A Review on Machine Learning for Channel Coding

Heimrih Lim Meng Kee

doi:10.1109/ACCESS.2024.3412192

A Review on Machine Learning for Channel Coding

2024-01-01

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DOI：10.1109/ACCESS.2024.3412192

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The usage of artificial intelligence and machine learning in wireless communications is the stepping stone towards a technological breakthrough in the current limitations of wireless communication systems. The trend of future coding schemes towards 6G appears to be based on rateless schemes and machine learning. Channel coding is important when transmitting data or information reliably as it provides error-correcting purposes. However, there is still a demand for more research regarding machine learning for channel coding. There is also a lack of a specific term or classification for existing machine learning applications for channel coding. This paper explores and compiles current trending machine learning techniques for channel coding. We are also introducing and proposing a new type of machine learning classification for channel coding purposes, as well as surveying some of the papers that fall under the respective class. This paper also discusses current challenges and future machine learning trends for channel coding, which are expected to impact future wireless communications development, especially in channel coding advancements.

Keywords:

Channel coding

5G mobile communication

Wireless communication

6G mobile communication

3GPP

Artificial intelligence

Deep learning

Reinforcement learning

Federated learning

6G

5G advanced

wireless communications

artificial intelligence

channel coding

machine learning

deep learning

reinforcement learning

federated learning

A Review on Machine Learning for Channel Coding

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