Precision planning for 5G Era networks with small cells
This paper, produced in collaboration with 5G Americas, shows significant savings in 5G deployment costs when using AI and machine learning for cell siting. This important piece of work is already proving influential, both in and outside SCF.
Accurate planning of the location of small cells has always been important. However, the challenges of planning the best sites to achieve well-targeted, interference-free signals for 4G and 5G, whilst keeping costs under control, have increased significantly in line with the need for very high levels of data usage and truly ubiquitous coverage.
A Manhattan case study shows how 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 examines why measurements of network quality, signal strength and quality, traffic patterns, and other topographical considerations are important for maximizing a network operator ROI, and demonstrates how including AI and machine learning models in small cell design and siting efforts can provide optimal coverage and throughput with the most efficient capital investment.