3GPP Radio Access Network (RAN) technology has evolved from the second through third, fourth and fifth generations, to significantly increase air-interface efficiency while supporting new services. During that time, commercial RAN deployments surged globally as operators rushed to expand network capacity to meet the ever-increasing user throughput demand.
While commercial rollouts were significantly increasing, the operations and management efforts were getting exponentially complicated. Lack of open interfaces and proprietary vendor solutions prevented the potential gains of Automation and Self Organizing Network (SON) from being fully realized.
This prompted the industry, mainly driven by network operators, to form the O-RAN Alliance to drive advancements in RAN by separating the management and control plane from the user data processing plane. This new disaggregated and open RAN architecture, utilizing software driven cloud technologies, leveraging open interfaces, intelligence and automation, laid the foundation to empower network operators to efficiently manage and operate their networks without being constrained by vendor proprietary solutions.
The cornerstone of the Open RAN architecture, the RAN Intelligent Controller (RIC), brings artificial intelligence to the wireless access network by leveraging advanced analytics and machine learning to make data-driven decisions. This capability enables more sophisticated optimization strategies, predictive maintenance and automated network adjustments.
Figure 1. O-RAN architecture overview
Driven by the different time scales required for real-time adjustments and modifications that impact long-term network behavior, RIC is split into two platforms:
To eliminate vendor lock-in, both RIC platforms rely on open interfaces that utilize publicly available and widely adopted protocols and open data models. For business logic (rApps) delivered by the Non-RT RIC, the REST (Representational State Transfer) paradigm is followed for the R1, A1 and O1 interfaces, while for time critical engineering logic (xApps), delivered by the Near-RT RIC, the SCTP (Stream Control Transmission Protocol) is used over the E2 interface.
Furthermore, MLOps features like data pipelining, model management, training and inference functions are also supported by the RIC, enabling an AI native network control and operations.
Aimed to improve operational efficiency, network performance and vendor interoperability, several use cases have been defined and demonstrated at events like Mobile World Congress (MWC), the Linux Networking Foundation (LNF) and The National Telecommunications and Information Administration (NTIA) RIC Forum.
These demonstrations span across:
Furthermore, to bring forward novel use cases, advance development and foster interoperability between RIC, rApp and xApp implementations take the stage, twice a year during the bi-annual O-RAN Global PlugFests. CableLabs has been a participating host lab since 2021 driving innovation and standardization in radio access networks.
Looking forward, several prominent themes are beginning to shape the landscape of RAN Intelligent Controller.
Despite significant progress in standardization, testing and live demonstrations over the past few years, RIC still faces challenges to deliver the operational efficiencies operators urgently need. Key areas requiring attention are:
As cloudification and softwarization continue to transform the wireless industry, the evolved Radio Intelligent Controller (RIC) will be a key component of future radio networks enabled by a robust interoperable multi-vendor ecosystem, leveraging AI/ML to offer ubiquitous and seamless connectivity while unlocking the hidden opportunities of shared spectrum.