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

Haoyun Li, Ming Xiao, Kezhi Wang, Dong In Kim, Merouane Debbah

TL;DR

An LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP can find the clear trade-off between C&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence.

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 Cramér-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.

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

TL;DR

An LLM-enabled decomposition-based multi-objective evolutionary algorithm (LEDMA) for solving the highly non-convex MOP can find the clear trade-off between C&S and outperforms baseline MOEAs in terms of obtained Pareto fronts and convergence.

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 Cramér-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.
Paper Structure (10 sections, 15 equations, 3 figures, 1 algorithm)

This paper contains 10 sections, 15 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Algorithm
  6. Initialization
  7. Evolution
  8. Update
  9. Numerical Results
  10. Conclusions

Figures (3)

  • Figure 1: An example prompt for generating points for the $j$-th sub-problem.
  • Figure 2: (a) Convergence curves of HV with respect to the number of evaluations. (b) Comparison of Pareto-optimal points distribution acquired by different algorithms.
  • Figure 3: (a) Optimized locations of two UAVs with respect to different Pareto points, where each pair of two UAVs is connected by a gray dotted line. (b) Optimized power control of two UAVs with respect to different Pareto points.