Complexity is driven in part by 5G itself, which uses a much broader set of frequency bands, can prioritize services based on latency, and supports huge increases in the number of network elements and end-user devices. But there is a plethora of other changes which further increase complexity. These include the evolution from physical hardware to virtual and cloud native networks, end-to-end network slicing, the adoption of Open Radio Access Network (RAN) technologies and the addition of new enterprise business services. There are also multi-technology networks with some communications service providers (CSPs) running 2G, 3G, 4G/LTE and 5G networks in parallel, as well as multi-vendor networks with typically two to four different RAN vendors deployed in the network. Artificial intelligence (AI) and machine learning (ML) are becoming commonplace in the telecoms industry and are often the only way to manage the complexity we see in today's multi-vendor, multi-technology networks.

The Ericsson Intelligent RAN Automation portfolio, shown in Figure 1, features end-to-end network automation that includes centralized and distributed SON solutions and new capabilities that support the transformation to a more open environment enabled for AI/ML, which empowers innovation and support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks. The objective of RAN automation is to boost RAN performance and operational efficiency by replacing the manual work of developing, installing, deploying, managing, optimizing and retiring of RAN functions with automated processes. The AI's role is to unlock more advanced network automation performance to make RAN network functions more autonomous and replace manual processes with intelligent tools that augment humans. Furthermore, it makes both AI/ML powered RAN network functions and tools more robust for deployment in different environments. Ericsson AI and automation foundations gives service providers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands.

Used correctly, these techniques have tremendous potential to overcome complex cross-domain automation challenges in radio networks. Our ongoing research reveals that an integrated framework of software enablers will be essential to success. Modern telecommunications and mobile networks are becoming increasingly complex from a resource management perspective, with diverse combinations of software and infrastructure elements that need to be configured and tuned for efficient operation with high QoS. The latest 5G mobile system is a good example of a sophisticated radio network that allows many deployment variations – such as centralized, distributed or various hybrids of both – while simultaneously supporting diverse categories of applications such as mission-critical control with ultra-reliability and low latency, massively concurrent Internet of Things device access and enhanced mobile broadband. It is well accepted in the communications community that appropriately dimensioned, efficient and reliable configurations of systems like 5G are a complex technical challenge.