AiltonOliveir/AI-Enhanced-MIMO-BeamTracking
This repository contains the code, datasets, and simulation tools for the paper "Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets", published at IEEE Latincom 2024.
This project helps wireless communication researchers and engineers to improve how vehicles maintain a strong signal connection with roadside infrastructure using millimeter-wave (mmWave) technology. It takes raw signal data from vehicle-to-infrastructure (V2I) scenarios and provides algorithms to continuously adjust the communication beam, resulting in more reliable high-speed wireless links. This is ideal for those working on next-generation connected vehicle systems.
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Use this if you are developing or researching advanced mmWave communication systems for vehicles and need to implement or evaluate machine learning-based beam tracking solutions to maintain signal integrity.
Not ideal if you are looking for a general-purpose simulation tool for wireless networks or if your focus is not specifically on mmWave beam tracking in V2I contexts.
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Language
Python
License
Apache-2.0
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Last pushed
Dec 09, 2024
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