Enriching CSP Networks with RIC Automation

Automation is one of the biggest benefits that Open RAN introduces and with the arrival of the RAN Intelligent Controller (RIC), automation possibilities are compelling. With all the excitement about new 5G applications, RIC is like an infusion of oxygen for the heart of the mobile network. Intended as a technology for all mobile generations, it seems for now that 5G use cases are currently driving the need for innovative RIC applications. The reason for this focus is that operators need a viable way to optimize their new investment in 5G network infrastructure, all while cultivating new revenue streams and elevating the customer experience.  The RIC is essential to these goals because it sits between applications and network infrastructure to control resources, traffic, and communication between users and the services they consume, monitors the network in real-time, and dynamically adjusts and repurposes the network.

To realize the potential of 5G, CSPs need to modernize the network with a RAN Intelligent Controller equipped with rich AI and ML algorithms. Where the RIC shines is its ability to synthesize AI/ML algorithms with deep insight into the CSP operational domain. Although the RIC is open and standardized, make no mistake – it’s not a commodity. RIC automation is only as valuable to CSPs as much as its intelligence algorithms can sort out which data to be applied to which algorithms at which time and for which use cases.

The RIC is a critical piece of the Open RAN disaggregation strategy, bringing multivendor interoperability, intelligence, agility, and programmability to radio access networks. The RIC enables the onboarding of third-party applications that automate and optimize RAN operations at scale while supporting innovative use cases that lower mobile operators’ total cost of ownership (TCO) and enhance customers’ quality of experience (QoE).

CSPs can make the best use of the network by combining deep CSP domain expertise with finely-tuned Artificial Intelligence and Machine Learning algorithms. Finely tuned in this context means the ability to adjust and select the best AI/ML algorithm for the specific use case, rather than a one-size-fits-all approach. The approach sounds simple, but that is because complexity is abstracted for the benefit of the network users.  The first step in the process is determining a general use case based on network data pattern analysis.   From there, complex mathematical modeling is applied and a specific algorithm is selected based on that modeling. That algorithm then gets adjusted using proper network data – meaning data that is deemed useful to the modeling and not extraneous.    The result is a fine-tuned AI/ML engine, the output of which is perfectly unique and built on years of telecom and data science expertise.

The RIC helps mobile operators reduce both infrastructure and operational costs, improve network performance, and increase business agility. It also helps them build new revenue streams with personalized services and network slicing. It is a way to add strategic value and differentiation to the CSP network with a framework that modernizes and automates RAN operational workflows. CSPs are finding that to maintain a high level of user experience, they need the ability to make intelligent network changes and automate processes 24 hours a day, seven days a week – an important capability to supply the necessary self-optimizing and self-healing Self-Organizing Network (SON) functions that pre-5G SON solutions failed to address.

The Numbers Don’t Lie

This all sounds great for CSPs, but how convincing are the business results?

The non-RT RIC has shown the ability to deliver drastic improvement in Coverage and Capacity Optimization and Mobility Robustness Optimization KPIs by training ML models with live data to enable dynamic and adaptive policy and control. Mavenir has worked with Tier 1 operators to demonstrate reinforcement learning for traffic steering use cases to show significant, qualitative improvements. For example, live trials of Mavenir’s RIC yielded a per-subscriber throughput increase by 28%, and results showed a significant reduction in handovers by 39% on average. In the future, the RIC is expected to make even more significant impacts on the mobile network.

The Near-RT RIC is responsible for fine-grained Radio Resource Management (RRM) of the control-plane and user-plane of the RAN protocol stack at a per-UE level over the E2 interface.  Mavenir’s near-RT RIC is the first to control RAN activity at BOTH the cell and individual user levels – which makes a difference in the experience offered to customers.  The containerized application hosts trained AI/ML applications to infer and control O-RAN elements in near-real-time. In the 2021 Airtel India Plugfest, Mavenir was able to show a live demonstration of the world’s first O-RAN standards-compliant Near Real-Time RAN Intelligent Controller (Near-RT RIC) with an AI-powered extensible application (xApp). The xApp controlled the traffic steering functionality of a 5G Radio Access Network (RAN), a key feature responsible for managing the connectivity and mobility of users in the network.  The results showed an improvement of throughput and spectral efficiency by 50% over traditional methods.

One unique feature of Mavenir’s Near-RT RIC is that the platform offers ‘Network Intelligence-as-a-Service (NIaaS)’ to the xApps with a built-in suite of sophisticated AI/ML models, an analytics framework, and cloud-native tools. NIaaS enables xApps to adopt scalable and practical ML-driven intelligence toward building programmable RAN optimization solutions for 5G and beyond.

In what looks to be a robust market for RIC in the coming years, what really matters for CSPs is how the RIC helps their business stay profitable and grow. Finding a partner who has a strong pedigree in cloud-native technology, RAN deployments, and applied AI/ML is paramount to reaping the benefits of the RIC.

-This article has been contributed by Neeraj Bhatnagar, Vice President – Sales, India & South Asia at Mavenir