To make the most out of RIS-assisted localization system, while providing
adaptability to ever changing conditions, an intelligent protocol that allows
the exploitation of the systems resources, namely space, frequency, and time
need to be developed. In more detail, the protocol should allow co-design of
beam code-words for both the source and the RIS, selects the optimum bandwidth
and timeslot for localization. Moreover, it should coordinate the overall
localization process allowing changing between beam codewords when needed.
Such a protocol should aim at maximizing the localization system energy
efficiency without compromising its accuracy and stability. Likewise, the
protocol should take into account the nature of the system, i.e. operation
frequency band, as well as the type of the RIS, i.e., analog, hybrid, digital.
Finally, the protocol should provide guidelines for the optimal RIS placement.
To design such protocol, researchers should formulate and solve a number of
optimization problems and combine their solution to a reinforcement learning
strategy that will ensure the system’s adaptability.

###  IX-D Heterogeneous localization system design

To counterbalance the physical limitations of high frequency wireless
localization systems, simultaneous exploitation of microwave and mmWave, THz,
and optical bands is required. However, localization systems in different
bands have different localization range and accuracy. Moreover, their range
and accuracy depends on different phenomena, like atmospheric conditions, size
of the scatterers and obstracles, and design parameters, such as antenna
gains, beamforming type, etc. Additionally, localization mechanisms have to
facilitate the co-existence of several technologies with different coverage
that follow different standards. As a result, localization system
heterogeneity can be: i) spectrum, and ii) technology heterogeneity.

####  IX-D1 Spectrum heterogeneity

refers to scenarios in which the localization agent uses both high (e.g.,
mmWave, THz, optical) and low frequencies (e.g., microwaves). The higher the
localization frequency, the higher the localization accuracy and the lower the
localization range. This observation drives the idea of designing multi-band
localizers capable of determining the position of the object of interest in a
hierarchical manner. Specifically, such a localizer would use lower frequency
to find the location of an object that is outside the high-frequency system
range with low accuracy, and high frequencies to increase the localization
accuracy of object that are inside their coverage area. A hierarchical
localization approach is required for such a localizer that will allow
efficient and fast localization of the object of interest and boost its
accuracy. Finally, an experimentally verified theoretical framework that
quantify the hierarchical localizer performance and provides design guidelines
is needed.

####  IX-D2 Technology heterogeneity

introduces two scenarios, namely: i) stand-alone and ii) integrated
localization systems. In stand-alone scenarios only one type of localization
technology, such as sensors, radards, mmaWave, THz, optical, is used. On the
contrary in integrated localization systems, more than one localization
technologies are used. In the later, the identification of the technologies
that could combined without significantly negatively impacting the localizers
energy efficiency needs to be performed. Another parameter that need to be
accounted for is the implementation cost. Finally, algorithms for combining
localization information from different sources are necessary to be developed.

###  IX-E Integrated localization and communication

A radio signal can carry both the TX information and localization-related
data. As a consequence, a unified framework for integrated communication and
localization seems to be a natural next step. Current cellular networks
implement localization functionalities separately from the communication-
related functions. The main reason behind this approach has been the different
design objectives that these functionalities have. In more detail, the
localization system aims at maximizing its accuracy, while the communication
system overall objective is to jointly maximize its reliability, data rate,
and transmission range through the wireless channel, which experience fading
and noise. Therefore, localization systems used to operate in high frequency
bands, where the short wavelength and antenna beamwidth allowed them to pin-
point the target with acceptable accuracy. On the other hand, communication
have only recently started to exploiting mmWave and THz transmissions. Thus,
the time of a localization and communication synergy has come [93, 466, 467,
468]. In order to devise efficient and low-cost integrated localization and
communication systems a number of strategically designed steps, starting from
suitable KPIs definition and finishing to the design of suitable protocols
capable of operating in heterogeneous network environments, need to be taken.
Next, we identify and analyze these steps.

####  IX-E1 KPIs definition

As discussed in [469, 470, 471, 472], there are several trade-offs between
localization and communication system goals. In more detail, in [469], the
authors discussed the localization accuracy and communication data rate trade-
off. In [470], the authors revealed the localization accuracy, power
efficiency, data rate trade-off. Similarly, in [471], the authors highlighted
the localization accuracy data rate trade-off in single-user mmWave systems,
while, in [472], the prior contribution was extended in multi-user scenarios.
The aforementioned contributions revealed that there are a number of trade-
offs that need to be understand and balanced. To achieve this, the current
localization and communication KPIs may be not enough. This motivates
rethinking and redefining or creating new KPIs capable of providing insights
that in turns optimize the localization and communication synergy. Moreover,
new KPIs may need to be defined that quantify the performance of specific
applications. For example, QoS may be a better KPI instead of localization
accuracy. Finally, for a number of applications in which the reliability of
position information is a key design parameter, such as RIS or UAV-empowered
wireless systems, the position integrity and availability may be the most
important KPIs.

####  IX-E2 Fundamental research

As we move towards mmWave, THz, and optical bands, the performance of
integrated localization and sensing systems are naturally expected to improve.
However, new issues and challenges need to be addressed, giving space to
fundamental research. Specifically, the research concerning THz channel
modeling as well as integrated localization and communication is still at the
early stage. Next, we focus in these two topics and provide some possible
research directions.

Most published contribution in high-frequency integrated localization and
communication neglect the impact of multiple scatters and obstacles and employ
deterministic channel models. However, in practice, the AOA as well as the
amplitude of the scattered signals are random processes. It is of high
importance to model the effect of non-LOS signals and of scatters in order to
allow the accurate integrated localization and communication system
performance evaluation. In this direction, new channel modeling methodologies
based on stochastic geometry, random shape theory and electromagnetic tools,
like ray tracing or the finite element method, need to be developed. Other
phenomena that should be taken into consideration in signal modeling are the
wideband effect as well as the impact of atmospheric conditions and/or the
transceivers hardware imperfections. Finally, the channel and signal model
should also account for the existance of smart materials, such as RISs.

Building upon the new channel and signal models, a new theoretical framework
for the analysis of the performance of integrated localization and
communication systems will be developed. This framework should return not only
conventional KPIs that quantify the performance either the localization or the
communication part of the system, but also the novel KPIs that would be
defined and allow the assessment of the integrated localization and
communication system. Moreover, different system models that employ different
types of localizers with and without RISs, need to be investigated. The
derived expressions are expected to bring insights and allow the optimization
of the integrated localization and communication system.

####  IX-E3 Advanced signal processing

Mobile terminals at various tiers of a multi-tiered network may communicate
with one another using distinct frequency bands and independent communication
paths. In such scenarios, efficient and dynamic maintenance of wireless
networks is a key concern for integrated localization and communication.
Maintenance and reconfiguration of wireless connections may be possible via
dynamic resource management and the utilization of multiple distinct frequency
bands. In addition, cross-layer data exchange depends on efficient signal
processing for its operation. In this respect, the use of common hardware for
localization and communication services can provide high efficiency for signal
processing technologies in integrated communication and localization services,
as well as the required reusability of channel estimation units for position
extraction. As a result, the most important challenge with respect to advanced
signal processing is achieving efficient co-design of signal processing
techniques and hardware architecture.

####  IX-E4 Waveform design

Despite the fact that localization and communication waveform designs share a
number of common key requirements, including low-latency, low transceiver
hardware complexity, and high reliability, in the 4G and the 5G eras, signals
and systems for localization and communications were designed separately [473,
474]. As we move towards higher frequency wireless systems, the idea of
designing a common signal for localization and communication arises. To
achieve this we need to determine optimal signal PSDs that satisfies both the
requirements of the localization accuracy and the communication robustness to
interference. In more detail, according to [473] and [475], the Cramer-Rao
lower bound of time-based ranging localization systems can be expressed as

$\displaystyle\mathrm{CR}=\frac{c^{2}}{8\pi^{2}\,b^{2}\,\frac{P}{N_{o}\,B}},$
(92)

where $P$ and $B$ are respectively the transmission power and bandwidth, while
$N_{o}$ is the noise PSD. Moreover, $b^{2}$ is the mean square bandwidth that
can be obtained as

$\displaystyle
b^{2}=\frac{\int_{-B}^{B}f^{2}\,\left|S(f)\right|^{2}\,\mathrm{d}f}{\int_{-B}^{B}\left|S(f)\right|^{2}\,\mathrm{d}f},$
(93)

with $S(f)$ being the Fourier transform of the transmission signal.

From (92) and (93), for the localization perspective, a signal that has its
power concentrated to the edges of the spectrum achieves better performance in
terms of localization accuracy, i.e., lower Cramer-Rao lower bound, in
comparison to signals that their power is either uniformly distributed or
concentrated at the center of their spectrum. From communications point of
view, in order to reduce the inter-symbol interference, signals are designed
in a way that their power is concentrated at the center of their spectrum.
This observation reveals that there is a trade-off in terms of the PSD
requirements for localization and communication. To find the equilibrium,
optimization problems for a number of different system models is necessary to
be formulated and suitable policies need to be extracted. Finally,
adaptability and flexibility in terms of bandwidth, signal power, PSD shape,
etc. should be allowed in order to make the most in real-time ever changing
wireless environments.

####  IX-E5 Pro-active radio environment mapping and radio resource management

In order to reap the communication benefits of the proactive radio resource
management in areas like beam alignment, channel prediction, cell selection,
and more, high-precision radio environment mappings will be necessary. The
majority of the radio environment mappings models that exist so far has
focused on 2D scenarios. With this in mind, more accurate 3D localization in
nLOS, and multi-path applications is required for future 6G networks. In spite
of the fact that a number of effective multi-path and NLOS mitigation
algorithms have been proposed in the literature, these algorithms are
typically quite complex and are only practical for remote localization systems
as opposed to self-localization systems where the UE must perform the
computation and estimation. However, for the sake of analytical simplicity,
these studies often only include first-order reflection, even though higher-
order reflections are common in dense multi-path settings and might be crucial
to localization in NLOS situations. As a result, further research is required
in order to develop more precise and inexpensive localization methods for
multi-path scenarios while maintaining the UE’s communication performance and
allocating radio resources based on its current position. Finally,despite the
fact that precise location data improves communication, a better understanding
of the underlying connection between varying levels of location precision and
transmission rates is required.

####  IX-E6 Design of integrated localization and communication protocols for
heterogeneous networks

Future networks will become more diverse by using a wide variety of standards
and frequency ranges. For mobile terminals that are constantly moving under
unstable radio conditions, the challenge of how to swiftly transition between
protocols is of paramount importance in order to maintain reliable
localization and communication. Since each network layer can use a unique set
of protocols, the creation of logical links between them can be achieved by
considering the protocols as gateways. However, the need for cross-layer,
-module, and -node information transfer constitutes the integrated protocol
design crucial. Finally, a reevaluation of the physical and MAC layers with
respect to the protocol design that takes into account localization in
addition to the communication metrics is required.

###  IX-F Low earth orbit (LEO) localization systems

In rural/remote areas that lack terrestrial infrastructure or when the signals
from the terrestrial infrastructure are blocked due to obstacles, such as in
mmWave/THz transmissions, terrestrial- and LEO-based localization can be used
in integrated terrestrial non-terrestrial networks to achieve the best from
the two options. In future communication, sensing, and localization
applications, and, especially, positioning applications with LEO signals,
there is currently a dichotomy of approaches. On the one hand, existing
systems can be utilized to offer novel services to the end users, while, on
the other hand, completely new systems, such as the ongoing efforts towards a
LEO-PNT concept, can be designed. In the former, the receiver is responsible
for most of the optimization work based on the assumption that the necessary
infrastructure is already in place. In the latter, a three-segment
optimization is required, which can be achieved either simultaneously or in
stages. In order to obtain higher precision for localization with worldwide
coverage, future efforts on constructing unique LEO-PNT constellations through
optimization are focused on (i) integration of 6G and LEO networks, (ii)
intelligent beamforming-based localization, (iii) LEO-based edge computing,
and (iv) equip LEO satellites with GNSS receivers.

##  X Conclusion

The potential of the future 6G wireless system for localization was
investigated in this article. In more detail, the key localization-based
applications and use cases for the next generation of wireless networks were
discussed, and the technologies that enable localization services were
investigated. Moreover, models of mmWave/THz as well as VLP localization
wireless systems were presented , while also taking into account both LOS and
NLOS channels that were generated by traditional reflectors as well as RISs.
Also, the major localization KPIs were described and mathematical expressions
were provided when possible. Furthermore, SotA traditional and learning-based
localization techniques were presented alongside their conceptual figures.
Finally, the impact of all design aspects of future 6G wireless systems,
namely characteristics, assumptions, localization problem formulation, and
optimization, were discussed, while lessons learned and future directions were
extracted.

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|  Stylianos E. Trevlakis was born in Thessaloniki, Greece in 1991. He
received the Electrical and Computer Engineering (ECE) diploma (5 year) from
the Aristotle University of Thessaloniki (AUTh) in 2016. Afterwards, Dr.
Trevlakis served in the Hellenic Army in for nine months in the Research
Office as well as at the Office of Research and Informatics of the School of
Management and Officers. During 2017, he joined the Information Technologies
Institute, while from October 2017 until April 2022, he was part of WCIP as a
PhD candidate in AUTh. During the same period, he was a teaching assistant at
the department of ECE of AUTh. From April 2022 until now, Dr. Trevlakis is
working at InnoCube as a postdoctoral researcher with focus on state-of-the-
art research in conventional & AI-enabled Wireless Communication Systems. Dr.
Trevlakis’s research interests lie in the area of Wireless Communications,
with emphasis on conventional & AI-enabled Wireless Communication Systems, as
well as Communications & Signal Processing for Biomedical Engineering.
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|  Alexandros-Apostolos A. Boulogeorgos (Senior Member, IEEE) was born in
Trikala, Greece, in 1988. He received the Diploma degree in Electrical and
Computer Engineering (ECE) and the Ph.D. degree in wireless communications
from the Aristotle University of Thessaloniki (AUTh) in 2012 and 2016,
respectively. From 2022, he is an Assistant Professor at the Department
Electrical and Computer Engineering of the University of Western Macedonia,
Greece. From 2017 to 2022, he served as a Senior Researcher at the Department
of Digital Systems, University of Piraeus, where he conducted research in the
area of wireless communications. From October 2012 to September 2016, he was a
Teaching Assistant with the Department of ECE, AUTh, and from February 2017 to
September 2022, he served as an Adjunct Professor with the Department of ECE,
University of Western Macedonia, and as an Visiting Lecturer with the
Department of Computer Science and Biomedical Informatics, University of
Thessaly. He has worked in a number of EU and national projects. Dr
Boulogeorgos has (co-)authored more than 120 technical papers, which were
published in scientific journals and presented at prestigious international