Near-real-time RIC: enabling AI/ML-driven extreme automation and granular control of Open RAN

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Mobile network operators (MNOs) recognize the need to reduce the total cost of ownership of 5G networks and accelerate innovation, even as they look to increase network efficiency, rapidly roll out new sites and bring to market new revenue-generating services. To this end, MNOs are taking steps to take control of the 5G network and are considering new virtual and Open radio access network (RAN) architecture, which follows the trend of network disaggregation and openness to enable an open ecosystem of multi-vendor RAN solutions. However, to truly realize the benefits of Open RAN, MNOs are also rethinking how they control, operate, and manage the RAN. Extreme network automation is at the heart of this new thinking and the RAN Intelligent Controller (RIC) is at the heart of the new control and management architecture for RAN automation.

MNOs expect the RIC to give them granular control of the RAN resources, which would enable them to take intelligent, insights-driven closed-loop automated actions based on the evolving network conditions. In addition, the RIC is deemed as essential for enabling MNOs to operationalize a best-of-breed Open RAN. According to the widely accepted specifications published by the O-RAN Alliance, the RIC architecture consists of two key components: the near-real-time RIC with xApps and the non-real-time RIC with rApps within service management and orchestration (SMO).

Analysys Mason conducted a market study for the Telecom Infra Project (TIP) to assess the current state of the industry’s thinking on the RIC platform, with a specific focus on the near-real-time (near-RT) RIC. This paper provides in-depth analysis based on the results of the study, which is based on a survey of 35 MNOs (Tier 1 and 2) and alternative network providers worldwide, and Analysys Mason’s existing RAN research. The survey focused on many areas relating to deploying the near-RT RIC, including the business and commercial rationale, use case priorities, deployment timelines and the role of artificial-intelligence and machine-learning (AI/ML) techniques. Key results of the study are summarized in this report.