Feature: RF
AI and ML to solve optimisation challenges. For example, AI could be used to optimise the power consumption of the network by turning on and off components based on real-time operating conditions. Today, xApps and rApps accomplish this at the base station by turning on and off power-hungry components like power amplifiers when they are not in use. However, the ability of AI to quickly solve challenging
compute problems and analyse large amounts of data opens the possibility of optimising networks at a larger, city-wide or national scale. Entire base stations could be turned off during low use, and cells could be reconfigured to service real-time demand in an energy-optimised way using the least amount of resources. It is not possible to reconfigure base stations and city-wide networks in this way today – it takes days or weeks to reconfigure and test any changes made to network configurations. But, advances in different AI techniques remain promising for infrastructure providers.
AI in wireless networks Wireless networks will not wait for 6G to start leveraging the power of AI. Active research is under way across the entire ecosystem to develop new models and integrate them into current and future wireless systems. However, these models are still new and need to be evaluated for rigour and reliability. Properly training AI models on diverse data sets, quantifying their improvement over traditional techniques, and defining new test methodologies for AI-enabled modules are critical steps that must be taken as this new technology is adopted. As AI models and testing best practices maturex, there is no doubt that AI will revolutionise wireless communications in the next 5-10 years.
Figure 1: Open Radio Access Network (ORAN)
Figure 2: ORAN 6G network
Figure 3: Progression from AI-infused to AI-native networks
www.electronicsworld.co.uk February 2024 17
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