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Terahertz Integrated Sensing and Communication-Empowered UAVs in 6G: A Transceiver Design Perspective

Ruoyu Zhang, Wen Wu, Xiaoming Chen, Zhen Gao, Yueming Cai

arXiv:2502.04877v1eess.SP

TL;DR

THz-ISAC-UAVs promise integrated communication and sensing but must address complex propagation, payload limitations, and high-dimensional channel acquisition. This article analyzes the propagation environment and transceiver design, then surveys hybrid beamforming, power-efficient waveforms, and channel-state-information acquisition, while identifying research directions and open problems.

  • Problem

    THz-ISAC-UAV transceivers face complex propagation, severe power and payload constraints, and difficult high-dimensional communication-and-sensing channel acquisition.

  • Method

    The article studies propagation and payload design, then elaborates hybrid beamforming, power-efficient waveform design, and communication-and-sensing channel-state-information acquisition.

  • Results

    The article identifies dominant barriers, transceiver-design peculiarities, three key technologies, and potential research directions for THz-ISAC-UAV.

  • Takeaways & Limitations

    THz-ISAC-UAV research must jointly address energy and hardware efficiency, communication-and-sensing distance, and mobility issues through transceiver-focused designs.

Abstract

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Due to their high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are expected to play a pivotal role in not only communication, but also sensing. Especially by exploiting the ultra-wide bandwidth of terahertz (THz) bands, integrated sensing and communication (ISAC)-empowered UAV has been a promising technology of 6G space-air-ground integrated networks. In this article, we systematically investigate the key techniques and essential obstacles for THz-ISAC-empowered UAV from a transceiver design perspective, with the highlight of its major challenges and key technologies. Specifically, we discuss the THz-ISAC-UAV wireless propagation environment, based on which several channel characteristics for communication and sensing are revealed. We point out the transceiver payload design peculiarities for THz-ISAC-UAV from the perspective of antenna design, radio frequency front-end, and baseband signal processing. To deal with the specificities faced by the payload, we shed light on three key technologies, i.e., hybrid beamforming for ultra-massive MIMO-ISAC, power-efficient THz-ISAC waveform design, as well as communication and sensing channel state information acquisition, and extensively elaborate their concepts and key issues. More importantly, future research directions and associated open problems are presented, which may unleash the full potential of THz-ISAC-UAV for 6G wireless networks.

I. INTRODUCTION

THz-ISAC-UAVs combine UAV flexibility with THz bandwidth and integrated communication-sensing functions, but face complex propagation and stringent payload constraints. The article surveys these challenges, transceiver-design peculiarities, key technologies, and future research directions.

  • UAVs support communication and sensing applications through airborne base stations, access points, relays, localization, detection, and imaging.
  • THz-ISAC integrates communication and sensing hardware, reducing payload size, weight, power consumption, and computational complexity while enabling compact transceivers.
  • THz-ISAC-UAVs face complex wireless propagation, high transceiver power demand, and stringent implementation constraints on moving platforms.
  • The article examines propagation characteristics, antenna design, RF front-end, and baseband processing from a THz-ISAC-UAV transceiver perspective.
  • Three highlighted technologies are hybrid beamforming for ultra-massive MIMO-ISAC, power-efficient waveform design, and communication-and-sensing channel-state-information acquisition.
  • Potential research directions are identified to facilitate THz-ISAC-UAV applications in future 6G wireless networks.

II. PROPAGATION CHARACTERISTIC OF THZ-ISAC-UAV

THz-ISAC-UAV propagation differs from ground-based and lower-frequency channels, requiring propagation analysis to guide transceiver design. Ultra-massive arrays provide high-gain pencil beams but introduce frequency-dependent beam-split effects.

  • Propagation-characteristic analysis is presented as a prerequisite for designing THz-ISAC-UAV systems.
  • Ultra-massive antenna arrays compensate THz attenuation by forming pencil beams with ultra-high array gains.
  • Wide THz bandwidth can cause narrow beams to disperse and split toward different directions across frequencies.

2) Non-Stationary Environment:

High-mobility UAVs create a non-stationary THz propagation environment through Doppler effects, wind-induced motion, and sensing-specific uncertainties. These effects constrain channel coherence and reduce sensing signal quality.

  • 2) Non-Stationary Environment:: High UAV mobility and THz carrier frequencies produce severe Doppler effects and significantly reduce channel coherence time.
  • 2) Non-Stationary Environment:: Wind-induced random UAV movement and rotation generate additional Doppler shifts and further constrain coherence time.
  • 2) Non-Stationary Environment:: Sensing signals experience round-trip propagation loss and Doppler shifts, resulting in lower signal-to-noise ratio.
  • 2) Non-Stationary Environment:: Target size, distance, and radar cross-section introduce additional uncertainties into sensing-channel propagation.
  • 2) Non-Stationary Environment:: Payload design must account for propagation uniqueness together with limits on size, weight, and power consumption.

A. Antenna Design of THz-ISAC-UAV

THz-ISAC-UAV antenna and RF-front-end design must provide directional, isolated, and multifunctional operation within compact payloads. The section highlights antenna technologies, full-duplex isolation, and integrated-circuit RF front ends.

  • A. Antenna Design of THz-ISAC-UAV: THz-ISAC-UAV antennas require sufficiently large gain and high directionality to enhance equivalent isotropic radiated power.
  • A. Antenna Design of THz-ISAC-UAV: Antenna designs use photoconductive, metallic, dielectric, graphene, liquid-crystal, and metamaterial approaches.
  • A. Antenna Design of THz-ISAC-UAV: Full-duplex ISAC antennas require sufficient transmit-receive isolation to prevent receiver saturation and preserve dynamic range for target reflections.
  • B. RF Front-End Design of THz-ISAC-UAV: Immature THz semiconductor transistor technology constrains high-efficiency power amplifiers and high-sensitivity low-noise amplifiers, limiting communication and detection distance.
  • B. RF Front-End Design of THz-ISAC-UAV: IC-based RF-front-end design can integrate THz circuits, share components, and support multifunctional on-chip microsystems.

C. Baseband Signal Processing of THz-ISAC-UAV

Baseband processing must jointly support waveform generation, communications decoding, and target-parameter acquisition under resource- and power-limited UAV payload constraints. Key techniques exploit THz UM-MIMO architectures, joint waveform optimization, and channel and target acquisition in high-mobility environments.

  • Baseband processing generates THz-ISAC waveforms, decodes communication information, and acquires target parameters.
  • Resource-limited UAV payloads require careful coordination between communication and sensing functions and power-limited THz RF devices.
  • Advanced processing should exploit THz UM-MIMO architectures, joint ISAC waveform design, and channel and target-parameter acquisition in high-mobility UAV environments.
  • Power-efficient ISAC waveforms sharing signaling resources are identified as one approach to address communication-and-sensing design conflicts.
  • The paper highlights hybrid beamforming, power-efficient waveform design, and communication-and-sensing channel-state-information acquisition as key THz-ISAC-UAV technologies.

A. Hybrid Beamforming for UM-MIMO-ISAC

Hybrid beamforming combines analog and digital processing to reduce RF-chain requirements for ultra-massive MIMO-ISAC on payload-constrained UAVs. True-time-delay augmentation addresses beam-split effects and supports frequency-dependent wideband beamforming.

  • 1) Concepts:: Hybrid beamforming combines analog and digital processing to reduce RF-chain requirements while maintaining comparable performance for THz-ISAC-UAV.Fully digital architectures require one RF chain per antenna, creating prohibitive hardware complexity for UAV payloads.
  • 1) Concepts:: Thousands of antennas are needed in ultra-massive MIMO to overcome THz communication and sensing distance limitations.
  • 1) Concepts:: Phase-shifter-only hybrid beamforming suffers beam-split effects and additional array-gain loss because its weights and phases are frequency independent.
  • 2) Architectures:: The three coupling cases illustrate that communication and sensing channels can have different coupling relationships in THz-ISAC-UAV systems.
  • 1) Concepts:: A true-time-delay layer generates frequency-proportional beamforming weights for frequency-dependent hybrid beamforming.The described delay-phase architecture inserts true-time delay between RF chains and the phase-shifter network.

3) Algorithms:

THz-ISAC-UAV algorithms must coordinate communication and sensing objectives while accounting for distinct beam requirements and coupled channels. Waveform design can use orthogonal resources or a shared signal according to task priorities and signal characteristics.

  • 3) Algorithms:: Hybrid beamforming algorithms must coordinate sensing scan beams or robust beampatterns with stable directional communication beams.
  • 3) Algorithms:: Algorithm performance is affected by the interrelationship between communication and sensing channels.
  • 3) Algorithms:: Orthogonal communication-and-sensing signals can be separated in temporal, spectral, or spatial domains to avoid mutual interference.
  • 3) Algorithms:: Fully unified waveforms can use one shared signal for both functionalities to improve resource efficiency.
  • 3) Algorithms:: Communication-centric and sensing-centric waveform designs should reflect underlying signal characteristics and task priorities on resource-constrained UAV platforms.

2) Communication-Centric Waveform Design:

Communication-centric waveform design reuses communication signals for sensing, with OFDM offering sensing flexibility but creating PAPR-related power costs. Single-carrier alternatives reduce PAPR, while sensing-centric designs can embed communication bits into known sensing waveforms and use index modulation to improve spectral efficiency.

  • 2) Communication-Centric Waveform Design:: Communication-centric design extracts target information from existing communication waveforms to enable sensing.
  • 2) Communication-Centric Waveform Design:: OFDM provides compatibility with cellular networks and flexible sensing capability but introduces PAPR that can require THz RF-front-end power back-off.
  • 2) Communication-Centric Waveform Design:: Single-carrier signals generally have lower PAPR than OFDM but offer limited data transmission rates.
  • 3) Sensing-Centric Waveform Design:: Sensing-centric design embeds communication information bits into known sensing waveforms while maintaining near-optimal sensing performance and high power efficiency.
  • 3) Sensing-Centric Waveform Design:: Fig. 5 compares target-sensing RMSE, communication achievable rate, and sensing RMSE under UAV location uncertainty across estimation methods and frequency bands.
  • 3) Sensing-Centric Waveform Design:: Index modulation conveys additional information through resource-block indices while retaining low PAPR and improving spectral efficiency.

C. C&S Channel State Information Acquisition

THz-ISAC-UAV requires accurate communication and sensing channel state information despite high-dimensional channels and narrow-beam search overhead. Unified channel acquisition can exploit shared physical parameters, and a tensor-based case study improves estimation and rate performance under constrained RF chains.

  • Challenges: High-dimensional C&S channels and ultra-narrow UM-MIMO beams make channel estimation and target search costly.Large bandwidth and antenna counts increase channel dimensionality, while narrow beams sharply increase the number of required search beams.
  • Concepts: CCSI supports beamforming and decoding, whereas SCSI extracts target parameters from echo signals.Both are important for the communication and sensing functions of THz-ISAC-UAV.
  • Unified acquisition: CCSI and SCSI acquisition can be linked through shared physical parameters such as angles and path gains.Known pilots and probing signals provide the basis for estimating propagation and target information in a unified framework.
  • Unified acquisition: A unified tensor method improves estimation accuracy, sensing resolution, and overhead relative to conventional acquisition approaches.The cited method addresses the joint CCSI and SCSI acquisition problem in a unified tensor framework.
  • Performance evaluation: At 0.15 THz with 3.2 GHz bandwidth, the tensor method achieves rates comparable to perfect CCSI, while compressed sensing suffers rate loss from limited training overhead.The case study uses a 0.1 m array aperture and RF chains equal to the number of users plus targets.
  • Performance evaluation: A 0.1 m UAV-location bias degrades angle and distance estimation, with stronger sensing degradation at THz than at mmWave.The case study reports this degradation under location uncertainty.

VI. FUTURE DIRECTIONS

Future THz-ISAC-UAV research must address unresolved performance limits, lightweight learning under constrained payloads, and metrics suited to multifunctional operation. The highlighted directions emphasize energy efficiency, information freshness, small datasets, and limited computational resources.

  • A. THz-ISAC Performance Metrics and Limits: Fundamental-limit studies should add energy-efficiency metrics for resource-constrained, multifunctional THz-ISAC-UAV payloads.Existing work emphasizes achievable rate, estimation accuracy, and transmit beampattern while generally overlooking energy efficiency.
  • A. THz-ISAC Performance Metrics and Limits: Sensing information freshness remains insufficiently defined, motivating age-of-information metrics alongside communication timeliness measures.AoI is established for communication systems, but its sensing interpretation is not clearly defined in the passage.
  • B. Lightweight Machine Learning-Based Methods: Machine learning can use received signals to infer channel structure and predict future beams from delay, Doppler, or angle information.The described approach establishes a nonlinear mapping from low-dimensional signals to high-dimensional channels.
  • B. Lightweight Machine Learning-Based Methods: Lightweight data-driven or model-driven learning is needed because UAV payloads face limited power, computation, storage, and delay budgets.The passage identifies these constraints as obstacles to directly applying data-driven methods.
  • B. Lightweight Machine Learning-Based Methods: High-speed UAV motion and ultra-narrow UM-MIMO beams create challenges for training-dataset size and transfer learning.These conditions make small-data learning particularly important for UAV-mounted THz-ISAC payloads.

C. Non-Ideal Factors and Interference Mitigation

THz-ISAC-UAV transceivers face non-ideal hardware and synchronization effects that can limit communication and sensing performance. The paper places these issues within a broader transceiver-design treatment spanning antennas, RF front ends, baseband processing, and key enabling technologies.

  • Non-Ideal Factors and Interference Mitigation: THz RF-front-end impairments such as oscillator phase noise and PA nonlinear distortion can limit communication and sensing performance.These impairments introduce complicated nonlinear distortion into UAV transceiver payloads.
  • Non-Ideal Factors and Interference Mitigation: The paper studies THz-ISAC-UAV barriers and techniques across antenna design, RF front ends, and baseband signal processing.Its transceiver perspective also covers hybrid beamforming, waveform design, and C&S channel-state-information acquisition.
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