national digital infrastructure strategies for mobile broadband, focussing on 4G and 5G technologies.
The Python Telecommunications Assessment Library (pytal) enables the assessment of fixed and wireless telecommunication infrastructure, with the ultimate aim of helping to connect more people to a faster internet.
The Longley-Rice Irregular Terrain Model (itmlogic) is a Python implementation of the classic Longley-Rice propagation model (v1.2.2) and capable of estimating the signal propagation effects resulting from irregular terrain.
Policy Options for Digital Infrastructure Strategies (podis) allows transparent and reproducible analysis of policies options for improving digital infrastructure access, thus contributing to sustainable economic development.
Telecoms Analytics for Demand using Deep Learning (taddle) is a predictive codebase for estimating telecoms demand in areas of poor data availability. We provide the codebase to both recreate the developed models, and demonstrate how to use these models to make spatially granular prediction maps.
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