PACE: A Pragmatic Agent for Enhancing Communication Efficiency Using
  Large Language Models
Jiaxuan Li Minxi Yang Dahua Gao Wenlong Xu Guangming Shi
Current communication technologies face limitations in terms of theoretical
capacity, spectrum availability, and power resources. Pragmatic communication,
leveraging terminal intelligence for selective data transmission, offers
resource conservation. Existing research lacks universal intention resolution
tools, limiting applicability to specific tasks. This paper proposes an image
pragmatic communication framework based on a Pragmatic Agent for Communication
Efficiency (PACE) using Large Language Models (LLM). In this framework, PACE
sequentially performs semantic perception, intention resolution, and
intention-oriented coding. To ensure the effective utilization of LLM in
communication, a knowledge base is designed to supplement the necessary
knowledge, dedicated prompts are introduced to facilitate understanding of
pragmatic communication scenarios and task requirements, and a chain of thought
is designed to assist in making reasonable trade-offs between transmission
efficiency and cost. For experimental validation, this paper constructs an
image pragmatic communication dataset along with corresponding evaluation
standards. Simulation results indicate that the proposed method outperforms
traditional and non-LLM-based pragmatic communication in terms of transmission
efficiency.