matteonerini/ml-based-csi-feedback
Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
This project helps wireless communication engineers efficiently manage large-scale antenna systems (Massive MIMO) in Frequency Division Duplex (FDD) networks. It takes raw channel state information (CSI) measurements and processes them to generate a highly compressed, variable-length feedback message. This improved feedback allows for more effective beamforming and overall network performance, and would be used by wireless network architects, researchers, or anyone involved in optimizing 5G and future wireless systems.
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Use this if you are designing or optimizing FDD Massive MIMO systems and need to compress Channel State Information (CSI) for efficient feedback with variable message lengths.
Not ideal if you are working with Time Division Duplex (TDD) systems or do not require machine learning-based compression for your CSI feedback.
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25
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4
Language
MATLAB
License
GPL-3.0
Category
Last pushed
Feb 20, 2024
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