What is RAN Intelligent Controllers and Types in ORAN

The Open Radio Access Network (ORAN) concept represents a major shift in how Radio Access Networks (RAN) are designed and deployed. Traditionally, RAN architectures have been closed systems, where hardware and software are tightly integrated, supplied by a single vendor. This created limitations in flexibility, innovation, and cost. ORAN, on the other hand, promotes open interfaces, interoperability, and multi-vendor ecosystems, thus fostering greater innovation and competition.

A crucial component of the ORAN architecture is the RAN Intelligent Controller (RIC). It plays a central role in bringing intelligence, optimization, and programmability to the RAN. In a nutshell, the RIC is responsible for controlling and managing the RAN elements dynamically, using AI/ML-driven policies and algorithms to optimize network performance.

What is a RAN Intelligent Controller (RIC)?

The RAN Intelligent Controller (RIC) is a key function of the ORAN architecture, designed to enable real-time optimization, resource management, and network automation. The RIC introduces an abstraction layer between the RAN infrastructure and network functions, providing a centralized platform for control and orchestration. It leverages open interfaces and APIs to enable multi-vendor operability and supports advanced AI and Machine Learning (ML) applications to optimize RAN operations.

The RIC allows network operators to deploy applications that manage radio resources, enhance Quality of Service (QoS), optimize user experience, and enable real-time policy control.

It can be divided into two categories:

• Near-Real-Time (Near-RT) RIC
• Non-Real-Time (Non-RT) RIC

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In Short, we can say that RIC introduces:

• Centralized control: It brings intelligence and centralized control over RAN components and radio resources.
• Programmability: It enables the programmability of the RAN via applications (known as xApps and rApps) that can be integrated on-demand.
• AI/ML-driven Optimization: It supports the application of AI/ML algorithms for predictive analysis, anomaly detection, and network optimization.
• Open APIs and Interfaces: Promotes interoperability through open interfaces, allowing integration with equipment from different vendors.

Why RAN Intelligent Controllers (RICs)?

With the introduction of 5G, RAN architectures have become increasingly complex, demanding enhanced scalability, automation, and real-time network adaptability. The RIC addresses these challenges in several ways:

• Efficient Use of Network Resources: The RIC ensures efficient allocation and usage of network resources, optimizing traffic handling and reducing congestion.
• Automation and AI/ML: The use of AI and ML enables predictive analytics and automation, reducing human intervention and improving network efficiency.
• Flexibility: The RIC allows operators to introduce new services and innovations quickly by simply updating the xApps and rApps deployed on the RIC platform without affecting the underlying hardware.
• Improved Customer Experience: By dynamically managing network resources, the RIC helps in maintaining high quality of service (QoS) for end users, ensuring consistent performance even during high traffic periods.

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Types of RAN Intelligent Controllers in ORAN

In ORAN architecture, the RIC is split into two types based on the time domain in which they operate and the specific functionalities they cater to. These are:

Near-Real-Time RAN Intelligent Controller (Near-RT RIC)

The Near-RT RIC is responsible for controlling and optimizing RAN functions that require quick decision-making, typically within a time frame of 10ms to 1 second. The Near-RT RIC resides within the RAN and interacts directly with the CU (Centralized Unit) and DU (Distributed Unit) elements to manage resources and apply real-time control policies.

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Key Features of Near-RT RIC:

• Latency-Sensitive Control: The Near-RT RIC handles latency-sensitive tasks that require immediate feedback and adjustment of network parameters, such as scheduling decisions, handover management, and interference mitigation.
• xApps: Near-RT RIC is designed to host applications known as xApps. These xApps are responsible for specific control tasks, such as radio resource management (RRM), user mobility management, and interference management. xApps can be easily added, updated, or removed, providing flexibility to introduce new capabilities as needed.
• Interoperability: The Near-RT RIC communicates with other RAN elements using standard interfaces like E2 (based on the ORAN E2 interface). This ensures that it can control multiple vendors’ equipment within the RAN, promoting interoperability.
• AI/ML-driven Decisions: The Near-RT RIC leverages AI/ML models for decision-making, learning from real-time network conditions to predict and optimize actions such as resource allocation or beamforming.

Functions of Near-RT RIC:

• Radio Resource Management (RRM): Allocates resources dynamically to meet the varying needs of users, optimize spectral efficiency, and minimize interference.
• User Mobility Management: Manages the handover process for users transitioning between cells, ensuring smooth connectivity with minimal service disruption.
• QoS Optimization: Ensures that QoS requirements are met by adjusting scheduling and resource allocation dynamically in real-time.
• Interference Management: Mitigates co-channel interference between cells or users to enhance overall network performance.

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Use Cases:

• Dynamic Spectrum Sharing: The Near-RT RIC can dynamically allocate spectrum based on real-time network demand, allowing different network slices or operators to share resources efficiently.
• Massive MIMO Optimization: With the help of AI/ML, the Near-RT RIC can dynamically adjust beamforming parameters for massive MIMO (Multiple Input Multiple Output) systems to improve coverage and throughput.
• Real-Time Traffic Steering: The Near-RT RIC can direct traffic to different base stations based on real-time load conditions, improving the user experience and network utilization.

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Non-Real-Time RAN Intelligent Controller (Non-RT RIC).

The Non-RT RIC operates at a higher layer of the RAN architecture, focusing on long-term network planning, policy management, and optimization tasks that are not time-sensitive. The Non-RT RIC typically handles tasks with a time frame greater than 1 second and resides within the network's service management layer (SMO – Service Management and Orchestration).

Its primary role is to handle non-real-time functions such as long-term policy management, data analytics, and AI/ML model training. It performs tasks that are not latency-sensitive but require more in-depth analysis of network data.

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Key Features of Non-RT RIC:

• Policy and Data-Driven Control: Unlike the Near-RT RIC, which focuses on real-time control, the Non-RT RIC is focused on collecting and analyzing data over time to implement policies that drive network optimization in the long term.
• rApps: The Non-RT RIC hosts applications called rApps, which are responsible for non-time-sensitive tasks such as policy control, AI/ML model training, and network performance analytics. Like xApps, rApps can be added, updated, or removed dynamically, allowing operators to introduce new optimization strategies.
• AI/ML Model Training: The Non-RT RIC is responsible for training and updating the AI/ML models that are deployed in the Near-RT RIC. By analyzing historical data, it can fine-tune these models for better performance and decision-making in the real-time environment.
• Open Interfaces: Non-RT RIC uses open interfaces such as A1 to communicate with the Near-RT RIC and other RAN elements. This ensures smooth coordination between real-time and non-real-time functions.

Functions of Non-RT RIC:

• Policy Management: Implements and updates network policies based on long-term analysis and operator-defined strategies, such as load balancing, energy savings, and service-level agreements (SLA).
• AI/ML Model Training: Continuously collects data from the RAN and trains AI/ML models for tasks such as anomaly detection, fault prediction, and traffic forecasting. The models are then deployed in the Near-RT RIC for real-time decision-making.
• Performance Analytics: Provides insights into the overall performance of the RAN, identifying trends, inefficiencies, and areas for improvement.

Use Cases:

• Network Slicing Optimization: Non-RT RIC can analyze long-term traffic patterns and optimize network slices to meet the requirements of different use cases, such as IoT, enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (URLLC).
• AI/ML Model Tuning: By analyzing historical network data, the Non-RT RIC can fine-tune AI/ML models to improve their accuracy in predicting network behaviour and optimizing resource allocation in real-time.
• Energy Savings: The Non-RT RIC can implement energy-saving policies by analyzing network usage patterns and switching off unused or underutilized resources during low-traffic periods.

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Interplay between Near-RT and Non-RT RICs

The Near-RT and Non-RT RICs work in tandem to deliver an efficient and optimized network. While the Non-RT RIC focuses on long-term strategy and model training, the Near-RT RIC is responsible for applying those models and strategies in real-time to manage network operations.

The A1 Interface plays a critical role in enabling communication between the Non-RT and Near-RT RICs. Through this interface, the Non-RT RIC can push policies and AI/ML models to the Near-RT RIC, which applies them in real-time. Similarly, the Near-RT RIC can provide feedback to the Non-RT RIC based on its performance, enabling continuous improvement and optimization.

This division of labor between the Near-RT and Non-RT RICs ensures that network operations are both agile (through real-time control) and strategic (through long-term policy and optimization).

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Non-RT RIC operates at a higher abstraction layer, focusing on non-real-time tasks such as policy management and model training, while the Near-RT RIC handles real-time network optimization and control. Both work together using the A1 and E2 interfaces, enabling the ORAN architecture to dynamically and efficiently manage network resources.

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