Search Results for author: Francesc Wilhelmi

Found 10 papers, 7 papers with code

Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

1 code implementation19 Sep 2023 Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz, Francesc Wilhelmi, Elia Guerra, Marco Miozzo, Pavlos S. Efraimidis, Paolo Dini, Remous-Aris Koutsiamanis

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation.

Federated Learning Traffic Prediction

The Cost of Training Machine Learning Models over Distributed Data Sources

1 code implementation15 Sep 2022 Elia Guerra, Francesc Wilhelmi, Marco Miozzo, Paolo Dini

Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data.

Federated Learning

On the Decentralization of Blockchain-enabled Asynchronous Federated Learning

no code implementations20 May 2022 Francesc Wilhelmi, Elia Guerra, Paolo Dini

Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments.

Edge-computing Federated Learning

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

no code implementations20 Mar 2022 Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz, Emre Ozfatura, Kerem Ozfatura, Ozlem Yildiz, Deniz Gündüz, Hao Chen, Xiaoying Ye, Lizhao You, Yulin Shao, Paolo Dini, Boris Bellalta

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.

Federated Learning

Analysis and Evaluation of Synchronous and Asynchronous FLchain

1 code implementation15 Dec 2021 Francesc Wilhelmi, Lorenza Giupponi, Paolo Dini

As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case.

Federated Learning

Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs

no code implementations29 May 2021 Francesc Wilhelmi, David Góez, Paola Soto, Ramon Vallés, Mohammad Alfaifi, Abdulrahman Algunayah, Jorge Martin-Pérez, Luigi Girletti, Rajasekar Mohan, K Venkat Ramnan, Boris Bellalta

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications.

BIG-bench Machine Learning

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

2 code implementations17 May 2020 Francesc Wilhelmi, Marc Carrascosa, Cristina Cano, Anders Jonsson, Vishnu Ram, Boris Bellalta

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems.

BIG-bench Machine Learning

Spatial Reuse in IEEE 802.11ax WLANs

2 code implementations9 Jul 2019 Francesc Wilhelmi, Sergio Barrachina Muñoz, Cristina Cano, Ioannis Selinis, Boris Bellalta

In particular, the main objective of the SR operation is to maximize the utilization of the medium by increasing the number of parallel transmissions.

Networking and Internet Architecture

On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs

1 code implementation19 Jun 2019 Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta

The Spatial Reuse (SR) operation included in the IEEE 802. 11ax-2020 (11ax) amendment aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS).

Networking and Internet Architecture

Implications of Decentralized Q-learning Resource Allocation in Wireless Networks

1 code implementation30 May 2017 Francesc Wilhelmi, Boris Bellalta, Cristina Cano, Anders Jonsson

Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results.


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