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Resource Allocation Based on Optimal Transport Theory in ISAC-Enabled Multi-UAV Networks

Yufeng Zheng, Lixin Li, Wensheng Lin, Wei Liang, Qinghe Du, Zhu Han

TL;DR

This paper proposes the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively and demonstrates that the AIBOT can improve the system sum rate and reduce the localization Crámer-Rao bound by almost 29% compared to benchmark algorithms.

Abstract

This paper investigates the resource allocation optimization for cooperative communication with non-cooperative localization in integrated sensing and communications (ISAC)-enabled multi-unmanned aerial vehicle (UAV) cooperative networks. Our goal is to maximize the weighted sum of the system's average sum rate and the localization quality of service (QoS) by jointly optimizing cell association, communication power allocation, and sensing power allocation. Since the formulated problem is a mixed-integer nonconvex problem, we propose the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively. Simulation results demonstrate that the AIBOT can improve the system sum rate by nearly 12% and reduce the localization Cr'amer-Rao bound (CRB) by almost 29% compared to benchmark algorithms.

Resource Allocation Based on Optimal Transport Theory in ISAC-Enabled Multi-UAV Networks

TL;DR

This paper proposes the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively and demonstrates that the AIBOT can improve the system sum rate and reduce the localization Crámer-Rao bound by almost 29% compared to benchmark algorithms.

Abstract

This paper investigates the resource allocation optimization for cooperative communication with non-cooperative localization in integrated sensing and communications (ISAC)-enabled multi-unmanned aerial vehicle (UAV) cooperative networks. Our goal is to maximize the weighted sum of the system's average sum rate and the localization quality of service (QoS) by jointly optimizing cell association, communication power allocation, and sensing power allocation. Since the formulated problem is a mixed-integer nonconvex problem, we propose the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively. Simulation results demonstrate that the AIBOT can improve the system sum rate by nearly 12% and reduce the localization Cr'amer-Rao bound (CRB) by almost 29% compared to benchmark algorithms.
Paper Structure (12 sections, 1 theorem, 20 equations, 4 figures, 1 table, 3 algorithms)

This paper contains 12 sections, 1 theorem, 20 equations, 4 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Communication Model
  5. Sensing Model
  6. Problem Formulation
  7. The AIBOT-Based Joint Cell Association and Power Allocation Optimization
  8. Cell Association Design
  9. Transmitting Power Design
  10. The alternating iteration algorithm based on OT
  11. Numerical Results
  12. Conclusions

Key Result

Theorem 1

The optimal 3D cellular association for a dual-functional ground base station can be expressed as

Figures (4)

  • Figure 1: The system model.
  • Figure 2: The system objective function versus different number of iterations.
  • Figure 3: The CRB for non-cooperative UAV localization versus different number of iterations.
  • Figure 4: The localization CRB of UAV versus the threshold of the communication sum rate.

Theorems & Definitions (1)

  • Theorem 1