‘Precision Planning for 5G Era Networks with Small Cells’ was created by teams at the two industry associations and includes contributions from AT&T, iBwave, Keima and Nokia. It identifies using Machine Learning (ML) and Artificial Intelligence (AI) in network design to help reduce the cost of deployments while improving coverage over traditional manual methods.
The ever-increasing demand for mobile data is driving network densification with the deployment of small cells. Small cells have a lower cost than macro towers, but their compact, low-power nature means they also serve a smaller area. This in turn means they need to be located closer to demand hotspots in order to effectively cover the mobile data demands of customers.
The white paper includes an example of how AI and algorithmic ML automated design processes can provide coverage and dominance while reducing the number of sites required. It demonstrates how Manhattan, New York can reduce the number of sites required from 185 to 111, with little impact on the coverage achieved. This 40 per cent reduction provided significant savings while expanding coverage.
Prabhakar Chitrapu, chair of Small Cell Forum, commented, “Small cells will form one of the foundations on which 5G is built, particularly through dense HetNets in spectrum-hungry urban areas. It is essential that as an organisation we consider the implications of this, and work to ensure that processes are in place to make the deployment of these cells viable. The potential for AI and ML is tremendous, and investing in good planning of small cells now can reap huge rewards later.”
Chris Pearson, president of 5G Americas added, “Machine intelligence and algorithms are tools that can enable excellent small cell siting efficiencies. It will be imperative for operators and their vendor partners to work on integrating massive amounts of data with these new network design capabilities.”
The paper examines why measurements of network quality, signal strength and quality, traffic patterns, and other topographical considerations are important for maximising a network operators’ return on capital investment.
It also demonstrates how including AI and ML models in small cell design and siting efforts can provide optimal coverage and throughput with the most efficient capital investment.
The paper is available here.
