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Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization

Hadi Sarieddeen, Nasir Saeed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

arXiv:1909.10462v2eess.SP

TL;DR

THz communications face propagation and device challenges, while their broader value depends on integrating communication with sensing, imaging, and localization. The paper reviews these applications, presents proof-of-concept approaches, and discusses shared or dedicated resource allocation and machine learning. It concludes that merged THz applications can support environment-aware performance and real-time multifunctional operation, while implementation details remain premature because no THz network has yet been built.

  • Problem

    The paper addresses how THz communications can move beyond high data rates by integrating sensing, imaging, and localization despite propagation and device challenges.

  • Method

    The paper reviews THz application peculiarities, develops proof-of-concept simulations, and discusses implementation through shared or dedicated resources and machine learning.

  • Results

    The paper shows that multiple THz applications can be realized seamlessly in real time and can support environment-aware communication performance.

  • Takeaways & Limitations

    Merging THz sensing, imaging, and localization provides a direction for wireless communication research beyond 5G use cases.

Abstract

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Terahertz (THz)-band communications are celebrated as a key enabling technology for next-generation wireless systems that promises to integrate a wide range of data-demanding and delay-sensitive applications. Following recent advancements in optical, electronic, and plasmonic transceiver design, integrated, adaptive, and efficient THz systems are no longer far-fetched. In this paper, we present a progressive vision of how the traditional "THz gap" will transform into a "THz rush" over the next few years. We posit that the breakthrough that the THz band will introduce will not be solely driven by achievable high data rates, but more profoundly by the interaction between THz sensing, imaging, and localization applications. We first detail the peculiarities of each of these applications at the THz band. Then, we illustrate how their coalescence results in enhanced environment-aware system performance in beyond-5G use cases. We further discuss the implementation aspects of this merging of applications in the context of shared and dedicated resource allocation, highlighting the role of machine learning.

I. INTRODUCTION

THz communications are emerging as devices and transceiver technologies improve beyond the traditional THz gap. The paper argues that sensing, imaging, and localization, combined with communication, will broaden THz applications and system performance.

  • The traditional THz gap arose from a lack of compact and efficient devices, restricting applications mainly to imaging and sensing.
  • Recent advances in THz signal generation, modulation, and radiation make communication-based THz use cases foreseeable.
  • Electronic and photonic transceiver platforms offer different advantages: electronics generate higher power, while photonics provide higher data rates.Electronic platforms generate 100 µW to mW, compared with typical photonic levels of tens of µW.
  • The paper advocates merging THz sensing, imaging, and high-resolution localization with communications to boost future system performance and enable novel applications.

II. THZ COMMUNICATIONS

THz communications offer very large bandwidths and directional links for 6G, while differing from VLC in propagation behavior and deployment constraints. The section presents THz as complementary to optical communications rather than simply a faster alternative.

  • THz communications are considered for 6G because the band offers ultra-high bandwidth communication paradigms.
  • THz systems can achieve terabit-per-second rates through large bandwidths without additional spectral-efficiency techniques.
  • THz communications provide higher link directionality and reduced susceptibility to free-space diffraction and inter-antenna interference because of shorter wavelengths.
  • Compared with optical signals, THz signals are less affected by ambient light, atmospheric turbulence, scintillation, cloud dust, and temporary light-intensity variations.
  • A heterogeneous mmWave/THz and VLC setup can enhance availability because VLC is unsuitable for uplinks and experiences different blockage conditions.

B. Challenges and Solutions

THz communications face severe propagation, blockage, modeling, and hardware challenges. Dense antenna arrays, hybrid beamforming, and configurable AoSA architectures are presented as routes toward improved range, efficiency, and flexibility.

  • High propagation losses, power limitations, molecular absorption, misalignment, blockage, and scarce realistic channel models constrain THz communications.The channel is dominated by LoS and a few NLoS paths because reflection losses are significant while diffraction and scattering losses are high.
  • 1) UM-MIMO and RIS systems:: Very dense UM-MIMO arrays can provide beamforming gains that compensate for increasing path loss within compact footprints.
  • 1) UM-MIMO and RIS systems:: AoSA architectures enable hybrid beamforming that trades beamforming gain against multiplexing, improving communication range and spectral efficiency.
  • 1) UM-MIMO and RIS systems:: Configurable AoSA systems support multi-carrier communication and dynamically varied spatial, index, and antenna-level modulation functions.

2) Waveform and modulation:

THz waveform design must account for distance-dependent molecular absorption and severe baseband bandwidth mismatch. The section also connects low-complexity processing with sensing methods based on THz spectral and temporal responses.

  • 2) Waveform and modulation:: Distance-dependent absorption requires waveform, power, and beamforming optimization over available absorption-free spectral windows.THz-specific multi-carrier designs other than OFDM are favored because OFDM is complex and has a high peak-to-average power ratio.
  • 2) Waveform and modulation:: The terabit-per-second THz channel exceeds digital baseband capacity limited by clock speeds of a few GHz, motivating efficient parallelizable processing.
  • Fig. 2 evaluates 1%-accuracy water-vapor sensing over 5 m as SNR and the number of uniformly distributed 1–2 THz carriers vary.
  • THz-TDS probes samples with broadband pulses and derives optical properties from temporal profiles measured with and without the sample.
  • THz-TDS imaging measures waveforms across multiple object positions to extract amplitude, phase, or combined spectral information and produce high-contrast images.
  • Carrier-based gas sensing estimates channel responses from selected high-frequency signals and matches absorption spikes against HITRAN molecular spectra.

IV. THZ LOCALIZATION

THz localization research extends beyond ranging toward robust network-level position estimation using high-resolution measurements and MDS-based mapping.

  • Localization motivation: 6G mmWave and THz networks are envisioned to provide centimeter-level localization accuracy beyond conventional GPS and cell multilateration.High-frequency localization can use SLAM because relatively slow-moving objects allow time to process high-resolution THz measurements.
  • Existing approaches: Existing THz-specific work includes weighted least-squares localization using dielectric-resonator anchors and round-trip time-of-flight ranging.Round-trip time of flight avoids synchronization issues in time-based ranging methods.
  • MDS-based localization: MDS estimates sensor-node locations from pairwise distances corrupted by distance-dependent additive Gaussian noise.The distances may come from THz ranging schemes, and MDS maps nodes into a chosen dimensional space.
  • MDS-based localization: A 20-sensor example with four corner anchors illustrates MDS initialization by comparing actual node locations with an initial coordinate-free network map.The initial MDS map is centered around the origin rather than expressed in actual coordinates.
  • Open challenge: Robust and accurate THz localization algorithms remain insufficient because prior schemes primarily address ranging rather than broader network localization.The paper positions MDS as an approach to fill this gap.

V. 6G AND BEYOND USE-CASES

Beyond-5G THz use cases combine high-rate communication with sensing, imaging, and localization to support environment-aware wireless intelligence across multiple industries.

  • 6G vision: 6G visions associate THz technology with wireless cognition remoting, information shower, accurate localization, sensing, and imaging.These capabilities are discussed for transportation, robotics, augmented reality, entertainment, and health care.
  • Multifunctional systems: Large THz bandwidths and massive antenna arrays can enhance communication, sensing, imaging, and localization simultaneously.Machine-type communication densification further contributes to this multifunctional performance.
  • Multifunctional systems: 3D beamforming can support precise positioning and tracking while creating physical-space images by monitoring signals across many angles.The technique was originally proposed to extend communication range and improve spectrum efficiency.
  • Application scope: The paper identifies indoor and outdoor mid-range mobile wireless communications as the central target beyond high-capacity backhaul and data-center links.This scope distinguishes mobile use cases from already-advocated fixed high-capacity applications.

A. Vehicular and Drone-to-Drone Communications

Vehicular and drone communications illustrate how THz bandwidth can support demanding services while sensing, localization, and reflection assistance address reliability and positioning needs.

  • Vehicular communications: Vehicular applications require stringent communication targets, including 50 Mbps and 50 ms for see-through vision and bird’s eye view.Automated overtake additionally demands 10 ms delay and 99.999% reliability.
  • Vehicular communications: High bandwidths and sufficiently dense networks can provide high reliability despite uncertainty in the THz channel.The passage cites THz virtual reality as an example supporting this claim.
  • Drone-to-drone communications: THz drone-to-drone links enable broadband FANET communication over large areas with greater flexibility than FSO’s stringent pointing and acquisition requirements.Operating drones at THz would require millimeter-level positioning accuracy.
  • Reflection-assisted communications: THz sensing, imaging, and localization can improve RIS route selection, environment-aware beamforming, and alternative-path discovery.RIS-assisted non-line-of-sight imaging and localization can support rescue, surveillance, autonomous localization, and navigation.
  • Reflection-assisted communications: Surface-material sensing can identify better non-line-of-sight routes by detecting reflection-induced changes in signal characteristics.Real-time environment maps guide intelligent beamforming with reflecting surfaces.

VI. IMPLEMENTATION ASPECTS

THz multifunctional networks remain an emerging implementation challenge, requiring adaptive AoSA-based hybrid processing and future joint optimization of communications, sensing, imaging, and localization.

  • Implementation outlook: No THz network, including a multifunctional network, has yet been built, so specific implementation details remain premature.The paper identifies joint algorithm and hardware design optimization as an expected near-term research topic.
  • System architecture: The typical THz system model uses adaptive AoSAs at the transmitter to serve multiple receiving devices.Each subarray can be tuned to a frequency or assigned a modulation type.
  • System architecture: Hybrid signal processing separates digital and analog domains through DAC and ADC blocks.The architecture includes dedicated RF chains feeding each subarray after DAC and before ADC.
  • Signal processing: Highly directional pencil beams reduce neighboring-subarray coupling and limit baseband precoding primarily to subarray utilization.The system requires very high-bandwidth, low-resolution ADCs and DACs.

A. Piggybacked Implementation

THz systems can combine sensing, imaging, and localization with communications through shared resources, but some applications require dedicated allocations because their frequency and coordination needs differ.

  • A. Piggybacked Implementation: Sensing, imaging, and localization can piggyback on THz communications by sharing space, time, and frequency resources across uplink, downlink, or both.Electronic smelling can identify gaseous components through channel-estimation variations regardless of the modulated data.
  • A. Piggybacked Implementation: Dedicated resource allocation is necessary when sensing and imaging require carriers tuned to absorption spectra that cannot carry information.Higher frequencies with congested absorption spectra favor sensing even though frequencies much above 1 THz are unlikely to support communications.
  • A. Piggybacked Implementation: Careful frequency planning prevents harmonic-product overlap when multiple THz applications are supported.Application-specific frequency needs make complete resource sharing impractical in some deployments.

C. Role of Machine Learning

Machine learning is positioned as a tool for improving THz communications and processing the large data volumes generated by integrated applications, while deployment must address health and privacy concerns.

  • C. Role of Machine Learning: Machine learning can improve THz spectrum utilization by replacing conventional channel estimation and channel coding in UM-MIMO systems.It is also identified for beamforming and efficient data detection in generalized index modulation schemes.
  • C. Role of Machine Learning: THz radiation raises health concerns because heating is considered the main risk, while possible skin-cancer effects remain uncertain.The stated risk is confined to skin-tissue heating because THz radiation does not penetrate the body.
  • C. Role of Machine Learning: High-resolution THz sensing, imaging, and localization create privacy risks through distant imaging, including potential misuse at user equipment or across the network.The paper states that these concerns should be addressed in both software and hardware, especially alongside machine learning.
  • C. Role of Machine Learning: The paper reports that multiple THz applications can be realized seamlessly in real time when communications, sensing, imaging, and localization are merged.This conclusion follows a review of application peculiarities and proof-of-concept simulations.

BIOGRAPHIES

The biographies identify the paper’s authors as researchers with backgrounds spanning communications, signal processing, navigation, mathematics, and wireless-system analysis.

  • BIOGRAPHIES: Hadi Sarieddeen is a KAUST postdoctoral fellow whose interests include communication theory and signal processing for wireless communications.He holds a Ph.D. in electrical and computer engineering from the American University of Beirut.
  • BIOGRAPHIES: Nasir Saeed is a KAUST postdoctoral fellow whose interests include cognitive radio networks and related wireless technologies.His training includes satellite navigation and electronics and communication engineering.
  • BIOGRAPHIES: Tareq Y. Al-Naffouri is a KAUST electrical-engineering professor whose research interests include sparse, adaptive, and statistical signal processing.His degrees span mathematics and electrical engineering, including a Ph.D. from Stanford University.
  • BIOGRAPHIES: Mohamed-Slim Alouini is a KAUST electrical-engineering professor focused on modeling, design, and performance analysis of wireless communication systems.He previously served on faculties at the University of Minnesota and Texas A&M University at Qatar.
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