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Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities

Akram Shafie, Nan Yang, Chong Han, Josep Miquel Jornet, Markku Juntti, Thomas Kurner

arXiv:2207.11021v1eess.SP

TL;DR

Rapidly increasing wireless traffic and 6G data-rate demands motivate research beyond current systems, while end-to-end THzCom remains insufficiently studied. This article presents a holistic review across physical, link, and network layers, covering spectrum management, antennas and beamforming, and integrated 6G technologies. It identifies challenges, advancements, and research opportunities for realizing THzCom in 6G and beyond.

  • Problem

    End-to-end THzCom research remains limited despite THzCom’s potential to support 6G applications requiring very high data rates.

  • Method

    The article holistically examines THz spectrum management, THz antennas and beamforming, and four integrated 6G-enabling technologies across physical, link, and network layers.

  • Results

    The article identifies challenges, key advancements, and opportunities for developing end-to-end THzCom systems in 6G and beyond networks.

  • Takeaways & Limitations

    The paper aims to spur further research into using the THz band for 6G and beyond wireless networks.

Abstract

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The unprecedented increase in wireless data traffic, predicted to occur within the next decade, is motivating academia and industries to look beyond contemporary wireless standards and conceptualize the sixth-generation (6G) wireless networks. Among various promising solutions, terahertz (THz) communications (THzCom) is recognized as a highly promising technology for the 6G and beyond era, due to its unique potential to support terabit-per-second transmission in emerging applications. This article delves into key areas for developing end-to-end THzCom systems, focusing on physical, link, and network layers. Specifically, we discuss the areas of THz spectrum management, THz antennas and beamforming, and the integration of other 6G-enabling technologies for THzCom. For each area, we identify the challenges imposed by the unique properties of the THz band. We then present main advancements and outline perspective research directions in each area to stimulate future research efforts for realizing THzCom in 6G and beyond wireless networks.

I. INTRODUCTION

THzCom is presented as a promising 6G technology because its broad bandwidth and short wavelengths could address escalating traffic demands, but its distinctive propagation losses create major system-design challenges. The article therefore takes a holistic end-to-end view across physical, link, and network layers, emphasizing spectrum management, antennas and beamforming, and integration of four 6G-enabling technologies.

  • By 2030, wireless traffic is predicted to reach 5 zettabytes per month and connected devices could exceed 50 billion.
  • THzCom spans 0.1–10 THz and offers tens to hundreds of gigahertz of bandwidth for applications requiring extremely high data rates.
  • Above 300 GHz, severe spreading loss, channel sparsity, molecular absorption, blockage vulnerability, and strong reflection and scattering losses complicate propagation.
  • The article addresses limited end-to-end THzCom research through a multidimensional analysis of physical, link, and network layer challenges, advancements, and opportunities.
  • Its three focal areas are THz spectrum management, THz antennas and beamforming, and joint integration of four 6G-enabling technologies.
  • Most prior studies considered sub-THz systems from 100 to 300 GHz, whereas this article considers carrier frequencies above 300 GHz with much larger bandwidths.

II. THZ BAND SPECTRUM MANAGEMENT

The section motivates THz spectrum management by linking 6G’s anticipated data-rate requirements to the THz band’s frequency-dependent absorption loss. This loss partitions the band into transmission windows and absorption-coefficient peak regions.

  • 6G networks may require peak data rates up to 1 Tb/s, or 100 times the 5G target.
  • Frequency-dependent absorption loss divides the THz band into ultra-wideband transmission windows and absorption coefficient peak regions.

A. THz Transmission Windows

THz transmission-window allocation must adapt to distance because molecular absorption varies across frequency and becomes more significant over longer links. The section contrasts pulse-based, multi-TW, and multi-band approaches, highlighting efficiency, interference, and fairness trade-offs.

  • For distances below one meter, nearly uniform low absorption permits treating the entire THz band as one band for pulse-based allocation.
  • For longer distances, significant within-band absorption variation makes single-band pulse allocation impractical and motivates carrier-based allocation within transmission windows.
  • Multi-band allocation divides transmission windows into non-overlapping sub-bands, achieving high spectrum efficiency and low complexity when absorption varies strongly within windows.
  • 1) Multi-band-based Spectrum Allocation Scheme:: Inter-band interference from frequency-multiplier harmonics limits multi-band performance, especially when users require multiple adjacent sub-bands.
  • 1) Multi-band-based Spectrum Allocation Scheme:: Distance-aware multi-carrier allocation assigns center-window sub-bands to longer links and edge sub-bands to shorter links to improve throughput fairness.
  • 1) Multi-band-based Spectrum Allocation Scheme:: Adaptive sub-band bandwidth and selective exclusion of high-loss window edges are proposed opportunities, evaluated through simulations with one access point, 10 users, and 50 GHz.

2) Multi-TW-based Spectrum Allocation Scheme:

The multi-TW scheme assigns complete transmission windows to separate high-speed links, but multiuser operation requires sharing limited windows through spatial and temporal multiplexing. Hierarchical bandwidth modulation is identified as one possible approach.

  • 2) Multi-TW-based Spectrum Allocation Scheme:: The multi-TW scheme allocates individual transmission windows to separate links using signals whose bandwidth equals each window’s bandwidth.
  • 2) Multi-TW-based Spectrum Allocation Scheme:: Key open problems include selecting usable bandwidth and modulation order while limiting distance-dependent absorption impairment and designing multi-GHz power amplifiers and transceivers.
  • 2) Multi-TW-based Spectrum Allocation Scheme:: Because available transmission windows are limited, multiuser systems must share the same spectrum through spatial and temporal multiplexing.
  • 2) Multi-TW-based Spectrum Allocation Scheme:: Hierarchical bandwidth modulation can transmit multiple streams simultaneously within one window by adapting symbol duration and modulation order to link distance.

B. THz Absorption Coefficient Peak Regions

Absorption coefficient peak regions can support secure short-range THz links despite their high loss. Distance-adaptive modulation and temporal broadening are presented as mechanisms for improving covertness.

  • B. THz Absorption Coefficient Peak Regions: High absorption loss makes ACPRs unfavorable for ultra-high-data-rate applications but potentially useful for secure short-range THz links.The paper exploits absorption's exponential distance dependence to limit unintended reception.
  • B. THz Absorption Coefficient Peak Regions: Distance-adaptive absorption peak modulation enhances covertness when the illegitimate user is farther from the transmitter than the legitimate user.The scheme adapts transmission within absorption peak regions to the relative link distances.
  • B. THz Absorption Coefficient Peak Regions: A transmit-beamwidth trade-off can balance secrecy against antenna directional gain to compensate for path loss.The beamwidth choice is part of the security-oriented design.
  • B. THz Absorption Coefficient Peak Regions: Temporal broadening within ACPRs can also support secure communications because molecular absorption selectively broadens pulses with distance.The broadening increases exponentially with distance under the described THz propagation behavior.

III. THZ BAND ANTENNAS AND BEAMFORMING

THz's short wavelengths enable compact ultra-massive antenna arrays and high directional gains, while hardware advances are gradually closing the technology gap. Beamforming and device implementations remain central to exploiting these capabilities.

  • III. THZ BAND ANTENNAS AND BEAMFORMING: Short THz wavelengths allow many antennas to fit into small transceiver footprints, forming ultra-massive MIMO arrays.Carefully designed arrays can provide 30-50 dBi directional gains and support short-range multiuser multiplexing.
  • III. THZ BAND ANTENNAS AND BEAMFORMING: Directional gains from THz antenna arrays can compensate for severe spreading and absorption losses.Multi-beam architectures can additionally support massive user multiplexing over short ranges.
  • III. THZ BAND ANTENNAS AND BEAMFORMING: Advances in device technologies are progressively closing the THz technology gap that previously constrained on-chip signal generation, modulation, and radiation.The paper identifies this progress as a basis for increased THz-band utilization.
  • III. THZ BAND ANTENNAS AND BEAMFORMING: Current THz systems mainly use frequency up-conversion, while many testbeds remain single-input single-output with external high-gain antennas.The passage contrasts established implementations with more recent fully digital antenna-array developments.
  • III. THZ BAND ANTENNAS AND BEAMFORMING: Graphene is proposed for THz sources, modulators, antennas, and arrays because it can support surface plasmon polariton waves.The proposal targets operation at frequencies as low as 100 GHz in theory.

B. Beamforming

THz hybrid beamforming must address sparse, highly correlated channels and channel squint while retaining spectral and energy efficiency. Candidate architectures combine widely spaced subarrays, adjustable connections, and true-time delays.

  • B. Beamforming: Hybrid beamforming is attractive for THz communications because it combines low hardware complexity with high spectral and energy efficiency.Its applicability is difficult because THz channel characteristics impose distinct design challenges.
  • B. Beamforming: Widely spaced subarrays can reduce inter-subarray correlation and improve multiplexing in sparse THz channels.The design groups antennas into subarrays separated by hundreds of wavelengths.
  • B. Beamforming: Widely spaced architectures can also act as frequency diverse arrays for decoupling legitimate and illegitimate-user channels and enhancing security.Their spacing prevents one RF chain from connecting to multiple subarrays simultaneously, producing a block-diagonal analog beamforming matrix.
  • B. Beamforming: True-time delays can replace phase shifters to overcome THz channel squint, while adjustable connections accommodate dynamic channel requirements.A comprehensive architecture can combine widely spaced subarrays, dynamic array-of-subarray connections, and TTDs.

A. Out-of-band Channel Estimation

THz channel estimation is challenged by blockage and high-dimensional sparse channel representations. Out-of-band estimation translates lower-frequency spatial-correlation measurements to the THz band, while multi-connectivity addresses blockage-related reliability.

  • A. Out-of-band Channel Estimation: THz channel-state information can be blocked, and estimating highly dimensional sparse channels can impose substantial overhead.These properties complicate transmission and signal-processing design.
  • A. Out-of-band Channel Estimation: Out-of-band estimation derives THz spatial-correlation estimates from measurements collected at lower frequencies.The translation is challenged by different antenna-array sizes within the same aperture across frequencies.
  • A. Out-of-band Channel Estimation: Multi-connectivity lets users communicate with multiple APs simultaneously to mitigate reliability degradation caused by blockages.It can also combine with hybrid beamforming to form distributed beamforming architectures.
  • A. Out-of-band Channel Estimation: The connection-probability analysis models APs and human blockers as spatial point processes with specified blocker geometry and mobility.The figure considers AP density 0.05 m^-1, blocker heights 1.7 m, and widths 0.6 m and 0.3 m.
  • A. Out-of-band Channel Estimation: Multi-connectivity produces substantially higher connection probability than single connectivity, especially with dense blockages and more associated APs.The simulations identify a pronounced reliability improvement from the strategy.
  • A. Out-of-band Channel Estimation: Multi-connectivity complicates system design through added interference, centralized processing and synchronization needs, backhaul demands, scheduling requirements, and AP switching.The paper identifies packet duplication or splitting as possible scheduling directions.

C. Reconfigurable Intelligent Surfaces

RISs steer incident THz beams to create virtual LoS links and can mitigate blockage vulnerability, but RIS-assisted THzCom remains at an early research stage with substantial design challenges.

  • RISs adjust incident-wave phase shifts to steer beams and transform LoS THz channels into rich multipath, software-defined channels with virtual LoS links.
  • RISs can improve THzCom reliability by mitigating signal blockage, including through active RISs that act as distributed THz access points or repeaters.
  • Active RISs can form virtual multi-connectivity links when few access points are available or access points are not nearby.
  • RIS-assisted THzCom requires low-loss reflector materials, validated channel models, practical channel-state acquisition, and dedicated infrastructure control protocols.At 1 THz and beyond, graphene is identified as a possible reflector material because of its tunable properties and low reflection coefficient.

D. Machine Learning Aided THzCom

Machine learning is presented as a flexible tool for near-real-time THzCom optimization, while data, non-stationarity, and distributed-intelligence constraints remain unresolved.

  • ML is proposed as a flexible and scalable tool for proactively optimizing THzCom resources under uncertain channels and near-real-time operation requirements.
  • THzCom ML research must address difficult training-data acquisition caused by blockages and the frequency-dependent nature of THz channels.
  • Highly non-stationary THz training data challenges current ML algorithms, which remain in their early stages for non-stationary data.
  • Distributed network intelligence must account for device power, memory, computation, security, privacy, data-transfer cost, and training complexity.
  • ML-aided THzCom is projected to improve channel estimation, beam tracking, blockage prediction, interference mitigation, and resource allocation.

BIOGRAPHIES

The biographies identify the authors’ affiliations and research interests across terahertz communications, wireless networks, signal processing, information theory, propagation, and network planning.

  • Akram Shafie is pursuing a Ph.D. at the Australian National University, researching terahertz, millimeter-wave, and machine-learning wireless communications.
  • Nan Yang is an Associate Professor at the Australian National University, researching terahertz communications, ultrareliable low-latency communications, cyber-physical security, and molecular communications.
  • Chong Han is an Associate Professor at the University of Michigan-Shanghai Jiao Tong University Joint Institute, researching terahertz and millimeter-wave networks and electromagnetic nanonetworks.
  • Josep Miquel Jornet is an Associate Professor at Northeastern University, researching terahertz-band communications and wireless nano-bio-communication networks.
  • Markku Juntti and Thomas Kürner are professors whose research spans wireless signal processing, communication theory, propagation modeling, channel characterization, and radio network planning.Both are identified as Fellows of the IEEE.
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