  Large language models (LLMs) have received considerable attention recently
due to their outstanding comprehension and reasoning capabilities, leading to
great progress in many fields. The advancement of LLM techniques also offers
promising opportunities to automate many tasks in the telecommunication
(telecom) field. After pre-training and fine-tuning, LLMs can perform diverse
downstream tasks based on human instructions, paving the way to artificial
general intelligence (AGI)-enabled 6G. Given the great potential of LLM
technologies, this work aims to provide a comprehensive overview of LLM-enabled
telecom networks. In particular, we first present LLM fundamentals, including
model architecture, pre-training, fine-tuning, inference and utilization, model
evaluation, and telecom deployment. Then, we introduce LLM-enabled key
techniques and telecom applications in terms of generation, classification,
optimization, and prediction problems. Specifically, the LLM-enabled generation
applications include telecom domain knowledge, code, and network configuration
generation. After that, the LLM-based classification applications involve
network security, text, image, and traffic classification problems. Moreover,
multiple LLM-enabled optimization techniques are introduced, such as automated
reward function design for reinforcement learning and verbal reinforcement
learning. Furthermore, for LLM-aided prediction problems, we discussed
time-series prediction models and multi-modality prediction problems for
telecom. Finally, we highlight the challenges and identify the future
directions of LLM-enabled telecom networks.
