Page 24 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 7 – Terahertz communications

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 7

respective positions optimally in order kernel-based learning, a Gaussian process regression is
to route and maintain Tbps links among commu- studied in [100] for the channel estimation of a
nicating drones. In what follows, we review some AI/ UM‑MIMO multi‑user system over the THz band (0.06‑
ML‑based approaches possibly implementable for 10 THz). It is to be noted here that the AI/ML techniques
THz‑enabled drone networks for channel estimation, mentioned above are proposed for 6G wireless commu‑
UM‑MIMO, and Mobile edge computing. nications and networks at sea level in general. There‑
fore, there will be a need to tailor these AI/ML
5.1 Channel estimation techniques speci ically for the THz‑enabled drone
networks and DSNs keeping in view the intrinsic nature
As discussed earlier in Section 1, irst, the THz band is of the communicating drones i.e., mobility, energy‑
highly affected from absorption loss due to water vapor constrained resources etc.
molecules in the atmosphere, contributing signi icantly to
the total loss. Second, the spread loss is also massive at 5.2 UM‑MIMO
THz frequencies. Third, the THz band channels are non‑
stationary, particularly for mobile use cases i.e., hovering In the realm of UM‑MIMO, ML can be employed in
drones in our case, where both the Tx and Rx drones will various use cases. One instance is when an existing
be mobile. Hence, conventional assumptions of quasi‑ model is erroneous, and/or it is only a sparse
stationary or stationary channel models may not be ap‑ approximate of the actual model. Such an instance can
plied to the THz band channels. More speci ically, arise within the linear channel models, where the
channel estimation over the THz band becomes more non‑linearities induced by certain practical circums-
challenging in the drone scenarios under mobility, where tances and hardware are neglected. Also, ML can be
precise Channel State Information (CSI) is needed, e.g., utilized for improving the solu‑ tions obtained using
in beam‑forming. Therefore, the traditional channel approximations of the linear mod‑ els. Another instance
estimation methodologies are required to be revisited of ML‑based solution in UM‑MIMO is possible when the
[52]. Overall, for reducing the complexity of the THz optimized solutions are computa‑ tionally expensive i.e.,
channel estimation, several techniques can be not feasible for the state‑of‑the‑art hardware. Here, ML
employed includ‑ ing: compressed sensing, fast channel can be effectively utilized for inding suboptimal
tracking‑based algorithms, etc. ML‑based algorithms, in solutions having less complexity, with some obvious/
this context, can be employed for evaluating the THz acceptable lower performance. Examples in this context
band communication data by anticipating the THz include channel estimation, maximum likelihood
signal loss in a certain unknown channel. Consequently, detection, etc. Moreover, optimum spectrum utilization
various AI or ML‑based algorithms are applicable to the in UM‑MIMO can be made possible using machine learn‑
physical layer of the forth‑coming 6G wireless networks ing techniques [22].
for addressing the above‑mentioned THz channel model
and estimation [92, 93]. Supervised Learning (SL) [94] 5.3 Mobile edge computing
can aid in predicting THz shadowing and path loss.
Moreover, SL can be employed for localization, channel Mobile Edge Computing (MEC) has recently emerged as
estimation, interference management, etc. Employable a technique for 5G networks, in which cloud computing‑
SL models and algorithms are K‑Nearest Neighbor like functionalities are processed at the edges of the cellu‑
(KNN), Support Vector Machine (SVM), feed‑forward lar networks [101]. MEC can equip mobile devices, such
neural networks, and radial basis function neural as drones with constrained processing capabilities, to
networks, etc. Various challenges related to the THz hand over their processing tasks to the nearest network’s
channel modeling and estimation including multi‑path edge. Conversely, within a THz‑enabled DSN, mobile user
tracking, interference mitigation, node clustering, equipment can of load its computationally expensive jobs
optimized modulation, etc., can be tackled by using Unsu‑ to the serving drones with MEC functionalities. Low la‑
pervised Learning (UL) techniques [95] such as: Fuzzy tency systems for sporadic access such as cyber‑physical
C‑means, K‑means, clustering algorithms, etc. Deep communication systems (a.k.a., Tactile Internet) [102]
Learning (DL) (both SL and UL) can be employed in require latencies within sub‑ms for controlling hovering
many aspects of channel modeling, such as for signal objects (drones in our case). Such alike systems will also
detection, and estimating Channel State Information be a requirement for evolved industry 4.0 applications
(CSI). Techniques including Deep Neural Networks [103]. It is predicted that the transport methods over the
(DNNs), Recurrent Neural Networks (RNNs), Convo- physical layer will be linked with edge computing such as
lutional Neural Net‑ works (CNNs) can be anticipated MEC, or real‑time cloud computing within the vicinity
as appropriate candidate DL algorithms [96]. Reinfor- of the communication network. The main objective
cement Leaning (RL)[97] can be used for channel here is to deliver resources/solutions to the evolving
selection and tracking, iden‑ ti ication of radio bands, IoT protocols comprising of a massive number of
selection of modulation modes, etc. Appropriate RL inter‑connected devices with constrained energy and
models and techniques include Q‑learning, fuzzy RL, storage requirements such as in drone networks, as
etc. [98]. Finally, learning‑based schemes for THz well with some latency requirements. For overcoming
channel estimation is highly ef icient particularly over these constraints, global 6G research is moving towards
higher dimensions [99]. As an exam deep distributed computation techniques (MEC here) [104, 105].
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