HEAVY.AI Launches Industry’s First Digital Twin for Telco Network Planning, Building and Operations, Leveraging the NVIDIA Omniverse Platform
HeavyRF solution offers a comprehensive view of how network assets serve customer experience at any scale; Charter Communications to collaborate for first deployment.
HEAVY.AI, an innovator in advanced analytics, announced HeavyRF, an extension of the company’s deep analytics platform that uses NVIDIA Omniverse to create digital twins that help telco network operators speed deployments of wireless networks.
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The industry’s first radio frequency (RF) digital twin solution, HeavyRF enables telcos to simulate potential city-scale deployments as a faster, more efficient way of optimizing cellular-tower and base-station placements for best coverage.
“5G infrastructure is fundamentally very different from 4G infrastructure: It requires far more towers and antennas to provide the highest level of coverage and hence customer satisfaction. Because of that heightened demand, telcos can’t spend the same amount of time planning and locating each piece of 5G infrastructure,” said Jon Kondo, CEO of HEAVY.AI.
HeavyRF allows telcos to review historic and current network-performance data as they test site-configuration scenarios against physical impediments, such as trees and dense buildings. It uses GPU-accelerated analytics to seamlessly combine customer-demographic and behavioral data with real-time geophysical mapping.
HEAVY.AI also announced it is extending its collaboration with Charter Communications, the nation’s second largest cable provider. “With the advent of 5G and an expanding user base, Charter needed a solution to help us map increasingly complex RF scenarios. HEAVY.AI’s new digital twin capabilities enable us to explore and fine-tune our expanding 5G networks in ways that weren’t possible before,” said Jared Ritter, Senior Director, Analytics & Automation at Charter Communications.
With NVIDIA Omniverse, HeavyRF is instantly compatible with a large set of complementary tools for reality capture, as well as advanced antenna and network tools leveraging machine learning. These can be used to design networks even in complexly dynamic environments such as ports and to simulate software-defined networks down to the packet level.
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“NVIDIA Omniverse is the ideal foundation for HEAVY.AI’s new HeavyRF module and digital twin application framework,” said Ronnie Vasishta, Senior Vice President of Telecom at NVIDIA. “Omniverse is a development platform designed to help customers solve real-world problems with custom-built 3D and simulation pipelines — and with HEAVY.AI’s massive data analytics platform, telcos can virtually place 4G and 5G towers long before trucks roll in for installation.”
Telcos are increasingly moving to a “plan, build, operate” model to accelerate business decision-making while optimizing capital expenditures. With this new module, the HEAVY.AI platform supports this approach by tightly integrating key business data; such as mobility and parcels data, customer experience data and RF planning workflows.
HeavyRF allows telcos to minimize site-deployment costs while maximizing quality of service for both entire populations as well as targeted demographic and behavioral profiles. The module supports network planning and coverage mapping at unprecedented speed and scale. This can be used to rapidly develop and evaluate strategic rollout options, including thousands of microcells and non-traditional antennas. Simulations can be run against full-resolution, physically precise LiDAR and clutter data interactively at metro regional scale, which avoids downsampling needs and false service qualifications.
Unlike conventional engineering tools, HeavyRF achieves accurate results in seconds and supports live and interactive analysis of relevant business metrics. As a result, engineers and business subject-matter experts can rapidly collaborate, radically reducing planning cycles. In addition, HeavyRF is the only RF propagation solution that can analyze multiple layers of data at once, allowing users to understand the full context of the simulated environment, including the location and material composition of obstructions, as well as customer locations and activity, so they can make the most informed and profitable tower-placement and optimization decisions.
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