ekagra-ranjan/Auto-SCMA
NCC 2021 - Auto-SCMA: Learning Codebook for Sparse Code Multiple Access using Machine Learning
This project helps wireless communication engineers design more efficient Sparse Code Multiple Access (SCMA) systems. It takes in communication channel parameters and automatically generates optimized 'codebooks' for transmitting data. The output is a highly effective codebook that improves signal transmission and reception quality. This is for researchers and practitioners in telecommunications who are working on advanced multiple access techniques.
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Use this if you need to create or optimize codebooks for Sparse Code Multiple Access (SCMA) systems, especially in challenging wireless environments like Rayleigh fading channels, and want to leverage machine learning for superior performance without manual design.
Not ideal if your work does not involve designing or evaluating Sparse Code Multiple Access (SCMA) systems or if you are looking for a general-purpose machine learning library.
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Sep 26, 2021
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