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UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Kaitao Meng, Qingqing Wu, Jie Xu, Wen Chen, Zhiyong Feng, Robert Schober, A. Lee Swindlehurst

arXiv:2206.03408v3cs.IT

TL;DR

UAV-enabled ISAC addresses limited terrestrial sensing coverage using mobile aerial platforms with strong air-ground LoS channels, while facing SWAP and interference constraints. The article surveys joint S&C protocols, optimization methods, and mutual-assistance scenarios; representative simulations verify benefits, while multi-UAV cooperation and secure operation remain challenging.

  • Problem

    UAV-enabled ISAC must improve coverage and S&C performance despite terrestrial sensing limits, UAV SWAP constraints, and severe LoS-induced interference.

  • Method

    The article surveys joint S&C protocols, UAV motion control, resource allocation, interference management, mutual-assistance scenarios, and future research directions.

  • Results

    Representative simulations verify benefits from coordinated interference management, cooperative ISAC, sensing-assisted communication, and communication-assisted sensing.

  • Takeaways & Limitations

    Mutual assistance between sensing and communication provides a coordination gain for UAV-enabled ISAC.

Abstract

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Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the UAVs' size, weight, and power (SWAP) constraints, controllable mobility, and line-of-sight (LoS) air-ground channels, UAV-enabled ISAC introduces both new opportunities and challenges. This article provides an overview of UAV-enabled ISAC, and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C, and discuss UAV motion control, wireless resource allocation, and interference management for the cases of single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

I. INTRODUCTION

UAV-enabled ISAC uses aerial mobility and air-ground LoS channels to extend sensing and communication coverage, while introducing SWAP, interference, and coordination challenges. The article surveys joint S&C designs, mutual assistance scenarios, and future research directions.

  • Terrestrial ISAC has limited sensing range because obstacles can block LoS links to long-range targets.
  • UAVs provide enhanced ISAC services for rescue, eavesdropper monitoring, and temporary hotspots through 3D mobility and strong air-ground LoS channels.
  • UAV-enabled ISAC must address SWAP constraints, severe LoS-induced interference, and mobility-dependent placement and trajectory design.
  • Joint S&C design targets improved spectrum efficiency, hardware reuse, and reduced power consumption while balancing distinct communication and sensing requirements.
  • S&C can mutually assist through sensing-assisted communication and communication-assisted sensing, including trajectory and resource decisions informed by sensing results.
  • The article surveys single- and multi-UAV joint design, coordination scenarios, and research directions for UAV-enabled ISAC.

II. UAV-ENABLED JOINT SENSING AND COMMUNICATION

UAV-enabled joint S&C designs protocols that support communication and sensing either simultaneously or in coordinated temporal patterns. Co-ISAC, TDM-ISAC, and Hybrid-ISAC trade sensing coverage, interference, trajectory flexibility, efficiency, and cost.

  • UAV-enabled joint S&C serves ground users while concurrently detecting or estimating targets, with separate single- and multi-UAV cases.
  • ISAC Frame Protocol Design: Co-ISAC senses all targets simultaneously, but divergent beams and divided transmit power reduce trajectory flexibility.
  • ISAC Frame Protocol Design: TDM-ISAC senses one intended target per time instant alongside one communication user, while other targets’ echoes become clutter or interference.
  • ISAC Frame Protocol Design: Hybrid-ISAC groups targets spatially, applying Co-ISAC within groups and TDM-ISAC across groups to improve intra-group efficiency and avoid inter-group interference.
  • ISAC Frame Protocol Design: The relative performance of the three protocols depends on S&C QoS requirements, user and target locations, and mobility.

2) Joint Resource Allocation, Waveform, and Deployment/Trajectory Design:

UAV deployment and trajectory affect angular separations, so resource allocation, beamforming, user association, and trajectory must be jointly designed. Long flight periods create complexity concerns that motivate frame-based trajectory construction.

  • UAV location changes user-target angular separations, requiring joint design of association, beamforming, and trajectory to maximize communication while meeting sensing power and frequency requirements.
  • Fig. 2 compares UAV trajectories and achievable communication rates for 4 users and 4 targets under TDM-ISAC.
  • A two-layer penalty-based algorithm decomposes coupled integer optimization variables to find high-quality solutions.
  • The evaluated setup uses 16 UAV antennas, 30 m/s maximum horizontal speed, 40 m altitude, and 40 s flight duration.
  • Trajectory-design complexity may become intractable for long flights; partitioning operations into limited-duration ISAC frames can reduce algorithmic complexity.

B. Multi-UAV-Enabled ISAC

Multi-UAV ISAC can extend coverage and improve resource efficiency for geographically distributed, time-critical tasks, but requires mitigation of severe inter-UAV interference from LoS-dominant channels.

  • A single UAV may provide low S&C performance for geographically distributed and time-critical tasks because of limited sensing range and communication rate.
  • Multi-UAV collaboration can improve resource efficiency and coverage, but strong LoS-dominant air-ground channels create potentially severe inter-UAV interference.

1) Coordinated Interference Management:

Multi-UAV ISAC can combine distributed sensing and coordinated communications, but deployment must balance LoS sensing benefits against communication interference, signaling overhead, and synchronization demands.

  • 1) Coordinated Interference Management:: UAV mobility, beamforming, and power control can be used to reduce interference between UAVs and adjacent unassociated users or targets.Interference can otherwise limit the S&C range and performance.
  • 1) Coordinated Interference Management:: Cooperative ISAC combines distributed MIMO radar sensing with coordinated multi-point transmission and reception.UAVs can exchange correlated signals and share or fuse sensing results.
  • 1) Coordinated Interference Management:: Higher altitudes and open environments can strengthen LoS sensing links, while LoS-dominated communication channels may create harmful interference and fewer MIMO degrees of freedom.Preferred deployments require strong LoS links to intended sensing targets and enough NLoS communication links to form high-rank channels.
  • 1) Coordinated Interference Management:: Distributed multi-static ISAC introduces high signaling overhead and strict time-synchronization requirements.These practical challenges remain unresolved in cooperative UAV deployments.
  • 1) Coordinated Interference Management:: Reflected signals from served ISAC users can replace pilot transmission and feedback for localization, beam tracking, and beam alignment.This creates a sensing gain by reducing signaling overhead and improving communication performance.

A. Sensing Gain

Sensing gain quantifies communication improvement from using reflected ISAC signals for prediction instead of conventional beam training. The gain depends on estimation accuracy and generally weakens as UAV-to-vehicle distance increases.

  • A. Sensing Gain: Sensing gain is the communication performance improvement obtained when reflected ISAC signals support beam tracking and alignment instead of pilots or feedback.The comparison is against conventional beam training, which incurs pilot overhead and beam-misalignment SNR loss.
  • A. Sensing Gain: More accurate target or channel estimation produces larger sensing gain.For LoS-dominated channels, location estimation error depends on the fourth power of the UAV-to-vehicle link distance.
  • A. Sensing Gain: Higher sensing gain occurs when the ground vehicle is closer to the hovering UAV.As distance increases, weaker echo power reduces ISAC prediction accuracy and can cause it to fluctuate.
  • A. Sensing Gain: Joint beamwidth and UAV trajectory design is identified as a promising way to further improve ISAC performance.UAV mobility can shorten the link distance, reducing path loss while strengthening sensing-assisted communication improvement.
  • A. Sensing Gain: Collaborative sensing in multi-UAV scenarios may improve communication performance but requires sophisticated cooperation schemes.Efficient and reliable sensing-data exchange and fusion remain open problems.

B. Sensing-assisted Beam Tracking

Sensing-assisted beam tracking must adapt beamwidth and trajectory to target geometry and mobility. The design balances sensing reliability, tracking robustness, communication throughput, and network-level service quality.

  • B. Sensing-assisted Beam Tracking: Long-distance point-like users favor narrow sensing beams, whereas nearby angularly extended users require wider beams for coverage.A narrower communication beam can still improve performance when aligned with receiver antennas.
  • B. Sensing-assisted Beam Tracking: Dynamic waveforms can adjust ISAC beam width and center in real time according to receiver position and estimated object contours.Wide beams support sensing accuracy and tracking, while narrow beams improve communication near the target.
  • B. Sensing-assisted Beam Tracking: High-mobility targets favor wide beams at larger distances and narrow beams near the target.This produces a fundamental trade-off between communication throughput and sensing reliability in joint beamwidth and trajectory design.
  • B. Sensing-assisted Beam Tracking: Multi-UAV networks face dynamic load-balancing and seamless-coverage challenges because user distributions and mobility create uneven S&C traffic loads.Uneven loads can degrade service time and quality.
  • B. Sensing-assisted Beam Tracking: Communication functionality can assist sensing by improving sensing robustness, efficiency, and accuracy.This complementary direction motivates communication-assisted UAV sensing.
  • B. Sensing-assisted Beam Tracking: Limited onboard computation and low-latency requirements make local processing of all sensing echoes impractical for delay-sensitive missions.Offloading raw or processed data to nearby edge servers is proposed as one solution.

B. Information Sharing and Fusion

Information sharing and fusion extend sensing beyond a single UAV’s limited range and performance. They support joint processing, efficient mission assignment, seamless tracking, and richer target information, but add latency and communication costs.

  • B. Information Sharing and Fusion: Multiple UAVs can share and integrate position and velocity estimates for joint sensing and more efficient mission assignment.Shared information can guide sensing tasks in the next ISAC frame.
  • B. Information Sharing and Fusion: Sharing motion directions and environmental changes enables collaborative multi-UAV coverage and tracking.Information exchange also avoids repetitive target detection and excessive target searching.
  • B. Information Sharing and Fusion: A UAV or ground base station can collect and fuse sensing results to improve sensing accuracy and obtain richer target information.The data-center role centralizes collection and fusion of distributed results.
  • B. Information Sharing and Fusion: Information sharing and fusion introduce transmission latency and consume communication resources.The paper identifies low-cost, highly efficient sharing and fusion as an open design problem.
  • B. Information Sharing and Fusion: Environmental obstacles can block LoS sensing links or create clutter interference, degrading S&C performance in unknown or urban areas.Historical-measurement environment maps can help predict UAV-ground and target links.

V. DIRECTIONS FOR FUTURE RESEARCH

Future UAV-enabled ISAC research must address interference, channel reconfiguration, and security challenges arising from UAV mobility and strong LoS air-ground links.

  • A. ISAC for UAVs: Low-altitude UAV monitoring can improve communication through tracking and beam prediction, but strong LoS links increase interference to terrestrial users and base stations.Cooperative interference management and cancellation are therefore identified as needed for heterogeneous ISAC networks.
  • B. IRS-assisted UAV-enabled ISAC: IRS-assisted UAV-enabled ISAC can create virtual LoS links to blocked users, expanding coverage and increasing deployment and trajectory flexibility.The joint design changes wireless channels through IRS phase shifts and UAV trajectory design.
  • B. IRS-assisted UAV-enabled ISAC: IRS channel estimation may require significant signaling overhead, while joint IRS and UAV design can entail high complexity.
  • C. Secure UAV ISAC: LoS-dominated air-ground channels increase UAV-enabled ISAC risks of eavesdropping and jamming, including threats from unauthorized malicious UAVs.Protecting target and user information, sensing accuracy, and communication reliability remains challenging when eavesdropper locations and channels are uncertain.

D. UAV ISAC Meets Artificial Intelligence

AI and federated learning are presented as approaches for adapting UAV-enabled ISAC to dynamic environments while using sensing data and preserving distributed-data privacy.

  • D. UAV ISAC Meets Artificial Intelligence: AI-based designs can address highly dynamic scenarios while avoiding the time-consuming iterations of traditional optimization algorithms.Sensing information can support prediction of future network states and online UAV action adjustment.
  • D. UAV ISAC Meets Artificial Intelligence: ISAC sensing can provide training data for AI-enabled applications through wireless network sensing.
  • D. UAV ISAC Meets Artificial Intelligence: Federated learning lets each UAV update a local AI model from local ISAC data and send parameters to a central server for global-model updating.The article identifies efficient integration of the training algorithm and ISAC process as an open problem.
  • VI. CONCLUSIONS: The article proposes coordinated interference management and cooperative ISAC for performance improvement in multi-UAV-enabled ISAC networks.Representative simulation results are reported to verify the benefits of the proposed methods.

VII. BIOGRAPHIES

The biographies identify the authors’ affiliations and research interests across UAV communications, ISAC, wireless networking, signal processing, and related technologies.

  • Author biographies: Kaitao Meng is a post-doctoral researcher at the State Key Laboratory of Internet of Things for Smart City, University of Macau.His interests include ISAC, multi-UAV collaboration, and intelligent reflecting surfaces.
  • Author biographies: Qingqing Wu is an associate professor at Shanghai Jiao Tong University and was recognized in several highly cited and influential researcher listings.
  • Author biographies: Jie Xu is an associate professor at The Chinese University of Hong Kong, Shenzhen, whose interests include UAV communications, edge intelligence, and ISAC.
  • Author biographies: Wen Chen is a tenured professor at Shanghai Jiao Tong University and director of its Broadband Access Network Laboratory.
  • Author biographies: Zhiyong Feng is a professor at Beijing University of Posts and Telecommunications whose interests include wireless architecture, radio resource management, and ISAC.
  • Author biographies: Robert Schober holds the chair for Digital Communication at Friedrich-Alexander University of Erlangen-Nuremberg and works broadly in communication theory and signal processing.
  • Author biographies: A. Lee Swindlehurst is a professor in UC Irvine’s EECS Department and an IEEE Fellow with prior academic and industry experience.
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