UAV-Enabled ISAC: Towards On-Demand Sensing Services and Enhanced Communication

TL;DR

This paper proposes an optimized UAV-based integrated sensing and communication system that dynamically balances on-demand sensing accuracy with maximizing communication throughput using convex approximation and iterative algorithms.

Contribution

It introduces a novel joint trajectory and scheduling optimization framework for UAV-enabled ISAC, ensuring on-demand sensing constraints while enhancing communication performance.

Findings

The proposed optimization scheme effectively satisfies on-demand sensing constraints.

The iterative algorithm converges to a high-quality suboptimal solution.

Simulations demonstrate improved throughput and sensing accuracy.

Abstract

In this paper, we investigate an integrated sensing-and-communication (ISAC) network enabled by an unmanned aerial vehicle (UAV). The UAV is supposed to fly along a periodical circular trajectory at a fixed height for ISAC service supply from the sky. We consider on-demand sensing services, where on-demand detection and on-demand localization requests may be activated at any time toward any position within the targeted serving region. While guaranteeing satisfactory accuracy for both on-demand sensing tasks, we aim at maximizing the minimum achievable throughput among all communication users, via joint optimizing the UAV trajectory and communication user scheduling. To address the complicated problem with infinite sensing constraints, we characterize the on-demand detection constraint as a restricted deployment area for UAV and the on-demand localization constraint as Cramer-Rao Bound (CRB) constraints over finite reference target points, based on which the original problem is simplified to more tractable one. Afterwards, particularly aiming to ensure no violations of CRB constraints, we propose a convex approximation for the reformulated problem, where tight approximation is guaranteed at given local solution. The construction strategy for convex problem approximation allows an efficient iterative algorithm with verified convergence to a superior suboptimal solution. At last, with simulations, we verified the applicability of our developed optimization scheme in strictly fulfilling the on-demand sensing constraints and the effectiveness of our proposed solution for simultaneously enhancing the communication throughput in UAV-enabled ISAC.