RIC Connect’s AI-Engine drives innovation within private networks. From optimizing performance to enhancing security, our tailored solutions empower your network to thrive.
RIC Connect has been developed by a world leading research group at the University of York to address the significant challenge of making existing technologies Open RAN-compatible, a pivotal step in the industry advancement.
These elements collectively enhance network efficiency, reduce integration time, and support the transition to more flexible, future-proof network infrastructures.
RIC Connect not only ensures technological compatibility across different platforms but also offers significant cost savings by enabling the use of existing equipment within new frameworks.
These elements collectively enhance network efficiency, reduce integration time, and support the transition to more flexible, future-proof network infrastructures.
RIC Connect not only ensures technological compatibility across different platforms but also offers significant cost savings by enabling the use of existing equipment within new frameworks.
Enables equipment from various wireless telecom equipment vendors to communicate and operate cohesively, fostering the adaptability of Open RAN technologies.
Acts as the central intelligence, driving the development of x/rApps to optimise network functionality.
Specialised applications that use AI to enhance network operations, such as energy savings. These services are not only more secure and efficient but also customizable to meet specific requirements.
Our “Seamless Open RAN Integration” platform is specifically designed to tackle the substantial challenges that Mobile Network Operators (MNOs), particularly Tier 2 MNOs, encounter as they transition to Open RAN architectures. This transition, while crucial for modernizing and future-proofing their telecommunications infrastructure, introduces several pressing issues:
1. Excessive Investment Requirements: One of the primary challenges for Tier 2 MNOs is the significant cost associated with updating existing systems to achieve Open RAN compatibility. Traditional approaches often require heavy investment in either R&D or extensive hardware and software upgrades.
2. Time-Consuming Integration and Updates: For MNOs, the process of integrating and updating hardware and software in the shift towards Open RAN architectures is not only complex but also highly time-consuming. This lengthy integration process can lead to operational delays and increased costs as it often involves significant downtime and manual effort.
3. Complexity and Interoperability Issues: Open RAN aims to foster a more competitive and diverse supplier ecosystem but also introduces complexity and interoperability challenges. Different components and solutions from various vendors must seamlessly work together, often requiring additional layers of coordination and technical customization.
4. Legacy Equipment Integration: Integrating legacy equipment with new Open RAN systems poses significant compatibility challenges.
2. Time-Consuming Integration and Updates: For MNOs, the process of integrating and updating hardware and software in the shift towards Open RAN architectures is not only complex but also highly time-consuming. This lengthy integration process can lead to operational delays and increased costs as it often involves significant downtime and manual effort.
3. Complexity and Interoperability Issues: Open RAN aims to foster a more competitive and diverse supplier ecosystem but also introduces complexity and interoperability challenges. Different components and solutions from various vendors must seamlessly work together, often requiring additional layers of coordination and technical customization.
2. Time-Consuming Integration and Updates: For MNOs, the process of integrating and updating hardware and software in the shift towards Open RAN architectures is not only complex but also highly time-consuming. This lengthy integration process can lead to operational delays and increased costs as it often involves significant downtime and manual effort.
3. Complexity and Interoperability Issues: Open RAN aims to foster a more competitive and diverse supplier ecosystem but also introduces complexity and interoperability challenges. Different components and solutions from various vendors must seamlessly work together, often requiring additional layers of coordination and technical customization.
4. Legacy Equipment Integration: Integrating legacy equipment with new Open RAN systems poses significant compatibility challenges.
4. Legacy Equipment Integration: Integrating legacy equipment with new Open RAN systems poses significant compatibility challenges.
Our value proposition for Mobile Network Operators (MNOs) centres on leveraging “Seamless Open RAN Integration” technology to effectively tackle the complex challenges associated with transitioning to Open RAN architectures. Here’s how our solution addresses specific issues faced by MNOs and delivers substantial benefits:
Our Seamless Open RAN Integrator, an integral component of “RIC Connect”, stands as a potent intermediary platform, streamlining the transition to Open RAN with remarkable simplicity. By bolstering compatibility and interoperability among diverse network components from various vendors, addressing the pivotal need for operators venturing into the Open RAN market. This seamless integration empowers operators to efficiently align their offerings with Open RAN standards, fostering a smoother transition process and enhancing their competitive edge in the evolving telecom landscape.
We streamline the integration process of Open RAN systems, significantly cutting down the time and resources required for deployment. “Seamless Open RAN Integration” is designed to seamlessly interface with both legacy and new equipment, simplifying the integration of diverse technologies and reducing operational delays that can result from complex installation processes.
“Seamless Open RAN Integration” significantly reduces the financial burden associated with adopting Open RAN. By enabling Tier 2 MNOs to repurpose existing equipment and minimizing the need for extensive new hardware purchases, our solution helps reduce capital expenditure. This approach allows MNOs to achieve compliance with Open RAN standards without the typically associated high costs, providing a more cost-effective path to modernizing their networks.
Integrating legacy equipment with new Open RAN systems poses significant compatibility challenges.
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1. Resource Intensity in Understanding Open RAN Architecture: Open RAN architecture, though advantageous, is complex and necessitates a deep understanding which can be resource-intensive. MNOs often must invest considerable time and effort into training and development to effectively manage Open RAN systems.
1. Resource Intensity in Understanding Open RAN Architecture: Open RAN architecture, though advantageous, is complex and necessitates a deep understanding which can be resource-intensive. MNOs often must invest considerable time and effort into training and development to effectively manage Open RAN systems.
2. Overwhelming Traditional Network Setups: Traditional Network setups are often overwhelmed by the intensive demands of model training, data collection, and preprocessing, which critically impede the efficient management and automation of radio resources. Our solution optimizes these processes, enhancing the efficiency and capability of network management systems to handle these demands without overloading the infrastructure.
2. Overwhelming Traditional Network Setups: Traditional Network setups are often overwhelmed by the intensive demands of model training, data collection, and preprocessing, which critically impede the efficient management and automation of radio resources. Our solution optimizes these processes, enhancing the efficiency and capability of network management systems to handle these demands without overloading the infrastructure.
3. Ownership and Compatibility of AI Models: There is often a lack of ownership of AI models and difficulty in ensuring their compatibility across different systems. Our solution includes customizable AI-driven applications that ensure operators can maintain control over their data and the models used within their networks.
4. Network Security Risks: With the increased complexity and the integration of multiple vendors, ensuring the security of the network becomes more challenging.
“AI-Engine” optimizes network operations through automation and advanced data analytics, improving the efficiency of resource allocation, network management, and operational decision-making. This not only reduces operational costs but also enhances network performance, meeting the high standards required by MNOs.
Specifically developed for Open RAN architectures, the AI-Engine optimizes critical functions such as data preprocessing and model training, enhancing interoperability across various applications. This not only allows for seamless management of diverse services but also supports robust network operations and dynamic decision-making without heavy reliance on manual processes. Additionally, the AI-Engine facilitates seamless integration with equipment from multiple vendors, simplifying the management of mixed environments and significantly improving interoperability. Enhanced security features ensure the protection of data integrity and system stability throughout the development and deployment of ML/AI-based applications.
With security integrated into the core, operators can assure their customers of a secure and reliable network, which is essential for building trust and expanding their customer base.
Our solution gives private networks complete ownership over the AI-Engine and associated trained data. This ownership ensures that they can control and utilize their proprietary data and models, enhancing their offerings and maintaining competitive advantages.
Our technology enhances interoperability among disparate network components from various vendors offering AI-models. “AI-Engine” facilitates a multi-vendor environment that encourages innovation and reduces vendor lock-in, while ensuring all components function seamlessly together. This is crucial for MNOs as it allows greater flexibility in choosing suppliers and optimizes network efficiency.
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1. Need for Customized Network Solutions: MNOs often require customized solutions tailored to specific operational needs or industry regulations.
1. Need for Customized Network Solutions: MNOs often require customized solutions tailored to specific operational needs or industry regulations.
2. Vendor Lock-in Risk Reduction: While Open RAN is intended to reduce vendor lock-in, transitioning from existing single-vendor solutions to a multi-vendor environment involves its own risks, including increased complexity and potential difficulties in managing service level agreements (SLAs).
3. Scalability: As networks grow and data demands increase, ensuring that Open RAN systems can scale effectively without compromising performance or efficiency is a crucial challenge.
2. Vendor Lock-in Risk Reduction: While Open RAN is intended to reduce vendor lock-in, transitioning from existing single-vendor solutions to a multi-vendor environment involves its own risks, including increased complexity and potential difficulties in managing service level agreements (SLAs).
3. Scalability: As networks grow and data demands increase, ensuring that Open RAN systems can scale effectively without compromising performance or efficiency is a crucial challenge.
4. Reliability and Performance Concerns: There can be apprehensions about the reliability and performance of Open RAN systems compared to traditional RAN solutions, particularly in terms of managing traffic, handling peak loads, and ensuring consistent quality of service.
5. Regulatory Compliance: MNOs must comply with stringent regulatory standards, which can be challenging when implementing new technologies.
6. Market Readiness: The ecosystem of vendors and solutions for Open RAN is still developing. Finding mature and ready-to-deploy solutions can be a challenge for MNOs looking to transition swiftly and effectively.
7. Lack of Technical Expertise: MNOs may not always have the technical expertise required to manage and maintain advanced RAN technologies.
4. Reliability and Performance Concerns: There can be apprehensions about the reliability and performance of Open RAN systems compared to traditional RAN solutions, particularly in terms of managing traffic, handling peak loads, and ensuring consistent quality of service.
5. Regulatory Compliance: MNOs must comply with stringent regulatory standards, which can be challenging when implementing new technologies.
6. Market Readiness: The ecosystem of vendors and solutions for Open RAN is still developing. Finding mature and ready-to-deploy solutions can be a challenge for MNOs looking to transition swiftly and effectively.
7. Lack of Technical Expertise: MNOs may not always have the technical expertise required to manage and maintain advanced RAN technologies.
AI-powered xApps and rApps are fully customizable, enabling operators to tailor solutions to meet specific network requirements and operational goals, enhancing flexibility and efficacy in network management. Thus, these AI-powered xApps/rApps support specialized use-cases, enhancing the capability of MNOs to offer tailored solutions and thereby improving market adaptability.
Our technology facilitates the cost-effective enhancement of network functionality, including AI/ML-based xApps and rApps, allowing private networks to offer more advanced capabilities without significant investment, thereby appealing to a broader market.
As MNOs expand their services or need to scale operations, Our product provides the scalability needed to manage growth efficiently and supports the dynamic allocation of resources without performance degradation, ensuring that network capabilities grow in line with demand.
We offer tailored consultative support and training, helping MNOs maximize the benefits of our product and ensuring they can fully leverage the platform to meet their specific operational needs.
Allows for flexible integration of AI/ML technologies, enhancing functionality and network control, which is crucial for vendors looking to offer cutting-edge solutions.
Our product provides the flexibility required by the MNOs to quickly adapt to new regulations and market demands, enhancing their ability to respond to competitive pressures and opportunities.
Our Product simplifies the understanding and implementation of Open RAN architectures. It minimizes the learning curve and the resource allocation needed for network management, which is a significant advantage for operators who may have smaller technical teams.
This product not only saves time and reduces costs but also accelerates the adoption of Open RAN technologies. For mobile network operators, this means being able to offer advanced services and compete more effectively with major players without the prohibitive expense.
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Swarna Chetty, with her profound expertise in telecommunications, software development, and machine learning, is poised to serve as the entrepreneurial lead for this programme. Her extensive background, underscored by a PhD in 5G and Beyond Systems using Machine Learning, fortifies her role. Swarna's leadership in pivotal initiatives, including the development of AI-based energy efficiency and network slicing applications, along with ML-based dynamic resource allocation of network services, and her experience as a project manager for an NGO, underscores her technical and managerial prowess.
David Grace is a Professor (Research), the Advanced Communications Pillar Lead, in the Institute for Safe Autonomy, and the Director of the Centre for High Altitude Platform Applications at the University of York. In 2000, he jointly founded SkyLARC Technologies Ltd. and was one of its directors. From 2014 to 2018, he was the Non-Executive Director of Stratospheric Platforms Ltd. He is currently a Lead Investigator on government-funded YO-RAN and REACH, dealing with O-RAN development and experimentation. He was the Technical Lead on the 14-partner FP6 CAPANINA Project that dealt with broadband communications from high-altitude platforms.
Hamed Ahmadi is a Reader at the University of York, UK. He has a PhD in intelligent wireless communications and a graduate certificate in Management of Technology both from the National University of Singapore. He has published more than 100 peer-reviewed book chapters, journal and conference papers. He is the lead investigator in iTwins EPSRC IAA project dealing with green and privacy-aware machine learning for O-RAN. He is also a co-investigator in DSIT-funded YO-RAN and REACH projects. He is the associate Editor in Chief of IEEE Communication Standards magazine.
Luke Souter has both a technical and business background, as he has a PhD in biomechanics and an MSc in biomedical engineering. He also has a background in sales, has set up and run his consultancy firm and has worked in a spin-out company previously creating a diabetes therapeutic. He has been working in the commercialisation team at York for over 2 years and has supported 4 previous academic teams on ICURe accelerator programmes. He has expertise in spin-out formation and licensing university technologies. Luke’s contribution will be supporting the academic team throughout the programme with business advice, and helping manage and create the IP strategy for the technology.
To register your interest and learn more about our solutions, please use the contact form below to contact our team. We are particularly keen to hear from Mobile Network Operators.