Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks

TL;DR

This paper introduces an LLM-enabled multi-objective evolutionary algorithm to optimize UAV networks for integrated sensing and communication, balancing localization accuracy and network utility.

Contribution

It proposes a novel LEDMA that leverages large language models within a decomposition-based evolutionary framework for complex UAV network optimization.

Findings

LEDMA outperforms baseline algorithms in Pareto front quality.

The approach effectively balances sensing and communication objectives.

Numerical results validate the superiority of LLM integration in optimization.

Abstract

This letter investigates an unmanned aerial vehicle (UAV) network with integrated sensing and communication (ISAC) systems, where multiple UAVs simultaneously sense the locations of ground users and provide communication services with radars. To find the trade-off between communication and sensing (C\&S) in the system, we formulate a multi-objective optimization problem (MOP) to maximize the total network utility and the localization Cram\'er-Rao bounds (CRB) of ground users, which jointly optimizes the deployment and power control of UAVs. Inspired by the huge potential of large language models (LLM) for prediction and inference, we propose an LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP. We first adopt a decomposition-based scheme to decompose the MOP into a series of optimization sub-problems. We second integrate LLMs as black-box search operators with MOP-specifically designed prompt engineering into the framework of MOEA to solve optimization sub-problems simultaneously. Numerical results demonstrate that the proposed LEDMA can find the clear trade-off between C\&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence.