Nelson L. S. da Fonseca

Avaliação de Desempenho de Aplicações de Aprendizado Federado em Redes de Acesso Compartilhadas Open

Diogo M. Cunha, M. Guerra, Oscar J. Ciceri, Nelson L. S. da Fonseca, Carlos A. Astudillo · 2025

O aprendizado federado (FL) permite treinar modelos de aprendizado de máquina (ML) por clientes distribuídos sem a necessidade de compartilhar seus dados locais com um servidor central (CS). Ao compartilhar somente os parâmetros locais dos…

Guest Editorial Special Section on Generative AI and Large Language Models Enhanced 6G Wireless Communication and Sensing Open

Jiacheng Wang, Geng Sun, Dusit Niyato, Hina Tabassum, Gabriel‐Miro Muntean , et al. · 2025

An Approach Based on Incremental Deep Learning and Traffic-Flow Characteristics for Scheduling Elephant Flows in Software-Defined Data Center Networks Open

Felipe Estrada‐Solano, Oscar Maurício Caicedo Rendón, Nelson L. S. da Fonseca · 2025

Large Language Models for Zero Touch Network Configuration Management Open

Oscar G. Lira, Oscar M. Caicedo, Nelson L. S. da Fonseca · 2024

The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificia…

The Fog Node Location Problem Open

Rodrigo Augusto Cardoso da Silva, Nelson L. S. da Fonseca · 2024

This paper summarizes the thesis "The Fog Node Location Problem"', which attempted to answer the question of how fog nodes should be located to process end-user demands that are variable in time and space. The problem was studied from diff…

The President's Page Open

Xuemin Shen, Ana García Armada, Nelson L. S. da Fonseca · 2023

It has been a great honor and privilege for me to serve as the President of IEEE Communications Society (ComSoc) in 2022–2023. The IEEE ComSoc is a global professional organization that has been creating cutting-edge communications and net…

Data-driven Intra-Autonomous Systems Graph Generator Open

Caio Vinicius Dadauto, Nelson L. S. da Fonseca, Ricardo da Silva Torres · 2023

Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-f…

Medium Access Control Techniques for Massive Machine-Type Communications in Cellular IoT Networks Open

Carlos A. Astudillo, Nelson L. S. da Fonseca · 2023

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numer…

Medium Access Control Techniques for Massive Machine-Type Communications in Cellular Networks Open

Carlos A. Astudillo, Nelson L. S. da Fonseca · 2023

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communication (mMTC). However, numero…

The Fog Node Location Problem Open

Rodrigo A. C. da Silva, Nelson L. S. da Fonseca · 2023

This paper summarizes the thesis “The Fog Node Location Problem”, which attempted to answer the question of how fog nodes should be located to process end-user demands that are variable in time and space. The problem was studied from diffe…

IEEE GLOBECOM 2022 General Chair Welcome Message Open

Nelson L. S. da Fonseca, José Roberto B. da Marca, Stefano Bregni, Lisandro Zambenedetti Granville · 2022

On behalf of the Organizing Committee, we have been delighted to welcome you to the IEEE Global Telecommunications Conference (GLOBECOM 2022). The conference was held in Rio de Janeiro, Brazil from 4 to 8 December 2022. IEEE GLOBECOM is on…

Resource management at the network edge for federated learning Open

Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da Fonseca · 2022

Federated learning has been explored as a promising solution for training machine learning models at the network edge, without sharing private user data. With limited resources at the edge, new solutions must be developed to leverage the s…

Model-Based Reinforcement Learning with Automated Planning for Network Management Open

Armando Ordóñez, Oscar Maurício Caicedo Rendón, William Villota, Angela Rodriguez-Vivas, Nelson L. S. da Fonseca · 2022

Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores t…

Synthesis of Multi-band Reflective Polarizing Metasurfaces Using a Generative Adversarial Network Open

Parinaz Naseri, George Goussetis, Nelson L. S. da Fonseca, Sean V. Hum · 2022

Electromagnetic linear-to-circular polarization converters with wide- and multi-band capabilities can simplify antenna systems where circular polarization is required. Multi-band solutions are attractive in satellite communication systems,…

ComSoc Conferences Open

Xuemin Shen, Nelson L. S. da Fonseca · 2022

Conferences are where ComSoc members meet, network, and exchange ideas. They offer a premier venue for networking with our peers and learning about the latest developments in communications engineering. They play an important role in achie…

Design of aerial fog computing with fixed-wing UAVs Open

Rodrigo Augusto Cardoso da Silva, Nelson L. S. da Fonseca · 2022

One challenge in the deployment of a fog computing infrastructure is the support of demands which are variable in time and space as this can temporarily overload fog nodes. Mobile fog nodes can be promptly dispatched to support dynamic dem…

Design of aerial fog computing with fixed-wing UAVs Open

Rodrigo Augusto Cardoso da Silva, Nelson L. S. da Fonseca · 2022

One challenge in the deployment of a fog computing infrastructure is the support of demands which are variable in time and space as this can temporarily overload fog nodes. Mobile fog nodes can be promptly dispatched to support dynamic dem…

Special issue on selected papers from the IEEE Latin America on Communications (LATINCOM) Open

Fabrizio Granelli, Oscar Maurício Caicedo Rendón, Gustavo B. Figueiredo, Nelson L. S. da Fonseca · 2022

Performance evaluation of multi-ONU customers in ethernet passive optical networks Open

Oscar J. Ciceri, Carlos A. Astudillo, Nelson L. S. da Fonseca · 2022

A Novel Architecture for Future Classical‐Quantum Communication Networks Open

Fabrizio Granelli, Riccardo Bassoli, Janis Nötzel, Frank H. P. Fitzek, Holger Boche , et al. · 2022

The standardisation of 5G is reaching its end, and the networks have started being deployed. Thus, 6G architecture is under study and design, to define the characteristics and the guidelines for its standardisation. In parallel, communicat…

To Better Serve and Grow the ComSoc Community Open

Xuemin Shen, Ana García Armada, Nelson L. S. da Fonseca, Chengshan Xiao, Wei Zhang · 2022

It is a great honor and privilege for me to serve as the President of IEEE Communications Society (ComSoc) in 2022–2023. IEEE ComSoc is a global professional organization that has been creating cutting-edge communications and networking te…

Cognitive Control-Loop for Elastic Optical Networks with Space-Division Multiplexing Open

Silvana Trindade, Ricardo da Silva Torres, Zuqing Zhu, Nelson L. S. da Fonseca · 2021

This paper introduces a new solution to improve network performance by decreasing spectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link load imbalance. These problems degrade the performance of Elasti…

Federated Learning over Next-Generation Ethernet Passive Optical Networks Open

Oscar J. Ciceri, Carlos A. Astudillo, Zuqing Zhu, Nelson L. S. da Fonseca · 2021

Federated Learning (FL) is a distributed machine learning (ML) type of processing that preserves the privacy of user data, sharing only the parameters of ML models with a common server. The processing of FL requires specific latency and ba…

Passive Optical Networking for 5G and Beyond 5G Low-Latency Mobile Fronthauling Services Open

Oscar J. Ciceri, Carlos A. Astudillo, Gustavo B. Figueiredo, Zuqing Zhu, Nelson L. S. da Fonseca · 2021

Passive optical network (PON) technology offers an attractive cost-efficient alternative to support 5G and Beyond 5G mobile network fronthauling (MFH). However, MFH for such networks is challenging given its high bandwidth and strict laten…

A strategy to the reduction of communication overhead and overfitting in Federated Learning Open

Alex Barros, Denis Rosário, Eduardo Cerqueira, Nelson L. S. da Fonseca · 2021

Federated learning (FL) is a framework to train machine learning models using decentralized data, especially unbalanced and non-iid. Adaptive methods can be used to accelerate convergence, reducing the number of rounds of local computation…

Routing based on Reinforcement Learning for Software-Defined Networking Open

Daniela Maria Casas Velasco, Nelson L. S. da Fonseca, Oscar Maurício Caicedo Rendón · 2021

Traditional routing protocols employ limited information to make routing decisions, leading to slow adaptation to traffic variability and restricted support to applications quality of service requirements. This paper introduces the work de…

Admission Control and Resource Allocation in 5G Network Slicing Open

William F. Villota Jácome, Oscar Maurício Caicedo Rendón, Nelson L. S. da Fonseca · 2021

This paper summarizes the research in the master thesis entitled "Admission Control and Resource Allocation in 5G Network Slicing". We propose two solutions, SARA and DSARA, based on Reinforcement Learning algorithms to learn the admission…

Management of Resource at the Network Edge for Federated Learning Open

Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da Fonseca · 2021

Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge,…

Random Access Based on Maximum Average Distance Code for Massive MTC in Cellular IoT Networks Open

Carlos A. Astudillo, Ekram Hossain, Nelson L. S. da Fonseca · 2021

Code-expanded Random Access (CeRA) is a promising technique for supporting mMTC in cellular IoT networks. However, its potentiality is limited by code ambiguity, which results from the inference of a larger number of codewords than those a…

Random Access Based on Maximum Average Distance Code for Massive MTC in\n Cellular IoT Networks Open

Carlos A. Astudillo, Ekram Hossain, Nelson L. S. da Fonseca · 2021

Code-expanded Random Access (CeRA) is a promising technique for supporting\nmMTC in cellular IoT networks.\n However, its potentiality is limited by code ambiguity, which results from\nthe inference of a larger number of codewords than tho…

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