CTM - Indexed Articles in Conferences
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ItemDURIUS: A Multimodal Underwater Communications Approach for Higher Performance and Lower Energy Consumption( 2023)The exploration of the ocean has got an increasing interest, including activities such as offshore wind farms and deep-sea mining. However, the ocean environment and the high cost of operations, namely for manned missions, have led to the development of Autonomous Underwater Vehicles (AUVs) and other sensing platforms. AUVs play a vital role in these environments, relying on communications systems to operate and exchange sensor data. Yet, reliable and energy-efficient broad-band wireless communications underwater remain an unsolved challenge, despite the recent advances in the field. We present a novel multimodal approach, named DURIUS, that considers the movement of the AUV to convey the sensor data and selects the most suitable underwater wireless communications technology - acoustic, optical or radio - according to the underwater context, targeting maximum performance and minimum energy consumption. Our analytical results show that DURIUS increases data throughput and reduces energy consumption when compared with the state of the art approaches.
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ItemShort-Range Energy-Aware Optical Wireless Communications Module for ns-3( 2025)Optical Wireless Communications (OWC) has recently emerged as a viable alternative to radio-frequency technology, especially for the Internet of Things (IoT) domain. However, current simulation tools primarily focus on physical layer modelling, ignoring network-level issues and energy-constrained environments. This paper presents an energy-aware OWC module for ns-3 that addresses these limitations. The module includes specific PHY and MAC layers and integrates an energy model, a mobility model, and models of monochromatic transceivers and photodetectors, supporting both visible light and infrared (IR) communications. Verification against MATLAB simulations confirms the accuracy of our implementation. Additionally, mobility tests demonstrate that an energy-restricted end device transmitting via IR can maintain a stable connection with a gateway at distances up to 2.5 m, provided the SNR is above 10 dB. These results confirm the capabilities of our module and its potential to facilitate the development of energy-efficient OWC-based IoT systems.
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ItemDesign and Implementation of Scalable 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E( 2025)Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.
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Item1-bit Graphene-based Reconfigurable Intelligent Surface Design in Ka-Band( 2024)This paper presents a 1-bit graphene-based reflective reconfigurable intelligent surface (RIS), namely a reflectarray antenna, that operates in the Ka-band (27 - 31 GHz). The reflectarray unit-cell features a simple structure with one metal layer, a Rogers RT5880 substrate and a Graphene Sandwich Structure (GSS) on top. The GSS comprises two layers of graphene separated by a diaphragm paper and a thin PVC layer to enhance its durability. The reflectarray can ensure a 1-bit phase shift resolution, by alternating the bias voltage applied to the graphene. The unit-cell simulation shows that the losses are around 3 dB over the studied band for both unit-cell states. An equivalent circuit model is presented to facilitate the analysis and design of GSS-based unit-cells. The full-wave simulation results of a 32x32 reflectarray indicate a gain of 25 dBi for a steering angle of 10 deg., displaying a 1 dB gain bandwidth of 15%, confirming the promise of the graphene-based radiating elements.
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ItemMachine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3( 2022)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.