The O-RAN Alliance is designed to drive progress in RAN by separating the management and control plane from the user data processing plane
3GPP Radio Access Network (RAN) technology has evolved from second through third, fourth and fifth generations to significantly increase air interface efficiency while supporting new services. During that time, commercial RAN deployments boomed worldwide as operators rushed to expand network capacity to meet ever-increasing user throughput demands.
As commercial rollouts increased significantly, operational and management efforts became exponentially complex. The lack of open interfaces and proprietary solutions from vendors prevented the potential benefits of Automation and Self Organizing Network (SON) from being fully realized.
This prompted the industry, driven mainly by network operators, to form the O-RAN Alliance to drive progress in RAN by separating the management and control plane from the user data processing plane. This new, disaggregated and open RAN architecture, which leverages software-driven cloud technologies and open interfaces, intelligence and automation, has laid the foundation to enable network operators to efficiently manage and operate their networks without being limited by proprietary solutions. suppliers.
The cornerstone of the Open RAN architecture, the RAN Intelligent Controller (RIC), brings artificial intelligence to the wireless access network by using advanced analytics and machine learning to make data-driven decisions. This capability enables more advanced optimization strategies, predictive maintenance, and automated network adjustments.
Figure 1. Overview of O-RAN architecture
Driven by the different time scales required for real-time adjustments and adjustments that impact long-term network behavior, RIC is split into two platforms:
- Non-Real-Time RIC (Non-RT RIC) provides policy-based management and long-term network planning and optimization; it operates on timescales greater than 1 second.
- Near-Real-Time RIC (Near-RT RIC) provides dynamic control and resource optimization of radio resources while making fast decisions within the time scale of 10 milliseconds to 1 second.
To eliminate vendor lock-in, both RIC platforms rely on open interfaces that use publicly available and widely accepted protocols and open data models. For business logic (rApps) provided 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), provided by the Near-RT RIC , the SCTP (Stream Control Transmission Protocol) is used over the E2 interface.
Additionally, MLOps functions such as data pipelining, model management, training, and inference functions are also supported by the RIC, enabling AI-native network control and operations.
Use cases and implementations
With the goal of improving operational efficiency, network performance, and vendor interoperability, several use cases have been defined and demonstrated at events such as Mobile World Congress (MWC), the Linux Networking Foundation (LNF), and the National Telecommunications and Information Administration’s RIC Forum ( NTIA).
These demonstrations include:
- Radio resource utilization and optimization, including coverage, capacity, optimization, load balancing, traffic steering and segment resource optimization
- Optimizing the quality of the experience
- Energy saving
- Share spectrum
- Verticals (i.e. UAV, V2X)
Additionally, to highlight new use cases, advance development, and promote interoperability among RIC, rApp, and xApp deployments, we take the stage twice a year at the biennial O-RAN Global PlugFests. Since 2021, CableLabs has been a participating host laboratory that drives innovation and standardization in radio access networks.
Emerging trends and future directions
Looking ahead, several prominent themes are beginning to shape the RAN Intelligent Controller landscape.
- Leverage advanced AI/ML enhancements. Recent standardization efforts enable the exposure of machine learning workflows such as model training, registration, and deployment through the R1 and A1 interfaces, enabling independent lifecycle management between the application logic (rApp, xApp) and ML models. As a result, engineers can repeat model training without changing the underlying application code, leading to faster model iteration. This also simplifies model updates and replacements, without impacting the application’s code base. By decoupling machine learning from application logic, operators can leverage the latest advances in artificial intelligence and machine learning to more quickly deploy enhanced capabilities such as predictive maintenance and ultimately autonomous network management.
- Greater interoperability between suppliers. While most RIC use cases are still deployed on single-vendor solutions, there is a growing demand within the O-RAN Certification and Badging program to finalize certification criteria, including security, for the A1, R1, and E2 interfaces. This will facilitate the development of a robust multi-vendor ecosystem for xApps and rApps, allowing operators to select best-in-class solutions tailored to their specific network needs.
- Shared spectrum. Existing challenges in shared spectrum management in CBRS and emerging bands such as 3.1 GHz could be addressed by the RIC, enabling efficient dynamic spectrum sharing between government and private sector through regulator-approved xApps, rApps and possibly dApps (when sub-MS control over radio resources is required). required).
- Seamless connectivity In the network of the future, also described in CableLabs’ Technology Vision, users will be able to access information anywhere and on any device, regardless of the underlying access technology. To deliver this seamless connectivity, service providers will go beyond traditional roaming agreements and offer integrated access services. We anticipate that the future evolution of RIC will play an important role in orchestrating and coordinating resources across diverse and heterogeneous network environments, tailored to a user or application QoE profile.
Challenges and outstanding issues
Despite significant progress in standardization, testing and live demonstrations in recent years, RIC still faces challenges in delivering the operational efficiencies that operators urgently need. Key areas of focus are:
- Implementation Challenges: The widespread deployment of 4G and 5G radio resources on vendor hardware and software has created a significant barrier for RIC. stake. The lack of a clear migration path from these legacy systems, combined with the long baseband hardware refresh cycle, hinders the transition to an Open RAN architecture.
- Security issues related to AI and the threats they can pose to network operations. ML models will need safeguards to prevent misconduct. Additionally, a trusted repository may be needed where these applications can be monitored for compliance, interoperability, and security.
- Lack of publicly available open datasets for training ML models that would power rApps and xApps.
Conclusion
As cloudification and softwareization 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 deliver ubiquitous and seamless connectivity while simultaneously unlocking the hidden potential of shared spectrum.
