  The advent of Large Language Models (LLMs) has revolutionized language
understanding and human-like text generation, drawing interest from many other
fields with this question in mind: What else are the LLMs capable of? Despite
their widespread adoption, ongoing research continues to explore new ways to
integrate LLMs into diverse systems.
  This paper explores new techniques to harness the power of LLMs for 6G (6th
Generation) wireless communication technologies, a domain where automation and
intelligent systems are pivotal. The inherent adaptability of LLMs to
domain-specific tasks positions them as prime candidates for enhancing wireless
systems in the 6G landscape.
  We introduce a novel Reinforcement Learning (RL) based framework that
leverages LLMs for network deployment in wireless communications. Our approach
involves training an RL agent, utilizing LLMs as its core, in an urban setting
to maximize coverage. The agent's objective is to navigate the complexities of
urban environments and identify the network parameters for optimal area
coverage. Additionally, we integrate LLMs with Convolutional Neural Networks
(CNNs) to capitalize on their strengths while mitigating their limitations. The
Deep Deterministic Policy Gradient (DDPG) algorithm is employed for training
purposes. The results suggest that LLM-assisted models can outperform CNN-based
models in some cases while performing at least as well in others.
