researching internet based telephony
IDC indicates that 63.5% of operators are investing in AI systems to improve their infrastructure. Some popular AI use cases in telecom include:
ZeroStack’s ZBrain Cloud Management, which analyzes private cloud telemetry storage and use for improved capacity planning, upgrades and general management
Aria Networks, an AI-based network optimization solution that counts a growing number of Tier 1 telecom companies as customers
Sedona Systems’ NetFusion, which optimizes the routing of traffic and speed delivery of 5G-enabled services like AR/VR
Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning, and to predict service degradations on cell sites up to seven days in advance.
AI for Predictive Maintenance
AI-driven predictive analytics are helping telcos provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to can monitor the state of equipment, anticipate failure based on patterns, and proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes.
In the short term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs. An innovative solution by AT&T is using AI to support its maintenance procedures: the company is testing a drone to expand its LTE network coverage and to utilize the analysis of video data captured by drones for tech support and maintenance of its cell towers.
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