Open RAN Use Cases: Network Controller

Network Controller: RIC is the RAN Intelligent Controller

The RAN intelligent network controller (RIC) plays a crucial role as an essential logical function for fulfilling open RAN use cases. O-RAN Alliance has created extensive documentation about its RIC. There is documentation that ties the RIC to various use cases included in ORAN’s “O-RAN Use Cases and Deployment Scenarios White Paper, February 2020,” and its “O-RAN Use Cases Detailed Specification 3.0 – July 2020.”

In the same way as any network controller, the RIC connects the application layer to the infrastructure layer. The application layer contains virtual network functions (VNFs) run as software on the controller. On the other hand, the infrastructure layer houses the radio components themselves. Using a southbound interface, the applications establish policies that the controller enforces on the radios.

Operators who use the controller can avoid vendor lock-in because it is based on open standards. Because underlying infrastructure and the applications used to run the network can come from different vendors. The vendors involved must all use available standard interfaces for interoperability to achieve success.

RAN Handover Management

The Open RAN Use Cases
O-RAN (Open RAN) Source:

When a device moves between the ranges of RAN base stations, the RAN must hand over connections. Seamless handover ensures an uninterrupted connection when the user passes from one base station to another. For autonomous vehicles and the devices inside, keeping constant contact while driving at high speeds is a specific RAN use case that relies on this capability.

The near real-time RAN intelligent controller (RIC) of the O-RAN Alliance manages various aspects of handover management. Data on traffic, navigation, radio, and handover is compiled and maintained. Radio and traffic conditions are monitored in real-time by the controller. Artificial intelligence (AI) and machine learning are used to detect and predict the irregularities that affect devices during handover in near-real-time. In addition, the near real-time RIC is responsible for the development and maintenance of real-time applications that expect, prevent, and mitigate handover irregularities.

Near real-time RICs manage network infrastructure components with less than one-second latency. Non-real-time controllers control aspects of the network infrastructure that can wait longer than one second for a response.

The optimization use case for RAN

Network resources are allocated to the users and services that need the most to maintain a good user experience. With open RAN interface standards, artificial intelligence (AI) can more easily communicate with the rest of the network and improve user quality of experience (QoE).

Using algorithms, AI software can identify traffic patterns and network health to determine resources. Since they do not have to respond to anything in real-time, machine learning models can train AI offline in a non-real-time RIC. AI software enforces decisions made by the RIC in near real-time. Decisions have to be implemented in near real-time due to the impact of timing on QoE.

Quality of service (QoS) is similar to Quality of Experience (QoE); however, QoS maintains the performance of a network service. However, QoE emphasizes the quality of the user’s connection with the network. Traffic patterns change quickly in a 5G RAN, and the infrastructure is heterogeneous. Thus, policies regarding the quality of service cannot be generalized.

Both non-real-time and near-real-time RICs use policy-controlled resource allocation to optimize how resources are distributed among users of the same service. A non-real-time RIC can collect information on resource demand and select users to allocate resources accordingly. The RIC can enforce resource allocation across the RAN’s central and distributed units in near real-time.

The traffic steering system

Traffic steering, in which network software determines where traffic is sent across the network, requires automation and intelligence on the software. In a RIC, artificial intelligence allows the controller to predict network conditions, such as congestion, to find an optimal path for traffic.

5G networks use massive multiple-input, multiple-output (MIMO) antenna configurations. In a multi-cell and multi-vendor deployment area, huge MIMO optimization continuously enhances network QoS and QoE. It is up to the network operator how deployment with massive MIMO looks using multiple cells or multiple vendors.

The Resource Sharing Use Case for Open RAN

The RAN infrastructure can be shared between different operators based on open standards. It would be the responsibility of one network operator to own and host the deployment. Hosted network operators need available interfaces for configuring and controlling the resources that serve their customers.

In this scenario, the deployment owner provides a portion of the deployment’s resources to the hosted network operator. Hosting operators can then use the near-real-time RIC of the O-RAN Alliance and its interfaces to remotely manage the virtual network functions (VNFs) that run the network. It is also possible for the hosted operator to monitor and interact with the RAN infrastructure, which is essential for meeting service level agreements (SLAs).

Network operators and their customers establish SLAs with various key performance indicators (KPIs). The same holds for 5G networks as it does for any other type of network. Network slicing distinguishes 5G networks from other mobile networks.

Network slicing allocates different portions of a network connection to various data types. Network slices can be designed to meet specific KPIs. High demand for traffic capacity, for example, could be addressed by allocating more frequency spectrum to a particular data type, allowing more data to pass through without interference.

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