Page 150 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

Fig. 6 – The proposed framework includes lexible key enabler platform and lexible cognitive engine. The lexible cognitive engine can be de ined as a
bridge between the requirements and potential technology options with the related con igurations.

3.8 Flexibility Challenges and Opportunities can include multiple systems together with the commu-
in 6G nications networks. Generally, the amount of sensing
information increases in parallel to awareness capabili-
The exempli ied key enablers show that 6G will have ties. However, processing the sensing information causes
many different lexibility options while 5G systems have computational burdens. Additionally, investigating the
limited lexibilities. However, each lexibility is coming ways of exploiting this information to enrich the commu-
with unique challenges. In other words, lexibility oppor- nications systems is another important challenge.
tunities bring new challenges for the 6G networks. For the lexibility challenges of intelligent communica-
For lexible multi-band utilization, operating the cellular tions, irst of all, an ef icient work distribution between
system at multiple frequency bands needs advanced conventional and ML methods is required. A large data
front-end hardware. Additionally, spectrum coexistence sets and useful features need to be developed to make
of different networks causes new interference problems. ML mechanisms more functional. Additionally, edge
If the lexibility challenges on the PHY and MAC layer computing algorithm structures should be designed to
are investigated, one of the most important problems reduce the workload at transmission points.
is the necessity of a lexible waveform system. At that
point, either a single but an ultra- lexible waveform can If wesummarize the challenges and opportunities, the fol-
be designed or multiple waveforms can be employed in lowing items can be listed:
the same frame. Designing a single waveform to meet all
types of requirements did not work for 5G networks. It • Need for a rich set of algorithms and techniques at
will be more dif icult for 6G with more types of require- different layers of the protocol stack that are opti-
ments. Moreover, waveform coexistence in the same mized for different applications with their own re-
frame causes new interferences (like inter-numerology quirements.
interference in 5G). Similarly, partial and fully overlapped
NOMA systems have the same interference problem. Con- • Integration of these rich sets of algorithms into the
trol and mitigation of these interferences is expected as lexibility framework with minimal overhead and
another challenge. complexity.
• Development of techniques that allows lexibility
Flexibility challenges for heterogeneous networks can be with a simple parameter change without signi i-
exempli ied with the developing optimal positioning and cantly impacting the rest of the system design.
relaying algorithms for lying access points. In addition
to these algorithms, interference management during • Integration of AI and ML techniques to solve complex
the coexistence of different networks is necessary. As system problems together with the classical model
another challenge, network MIMO structures provide based approaches. AI/ML can be applied in different
multi-cell lexibility, however, large amounts of data parts of our proposed framework, i.e. it can be ap-
need to be transferred at the backhaul systems and the plied for better sensing and learning, or for optimal
amount of burden increases. use of the given set of algorithms and approaches, or
developing better solutions in the transmission, re-
As discussed in the previous subsections, ISAC systems ception, and modeling of the system.

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