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

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

###### Abstract

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

We live in an era of remarkable expansion of wireless data traffic. Although
the recently launched fifth-generation (5G) networks can offer significant
advancements compared to 4G Long-Term Evolution, such networks are inherently
limited in supporting the data and connection demands in 2030s, since these
demands go far beyond the initial premise of 5G networks. Specifically, the
International Telecommunication Union (ITU) predicts that by 2030, the global
wireless data traffic will reach an astonishingly 5 zettabytes per month and
the number of connected devices might surge beyond 50 billions. This has
prompted industries and academia to look beyond 5G and conceptualize the
sixth-generation (6G) networks [1].

Among various cutting-edge and promising solutions, terahertz (THz)
communications (THzCom), which refers to the communications in the ultra-wide
THz band ranging from 0.1 to 10 THz, is envisioned as a highly promising
technology for 6G and beyond era [2]. In particular, the huge available
bandwidths in the order of tens up to a hundred gigahertz (GHz) and extremely
short wavelengths offer enormous potentials to alleviate the spectrum scarcity
and break the capacity limitation of 5G networks, thereby enabling emerging
applications that demand an explosive amount of data, such as holographic
telepresence, extended reality, and ultra high-speed wireless backhaul. Thus,
similar to that millimeter wave (mmWave) communications was first envisioned
and then utilized for 5G, THzCom will inevitably play an indispensable role in
developing 6G and beyond wireless networks [3].

Despite the prospects, THz band, especially the carrier frequencies above 300
GHz, brings new challenges that have never been addressed at sub-6 GHz and
even mmWave frequencies [4]. Particularly, in addition to the severe spreading
loss and high channel sparsity, the THz band is characterized by the unique
molecular absorption loss which is frequency and distance dependant. Moreover,
the very short wavelength of THz signals makes THz signal propagation highly
vulnerable to blockages, since objects with small dimensions can act as
impenetrable blockers at THz frequencies. Furthermore, high reflection and
scattering losses attenuate the non-line-of-sight (nLoS) rays significantly,
triggering the need for line-of-sight (LoS) links for reliable transmission.
All these phenomena lead to distinctive propagation characteristics at the THz
band, which necessitates the design and development of new communications and
signal processing mechanisms for THzCom.

Figure 1: Illustration of the three pivotal areas for the development of end-
to-end THzCom systems.

Following the recent breakthroughs on THz hardware technologies, the research
on THzCom has accelerated rapidly in recent years and primarily focused on
channel measurement and modeling, device technologies, experimental results,
and standardization, e.g., [5, 4]. Despite such research has uniquely advanced
our understanding of THz technologies, the research on end-to-end THzCom
systems is still limited. For example, a recent study [6] examined the end-to-
end THzCom systems, with a focus on the network layer challenges. Differently,
this article provides a holistic and multidimensional view on the challenges,
key advancements, and opportunities of end-to-end THzCom systems in the 6G and
beyond era, with the primary focus on physical, link, and network layers. To
this end, we examine three pivotal areas of THzCom which will pave the way for
implementing end-to-end THzCom systems in the 6G and beyond era. Specifically,
as depicted in Fig. 1, we discuss:

  * •

THz band spectrum management to unlock the inherent potentials offered by the
huge bandwidth at the THz band.

  * •

THz band antennas and beamforming to reap the benefits of processing THz
signals which have small wavelengths.

  * •

Joint integration of four 6G-enabling technologies for THzCom to materialize
end-to-end THzCom systems for achieving various QoS targets anticipated by 6G
and beyond networks.

The first two key areas explore the inherent benefits of the THz band and the
third key area discusses the integration of four 6G-enabling technologies into
THzCom. We note that most of previous relevant studies have discussed THzCom
systems operating at the sub-THz band, i.e., the carrier frequency from 100
GHz to 300 GHz, with bandwidth of up to several tens of GHz, e.g., the first
THzCom standard targeting 6G – IEEE 802.15.3 [5]. Differently, in this
article, we consider a much broader scope by discussing the effects relevant
to THzCom systems operating at carrier frequencies above 300 GHz with much
larger bandwidths. A brief summary of the challenges, key advancements, and
opportunities discussed in this paper are presented in Table I.

##  II THz Band Spectrum Management

The data rate demands of 6G networks are anticipated to be much higher than
what the state-of-the-art wireless systems can offer, e.g., 6G networks would
demand the peak data rate of up to one terabit per second (Tb/s), which is 100
times that of 5G [7]. To meet this demand, the enormous potential offered by
the huge available bandwidths of the THz band must be explored. The frequency-
dependent nature of absorption loss divides the entire THz band into ultra-
wideband transmission windows (TWs) and absorption coefficient peak regions
(ACPRs), as shown in the left of Fig. 1. We next discuss the challenges
brought by utilizing the spectra in TWs and ACPRs, and present promising
future research directions.

TABLE I: Summary of the challenges, key advancements, and opportunities discussed in this paper. Key Area |  Challenges |  Key Advancements |  Opportunities/ Future Research Directions
---|---|---|---
THz band Spectrum Management |  $\diamond$ Frequency mapping $\diamond$ Distance and frequency dependency of molecular absorption loss $\diamond$ Inter-band interference (IBI) $\diamond$ Temporal broadening effect $\diamond$ Impact of the pressure, temperature, and relative humidity condition of propagation environment |  $\diamond$ Single-band pulse-based spectrum allocation $\diamond$ Multi-band-based spectrum allocation $\diamond$ Distance-aware multi-carrier (DAMC) based spectrum allocation $\diamond$ Hierarchical bandwidth modulation (HBM) $\diamond$ Distance-adaptive absorption peak modulation (DA-APM) |  $\diamond$ Design of power amplifiers and transceivers that are capable of handling multi-GHz bandwidths $\diamond$ IBI modelling, analyzing, suppression, and elimination $\diamond$ Multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB) $\diamond$ Optimally avoiding spectra at the edges of transmission windows (TWs) $\diamond$ HBM when (i) the available bandwidths of different links are similar and (ii) the number of transmission links are higher than two $\diamond$ Secure communications exploiting the temporal broadening effect $\diamond$ Dynamic spectrum allocation
THz band Antennas and Beamforming |  $\diamond$ Device technology and circuit design $\diamond$ Low multiplexing gain $\diamond$ Channel squit effect $\diamond$ Sparse channel $\diamond$ Poor multiplexing gain $\diamond$ Dynamic channel |  $\diamond$ Frequency up-converting systems  $\diamond$ Spatial multiplexing designs $\diamond$ Widely-spaced hybrid beamforming architectures (HBAs) $\diamond$ Dynamic array-of-subarray (DAoS) HBAs |  $\diamond$ Graphene technology $\diamond$ Low-cost and energy-efficient nano-transceiver design $\diamond$ Channel models with spherical wave propagation $\diamond$ THz true-time delays (TTDs) $\diamond$ Unified HBAs
6G-enabling Technologies for THzCom |  Out-of-band Channel Estimation |  $\diamond$ Accurate channel estimation $\diamond$ Mismatch in the angles-of arrival and the angle spread between lower frequencies and THz signals |  $\diamond$ Out-of-band for millimeter wave (mmWave) communications, i.e., estimating the mmWave channels using sub-6G channel measurements |  $\diamond$ Exploit the structure of the spatial correlation matrix along with interpolation/extrapolation techniques to determine the THz band correlation matrix $\diamond$ Exploit the theoretical expressions for the THz band correlation in conjunction with the measured lower frequency correlation to determine the THz band correlation matrix
Multi- Connectivity (MC) |  $\diamond$ Reliability degradation caused by blockages $\diamond$ Cell-free architecture with centralized controlling, data processing, and combining strategies. $\diamond$ Increase in interference |  $\diamond$ Theoretical evaluation of the benefits of MC |  $\diamond$ Jointly deployment of MC and HBAs $\diamond$ Dynamic AP selection strategies $\diamond$ “Lightweight” solutions, such as the reactive MC strategy $\diamond$ Interference management and mitigation techniques $\diamond$ Promising packet scheduling methods for latency reduction, e.g., packet duplication and splitting
RIS |  $\diamond$ Reliability degradation caused by blockages $\diamond$ Reflector array material selections $\diamond$ RIS channel characterization |  $\diamond$ Design of RIS-aided THzCom systems |  $\diamond$ Use of both passive and active RIS $\diamond$ Form virtual MC links with RIS $\diamond$ Graphene for RIS $\diamond$ Experimental validation of THz band RIS channels
Machine Learning (ML) |  $\diamond$ Near-real-time operation requirement $\diamond$ Computational complexity $\diamond$ Data acquisition for the ML-training process |  $\diamond$ Development of different ML approaches for wireless communications systems |  $\diamond$ Techniques to process highly non-stationary THz band training data $\diamond$ Efficiently distribute intelligence in the network

###  II-A THz Transmission Windows

For very short transmission distances (less than one meter), the absorption
loss is very low and remains almost the same within the whole THz band. Due to
this phenomenon, single-band pulse-based spectrum allocation schemes, where
the whole THz band is considered as a single band, as depicted in case (i) of
Fig. 2, can be used for applications with very short transmission distances,
e.g., nanonetworks and device-to-device communications [8]. Although such
schemes can theoretically achieve Tb/s data rates using one-hundred-femto
second-long pulses, it is challenging to design low-cost and energy-efficient
nano-transceivers that can support smart physical- and link-layer solutions.

When transmission distances are relatively long, the molecular absorption
varies significantly within the THz band, making it impractical to use single-
band pulse-based spectrum allocation schemes. This requires us to explore
efficient spectral allocation strategies, based on carrier-based modulation
schemes, for utilizing the bandwidths available within TWs towards micro- and
macro-scale THzCom applications. In THz band carrier-based systems, the to-be-
transmitted data is first modulated into single-carrier waveforms and/or
pulses and then the modulated signals are upshifted to higher THz band
frequencies via cascaded frequency multipliers. We next discuss two carrier-
based spectrum allocation schemes for THzCom systems.

####  II-A1 Multi-band-based Spectrum Allocation Scheme

In this scheme, as depicted in case (iii) of Fig. 2, the TWs are divided into
a set of non-overlapping sub-bands whose bandwidths are reasonably low as
compared to the bandwidths of TWs. Then, these sub-bands are exploited to
serve a single user or multiple users that simultaneously demand ultra-high
data rates. By doing so, the multi-band-based scheme achieves high spectrum
efficiency (SE) and low complexity, especially when the absorption loss varies
significantly within TWs.

The inter-band interference (IBI) is an important performance limiting factor
in the multi-band-based spectrum allocation scheme, especially when each user
demands multiple sub-bands to satisfy its QoS requirements. The IBI at the THz
band is caused by the power leakage from adjacent sub-bands, due to harmonics
generated in frequency multiplying chains during frequency upshifting. Given
the high bandwidth observed at the THz band, sub-bands can be well spaced to
avoid the effect of IBI. Hence, THzCom systems adopting well-spaced sub-bands
can achieve much higher data rates than those envisioned by the state-of-the-
art mmWave systems. Despite so, the need to fully utilize the bandwidths
available in TWs may arise, in order to satisfy the QoS requirements of 6G and
beyond networks. Due to this, when sub-bands are close to each other, the
impact of IBI needs to be precisely modeled, analyzed, and then suppressed or
eliminated to reap the benefits of multi-band-based schemes. It is noted that
the Gaussian approximation has been widely used to model IBI at sub-6 GHz and
mmWave frequencies, assuming that neighboring sub-bands have similar channel
quality [9]. However, the applicability of this approximation at the THz band
needs to be re-examined, since the adjacent THz sub-bands exhibit different
channel qualities, due to absorption loss. As for IBI suppression, time-domain
front-end filtering, subcarrier weighting, windowing, and orthogonal precoding
methods are worth careful investigation. Furthermore, owing to the narrow
beams observed at the THz band, beam management solutions can be developed to
overcome IBI in multiuser THzCom systems such that the adjacent bands are
assigned to transmission links that are located far apart.

When considering multi-band-based spectrum allocation in multiuser THzCom
systems, novel challenges are caused by the distance-dependent nature of
absorption loss. To address such challenges, distance-aware multi-carrier
(DAMC) based spectrum allocation schemes can be utilized to improve the
throughput fairness among users in multiuser THzCom systems [9]. In DAMC-based
spectrum allocation schemes, the sub-bands existing in the center region of
the TW are assigned to the links with longer distances, while the sub-bands
existing at the edges of the TW are assigned to the links with shorter
distances. We note that the state-of-the-art DAMC-based multi-band spectrum
allocation schemes have only considered equal sub-band bandwidth (ESB), where
the TW is divided into sub-bands with equal bandwidth. However, it would be
beneficial to explore novel multi-band spectrum allocation schemes with
adaptive sub-band bandwidth (ASB) to improve the SE, by dividing the TW into
sub-bands with unequal bandwidths. Moreover, since absorption loss is very
high at edge sub-bands, it would be highly beneficial if some spectra at the
edges of TWs are not used during spectrum allocation. To illustrate the
advantages of the DAMC principle and ASB in multiuser THzCom systems, we use
Monte Carlo simulations to plot the aggregated multiuser data rate in Fig. 3.
In this figure, we consider a three-dimensional (3D) THzCom system where an
access point (AP) at the center of the indoor environment serves 10 users
using 50 GHz spectrum in the first TW above 1 THz. As shown in this figure,
DAMC-based spectrum allocation schemes achieve a profound data rate
improvement compared to random spectrum allocation schemes. Also, the use of
ASB brings noticeable data rate improvement, compared to the use of ESB.

####  II-A2 Multi-TW-based Spectrum Allocation Scheme

In this scheme, individual TWs are fully allocated to separate high-speed
communications links while exploring wideband signals with bandwidths equal to
those of TWs, as shown in case (ii) of Fig. 2 [8]. Open problems related to
this scheme include: Determining the optimal usable bandwidth and modulation
order that guarantee very high throughput while ensuring the minimal
impairment caused by the distance-dependent nature of absorption loss within
TWs, and designing power amplifiers and transceivers that are capable of
handling multi-GHz bandwidths.

When this scheme is adopted in multiuser THzCom systems, the same spectrum
needs to be shared among multiple users, as shown in case (ii) of Fig. 2,
since the number of available TWs within the entire THz band is limited. This
necessitates the exploration of spatial and temporal multiplexing strategies.
Motivated by this, hierarchical bandwidth modulation (HBM) can be explored,
where multiple data streams are transmitted over links with different
distances at the same time and within the same TW, by simultaneously adapting
both symbol duration and modulation order [8]. Notably, the symbol duration
for the links with shorter distances and larger available bandwidths needs to
be smaller than that for the links with longer distances.

Figure 2: Illustration of bandwidth allocation for Case (i) single-band pulse-
based schemes (top), Case (ii) multi-TW-based schemes (middle), and Case (iii)
multi-band-based schemes (bottom).

###  II-B THz Absorption Coefficient Peak Regions

Conventionally, ACPRs are not favorable for applications that demand ultra-
high data rates, due to the high absorption loss experienced at the spectra in
ACPRs. However, the highly exponential nature of absorption loss with
transmission distance in ACPRs can be exploited to materialize secure short
range THzCom links. Recently, the distance-adaptive absorption peak modulation
(DA-APM) has been proposed to enhance covertness when the illegitimate user is
located further than the legitimate user, by dynamically modulating signals at
ACPRs [10]. Inspired by this, open problems related to DA-APM include:
Selection of appropriate operating frequency and bandwidth within the ACPR
based on the distance of the legitimate link and the required level of secrecy
performance, signal design which guarantees the smoothness of its power
spectral density to avoid local power leakage and enhance security, and the
determination of the optimal transmit beamwidth such that a compromise between
secrecy and antenna directional gains is achieved to compensate for path loss.

Apart from DA-APM, the impact of temporal broadening within ACPRs can also be
exploited for secure communications, when the illegitimate user is located
further than the legitimate user. It is noted that at the THz band, temporal
broadening, i.e., pulse broadening in the time domain, is mainly caused by the
frequency selective nature of molecular absorption loss that is significant
within ACPRs. Specifically, since temporal broadening increases exponentially
with distance and the molecular absorption coefficient, we need to determine
appropriate operating frequency, bandwidth, and time interval between two
consecutive pulses, based on the distance of the legitimate link. This ensures
that non-overlapping broadened pulses are detected at the legitimate user, but
overlapping broadened pulses are detected at the illegitimate user, which
enhances the secrecy performance.

Figure 3: Aggregated multiuser data rate versus the power budget, where
$B_{\textrm{max}}$ is the upper bound on the sub-band bandwidth. An indoor
environment of size $20~{}\textrm{m}\times 20~{}\textrm{m}$ is considered,
where the AP and users are equipped with directional antennas with
$25~{}\textrm{dBi}$ gains.

##  III THz Band Antennas and Beamforming

The very short wavelength of THz signals enables the integration of a huge
number of antennas into transceivers in a very small footprint to form ultra-
massive multiple-input multiple-output (UM-MIMO) antennas [11]. It is
important to note that such antenna arrays with carefully designed signal
processing algorithms can deliver extremely high directional gains, e.g.,
30-50 dBi [3]; thus, they can be utilized to compensate for the severe
spreading and absorption losses experienced at THz band. Moreover, multi-beam
architectures can be leveraged for massive user multiplexing in the short
range. In the following, we discuss the challenges related to device
technologies and a pivotal signal processing algorithm in THzCom systems,
namely, beamforming, and present promising future research directions.

###  III-A Device Technology

For many years, the challenges associated with on-chip generation, modulation
and radiation of THz signals has limited the utilization of the THz band.
However, the THz technology gap is progressively being closed, thanks to the
major advancements across different device technologies [11]. Today, the
majority of THzCom systems rely on frequency up-converting systems. Different
processes can be utilized to build such up-converters, ranging from silicon
complementary metal-oxide-semiconductor (CMOS) at frequencies of up to 200-300
GHz to the III-V semiconductor-based Hight Electron Mobility Transistor (HEMT)
or the Schottky diode technology at frequencies above 1 THz. For now, most of
the THz testbeds are single-input single-output and rely on external high-gain
directional antennas (e.g., horn and dish antennas). Only recently, fully
digital antenna arrays with 8 to 16 channels have been demonstrated at
frequencies between 100 and 200 GHz [11].

Beyond well-established technologies, innovative solutions are possible when
embracing new materials that open doors to new physics. Inspired by this, the
use of graphene has been proposed to develop signal sources, modulators,
antennas and antenna arrays, due to its ability to support the propagation of
surface plasmon polariton (SPP) waves, theoretically for frequencies as low as
100 GHz [11]. However, for graphene-based plasmonic devices to operate
efficiently in the lower THz band, high electron relaxation times are
required. Although theoretically possible, it remains experimentally very
challenging; thus, at present, the majority of graphene-based plasmonic
devices for THz applications are found at frequencies of 1 THz and beyond
[11]. Interestingly, this is also the frequency range where the conventional
electronic approaches to THz wave generation decay, making graphene a
promising candidate.

###  III-B Beamforming

To materialize the high directional gains promised by THz antenna arrays,
efficient beamforming architectures that achieve high SE and energy efficiency
EE are desired. Among different beamforming architectures, hybrid beamforming
architectures (HBAs) are envisioned for THzCom systems due to their low
hardware complexity and capability of achieving simultaneously high SE and EE
[3, 12]. Although HBAs have been studied at lower frequencies, the
applicability of those HBAs for THzCom systems is arduous since the
characteristics of THz channels impose unique challenges. Specifically, high
THz channel sparsity leads to poor multiplexing gain, even if a significantly
higher number of antennas that are critically spaced (spacing of half of the
wavelength between antennas) are used in antenna arrays. Moreover, channel
squint effects, i.e., phase shift errors, are introduced during beamforming at
the THz band since THzCom systems generally have large bandwidth. Furthermore,
the high susceptibility of THz signals towards blockages leads to dynamic
channel requirements, resulting in very low efficiency when traditional static
HBAs are adopted. These challenges require new HBAs that are different from
those designed at lower frequencies.

Figure 4: Illustration of potential hybrid beamforming architectures (HBAs)
for THz band antennas.

Spatial multiplexing designs, such as the are certainly worthwhile to explore
for overcoming the poor multiplexing gain caused by channel sparsity. One such
design consists of grouping the antennas into several subarrays and separating
these subarrays over hundreds of wavelengths, i.e., widely-spaced, as depicted
in Fig. 4, aiming to reduce the correlation among these subarrays. Moreover,
widely-spaced HBAs can also act as frequency diverse arrays (FDAs), which can
intentionally introduce frequency offsets across array antennas for decoupling
the highly correlated channels of legitimate users and illegitimate users,
thereby enhancing the security of THzCom systems. Note that the same radio
frequency (RF) chain cannot be connected to multiple widely-spaced subarrays
at once, which leads to a block diagonal analog beamforming matrix. This
necessitates novel design of beamforming matrices. Also, channel models with
spherical wave propagation must be considered if subarrays are widely-spaced.
Furthermore, the number of antenna elements per subarray, the number of
subarrays, and spacing between subarrays need to be carefully designed to
strike a balance between the multiplexing gain and array gain.

To overcome the channel squint effect at the THz band, THz true-time delays
(TTDs) can be used to replace phase shifters in THz HBAs. TTDs allow for
adjusting the phase shift relative to the carrier frequency, to generate
waveforms with desired phase shifts. Moreover, to satisfy the varying dynamic
channel requirements of THzCom systems, HBAs with adjustable hardware
connections can be utilized. For example, a dynamic array-of-subarray (DAoS)
HBA (DAoS-HBA) can be adopted, as depicted in Fig. 4, and interested readers
may refer to [12] for detailed discussion on DAoS-HBAs. Finally, a
comprehensive THz HBA that incorporates wide-spaced subarrays, the DAoS, and
TTDs can be designed to capture all the peculiarities of THz channels and
achieves ultra-high SE and EE.

##  IV Joint Integration of Other 6G-Enabling Technologies for THzCom

To meet the wide range of QoS requirements of 6G networks, especially the
connectivity, reliability, and latency requirements, e.g., ultra low-latency
of less than 1-millisecond end-to-end delay and massive connectivity in the
order of $10^{7}$ connections per $\textrm{km}^{2}$, several 6G-enabling
technologies are anticipated to be integrated into THzCom systems to fully
unlock the potential offered by the THz band [7, 6]. Motivated by this, in
this section we discuss four 6G-enabling technologies that can be used to
materialize end-to-end THzCom systems and outline the imposed challenges at
physical, link, and network layers.

###  IV-A Out-of-band Channel Estimation

Accurate channel estimation is of paramount importance to effectively design
and develop transmission and signal processing strategies of THzCom systems.
For channel estimation in THzCom systems, on one hand, the transmitted channel
state information (CSI) can be blocked due to the high susceptibility of THz
signals towards blockers. On the other hand, significant overhead can be a
problem since THz channels are highly dimensional and often very sparse due to
their beam representation and high reflection, diffraction, and scattering
losses. These necessitate the exploration of out-of-band channel estimation
for THzCom systems, i.e., estimating the CSI of THz channels using sub-6 GHz
and/or mmWave band channel measurements [13].

The key principle behind out-of-band channel estimation is to use spatial
correlation translation techniques, which can provide the estimates of spatial
correlation at the THz band from the measurements obtained from lower
frequencies. Notably, the translation of correlation information from lower
frequencies to the THz band poses several challenges. Specifically, the number
of antennas used in antenna arrays within the same aperture at lower
frequencies is less than that used at the THz band. Due to this, on one hand,
a mismatch in the angles-of-arrival and the angle spread occurs between lower
frequencies and THz signals. On the other hand, a large correlation matrix
must be estimated from the lower frequency data obtained from fewer antennas.
These challenges open up new research avenues. For example, the THz band
correlation matrix can be determined by exploiting (i) the structure of the
spatial correlation matrix and interpolation/extrapolation techniques and (ii)
the theoretical expressions for the THz band correlation in conjunction with
the measured lower frequency correlation. We note that the latter demands the
knowledge of the antenna array geometry at the receiver and the distribution
of angles-of-departures.

###  IV-B Multi-connectivity

Multi-connectivity (MC) allows users to associate and communicate with
multiple APs simultaneously [6]. MC strategies can be effectively utilized at
the THz band to overcome the reliability degradation caused by blockages.
Moreover, MC and HBAs can be jointly deployed to form distributed beamforming
architectures to further enhance the throughput/reliability of THzCom systems.
Fig. 5 illustrates the significance of MC strategies for a user operating at
the THz band, in the presence of dynamic human blockers. Through simulations,
we plot the connection probability, defined as the probability that at least
one associated AP has a line-of-sight (LoS) link with the user to connect and
communicate with. In this figure, we consider the locations of APs that exist
around the user to follow a Poisson point process with density
$0.05~{}\textrm{m}^{-1}$, model impenetrable dynamic human blockers according
to the double knife-edge model, and assume blockers’ mobility following the
random directional model [14]. We show that the connection probability with
the MC strategy is significantly higher than that without the MC strategy,
especially for a high blockage density and when the user associates and
communicates with a higher number of APs. This demonstrates the profound
reliability improvement brought by the MC strategy.

The design of MC-enabled THzCom systems is hugely beneficial but complicated,
since it requires advancements in several aspects of THzCom systems. First,
the adaptation of MC strategies is likely to increase the interference in
THzCom systems. This demands new techniques to manage and mitigate
interferences. Second, MC strategies requires the cell-free architecture with
centralized controlling, data processing, and combining strategies. This in
turn demands high capacity backhaul connections and precise clock
synchronization as well as promising packet scheduling methods for latency
reduction. For example, packet duplication-based scheduling can be explored
such that packets are duplicated and the original and duplicated versions of
the same packet are sent over different communication paths. Furthermore,
packet splitting-based scheduling can be explored such that one packet is
divided into several parts and each part of the packet is sent on different
communication paths. Third, dynamic AP selection strategies need to be wisely
designed to improve the reliability and throughput performance in MC-enabled
THzCom systems, at the cost of frequent AP switching. Given this cost,
“lightweight” solutions in terms of software and hardware implementations are
desired, aiming to provide a trade-off between improving the
reliability/throughput performance and AP switching.

Figure 5: Connection probability with different densities of human blockers
and the number of AP connections. In this figure, we consider the locations of
APs around the user to follow a Poisson point process (PPP) with density
$0.05~{}\textrm{m}^{-1}$. Also, we model the impenetrable dynamic human
blockers by rectangular absorbing screens (commonly referred to as the double
knife-edge model) with heights $1.7~{}\textrm{m}$ and widths
$0.6~{}\textrm{m}$ and $0.3~{}\textrm{m}$, their locations by a PPP, their
mobility by the random directional model, and their moving speeds by a
constant velocity model [14].

###  IV-C Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) can smartly steer the beams of
incident radio waves in different directions by adjusting the phase shifts
using a smart processor. This allows RIS to transform a LoS THz channel into a
rich multipath software-defined channel with virtual LoS links. Given this
benefit, RIS can be effectively utilized to mitigate the blockage
vulnerability of THz signals, thereby improving the reliability of THzCom
systems [3, 6]. We note that both passive and active RIS can be utilized at
the THz band. The active RIS has the advantage of serving as distributed THz
APs or signal repeaters to reflect THz signals towards specific directions by
introducing arbitrary phase shifts. Moreover, RIS can be utilized to form
virtual MC links when the number of APs in the system or close proximity is
limited.

The research on RIS-assisted THzCom systems is in its early stages and
enormous challenges lie ahead. First, given the high reflection loss at THz
band, the material of reflector arrays needs to be carefully selected to
support RIS at the THz band. For example, at frequencies of 1 THz and beyond,
graphene can be used for RIS, due to its tunable properties and low reflection
coefficient [3]. Second, tractable models that describe the THz band RIS
channel need to be developed and validated through experimental results.
Third, the challenges arising during the acquisition of the CSI between the
RIS and the involved AP and users need to be addressed. A viable method to
overcome this challenge can be that, instead of estimating the RIS-AP and RIS-
user channels separately, the concatenated AP-RIS-user channel can be
estimated based on direct AP-user channel measurements and some known RIS
reflection patterns. Fourth, to integrate RISs with the fixed wireless
infrastructure, the design of a dedicated control plane, with appropriate
protocols and networking procedures, is desired.

###  IV-D Machine Learning Aided THzCom

Designing effective and efficient communication paradigms in THzCom systems
are far more complex compared to those at lower frequency systems, as
discussed in previous sections, since channels at the THz band are known to be
more uncertain than those of other frequencies. To handle this complexity
while ensuring near-real-time operation, machine-learning (ML), one of the
main pillars of artificial intelligence, can be used as a flexible and
scalable tool to proactively optimize resources for achieving user-level and
network-level performance targets [1].

Recently, the benefits of various ML approaches, such as supervised and
unsupervised learning, reinforcement learning, and federated learning, have
been studied for wireless communications systems [15]. Based on such studies,
further research are worth to examine and reap the benefits of ML approaches
for THzCom systems, during which several newly posed challenges need to be
solved. First, data acquisition for the ML-training process can be difficult,
due to the vulnerability of THz signals towards blockages and the frequency-
dependent nature of THz channels. Second, processing highly non-stationary THz
band training data is very challenging, since ML training algorithms that deal
with non-stationary data are still in their early stages. Third, distributed
network intelligence should be carefully designed based on THz device power,
memory, computational limitations, security, and privacy issues related to
data sharing, data transfer cost, and training complexity. By solving these
challenges, ML aided THzCom systems will undoubtedly attain revolutionary
leaps in channel estimation, beam tracking, blockage prediction, interference
mitigation, and resource allocation, thereby bringing THzCom systems much
closer to reality.

##  V Conclusion

THzCom has been envisioned as a key technology for 6G and beyond networks.
Despite its promise, the research on end-to-end THzCom systems is still in its
infancy. In this article, we studied pivotal areas for the development of end-
to-end THzCom systems, with the emphasis on physical, link, and network
layers. In particular, we examined the areas of THz spectrum management, THz
band antennas and beamforming, and the integration of other 6G-enabling
technologies into THzCom. This article provides a holistic view on the
opportunities, key advancements, and challenges of end-to-end THzCom systems,
and is expected to spur further research efforts into the use of THz band in
the 6G and beyond era.

## References

  * [1] W. Saad _et al._ , “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” _IEEE Netw._ , vol. 34, no. 3, pp. 134–142, June 2020.
  * [2] K. M. S. Huq _et al._ , “Terahertz-enabled wireless system for beyond-5G ultra-fast networks: A brief survey,” _IEEE Netw._ , vol. 33, no. 4, pp. 89–95, July 2019.
  * [3] I. F. Akyildiz _et al._ , “Combating the distance problem in the millimeter wave and terahertz frequency bands,” _IEEE Commun. Mag._ , vol. 56, no. 6, pp. 102–108, June 2018.
  * [4] H. Elayan _et al._ , “Terahertz band: The last piece of RF spectrum puzzle for communication systems,” _IEEE Open J. Commun. Soc._ , vol. 1, pp. 1–32, Nov. 2019.
  * [5] V. Petrov _et al._ , “IEEE 802.15.3d: First standardization efforts for sub-terahertz band communications toward 6G,” _IEEE Commun. Mag._ , vol. 58, no. 11, pp. 28–33, Nov. 2020.
  * [6] M. Polese _et al._ , “Toward end-to-end, full-stack 6G terahertz networks,” _IEEE Commun. Mag._ , vol. 58, no. 11, pp. 48–54, Nov. 2020.
  * [7] M. Giordani _et al._ , “Toward 6G networks: Use cases and technologies,” _IEEE Commun. Mag._ , vol. 58, no. 3, pp. 55–61, Mar. 2020\.
  * [8] Z. Hossain _et al._ , “Hierarchical bandwidth modulation for ultra-broadband terahertz communications,” in _Proc. IEEE Int. Conf. Commun. (ICC)_ , Shanghai, China, May 2019, pp. 1–7.
  * [9] C. Han _et al._ , “Distance-aware bandwidth-adaptive resource allocation for wireless systems in the terahertz band,” _IEEE Trans. THz Sci. Technol._ , vol. 6, no. 4, pp. 541–553, July 2016.
  * [10] W. Gao _et al._ , “Distance-adaptive absorption peak modulation for terahertz covert communications,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 3, pp. 2064–2077, Nov. 2020.
  * [11] A. Singh _et al._ , “Design and operation of a graphene-based plasmonic nano-antenna array for communication in the terahertz band,” _IEEE J. Sel. Areas Commun._ , vol. 38, no. 9, pp. 2104–2117, Sept. 2020.
  * [12] L. Yan _et al._ , “A dynamic array-of-subarrays architecture and hybrid precoding algorithms for terahertz wireless communications,” _IEEE J. Sel. Areas Commun._ , vol. 38, no. 9, pp. 2041–2056, Sept. 2020.
  * [13] N. Gonzalez-Prelcic _et al._ , “Millimeter-wave communication with out-of-band information,” _IEEE Commun. Mag._ , vol. 55, no. 12, pp. 140–146, Dec. 2017.
  * [14] A. Shafie _et al._ , “Coverage analysis for 3D terahertz communication systems,” _IEEE J. Sel. Areas Commun._ , vol. 39, no. 6, pp. 1817–1832, June 2021.
  * [15] J. Du _et al._ , “Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service,” _IEEE Veh. Technol. Mag._ , vol. 15, no. 4, pp. 122–134, Sept. 2020.

## Biographies

Akram Shafie is currently pursuing the Ph.D. degree with the School of
Engineering, the Australian National University, Australia. His current
research interests include terahertz communications, millimeter-wave
communication, and machine learning in wireless communication systems.

Nan Yang is an Associate Professor with the Australian National University,
Australia. His research interests include terahertz communications, ultra-
reliable low latency communications, cyber-physical security, and molecular
communications.

Chong Han is an Associate Professor with University of Michigan-Shanghai Jiao
Tong University Joint Institute, China. His research interests include
Terahertz band and millimeter-wave communication networks, and electromagnetic
nanonetworks.

Josep Miquel Jornet is an Associate Professor with Northeastern University,
USA. His research interests are in terahertz-band communications and wireless
nano-bio-communication networks.

Markku Juntti is a Professor of communications engineering at the University
of Oulu, Finland. His research interests include signal processing for
wireless networks as well as communication and information theory. He is a
Fellow of the IEEE.

Thomas Kürner is a Professor of Mobile Radio Systems at the Institut für
Nachrichtentechnik, Technische Universität Braunschweig, Germany. His current
research interests include wave propagation modeling, radio channel
characterization, and radio network planning. He is a Fellow of the IEEE.