arXiv subjects

Rana M. Sohaib

Publications and source records attributed to Rana M. Sohaib.

4 recordsLinked to original sources

arXiv2026

Handling Interoperability Issues in 6G ORAN: Lessons Learned from Research Lab-based Integrations

As 6G networks continue to evolve, the Open Radio Access Network (ORAN) framework is gaining prominence for its promise of network flexibility, vendor-neutral architecture, and enhanced service delivery. However, achieving true interoperability across diverse hardware and software components from multiple vendors remains a critical challenge. This research focuses on identifying and addressing interoperability issues within 6G ORAN environments, leveraging insights from research lab-based integrations. By systematically analysing lab-based case studies, the study aims to uncover the root causes of integration failures, propose solutions to ensure smooth multi-vendor compatibility, and establish a set of best practices for future deployments. This work will contribute to developing robust guidelines for 6G ORAN ecosystems, accelerating seamless and scalable deployments in real-world networks.

cs.NI↗

arXiv2024

Towards Resilient 6G O-RAN: An Energy-Efficient URLLC Resource Allocation Framework

The demands of ultra-reliable low-latency communication (URLLC) in ``NextG" cellular networks necessitate innovative approaches for efficient resource utilisation. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimisation individually, often neglecting the intricate balance required to optimise both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximise energy efficiency (EE) while minimising URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks.

cs.NI↗

arXiv2024

DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN

This work addresses resource allocation challenges in multi-cell wireless systems catering to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) users. We present a distributed learning framework tailored to O-RAN network architectures. Leveraging a Thompson sampling-based Deep Reinforcement Learning (DRL) algorithm, our approach provides real-time resource allocation decisions, aligning with evolving network structures. The proposed approach facilitates online decision-making for resource allocation by deploying trained execution agents at Near-Real Time Radio Access Network Intelligent Controllers (Near-RT RICs) located at network edges. Simulation results demonstrate the algorithm's effectiveness in meeting Quality of Service (QoS) requirements for both eMBB and URLLC users, offering insights into optimising resource utilisation in dynamic wireless environments.

eess.SP↗

arXiv2024

Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.

eess.SP↗