arXiv subjects

Glauco Gonçalves

Publications and source records attributed to Glauco Gonçalves.

3 recordsLinked to original sources

arXiv2026

LoRaWAN Gateway Placement for Network Planning Using Ray Tracing-Based Channel Models

Network planning for long range wide area networks (LoRaWAN) relies heavily on the channel models used to estimate wireless coverage and connectivity. Consequently, the quality of gateway (GW) deployment decisions may be strongly affected by the propagation assumptions adopted during the planning process. Given this motivation, this work investigates how different channel models influence the placement of LoRaWAN GWs,formulating an optimization problem that contrasts stochastic and empirical models with ray-tracing-based models. To this end, we developed a framework that integrates ray tracing (RT) simulators with a discrete-event network simulator. Using this framework to generate LoRaWAN data metrics, we employ an optimization model that determines the optimal GW placement under different channel models, received power constraints, and network scenarios. Our results show that the optimized solution is highly sensitive to the chosen channel model, even when considering the same scenarios with different RT simulators, revealing a clear trade-off between computational cost and the fidelity of the solution to real-world conditions.

cs.NI↗

arXiv2025

Towards a Robust Transport Network With Self-adaptive Network Digital Twin

The ability of the Network digital twin (NDT) to remain aware of changes in its physical counterpart, known as the physical twin (PTwin), is a fundamental condition to enable timely synchronization, also referred to as twinning. In this way, considering a transport network, a key requirement is to handle unexpected traffic variability and dynamically adapt to maintain optimal performance in the associated virtual model, known as the virtual twin (VTwin). In this context, we propose a self-adaptive implementation of a novel NDT architecture designed to provide accurate delay predictions, even under fluctuating traffic conditions. This architecture addresses an essential challenge, underexplored in the literature: improving the resilience of data-driven NDT platforms against traffic variability and improving synchronization between the VTwin and its physical counterpart. Therefore, the contributions of this article rely on NDT lifecycle by focusing on the operational phase, where telemetry modules are used to monitor incoming traffic, and concept drift detection techniques guide retraining decisions aimed at updating and redeploying the VTwin when necessary. We validate our architecture with a network management use case, across various emulated network topologies, and diverse traffic patterns to demonstrate its effectiveness in preserving acceptable performance and predicting quality of service (QoS) metrics under unexpected traffic variation, such as delay and jitter. The results in all tested topologies, using the normalized mean square error as the evaluation metric, demonstrate that our proposed architecture, after a traffic concept drift, achieves a performance improvement in per-flow delay and jitter prediction of at least 64% and 21%, respectively, compared to a configuration without NDT synchronization.

cs.NI↗

arXiv2025

Network Digital Twin for Route Optimization in 5G/B5G Transport Slicing with What-If Analysis

The advent of fifth-generation (5G) and Beyond 5G (B5G) networks introduces diverse service requirements, from ultra-low latency to high bandwidth, demanding dynamic monitoring and advanced solutions to ensure Quality of Service (QoS). The transport network - responsible for interconnecting the radio access network and core networks - will increasingly face challenges in efficiently managing complex traffic patterns. The Network Digital Twin (NDT) concept emerges as a promising solution for testing configurations and algorithms in a virtual network before real-world deployment. In this context, this work designs an experimental platform with NDT in a transport network domain, synchronizing with the virtual counterpart and a recommendation system for what-if analysis, enabling intelligent decision-making for dynamic route optimization problems in 5G/B5G scenarios. Our NDT, composed of a Graph Neural Network (GNN), was evaluated across three different network topologies consisting of 8, 16, and 30 nodes. It achieved lower MAPE values for URLLC and eMBB slices, comparing latency predictions with actual latency after the solution implementation. These values indicate high accuracy, demonstrating the solution's effectiveness in generating precise insights into network performance if a particular solution were implemented.

cs.NI↗