AI and ML play crucial role in 5G adoption
With 5G, topologies will grow more complex, meaning machine intelligence will play a key role in assisting operators in laying out and operating networks
By using these techniques 15-20% average performance increase can be achieved. Performance can be boosted for edge of cell and overall cell capacity can be better utilized.
In today's architecture ML/AI neural networks are implemented on the Digital Baseband part of the radio site. In the future, these functions can be partially moved to the virtual RAN architecture part (Central Unit) and DU (Distributed Unit) according to the 3GPP architecture.
ML/AI in the future Radio Access NetworksIn future RAN networks we can envisage that ML/AI will bring multiple benefits to operators by simplifying tedious work of identifying the problem, suggesting radio parameters set for optimal performance and monitoring such a performance with automated tools. In Israel, mobile operators are constantly looking to improve network performance. SON (Self Organized Networks) systems have been deployed in some of the operators to enable a certain level of automation. These SON systems are basically looking at a network load as a whole and try optimizing it between different sites and frequencies. But with increasing spectrum (adding 700, 2600 MHz) in LTE together with 5G deployment, radio optimization becomes a very complex and time consuming task. Network powered by ML, can identify different types of traffic and suggest the optimization accordingly. For example, different radio optimization can be applied to a smartphone watching YouTube versus IoT traffic generated by smart electricity meters. Operators will benefit quicker from more adopted optimization of the radio based on the use case. Dmitry Filkovscky is the CTO of Ericsson Israel