NET1 – Radio Access Networks
Wednesday, 4 June 2025, 16:00-17:30, room 1.A
Session Chair: Alister G. Burr (Univ. of York, UK)
Radio Access Selection for Mobile Robots in Hybrid Li-Fi and mmWave Networks
Yijing Ren and Vasilis Friderikos (King’s College London, United Kingdom (Great Britain))
The emerging beyond fifth-generation (B5G) and sixth-generation (6G) wireless networks utilizing ultra-high bands are considered key enablers in supporting diversified applications for industrial mobile robots (MRs). Hybrid light fidelity (Li-Fi) and millimeter wave (mmWave) networks can provide high-speed data transmission and enhanced coverage for indoor mobile robot communications. The hybrid network scenario investigated in this paper relates to mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different workstations along planned trajectories. The selection of wireless access for mobile robot transmissions at workstations remains a challenge due to co-channel interference that might deteriorate overall network performance. To resolve this issue, a novel mmWave/Li-Fi access mode selection optimization problem is proposed via an integer programming formulation where the achieved data rate is considered jointly by considering both vertical and horizontal handovers. A wide set of numerical investigations reveal that the proposed optimal access mode selection (OAMS) can improve the overall network throughput by 14% and 46% compared with mmWave and Li-Fi communication access at workstations, respectively, while achieving a higher grade of individual transmission fairness and robustness.
An Open Source Digital Twin Framework for O-RAN
Hammad Zafar (Fraunhofer Heinrich Hertz Institute, Germany); Ehsan Tohidi (Fraunhofer HHI, Germany); Martin Kasparick (Fraunhofer Heinrich Hertz Institute & Technical University Berlin, Germany); Slawomir Stanczak (Technische Universität Berlin & Fraunhofer Heinrich Hertz Institute, Germany)
Open radio access network (O-RAN) offers interoperability and innovation by disaggregating traditional monolithic architecture. However, open networks’ complexity and dynamic nature pose significant optimization challenges. In this context, digital twins are crucial as they enable real-time monitoring, simulation, and optimization, leading to substantial performance improvements in the O-RAN network. This paper presents a framework that integrates the ns-3 network simulator, the Open Source Community (OSC) RAN Intelligent Controller (RIC), and xApps within O-RAN digital twins. By bridging the gap between simulation and real-world deployment, this research underscores the role of digital twins in the O-RAN ecosystem for developing and testing xApps. These digital twins enhance network performance optimization through real-time data analysis and simulation, enabling operators to seamlessly transition from simulated environments to actual telecom infrastructure. This integration not only promotes the programmability and disaggregation of network functions but also drives innovation in network management and control, leading to more intelligent and responsive wireless networks.
Enhancing 5G V-RAN Reliability via Reactive CU-UP Scaling
Dimitris Kefalas (Sorbonne University, France & University of Thessaly, Greece); Nikos Makris (University of Thessaly & CERTH, Greece); Serge Fdida (Sorbonne University, France); Thanasis Korakis (University of Thessaly, Greece)
The 5th generation and beyond of cellular networks (5G & B5G) adopted a cloud-based architecture, representing a significant evolution compared to monolithic approaches of previous generations. This transformation has even been devised for the Radio Access Network (RAN), with the Cloud-RAN (C-RAN), and its virtualized version the Virtual RAN (V-RAN) as crucial and key components. RAN cloudification has added up to the flexibility, reduced deployment and operational costs for the network operators. The high-speed demands, vast volumes of data, and the low latency response requirements of future applications (e.g. V2X, IoT, URLLC, and others) create significant stress on some V-RAN components. In the V-RAN architecture, the CU-UP is highlighted as the datapath bottleneck of the V-RAN under high traffic loads. This paper aims to enhance the reliability of 5G V-RAN by addressing the bottleneck issues of CU-UP by developing a controller that performs reactive horizontal scaling of the CU-UP component. The scaling mechanism is driven by an algorithm that estimates CPU usage based on a Linear Regression model, based on the rate of incoming packets, with the goal to increase the overall 5G network performance delivered to the end users. Using real-world traffic patterns, we tested the proposed scaling mechanism, conducting the experiments in a non-simulated real-world testbeds exposing our system to dynamic conditions. Our findings highlight the importance of adapting such a scaling mechanism to ensure the robust reliability of the 5G V-RAN.
An Open RAN Development Framework with Network Energy Saving rApp Implementation
Minhyun Kim, Kyoung Seok Lee, Soojung Jung and Jeehyeon Na (ETRI, Korea (South)); Salvatore D’Oro, Leonardo Bonati and Tommaso Melodia (Northeastern University, USA)
Open Radio Access Network (RAN) is the enabling technology to introduce interoperability and intelligence for the fifth/sixth generation (5G/6G) of mobile communication systems. We first describe an Open RAN architecture and then design and develop a framework that efficiently supports the implementation and testing of rApp prototypes. Specifically, the development framework provides the functionalities of non-real-time RAN Intelligent Controller (Non-RT RIC) and a simplified O1 interface with E2 Node functions that perform 5G RAN simulation and dataset generation. In addition, the front/back-end components are implemented to provide visualization related to E2 Node operation and energy saving performance. To validate the proposed framework, we design and implement an rApp prototype that supports network energy saving feature using an Artificial Intelligence/Machine Learning (AI/ML) model for Open RAN systems. We perform a conformance test and verify the efficiency of the framework through visualization results, and demonstrate that the proposed framework can be extended to the realization of a variety of O-RAN use cases.
Adaptive mMIMO Control in OPEN RAN: a Dynamic xApp Approach for Energy-Efficient Antenna Management
Vahid Kouh Daragh, Sr, David Grace and Hamed Ahmadi (University of York, United Kingdom (Great Britain))
This paper presents the development of a novel xApp designed to control mMIMO variables within the OPEN RAN framework. Despite the growing adoption of OPEN RAN, existing solutions lack efficient mechanisms to dynamically adjust mMIMO configurations, highlighting a critical research gap. To address this, we propose an xApp-based algorithm that fetches RAN KPIs to adjust mMIMO parameters and enhance energy efficiency. The proposed solution is validated using the TeraVM AI RSG, VIAVI RIC Tester, Emulator, demonstrating a significant reduction in energy consumption while maintaining optimal network performance. Our findings underscore the potential of xApps in enabling intelligent and energy-efficient mMIMO operations within 5G and 6G networks.
