<ul><li>Traditional deep reinforcement learning struggles in dynamic wireless network environments due to scattered and evolving feedback.</li><li>Large Language Models (LLMs) help by structuring unorganized network feedback into meaningful latent representations for more effective decision-making.</li><li>A contextualization-based adaptation method integrating learnable prompts into an LLM-augmented DRL framework is introduced for O-RAN network slicing.</li><li>The developed Prompt-Augmented Multi agent RL (PA-MRL) framework optimizes semantic clustering and RL objectives, leading to faster, more scalable, and adaptive resource allocation in O-RAN slicing.</li></ul>

Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing

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