Open radio access networks (O-RAN) have transformed the telecom landscape by infusing intelligence into the disaggregated radio access network (RAN) and implementing functionalities such as virtual network functions (VNFs) through open interfaces. Despite these advances, the dynamic nature of traffic conditions in real-world O-RAN environments often requires VNF reconfigurations during runtime, resulting in increased overhead costs and potential traffic instability.
Responding to this challenge, in a study recently published in IEEE Transactions on Network Service Management, researchers at the University of Surrey detail how they mathematically modeled the network and used ai to optimize the allocation of computing power. This innovative model offers the potential to significantly improve the efficiency of bandwidth utilization.
This approach minimizes VNF computational costs and overheads associated with periodic reconfigurations. The study used constrained combinatorial optimization coupled with deep reinforcement learning, employing an agent to minimize a penalized cost function derived from the proposed optimization problem. Evaluation of this innovative solution showed substantial improvements, achieving a notable reduction of up to 76% in VNF reconfiguration overhead, accompanied by a marginal increase of up to 23% in computational costs.
While O-RANs have transformed the telecommunications landscape by allowing providers to move 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-powered scheme could allow telecom providers to improve the efficiency of their networks, making them more resilient and energy efficient.
Telecom companies could apply their findings to further improve the efficiency of their networks. This could reduce energy consumption while strengthening the resilience of your systems.
The Surrey team will collaborate with industry partners on the HiperRAN Project, which aims to further test the proposed scheme and bring the technology closer to being ready for widespread adoption.
ai-mobile-networks-efficient.html”>Dr Mohammad Shojafar, Senior Lecturer, University of Surrey and co-author of the study, added that this approach attempts to create robust and intelligent applications for the traffic demands in Open RAN, a well-known next-generation telecommunications network. This research, which could be easily implemented, could shape the next generation of telecommunications networks.
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Rachit Ranjan is a consulting intern at MarktechPost. He is currently pursuing his B.tech from the 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 to exploring these fields.
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