The current transformation of telecom testing is fueled by the increasing complexity of networks and the integration of artificial intelligence. At a recent Test and Measurement Forum, experts emphasized the shift from point-in-time verification to continuous validation processes. This transition is crucial as networks are becoming more cloud-native and reliant on cross-technology integration. Real-world performance monitoring, which combines active and passive testing, is becoming essential for assuring network quality.
Artificial intelligence is reshaping both telecom networks and testing methodologies. AI models continuously learn and evolve, thus requiring innovative testing approaches. Yet, users must trust AI’s decisions. Therefore, AI systems need to justify their actions and allow for thorough validations of changes before and after implementation. This focus on AI transparency and accountability is vital to maintain trust in network operations.
Real-world performance increasingly dictates the success of telecom testing. Networks must provide consistent and reliable user experiences, rather than merely achieving average or peak performance metrics. Sylwia Kechiche, from Opensignal, highlighted that growing network complexity, driven by AI demands and the fusion of terrestrial and non-terrestrial networks (NTNs), is challenging operators. Users evaluate their connectivity experience based on real location and situation, underscoring the necessity for a broader testing scope.
Michael Thelander, president of Signals Research Group, noted that average network measurements can obscure problematic conditions impacting customers significantly more than average results suggest. This discrepancy can notably affect gaming and video conferencing applications. Monisha Ghosh from the University of Notre Dame observed that while 5G networks have improved over time, real-world conditions often fall short of theoretical promises. This insight is vital as attention turns to 6G development.
The forum focused heavily on AI’s emerging role in network testing. Unlike traditional automated systems, AI can adapt, necessitating a different testing approach. Per Kangru of Viavi Solutions stated that AI systems would require evolving tests as they learn and develop. Digital twins have emerged as a critical tool for validation. Furthermore, AI’s complexity means operators need assurance that AI conclusions are trustworthy and understandable.
Ross Cassan from Spirent Communications emphasized the need for AI systems to manage data efficiently while providing transparent results. Similarly, Mohamed Nabih from Rakuten Mobile pointed out the importance of aligning AI systems with service expectations, to build operational trust.
Challenges persist with multi-vendor environments and Open RAN deployment. The integration of diverse hardware and software creates new hurdles, often requiring lengthy manual processes. As the industry plans for 6G, past experiences with 5G guide future developments on issues like integration and interoperability.
In conclusion, as telecom networks continue their evolution, so must the methodologies to assure their performance. A balanced focus on AI, real-world application results, and continuous validation will guide successful operations in this rapidly advancing landscape.
