The transition to the 5th generation (5G) of mobile networks introduces significant advancements in telecommunications, notably in data transmission speeds, connectivity, and the accommodation of varied service requirements. Ultra-Reliable and Low-Latency Communications (URLLC) are at the cutting edge of 5G advancements, playing a critical role in enabling applications like autonomous driving, telemedicine, and the Industrial Internet of Things (IIoT). However, despite URLLC being a part of the 5G standards, achieving its goals of extremely low latency and high reliability is challenging. This paper employs the OpenairInterface (OAI) platform for a holistic end-to-end analysis of URLLC. Through the evaluation of various UPF implementations and RAN configurations, that can highly affect the perceived end-user network latency, in addition to system adjustments aimed at performance optimization, this work successfully reduces latency almost to half that of standard configurations and achieves a near-zero Block Error Rate (BLER). This study attempts to shed light on the practical difficulties of meeting URLLC standards in 5G networks and provide a basis for further research in real-world experimentation.

5G technology, the latest advancement in mobile networks, promises increased data speeds, reduced latency, and enhanced capacity. However, network performance and user experience can be critically impacted by malicious traffic, identified as anomaly traffic and intrusion methods. Addressing these challenges requires optimized resource sharing and robust network security measures. In this paper, we present an AI/ML-driven Network Intrusion Detection framework with dynamic resource allocation and user management within the O-RAN architecture. Our Anomaly Traffic Detector (ATD) enhances network security by mitigating Denial of Service (DoS) attacks through an xApp that classifies network traffic in real-time and dynamically adjusts network resources and user connections. Experimental evaluations show that our system effectively maintains low latency under attack conditions, nearly doubles the throughput for legitimate users, and reduces average CPU usage by up to 15%. We use as reference platforms the OpenAirInterface, and FlexRIC for programming the slice and user connectivity decisions at the RAN level, and evaluate our scheme under real-world settings in a testbed environment.

Multi-access Edge Computing (MEC) has been considered one of the most prominent enablers for low-latency access to services provided over the telecommunications network. Nevertheless, client mobility, as well as external factors which impact the communication channel can severely deteriorate the eventual user-perceived latency times. Such processes can be averted by migrating the provided services to other edges, while the end-user changes their base station association as they move within the serviced region. In this work, we start from an entirely virtualized cloud-native 5G network based on the OpenAirInterface platform and develop our architecture for providing seamless live migration of edge services. On top of this infrastructure, we employ a Deep Reinforcement Learning (DRL) approach that is able to proactively relocate services to new edges, subject to the user’s multi-cell latency measurements and the workload status of the servers. We evaluate our scheme in a testbed setup by emulating mobility using realistic mobility patterns and workloads from real-world clusters. Our results denote that our scheme is capable sustain low-latency values for the end users, based on their mobility within the serviced region.