This paper introduces WirelessLLM, a comprehensive framework for adapting and enhancing large language models (LLMs) to address the unique challenges and requirements of wireless communication networks. The framework is built on three foundational principles: knowledge alignment, knowledge fusion, and knowledge evolution. Knowledge alignment ensures that LLMs' outputs are aligned with the physical realities and constraints of wireless environments. Knowledge fusion involves synthesizing wireless physical signals to form a coherent understanding of the electromagnetic environment. Knowledge evolution requires continuously updating and refining the model based on dynamic channel conditions and user feedback. To build WirelessLLM, the paper investigates enabling technologies such as prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Three case studies demonstrate the practical applicability and benefits of WirelessLLM in solving typical problems in wireless networks: power allocation, spectrum sensing, and protocol understanding. The results show that incorporating domain-specific knowledge significantly improves the response quality of WirelessLLM compared to LLMs without wireless context. The paper also highlights key challenges in constructing WirelessLLM, including dataset collection for model training, computational complexity in inference, and security concerns related to wireless systems. Potential approaches and future research directions are discussed to mitigate these bottlenecks, facilitating the efficient implementation of WirelessLLM. LLMs have demonstrated remarkable capabilities in semantic understanding, knowledge retention, and adaptability. However, they face several limitations, including hallucinations, single modality, and outdated knowledge. WirelessLLM addresses these limitations by integrating domain-specific knowledge into LLMs, enabling them to understand and adapt to wireless environments. The paper discusses the challenges of training and deploying WirelessLLM, including data scarcity, resource constraints, and the need for efficient deployment on edge devices. It also addresses security and privacy concerns related to the integration of LLMs into wireless networks, proposing strategies such as data validation, adversarial training, and federated learning to mitigate these risks. The paper concludes that WirelessLLM has the potential to revolutionize wireless communication systems by enabling intelligent decision-making and optimization in complex and dynamic environments.This paper introduces WirelessLLM, a comprehensive framework for adapting and enhancing large language models (LLMs) to address the unique challenges and requirements of wireless communication networks. The framework is built on three foundational principles: knowledge alignment, knowledge fusion, and knowledge evolution. Knowledge alignment ensures that LLMs' outputs are aligned with the physical realities and constraints of wireless environments. Knowledge fusion involves synthesizing wireless physical signals to form a coherent understanding of the electromagnetic environment. Knowledge evolution requires continuously updating and refining the model based on dynamic channel conditions and user feedback. To build WirelessLLM, the paper investigates enabling technologies such as prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Three case studies demonstrate the practical applicability and benefits of WirelessLLM in solving typical problems in wireless networks: power allocation, spectrum sensing, and protocol understanding. The results show that incorporating domain-specific knowledge significantly improves the response quality of WirelessLLM compared to LLMs without wireless context. The paper also highlights key challenges in constructing WirelessLLM, including dataset collection for model training, computational complexity in inference, and security concerns related to wireless systems. Potential approaches and future research directions are discussed to mitigate these bottlenecks, facilitating the efficient implementation of WirelessLLM. LLMs have demonstrated remarkable capabilities in semantic understanding, knowledge retention, and adaptability. However, they face several limitations, including hallucinations, single modality, and outdated knowledge. WirelessLLM addresses these limitations by integrating domain-specific knowledge into LLMs, enabling them to understand and adapt to wireless environments. The paper discusses the challenges of training and deploying WirelessLLM, including data scarcity, resource constraints, and the need for efficient deployment on edge devices. It also addresses security and privacy concerns related to the integration of LLMs into wireless networks, proposing strategies such as data validation, adversarial training, and federated learning to mitigate these risks. The paper concludes that WirelessLLM has the potential to revolutionize wireless communication systems by enabling intelligent decision-making and optimization in complex and dynamic environments.