Dynamic traffic patterns and shifts in traffic distribution in Open Radio Access Networks (O-RAN) pose a significant challenge for real-time network optimization in 5G and beyond. Traditional traffic analytics methods struggle to remain accurate under such non-stationary conditions, where models trained on historical data quickly degrade as traffic evolves. This paper introduces AIDITA, an AI-driven Digital Twin for Traffic Analytics framework designed to solve this problem through autonomous model adaptation. AIDITA creates a digital replica of the live analytics models running in the RAN Intelligent Controller (RIC) and continuously updates them within the digital twin using incremental learning. These updates use real-time Key Performance Metrics (KPMs) from the live network, augmented with synthetic data from a Generative AI (GenAI) component to simulate diverse network scenarios. Combining GenAI-driven augmentation with incremental learning enables traffic analytics models, such as prediction or anomaly detection, to adapt continuously without the need for full retraining, preserving accuracy and efficiency in dynamic environments. Implemented and validated on a real-world 5G testbed, our AIDITA framework demonstrates significant improvements in traffic prediction and anomaly detection use cases under distribution shifts, showcasing its practical effectiveness and adaptability for real-time network optimization in O-RAN deployments.

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long Term Evolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia Broadcast Multicast Service over a Single Frequency Network (MBSFN) feature of an LTE network and the Multicast Broadcast Service (MBS) feature of an NR network. The proposed DSS scheme utilizes LTE and NR resource controllers to assign muted MBSFN subframes on the LTE band and muted MBS subframes on the NR band based on traffic needs. In contrast to the state-of-the-art, the proposed DSS scheme does not require a coordination signaling channel between the LTE and NR networks. Instead, a machine learning-based Technology Recognition and Traffic Characterization (TRTC) system is used to identify and characterize traffic patterns. The LTE and NR resource controllers use the TRTC to sense the muted subframes and offload traffic accordingly. On average, the proposed DSS, as compared to static band configuration, improves the LTE throughput, NR throughput, LTE band spectrum utilization efficiency, and NR band spectrum utilization efficiency by 13.5%, 8.3%, 11.8%, and 20.7%, respectively.

The need for supporting multimedia streaming services in cellular networks as standardized by 3GPP is expanding rapidly. Evolved Multimedia Broadcast Multicast Service (eM-BMS) was initially introduced in Release 9 and following releases have introduced several enhancements. Multimedia Broadcast Multicast Single Frequency Network (MBSFN) is one of the eMBMS enhancements targeting to reduce interference, however, its static parameter configuration yields inefficient resource allocation. Therefore, in this paper, an adaptive demand-driven MBSFN resource allocation algorithm is proposed aiming to efficiently utilize the radio resources. The algorithm flexibly assigns resources to multicast transmissions by varying MBSFN configuration parameters (the number and period of multicast subframes) and provides freed resources to unicast traffic. The proposed algorithm is implemented and evaluated using a Software Defined Radio platform which we made open source. As compared to the fixed MBSFN parameter configuration, our solution showcases an improvement of at least 24% and maximally by 40% in terms of multicast resource efficiency. Also, the total system throughput (multicast and unicast) improves by at least 4% and maximally by 24%.

One of the aims of the H2020 5G Blueprint project is to enable seamless cross border teleoperation use cases with 5G technology. The explored use cases are automated barge control, automated drive-in-loop docking, cooperative adaptive cruise control based platooning, and remote take-over operations. In this paper, we present an analysis of the network requirements of such use cases, and we present an end-to-end 5G architecture (with a focus on network slicing and seamless cross-border roaming) for the trial network based on lasted standardization work and implementation tools, from user equipment, radio, transport, core network to exposed network APIs. This work will be used as guideline for further network deployment and feature implementation in test labs and project pilot area within the project.

In the coming years, connectivity between vehicles with autonomous driving features and roadside infrastructure will become more and more a reality on our roads, pursuing to improve road safety and traffic efficiency. In this regard, two main communication standards are considered as key enablers, that is ITS-G5 (based on IEEE 802.11p) and C-V2X (3GPP). To assess the real performance of these technologies, there is still need for an objective and independent one-to-one comparison of these technologies using off-the-shelf hardware under identical and real-life traffic conditions. Until today, performance evaluations are limited to simulations, emulations or individual technology assessments in real-life circumstances. In this paper, an exhaustive and fair evaluation of the technologies has been conducted in a real-life highway environment under identical conditions. Tailored evaluation tools in combination with our in-house CAMINO vehicular framework has been utilized to perform the tests and analyze the results for different well-specified test cases. The performance evaluation shows that for the short-range technologies, C-V2X PC5 has, in general, a higher range than ITS-G5, while ITS-G5 offers lower latency than C-V2X PC5 in low-density scenarios. Long-range 4G C-V2X can be considered as an alternative for certain use cases. The outcome of this experimentation study can be used as valuable information for the further development of future (5G) connected and autonomous driving.

5G systems promise to enable autonomous vehicles by empowering road-, water-, and air-vehicles with ultra low latency communications and computing at edge in order to share and process data from multiple sensors. However, in order to realize such fully Connected and Automated Mobility (CAM) for cars, drones and vessels, a crucial intermediary step must be fully achieved: 5G-based tele-operated transport. In order to do so, the European project H2020 5G-Blueprint aims to design, test, and validate in real deployments a 5G-enabled tele-operated transport and its enabling functions in both a relevant and operational environment realised through cross-border trials on the road and on the water along 5G corridors in the Dutch and Belgian border area, resulting in a blueprint for future cooperation on 5G-enabled CAM between public, private and semi-private parties (e.g, ports), gaining new and innovative insights on the stringent particular requirements for safe CAM, on the architecture, on governance and relevant business models.

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors ς = 0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

Recently, the operation of LTE in unlicensed bands has been proposed to cope with the ever-increasing mobile traffic demand. However, the deployment of LTE in such bands implies sharing spectrum with mature technologies such as Wi-Fi. Several studies have discussed this coexistence problem by suggesting that LTE implements different adaptation mechanisms that allow transmission possibilities to Wi-Fi. While such adaptation mechanisms exist, they still negatively impact Wi-Fi performance, mainly due to the lack of collaboration/coordination mechanisms that inform about the co-located networks' activities. In this paper, we propose a distributed spectrum management framework that enhances the performance of Wi-Fi, as a particular case, by detecting harmful co-located wireless networks and changes the Wi-Fi's operating central frequency to avoid them. The framework is based on a Convolutional Neural Network (CNN) that can identify different wireless technologies and provides spectrum usage statistics. Experiments were carried out in a real-life testbed, and the results show that Wi-Fi maintains its performance when using our framework. This translates in an increase of at least 40% on the overall throughput compared to a non-managed operation of Wi-Fi.

The proliferation of mobile devices and the exponential growth of data transmitted over the air pushed the wireless community to find solutions in order to increase network capacity and fully exploit the available spectrum. Recently, 3GPP announced the operation of LTE in the unlicensed spectrum in order to offload the limited and expensive licensed spectrum. Concurrently, leading parties of the wireless community examine standalone operation of LTE in unlicensed spectrum. LTE was initially designed to operate in licensed spectrum and does not use any channel estimation mechanism to determine ongoing transmissions by other co-located networks. This introduces important coexistence challenges in unlicensed spectrum between LTE deployments and the current, well-established technologies, such as IEEE 802.11 (a.k.a. Wi-Fi). In this paper, we discuss the core differences between LTE and Wi-Fi, which lead to significant coexistence issues. We verify and showcase the problem by analyzing the performance degradation of Wi-Fi, when a traditional LTE network is co-located and operates in the same unlicensed frequency without any coexistence mechanism. The experiments are performed using open- source LTE and Wi-Fi implementations on real equipment in a fully controlled wireless environment. We conclude with showing the need for coexistence mechanisms, following the work that is being done within the standardization activities.

LTE, commonly known as 4G, stands for Long-Term Evolution and is a wireless communication technology standardized by 3GPP. LTE adoption has increased drastically over the last years, and today is integrated widely in a vast number of wireless devices. However, significant restrictions, such as the increased equipment cost and the acquisition of licensed spectrum, renders experimentation with real LTE equipment very difficult. Recently, iMinds w-iLab.t testbed has been extended with commercial LTE equipment and an EPC (Evolved Packet Core) software platform, offering the opportunity to orchestrate and execute real LTE experiments, hereby providing full access to the most relevant configurable LTE parameters of the network. In this paper, we describe an LTE demonstration scenario that showcases various use cases and the respective experimental settings that can be conducted on the LTE testbed.

In this paper we present the NITOS Bikes Net platform, a city-scale mobile sensing infrastructure that relies on bicycles of volunteer users. NITOS Bikes Net employs a custom-built embedded node that can be equipped with different types of sensors, and which can be easily mounted on a bicycle in order to opportunistically collect environmental and WiFi measurements in different parts of the city. Experimenters can remotely reserve and control the sensor nodes on bicycles as well as collect/visualize their measurements via the OMF/OML framework, which was extended in order to handle the intermittent connectivity and disconnected operation of the mobile nodes. We also provide a performance analysis of our node prototype in terms of sensing latency, end-to-end data transmission capability and power consumption, and report on a first experiment that was performed using NITOS Bikes Net in the city of Volos, Greece.