Suche

Wagen Jean-Frédéric

Professeur HES associé

Hauptkompetenzen

Professeur HES associé

Telefon-Nummer: +41 26 429 65 53

Büro: HEIA_C10.07

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH

Institut
HumanTech - Technology for Human Wellbeing Institute

BSc HES-SO en Informatique - Haute école d'ingénierie et d'architecture de Fribourg

Computer networking

BA HES-SO en Architecture - Haute école d'ingénierie et d'architecture de Fribourg

Computer networking (teleinformatique)
Mobile and wireless communications
TIC

2018

Reproducing measured MANET radio performances using the EMANE framework

Wissenschaftlicher Artikel ArODES

Alexandre Nidokemski, Jean-Frédéric Wagen, François Buntschu, Christophe Gisler, Jérôme Bovet

IEEE Communications Magazine, 2018, vol. 56, no. 10, pp. 151-155

Link zur Publikation

Zusammenfassung:

Simulation or emulation of mobile ad hoc networks (MANET) is used to predict or analyze the performance of MANETs under various scenarios. One challenge is to emulate realistically the MANET's radio performance. Running the Extendable Mobile Ad Hoc Network Emulator (EMANE) framework, we show how to reproduce measured characteristics, namely throughput and round-trip time, of real tactical radios using wideband or narrowband TDMA-based waveforms. Additionally, a solution to simulate rate adaptation is proposed. An introduction to EMANE and the EMANE radio model plugins is also provided.

TAKE—tactical ad-hoc network emulation

Wissenschaftlicher Artikel

Ruffieux Simon, Gisler Christophe, Wagen Jean-Frédéric, Buntschu François, Gérôme Bovet

2018 International Conference on Military Communications and Information Systems (ICMCIS), 2018 , pp. 1-8

Link zur Publikation

Reproducing measured manet radio performances using the emane framework

Wissenschaftlicher Artikel

Alexandre Nikodemski, Gisler Christophe, Wagen Jean-Frédéric, Buntschu François, Gérôme Bovet

IEEE Communications Magazine, 2018 , vol. 56, no 10, pp. 151-155

Link zur Publikation

2018

TAKE :

Konferenz ArODES

Tactical ad-hox network emulation

Simon Ruffieux, Christophe Gisler, Jean-Frédéric Wagen, François Buntschu, Jérôme Bovet

Proceedings of International Conference on Military Communications and Information Systems Emerging CIS challenges, 22-23 May 2018, Warsaw, Poland

Link zur Konferenz

Zusammenfassung:

The Swiss Army uses tactical radios to communicate between troops on the field. Due to the operating environment, these radios experience specific characteristics such as limited bandwidth, in addition to high delay and packet losses. This paper presents the work achieved to develop, evaluate and test a novel application-layer routing algorithm specifically designed for tactical MANET networks. In order to evaluate the proposed algorithm in real conditions, two platforms and specific Quality of Experience metrics have been developed.

2014

Adaptive proportional fair parameterization based LTE scheduling using continuous actor-critic reinforcement learning

Konferenz ArODES

Ioan Sorin Comsa, Sijing Zhang, Mehmet Aydin, Jianping Chen, Pierre Kuonen, Jean-Frédéric Wagen

Proceedings of the 2014 IEEE Global Communications Conference, 8-12 December 2014, Austin, TX, USA

Link zur Konferenz

Zusammenfassung:

Maintaining a desired trade-off performance between system throughput maximization and user fairness satisfaction constitutes a problem that is still far from being solved. In LTE systems, different tradeoff levels can be obtained by using a proper parameterization of the Generalized Proportional Fair (GPF) scheduling rule. Our approach is able to find the best parameterization policy that maximizes the system throughput under different fairness constraints imposed by the scheduler state. The proposed method adapts and refines the policy at each Transmission Time Interval (TTI) by using the Multi-Layer Perceptron Neural Network (MLPNN) as a non-linear function approximation between the continuous scheduler state and the optimal GPF parameter(s). The MLPNN function generalization is trained based on Continuous Actor-Critic Learning Automata Reinforcement Learning (CACLA RL). The double GPF parameterization optimization problem is addressed by using CACLA RL with two continuous actions (CACLA-2). Five reinforcement learning algorithms as simple parameterization techniques are compared against the novel technology. Simulation results indicate that CACLA-2 performs much better than any of other candidates that adjust only one scheduling parameter such as CACLA-1. CACLA-2 outperforms CACLA-1 by reducing the percentage of TTIs when the system is considered unfair. Being able to attenuate the fluctuations of the obtained policy, CACLA-2 achieves enhanced throughput gain when severe changes in the scheduling environment occur, maintaining in the same time the fairness optimality condition.

Errungenschaften

2014

DMAlert: Une réaction plus rapide pour les urgences médicales

2014 ; Fonds de soutien à l’innovation du canton de Fribourg

Collaborateurs: Wagen Jean-Frédéric

Link zur Errungenschaft

Le premier projet, DMAlert, doté d’un financement de 300 000 francs, a été lancé en partenariat par l’Ecole d’Ingénieurs et d’Architectes de Fribourg (EIA-FR), l’Université de Fribourg et Datamed SA, éditeur fribourgeois de logiciels pour laboratoires médicaux. Il vise à la mise en œuvre d’un système d’alerte en temps réel entre les médecins et les laboratoires médicaux afin d’accélérer la prescription d’un traitement pour les patients. Ces alertes devront à la fois être automatisées, fiables et sécurisées. Elles devront fonctionner sur les smartphones, tablettes ou/et PC et doivent automatiquement êter déviée vers un confrère en cas d’indisponibilité du médecin traitant.

Suche

Wagen Jean-Frédéric

Professeur HES associé

Hauptkompetenzen

Solutions finder

Wireless Communication

Computer networking

PhD co-advisor

Matlab

Software Defined Radio

Telco Cloud

Professeur HES associé