F-IRAN: Performance Analysis of 6G Fog Intelligent Radio Access Network

The next generation of wireless systems—Sixth Generation (6G) and beyond—is expected to support ultra-massive connectivity through a dense composition of small-and micro-scale networks with high traffic loads. To address the resulting demands for network flexibility, low-latency access, and energy-aware operation, conventional Radio Access Network (RAN) architectures require fundamental redesign. This paper proposes a novel Fog-based Intelligent Radio Access Network (F-IRAN) architecture for 6G, integrating Artificial Intelligence (AI), Tiny Machine Learning (TinyML), and Fog Radio Access Network (F-RAN) concepts across diverse deployment scenarios with varying numbers of Intelligent Remote Radio Heads (IRRHs).The proposed F-IRAN framework is realized through two deployment strategies: a virtualization-based design that leverages distributed computing resources to reduce power consumption and enhance energy efficiency, and a hardware separation-based design that embeds lightweight TinyML models at the fog layer to accelerate processing and reduce execution latency. Simulation results confirm that both strategies outperform conventional F-RAN in key performance indicators. The separation-based approach achieves Energy Efficiency (EE) gains of up to 70% and latency reductions exceeding 36%, while the virtualization-based approach delivers over 50% improvement in EE along with consistent Spectral Efficiency (SE) performance, even under varying IRRH densities and a latency reduction of roughly 11%. Compared to centralized architectures such as Cloud RAN (C-RAN) and Heterogeneous Cloud RAN (H-CRAN), F-IRAN significantly reduces execution delay by offloading computation to the network edge and alleviating fronthaul bottlenecks. Furthermore, when evaluated against recent architectures such as Open RAN (O-RAN) and Virtualized RAN (V-RAN), the proposed architecture demonstrates lower energy consumption and latency by avoiding coordination and virtualization overhead. These findings validate F-IRAN as a scalable, low-latency, and energy-efficient solution for deployment in Beyond 5G (B5G) and 6G network environments.

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Publication Details

Journal
IEEE Open Journal of the Communications Society
Published
2025-01-01
DOI
https://doi.org/10.1109/ojcoms.2025.3604282
Primary Topic
Advanced MIMO Systems Optimization
Type
article
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article

F-IRAN: Performance Analysis of 6G Fog Intelligent Radio Access Network

Eman A. Anowr, Mona Nashaat, Heba M. Abdel‐Atty, Mostafa M. Fouda et al.
IEEE Open Journal of the Communications Society
Advanced MIMO Systems Optimization
article

F-IRAN: Performance Analysis of 6G Fog Intelligent Radio Access Network

Eman A. Anowr, Mona Nashaat, Heba M. Abdel‐Atty, Mostafa M. Fouda, Mohamed A. Mohamed, M. Ismail
article en

Abstract

The next generation of wireless systems—Sixth Generation (6G) and beyond—is expected to support ultra-massive connectivity through a dense composition of small-and micro-scale networks with high traffic loads. To address the resulting demands for network flexibility, low-latency access, and energy-aware operation, conventional Radio Access Network (RAN) architectures require fundamental redesign. This paper proposes a novel Fog-based Intelligent Radio Access Network (F-IRAN) architecture for 6G, integrating Artificial Intelligence (AI), Tiny Machine Learning (TinyML), and Fog Radio Access Network (F-RAN) concepts across diverse deployment scenarios with varying numbers of Intelligent Remote Radio Heads (IRRHs).The proposed F-IRAN framework is realized through two deployment strategies: a virtualization-based design that leverages distributed computing resources to reduce power consumption and enhance energy efficiency, and a hardware separation-based design that embeds lightweight TinyML models at the fog layer to accelerate processing and reduce execution latency. Simulation results confirm that both strategies outperform conventional F-RAN in key performance indicators. The separation-based approach achieves Energy Efficiency (EE) gains of up to 70% and latency reductions exceeding 36%, while the virtualization-based approach delivers over 50% improvement in EE along with consistent Spectral Efficiency (SE) performance, even under varying IRRH densities and a latency reduction of roughly 11%. Compared to centralized architectures such as Cloud RAN (C-RAN) and Heterogeneous Cloud RAN (H-CRAN), F-IRAN significantly reduces execution delay by offloading computation to the network edge and alleviating fronthaul bottlenecks. Furthermore, when evaluated against recent architectures such as Open RAN (O-RAN) and Virtualized RAN (V-RAN), the proposed architecture demonstrates lower energy consumption and latency by avoiding coordination and virtualization overhead. These findings validate F-IRAN as a scalable, low-latency, and energy-efficient solution for deployment in Beyond 5G (B5G) and 6G network environments.

IEEE Open Journal of the Communications SocietyVol. 6
Idaho State University (US), Mansoura University (EG), Port Said University (EG)
Openalex Percentile: Top 78%
Advanced MIMO Systems Optimization
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