Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

UAV-Enabled ISAC with Fluid Antennas for Low-Altitude Wireless Networks

Wenchao Liu, Xuhui Zhang, Jinke Ren, Weijie Yuan, Changsheng You, Shuangyang Li

Abstract

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed-position antennas limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antennas (FAs), where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cramér-Rao bound (CRB) for the angle estimation of a single target. Due to excessively frequent updates of FA positions may lead to response delay, a three-timescale optimization framework is developed to jointly optimize transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.

UAV-Enabled ISAC with Fluid Antennas for Low-Altitude Wireless Networks

Abstract

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed-position antennas limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antennas (FAs), where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cramér-Rao bound (CRB) for the angle estimation of a single target. Due to excessively frequent updates of FA positions may lead to response delay, a three-timescale optimization framework is developed to jointly optimize transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.

Paper Structure

This paper contains 29 sections, 1 theorem, 100 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Fluid Antenna Systems
  4. UAV-enabled ISAC Systems
  5. System Model and Problem Formulation
  6. Channel Model
  7. User Communication Model
  8. Target Sensing Model
  9. Performance Metrics
  10. Three-Timescale Optimization Framework
  11. Problem Formulation
  12. Proposed Solution
  13. Transmit Beamforming Optimization
  14. Transmit FA Position Optimization
  15. Receive FA Position Optimization
  16. ...and 14 more sections

Key Result

Proposition 1

The ergodic rate $\mathbb{E} [ R_{m}[n] ]$ in the objective function $\mathcal{F}$ can be approximated as ERate_1, where $\zeta^{\rm{los}}_{m}[n] = \frac{ \kappa_{m}[n] \beta_{m}[n] }{ \kappa_{m}[n] + 1 }$ and $\zeta^{\rm{nlos}}_{m}[n] = \frac{ \beta_{m}[n] }{ \kappa_{m}[n] + 1 }$.

Figures (11)

  • Figure 1: Illustration of the UAV-enabled ISAC system with transmit and receive FAs vertically mounted on the UAV.
  • Figure 2: Illustration of three-timescale optimization framework.
  • Figure 3: The convergence performance of the proposed scheme under different weight factors $\xi_{c}$.
  • Figure 4: The UAV trajectory under different weight factors $\xi_{c}$.
  • Figure 5: The sum rate of all users versus time slot index under different weight factors $\xi_{c}$.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof