Precision Planning for 5G Era Networks with Small Cells

Small Cell Forum, in partnership with 5G Americas has published a new whitepaper titled 'Precision planning for 5G Era networks with small cells'.

According to the press release:

The whitepaper 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 full whitepaper is available for download here.

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.

The report details recommended best practices for precision planning including:

• For maximum return on investment, small cells should be placed as close as possible to demand peaks; best practice is within 20-40m.
• Network operators would like equipment that estimates location of usage and quality reports to adopt smarter algorithms such as the machine learning approach demonstrated. Median locate errors less than 20m are expected for small cell planning purposes.
• 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.

In addition, the aggregation of very large data sets are 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 requirements with site owners, and numerous other considerations.

The paper is available for free download on the 5G Americas website, as well as the Small Cell Forum Release site. Blog posts by 5G Americas and Small Cell Forum are also available, along with presentation slides.

People interested in this topic can also check out the video by Small Cell Forum Chief Strategy Officer (CSO), Julius Robson below.



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