CTM

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This service operates in key areas within modern communications networks and services, especially in network architectures, telecommunications services, signal and image processing, microelectronics, digital TV and multimedia.

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Browsing CTM by Author "5179"
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    Improving ns-3 Emulation Performance for Fast Prototyping of Routing and SDN Protocols: Moving Data Plane Operations to Outside of ns-3
    ( 2019) Cardoso,T ; Manuel Ricardo ; Rui Lopes Campos ; Hélder Martins Fontes ; 4473 ; 5179 ; 651
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    Machine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3
    ( 2022) Eduardo Nuno Almeida ; Rui Lopes Campos ; Hélder Martins Fontes ; 6453 ; 4473 ; 5179
    The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3.
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    ns-3 NEXT: Towards a reference platform for offline and augmented wireless networking experimentation
    ( 2019) Rui Lopes Campos ; Manuel Ricardo ; Hélder Martins Fontes ; Vítor Hugo Fernandes ; 7592 ; 651 ; 4473 ; 5179
    In the past years, INESC TEC has been working on using ns-3 to reduce the gap between Simulation and Experimentation. Two major contributions resulted from our work: 1) the Fast Prototyping development process, where the same ns-3 protocol model is used in a real experiment; 2) the Trace-based Simulation (TS) approach, where traces of radio link qualities and position of nodes from past experiments are injected into ns-3 to achieve repeatable and reproducible experiments. In this paper we present ns-3 NEXT, our vision for ns-3 to enable simulation and experimentation using the same platform. We envision ns-3 as the platform that can automatically learn from past experiments and improve its accuracy to a point where simulated resources can seamlessly replace real resources. At that point, ns-3 can either replace a real testbed accurately (Offline Experimentation) or add functionality and scale to testbeds (Augmented Experimentation). Towards this vision, we discuss the current limitations and propose a plan to overcome them collectively within the ns-3 community. © 2019 ACM.
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    On the Reproduction of Real Wireless Channel Occupancy in ns-3
    ( 2020) Hélder Martins Fontes ; Rui Lopes Campos ; Manuel Ricardo ; José Ruela ; Renato Mendes Cruz ; 7543 ; 651 ; 174 ; 4473 ; 5179
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    Position-Based Machine Learning Propagation Loss Model Enabling Fast Digital Twins of Wireless Networks in ns-3
    ( 2023) Rui Lopes Campos ; Manuel Ricardo ; Hélder Martins Fontes ; Eduardo Nuno Almeida ; 4473 ; 651 ; 5179 ; 6453
    Digital twins have been emerging as a hybrid approach that combines the benefits of simulators with the realism of experimental testbeds. The accurate and repeatable set-ups replicating the dynamic conditions of physical environments, enable digital twins of wireless networks to be used to evaluate the performance of next-generation networks. In this paper, we propose the Position-based Machine Learning Propagation Loss Model (P-MLPL), enabling the creation of fast and more precise digital twins of wireless networks in ns-3. Based on network traces collected in an experimental testbed, the P-MLPL model estimates the propagation loss suffered by packets exchanged between a transmitter and a receiver, considering the absolute node's positions and the traffic direction. The P-MLPL model is validated with a test suite. The results show that the P-MLPL model can predict the propagation loss with a median error of 2.5 dB, which corresponds to 0.5x the error of existing models in ns-3. Moreover, ns-3 simulations with the P-MLPL model estimated the throughput with an error up to 2.5 Mbit/s, when compared to the real values measured in the testbed.
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    Repeatable and Reproducible Wireless Networking Experimentation through Trace-based Simulation
    ( 2019) Vítor Hugo Fernandes ; Rui Lopes Campos ; Manuel Ricardo ; Ruela,J ; Tiago Telmo Oliveira ; Hélder Martins Fontes ; 7592 ; 651 ; 4473 ; 5179 ; 6431