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

TL;DR

This paper introduces an optimal transport theory-based algorithm for resource allocation in ISAC-enabled multi-UAV networks, improving data rate and localization accuracy.

Contribution

It proposes the AIBOT algorithm to jointly optimize resource allocation, addressing the non-convex problem effectively in UAV cooperative networks.

Findings

System sum rate increased by nearly 12%.

Localization CRB reduced by almost 29%.

AIBOT outperforms 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.