The rapid evolution of wireless communication systems toward 6G and beyond poses unprecedented challenges in resource allocation, interference management, and spectrum efficiency. Conventional optimization-based methods often fail to scale in highly dynamic environments with massive connectivity. Deep reinforcement learning (DRL) has emerged as a promising paradigm for adaptive wireless resource management, yet it struggles with coordination, interpretability, and convergence in large-scale multi-agent scenarios. This project proposes a novel multi-LLM agent deep reinforcement learning framework for wireless communications. The central idea is to leverage large language models (LLMs) as reasoning and coordination agents in multi-agent DRL environments, where each LLM represents a wireless entity such as a base station, user equipment, or spectrum regulator. The LLM agents will be responsible for high-level negotiation, cooperation, and policy adaptation, while DRL policies execute low-level control tasks such as power allocation, beamforming, and scheduling. Our approach integrates domain knowledge of wireless protocols into LLM prompts, enabling emergent coordination strategies that go beyond traditional DRL. For example, LLM agents can negotiate interference-mitigation strategies or dynamically form coalitions for spectrum sharing, leading to more efficient and fair communication outcomes. This hybrid design allows DRL to focus on numerical optimization while LLMs provide strategic reasoning and cooperative decision-making. The research objectives are threefold: 1) Framework Design: Develop a scalable architecture combining DRL policies with LLM-based reasoning agents, including prompt engineering and parameter-efficient fine-tuning (LoRA/PEFT) for wireless-specific knowledge; 2)Simulation and Evaluation: Implement large-scale wireless communication environments (multi-cell, multi-user) with fading channels, interference, and mobility, and benchmark against conventional DRL-only and optimization-based baselines; 3)Emergent Cooperation Analysis: Study how LLM-mediated communication influences fairness, energy efficiency, spectral efficiency, and latency, and evaluate the interpretability of emergent negotiation strategies. The anticipated outcomes include: 1) A proof-of-concept multi-LLM + DRL framework for wireless communications; 2)Performance gains in spectral and energy efficiency compared to baseline DRL systems; 3)Insights into emergent multi-agent cooperation, potentially applicable to other domains such as IoT coordination and edge computing; 4)Publications in leading journals and conferences (IEEE Transactions on Wireless Communications, IEEE JSAC, NeurIPS ML for Wireless workshops). By bridging wireless communications, reinforcement learning, and large language models, this project aims to pioneer a new paradigm in AI-driven network management, pushing the boundaries of what is possible in autonomous wireless systems.