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Network Services

TelecomTV TRACKER

Sourced by TelecomTV's TRACKER platform
from Huawei Corporate News

Network Services

Huawei releases VP Master to empower efficient capacity expansion

Via Huawei Corporate News

Nov 19, 2018

London, UK, November 19, 2018: During the 2018 Global MBB Forum, Huawei formally released its Value Package Master (VP Master), a professional service solution designed to enable global carriers to increase network capacity. Leveraging the advantages of machine learning, this solution allows for a quick capacity expansion through accurate scenario-specific feature deployment and parameter optimization. So far, this solution has been put into pre-commercial use in UMTS networks, helping carriers in emerging markets expand UMTS capacity while laying a solid foundation for transitioning networks to 4.5G and 5G.

Continuously growing DOU requires mobile networks to boost their capacity, which is commonly based on adding sites or carriers and applying software-based optimization. Software-based optimization supports quick delivery, but requires accurate scenario-specific match. Scenarios may vary vastly in desired features and feature parameter settings, and they are likely to change over time within a day at the same sites. With a surging number of sites on mobile networks, it is a complicated and challenging mission to match site scenarios with desired features and feature parameters. In most cases, this mission is totally beyond the reach of existing solutions that are based on manual operations. Statistics show that the feature activation rate is below 20% on mobile networks, indicating software-based optimization fails to achieve the greatest potential.

Huawei's VP Master takes advantages of the abundant experience with feature deployment and parameter optimization of over 300 networks. Benefiting from an expert library created by logging such experience onto the GenexCloud, VP Master is able to define a broad category of scenarios for network deployments based on the powerful features of machine learning. With the detailed characteristics extracted for a target site during a deployment, this solution groups the scenario into an appropriate category and then provides recommended features and parameter settings that are the most suitable to this scenario through fitted regression. After the features are deployed at the site, this solution proceeds with iterative optimization of feature parameters on the network side to bring a full play to the performance of the features.

This solution breaks down the barrier of low efficiency in software-based optimization and enables scaled, real-time feature deployment and optimization. It has already been successfully verified in the commercial networks in a number of countries, South Africa included, concluding that it can deliver a 30% capacity increase in UMTS networks.

Liu Feng, General Manager of Huawei's SmartCare® CEM Domain, said:

"We are dedicated to working closely with our customers and partners to reduce mobile network costs through constant innovation. We are happy to see that VP Master has been successfully used in UMTS networks to help raise the efficiency of network optimization. With maturing automated processing, combining software-based optimization with professional services will be essential for carriers to improve network performance."

The 9th Global Mobile Broadband Forum will be hosted by Huawei with our industry partners GSMA and GTI in London during November 19th-21st, 2018. This annual event gathers operators, regulators, vertical industry partners, media, and analysts from around the world to shape the future of the mobile industry. For more information, please visit us online at: Link.

Related Topics
  • Global,
  • Huawei,
  • Network Services,
  • Product Launch,
  • Telecoms Vendors & OEMs,
  • Tracker

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This content extract was originally sourced from an external website (Huawei Corporate News) and is the copyright of the external website owner. TelecomTV is not responsible for the content of external websites. Legal Notices

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