Precision Planning for 5G Era Networks with Small Cells, which explores the precision planning process of small cell siting and identifies how employing Machine Learning (ML) and Artificial Intelligence (AI) in network design can help to reduce the cost of deployments while optimizing coverage over traditional manual methods. The white paper was created by working teams at the two industry associations and includes project leadership contributions from: AT&T, iBwave, Keima and Nokia.
The ever-increasing demand for mobile data is driving network densification with the deployment of small cells. Although lower cost than macro towers, the compact, low-power nature of small cells 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.
Manhattan, New York was one example used in the white paper where AI and algorithmic ML automated design processes were able to provide coverage and dominance while reducing the number of sites required from 185 to just 111. This reduction provided significant savings while additionally creating optimized coverage.
The paper also examines why measurements of network quality, signal strength and quality, traffic patterns, and other topographical considerations are important for maximizing a network operators’ return on capital investment, and 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.