Architecture services for generation next

As users become more distributed and the needs of IT become more complex, the underlying network infrastructure becomes increasingly more important to business operations. The old, reactive way of collecting event logs and manually troubleshooting network problems just won’t work anymore. AI-driven operations (AIOps) is pivotal to supporting digital transformation requirements because it brings much-needed automation, insights and actions that lower costs and maximize user experiences in ways that simply cannot be achieved using traditional IT service models and tools.

AI can simplify network operations with real-time anomaly detection and event correlation to pinpoint the source of issues. It can also recommend actions for proactive correction and power self-driving network operations to correct issues before anyone even knows they exist. AI has also enabled new virtual network assistants that can answer network questions on par with domain experts. By leveraging natural language processing (NLP) and natural language understanding (NLU), IT teams can ask questions, and the system will come back with specific insights and recommendations.

The recent Covid-19 pandemic has accelerated the demand for AIOps. Overnight, many corporate environments went from a handful of branch offices to thousands (or tens of thousands) of remote locations. IT teams became flooded with new network issues and had to find ways to solve them remotely. Complicating the problem is the fact that every individual has a unique home network setup — there are huge inconsistencies with respect to home Wi-Fi, network design, internet service providers and more.

With support tickets increasing, AI systems can help by not only detecting the cause of a problem but also by resolving IT issues automatically through self-driving actions. Identifying potential issues before they reach remote workers can decrease the number of support tickets and save IT teams time and resources. AI can also gather insights into the network and share in-depth data with the IT team, streamlining the remote troubleshooting process.

5G and machine learning

5G is ushering in a new breed of ‘genius’ networks to deal with the increased levels of complexity, prediction and real-time decision-making that is required to deliver the performance gains promised not just in enhanced mobile broadband applications but also in IoT and mission critical use cases. 5G adds support for new antenna capabilities, high-density and heterogeneous network topologies, and uplink and downlink channel allocation and configuration based on payload type and application.

One of the hallmarks of a next-generation 5G base station is the use of advanced antenna capabilities. These capabilities include massive multiple-input multiple-output (MIMO) antenna arrays, beamforming, and beam steering. In order to fully realize the benefits of massive MIMO capability, beamforming and beam steering, machine learning is being utilized at the base station to provide real-time and predictive analysis and modeling to better schedule, coordinate, configure and select which arrays to use and when.

The new 5G network standard requires higher density deployments of smaller cells working with larger macro cells and multiple air interface protocols. The vision is for smaller cells to be designed for indoor locations or dense urban environments where GPS positioning is not always reliable and the radio frequency (RF) environment is far from predictable. Understanding the location of the devices interacting with the network is essential not only to application layer use cases but also to real-time network operation and optimization.

Machine learning is being applied to estimate user equipment location using RF data and triangulation techniques. The use of machine learning algorithms is yielding material improvements in terms of accuracy, precision, and viability of widespread use. One of the driving considerations for the development of 5G is to have one framework to address the varied and often conflicting requirements of 3 use cases, including Enhanced Mobile Broadband (eMBB), massive IoT, and mission critical applications.

The role of Glow

Network services are focusing on increasing operational excellence, driving growth and improving profits. The need for superior user experience supplemented with stringent SLAs increases the complexity significantly. To address this, service providers require standards-based end-to-end network performance management and visibility.

Glow Networks’ next generation network architecture services are designed to support the entire life-cycle of networks and applications, from conducting assessments, to developing strategies and solving technological issues. Glow also offers a suite of additional services and solutions ranging from DAS and small cells, to RF design and optimization, to supplemental staffing, and so much more. Our next generation network architecture services partnership has helped our customers to increase coverage areas, reach new customers and generate new service revenues.

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