Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G

TL;DR

This paper introduces a novel integrated sensing and communication system using HAPS and UAVs for 6G, optimizing position, power, and beamforming to enhance coverage, resource allocation, and system efficiency.

Contribution

It presents a new HAPS-UAV integrated framework for 6G ISAC, with a multi-objective optimization for power and beamforming, improving network performance and resource management.

Findings

Enhanced wireless coverage in remote areas.

Improved network performance and resource allocation.

Effective beamforming and target detection achieved.

Abstract

This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless coverage, particularly in remote areas. UAVs function as dual-purpose access points (APs), using their maneuverability and line-of-sight (LoS) aerial-to-ground (A2G) links to transmit combined communication and sensing signals. The scheme operates in two time slots: in the first slot, UAVs transmit dedicated signals to communication users (CUs) and potential targets. UAVs detect targets in specific ground locations and, after signal transmission, receive reflected signals from targets. In the second slot, UAVs relay these signals to HAPS, which performs beamforming to align signals for each CU from various UAVs. UAVs decode information from HAPS and adjust transmissions to maximize the beam pattern efficiency toward the desired targets. We formulate a multi-objective optimization problem to maximize both the minimum signal-to-interference-plus-noise ratio (SINR) for CUs and the echo signal power from the targets. This is achieved by finding the optimal power allocation for CUs in each UAV, subject to constraints on the maximum total power in each UAV and the transmitted beam pattern gain. Simulation results demonstrate the effectiveness of this approach in enhancing network performance, resource allocation, fairness, and system optimization. Using HAPS as the CPU, computational tasks are offloaded from UAVs, which conserves energy and improves network performance.