Optimizing the resources allocation of open radio access networks using predictive models

This paper presents an adaptable mechanism for optimising the bandwidth and power for the ORAN. This modelling takes into account many metrics such as power and bandwidth allocation, predictive traffic forecasting, and the interference management strategy to optimize the QoS and energy efficiency (EE). This ensures the user-centric services—such as videos and live communications— will receive the required prioritization during the process of resource allocations. To mitigate the risk of network overloading, this paper also considers the dynamic behaviour of the network, and thereby, allocating the users resources based on the users need. This work also extends to a multi-vendor, multi-operator setup. This is where different network providers rely on shared components of the network, such as servers, software, antennas, and more. The complexity involved in such connectivity requires more advanced management techniques. This is self-explanatory as effective coordination and allocation of resources are needed to run the components. Via the use of predictive models in this work, the traffic fluctuations considering power and bandwidth is properly realized, which enhances the network efficiency expressions.

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Publication Details

Journal
IET conference proceedings.
Published
2026-01-19
DOI
https://doi.org/10.1049/icp.2025.4394
Primary Topic
Network Traffic and Congestion Control
Type
article
Field-Weighted Citation Impact
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article

Optimizing the resources allocation of open radio access networks using predictive models

S. H. Mehdi, Bilal R. Al-Kaseem, Raad S. Alhumaima, Basit N. Khalaf et al.
IET conference proceedings.
Network Traffic and Congestion Control
article

Optimizing the resources allocation of open radio access networks using predictive models

S. H. Mehdi, Bilal R. Al-Kaseem, Raad S. Alhumaima, Basit N. Khalaf, Abdulla Amer
article en

Abstract

This paper presents an adaptable mechanism for optimising the bandwidth and power for the ORAN. This modelling takes into account many metrics such as power and bandwidth allocation, predictive traffic forecasting, and the interference management strategy to optimize the QoS and energy efficiency (EE). This ensures the user-centric services—such as videos and live communications— will receive the required prioritization during the process of resource allocations. To mitigate the risk of network overloading, this paper also considers the dynamic behaviour of the network, and thereby, allocating the users resources based on the users need. This work also extends to a multi-vendor, multi-operator setup. This is where different network providers rely on shared components of the network, such as servers, software, antennas, and more. The complexity involved in such connectivity requires more advanced management techniques. This is self-explanatory as effective coordination and allocation of resources are needed to run the components. Via the use of predictive models in this work, the traffic fluctuations considering power and bandwidth is properly realized, which enhances the network efficiency expressions.

IET conference proceedings.Vol. 2025(41)
Imam Sadiq University (IR), Artificial Intelligence in Medicine (Canada) (CA), University of Diyala (IQ)
Affordable and clean energy
Openalex Percentile: Top 87%
Network Traffic and Congestion Control
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Optimizing the resources allocation of open radio access networks using predictive models — S. H. Mehdi, Bilal R. Al-Kaseem, et al. · IET conference proceedings. (2026) | TGRS Research Map | TGRS