by TIP News

The flexible and scalable architecture enabled by Open RAN can only be truly realized with revisiting how the RAN is managed and operated. The RAN Intelligent Controller (RIC) will play a crucial role in supporting increased levels of network automation, providing fine-grained, use case-driven control and management of the RAN resources. The non-realtime and near-realtime variants allow specialized software from different vendors to be deployed for network management, optimization, and innovation.

The RIC’s promotion of interoperable solutions is vital to solving complex connectivity problems, creating new capabilities, and accelerating the deployment of AI/ML driven networks of the future. The ARI-5G consortium funded by DSIT implements, tests, and demonstrates a standards-based RIC platform with specific software solutions (‘xAPPs and rAPPs’), building on TIP’s RAN Intelligence and Automation (RIA) Subgroup activities that nine leading MNOs have prioritized.

ARI-5G Aims

ARI-5G has two main aims.  First, to demonstrate the successful interoperability of components developed to OpenRAN standards.  Second, focused on the RIC, as a distinguishing feature of OpenRAN architectures, leveraging software-based intelligent designs enabled through artificial intelligence and machine learning (AI/ML) to provide more efficient and optimized control of RAN operations. 

It involves indoor/outdoor deployments of a standalone 5G network in a simulator along with an outdoor physical network, both located at BT’s Adastral Park test facility, which is underpinned by AttoCore’s Release 16 Next Generation Core (NGC). Now integrated and tested, this environment is being used for testing the four RAN Intelligent Controller (RIC) based applications (known as xApps & rApps with Open RAN) that will optimize the following facets of the network.  

  • Capacity and Coverage Optimisation
  • Interference Mitigation and Management
  • Massive Multiple-Input Multiple-Output (mMIMO) Spectrum Efficiency
  • Energy Management and Power Savings

Direct benefits of the project

  • Demonstration of interoperable multi-vendor solutions
  • Improvements in spectral efficiency, coverage, capacity, energy management and throughput that leads to reduced need for network expansion. 
  • Improved vendor conformity to Open RAN standards promulgated by organisations such as O-RAN Alliance and 3GPP. . 

These benefits will be cumulatively demonstrated across the lifecycle of the ARI-5G project through the staggered design, testing, deployment, and demonstration of the four solutions.   



Contribution to Open RAN commercialisation

  • Increased development and deployment of Open RAN 5G technologies leading to faster time to market for vendor-prioritized solutions. 
  • Lower barrier to entry for xAPPs and rAPPs development by introducing E2 open interface node simulation that partially decouples developers from needing physical networks. We expect this to lead to a proliferation of deployable xAPPs and rAPPs available through a marketplace with 80-100 UK vendors by 2025. 
  • Lower barrier to entry for Small Medium Enterprises (SMEs) and universities to monetise their algorithms in a very short time frame (<6 months from conception to deployment through a marketplace) by providing open RIC platforms with abstracted network configuration and measurement data. 
  • Catalyst for ecosystem growth leading to participation by new entrants. 
  • New RIC platforms that allow vendors to build and sell new network automation applications. We expect this to lead to 3-8 UK vendors that build and deploy RIC platforms for both domestic and international markets. 

Success so far

The 5G stand-alone network has been successfully built at Adastral Park. The four applications have been developed using VIAVI’s RIC Test to simulate the network and UEs. The 5G Capacity and Coverage Optimization application has successfully been tested over-the-air on the 5G SA network over-the-air on the 5G SA network.  As part of the tests, BT conducted before and after quality assessments (walk tests) on the network and were able to measure good mobile signal strength in 95% of measurements vs 48% prior to optimization.

What’s next 

The test conditions for the Energy Management application are being setup.  This application uses a learning algorithm that requires an extended period of traffic generation over the network.  

  • Traditional macrocell/small cell coverage and capacity multifrequency network (MFN) layers where the capacity layer can be enabled/disabled via RF transceiver switch on/off (this is the most typical MNO-oriented deployment using MNO licensed bands in low bands and mid-bands). 
  • 2) High-density small cell deployment based on a single frequency network (SFN) where The TX power of the small cells is managed dynamically, and small cells can be switched on/off as needed (this is also an important deployment topology for dense urban or enterprise indoor/outdoor campus network scenarios where a neutral host or an enterprise may use private or shared spectrum only in mid-bands).

After this, Interference Management and Mitigation, which uses UE-centric data to drive an optimization model that mitigates inter-cell interference, will be the focus. The implementation as an rAPP is being completed and will then be demonstrated on the RIC Test simulated environment.

Please come and hear progress at Fyuz October 9-11, Madrid, where companies involved in ARI will discuss project progress. To register for Fyuz, visit: