University of Surrey Researchers Developed a new Artificial Intelligence (AI) Model that Could Help the Telecommunications Network Save up to 76% in Network


Harness the Potential of AI Tools with ChatGPT. Our blog offers comprehensive insights into the world of AI technology, showcasing the latest advancements and practical applications facilitated by ChatGPT’s intelligent capabilities.

Open Radio Access Networks (O-RANs) have transformed the telecommunications landscape by infusing intelligence into the disaggregated Radio Access Network (RAN) and implementing functionalities as Virtual Network Functions (VNF) through open interfaces. Despite these advancements, the dynamic nature of traffic conditions in real-world O-RAN environments often necessitates VNF reconfigurations during runtime, leading to increased overhead costs and potential traffic instability. 

In response to this challenge, In a study recently published in the IEEE Transactions on Network Service Management, researchers from the University of Surrey detail how they mathematically modelled the network and utilized AI to optimize the allocation of computing power. This innovative model offers the potential to enhance the efficiency of bandwidth utilization significantly.

This approach minimizes VNF computational costs and the overhead associated with periodic reconfigurations. The study utilized constrained combinatorial optimization coupled with deep reinforcement learning, employing an agent to minimize a penalized cost function derived from the proposed optimization problem. The evaluation of this innovative solution showcased substantial improvements, realizing a remarkable up to 76% reduction in VNF reconfiguration overhead, accompanied by a marginal increase of up to 23% in computational costs.

While O-RANs have transformed the telecom landscape by enabling providers to shift computing power across their network in response to changing demand, the study emphasizes that existing technology struggles to adapt to rapid changes in network demand. The researchers believe that the proposed AI-driven scheme could empower telecom providers to enhance the efficiency of their networks, making them more resilient and energy-efficient.

Telecom companies could apply their findings to improve the efficiency of their networks further. This could reduce energy consumption while simultaneously strengthening the resilience of their systems.

The Surrey team will collaborate with industry partners on the HiperRAN Project, which aims to test the proposed scheme further and get the technology closer to being ready for widespread adoption.

Dr. Mohammad Shojafar, a senior lecturer at the University of Surrey and co-author of the study, added that this approach attempts to create robust, intelligent applications for traffic demands on Open RAN, a well-known next-generation telecom network. The next generation of telecommunications networks could be shaped by this research, which could be easily implemented.

Check out the Paper. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 32k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

If you like our work, you will love our newsletter..

We are also on Telegram and WhatsApp.

Rachit Ranjan is a consulting intern at MarktechPost . He is currently pursuing his B.Tech from Indian Institute of Technology(IIT) Patna . He is actively shaping his career in the field of Artificial Intelligence and Data Science and is passionate and dedicated for exploring these fields.

🔥 Meet Retouch4me: A Family of Artificial Intelligence-Powered Plug-Ins for Photography Retouching

Discover the vast possibilities of AI tools by visiting our website at to delve deeper into this transformative technology.


There are no reviews yet.

Be the first to review “University of Surrey Researchers Developed a new Artificial Intelligence (AI) Model that Could Help the Telecommunications Network Save up to 76% in Network”

Your email address will not be published. Required fields are marked *

Back to top button