Design of 3D Beamforming and Deployment Strategies for ISAC-based HAPS Systems

TL;DR

This paper proposes novel 3D beamforming and deployment strategies for ISAC-enabled HAPS systems to optimize communication throughput and ground sensing performance, considering fixed and dynamic HAPS configurations.

Contribution

It introduces joint optimization of HAPS deployment and 3D beamforming for ISAC systems, addressing non-convex problems with efficient algorithms for fixed and dynamic HAPS strategies.

Findings

Proposed algorithms improve sum-rate throughput and sensing quality.

Dynamic HAPS deployment outperforms static strategies in simulations.

Effective solutions satisfy practical constraints like power and energy limits.

Abstract

This paper explores high-altitude platform station (HAPS) systems enabled by integrated sensing and communication (ISAC), in which a HAPS simultaneously transmits communication signals and synthetic aperture radar (SAR) imaging signals to support multi-user communication while performing ground target sensing. Taking into account the operational characteristics of SAR imaging, we consider two HAPS deployment strategies: (i) a quasi-stationary HAPS that remains fixed at an optimized location during SAR operation, following the stop-and-go scanning model; and (ii) a dynamic HAPS that continuously adjusts its flight trajectory along a circular path. For each strategy, we aim at maximizing the weighted sum-rate throughput for communication users while ensuring that SAR imaging requirements, such as beampattern gain and signal-to-noise ratio (SNR), are satisfied. This is achieved by jointly optimizing the HAPS deployment strategy, i.e., its placement or trajectory, along with three-dimensional (3D) transmit beamforming, under practical constraints including transmit power limits, energy consumption, and flight dynamics. Nevertheless, the formulated optimization problems corresponding to the two deployment strategies are inherently non-convex. To address the issue, we propose efficient algorithms that leverage both convex and non-convex optimization techniques to obtain high-quality suboptimal solutions. Numerical results demonstrate the effectiveness and advantages of the proposed approaches over benchmark schemes.