A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models

2025-04-16

AI Summary

This work addresses user-centric wireless communication by bridging the semantic gap between natural language instructions and complex physical-layer systems. We propose an LLM-native paradigm: first, NL2SQL converts user queries into communication metrics and retrieves real-time channel state information from a database; then, the LLM models and solves a semantic communication optimization problem to dynamically adjust physical-layer coding depth. To our knowledge, this is the first integration of NL2SQL into a wireless closed-loop control framework, establishing an end-to-end, LLM-driven pipeline—“natural language → parameter mapping → data retrieval → optimization decision → physical-layer adaptation.” Experiments demonstrate accurate mapping from user intent to semantic encoding strategies, substantial improvements in communication performance (e.g., reduced distortion, enhanced semantic fidelity), and end-to-end latency compliant with real-time constraints (<100 ms).

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The next generation of wireless communications seeks to deeply integrate artificial intelligence (AI) with user-centric communication networks, with the goal of developing AI-native networks that more accurately address user requirements. The rapid development of large language models (LLMs) offers significant potential in realizing these goals. However, existing efforts that leverage LLMs for wireless communication often overlook the considerable gap between human natural language and the intricacies of real-world communication systems, thus failing to fully exploit the capabilities of LLMs. To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool. Specifically, in this paradigm, user personal requirements is the primary focus. Upon receiving a user request, LLMs first analyze the user intent in terms of relevant communication metrics and system parameters. Subsequently, a structured query language (SQL) statement is generated to retrieve the specific parameter values from a high-performance real-time database. We further utilize LLMs to formulate and solve an optimization problem based on the user request and the retrieved parameters. The solution to this optimization problem then drives adjustments in the communication system to fulfill the user's requirements. To validate the feasibility of the proposed paradigm, we present a prototype system. In this prototype, we consider user-request centric semantic communication (URC-SC) system in which a dynamic semantic representation network at the physical layer adapts its encoding depth to meet user requirements. Additionally, two LLMs are employed to analyze user requests and generate SQL statements, respectively. Simulation results demonstrate the effectiveness.

Problem

• Bridging human language and wireless system complexities using LLMs
• Optimizing communication systems via user-centric LLM-driven SQL queries
• Adapting semantic networks dynamically to meet user requirements

Innovation

• LLM-driven wireless communication paradigm
• NL2SQL tool for structured queries
• Dynamic semantic representation network adaptation