Researchers from the University of Surrey have developed an AI-driven model to optimize the allocation of computing power in Open Radio Access Networks (O-RANs). By minimizing VNF computational costs and reducing overhead associated with reconfigurations, the model has the potential to significantly enhance bandwidth utilization efficiency. The study showcased up to a 76% reduction in VNF reconfiguration overhead and a marginal increase of up to 23% in computational costs. This research could empower telecom providers to improve the resilience and energy efficiency of their networks.
University of Surrey Researchers Developed a new Artificial Intelligence (AI) Model that Could Help the Telecommunications Network Save up to 76% in Network
Open Radio Access Networks (O-RANs) have revolutionized the telecommunications industry by bringing intelligence to the disaggregated Radio Access Network (RAN) and implementing functions as Virtual Network Functions (VNF) through open interfaces. However, the dynamic nature of traffic conditions in real-world O-RAN environments often leads to increased costs and potential traffic instability.
To address this challenge, researchers from the University of Surrey have mathematically modeled the network and utilized AI to optimize the allocation of computing power. This innovative model offers the potential to significantly enhance the efficiency of bandwidth utilization.
The approach minimizes VNF computational costs and the overhead associated with periodic reconfigurations. The study utilized constrained combinatorial optimization coupled with deep reinforcement learning to minimize costs while maximizing performance. The evaluation of this solution demonstrated substantial improvements, with up to a 76% reduction in VNF reconfiguration overhead and a marginal increase of up to 23% in computational costs.
This research highlights that while O-RANs have transformed the telecom landscape by allowing providers to adjust computing power based on demand, existing technology struggles to adapt to rapid changes in network demand. The proposed AI-driven scheme empowers telecom providers to enhance the efficiency of their networks, making them more resilient and energy-efficient.
Telecom companies can apply these findings to improve the efficiency of their networks, reducing energy consumption and strengthening system resilience.
The University of Surrey team plans to collaborate with industry partners on the HiperRAN Project to further test and advance the proposed scheme for widespread adoption.
If you’re interested in the full research paper, please check it out. The credit for this research goes to the researchers involved in the project. Additionally, don’t forget to join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter to stay updated on the latest AI research news and projects.
If you want to leverage AI to evolve your company and stay competitive, consider the new AI model developed by the University of Surrey Researchers. It has the potential to save up to 76% in the telecommunications network. Discover how AI can redefine your work processes and identify areas where automation can be beneficial. Define key performance indicators (KPIs) to measure the impact of AI on your business outcomes. Select an AI solution that suits your needs and can be customized. Implement AI gradually, starting with a pilot project and expanding its usage strategically.
To receive guidance on managing AI KPIs and for continuous insights into leveraging AI, connect with us at hello@itinai.com. You can also stay tuned to our Telegram channel or Twitter for the latest updates.
One practical AI solution to consider is the AI Sales Bot from itinai.com/aisalesbot. This bot is designed to automate customer engagement 24/7 and manage interactions across all stages of the customer journey. Explore how AI can redefine your sales processes and customer engagement by visiting itinai.com.
