OPTIMIZE YOUR FWA NETWORK EXPERIENCE AND MAXIMIZE PROFITABILITY
The role of automation and AI in optimizing FWA profitability Author: Renata Da Silva, VP Product Marketing
The ambition of ‘autonomous networks’ has become a game-changer in the rapidly evolving landscape of modern telecommunications and networking. But how applicable is it to Fixed Wireless Access? The complexity of 5G is compounded by the unique requirements of FWA. From planning a network for FWA to deploying and optimizing it, from dimensioning the
Data without automation is a wasted opportunity, automation without data is wasted investment.
across silos and create actionable insights. Only then does automation deliver the promised CAPEX and OPEX benefits. Or to put it bluntly: Data without automation is a wasted opportunity, automation without data is wasted investment. Planning FWA – with automated use case-centric workflows We’ve already explored the challenges of planning and scaling FWA profitably, but let’s dig in further and look at the role data- driven automation can play in this. Traditionally, RF planning tools had a single desktop-based user interface. With one common interface to address all the required use cases, this meant it was extremely powerful but invariably complicated to learn and inefficient to use. FWA has its own specific RF planning challenges and requirements, so use case- centric workflows specifically designed and optimized for that FWA can ensure maximum simplicity and efficiency.
network for data-intensive usage to assuring the user experience of new apps and services, the prospect of an autonomous FWA network can seem a distant dream. However, by embracing a practical approach to automation, CSPs can start realizing today the benefits of real-time data-driven automation that streamlines processes, improves operational efficiency and helps them deliver on their FWA monetization objectives. Automation can be classified into two primary areas: data intelligence, and orchestration. While orchestrators serve as automation engines, they lack the intelligence on what exactly to orchestrate. The initial challenge lies in comprehending the vast volumes and diverse data types to enable precise automation. Data has to be at the heart of automation. How that data is then used to streamline workflows, improve accuracy or modeling or accelerate processes is down to the ability to extend automation
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