RAN Intelligent Controller

Mobile communication is now being revolutionised by two concurrent, dynamic and synergistic phenomena. One is the adoption of 5G technology in networks around the world. The other is the large and growing international effort to commercialise the Open-Radio Access Network (O-RAN) architecture.CTA

Compared to its predecessor, the principal attractions of 5G include: much higher connection speeds; lower latency; massively increased capacity; an extended service offering; network slicing and much enhanced user experiences. As a replacement for previous hardware-centric, inflexible and vendor proprietary RAN models, the software-centric, virtualised O-RAN is based on interoperability and standardisation of RAN elements, including a unified interconnection standard for commodity white-box hardware and open-source software elements from different vendors. Together, O-RAN underscores streamlined 5G RAN performance objectives through the common attributes of efficiency, intelligence and versatility.

At the heart of the O-RAN is the RAN Intelligent Controller (RIC). But, what is a RAN Intelligent Controller? how does it help operators, and how can they validate it before deployment? Here’s an overview.

Cloud-Native

The RIC is a cloud-native central component of an open and virtualised RAN network. It is a super optimisation engine that will be able to take live data feeds from the RAN, and make decisions and take actions in near real time. It acts as a platform for ML applications that can learn from events and programme actions.

It features a standardised interface – named the E2 interface – over which it receives measurements from the RAN about the performance of the network.

With the help of xApps sitting on the RIC it makes intelligent decisions to improve the overall running of the network in areas such as traffic steering, QoS optimization, energy optimisation and network slice attainment. Using built in algorithms, machine learning capability and Artificial Intelligence the RIC makes decisions to adapt variables in order to optimise the subscriber experience and network performance. To effect these improvements, RAN changes are sent back across the E2 interface to the RAN where they are implemented and observed to seeif the expected improvement is achieved.

Non RT And Near RT RAN Intelligent Controller

In practice there are two types of RAN Intelligent Controller (RIC) – a non-real time (Non RT) and a near real time (Near RT). As the names suggest, the main difference between them is the response time they take to effect changes in the RAN. The Near RT RIC will turn around decisions in between 1 millisecond and 1 second. The Non-RT RIC has a response time of 1 second or greater, gathers datafrom RAN elements and provides a source of data into the near real time RIC

Near RT control functionalities enable things like the partitioning or slicing of the RAN for different applications, Quality of Service (QoS) control, and radio resource management. Also enabled here are functions such as per user equipment (UE) controlled load balancing, radio database management, and interference detection and mitigation.

As described by the Open RAN Alliance, the primary goal of the Non-RT RIC is to support non-real-time management of radio resources, high level optimisation of procedures and policies, and the provision of guidance, parameters, policies and Artificial Intelligence/Machine Learning (AI/ML) models to support the operation of Near RT RIC functions in the RAN and so achieve higher-level non-real-time objectives. Some RICs can include both Near RT and Non RT capabilities.

RAN Intelligent Controller Automation Apps

RICs host two types of software automation applications called rApps and xApps, the first running on Non RT RICs and the second on Near RT RICs.

In the view of Ericsson, there are five general areas of rApp application:

  • Network evolutions
  • Network deployment
  • Network optimisation
  • Network healing
  • Automation and AI.

For their part, xApps use inputs to perform computations and propose changes to improve an aspect of the RAN such as: network admission control, resource utilisation, QoS fulfilment and other RAN network Key Performance Indicators (KPIs).

Examples of rApps could be used to: facilitate frequency layer management, by optimising the automated load balancing features of the radio network between different frequency layers; and provide platforms that intelligently automate the radio access network using AI and radio network applications.

Test and monitoring key to commercial success

A colossal amount of financial and intellectual capital is being invested in 5G and the O-RAN. Faultless, guaranteed network operation will be key to recouping and increasing the value of this investment. To this end the acquisition, deployment and application of best-in-class network element and performance test and monitoring solutions and methodologies will be essential, and could make all the difference between commercial success and failure for Mobile Network Operators (MNOs) and system suppliers alike. The RIC itself will become a differentiator for MNOs to attract new customers by showing how their network is outperforming the competition with real benchmarking statistics.

However, as you might anticipate, RIC testing throws up some new non-trivial challenges and requirements for participants in the 5G and O-RAN ecosystem. For example, all the messages coming from the RAN into RIC need to go over the E2 interface. However, most developers won’t have a RAN to test the RAN Intelligent Controller with. They therefore need a way to emulate the RAN metrics volume and RAN traffic scenarios in order to test the compliance and functions of the RIC. They also need to be able to test in real time, given that much of the RIC’s functionality is performed in real time or near real time.

Mobile Network Operators

MNOs need a way to do all of this in a lab environment, before placing their trust in a node which can effect changes in their RAN in real time.

More specifically, MNOs need to address the following questions:

  • How do they ensure the decisions made by RIC and proposed changes won’t cause degradation rather than benefit the RAN?
  • How do they make sure the AI/ML models trained and tested in a certain network environment will perform reasonably in different network environments?
  • How do they manage trust in AI/ML models that they don’t have 100% control of?
  • How do they port and combine between different vendors O-RAN components, xApps, rApps and RICs with confidence and without adversely impacting network operations and performance?

Testing the Near RT RAN Intelligent Controller With TeraVM

At VIAVI we provide complete and effective answers to the thorny test and monitoring questions raised by the development, deployment and commercialisation of the RAN Interface Controller element of the O-RAN.

The VIAVI RIC Test provides a comprehensive validation test suite for the RAN Interface Controller element of the O-RAN ecosystem. It emulates UEs, Next Generation Node B (gNBs), O-DUs, O-CUs, mobility patterns, path loss propagation models and much more.

Among its main functional repertoire are:

  • E2 Interface Performance Testing, which involves ensuring the RIC complies to the E2 interface standard.
  • RAN Scenario Generator, which enables xAPPs to train their algorithm under different scenarios for a complete and all round RAN experience. With TeraVM’s RAN scenario generator traffic scenarios can be emulated and pushed towards the xAPPs to offer a diverse set of UE behaviours and traffic models thereby fully training the xApp prior to live network deployment
  • xAPP Functionality Testing, which establishes how effective are the decisions taken by the xAPPs housed on the Near RT RIC. The system measures the effectiveness of xAPP recommendations by performing the changes in the emulated RAN, and measuring the change over time to see if the desired improvement is achieved. Areas addressed, but not limited to, are: traffic steering; anomaly detection and mitigation; geolocation xAPP; and network slice QoS assurance.

It’s worth noting that VIAVI was the first to market Near RT RIC Test compliant with O-RAN Alliance standard. TeraVM is also an invaluable tool for validating Non RT RIC performance. It enables end-to-end testing and monitoring in public, private and edge environments of this type of RIC, and supports across-the-board testing of rApps functionality.

The Beat Goes On

The 5G and O-RAN combo revolution continues to build a head of steam. The O-RAN Alliance states it is now a worldwide community of more than 300 mobile operators, vendors, and research and academic institutions – up from five founding operators in early 2018.

In this context it’s a given that best-of-breed test solutions for RAN Intelligent Contoller (RIC) conformance, performance and interoperability will continue to play a pivotal role in advancing this twin mobile communications transformation. It’s a given that VIAVI, a regular contributor to O-RAN Alliance work groups helping define the RIC interface standards, and a major supplier of advanced test, monitoring and assurance systems to the global networking and communications community, will continue to play a leading part in all of this.

 

 

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TeraVM Marketing Manager

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