Sự Kiện

In this webinar, you will learn how we generate synthetic waveforms with embedded impairments, which are then broadcasted and received using Software-Defined Radios (SDRs). The captured signals will be employed to train and evaluate a Convolutional Neural Network (CNN) AI model.

In this webinar, we show how easy it is to apply artificial intelligence (AI) capabilities to solve wireless communications problems in MATLAB. You learn how to be more efficient by using ready-to-use algorithms and data generated with MATLAB and wireless communications products

5G and 6G communication systems will employ mm-Wave frequencies. This has made the development of highly integrated antenna arrays and RF front ends a standard practice. Engineers need to integrate RF,...

This webinar demonstrates the deployment of a Wireless System on an RFSoC device using Model-Based Design with MATLAB and Simulink. The talk emphasizes the benefits of this approach including system-level modeling, automated code generation, earlier verification, and faster adaptation to specification changes.

MATLAB EXPO brings together engineers, researchers, and scientists to hear real-world examples, get hands-on demonstrations, and learn more about the latest features and capabilities of MATLAB and Simulink.

In this webinar, you will learn about single- and multi-user MIMO in 5G NR, as well as common beamforming techniques and scenarios. We will cover different techniques to estimate the channel or channel...

In this session you will learn the basics of Simulink for modIn this session you will learn the basics of Simulink for modeling, simulating, and analyzing multidomain dynamical systems. You will see how to build simulation models using Simulink’s block diagramming interface, customizable set of libraries, and connectivity to MATLAB.eling, simulating, and analyzing multidomain dynamical systems. You will see how to build simulation models using Simulink’s block diagramming interface, customizable set of libraries, and connectivity to MATLAB.

Learn how synthesized radar data can be used to improve your design choices, how design models can be shared across organizations, and how testing data can be used to shorten integration cycles.

MATLAB® and Simulink® with deep learning frameworks, TensorFlow and PyTorch, provide enhanced capabilities for building and training your machine learning models. Via interoperability, you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.