Precision planning process of small cell siting and implementation of Machine Learning (ML) & Artificial Intelligence (AI) in 5G network design can reduce the cost of deployments while optimizing coverage.
The demand for mobile data is driving network densification with the deployment of small cells. Though 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.
Telecom engineers must focus on measurements of network quality, signal strength and quality, traffic patterns and other topographical considerations for maximizing a network operators’ return on capital investment.
Artificial Intelligence & Machine Learning models in small cell design and siting efforts can provide optimal coverage and throughput with the most efficient capital investment.
By using big data analytics, including machine learning to digitally model specific use cases, will deliver better returns on investment (RoI) for network evolution plans and hence a better business outcome.
Artificial Intelligence & Machine Learning technologies can enable remarkable capital and operational efficiencies, where the design software learns and adapts to draw on many inputs, each providing an immense amount of granular data to inform decisions.
Machine learning models should be part of any small cell design effort. Different inputs and assumptions will be factors in the resulting models that are generated.
The aggregation of very large data sets is important to provide algorithms with sufficient test data to inform results. These data sets provide algorithms with information on factors such as power and backhaul availability, signal to interference ratio, spectral efficiency, line of sight, traffic estimates, overlapping cell coverage, agreement with site owners and other considerations.