AI RAN – 5G – 6G
Link to the White paper here.
RAN Scenario Generators and Their Critical Role for Future-Proofing AI-Native RAN in Advanced 5G and 6G Networks
The role of artificial intelligence within telecommunications is evolving quickly, shifting from speeding discrete automation tasks to intelligent, context-aware decision making in which it becomes a critical element of network operations. This change is particularly visible in the way that AI is being used in new and emerging 5G and 6G deployments, not least when it comes to MU-MIMO. To date networks have deployed AI as an add-on. It is being used to optimize and enhance existing systems and has enabled the dynamic allocation of network slices, the better management of resources, and the improved detection of both potential issues and security threats. However, new 5G and 6G implementations built around “AI-native” architectures and MU-MIMO will shift AI from the periphery to the very heart of the network. This will enable autonomous operation across immensely complex, heterogeneous Service Management and Orchestration (SMO) Networks built on principles such as Open RAN and managed by programmable platforms such as the RAN Intelligent Controller (RIC). This shift, however, presents a fundamental challenge: How do you ensure that AI is making the correct decisions for the network, especially when it needs to scale rapidly across heterogeneous, dynamic environments?
Training an AI (and ensuring its long-term viability) requires data. As well as being accurate and reliable, this data must be representative of a real-world, dynamic network rather than an ideal or a snapshot at a particular time. Using this data, the AI applications used to run and maintain the network should then be continuously tested and challenged to prevent drift and to ensure readiness for change and unforeseen scenarios.
In this paper we examine the challenges and trade-offs of real-world data and synthetic data to demonstrate why a hybrid data layer with RAN scenario generator (RSG) testing is the optimal approach for training AI applications for the specific network. We will also identify how this approach can be used to prevent AI drift and how it can prepare for change (including upgrades and attacks). Finally, we will break down how these techniques can be used to improve the network’s energy efficiency, plan for 6G deployment, enhance QoS, and optimize for massive MIMO.
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AI RAN – 5G – 6G
