arXiv subjects

Cleverson Nahum

Publications and source records attributed to Cleverson Nahum.

9 recordsLinked to original sources

arXiv2026

Ray Tracing-Based LoRaWAN Gateway Placement for Reliable Connectivity in Amazonian Regions

Network planning is an important task in wireless communications, as it helps network operators avoid unnecessary costs. In the context of the internet of things, using long-range wide-area network technologies in the Amazon rainforest, a key challenge is ensuring reliable communication between end-devices and gateways (GWs). In this sense, this reliability is strongly affected by channel conditions. Thus, during the planning phase, choosing the appropriate channel model is an important decision for accurate simulations. Given this motivation, in this work, we propose an optimization model to evaluate the impact of different types of channels on coverage and packet delivery ratio in a forest scenario. We used channels from ray tracing, empirical, and stochastic approaches to assess how decisions made during the network planning phase, in terms of the channel used, affect GW placement and, specifically, the percentage of end-devices covered and the reliability of the communication system. Our results show that GW placement based on site-independent channels can overestimate the number of GWs required to meet the network requirements, whereas using site-specific channels allows us to satisfy the same requirements with fewer GWs.

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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.

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arXiv2026

Toward Scalable VR-Cloud Gaming: An Attention-aware Adaptive Resource Allocation Framework for 6G Networks

Virtual Reality Cloud Gaming (VR-CG) represents a demanding class of immersive applications, requiring high bandwidth, ultra-low latency, and intelligent resource management to ensure optimal user experience. In this paper, we propose a scalable and QoE-aware multi-stage optimization framework for resource allocation in VR-CG over 6G networks. Our solution decomposes the joint resource allocation problem into three interdependent stages: (i) user association and communication resource allocation; (ii) VR-CG game engine placement with adaptive multipath routing; and (iii) attention-aware scheduling and wireless resource allocation based on motion-to-photon latency. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance while significantly reducing computational time. We introduce a novel user-centric QoE model based on visual attention to virtual objects, guiding adaptive resolution and frame rate selection. A dataset-driven evaluation demonstrates that, when compared against state-of-the-art approaches, our framework improves QoE by up to 50\%, reduces communication resource usage by 75\%, and achieves up to 35\% cost savings, while maintaining an average optimality gap of 5\%. Our proposed heuristics solve large-scale scenarios in under 0.1 seconds, highlighting their potential for real-time deployment in next-generation mobile networks.

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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.

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arXiv2025

Intent-based Radio Scheduler for RAN Slicing: Learning to deal with different network scenarios

The future mobile network has the complex mission of distributing available radio resources among various applications with different requirements. The radio access network slicing enables the creation of different logical networks by isolating and using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources available among the slices to fulfill their service-level agreement (SLA) requirements, prioritizing critical slices while minimizing the number of intent violations. Moreover, ensuring that the RRS can deal with a high diversity of network scenarios is essential. Several recent papers present advances in machine learning-based RRS. However, the scenarios and slice variety are restricted, which inhibits solid conclusions about the generalization capabilities of the models after deployment in real networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a radio access network (RAN) slicing context. The proposed method protects high-priority slices when the available radio resources cannot fulfill all the slices. It uses transfer learning to reduce the number of training steps required. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines. The results show that by using transfer learning, the required number of training steps could be reduced by a factor of eight without hurting performance.

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arXiv2021

System Intelligence for UAV-Based Mission Critical with Challenging 5G/B5G Connectivity

Unmanned aerial vehicles (UAVs) and communication systems are fundamental elements in Mission Critical services, such as search and rescue. In this article, we introduce an architecture for managing and orchestrating 5G and beyond networks that operate over a heterogeneous infrastructure with UAVs' aid. UAVs are used for collecting and processing data, as well as improving communications. The proposed System Intelligence (SI) architecture was designed to comply with recent standardization works, especially the ETSI Experiential Networked Intelligence specifications. Another contribution of this article is an evaluation using a testbed based on a virtualized non-standalone 5G core and a 4G Radio Access Network (RAN) implemented with open-source software. The experimental results indicate, for instance, that SI can substantially improve the latency of UAV-based services by splitting deep neural networks between UAV and edge or cloud equipment. Other experiments explore the slicing of RAN resources and efficient placement of virtual network functions to assess the benefits of incorporating intelligence in UAV-based mission-critical services.

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arXiv2020

Virtualized C-RAN Orchestration with Docker, Kubernetes and OpenAirInterface

Virtualization is a key feature in Cloud Radio Access Network (C-RAN). It can help to save costs since it allows the use of virtualized base stations instead of physically deployment in different areas. However, the creation and management of virtual base stations pools is not trivial and introduces new challenges to C-RAN deployment. This paper reports a method to orchestrate and manage a container-based C-RAN. We used several instances of the OpenAirInterface software running on Docker containers, and orchestrated them using Kubernetes. We demonstrate that using Kubernetes it is possible to dynamically scale remote radio heads (RRHs) and baseband units (BBUs) according to requirements of the network and parameters such as the server resources usage.

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arXiv2020

Downlink Fronthaul Compression in Frequency Domain using OpenAirInterface

This paper presents a compression scheme developed for the transport of downlink radio signals in packet fronthaul of centralized-radio access networks (C-RAN). The technique is tailored to frequency-domain functional splits, in which inactive LTE (or 5G NR) resource elements may not be transmitted in the packets. This allows decreasing the link data rate, especially when the cell load is low. The compression scheme is based on two parts: transmission of side information to indicate active and inactive resource elements of the LTE, and nonuniform scalar quantization to compress the QAM symbols of the active resource elements. The method was implemented in the OpenAirInterface (OAI) software for real-time evaluation. The testbed results show a significant reduction in link usage with a low computational cost.

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arXiv2019

C-RAN Virtualization with OpenAirInterface

C-RAN virtualization is a research topic with great interest since it allows to share baseband processing resources.Therefore, in this work, we report the implementation of a virtualized LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI) with Docker. Using the test bed,we conducted a workload study to understand the computation resource demand of C-RAN software. Virtualization in containers has proven to be effective in creating a functional 4G network which achieves realistic results to facilitate research.

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