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The evolution of Beyond 5G (B5G) networks demands communication systems that are ultrareliable, low latency, and capable of handling diverse, data-intensive applications. Traditional source and channel coding methods, following Shannon’s separation principle, often fall short in dynamic and heterogeneous wireless environments. This chapter reviews the foundational principles of source and channel coding, highlighting key theoretical insights, and then delves into recent advances in deep learning, Generative AI (GenAI), and Large Language Model (LLM)-based joint source-channel coding techniques. Emphasizing semantic and task-oriented communication, these emerging approaches offer promising solutions for more intelligent, adaptable, and efficient transmission strategies tailored to the new requirements of B5G networks.

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GenAI and LLMs for Source and Channel Coding in B5G Networks

  • 章节

  • 发表日期：

    2026年

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  • Jaswanthi Mandalapu,
  • Abhishek Roy,
  • Navrati Saxena

摘要

The evolution of Beyond 5G (B5G) networks demands communication systems that are ultrareliable, low latency, and capable of handling diverse, data-intensive applications. Traditional source and channel coding methods, following Shannon’s separation principle, often fall short in dynamic and heterogeneous wireless environments. This chapter reviews the foundational principles of source and channel coding, highlighting key theoretical insights, and then delves into recent advances in deep learning, Generative AI (GenAI), and Large Language Model (LLM)-based joint source-channel coding techniques. Emphasizing semantic and task-oriented communication, these emerging approaches offer promising solutions for more intelligent, adaptable, and efficient transmission strategies tailored to the new requirements of B5G networks.