The AI-based propagation model enables mobile operators to drive 5G planning and delivery

Infovista, the network lifecycle automation company, has announced the availability of its Artificial Intelligence Model (AIM). The commercially available AI-based propagation model aims to transform the way wireless networks can be planned and optimised.

Régis Lerbour, VP Product & R&D, RAN Engineering at Infovista, said, “Operators are at different stages within the 5G rollout, but the majority are still faced with the massive task of selecting, testing and commissioning new sites.

“Our AI-based propagation model, successfully presented to our customers at Infovista RAN Summit, is, by design, cloud-ready and scalable to increase agility and the ability to adapt the network more dynamically, thus offering a way to automate and accelerate the planning and roll-out of 5G networks.”

Infovista’s AIM has been built around machine learning frameworks such as TensorFlow to focus on training and inference of deep neural networks. The project utilised over 10 million data points collected by the company during the last 15 years and spans multiple sub-6 GHz and millimetre wave bands, geographic locations, antenna heights, weather conditions, seasonal foliage variations and hundreds of additional variables – across urban, mixed and rural environments. 

The company emphasised that the AI-model predictions have been extensively validated against real-world measurement sampling data and are proven to deliver network plans that are 25% more accurate compared to those delivered using traditional propagation models. The initial testing shows that this improved accuracy translates into up to 20% CAPEX savings when it comes to radio site investments.

AIM avoids labour-intensive and repetitive calibration and parameter manipulation. It fully fits with the Network Lifecycle Automation vision of Infovista that aims to expand the reach of automation beyond network and service operations, into planning, testing and deployment, and reporting and monetization.

AIM is embedded into Infovista’s Planet software, which also includes an integrated feed of crowdsourced subscriber-centric data available in all geographies. Combining both provides mobile operators with higher accuracy and more efficient network planning workflows.

“Over time the combination of AIM with crowdsourced data will mean the new platform will enable operators to fully automate network planning thus allowing them to deploy in new frequency bands faster than ever,” Lerbour added. “Automated data collection and processing contribute to significantly reducing the cost of propagation model calibration and optimizing drive testing, helping accelerate 5G deployments to new levels.”

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Amazon Web Services (AWS) has revealed new and updated courses for partners in the areas of advanced migration, security governance and machine learning.  

Among the new courses the cloud vendor has made available via its AWS Training and Certification function is AWS Solutions Training for Partners: Advanced Migrating to AWS (Technical), an advanced course that provides prescriptive guidance on how to migrate workloads at scale.  

This course also covers common migration patterns and challenges in large-scale migrations.  

With this course, partners will learn how to identify use cases for re-hosting, re-platforming and re-factoring legacy applications to AWS Cloud. Partners will also learn how to determine which applications are good candidates for automated modernisation.

Also available is the AWS Solutions Training for Partners: Security Governance at Scale (Business) course, which is designed to help sales and business professionals learn how to qualify customer opportunities around security governance at scale.  

This course covers topics such as AWS account management, cost control, security and compliance through automation, along with best practices for establishing an automated, simplified, and secure framework for governance at scale.  

AWS has also launched a major update to its AWS Solutions Training for Partners: Machine Learning on AWS (Business) digital course.

This update will see partners learn how to identify and qualify customer machine learning (ML) opportunities, how to help customers navigate the ML solution-building process and how to use such experience to build an AWS ML solutions practice.

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