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.



Related Posts:

• The 3G4G Blog: Keima Automated 4G / 5G HetNet Design
• 3G4G Small Cells Blog: Small Cell Forum Releases 5G FAPI API Specifications

Small Cells Deployment Stories

I recently got an opportunity to hear about the small cell deployment studies, organised as SCWS pre-conference workshop. The combined slides from the presentation are embedded below and available to download from Small Cell Forum page here.

Summary of Small Cell Forum Champions day

Small Cell forum held its champions day in Rome this month. There were some interesting case studies and presentations (details below). I have embedded some presentations and provided links to others. Interested people, feel free to explore further.

The Small Cell Forum has identified six key work items where they will be focusing their energies. These are:

• Small cells in Enterprise
• License Exempt Spectrum
• HetNet & SON
• Virtualization of small cells
• Multi operator support
• The role of small cells in 5G, IOT & M2M

Spidercloud did a presentation on Enterprise small cells. They were also one of the sponsors for a study by analyst firm iGR that showed strong demand among Enterprises for Managed Services based on Small Cells.

Cisco shared a case study from a university campus deployment where existing WI-FI APs were ‘upgraded’ to add a small cell capability.

Quortus demonstrated the range of architectures possible with virtualized small cell core networks including the on site MEC server supporting small cells across an enterprise and mission critical small cells supporting public safety applications. See presentation below:

iBwave showed how deployment within the enterprise had improved, with a case study which reduced indoor small cell planning down to one site visit.

MVNO TalkTalk outlined their plans to add LTE small cells to their home routers enriching customer experience as well increasing traffic offload from the macro network. The residential 4G small cells use a dedicated 3.3MHz carrier frequency already compatible with existing 4G handsets to provide good coverage indoors and in the surrounding streets.

Nokia demonstrated the importance of 3D thinking when planning small cell HetNets in dense urban indoor and outdoor environments due to building and user topography.

Qualcomm described how their SON technology provides zero touch integration for both the small cells and the macros, optimizing handovers in both directions.

Huawei shared their vision for small cell evolution, incorporating emerging technologies which leverage license exempt spectrum. Their demonstration of LAA mobility with Vodafone notching up 600Mbps peak rates clearly showing the potential of a joined-up approach to spectrum.

Airspan trials with SoftBank demonstrated an early nFAPI implementation working in a virtualized small cell / macro HetNet. The small cells filled in coverage gaps, and their densification increased capacity. Centralised CoMP and eICIC were demonstrated over a pre-standard nFAPI which works over commonly available packet based transport with significantly less stringent performance requirements than required with CPRI based C-RAN.