cs.NISep 14, 2026

Toward Mission-Critical ISAC: Reliable Energy-Aware Coordination in UAV Swarms

Authors: Masoud Shokrnezhad, Tarik Taleb, Amir Javadpour

Organizations: ICTFICIAL Oy Finland · Ruhr University Bochum Germany

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Unmanned aerial vehicle (UAV) swarms deployed in mission-critical applications must simultaneously track a mobile aerial target and maintain reliable data links. However, active integrated sensing and communication (ISAC) operation imposes a dual energy burden on propulsion and transmission, threatening mission continuity through premature battery depletion. In this paper, we propose a two-tier UAV swarm architecture in which mission UAVs (MUAVs) execute cooperative ISAC for mobile aerial target tracking while dedicated charging UAVs (CUAVs), equipped with solar harvesting panels, replenish low-battery MUAVs via aerial UAV-to-UAV wireless power transfer (WPT). We formulate the joint minimization of the cooperative posterior Cramér-Rao bound (PCRB) over MUAV trajectories, per-slot sensing-communication time splits, WPT scheduling and admission, and CUAV rendezvous trajectories, subject to minimum uplink rate, dual-tier energy causality, WPT proximity, collision-avoidance, and speed constraints, yielding a non-convex mixed-integer program (MIP) that, to the best of our knowledge, is the first to jointly couple cooperative ISAC sensing quality with aerial WPT and dual-tier energy management. To solve it efficiently, we propose Receding-Horizon Alternating Optimization (RHAO), a four-block per-slot algorithm that decomposes the problem into: charging admission via the Hungarian algorithm, MUAV trajectory and time-split via successive convex approximation (SCA), CUAV rendezvous, and WPT power allocation, with monotone convergence guarantees. Simulation results demonstrate that RHAO reduces the mean PCRB by 16.8 times over a fixed-time-split baseline and 5.4 times over a static-trajectory scheme, while the aerial WPT subsystem sustains all MUAVs above the energy-critical threshold throughout the full mission horizon.

Figures & tables

Fig. 1: Two-tier uav swarm architecture. muav (blue) perform cooperative isac : they transmit radar waveforms toward the mobile target and relay sensing data to the bs via uplink. cuav (green) carry solar harvesting panels and recharge low-battery muav via aerial wpt .
Fig. 2: Simulation results averaged over 10 Monte-Carlo runs. (a) Sensing quality: ρt=tr(J[t]−1)\rho_{t}=\mathrm{tr}(\mathbf{J}[t]^{-1}) vs. slot (log scale). (b) Tracking accuracy: PCRB position bound tr(Jpos−1)\sqrt{\mathrm{tr}(\mathbf{J}^{-1}_{\mathrm{pos}})} vs. slot (log scale). (c) Energy sustainability: minimum muav battery vs. slot; dashed line marks the energy-critical trigger Bntrig=6 kJB_{n}^{\mathrm{trig}}{=}6\,\mathrm{kJ} (Block 1 admission threshold).
More in this topic

Integrated Sensing and Communication

Integrated sensing and communication combines wireless data links with radar-like perception using shared signals, spectrum, and hardware.

See all 47 papers

Explore similar work

CardsList
Jul 21, 2026

CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cramér-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.

Yi Yang, Qianqian Zhang, Huaxia Wang

Department of Electrical and Computer Engineering, Rowan University, NJ, USA

Trajectory OptimizationIntegrated Sensing and CommunicationWireless CommunicationsUAV Trajectory Optimization
AskPDF
Oct 8, 2026

An Embodied Multiagent Framework Based on Token Communications for Cooperative ISAC

The emerging low-altitude economy demands unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) for reliable connectivity and environmental awareness. In particular, embodied UAV agents offer a promising means of supporting autonomous operations through a closed loop linking perception, decision-making, and physical actions. However, each UAV has access only to local observations, and effective cooperation requires exchanging local states and intentions. Directly sharing such information can incur substantial signaling overhead and hinder timely coordination in dynamic environments. To deal with this problem, this paper investigates a cooperative ISAC network of embodied UAV agents and formulates a joint token communications (TokCom) and physical control problem to minimize total propulsion energy subject to communication and sensing rate requirements. Then, we propose a state--intent TokCom (SI-TokCom) framework driven by multi-agent embodied policy learning. Specifically, separate pretrained codebooks enable compact exchanges of local states and intentions, while UAV agents jointly learn to select and compose tokens and determine physical actions based on local observations and received tokens. Simulation results show that SI-TokCom achieves 98.9% and 99.1% of the centralized baseline's communication and sensing rates, respectively. Compared with the local baseline, it improves the corresponding rates by 5.0% and 43.6%, respectively, with essentially unchanged propulsion energy. These results highlight the potential of TokCom for communication-efficient cooperation among embodied UAV agents in ISAC systems.

Jiahe Guo, Jun Du, Chunxiao Jiang +1

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China · State Key Laboratory of Space Network and Communications, Tsinghua University, Beijing 100084, China · Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

Integrated Sensing and CommunicationMulti-Agent CommunicationAerial Robotics
AskPDF
Jul 27, 2026

Distributed Coordination for Resilient Multi-UAV Remote Sensing: A Photovoltaic Inspection Case Study

Deploying multiple UAVs for remote sensing enables proportional reductions in mission time, but realizing these benefits requires the fleet to coordinate at runtime: distributing sensing targets, responding to platform failures, and recovering from degraded data quality. In inspection campaigns, where mission value depends on complete coverage and the usability of every capture, a centralized ground-station coordinator is a single point of failure: a lost link or station fault leaves sensing gaps that cannot be filled without operator intervention. We propose the SwarmLink, an inter-agent communication infrastructure that non-invasively extends any existing aerial framework with peer-to-peer coordination capability, without modifying the host system. We apply it to photovoltaic plant inspection as a representative large-scale sensing campaign, extending Aerostack2 with a distributed auction that unifies initial sensing-target allocation, platform-failure recovery, and data-quality-triggered reassignment into a single runtime mechanism. All three disruption scenarios reduce to the same re-auction over remaining targets and active platforms, requiring zero modifications to the Aerostack2 core and no ground-station involvement during the mission.

Guillermo GP-Lenza, Miguel Fernandez-Cortizas, Martin Molina +1

Centre for Automation and Robotics C.A.R, (UPM-CSIC), Universidad Politécnica de Madrid, Spain · Department of Artificial Intelligence, Universidad Politécnica de Madrid,, Spain · Automation and Robotics Group (ARG-SnT), University of Luxembourg - SnT, Luxembourg

Resilient Multi-Robot CoordinationMulti-Robot CoordinationMulti-Robot Task AllocationUAV Remote Sensing
AskPDF