arXiv subjects

Aloizio Da Silva

Publications and source records attributed to Aloizio Da Silva.

5 recordsLinked to original sources

arXiv2026

Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency

The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the O-RAN architecture to optimize energy efficiency while managing its trade-off with QoS. The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-RT RIC periodically computes energy-aware policies from long-term historical data, while a Near-RT RIC enforces those policies through per-UE handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a MINLP model. This model optimizes UE association across O-RUs to minimize transmission power and autonomously deactivate underutilized cells, while enforcing per-UE throughput requirements as explicit constraints at each optimization cycle. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123-0.204 s/UE), confirming the solution's feasibility for autonomous and sustainable 5G Advanced networks. An offline physical-layer throughput evaluation further characterizes the trade-off between energy efficiency and QoS, establishing the operational limits of dynamic cell deactivations. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.

cs.NI↗

arXiv2025

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-integrated ORAN environments without human intervention. The SAJD framework forms a closed-loop system that includes near-realtime inference of radio signal jamming via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. In this demonstration, we will show how SAJD outperforms state-of-the-art jamming detection xApp (offline trained with manual labels) in terms of accuracy and adaptability under various dynamic and previously unseen interference scenarios in the O-RAN-compliant testbed.

cs.CR↗

arXiv2025

SAJD: Self-Adaptive Jamming Attack Detection in AI/ML Integrated 5G O-RAN Networks

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking (SDN), network function virtualization (NFV), and implementation of standardized open interfaces. It also facilitates closed loop control and (non/near) real-time optimization of radio access network (RAN) through the integration of non-real-time applications (rApps) and near-real-time applications (xApps). However, one of the security concerns for O-RAN that can severely undermine network performance and subject it to a prominent threat to the security & reliability of O-RAN networks is jamming attacks. To address this, we introduce SAJD-a self-adaptive jammer detection framework that autonomously detects jamming attacks in artificial intelligence (AI) / machine learning (ML)-integrated O-RAN environments. The SAJD framework forms a closed-loop system that includes near-real-time inference of radio signal jamming interference via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. Specifically, a labeler rApp is developed that uses live telemetry (i.e., KPIs) to detect model drift, triggers unsupervised data labeling, executes model training/retraining using the integrated & open-source ClearML framework, and updates deployed models on the fly, without service disruption. Experiments on O-RAN-compliant testbed demonstrate that the SAJD framework outperforms state-of-the-art (offline-trained with manual labels) jamming detection approach in accuracy and adaptability under various dynamic and previously unseen interference scenarios.

cs.NI↗

arXiv2025

Inter-DU Load Balancing in an Experimental Over-the-Air 5G Open Radio Access Network

This paper presents the first ever fully open-source implementation of Load Balancing (LB) in an experimental Fifth Generation (5G) New Radio (NR) Standalone (SA) network using Open Radio Access Network (O-RAN) architecture. The deployment leverages the O-RAN Software Community (SC) Near Real-Time RAN Intelligent Controller (Near-RT RIC), srsRAN stack, Open5GS core, and Software-Defined Radios (SDRs), with Commercial Off-The-Shelf (COTS) User Equipments (UEs). The implementation extends the srsRAN stack to support E2 Service Model for RAN Control (E2SM-RC) Style 3 Action 1 to facilitate Handovers (HOs) and adds Medium Access Control (MAC) downlink (DL) buffer volume reporting to srsRAN's E2 Service Model for Key Performance Measurement (E2SM-KPM). The deployment demonstrates Near-RT RIC closed-loop control where our Mobility Load Balancing (MLB) xApp makes HO decisions based on network load metrics for LB between two Open Distributed Units (O-DUs) operating at different frequencies in the same band.

cs.NI↗

arXiv2025

Experimental evaluation of xApp Conflict Mitigation Framework in O-RAN: Insights from Testbed deployment in OTIC

Conflict Mitigation (CM) in Open Radio Access Network (O-RAN) is a topic that is gaining importance as commercial O-RAN deployments become more complex. Although research on CM is already covered in terms of simulated network scenarios, it lacks validation using real-world deployment and Over The Air (OTA) Radio Frequency (RF) transmission. Our objective is to conduct the first assessment of the Conflict Mitigation Framework (CMF) for O-RAN using a real-world testbed and OTA RF transmission. This paper presents results of an experiment using a dedicated testbed built in an O-RAN Open Test and Integration Center (OTIC) to confirm the validity of one of the Conflict Resolution (CR) schemes proposed by existing research. The results show that the implemented conflict detection and resolution mechanisms allow a significant improvement in network operation stability by reducing the variability of the measured Downlink (DL) throughput by 78%.

cs.NI↗