# Role of Reconfigurable Intelligent Surfaces in 6G Radio Localization: Recent
Developments, Opportunities, Challenges, and Applications

Anum Umer, Ivo Müürsepp, Muhammad Mahtab Alam, Henk Wymeersch This work has
received funding partly from the European Union’s Horizon 2020 Research and
Innovation Program under Grant 951867 ‘5G-ROUTES’ and Grant 101058505
‘5G-TIMBER’. Henk Wymeersch was supported by the European Commission through
the H2020 project RISE-6G (grant agreement no. 101017011)A. Umer, I. Müürsepp,
and M. M. Alam are with Thomas Johann Seebeck Department of Electronics,
Tallinn University of Technology, 12616 Tallinn, Estonia (e-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS>muhammad.alam@taltech.ee).H.
Wymeersch is with Department of Electrical Engineering, Chalmers University of
Technology, 412 58 Gothenburg, Sweden (e-mail: henkw@chalmers.se).

###### Abstract

Reconfigurable intelligent surfaces (RISs) are seen as a key enabler low-cost
and energy-efficient technology for 6G radio communication and localization.
In this paper, we aim to provide a comprehensive overview of the current
research progress on the RIS technology in radio localization for 6G.
Particularly, we discuss the RIS-assisted radio localization taxonomy and
review the studies of RIS-assisted radio localization for different network
scenarios, bands of transmission, deployment environments, as well as near-
field operations. Based on this review, we highlight the future research
directions, associated technical challenges, real-world applications, and
limitations of RIS-assisted radio localization.

###### Index Terms:

6G, Localization, Reconfigurable Intelligent Surfaces, RIS.

##  I Introduction

Reconfigurable intelligent surfaces (RISs) are advanced metasurfaces designed
with the remarkable capability of being able to be reprogrammed to alter their
electromagnetic properties and functionalities according to specific
requirements [1]. These intelligent surfaces enable dynamic control over the
reflection, transmission, and absorption of electromagnetic waves, allowing
for unprecedented flexibility and adaptability in manipulating wireless
signals and optimizing wireless communication systems. Through their
programmability, RISs empower researchers and engineers to explore a wide
range of applications, including wireless communication networks, smart
environments, Internet of Things (IoT) connectivity, radar systems, and more
[2]. By harnessing the potential of RIS technology, we can revolutionize the
way we interact with and shape the electromagnetic world around us. They
provide the ability to control and program the wireless communication channel,
making it a highly versatile tool for wireless communication [3]. This feature
makes RISs favorable for radio communication and localization since we can
control the illumination of non-line-of-sight (NLoS) areas where direct
signaling from the base station (BS) is not possible. Moreover, radio
localization typically requires more than one anchor to function, in contrast,
RISs offer a cost-effective and energy-efficient solution to replace
additional BSs and relays [4]. This is due to the simpler hardware
implementation of RISs, which are easier to deploy and maintain. RISs, with
their limited power requirements, can be installed on surfaces like walls,
billboards, or even unmanned aerial vehicles for emergency services [5].

Some key features make RIS suitable for use in wireless networks, besides
reconfigurability, such as being a low-cost solution for enhancing wireless
communication systems. RIS can be fabricated using low-cost materials such as
printed circuit boards, making it affordable for widespread deployment [6]. It
can significantly reduce the energy consumption of wireless communication
systems. By reflecting and focusing the signal towards the intended receiver,
RIS can reduce the need for high-power transmitters and increase the energy
efficiency of the system [7]. It can mitigate interference in wireless
communication systems. By reflecting and manipulating the signal, RIS can
create nulls in the signal where interference is present, leading to improved
signal quality and capacity [8, 9].

RISs are, thus, perceived as state-of-the-art technology for the localization
of users in Sixth Generation (6G) networks provided the location of the RIS is
already known [1]. The research community is actively working on modeling and
optimization of various aspects of RIS-assisted radio localization to enable
bigger-impact techniques and applications for 6G networks, such as
simultaneous localization and communication (SLAC), simultaneous localization
and mapping (SLAM), and numerous inventive applications in the realm of
Industry 4.0, as elaborated further in the paper. While research in this area
of radio localization is advancing rapidly, it is essential to consolidate the
progress made in this field and pinpoint both past accomplishments and future
avenues for exploration.

TABLE I: SUMMARY OF RECENT RIS TECHNOLOGY AND LOCALIZATION SURVEYS AND TUTORIALS | Year | Article | Type | Transmission bands | Environment | Applications
---|---|---|---|---|---|---
|  $<$30GHz | mmW | THz | Indoor | Outdoor | SLAM | ISAC | JCAL
Localization | 2017 | [10] | Survey |  |  |  |  | ✓ | ✓ |  |
2017 | [11] | Survey |  |  |  | ✓ |  |  |  |
2018 | [12] | Survey |  |  |  |  | ✓ |  |  |
2018 | [13] | Survey | ✓ |  |  | ✓ |  | ✓ |  |
2018 | [14] | Survey | ✓ |  |  |  |  |  |  |
2019 | [15] | Survey |  |  |  | ✓ |  |  |  |
2019 | [16] | Survey | ✓ |  |  |  |  |  |  |
2019 | [17] | Survey |  | ✓ |  |  |  |  |  |
2020 | [18] | Survey | ✓ | ✓ | ✓ | ✓ |  |  |  |
2020 | [19] | Survey |  |  |  | ✓ |  |  |  |
2021 | [20] | Survey | ✓ |  |  |  |  |  |  |
2021 | [21] | Survey |  |  |  | ✓ |  |  |  |
2021 | [22] | Survey | ✓ | ✓ |  |  |  |  |  |
2022 | [23] | Survey |  | ✓ |  |  |  |  |  |
2022 | [24] | Survey |  |  |  |  |  |  |  |
2022 | [25] | Survey |  |  |  |  |  |  | ✓ |
2023 | [26] | Survey | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
RIS- Assisted Localization | 2021 | [27] | Survey |  |  |  |  |  | ✓ |  |
2022 | [28] | Tutorial |  |  | ✓ |  |  |  |  |
2022 | [29] | Survey |  | ✓ |  | ✓ |  |  |  |
2022 | [30] | Survey |  | ✓ |  |  |  |  |  |
| this work | Survey | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
|  |  |  |  |  |  |  |  |  |  |

###  I-A Related Work and Motivation

Several review studies exist on localization that share the localization
basics in common but each of them is primarily focused on either the type of
signals used for localization, the localization environment, or the techniques
for localization. For instance, indoor localization is discussed in [11, 13,
15, 18, 19, 21], outdoor localization in [10, 12, 31]. As for the types of
signals, radio signals are covered in [13, 14, 21] and visible light
positioning (VLP) in [32]. Localization techniques such as SLAM [10, 13],
multi-dimensional scaling [16], machine learning (ML) [18, 19, 20] have also
been discussed in the surveys. Several surveys exist on the application of
localization such as device-free localization (DFL) [31], autonomous driving
[10, 12, 24], pedestrian localization [20], emergency response [11] and
network localization [13]. Recent studies focus on envisioned applications and
use cases of localization in 6G [26], technological enablers for beyond 5G and
6G localization including RIS [1, 2], surveys on localization signal
processing techniques and algorithms for 6G [33, 27, 30], as well as the
convergent communication, localization, and sensing including integrated
sensing and communications (ISAC) [25, 2], and high-frequency localization [1,
2, 28, 33]. Several studies explore the potential and applications of RIS in
6G systems. Research has been conducted on the potential of RIS in radio
localization and mapping, which are detailed in [1, 2]. Further studies and
surveys discuss signal processing in RIS-assisted networks, as can be found in
[33, 27, 30]. A tutorial that gives an overview on radio localization with RIS
at higher frequencies is presented in [28]. IoT positioning is another area of
interest, with a comprehensive survey available in [29]. The concept of ISAC
with RIS is explained in a tutorial overview in [3]. Lastly, the study in [34]
examines the use of RIS in different network scenarios, such as single-input,
single-output (SISO), multiple-input, multiple-output (MIMO), multiple-input,
single-output (MISO), and single-input, multiple-output (SIMO). A recent study
gave an overview of the situations in which RISs will provide significant
performance improvements over traditional network designs smart wireless
environments enabled by RIS from the perspective of network architecture for
deployment scenarios, bandwidth, and area of influence [35]. A summary of
recent articles on localization and RIS-assisted radio localization is
provided in Table I.

In contrast to these existing surveys and tutorials, we provide a
comprehensive overview of the recent studies on the role of RIS in radio
localization in 6G networks. Our work consolidates and builds upon existing
knowledge to promote the advancement of RIS-assisted localization in 6G
networks. The research community is actively exploring various aspects of RIS
in localization. These include modeling, analysis, and optimization of various
localization scenarios with BS, user equipment (UE), and RIS, determining the
number and type of RIS elements, as well as designing phase control and
coefficients for enhanced localization [36, 37, 38, 39, 40, 6, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52, 53, 34, 30, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 66, 67]. Other factors being studied primarily include the
placement of RIS in indoor and outdoor scenarios with variations in the number
of antennas on BS and UE, and RIS operation at multiple frequency bands, i.e.,
frequency range 1 (FR1) (450 MHz to 6 GHz), frequency range 2 (FR2) (between
24.25 GHz and 52.6 GHz), millimeter wave frequency band (mmW) (30-300 GHz) and
terahertz band (THz) (0.1-10 THz), as well as the near-field and far-field
operation of RIS assisted localization [28], which are the focus of this
article. Overall, there is significant interest and effort being dedicated to
advancing the use of RIS for localization. In order to determine the
unexplored research avenues within this field, it is essential to carry out a
comprehensive survey and compile the existing literature. This will facilitate
the identification of specific areas for the research community. In this
study, we seek answers to the following questions:

  1. 1.

How has RIS been used for localization in 6G networks?

  2. 2.

What are the current trends and developments in the use of RIS technology for
radio localization?

  3. 3.

What are the future directions and potential applications of RIS-assisted
radio localization in 6G and what are the associated technical challenges, and
limitations?

###  I-B Review Method

After having identified the research questions, we defined our search string
and identified the relevant databases to find the most relevant literature
based on the inclusion and exclusion criteria. For the search string, we
selected the keywords from our research questions, based on which two search
strings were defined. The first string limits the investigation to the RIS and
its synonyms. Similarly, the second string limits the literature to
localization and its synonyms. Both search strings were combined using logical
operators before being applied to the literature databases. The final search
string is: (”intelligent reflecting surface” OR ”reconfigurable intelligent
surface” OR ”RIS” OR ”IRS” OR ”LIS” OR ”large intelligent surface”) AND
(”localization” OR ”positioning”). The search string was used on IEEE Xplore,
ACM Digital Library, and Scopus to identify the most pertinent papers. These
databases were chosen because they encompass the majority of publications in
the fields of radio communication and localization. We searched for journal,
workshop, and conference papers in the selected databases. As various studies
use diverse terms to describe RIS-assisted radio localization, it is possible
that our search string might not capture all relevant works. Therefore, we
also performed comprehensive backward referencing. To ensure we did not miss
pertinent articles, we included full texts in our analysis, even if they did
not have our search terms in their titles or abstracts.

After having identified all the relevant studies, we excluded all the studies
that fulfilled any of the following exclusion criteria: The study cannot be
accessed digitally, or the study is a duplicate. The remaining set of studies
was evaluated for the following inclusion criteria: the study is relevant to
the topic of RIS in radio localization in 6G, and it showcases or illustrates
a method, approach, or technique for RIS-assisted radio localization.

In the subsequent sections, we present the results of our review. In Section
II, we give the comprehensive background of RIS technology and discuss it from
the perspective of its potential for radio localization. In Section III, the
developments in RIS-assisted radio localization are consolidated in terms of
research in various frequency bands, deployment scenarios, and RIS placement
for enhanced localization. In Section IV, we outline the limitations and
unexplored research directions of RIS for localization, followed by
conclusions in Section V. The sections and main topics of this article are
shown in Figure 1. A list of definitions of frequently used abbreviations is
given in Table II.

I Introduction II RIS In Radio Localization A. Overview of RIS Operation  B. RIS Support for Radio Localization C. Potential Applications D. RIS-Assisted Localization Taxonomy III State of the Art in RIS-Assisted Radio Localization A. Outdoor and Far-field Localization B. Indoor Localization  C. Near-field Localization  IV Challenges and Research Outlook V Conclusion Figure 1: The overall outline of the article. TABLE II: Abbreviations 3D | three dimensional
---|---
5G | fifth generation
6G | sixth generation
AI | artificial intelligence
AOA | angle-of-arrival
AOD | angle-of-departure
BS | base station
CRF | conventional radio frequency
CRLB | Cramér-Rao lower bound
CS | compressive sensing
CSI | channel state information
DFL | device-free localization
DL | downlink
DNN | deep neural network
DSP | digital signal processors
FIM | Fisher information matrix
FPGA | field-programmable gate arrays
GDoP | geometric dilution of precision
GNSS | global navigation satellite systems
IoT | internet of things
ISAC | integrated sensing and communication
JCAL | joint communication and localization
LoS | line-of-sight
mmW | millimeter wave
MIMO | multiple-input, multiple-output
MISO | multiple-input, single-output
MCRB | misspecified Cramér-Rao bound
ML | machine learning
MLE | maximum likelihood estimation
MSE | mean square error
MUSIC | multiple signal classification
NLoS | non-line-of-sight
PEB | position error bound
REB | rotation error bound
RIS | reconfigurable intelligent surfaces
RL | reinforcement learning
RSS | received signal strength
SISO | single-input, single-output
SIMO | single-input, multiple-output
SL | sidelink
SLAC | simultaneous localization and communication
SLAM | simultaneous localization and mapping
SNR | signal-to-noise ratio
OEB | orientation error bound
THz | terahertz band
TOA | time-of-arrival
UE | user equipment
UL | uplink
UWB | ultra wide band
VLP | visible light positioning

##  II RIS In Radio Localization

In this section, we briefly discuss the RIS operation and the different types
of its working operations. We focus on the reflective RIS signal and channel
modeling, as it is the most commonly researched type of RIS operation.
Subsequently, we highlight why RIS is an advantageous technology for 6G radio
localization, followed by a discussion on the taxonomy of RIS-assisted
localization.

###  II-A Overview of RIS Operation

RISs represent a novel electromagnetic surface capable of altering the
behavior of wireless signals, paving the way for more efficient and adaptable
wireless communication systems. A RIS comprises a multitude of tunable
elements designed to regulate the amplitude, phase, and polarization of
electromagnetic waves either passing through or reflecting off the surface
[68]. These individual elements are generally small, cost-effective, and
programmable to accommodate varying channel conditions and modulation methods.
The structure of RISs operating across different modes is depicted in Figure
2.

The RIS operates by modifying the propagation of electromagnetic waves by
reflecting, refracting, or scattering them [1]. Typically, the tunable
elements within RISs are diminutive antennas or resonators that can be
electrically manipulated to adjust their electromagnetic properties, including
resonant frequency, impedance, and polarization [69]. By altering the
impedance of each tunable element, the RIS can govern the amplitude and phase
of the reflected or transmitted wavefront, effectively directing the wave
towards a desired direction or concentrating it on a specific location [2].
Entrusted with the role of tweaking the electrical parameters of these tunable
elements in real-time, the RIS controller uses feedback from the wireless
channel conditions, modulation scheme, and performance targets. It employs
feedback from the receiver to fine-tune the settings of the tunable elements,
optimizing signal quality while curbing interference and noise [3]. Various
hardware and software technologies, ranging from analog, digital, or hybrid
controllers to software-defined or artificial intelligence (AI)-based
controllers, can implement the RIS controllers [69]. The selection of a
controller hinges on the RIS’s application, complexity, and performance
prerequisites [70].

(a) Passive Reflective RIS

(b) Active Reflective RIS

(c) Transmissive/STAR RIS which can be active or passive

Figure 2: Comparative structure of RIS under different modes of operation, (a)
Passive Reflective RIS can alter the phase of the incident signal only, (b)
Active Reflective RIS can amplify and alter the phase of the incident signal,
(c) Transmissive RIS passes the signal through while STAR RIS can perform both
transmission and reflection simultaneously. The reflection coefficient of each
RIS element is reconfigurable in real time via the controller.

Here, we briefly discuss some of the RIS working operations:

  * •

Reflective RIS: This is the most common type, shown in Figure 2(a), where
elements of the surface can alter the phase of the incident signal. These
essentially act as programmable mirrors that shape and direct the radio waves
toward a specific direction [71]. Each RIS element reflects the incoming
signal due to the copper backplane [72]. An RIS can reflect electromagnetic
signals independently in reflection mode using its $N$ reflection units. The
magnitude, $\alpha_{n}\in[0,1]$, and phase, $\phi_{n}\in[0,2\pi)$, of the
reflection coefficient of each reflection unit are reconfigurable via the
controller. This leads to a baseband signal model of,
$\mathbf{y}_{n}=\alpha_{n}e^{j\phi_{n}}\mathbf{x}_{n},$ for each unit, where
$\mathbf{x}_{n}$ is the incident signal and $\mathbf{y}_{n}$ is the reflected
signal, respectively. For the entire RIS surface, the relationship between the
incident and reflected signals can be represented by a diagonal matrix, as the
reflection units are independent, given as,
$\mathbf{y}=\text{diag}(\alpha_{1}e^{j\phi_{1}},...,\alpha_{1}e^{j\phi_{n}},...,\alpha_{1}e^{j\phi_{N}})\mathbf{x}=\boldsymbol{\Omega}\mathbf{x},$
where $\boldsymbol{\Omega}$ is the reflection coefficient matrix of RIS. The
RIS is designed to reflect incident signals maximally, i.e., ideally
$\alpha=1$. However, $\alpha$ in practice may not be equal to 1. It is usually
a constant with a value dependent on the specific circuit [73]. The magnitude
and phase, $\alpha$ and $\phi$, can be varied within an interval with the
limitation on cost and complexity. This leads to three practical reflection
coefficient types: constant amplitude with continuous phase shift, optimized
amplitude with continuous phase shift, and constant amplitude with discrete
phase shift. Continuous phase shift is assumed in some papers, but it is
limited by high hardware costs. Thus, a discrete phase shift is often used to
increase cost-effectiveness. It is worth mentioning that the amplitude and
phase control are not necessarily independent, i.e., when the phase is varied,
this also varies the amplitude.

  * •

Transmissive RIS: This type allows signals to pass through the surface,
modifying their characteristics in the process. This provides an additional
degree of flexibility in controlling the wave propagation. Incident signal
penetrates the RIS elements due to the absence of copper backplane as shown in
Figure 2(c) [72].

  * •

Hybrid RIS: Common RIS designs feature metasurfaces made of passive meta-atoms
that can reflect incoming waves in adjustable ways. However, this exclusive
reflection method poses considerable coordination challenges in wireless
networks. For instance, RISs don’t possess the needed data to modify their
reflection patterns independently; this data must be gathered by other network
components and then relayed to the RIS controller. Moreover, gauging the
communication channel, vital for coherent RIS-aided communication, is
problematic when using existing RIS models. Hybrid Reflecting and Sensing RISs
offer a solution by allowing metasurfaces to not only adjustably reflect the
incoming signal but also sense a fraction of it [74]. This sensing ability of
hybrid RISs supports many network management tasks, like estimating channel
parameters and pinpointing locations, paving the way for potentially self-
regulating and self-setting metasurfaces.

  * •

Active RIS: In the case of passive RISs, the path loss between the
transmitter-RIS-receiver connection is determined by multiplying, rather than
adding, the path losses of the transmitter-RIS and RIS-receiver connections.
This value is typically many times greater than the direct link’s path loss
[75]. Consequently, this ”multiplicative fading” phenomenon often renders it
highly challenging for passive RISs to realize significant capacity gains in
numerous wireless settings [76]. It is, thus, a significant performance
hindrance to passive RIS operation [77]. Active RIS was introduced as a
solution. Like its passive counterpart, it can reflect incident signals with
adjustable phase shifts, but it can also amplify these signals, as shown in
Figure 2(b). Its hardware architecture is, thus, different from the passive
RIS such that its design involves reflection-type amplifiers in addition to
the phase shift circuits. Active RIS needs additional power to operate [78].

  * •

Simultaneously transmitting and reflecting (STAR) RIS: This variant allows the
RIS to perform both transmission and reflection simultaneously, making it
highly efficient and versatile for various communication needs [79].
Conventional RISs, due to their hardware design, are capable of only
reflecting incident signals, serving wireless devices situated on the same
side. This restricts their deployment adaptability and coverage span [80, 81].
To overcome these limitations, a new type of metamaterial called simultaneous
transmitting and reflecting RIS (STAR-RIS) has been introduced [82, 83]. STAR-
RIS supports both electric-polarization and magnetization currents, enabling
it to reflect and/or transmit the incident signals [84]. In contrast to
conventional RIS, STAR-RIS can offer full-space service coverage (i.e., 360
degrees), leading to enhanced deployment flexibility.

  * •

RIS with Non-Diagonal Control: In conventional RIS structures, it is assumed
that a signal hitting a specific element can only be reflected from that same
element after the phase shift adjustment. There was no deliberate association
between the RIS elements. The phase shift matrix in such designs was diagonal,
such that each RIS element is connected to the load disassociated from the
other elements on the surface, leaving untapped potential for system
performance enhancement through RIS. On the contrary, RIS with non-diagonal
control has a design based on non-reciprocal connections, allowing the signal
impinging on one element to be reflected from a different element after phase
shift adjustment [85]. Consequently, the phase shift matrix can be non-
diagonal. This allows for greater adaptability in configuring the RIS
structure to optimize system performance. They can increase reflected power,
enhance aggregate data rate, and provide versatility in a variety of
deployment scenarios.

Figure 3: Illustration of RIS-assisted localization network. Localization
without RIS requires multiple BSs while the use of RIS makes localization
possible with lesser infrastructure with the added advantage of energy
efficiency, minimal deployment, and maintenance cost.

To get an idea of how RIS works in conjunction with BS for UE localization,
consider the scenario showing the RIS-assisted radio localization environment
in Figure 3 where a multiantenna BS equipped with $N_{\text{BS}}$ antennas is
located at ($x_{\text{BS}},y_{\text{BS}},z_{\text{BS}}$). RIS has
$N_{\text{RIS}}$ reflecting elements with its centre located at
($x_{\text{RIS}},y_{\text{RIS}},z_{\text{RIS}}$) and the UE with
$N_{\text{UE}}$ antennas is at ($x_{\text{UE}},y_{\text{UE}},z_{\text{UE}}$).
Two propagation paths are available in this scenario, i.e., a direct path from
the BS to the UE and a reflected path through the RIS to the UE. We consider
the generic channel model and frequency domain representation of the received
signal and channel model for $N$ samples spaced $\triangle f$ apart [86]. The
received signal at the UE for frequency $n\in\\{0,....,N-1\\}$ and symbol
$k\in\\{0,....,k-1\\}$ can be represented as,
$\mathbf{y}_{n,k}=\mathbf{H}_{n,k}\mathbf{x}_{n,k}+\mathbf{n}_{n,k}$, where
$\mathbf{y}_{n,k}$ is the received signal at the UE, $\mathbf{x}_{n,k}$ is the
transmitted signal from the BS, and $\mathbf{n}_{n,k}$ is the additive
Gaussian noise. The
$\mathbf{H}_{n,k}=\mathbf{H}_{n,k}^{\text{direct}}+\mathbf{H}_{n,k}^{\text{RIS}}$,
is the channel response of the direct and RIS reflected path. Here,
$\mathbf{H}_{n,k}^{\text{direct}}\in\mathbb{C}^{N_{\text{BS}}\times
N_{\text{UE}}}$ is the channel response of the direct path, given as
$\mathbf{H}_{n,k}^{\text{direct}}=\sum_{l=1}^{L}\alpha_{l}\mathbf{a}_{\text{UE}}(\theta_{l})\mathbf{a}_{\text{BS}}(\phi_{l})e^{-j2\pi
n\Delta f\tau_{l}}$, where, $\alpha_{l}$ is the complex channel gain with $L$
being the number of signal propagation paths,
$\mathbf{a}_{\text{UE}}(\theta)\in\mathbb{C}^{N_{\text{UE}}}$ is the UE array
response as the function of angle-of-arrival (AOA) $\theta\in\mathbb{R}^{2}$
in azimuth and elevation,
$\mathbf{a}_{\text{BS}}(\phi)\in\mathbb{C}^{N_{\text{BS}}}$ is the BS array
response as function of angle-of-departure (AOD) $\phi\in\mathbb{R}^{2}$ in
azimuth and elevation, and $\tau$ is the time-of-arrival (TOA). The
$\mathbf{H}_{n,k}^{\text{RIS}}$ is the RIS incident and reflected signal
channel response such that
$\mathbf{H}_{n,k}^{\text{BS,RIS}}\in\mathbb{C}^{N_{\text{BS}}\times
N_{\text{RIS}}}$ is the channel response of the path from the BS to the RIS
and $\mathbf{H}_{n,k}^{\text{RIS,UE}}\in\mathbb{C}^{N_{\text{RIS}}\times
N_{\text{UE}}}$ is the channel response from the RIS to the UE, collectively
given as,
$\mathbf{H}_{n,k}^{\text{RIS}}=\mathbf{H}_{n,k}^{\text{BS,RIS}}\mathbf{H}_{n,k}^{\text{RIS,UE}}=\alpha^{\text{RIS}}_{k}\mathbf{a}_{\text{UE}}(\theta_{\text{RIS}})\mathbf{a}_{\text{BS}}(\phi_{\text{RIS}})e^{-j2\pi
n\Delta f\tau_{\text{RIS}}}$. where $\alpha_{\text{RIS}}^{k}=\alpha_{\text{BS-
RIS}}\alpha_{\text{RIS-UE}}\mathbf{a}^{\top}_{\text{RIS}}(\phi_{\text{RIS-
UE}})\boldsymbol{\Omega}_{k}\mathbf{a}_{\text{RIS}}(\theta_{\text{BS-RIS}})$.
Here, $\alpha_{\text{RIS}}^{k}$ is controllable such that $\alpha_{\text{BS-
RIS}}$ is the complex gain from BS to RIS, $\alpha_{\text{RIS-UE}}$ is the
complex gain from RIS to the UE, $\mathbf{a}_{\text{RIS}}(.)$ is the RIS
response function as the function of AOA from BS, $\theta_{\text{BS-RIS}}$,
and the AOD to the UE, $\phi_{\text{RIS-UE}}$ and $\Omega_{k}$ determines the
RIS configuration [87]. For the localization of the UE, its position,
orientation, and clock bias information are inferred from the received signal
$\mathbf{y}_{n,k}$, details of which are well summarized in [88, 87]. The
process encompasses three steps: first, the channel parameters (TOAs, AOAs,
AODs) are estimated. Second, the LOS parameters and RIS path parameters are
extracted, and finally, the UE is localized.

RISs have a wide range of potential applications in wireless communication
systems. It includes improvement in the coverage, capacity, localization, and
security of wireless networks. RIS has the potential to enhance the accuracy
of wireless localization as it can create unique spatial patterns that can be
used to accurately locate wireless devices. This feature is particularly
useful in indoor environments where global navigation satellite systems (GNSS)
signals are weak or unavailable. Moreover, RIS is highly scalable and can be
easily integrated into existing wireless communication systems. This
scalability makes it an attractive solution for enhancing the performance of
radio localization key performance matrices such as accuracy, coverage,
latency, update rate, stability, scalability, mobility, and system complexity
[28].

###  II-B RIS Support for Radio Localization

In order to enhance communication and localization performance, next-
generation cellular networks will develop ”smart radio environments” where
walls and other objects will be covered with RISs. The concept of a smart
radio environment is such that when RIS effectively reconstructs radio signals
and modifies their inherent properties, such as the direction of transmission,
the polarization of the electromagnetic signal, etc., the wireless channel
becomes an intelligent transmission environment [69, 89]. New opportunities
for radio communication, sensing, localization, and computation are made
possible by the ability to intelligently govern the wireless signal
propagation channel. [90]. Authors have stated in [1] that RISs improve
localization accuracy and aid in extending the physical coverage provided that
the appropriate models and algorithms are built for the desired outcomes. RIS
has many advantages over traditional MIMO systems from the perspective of
localization. The RIS has a large surface area, which enables it to transmit,
receive or reflect radio signals more effectively. The CRLB for UE
localization decreases as the area of the RIS increases, with the exception
when it is located on the central perpendicular line of the RIS [7, 36]. The
author concludes that distributed deployments of RIS enhances the CRLB and
enlarges the coverage of localization. Authors in [38] proposed a powerful
online wireless RIS optimum phase design method that enhances the user’s
received signal strength (RSS) by concentrating transmission energy on the
projected UE position using deep learning. This paper highlights that RIS’s
large geometric size allows for high-precision radio localization and sensing
by finely estimating the position of UE and devices. Authors in [39] analyze
the RIS-aided downlink localization problem from the perspective of Fisher
Information analysis and show their coverage and accuracy benefits over the
reflecting surfaces and scatter points. Both papers demonstrate the potential
of RIS in improving the performance of radio localization systems.

It is worthwhile to mention that quantifying the phase and amplitude
accurately is crucial for achieving good localization accuracy with RIS.
However, measuring phase and amplitude with full resolution can be costly, so
it is deemed necessary to look into the effects of their quantization on the
localization with RIS. Authors in [36] assumed full-resolution phase and
amplitude measurements to study the impact of variations in phase and
amplitude quantization resolutions on the CRLB of estimation, whereas, [37]
studied the effects of quantized amplitude and phase on the CRLB of
localization. The numerical results showed that there is no significant
difference between the CRLB loss in both cases. However, the impact of the
phase quantization is much more prominent compared to the amplitude
quantization. This discovery drives the practical use of RIS-assisted
localization, which improves the phase resolution at the RIS to produce
improved localization accuracy. To theoretically study the CRLB of location
estimate, the authors in [7, 36, 37] looked into RIS-assisted UE localization
using the LOS propagation assumption.

It is challenging to utilize explicit geometric information in wireless
channels below 6 GHz due to limitations in delay and angle resolution, as well
as weak connectivity of paths to the environment geometry. On the other hand,
in frequencies above mmW, paths are more closely linked to the environment
geometry and can be more effectively resolved [88, 91]. Moreover, the high
bandwidth and the large antenna arrays at the mmW and THz band results in high
spatial resolution as well as high temporal resolution. These are the
prominent reasons that the majority of the studies in contemporary literature
are based on higher frequency bands, respectively [92, 93, 94, 95, 40]. These
studies usually assume an indoor scenario due to the limited range of
transmission of mmW or THz frequencies. The GNSS can provide outdoor
localization services with acceptable accuracy. Nevertheless, GNSS-based
localization becomes disadvantageous in a setting where GNSS signal strength
is negligible, such as indoors. When RIS is utilized in such an environment,
it not only helps users localize themselves accurately in their surroundings,
but it also helps reduce communication congestion brought on by obstructions.

(a) Collaboration among humans and robots in an intelligent factory

(b) Traffic monitoring and landscape sensing for an immersive smart city

(c) Local coverage/infrastructure less network extensions

(d) Telepresence and multisensory extended reality for enhanced interactions

(e) Interactive IoT networks for local trust zones for humans and machines

(f) Wireless brain-computer interfaces for patient tracking and monitoring in
local trust zones for human and machines

(g) Telecontrol and gesture recognition in human-machine interface for
enhanced interactions

Figure 4: Illustration of RIS-assisted localization applications in 6G
networks from the use case families in [96]. Use cases prominently include
sustainable development, massive twinning, telepresence, robots to
collaborative robots, and local trust zones.

There is another important factor that is worth mentioning when looking at the
algorithms and methods developed for RIS-assisted localization, that is,
methods developed with the far-field assumption are not applicable in near-
field since there is a clear distinction between the two [1]. In far-field we
have energy traveling away from the source and the plane wave assumption holds
while in near-field the energy is periodically stored and returned to the
source and the radiation pattern in near-field is not fixed but it depends on
distance from the source, i.e., spherical wave model assumption holds. The
near-field region in RIS-assisted networks is directly related to the surface
area of the RIS. When the distance to the RIS is only moderate, near-field
propagation takes place, resulting in a curvature of the wavefront. This
curvature needs to be correctly represented and factored into the
communication system. It has been shown that the near-field area of RIS has an
inverse relationship with the wavelength of the incident signal. The
variations in far-field distance in relation to the frequency of the incident
wave for various RIS sizes is shown in [97] (Figure 7 and 8). The near-field
region of RIS, in particular, rises with operation frequency and surface area,
and the UE is most likely to be situated in this region. Thus, especially in
indoor settings, at mmW and THz operating frequencies, far-field models are
not truly applicable.

###  II-C Potential Applications of RIS-Assisted Localization

RIS technology has the potential to revolutionize many applications that
require centimeter level accuracy and low latency, less infrastructure, cost
reduction and reduced power consumption such as IoT networks, smart cities and
automated factories as presented in Figure 4 [96]. Localization and sensing
use cases with there relevant key performance indicators (KPIs) are discussed
in detail in [96].

RIS-assisted localization can play a crucial role in enabling high
localization performance for intelligent interactive IoT networks [71]. In the
context of intelligent interactions, RIS can optimize the propagation of
signals for people-to-people, people-to-machine, and machine-to-machine
communication. For multisensory extended reality (XR) applications, RIS can
assist in achieving accurate device tracking by optimizing signal propagation
between XR devices and the control center, as shown in Figure 4(d). By
intelligently manipulating the wireless links, RIS can minimize signal delay
and ensure precise localization, thus enhancing the user experience and
reducing issues like cybersickness. For tele-presentation and tele-control
technologies, RIS can assist in achieving accurate location information and
reducing mismatch errors, as in Figure 4(g) [29]. By optimizing signal
propagation and calibration between remote and neighboring localization
systems, RIS can enhance real-time environment capturing, information
transmission, and 3D mapping [98]. In the case of wireless brain-computer
interfaces (WBCI), RIS can enhance localization accuracy by intelligently
manipulating the wireless signals between human-centric IoT devices [99], as
in Figure 4(f). By optimizing the signal paths, RIS can minimize interference
and improve the reliability and data rate required for WBCI applications
[100]. This enables seamless patient tracking and monitoring irrespective of
their location. This is particularly critical for applications like tele-
surgery, where highly accurate, ultra-reliable, and low-latency localization
is vital.

In the context of smart indoor services, RIS-assisted localization can address
the challenges posed by NLoS signal propagation by providing local coverage,
as in Figure 4(c). By intelligently manipulating wireless signals, RIS can
optimize the signal paths and mitigate the degradation of localization
accuracy caused by NLoS scenarios [101]. Additionally, RIS can enhance privacy
protection by selectively controlling the accessibility of location
information, ensuring that sensitive data remains secure while enabling
appropriate access for public devices. Smart transportation, on the other
hand, requires advanced localization technologies for autonomous driving and
vehicle-to-vehicle (V2V) communications. For autonomous driving, RIS-assisted
localization can provide precise distance information between vehicles and
obstacles, facilitating real-time 3D mapping and the construction of an
accurate environment model. By optimizing wireless signal propagation, RIS can
improve the accuracy of distance measurements, enhancing the safety and
efficiency of autonomous vehicles. Moreover, RIS can support wide coverage and
robust localization in highly mobile scenarios, a critical requirement for
smart transportation systems. By intelligently manipulating wireless signals,
RIS can extend the coverage of localization systems, ensuring that vehicles
can maintain accurate positioning even in dynamic and fast-paced environments.
This enables reliable V2V communications and the effective coordination of
smart transportation networks and immersive smart cities, as in Figure 4(b).

RIS-assisted localization can enable accurate, low-latency and robust
localization for the development of automatic factories and connected robotics
and autonomous systems (CRAS), as in Figure 4(a) [102]. By integrating RIS
into the factory environment, it becomes possible to optimize wireless signal
propagation and enhance localization capabilities. In automatic factories,
where effective cooperation among IoT devices is paramount, RIS-assisted
cooperative localization can provide highly accurate, low-latency, and
reliable location information for the multitude of devices involved [99]. By
intelligently manipulating wireless signals, RIS can optimize communication
links and improve the end-to-end (E2E) connectivity among the devices. This
enables seamless collaboration between autonomous robotics, drone-delivery
systems, and other IoT devices, facilitating efficient smart storage,
autonomous production, and autonomous delivery within the automatic factory
setting.

Overall, there are many open challenges to fulfill the KPIs for the envisioned
use cases of localization and sensing in 6G networks as listed in Section IV.
However, the integration of RISs into communication networks can significantly
enhance localization and sensing performance. By intelligently manipulating
wireless signals, RIS can optimize signal paths, minimize delay, and improve
accuracy, thereby enabling intelligent interactions across various
applications.

###  II-D RIS-Assisted Localization Taxonomy

Figure 5: Taxonomy of RIS-assisted localization. Signal processing involving
localization measurements and algorithms could take place at the UE or BS
depending on the mode of communication, i.e., uplink or downlink.

RIS-assisted localization works by estimating the location and orientation of
a UE with the help of anchor nodes (BS and RIS) provided the location of
anchor nodes is already known [1]. To locate itself, UE sends out a known
uplink pilot signal to the BS or receives a downlink pilot signal from the BS.
The signal’s behavior is influenced by the propagation channel, which depends
on the location and orientation of the BS and UE, as well as the environment
surrounding them. The level of distortion in the received signal is determined
by reflections from the RIS and other objects in the vicinity [87]. The direct
unobstructed link from the BS to the UE is called the line-of-sight (LoS)
path, the path of the signal reflected by the RIS is RIS path, and all the
other NLoS paths from walls and objects in the environment are classified as
the reflected paths and the scattered paths, respectively. Successful modeling
of the pilot signal and the channel allows us to estimate the channel state
information and identify the parameters for the signal paths [28]. These
parameters, which aid in localization, include TOA/delay, AOA, and AOD. UE
location can be estimated based on these parameters and their geometrical
relationships with the BS and RIS location [88]. RIS-assisted radio
localization scenarios can be in indoor or outdoor environments, based on
which the channel modeling is different.

RIS-assisted localization can be classified on the basis of the application
scenario, localization technique, functionality of RIS employed and its
configuration and deployment details, localization method as well the
localization performance matrices. A brief taxonomy of RIS-assisted
localization systems is shown in Figure 5 and illustrated in Figure 6,
respectively.

####  II-D1 Localization Measurements

Geometry-based techniques are widely used for radio localization and typically
involve timing-based (TOA) and angle-based (AOA/AOD) methods [22]. Time-of-
flight (ToF) is the time taken by the signal to travel from the BS to the UE.
Timing-based localization technique, named trilateration, uses the measured
ToF while considering the effects of RIS reflections, to estimate the
location. The technique requires at least three BSs to get an unambiguous 2D
estimate of the UE location when RISs are not considered. An alternative is to
estimate round-trip time (RTT) by recording signal transmission, processing
and reception times, providing necessary TOA information. Well-synchronized
systems can directly infer TOA from signals, with resolution dependent on
signal bandwidth. Angle-based localization technique, named triangulation,
estimate the angle from which signals arrive at the receiver, incorporating
RIS-assisted reflections, to determine the location [103]. It is typically
employed when an antenna array is available at the BS. Time-difference-of-
arrival (TDOA) estimation, AOD-based estimation that is applicable when UE is
equipped with an antenna array, angle-difference-of-arrival (ADOA) location
and orientation estimation are some of the other estimation techniques rooted
in TOA and AOA.

RSS-based localization techniques utilize the received signal strength
measurements from RIS-assisted reflections to estimate the location of the
receiver [38]. It assists with the geometry-based trilateration and
fingerprinting localization algorithms [66]. This method capitalizes on the
sensitivity of RSS to spatial variations, allowing for accurate localization
even in complex indoor or outdoor urban scenarios [104]. The reconfigurability
of RISs enables real-time adaptation to changing propagation conditions,
enhancing the precision and robustness of the localization system.

Channel state information (CSI)-Based localization methods exploit the fine-
grained channel state information obtained through RIS-assisted reflections
for accurate localization. CSI contains valuable insights into the wireless
propagation environment, including path loss, multipath components, and
spatial signatures [61]. Using advanced signal processing and machine learning
algorithms, the collected CSI data can be used to estimate the position of the
devices [63]. The integration of RISs with CSI-based localization enabled
radio localization in dynamic scenarios with changing propagation conditions.

####  II-D2 RIS type

The modeling and optimization of RIS-assisted localization is tightly bound
with the operation method of RIS [71]. RIS can be employed in communication
systems in an active or passive state [78]. Passive RIS are made of passive
elements that can adjust the phase of the incoming electromagnetic wave
without the need for a power source. They are simpler in structure and require
less power than active RIS, making them more cost-effective and potentially
easier to implement. However, they offer less control over the signal than
active RIS, as they can typically only adjust the phase of the signal, not the
amplitude. They are deployed strategically and their phase shifts are designed
and optimized carefully to act as reflective surfaces to enhance the
constructive interference and signal quality. Active RIS are equipped with
active elements, which typically include integrated circuits for signal
amplification. They are capable of independently manipulating the phase and
amplitude of the incoming signals [76]. This independent control allows for
more complex and potentially more beneficial manipulation of signals, but it
also requires power and more complex design and control strategies. Since the
elements can dynamically adjust their phase, the phase configuration becomes
even more versatile. Active RIS elements allow for more sophisticated control
over the signals, including dynamic beamforming, signal amplification, and
cancellation of interference [105]. In the context of radio localization,
active RISs are strategically placed in the environment to actively modify
wireless propagation by intelligently adjusting the directionality of the
transmitted and reflected signals. Active RIS-based localization systems offer
benefits such as enhanced flexibility, adaptability to dynamic environments,
and the potential for improved localization performance in challenging
scenarios. Passive RISs are more energy efficient, easily scalable and
deployable compared to active RISs.

####  II-D3 RIS configuration and deployment

The configuration and deployment of RISs play a crucial role in RIS-assisted
radio localization. The optimization of the phase configuration in RIS systems
holds immense significance in unlocking its full potential. By precisely fine-
tuning the phase shifts of the RIS elements, we can achieve remarkable control
over signal propagation, allowing for unprecedented customization and
optimization of wireless communication links [1]. The optimization process
involves carefully analyzing the channel characteristics, understanding the
desired signal characteristics, and employing advanced algorithms to determine
the optimal phase shifts for each RIS element [87]. Through this optimization,
we can exploit constructive interference, nullify destructive interference,
and shape the signal to match specific requirements, such as maximizing
coverage, minimizing signal attenuation, or focusing energy in desired
directions [28].

The placement and arrangement of RISs in the environment directly impact the
accuracy, coverage, and performance of the localization system [78].
Determining the optimal locations for deploying RIS elements is fundamental to
achieving the desired coverage area, signal propagation characteristics, and
localization requirements. Proper deployment ensures optimal coverage of the
desired area, minimizing blind spots and maximizing the availability of RIS
reflections for localization purposes [1]. Additionally, the number of RIS
elements deployed per unit area or volume affects the granularity of control
and the accuracy of localization. The configuration of the RIS elements,
including their reflection coefficients and phase shifts, is vital in
manipulating the signal propagation and optimizing the received signal at the
receiver [106]. The dynamic reconfigurability of RISs further enhances their
role, allowing for real-time adaptation to changing propagation conditions and
environmental dynamics [107]. Moreover, ensuring proper synchronization among
the RIS elements to avoid interference is equally important to configure. By
intelligently configuring and deploying RISs, the localization system can
achieve improved accuracy, robustness, and scalability, enabling a wide range
of location-based applications in various scenarios.

Figure 6: Illustration of RIS-assisted localization taxonomy. Signal
processing involving localization measurements and algorithms could take place
at the UE or BS depending on the mode of communication, i.e., uplink or
downlink.

####  II-D4 Localization algorithms and optimization

RIS-assisted localization algorithms can be broadly classified as model-based
and learning-based [28]. Model-based methods include deductive (physics-based)
techniques such as geometry-based location estimation algorithms, Kalman
filters, fingerprinting, and SLAM. On the other hand, learning-based
techniques are inductive (data-driven) and leverage machine learning
algorithms such as neural networks to learn and model the relationship between
RIS-assisted signals and the receiver’s location. The advantages of model-
based approaches versus data-driven methods are numerous. They are supported
by performance constraints that give solid assurances of optimality and
dependability, rely on well-established signal processing techniques, and
offer typically less complexity than data-driven systems. Some of the model-
based approaches that are commonly employed in RIS-assisted localization are
briefly discussed below.

  * •

Geometry-based methods: Geometry-based methods rely on the TOA and AOA
measurements or their combination to determine the 2D or 3D location of the UE
[88, 87]. In traditional systems, such methods require a combination of
measurements from multiple BSs to determine the UE location. However, the
location of the UE can be estimated with the help of one BS and a RIS [39],
more details in [34]. Location estimation typically entails creating an
objective function that incorporates geometric information and solving an
optimization problem with geometric constraints. Geometry-based localization
techniques are characterized by being free from training requirements, easily
analyzable from a theoretical standpoint, and scalable across various
environments.

  * •

Kalman Filter: The Kalman filter is a recursive estimation algorithm that
optimally fuses noisy measurements with a dynamic model to estimate the state
of a system. In the context of localization, the Kalman filter predicts the
device’s position based on the previous state estimate and motion dynamics and
then updates it using RIS-assisted measurements such as RSS or ToA [108]. The
Kalman filter-based approach can effectively mitigate the impact of noise,
multipath, and other propagation effects on localization accuracy by
iteratively updating the state estimate and incorporating RIS-assisted
measurements [87]. Despite its widespread use in localization, Kalman
filtering has several limitations. Its assumptions of system linearity and
Gaussian noise can be inaccurate in complex, real-world scenarios [28]. The
initial state, which the filter requires, may not always be accurately known,
and any errors in it can propagate, causing inaccuracies in state estimation.
Furthermore, Kalman filters assume constant process and measurement noise
covariances, an assumption often violated in real-world conditions. For
nonlinear challenges, one can utilize an extended Kalman filter [109]. This
approach estimates the state distribution by employing a Gaussian random
variable and advances it through first-order linearization. Finally, Kalman
filters are sensitive to model mismatches and outliers, which can
significantly affect their performance [28].

  * •

Fingerprinting: In the fingerprinting approach, a database of signal
fingerprints is created by collecting and mapping the received signal
characteristics at various locations in the environment [14]. The fingerprint
database contains information such as RSS, CSI, or signal amplitude patterns
specific to each location. RSS has limited precision and CSI demands
significant computational resources, spatial beam signal-to-noise ratios
(SNRs) are chosen as an intermediate channel measurement with moderate
granularity [110]. When a device needs to be localized, it measures the RSS or
other signal characteristics at multiple RIS-assisted points in the
environment. These measurements are then compared with the fingerprint
database to find the closest match. The RISs play a crucial role in this
process by manipulating the wireless channel to enhance the quality and
reliability of the measurements. By adjusting the reflection coefficients of
the RIS elements, the received signal can be optimized, leading to more
accurate and consistent measurements. However, if the configuration of the RIS
changes over time, it would effectively alter the environmental
characteristics that the fingerprint is based on, potentially decreasing the
accuracy of location estimates. To guarantee the stationarity of the
environment when employing RIS in fingerprinting, the configuration of the RIS
should be kept constant during both the fingerprinting process and when using
the fingerprint for location estimation. This means that the phase shifts or
other manipulations applied to signals by the RIS should be fixed and not vary
over time. In practice, this might require careful design and control of the
RIS, and thorough testing to ensure its configuration remains stable under
different conditions. This issue is resolved by including in the fingerprint
the RIS configuration. This provides a richer set of fingerprints. The
fingerprint matching process can utilize techniques such as pattern matching
or machine learning algorithms, deep learning methods such as deep neural
networks (DNN) and convolutional neural networks (CNN) to find the best match
between the measured signals and the fingerprints in the database [47, 110,
28, 111]. Once the closest match is found, the device’s location is estimated
based on the known location associated with the matched fingerprint [66].
Fingerprinting can handle complex indoor or outdoor environments where
multipath and NLoS conditions pose challenges for traditional localization
techniques [19].

  * •

Simultaneous localization and mapping: SLAM is a well-established method used
to estimate the position of a device while constructing a map of its
surroundings [10]. In this approach, RISs are strategically deployed in the
environment to manipulate the wireless channel, enhancing the quality of
received signal measurements [112]. During the SLAM process, the device
measures parameters such as RSS or ToA at multiple RIS-assisted points. These
measurements, along with the known positions of the RISs, are used to estimate
the UE location and construct a map of the environment [113]. The RISs play a
crucial role in improving the accuracy and reliability of the localization and
mapping process by optimizing the quality of the received signals [113]. It
offers the advantage of accurate localization in complex environments where
multipath propagation and NLoS conditions may exist. Additionally, RISs can
adaptively adjust their reflection coefficients, enhancing the performance and
robustness of the SLAM-based localization system.

Explicit modeling of geometry information becomes difficult in complicated
cases when there are several non-resolvable NLoS pathways [22]. So learning-
based approaches are recommended [18, 20, 114]. ML-based techniques need
offline training, which drastically decreases online calculations, in contrast
to the practical algorithms employed in geometry-based localization [18]. To
train the models, however, a significant amount of system data must be
gathered, and the learned models must be updated on a regular basis to account
for changes in the environment. The DNN are used to perform environmental
sensing to achieve the best performance in RIS-assisted radio localization
[66, 47, 115, 27]. Since the channels are sparse at the higher frequency
bands, therefore, most of the studies use geometry-based algorithms [28].

####  II-D5 Localization performance evaluation

A radio localization system is designed on the basis of a number of
performance objectives that include accuracy, coverage, latency, robustness,
resolution, update rate, stability, scalability, mobility and system
complexity, etc [28]. Here we discuss only a few of them.

Accuracy and precision are metrics to assess the accuracy of the estimated
location compared to the ground truth, and the level of precision in
determining the location, often represented by the standard deviation or
confidence interval. Accuracy is the most widely used localization metric in
state-of-the-art studies as it accounts for localization resolution as well as
identifiability. Cramér-Rao lower bounds (CRLB) are the commonly used bounds
on the achievable accuracy in studies. The deployment geometry, also known as
geometric dilution of precision (GDoP) or UE relative position with respect to
BSs, determines accuracy in addition to link-level SNR [34].

The separability of completely correlated radio propagation channels in at
least one domain is referred to as resolution. Unresolvable signal paths will
be treated as a single path, limiting accuracy (regardless of SNR), and
producing worse performance than predicted by analytical bounds. The
resolution is constrained by the physical resources available, such as antenna
array aperture for angle resolution and bandwidth for delay/distance
resolution [88]. Despite having a high degree of resolution, radio
localization can nevertheless suffer from ambiguity and non-identifiability
[34]. This indicates that the localization problem of UE might have many
solutions or a continuous space of solutions. This might happen when there are
barriers in the way of specific BS signals, or when infrastructure rollout or
coverage is insufficient. Ambiguity, which emerges as numerous unique
locations, is frequently addressed by prior knowledge or external signals. On
the other hand, non-identifiability poses a more significant challenge as
there are numerous equally valid solutions to the localization problem, and it
is difficult to discard them based on external information.

The robustness of the localization algorithm evaluates its performance under
various environmental conditions, such as multipath fading, interference, and
mobility. It accounts for availability, latency, and update rate under such
conditions [28]. Likewise, assessing the energy consumption of RIS-assisted
localization techniques, considering both RIS elements and the receiver device
is an important measure in RIS-assisted localization in comparison to the
systems without RISs. Energy-efficient techniques aim to minimize energy
consumption while achieving accurate localization. Computational complexity
quantifies the computational resources needed to perform RIS-assisted
localization at the hardware and algorithm level. It includes the processing
power, memory requirements, and time complexity of the localization algorithm.
Lower computational complexity allows for faster and more efficient
localization.

##  III State of The Art in RIS-Assisted Radio Localization

Figure 7: State of the art literature breakdown. Studies can be broadly
categorized into outdoor, far-field, indoor and near-field RIS-assisted
localization. Within these categories, we notice trend towards investigation
of systems on mmW and THz frequencies. Further, the passive reflective RIS is
the most common type of RIS used in literature for RIS-assisted localization.

In this section, we discuss the recent literature on RIS-assisted radio
localization for 6G networks. The trends in the latest studies are on
developing algorithms, optimization, and investigation of RIS-assisted
localization systems from the perspective of accuracy and availability at
various frequency bands, near-field and far-field modeling, and indoor and
outdoor scenarios, summarized in Figure 7.

In the realm of RIS-assisted localization, we classify the frequency bands
into two categories: conventional radio frequency (CRF) bands localization and
high frequency bands localization. Conventional frequency bands typically
refer to those below 24 GHz, encompassing well-known sub-bands such as FR1,
which usually includes frequencies from 450 MHz to 6 GHz. CRF bands are
recognized for their longer wavelengths, better penetration capabilities, and
widespread application in various wireless services. For outdoor localization,
GNSS is predominantly employed, providing meter-level precision with support
from long-term evolution (LTE) communication signals. Yet, this method proves
ineffective for indoor environments, where intricate surroundings and LoS
channel obstructions are common challenges. As alternatives, there are
documented localization systems utilizing UWB [116], WiFi [117], WLAN [118],
and LoRA [119] [15]. By leveraging CRF bands systems in association with the
RISs, advantages are gained in location-centric services, including navigation
and identification of nearby amenities.

On the other hand, we refer to the localization services in the range of
spectrum above 24 GHz as high frequency bands localization. This category
consists of FR2 frequencies, mmW bands ranging from 30 to 300 GHz and THz
band, extending even beyond 300GHz. The range of 100- 300 GHz in mmW band is
also referred to as the sub-terahertz (sub-THz) according to the deliverable
D2.1 of the European HEXA-X project [120]. These higher frequency bands offer
significant advantages in terms of data rate, capacity and localization
performance but come with challenges related to propagation and penetration.
Utilizing antenna arrays at the UE enables orientation estimation [92].
Furthermore, through the use of NLOS paths [121] and RISs [40, 1] localization
tasks can be accomplished with only one BS. THz systems are anticipated to
complement mmW systems in diverse environments, and the comparison between the
two reveals distinct advantages and challenges in terms of localization [28].
As technology progresses from CRF to 5G and onto 6G, expectations include
higher frequencies, increased bandwidths, more compact footprints, and larger
array sizes [122]. These changes will affect path loss, delay estimation
resolutions, and antenna array design, among other features. Challenges may
arise with hardware imperfections and synchronization at THz frequencies. The
design of localization algorithms must also consider the specific properties
of THz signals, such as the beam split effect and high path loss [28].
Ultimately, the adaptations and innovations within the THz systems are
expected to lead to improved localization performance in 6G networks.

We group the studies as the development of methods for localization of UE in
outdoor and far-field, indoor and near-field, respectively. To enhance reader
clarity, it is important to emphasize that the first group predominantly
encompasses studies centered around far-field and outdoor scenario
investigations. Distinct groupings have been established to specifically
address indoor scenarios and near-field localization, both of which are
considered specialized cases. Studies are presented in comprehensive detail
and tabular format in the subsequent subsections.

###  III-A RIS-Assisted Outdoor and Far-Field Localization

In this subsection, we explore the latest advancements and contemporary
studies focused on leveraging RIS technology to augment outdoor and far-field
localization.

####  III-A1 RIS-Assisted Conventional Radio Frequency Bands Localization
(Below 24 GHz)

TABLE III:  SUMMARY OF RIS-ASSISTED OUTDOOR AND FAR-FIELD LOCALIZATION ARTICLES AT CONVENTIONAL RADIO FREQUENCY BAND. HERE ”R” REFERS TO ”REFLECTIVE RIS” RIS |  Year |  Ref |  $f_{c}$ |  Link |  System |  Purpose |  Technique |  Performance metric
---|---|---|---|---|---|---|---|---
R |  2022 |  [123] |  1.5 GHz |  DL |  MISO |  RIS for sensing/localizing targets in wireless networks |  Self-sensing RIS architecture, customized MUSIC algorithm, CRLB |  accuracy
R |  2022 |  [124] |  2 GHz |  DL |  MISO |  Localization with obstructed LoS and three RISs |  Elimination of destructive effect of the AoD |  accuracy
R |  2023 |  [125] |  2.5 GHz |  DL |  MIMO |  Joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size |  Non-convex optimization |  detection probability, SNR
R |  2022 |  [126] |  2 GHz |  DL |  SISO |  UE localization assisted by multiple RISs |  Localization algorithm design based on nodes distances |  accuracy
R |  2021 |  [127] |  3 GHz |  DL |  SISO |  RSS fingerprinting based multi-user outdoor localization using RIS and single BS |  localization error minimization (LEM) problem, LEM algorithm |  accuracy
R |  2022 |  [65] |  3 GHz |  |  SISO |  Fundamental limits of RIS-aided localization and communication system with RIS as continuous and discrete intelligent surface |  RIS phase design, FIM |  accuracy, spectral efficiency, SNR
R |  2022 |  [128] |  3 GHz |  DL |  SISO |  Wideband localization with RISs |  FIM |  accuracy
R |  2022 |  [129] |  3 GHz |  DL |  SISO |  JCAL system design |  PEB, joint RIS discrete phase shifts design and subcarrier assignment using Lagrange duality and penalty-based optimization |  accuracy and data rate
R |  2022 |  [130] |  3 GHz |  DL |  SISO |  Multi-user localization system using modulated RIS |  TDOA |  accuracy
R |  2023 |  [131] |  3.4/3.5/28 GHz |  |  MIMO |  Localization technique that does not require RIS codewords for online location inference |  Domain adversarial neural network, fingerprinting |  accuracy
R |  2021 |  [55] |  4.9 GHz |  DL |  MIMO |  Analysis of multiple-RIS-aided localization system |  CRLB, RIS phase shift design using particle swarm optimization (PSO) algorithm |  accuracy
R |  2023 |  [132] |  4.9 GHz |  UL |  MIMO |  UE position estimation |  compressed sensing orthogonal simultaneous matching pursuit (DCS-SOMP) algorithm, MLE, discrete Fourier transform (DFT), space-alternating generalized expectation maximization (SAGE) algorithm, CRLB |  accuracy
R |  2022 |  [133] |  5 GHz |  DL |  MIMO |  Lower bounds on the location estimation error for multiple RISs -aided mmW system |  CRLB, PEB, REB |  accuracy
R |  2023 |  [134] |  10 GHz |  DL |  MIMO |  Bayesian analysis of the information in LoS and RIS reflected signal |  Bayesain analysis, FIM |  accuracy
R |  2022 |  [62] |  20 GHz |  UL |  MISO |  To localize a large number of energy-limited devices simultaneously and accurately |  Triangulation-based localization framework, optimization |  accuracy, energy efficiency
|  |  |  |  |  |  |  |

Studies are briefly summarized in Table LABEL:table1. We attempt to categorize
the works into thematic groups based on their core focus as follows.

##### Foundational Studies

Authors in [127] introduce RIS to enhance RSS fingerprinting-based outdoor
localization using just one BS. By adjusting the RIS phase shifts, the
approach creates distinct RSS values at the same location, optimizing this
through a localization error minimization (LEM) algorithm. Simulations confirm
the scheme’s efficacy. Authors in [55] derive the Cramér-Rao bound (CRB) for a
multiple-RIS-aided MIMO positioning system and investigate the impact of the
number, sizes, and phase shifts of RISs on positioning performance. Results
demonstrate that the use of multiple RISs improves the position error bound
(PEB) and rotation error bound (REB), highlighting the potential of RISs to
achieve high positioning accuracy. Article [65] introduces the concept of
continuous intelligent surfaces (CIS) and investigates the fundamental limits
of RIS-aided ISAC systems. The paper proposes a general signal model, derives
theoretical limits on localization and communication performance, and performs
Fisher information analyses. The numerical results demonstrate that optimized
RISs can improve the SNR and spectral efficiency of communication, as well as
enhance localization accuracy. Authors in article [62] propose a RIS-assisted
positioning method for simultaneously localizing multiple energy-limited IoT
devices in location-based IoT services. The proposed method utilizes
triangulation-based localization, estimating the propagation delay difference
between the direct and reflected paths using cross-correlation. By optimizing
the multi-antenna BS and RIS to minimize total transmission power, significant
power gain and decimeter-level positioning accuracy are achieved,
demonstrating the effectiveness of the proposed optimization approach compared
to unoptimized RIS-assisted localization.

##### Novel Techniques and Systems

In [123], the authors propose a new RIS self-sensing system where the RIS
controller transmits probing signals and dedicated sensors at the RIS are used
for location and angle estimation based on the reflected signals by the
target. The multiple signal classification (MUSIC) algorithm is applied to
accurately estimate the direction-of-arrival (DOA) of the target in the RIS’s
vicinity, and the RIS passive reflection matrix is optimized to maximize the
received signals’ power at the RIS sensors, leading to minimized DOA
estimation mean square error (MSE). The results demonstrate the benefits of
using the RIS controller for probing signals and provide the CRLB for target
DOA estimation. In [124], a positioning algorithm is introduced for RIS-
assisted networks, focusing on multi-antenna BS and single-antenna UE.
Leveraging three RISs and specific phase shifter adjustments, the method
effectively overcomes LoS obstructions and minimizes the adverse effects of
the AOD, resulting in improved localization accuracy compared to non-AoD
estimating algorithms. The study [125] introduces a joint active and passive
beamforming design for RIS-enabled ISAC systems, accounting for target size.
Through an alternative optimization method, the paper addresses non-convex
problems involving beamforming solutions and RIS phase shifts, with the
developed algorithm showcasing superior target detection performance in
simulations, especially for practical target sizes, against existing
benchmarks. The article [134] presents a Bayesian analysis of the information
contained in a signal received by a UE from a BS that includes reflections
from RISs. The analysis considers both near and far-field scenarios and
incorporates prior information about the UE and the RISs for localization. The
results indicate that the orientation offset of the RISs affects the pathloss
of the RIS paths when the RIS elements are spaced half a wavelength apart. In
the far-field regime, an unknown phase offset in the received signal prevents
the correction of the RIS orientation offset. However, in the near-field
regime, the estimation of the RIS orientation offset is possible when the UE
has multiple receive antennas. The article also demonstrates that accurate
localization with RISs is only possible when there is prior knowledge of their
locations. Finally, numerical analysis shows the loss of information when
applying a far-field model to signals received in near-field propagation.

Unlike the methods that rely on channel matrices or RIS codewords, the authors
in [131] proposed an approach that uses a domain adversarial neural network to
extract codeword-independent representations of fingerprints for online
location inference in RIS-assisted localization network. The solution is
evaluated using the DeepMIMO data set, and the results show that the proposed
method performs significantly closer to the theoretical upper bound (oracle
case) than the lower bound (baseline case), indicating its effectiveness and
robustness. Authors in [132] article investigate the estimation of position
and angle of rotation for a UE in a MIMO system with the assistance of a RIS.
The RIS creates a virtual LoS (VLoS) link, along with NLoS links from
scatterers in the environment, to aid in the estimation process. A two-step
positioning scheme is utilized, where channel parameters are acquired first
and then position-related parameters are estimated. Coarse estimation is
performed using various algorithms, followed by joint refinement using the
space-alternating generalized expectation maximization (SAGE) algorithm. The
performance of the proposed algorithms is demonstrated to be superior through
simulation results, and theoretical quantification is done using the CRLB.

####  III-A2 RIS-Assisted High Frequency Bands Localization (24 GHz and above)

Higher frequency bands can be broadly categorized as mmW Band (30-300 GHz) and
THz (0.1-10 GHz) bands of radio communication [28]. Studies on RIS-assisted
localization in this band of operation are briefly summarized in Table
LABEL:table2 and Table LABEL:table3 and discussed as follows.

##### Foundational Studies

Authors in [39] discussed the use of RIS in 5G radio positioning. The authors
propose a two-step optimization scheme that selects the best combination of
RIS and controls their constituent elements’ phases to improve positioning
performance. Preliminary simulation results demonstrate gains in coverage and
accuracy compared to natural scattering, but limitations are identified in
terms of low SNR and inter-path interference. Assuming the LOS route between
the BS and the MS is present, authors in [40] introduced RIS as a reflector
into the mmW MIMO positioning system. The CRLB of the positioning as well as
the orientation estimation error are obtained by calculating FIM, which
reveals that the RIS-aided mmW MIMO positioning system offers better
localization accuracy and coverage as compared to the conventional
localization system comprising BS nodes only. It has also been demonstrated
that one BS with the help of reflection from RIS can also achieve promising
positional precision. Nevertheless, nothing is discussed about how to localize
the UE in a LOS-obscured environment. To determine the absolute location of
the MS under the NLoS scenario, authors in [41] developed the CRLB based on
FIM. The study suggests that, in the given setup, the localization can reach
the decimeter level of accuracy by refining the reflect beamforming
architecture to reduce CRLB. Authors in [58] present localization and
synchronization in a wireless system with a single-antenna UE, a single-
antenna BS, and a RIS. They calculate the CRB and develop a low-complexity
estimator to determine the AOD from the RIS, as well as the delays of direct
and reflected signals. The results indicate that efficient 3D localization and
synchronization are achievable in the considered system, showcasing the
potential of RIS for enabling radio localization in simple mmW wireless
networks.

TABLE IV:  SUMMARY OF RIS-ASSISTED OUTDOOR AND FAR-FIELD LOCALIZATION ARTICLES AT HIGH FREQUENCY BANDS. HERE ”R” REFERS TO ”REFLECTIVE RIS”, , ”S” REFERS TO ”STAR RIS”, ”A” REFERS TO ”ACTIVE RIS” RIS |  Year |  Ref |  $f_{c}$ |  Link |  System |  Purpose |  Technique |  Performance metric
---|---|---|---|---|---|---|---|---
R |  2022 |  [108] |  24 GHz |  DL |  MISO |  RIS to replace the function of a remote cell in the DL-TDOA measurement |  Extended Kalman filter positioning and tracking algorithm |  accuracy
R |  2020 |  [39] |  28 GHz |  DL |  SISO |  Analysis of a RIS-aided localization problem |  FIM, two step optimization |  accuracy, coverage
R |  2021 |  [43] |  28 GHz |  UL |  MIMO |  Beam training designs to estimate optimal beams for BS and UE, RIS reflection pattern and link blockage |  maximum likelihood estimation (MLE), positioning algorithm design |  accuracy
R |  2021 |  [61] |  28 GHz |  DL |  SISO |  Use of 3D localization technology to achieve the low-complexity channel estimation |  reflecting unit set (RUS) concept, coplanar maximum likelihood-based (CML) based 3D positioning method, CRLB |  accuracy, SNR
R |  2022 |  [64] |  28 GHz |  UL |  MIMO |  Localization and channel reconstruction in extra large RIS-assisted MIMO systems |  Low-overhead joint localization, channel reconstruction scheme |  accuracy
R |  2022 |  [135] |  28 GHz |  DL |  MISO |  Exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance |  PEB, MLE |  accuracy
S |  2022 |  [136] |  28 GHz |  UL |  MISO |  STAR RIS potential for enhanced concurrent indoor and outdoor localization |  CRLB, FIM, optimization |  accuracy
R |  2022 |  [137] |  28 GHz |  DL |  MIMO |  Joint beamforming and localization for RIS-aided mmW localization system |  JLBO algorithm |  accuracy
R |  2022 |  [138] |  28 GHz |  |  |  Enabling the user to estimate its own position by transmitting OFDM pilots and processing the signal reflected from the RIS |  CRLB, low-complexity position estimation algorithm, temporal coding on RIS phase |  accuracy
R |  2022 |  [139] |  28 GHz |  UL |  MIMO |  Channel estimation and user localization |  RIS training coefficients designs, array signal processing, atomic norm denoising techniques |  accuracy
R |  2022 |  [140] |  28 GHz |  DL |  SIMO |  Joint RIS calibration and user positioning (JrCUP) scheme |  FIM |  accuracy
R |  2022 |  [141] |  28 GHz |  DL |  MIMO |  User localization and tracking |  Bayesian user localization and tracking (BULT) algorithm |  accuracy
R |  2022 |  [142] |  28 GHz |  |  SISO |  Cooperative localization to improve accuracy in RIS-assisted system |  Beam sweeping, optimization, neural network |  accuracy
R |  2022 |  [106] |  28 GHz |  |  |  Cooperative localization with no access point |  FIM, CRLB, RIS configuration optimization |  accuracy
Rx |  2023 |  [143] |  28 GHz |  UL |  |  Localization of UE with partially connected receiving RIS (R-RISs) only |  Atomic norm minimization (ANM), MUSIC, CRLB |  accuracy
A |  2023 |  [105] |  28 GHz |  UL |  SIMO |  Joint RIS calibration and user positioning problem with an active RIS |  Tensor-ESPRIT estimator, least-squares, 2D search-based algorithm, CRLB |  accuracy
R |  2023 |  [144] |  28 GHz |  DL |  SISO |  Misspecified Cramér-Rao bound (MCRB) with RIS geometry mismatch |  Method for pseudo-true parameter determination for MCRB analysis |  accuracy
R |  2023 |  [145] |  28 GHz |  UL |  MISO |  Localization of UE using distributed passive RIS |  compressive sensing (CS) approach based on ANM, ML estimation, CRLB |  accuracy
R |  2023 |  [146] |  28 GHz |  |  MIMO |  Device-free target sensing via joint location and orientation estimation |  Target based method for angle estimation, gradient descent method, manifold optimization |  accuracy
A |  2023 |  [147] |  28 GHz |  UL |  MISO |  ISAC using sparse active RIS |  MUSIC algorithm, optimization |  accuracy
R |  2023 |  [107] |  28 GHz |  DL |  MIMO |  JCAL framework |  Novel RIS optimization and channel estimation methods |  accuracy, data rate
R |  2023 |  [148] |  28 GHz |  SL |  SISO |  UE localization without BS involvement |  Two-stage 3D sidelink positioning algorithm, CRLB |  accuracy
R |  2021 |  [58] |  30 GHz |  DL |  SISO |  Joint three-dimensional localization and synchronization for a SISO multi-carrier system |  CRLB, design of low complexity estimation algorithm |  accuracy
R |  2021 |  [149] |  30 GHz |  DL |  SISO |  RIS in a multi-user passive localization scenario |  Low complexity TOA based positioning algorithm, CRLB |  accuracy
R |  2022 |  [150] |  30 GHz |  DL |  SISO |  Positioning UE by taking into account the its mobility spatial- WB effects |  CRLB, low-complexity estimator design |  accuracy
R |  2022 |  [112] |  30 GHz |  |  |  RIS-enabled radio SLAM without the intervention of BS |  RIS phase profile design, marginal Poisson multi-Bernoulli SLAM filter modification, CRLB |  accuracy
|  |  |  |  |  |  |  |
TABLE V:  SUMMARY OF RIS-ASSISTED OUTDOOR AND FAR-FIELD LOCALIZATION ARTICLES AT HIGH FREQUENCY BANDS. HERE ”R” REFERS TO ”REFLECTIVE RIS”, ”H” REFERS TO ”HYBRID RIS”, ”A” REFERS TO ”ACTIVE RIS” RIS |  Year |  Ref |  $f_{c}$ |  Link |  System |  Purpose |  Technique |  Performance metric
---|---|---|---|---|---|---|---|---
R |  2022 |  [151] |  |  DL |  MIMO |  ISAC with RIS |  Compressive sensing, expectation-maximization (EM) algorithm, Bayesian Cramér-Rao bound (BCRB) |  accuracy
H |  2022 |  [152] |  30 GHz |  DL |  MISO |  Joint localization of a hybrid RIS and a user |  CRLB |  accuracy
R |  2022 |  [153] |  30 GHz |  DL |  SISO |  Cooperative localization in a RIS-aided mmW system |  FIM, CRLB, block coordinate descent (BCD)-based reflect beamforming design algorithm |  accuracy
R |  2023 |  [154] |  30 GHz |  |  MISO |  Location information assisted beamforming design without the requirement of the channel training process |  Relaxed alternating optimization process (RAOP) |  data rate
R |  2021 |  [41] |  50 GHz |  DL |  MIMO |  Channel modeling, positioning bounds and enhanced optimization methods to optimize RIS beamforming under NLoS |  CRLB, optimization |  accuracy
R |  2020 |  [40] |  60 GHz |  DL |  MIMO |  Theoratical bounds for LIS |  CRLB based PEB and OEB |  accuracy
R |  2020 |  [6] |  60 GHz |  DL |  MIMO |  Improving the positioning accuracy and date rate |  Adaptive phase shifter design based on HCB and feedback from the UE |  accuracy, data rate
R |  2020 |  [44] |  60 GHz |  UL |  MIMO |  Localization of UE |  Two stage positioning method with dual RISs |  accuracy
R |  2021 |  [42] |  60 GHz |  DL |  MIMO |  Utilizing RIS in mmW MIMO radar system for multi-target localization |  Adaptive localization algorithm utilizing the concept of hierarchical codebook design (HCB) |  accuracy
R |  2021 |  [59] |  60 GHz |  DL |  MISO |  Joint localization and synchronization |  MLE |  accuracy
R |  2022 |  [28] |  60 GHz |  UL |  MIMO |  RIS-assisted localization at THz band in comparison with the mmW band |  Geometrical modeling and simulations |  accuracy
R |  2022 |  [155] |  60 GHz |  UL |  MIMO |  Potential of RIS for cooperative localization performance |  CRLB, manifold optimization |  accuracy
R |  2022 |  [156] |  60 GHz |  UL |  MIMO |  To optimize the worst-case localization performance by jointly optimizing beamforming vectors at RIS and UE |  Joint array gain and path loss search (JAPS) algorithm, difference of convex (DC)-based algorithm |  accuracy
A |  2022 |  [76] |  60 GHz |  DL |  MIMO |  UE localization with active RIS |  Multiple signal transmissions, particle filtering (PF), CRLB |  accuracy
R |  2023 |  [34] |  60 GHz |  DL |  SISO |  Overview of RIS enabled localization scenarios |  Experimental demonstration |  accuracy
R |  2023 |  [54] |  60 GHz |  DL |  MIMO |  Joint optimal point of the user position/orientation estimation error bound (POEB) and effective achievable data rate (EADR) |  Worst-case robust beamforming and time allocation optimization approach, majorize-minimization (MM) based algorithm |  accuracy, data rate
R |  2023 |  [157] |  60 GHz |  DL |  MISO |  Investigate the potential of employing RIS in dual-functional radar-communication (DFRC) vehicular networks |  Codebook design for optimal phase shift of RIS, position-based CSI design |  accuracy, data rate
R |  2023 |  [158] |  100 GHz |  UL |  MISO |  Sensing of channel and location under the unique hybrid far-near field effect and the beam squint effect |  Location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm, dictionary-based localization (CDL) scheme, polar-domain gradient descent (PGD) algorithm |  accuracy
|  |  |  |  |  |  |  |

Authors in [108] investigate the potential of RISs in replacing the function
of a remote cell for downlink TDOA (DL-TDOA) measurements in 3GPP new radio
(NR) positioning. The study demonstrates that the TDOA between the LoS path
and the reflected path through the RIS can effectively replace DL-TDOA
measurements, enabling accurate localization within a single cell. Simulation
results indicate that RIS-enabled localization achieves positioning accuracy
comparable to the traditional two-cell structure, offering a cost-effective
solution. Authors present an efficient CSI acquisition method for a RIS-aided
communication system in [61]. They propose a compressed maximum likelihood
(CML)-based 3D localization approach and utilize the concept of random unitary
subspaces (RUS) to acquire channel information with minimal training
resources. Study indicates substantial performance improvements in terms of
the SNR of the received signal. Article [64] presents a low-overhead joint
localization and channel reconstruction scheme for extra-large RIS-assisted
MIMO systems. The proposed scheme accurately identifies the visibility region
(VR) of each user, achieves centimeter-level user localization accuracy and
obtains more accurate channel reconstruction results compared to existing
works. The results demonstrate the potential of RIS for improving
communication and sensing integration. Authors in [139] focus on the
challenges of channel estimation and user localization in an RIS-assisted
MIMO-OFDM system. The article proposes a unique twin- RIS structure that
incorporates spatial rotation to extract the 3D propagation channel. They
employ tensor factorization, sparse array processing, and atomic norm
denoising techniques to design training patterns and recover the associated
parameters. By decoupling the channel’s angular and temporal parameters, they
achieve precise channel parameter extraction and centimeter-level positioning
resolution. A two-stage method is proposed in [145], utilizing the tunable
reflection capability of passive RISs and the multi-reflection wireless
environment. The first stage employs an off-grid compressive sensing (CS)
approach to estimate the angles of arrival associated with each RIS, followed
by a maximum likelihood location estimation in the second stage. The study
demonstrates the high accuracy of the proposed 3D localization method,
consistent with the theoretical CRLB analysis.

##### Advanced Techniques and Systems

In [136], the authors investigate the potential of STAR-RISs for enhanced
indoor and outdoor localization. They study the fundamental limits of 3D
localization performance using Fisher information analysis and optimize the
power splitting between refraction and reflection at the STAR-RIS, as well as
the power allocation between the UE. The results indicate that high-accuracy
3D localization can be achieved for both indoor and outdoor UEs when the
system parameters are well optimized, demonstrating the potential of STAR-RISs
in concurrent localization. Existing RIS-aided localization approaches assume
perfect knowledge of the RIS geometry, which is not realistic due to
calibration errors. The authors in [144] derive the MCRB for localization with
RIS geometry mismatch and propose a closed-form solution for determining
pseudo-true parameters. Numerical results validate the derived parameters and
MCRB, demonstrating that RIS geometry mismatch leads to performance saturation
in high SNR regions. The article [138] introduces a concept of 3D UE self-
localization using a single RIS. The approach involves the UE transmitting
multiple OFDM signals and processing the reflected signal from the RIS to
estimate its position. The estimation process includes separating the RIS-
reflected signal from the undesired multipath, obtaining a coarse position
estimate, and refining the estimation through maximum likelihood (ML)
techniques. The performance of the estimator is evaluated in terms of
positioning error and compared to an analytical lower bound. The results
demonstrate the potential of RIS as an enabling technology for radio
localization, offering improved positioning accuracy. Authors in [143]
introduce the concept of partially-connected receiving RIS (R-RISs) that can
sense and localize users emitting electromagnetic waveforms. The R-RIS
hardware architecture consists of meta-atom subarrays with waveguides that
direct the waveforms to reception RF chains for signal and channel parameter
estimation. The focus is on far-field scenarios, and a 3D localization method
is presented based on narrowband signaling and AoA estimates using phase
configurations of meta-atoms. The results include theoretical CRLBs and
extensive simulations, demonstrating the effectiveness of the proposed R-RIS-
empowered 3D localization system, providing cm-level positioning accuracy. The
impact of various system parameters on localization performance is also
evaluated, such as training overhead, distance between R-RIS and the user, and
spacing among R-RIS subarrays and their partitioning patterns. The joint
calibration and positioning problem in an uplink system with an active RIS is
addressed in [105]. Existing approaches often assume known positions and
orientations for RISs, which is not realistic for mobile or uncalibrated RISs.
The proposed two-stage method includes a tensor-ESPRIT estimator followed by
parameter refinement and a 2D search-based algorithm to estimate user and RIS
positions, RIS orientation, and clock bias. The derived CRLBs verify the
effectiveness of the algorithms, and simulations show that the active RIS
significantly improves localization performance compared to the passive case.
Blind areas that limit localization performance can be mitigated by providing
additional prior information or deploying more BSs.

##### Advanced Beamforming and Phase Shifter Designs

The adaptive beamforming of RIS-assisted mmW MIMO placement with obstructed
LoS between the MS and the UE is studied in [6]. The authors suggest a
hierarchical codebook (HCB) and receiver feedback-based adaptive phase shifter
architecture to optimize the phase of each of the RIS units and in turn
optimize performance in terms of localization accuracy and data rate. Authors
in [44] have proposed a two-stage localization technique using dual RISs. The
reflecting element’s phase shift is first designed for each RIS, and then in
the second stage, the location data is calculated and it demonstrates the
localization accuracy in the range of $10^{-5}$–$10^{-4}$ meters. Article
[155] explores the potential of RIS for improving cooperative localization
performance in mmW MIMO systems. The paper presents a study on the fundamental
limits of cooperative localization using the CRLB and proposes an optimal
phase design at the RIS to enhance position accuracy. The study demonstrates
that the proposed optimal passive beamforming (PBF) algorithm significantly
improves localization accuracy, and a low-complexity closed-form PBF design
achieves near-optimal performance with minimal computational complexity.
Authors in [43] suggest a simultaneous beam training and placement technique
to address the LoS obstruction in mmW MIMO network. The UE estimates its
location using the angle of departure, which is determined via beam training.
The location of UE, in turn, helps to improve the beam training. Results
demonstrate that the proposed approach can obtain centimeter-level multi-user
localization accuracy. A low-complexity method for joint localization and
synchronization in CRF systems using RISs is proposed in [135]. Their approach
involves optimizing the beamforming strategies of the BS active precoding and
RIS passive phase profiles, considering a single-antenna receiver. The results
indicate that the proposed joint BS-RIS beamforming scheme achieves enhanced
localization and synchronization performance compared to existing solutions,
with the proposed estimator achieving the theoretical bounds even under
challenging conditions such as low SNR and uncontrollable multipath
propagation. The authors focused on the successive localization and
beamforming design of a RIS-aided CRF communication system in [137]. They
formulated the problem as a multivariable coupled non-convex problem and
proposed an alternating optimization algorithm to solve it. The results showed
that their proposed scheme, called joint localization and beamforming
optimization (JLBO), significantly improved the performance compared to
existing joint localization and beamforming methods, as demonstrated through
simulation results.

##### Multiuser and Joint Communication and Localization Approaches

Authors in [42] examined a multiuser localization method based on an HCB
design in light of the LoS obstruction scenario. The study results under
various SNR situations demonstrate that, with the right HCB design, the
suggested approach has the ability to provide multiuser localization in mmW
MIMO radar systems. In [54], authors present an RIS-aided mmW- MIMO system for
JCAL. They derive closed-form expressions of CRLB for position/orientation
estimation errors and effective achievable data rate (EADR) based on RIS phase
shifts. They propose a joint optimization algorithm to balance the trade-off
between the two metrics, and simulation results demonstrate the effectiveness
of the algorithm in terms of estimation accuracy and EADR, even in the
presence of estimation errors and user mobility. Authors in article [59]
address the problem of joint localization and synchronization in a mmW MISO
system using a RIS. They formulate the joint ML estimation problem in the
position domain and propose a reduced-complexity decoupled estimator for
position and clock offset. Simulation results demonstrate that their approach
achieves high accuracy in localization and synchronization, even in low SNR
scenarios, without the need for optimizing transmit beamforming, RIS control
matrix, or prior knowledge of the clock offset.

##### Localization in Special Scenarios

Authors in [150] address the challenge of positioning a single-antenna user in
3D space by considering the received signal from a single-antenna base station
and the reflected signal from an RIS. They take into account both user
mobility and spatial-wideband (WB) effects. Initially, a spatial-WB channel
model is derived under the assumption of far-field conditions, focusing on
OFDM signal transmission with a user of constant velocity. CRLB are derived as
a benchmark, and a low-complexity estimator is developed to achieve these
bounds under high SNR ratios. The proposed estimator compensates for user
mobility by estimating radial velocities and iteratively accounting for their
effects. The results indicate that spatial-wideband effects can have a
detrimental impact on localization accuracy, especially for larger RIS sizes
and signal bandwidths deviating from the normal of the RIS. However, the
proposed estimator demonstrates resilience against spatial-wideband effects up
to a bandwidth of 140 MHz for a 64x64 RIS. Notably, user velocity does not
significantly affect the bounds or accuracy of the estimator, indicating that
high-speed users can be localized with similar precision as static users. The
potential of 6G THz systems for localization and comparison with mmW
localization systems is performed in [28]. They compare various aspects
including system properties, channel modeling, localization problem
formulation, and system design. Preliminary simulations demonstrate the
potential of THz localization in terms of PEB and OEB compared to mmW systems.
The article provides recommendations for efficient localization algorithm
design for RIS-assisted adaptive optics-based spatial modulation (AOSA) MIMO
systems and highlights the anticipated applications in future communication
systems, such as intelligent networks, autonomous transportation, and tactile
internet. A framework for RIS-enabled SLAM without the need for access points
is proposed in [112]. They design RIS phase profiles based on prior
information about the UE, allowing for uniform signal illumination in the UE’s
probable location. They also modify the Poisson multi-Bernoulli SLAM filter to
estimate the UE state and landmarks, facilitating efficient mapping of the
radio propagation environment. Theoretical CRLB are derived for the estimators
of channel parameters and UE state. The proposed method is evaluated under
scenarios with a limited number of transmissions and considering the channel
coherence time. Study demonstrates that RISs can solve the radio SLAM problem
without the need for access points, and incorporating the Doppler shift
improves UE speed estimates.

##### High Frequency Sensing Operations

The researchers in [158] perform the sensing of the user’s uplink channel and
location in terahertz extra-large RIS (XL-RIS) systems. The authors propose a
joint channel and location sensing scheme that includes a location-assisted
generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-
OMP) algorithm for channel estimation and a complete dictionary-based
localization (CDL) scheme. They address challenges such as the hybrid far-near
field effect and beam squint effect caused by the XL array aperture and XL
bandwidth. The proposed schemes demonstrate superior performance compared to
existing approaches, as indicated by simulation results. They also introduce a
partial dictionary-based localization scheme to reduce sensing overhead, where
the RIS serves as an anchor for user localization using time difference of
arrival. An ISAC scenario using RISs is investigated in [151] where multiple
devices communicate with a BS in full-duplex mode while simultaneously sensing
their positions. RISs are mounted on each device to enhance reflected echoes,
and device information is passively transferred to the BS through reflection
modulation. The problem of joint localization and information retrieval is
addressed by constructing a grid-based parametric model and formulating it as
a CS problem. An expectation-maximization (EM) algorithm is applied to tune
the grid parameters and mitigate model mismatch. The efficacy of various CS
algorithms is analyzed using the Bayesian Cramér-Rao bound (BCRB). Numerical
results demonstrate the feasibility of the proposed scenario and the superior
performance of the EM-tuning method.

##### Practical Localization Scenarios and Applications

The authors in [107] focus on leveraging RISs to enhance communication
performance when the LoS path between the UE and BS is blocked. The authors
propose a novel framework that integrates localization and communications by
fixing RIS configurations during location coherence intervals and optimizing
BS precoders every channel coherence interval. This approach reduces pilot
overhead and the need for frequent RIS reconfiguration. The framework utilizes
accurate location information from multiple RISs, along with novel RIS
optimization and channel estimation methods. The results indicate improved
localization accuracy, reduced channel estimation error, and increased
achievable rate, demonstrating the effectiveness of the proposed approach.
Authors in [159] focus on the requirements of localization and sensing (L&S)
in the context of smart cities and highlight the limitations of traditional
communication infrastructure for meeting L&S demands. The authors argue that
RISs and sidelink communications are promising technologies that can address
the L&S needs of smart cities. They propose and evaluate AP-coordinated and
self-coordinated RIS-enabled L&S architectures, considering different
application scenarios such as low-complexity beacons, cooperative
localization, and full-duplex transceivers. The article also discusses
practical issues and research challenges associated with implementing these
L&S systems. Authors in [148] address the importance of localization in
intelligent transportation systems (ITS) and explore the use of reflective
RISs to enhance high-precision localization. The authors propose a two-stage
3D sidelink positioning algorithm that utilizes at least two RISs and sidelink
communication between UEs to achieve localization without the involvement of
BSs. They evaluate the effects of multipath and RIS profile designs on
positioning performance, analyze localizability in various scenarios, and
propose solutions to eliminate blind areas. The study demonstrates the
promising accuracy of the proposed BS-free sidelink communication system in
challenging ITS scenarios.

TABLE VI:  SUMMARY OF RIS-ASSISTED INDOOR LOCALIZATION ARTICLES. HERE ”R” REFERS TO ”REFLECTIVE RIS” RIS |  Year |  Ref |  $f_{c}$ |  Link |  System |  Purpose |  Technique |  Performance metric
---|---|---|---|---|---|---|---|---
R |  2020 |  [47] |  2.4 GHz |  |  SISO |  Exploiting RISs to generate and select easily differentiable radio maps for use in wireless fingerprinting |  Machine Learning and fingerprinting |  accuracy
R |  2021 |  [45] |  2.4 GHz |  UL |  SISO |  Enhancing the accuracy of RSS based localization |  Configuration optimization iterative algorithm |  accuracy
R |  2021 |  [46] |  2.4 GHz |  DL |  SISO |  Enhancing the accuracy of RSS based positioning |  PSO algorithm |  accuracy
R |  2021 |  [48] |  2.4 GHz |  DL |  SIMO |  Employment of RIS in indoor localization |  UWB technique, CRLB |  accuracy
R |  2021 |  [160] |  2.4 GHz |  DL |  SISO |  Fingerprinting localization estimation using RISs |  Supervised learning feature selection method, localization heuristics states selection framework |  accuracy
R |  2022 |  [66] |  2.4 GHz |  UL |  MISO |  Investigating multiple RSS fingerprint based localization |  CRLB, projected gradient descent (PGD) optimization, DNN |  accuracy
R |  2023 |  [161] |  2.4 GHz |  DL |  SIMO |  Passive person localization |  Phase control optimization algorithm, side-lobe Cancellation Algorithm |  accuracy
R |  2023 |  [162] |  2.4 GHz |  DL |  SIMO |  Passive multi person localization |  Phase control optimization algorithm, side-lobe Cancellation Algorithm |  accuracy
R |  2019 |  [38] |  2.6 GHz |  UL |  MISO |  Method for efficient online wireless configuration of RISs |  Deep learning |  Throughput
R |  2023 |  [163] |  3.5 GHz |  |  |  Radio sensing |  ML and computer vision (clustering, template matching and component labeling) |  accuracy
R |  2022 |  [164] |  5 GHz |  DL |  SISO |  Fingerprint-based indoor localization system using RIS |  RIS configuration design |  accuracy
R |  2023 |  [165] |  5 GHz |  DL |  MISO |  RIS-enabled fingerprinting-based localization |  Deep Reinforcement learning |  accuracy
R |  2023 |  [166] |  5 GHz |  UL |  MISO |  Distributed RISs assisted localization |  Two-step positioning approach, CRLB, theoretical analysis |  accuracy
R |  2021 |  [113] |  10 GHz |  DL |  SIMO |  Indoor wireless SLAM system assisted by a RIS |  RIS-assisted indoor SLAM optimization problem and design of error minimization algorithm |  accuracy
R |  2022 |  [167] |  30 GHz |  |  |  User localization with multiple RISs |  Maximum likelihood position estimation, least squares line intersection technique |  accuracy
R |  2022 |  [168] |  60 GHz |  DL |  MIMO |  RIS-assisted downlink mmW indoor localization framework |  Coarse-to-fine localization algorithm with low-complexity grid design |  accuracy
R |  2023 |  [169] |  90 GHz |  UL |  MIMO |  RIS aided UE localization |  Space-time channel response vector (STCRV), residual convolution network regression (RCNR) learning algorithm |  accuracy
R |  2022 |  [170] |  150 GHz |  DL |  SISO |  Optimal RIS placement with respect to position and orientation |  Analytical modeling |  received power
|  |  |  |  |  |  |  |

###  III-B RIS-Assisted Indoor Localization

Studies on RIS-assisted indoor localization are briefly summarized in Table
LABEL:table4 and discussed as follows.

####  III-B1 Foundational Techniques

The multipath signal traveling through each RIS may be labeled, which offers a
workable concept for processing them, provided each of the RIS elements has a
unique phase, i.e., $\phi_{1}\neq\phi_{2}......\neq\phi_{n}\in[0,2\pi)$.
Taking advantage of the high multipath resolution of UWB signals and the
capability of RIS to identify multipath channels, researchers in [48] created
a unique indoor RIS-assisted localization technique. The suggested
localization scheme’s CRLB is calculated, demonstrating how RIS has the
ability to provide precise location with just one access point. Also, the
suggested system offers a more precise and economical option for indoor
placement because it only calls for a single access point and a few
inexpensive RIS devices. RIS may be used to complement the RSS-based
localization technique in many ways, such as, strengthening the signal
received, diminishing co-channel interference, and providing additional
propagation paths. As a result, RIS can significantly improve the RSS-based
localization algorithms that rely on it. Nevertheless, because it is
challenging to tell apart nearby RSS data, the accuracy of such algorithms is
constrained. A deep learning method for efficient online wireless
configuration of RIS in indoor communication environments is proposed in [38].
They use a database of coordinate fingerprints to train a DNN that maps user
location information to the optimal phase configurations of the RIS,
maximizing the RSS at the intended location. Simulations in a 3D indoor
environment show that the proposed DNN-based configuration method effectively
increases the achievable throughput at the target user location in all
considered cases.

A RIS-assisted localization scheme utilizing multiple RSS fingerprints and a
DNN is presented in [66]. The scheme utilizes RSS values obtained under
different RIS configurations as fingerprints and employs an optimization
method based on the CRLB to find the optimal RIS configurations. A DNN
regression network is trained for localization. The simulation results
demonstrate that the proposed scheme achieves robust and accurate location
estimation, with an accuracy of approximately 0.5 meters in the NLoS scenario.
The researchers in [47] propose and evaluate a novel machine learning method
for wireless fingerprinting localization in RIS-assisted environments. The
approach combines off-the-shelf components such as k-nearest neighbors (k-NN)
localization and genetic algorithms, leveraging the capabilities of RIS to
create a smart reconfigurable radio environment. The results demonstrate that
this approach achieves excellent localization accuracy, eliminating the need
for multiple access nodes and extensive fingerprint grid sample points. The
study suggests that RIS and smart radio environments have the potential to
enable sub-meter localization accuracy, and future research should explore
more challenging scenarios involving mixed LoS and NLoS environments, higher
frequencies, multiple RIS elements, and multiple RIS deployments.

####  III-B2 Accuracy Improvement with RIS

By theoretical analysis and practical testing, it has been shown in [104] that
RIS can in fact customize the wireless environment. Authors have clearly shown
with the help of measurements that RIS configuration changes the RSS at a
particular location. Thus, the issue of similar RSS values from nearby sites
can be resolved in smart radio environments enabled by the RISs. Authors in
[68] have designed an RIS-assisted localization algorithm that is focused on
enhancing localization accuracy. To do so, an iterative configuration
optimization algorithm is proposed whose purpose is to select the RIS
configuration that improves the localization accuracy. The localization
accuracy of the suggested technique is substantially more than that of the
localization method without RIS. The authors also designed a Phase shift
optimization (PSO) technique to address the same issue in [46]. This approach
offers a unique solution to the multiuser localization problem and can
minimize localization error by at least three times when compared to the
conventional RSS-based solution.

TABLE VII:  SUMMARY OF RIS-ASSISTED NEAR-FIELD LOCALIZATION ARTICLES. HERE ”R” REFERS TO ”REFLECTIVE RIS”, ”H” REFERS TO ”HYBRID RIS” RIS |  Year |  Ref |  $f_{c}$ |  Link |  System |  Purpose |  Technique |  Performance metric
---|---|---|---|---|---|---|---|---
R |  2022 |  [171] |  3 GHz |  DL |  SISO |  RSS based localization algorithms |  Weighted least square (WLS) and alternate iteration methods |  accuracy
R |  2022 |  [172] |  3.5/28 GHz |  DL |  SISO |  UE localization under LoS and NLoS conditions |  Practical signaling and positioning algorithms design based on an OFDM, RIS time-varying reflection coefficients design |  coverage, accuracy
R |  2021 |  [49] |  28 GHz |  DL |  SISO |  Localization of a transmitter using a RIS-based lens |  FIM, two stage localization algorithm |  accuracy
R |  2021 |  [50] |  28 GHz |  UL |  MIMO |  Localization performance limits in single BS and RIS-assisted UE localization |  CRLB, signaling model design applicable for near and far-field localization |  accuracy
R |  2021 |  [51] |  28 GHz |  DL |  SISO |  Potential to exploit wavefront curvature in geometric near-field conditions |  FIM |  accuracy
R |  2021 |  [52] |  28 GHz |  DL |  SISO |  Localization of UE under NLOS |  Propose a low complexity algorithm |  accuracy, latency, robustness, coverage
R |  2022 |  [60] |  28 GHz |  UL |  MISO |  Performance limits of the RIS-based near-field localization in the asynchronous scenario, impact of cascaded channel on the localization performance |  FIM, PEB |  accuracy
R |  2022 |  [173] |  28 GHz |  DL |  SISO |  Near-field regional target localization with the RIS-assisted system |  IER based algorithm for RIS phase design, near-field target localization algorithm |  accuracy
R |  2022 |  [174] |  28 GHz |  DL |  SISO |  RIS-assisted near-field localization system under hardware impairment |  MCRB, PEB, mismatched maximum likelihood (MML) estimator |  accuracy
R |  2022 |  [175] |  28 GHz |  DL |  SISO |  Suitable phase profiles design at a reflective RIS to enable NLoS localization |  PEB, localization-optimal phase profile design |  accuracy
R |  2022 |  [176] |  28 GHz |  UL |  MIMO |  UE localization in near- field |  Atomic norm minimization |  accuracy
R |  2022 |  [177] |  28 GHz |  DL |  SISO |  Multiuser localization using RIS and cooperative links |  CRLB, iterative searching (IS) algorithm |  accuracy, power allocation
R |  2023 |  [67] |  28 GHz |  |  MISO |  Integration of holographic RIS into mmW localization system |  FIM, CRLB, iterative entropy regularization (IER) based RIS phase optimization |  accuracy
R |  2023 |  [53] |  28 GHz |  DL |  SISO |  Near-field localization of a UE under phase-dependent amplitude variations at each RIS element |  Low-complexity AMML estimator, iterative refinement algorithm to update individual parameters of the RIS amplitude model, MCRB |  accuracy
R |  2023 |  [178] |  5.15/28 GHz |  DL |  SISO |  Optimizing the precoders that control RIS under hardware constraints |  Low-complexity algorithm design for RIS configuration, FIM, PEB |  accuracy
R |  2023 |  [179] |  28 GHz |  DL |  SISO |  RIS-aided Localization under Pixel Failures |  MCRB, joint localization and failure diagnosis (JLFD) method |  accuracy
H |  2023 |  [180] |  28 GHz |  UL |  MISO |  Hybrid RIS-assisted UE localization |  CRLB, automatic differentiation-based gradient descent approach |  accuracy
R |  2023 |  [181] |  30 GHz |  DL |  SIMO/ SISO |  Design RIS coefficient to convert planar waves into spherical waves and cylindrical wave |  MLE, focus scanning method, PEB |  accuracy, energy leakage
R |  2022 |  [182] |  30 GHz |  |  SISO |  RIS localization |  FIM, multistage low-complexity RIS localization algorithm, quasi-Newton method |  accuracy
R |  2022 |  [183] |  45 GHz |  UL |  MISO |  Near-field localization |  Second-order Fresnel approximation, RIS training phase shifts and pilots design |  accuracy
R |  2022 |  [63] |  90 GHz |  UL |  MISO |  Spherical wavefront propagation in the near-field of the subTHz system with the assistance of a RIS |  Near-field channel estimation and localization (NF-JCEL) algorithm based on second-order Fresnel approximation of the near-field channel |  accuracy
R |  2022 |  [184] |  100 GHz |  UL |  MIMO |  UE localization under beam squint effect |  Polar-domain gradient descent algorithm, MUSIC algorithm |  accuracy
R |  2023 |  [185] |  320/325/ 330 GHz |  UL |  MISO |  Spherical wavefront propagation in the near-field of the RIS-assisted THz system |  Proposed NF-JCEL algorithm based on second-order Fresnel approximation of the near-field channel |  accuracy
|  |  |  |  |  |  |  |

####  III-B3 Wireless Indoor Simultaneous Localization and Mapping

Researchers developed a RIS-assisted wireless indoor SLAM system in [113].
Channel models incorporating RIS are proposed, and a RIS-aided SLAM protocol
is introduced to coordinate the RIS and the agent. An optimization problem for
SLAM is formulated and solved using a particle filter-based localization and
mapping algorithm. The study demonstrates that the RIS significantly enhances
channel amplitudes compared to scattered environments. Furthermore, the RIS-
assisted SLAM system reduces agent estimation errors by 0.1 meters compared to
non-RIS wireless SLAM systems. The article [161] presents a framework for
passive human localization using WiFi signals enhanced by RIS. The RIS,
consisting of controllable reflective elements, overcomes the limited spatial
resolution of WiFi devices to achieve accurate localization. The proposed
framework includes a phase control optimization algorithm to maximize the
discrepancy between human reflection and multipath interference. Additionally,
a Side-lobe Cancellation Algorithm is introduced to address the near-far
effect in multi-person scenarios. Simulation results demonstrate sub-
centimeter accuracy in locating moving individuals passively, even in the
presence of noise and multipath interference. As an extension, the article
[162] addresses the challenge of achieving accurate passive multi-human
localization using commodity WiFi devices. To overcome the limited spatial
resolution of WiFi signals, the authors propose utilizing RIS with
controllable reflective elements. In single-person scenarios, they derive a
closed-form solution for optimizing the phase shift of the RIS elements. For
multi-person scenarios, a Side-lobe Cancellation Algorithm is introduced to
achieve accurate localization iteratively. Results indicate that the proposed
framework enables sub-centimeter accurate localization of multiple moving
individuals without modifications to existing WiFi infrastructure, even in the
presence of multipath interference and random noise.

###  III-C RIS-Assisted Near-Field Localization

Studies on RIS-assisted near-field localization are briefly summarized in
Table LABEL:table5 and discussed as follows.

####  III-C1 Foundational Studies

A two-stage positioning technique for determining the transmitter’s location
with a RIS, employed as a lens, running at mmW frequency demonstrates the
capability of decimeter-level localization accuracy in the near-field region
[49]. A generic model of near-field as well as the far-field placement was
constructed in [50], and it suggests an SNR-based RIS phase design algorithm
for CLRB reduction. The suggested technique can reduce PEB and directional
error bounds significantly compared to the conventional system without RIS.
Both of these RIS-assisted near-field localization studies disregard the
scenario of LoS obstruction, however, it is necessary to take care of to cater
for the successful localization in real-world scenarios. Authors in [173]
propose a general framework for RIS-assisted regional localization, including
RIS phase design and position determination. The results demonstrate the
effectiveness of the proposed framework, showing that the designed RIS phase
schemes lead to near-optimal localization performance. Authors in article [63]
investigate the localization and CSI estimation scheme for a near-field sub-
THz system with a RIS. The authors propose a near-field joint channel
estimation and localization (NF-JCEL) algorithm, which demonstrates superior
performance in terms of localization and CSI estimation root mean square error
(RMSE) compared to conventional far-field algorithms. The complexity of near-
field CSI estimation is influenced by the array steering vector formulation,
which takes into account the reflection elements and their coupling effects,
leading to higher resolution accuracy. The study highlights the importance of
considering the near-field effects and angle separations between UEs for
achieving high-precision localization with a single RIS panel. Additionally,
it is emphasized that the inclusion of a large RIS panel with more elements
must consider the spherical wavefront feature to avoid performance
degradation.

####  III-C2 LoS Blockages

Researchers in [51] have investigated the SISO system’s near-field
localization capabilities in the presence of a significant LoS blockage. A
two-step localization algorithm based on TOA was presented in [52], and the
results supported the feasibility of retaining high localization accuracy even
under the situation of significant blockage in the near-field region of RIS.
Using the Jacobi-Anger expansion and taking into account the RIS amplitude, a
low complexity near-field localization approach, termed approximation MML
(AMML), has been devised in [53]. It also suggests an iterative refinement
approach for joint localization and RIS amplitude model parameter updating,
using the result as the initial location estimate. The suggested low-
complexity localization technique performs well in simulations, and the
iterative algorithm’s localization accuracy is asymptotically approaching
CRLB.

####  III-C3 Performance and Considerations

RIS-based asynchronous localization is studied in [60] by examining the PEB
and equivalent Fisher information (EFI) for the intermediate parameters
involved. The study considers multi-paths between the BS and the RIS, taking
into account amplitude differences. The results indicate that near-field
spherical wavefront modeling enables UE localization in the asynchronous
scenario, but the EFI decreases as the distance between the UE and RIS
increases. The study also highlights the performance difference between
spatial gain and power gain in the BS-RIS channel, and cautions against using
the SNR-maximizing focusing control scheme for RIS in localization
applications. In article [67], the performance of a holographic RIS (HRIS)
assisted mmWave near-field localization system is investigated. The FIM and
CRLB are derived, considering the radiation pattern of antennas. The
theoretical analysis demonstrates that the position accuracy improves
quadratically with the size of the HRIS. An iterative entropy regularization
(IER)-based method is proposed to minimize the worst-case CRLB by optimizing
the HRIS phases. Researchers in [178] propose a low-complexity approach to
optimize the precoders that control the RISs, considering hardware
constraints. The method approximates desired beam patterns using pre-
characterized reflection coefficients. The evaluation includes beam fidelity
for different RIS hardware prototypes and theoretical analysis of the impact
on near-field downlink positioning in NLoS conditions. Results demonstrate the
effectiveness of the proposed optimization scheme in producing desired RIS
beams within hardware limitations, while also highlighting the sensitivity to
hardware characteristics and the specific requirements of RIS-aided
localization applications.

Researchers address the problem of near-field localization using RIS in the
presence of phase-dependent amplitude variations at each RIS element in [73].
The authors analyze the performance limitations using a MCRB and demonstrate
that performance penalties can occur, particularly at high SNRs, when the UE
is unaware of the amplitude variations. They propose a low-complexity AMML
estimator, leveraging Jacobi-Anger expansion, to mitigate performance loss.
The method shows fast convergence and performance close to the CRB, indicating
the effectiveness of the proposed method in recovering performance and
calibrating the RIS amplitude model. The issue of RIS pixel failures is
studied in [179], which can severely impact localization accuracy. The paper
investigates the impact of pixel failures on accuracy and develops two
strategies for joint localization and failure diagnosis (JLFD) to detect
failing pixels while accurately locating the UE. The proposed JLFD algorithms
demonstrate significant performance improvements over conventional failure-
agnostic approaches, enabling successful localization in the presence of pixel
failures.

##  IV Challenges and Research Outlook

Based on the review performed in the previous section, in this section, we
present a detailed overview of the limitations, open areas of research and
challenges in RIS-assisted radio localization that need to be investigated to
make it a practical and feasible solution to radio localization in 6G
networks.

While RIS offers significant benefits for localization, there are some
associated limitations, as shown in Figure 8 [71]. Implementing RIS-assisted
localization may involve significant costs, including the installation and
maintenance of RIS devices throughout the target environment. The deployment
of RIS infrastructure can be challenging and require careful planning.
Inferring from the reviewed literature, RIS devices typically rely on LoS
communication with the devices they assist in localizing. This means that
obstacles, such as walls or objects, can obstruct the signal path and
potentially degrade the accuracy or reliability of localization. While RIS can
optimize signal propagation, there may still be scenarios where NLoS signal
paths exist, leading to potential inaccuracies in localization. Overcoming
NLoS challenges in complex environments with reflective or obstructive
surfaces can be a limitation for RIS-assisted localization. RIS devices
require power and connectivity to function effectively. Ensuring an adequate
power supply and reliable connectivity to each RIS device can be a logistical
challenge, particularly in large-scale deployments or areas with limited
infrastructure. RIS-assisted localization may face scalability limitations
when applied to larger or more complex environments. As the number of devices
and users increases, coordinating and optimizing the RIS network can become
more challenging. Additionally, adapting the RIS configuration to accommodate
changes in the environment or user requirements may require significant
adjustments and maintenance. In situations where multiple RIS devices are
deployed in proximity, potential interference and coexistence challenges may
arise. Careful planning and coordination are necessary to ensure that RIS
devices do not interfere with each other or with other wireless communication
systems operating in the same frequency band. These limitations should be
carefully considered and addressed during the planning, deployment, and
operation of RIS systems to maximize their effectiveness.

Here we discuss the research directions related to the limitations and open
areas of research in the path of practical applications of RIS-assisted radio
localization in 6G networks that includes the technical as well as the
deployment challenges.

(a) Absence of LoS path

(b) Installation and maintenance cost

(c) Power supply, calibration and reliable connectivity

(d) Scalability, interference and coexistence challenge

Figure 8: Illustration of limitations to RIS-assisted radio localization. (a)
NLoS challenges in complex environments with reflective or obstructive
surfaces can be challenging, (b)The deployment, installation and maintenance
of RIS infrastructure can be challenging and require careful planning (c)
Ensuring an adequate power supply, RIS calibration and reliable connectivity
to each RIS device can be a logistical challenge, (d) when multiple RIS
devices are deployed in proximity, potential interference and coexistence
challenges may arise.

  * •

Availability, Scalability, Privacy and Security: It is observed in the
previous section that the theoretical approaches being developed in the
literature are primarily focused on the accuracy of localization. While
devising new methods for RIS-assisted localization in 6G networks, it is an
important factor to consider also the coverage area and availability of the
service [186]. Techniques must be developed in a fashion that it is scalable
without any major hardware as well as the software limitations, as shown in
Figure 8(a) and 8(d). Lastly, user privacy in such networks is an interesting
area of investigation since the transmission and processing of data within RIS
systems can be susceptible to eavesdropping, leading to the leakage of
sensitive information [34].

  * •

Mobile User Localization: Mobility of the UE is an important factor in a real-
world scenario that needs to be considered in addition to the 3D position and
3D orientation to localize the user with maximum accuracy [87]. If the Doppler
delay effects due to user mobility are ignored, it will negatively impact the
location estimates. It is, thus, necessary to account for the velocity of the