Aldebaro Klautau

João Borges, Felipe Bastos, Ilan Correa et al.

Digital twins are an important technology for advancing mobile communications, specially in use cases that require simultaneously simulating the wireless channel, 3D scenes and machine learning. Aiming at providing a solution to this demand, this work describes a modular co-simulation methodology called CAVIAR. Here, CAVIAR is upgraded to support a message passing library and enable the virtual counterpart of a digital twin system using different 6G-related simulators. The main contributions of this work are the detailed description of different CAVIAR architectures, the implementation of this methodology to assess a 6G use case of UAV-based search and rescue mission (SAR), and the generation of benchmarking data about the computational resource usage. For executing the SAR co-simulation we adopt five open-source solutions: the physical and link level network simulator Sionna, the simulator for autonomous vehicles AirSim, scikit-learn for training a decision tree for MIMO beam selection, Yolov8 for the detection of rescue targets and NATS for message passing. Results for the implemented SAR use case suggest that the methodology can run in a single machine, with the main demanded resources being the CPU processing and the GPU memory.

Cláudio Modesto, Lucas Mozart, Glauco Gonçalves et al.

Network planning is a fundamental task in wireless communications, primarily focused on guaranteeing adequate coverage for every network device. In this context, the quality of any planning effort strongly depends on the channel model adopted in the design process of the simulations. Given this motivation, this work investigates how different channel models influence the placement of Long Range Wide Area Network (LoRaWAN) gateways (GWs), formulating an optimization problem that contrasts stochastic and empirical models with ray-tracing-based models. To this end, we developed a framework that integrates ray tracing (RT) simulators with a discrete-event network simulator. Using this framework to generate long range wide area network (LoRaWAN) wireless data metrics, we employ an optimization model that determines the optimized GW placement under different channel models and power constraints. Our results show that the optimized solution is highly sensitive to the chosen channel model, even when considering the same scenarios with different RT simulators, revealing a clear trade-off between computational cost and the fidelity of the solution to real-world conditions.

Ailton Oliveira, Felipe Bastos, Isabela Trindade et al.

Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.

Aldebaro Klautau

Introduction Several speech processing algorithms assume the signal is stationary during short intervals (approximately 20 to 30 ms). This assumption is valid for several applications, but it is too restrictive in some contexts. This work investigates the application of adaptive signal processing to the problem of estimating the formant frequencies of speech. Two algorithms were implemented and tested. The first one is the conventional Least-Mean-Square (LMS) algorithm, and the second is the conventional Recursive Least-Squares (RLS) algorithm. The formant frequencies are the resonant frequencies of the vocal tract. The speech is the result of the convolution between the excitation and the vocal tract impulse response [Rabiner, 78], thus a kind of "deconvolution" is required to recover the formants. This is not an easy problem because one does not have the excitation signal available. There are several algorithms for formant estimation [Rabiner, 78], [Snell, 93], [Laprie, 94