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# A 5G-Based Architecture for Localization Accuracy

Maria Belesioti, Kostas Tsagkaris, Aristotelis Margaris, Ioannis P. Chochliouros

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Maria Belesioti, Kostas Tsagkaris, Aristotelis Margaris, Ioannis P. Chochliouros. A 5G-Based Architecture for Localization Accuracy. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.23-33, 10.1007/978-3-031-08341-9_2. hal-05335648

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# A 5G-Based Architecture for Localization Accuracy

Maria Belesioti<fnref n="1" />(✉) ID, Kostas Tsagkaris<fnref n="2" />, Aristotelis Margaris<fnref n="2" />, and Ioannis P. Chochliouros<fnref n="1" />

**Abstract.** Future communications technologies will radically transform the way we communicate, by introducing a vast array of capabilities and services. In current 5G networks, key elements such as increased bandwidth, smaller cells, high density, multiple radio access technologies and device-to-device (D2D) communication can offer great benefit in localization services. Telecom operators and ICT companies have accepted the challenge to develop and integrate mobile localization technologies, powered by AI algorithms and machine-learning techniques, which will exploit the location information while, at the same time, preserve end-users' privacy. The use of these technologies will enhance location-based communication and network management techniques as well as mobility and radio resource management. In this paper, we present the ambition coming from the framework of the LOCUS EU-funded project [1]. LOCUS aims to design and implement an innovative location management layered platform which will be able to improve localization accuracy, taking into consideration localization security and privacy concerns, to extend localization with physical analytics and finally to extract value out from the combined interaction of localization and analytics, while guaranteeing users' privacy.

**Keywords:** 5G · Artificial Intelligence (AI) · Localization analytics & services · Localization awareness · Machine learning (ML) · Network function (NF) · Privacy

## 1 Introduction

The deployment of 5G networks is expected to provide improvement in terms of capacity, number of connected devices and latency. Location awareness in mobile communication systems will enable many location-based applications and services while improving communication system performance [2]. However, the performance of these applications depends on the location accuracy [3]. Global Navigation Satellite System (GNSS), can offer in urban environments an approximate accuracy of five meters, when in dense urban areas, wireless local area network (WLAN) fingerprinting techniques can offer a

<footnote n="1">¹ Hellenic Telecommunications Organization (OTE) S.A., 99 Kifissias Avenue, 15124 Maroussi-Athens, Greece
mbelesioti@oteresearch.gr</footnote>

<footnote n="2">² Incelligent, Athens, Greece</footnote>
3–4 m accuracy. When it comes to 5G networks, the expectation have been risen to one meter or even below [4], with the adoption of disruptive technologies such as device-centric architectures, millimeter wave, massive multiple-in multiple-out (MIMO) and native support for machine-to-machine (M2M) communications [5]; this is significantly better than long term evolution (LTE) systems by observed time difference of arrival (OTDOA)-based techniques.

In addition, context-awareness is essential for many existing and emerging applications and it mainly relies on location information of people and things. Localization, together with analytics and their combined provision “as a service”, will greatly increase the overall value of the 5G ecosystem, allowing network operators to better manage their networks and to dramatically expand the range of offered applications and services.

In the scope of the LOCUS vision, location information supplementary to context information will address several challenges in 5G networks by complementing already existing technologies. For instance, in 2G, user’s position could be identified based on the cell serving his mobile phone with the accuracy ranging from tens of meters to dense urban areas to hundreds of meters to rural. However, in the 5G framework, seamless and ubiquitous location awareness will enforce the precision of location accuracy and will enable new services based on modern positioning technologies. To this respect, during the project’s lifetime, integration of both 3GPP and non-3GPP technologies in 5G Core will be attempted and a virtualization and software platform that will enhance location accuracy and serve localization analytics for new service(s) will be developed.

The second Section of the paper provides an overview of 5G localization techniques and technologies, while the third one introduces the concept of locations awareness. Sections 4 and 5 refer exclusively to the context of the LOCUS project; its concept and dedicated objectives are highlighted and the proposed fundamental architecture along with the key functionalities is thoroughly analyzed. Section 6 is dedicated to privacy in localization-based services, while Sect. 7 concludes the paper.

## 2 Technologies for 5G Localization

Localization techniques become more accurate as cellular networks are evolving. In previous mobile networks generations, localization performed on user equipment (UEs) could not achieve high accuracy, especially concerning indoor environments. In Long Term Evolution (LTE) networks, positioning methods were dependent on radio access techniques (RATs) exploiting LTE signals or on using other signals such as Global Positioning System (GPS) which were independent of the RATs. The use of uplink time difference-of-arrival (UTDOA) allowed enhanced NodeBs (eNBs) to collect time difference-of-arrival (TDOA) measurements of the signal transmitted by UEs and estimate their exact position [6]. Future 5G devices will depend on ubiquitous location awareness, supported by accurate location information utilized by 5G networks across all layers of the communication protocol stack [7].

5G uses cases (cf. Fig. 1) are driven by the needs and requirements of Industry 4.0 verticals, acting as key enablers, and are designed to “address” specific challenges of these verticals. In 5G, disruptive technologies such as device-centric architectures, mmWave, massive multiple-in multiple-out, smart devices, and native support for machine-to-machine (M2M) communications will be implemented in a way also to assist localization
accuracy [5]. These technologies require wide bandwidths so as to meet capacity requirements and with the use of higher frequency bands such as mm waves, they can provide higher system capacity, especially in dense urban environments where the deployment of a large number of small cells enables massive MIMO and very accurate beamforming. The 5G networks shall exploit frequently transmitted uplink (UL) signals which, together with wide bandwidths enable accurate time of arrival (ToA) estimates which, in turn, provide an opportunity for positioning with remarkably high accuracy [8]. These features will enhance UE positioning accuracy both inside buildings and in dense urban areas, providing awareness of the current but also of the past UE location and in some cases, with the use of predictive algorithms, can even predict the UE location.

Related 3GPP specifications [9] analyze other access technologies, such as Wi-Fi, that can provide flexibility in the access of non-3GPP networks. As it is stated in Release 16 [10], non-3GPP access networks can use the same 5G Core for service provisioning, enhancing integration between these heterogeneous access networks as 3GPP ones.

<figure><img src="image_4.png" /><figcaption>Fig. 1. 5G usage scenarios</figcaption></figure>

As far as indoor location is concerned, Wi-Fi and Bluetooth are the most commonly used technologies providing high accuracy, but with significant shortcomings in terms of range and penetration. So, as it is evident, there is a need for a technology that “meets globally” the augmented demands of localization. 5G has all the potential to fulfill the requested requirements and, moreover, with the use of analytics and advanced location aware techniques can reduce overheads and delays, due to their ability to predict channel quality beyond traditional time scale.

## 3 Location Awareness in 5G Networks

The Global Positioning System (GPS) enabled smartphones to allow users locating their position, within accuracy of about 5 m, with a unidirectional flow of information meaning that the system cannot determine UEs location automatically and send this information to satellite [11]. Location awareness is the “key element” for a number of emerging applications in the 5G context and is expected to increase further in future 6G networks, receiving intense attention from both the business sector and the research community.
Location awareness is based on network's knowledge about the user's current location and can be utilized in many ways, in order to sufficiently meet 5G challenges. It is directly related with the use of information from a network owner—or an OTT (over-the-top) player—that provides information and services to specific user(s), all related to its current position [12]. In 5G and beyond networks, UEs will be able to determine their exact position in terms of coordinates with high accuracy, so specific information relevant to each user's position, in a given moment, can be forwarded to him/her.

Actually, location information is considered as one among the most important aspects in 5G networks and as the key enabler of location-based services. The performance of such services is purely dependent on the location accuracy. Location analytics are meant to exploit this information by incorporating it into dedicated network features, so as to utilize it in terms of real-time (RT) tracking of a UE location with high precision and accuracy. This sort of sophisticated spatial information will provide better knowledge related to processes as well as behavioral mechanism(s) to enhance predictive and coverage models and to enable the offering of application based on it. Location-aware resource allocation techniques can reduce overheads and delays due to their ability to predict channel quality beyond the traditional time scales [13].

On the other hand, 5G devices will rely upon ubiquitous location awareness, provided by 5G networks and supported by new advanced technological developments [14]. Signal-to-Noise Ratio (SNR) is reduced with distance due to path loss, so location awareness together with distance cognition can be an indicative instance of the received power and interference level [13]. Thus, if we neglect shadowing, the best multihop path between a source-destination pair in a network deployed in dense urban environment, is the one that is shortest in terms of distance.

## 3.1 Location Based Services

Location Based Services (LBSs) are in reality services that provide information to users exploiting the utilization of geographical raw data and processed information, in real time. The arrival of 5G cellular networks has contributed in the enhancement of LBS in terms of accuracy, speed and latency. Although, 3GPP distinguishes LBSs and location services, most times the terms are being confused. More specifically, location services deal with the automated localization of UEs and with the provision of location data to external stakeholders, without necessarily processing it. In addition, LBSs are often considered as context aware services, due to their ability to adapt their behavior [2].

5G uses massive MIMO beamforming and mmWave signals. The use of mmWave technology offers a two-fold advantage when it comes to LBSs, that is: the availability of large bandwidth and the ability to have many antenna elements even in small spaces [15].

The aim of achieving accurate location awareness in mobile networks implicates for the need of integration of sensing and communication where optimization, signal processing and data fusion will be under a common framework [16]. Since most 5G terminals will be associated with people, their mobility pattern could be considered to be predictable and this information could be exploited by a variety of LBSs.
# 4 The Fundamental LOCUS Concept

LOCUS aims to design and develop a location management layered infrastructure capable of improving localization accuracy and security as well as to extend it with physical analytics, extracting value out of it, meanwhile guaranteeing the end-users' right to privacy [17].

The project envisages to make localization and related analytics a first class priority in the cellular world; this is due to the appearing market trends that expected that evolution of 5G, both in the short and in the long term, shall address not only communication but also localization functionalities. Starting from the context of the 3GPP Release 16 [18], this is extending the functionality of 5G infrastructures to enable positioning reference signals, measurements and procedure information. Building on top of these components, adequate low complexity algorithms and scenario-dependent deployment designs can enable 5G and beyond networks to: (i) provide accurate and ubiquitous information on the location of physical targets as a network-native service, and; (ii) derive complex features and behavioral patterns from raw location and physical events, which can be exposed to application developers.

Localization, appropriate dedicated analytics and their combined provision “as a service” will greatly increase the overall value of the 5G ecosystem and beyond, and will allow network operators to dramatically expand their range of offered services, enabling holistic sets of user-, location- and context-targeted applications.

Moreover, LOCUS localization solutions, aim to extend the 3GPP Rel.16 positioning service levels, meeting the enhanced requirements in terms of accuracy, availability and latency dictated by 5G challenges, and in addition to develop new applications or improve already existing ones that are based on high location accuracy and low response times. Currently, outdoor localization depends on the accuracy of GNSS. The LOCUS ecosystem is expected to improve both outdoor and indoor accuracy with the use of analytics tools for processing huge amounts of target-based and/or context-based location information and with the exploitation and coexistence of wireless and cellular networks. As far as 5G is concerned, LOCUS will exploit the benefits of 5G networks for localization and analytics services as follows:

* 5G network devices can easily derive both time- and environment-related information and exploit them to strongly improve localization accuracy.

* 5G network devices are expected to be very close to users, hence they can provide low-latency location services, much faster than remote servers in the Internet.

* 5G will avail itself of virtualization tools for network functions and services.

* 5G virtualization platforms can be exploited to perform localization and the related analytics; primitive virtualized functions can also be specialized to match the requirements of vertical applications in a multi-tenancy scenario.

# 5 The LOCUS Architecture

The convergence of 5G systems and non-3GPP access networks is expected to have significant impact in the field of Information and Communication Technology. What is
new in 5GCN (5G Core Network), compared to 4G/Evolved Packet Core (EPC) is that the 5G Core can be considered as a collection of Network Functions (NFs) which provide the specific core functionalities. More specifically, each NF can expose its services to other NFs, thus acting as a service provider. Additionally, as a consumer, a NF can utilize the services provided by other related NFs. This ability to expose and make services available, characterizes the Service-Based Architecture [20].

The proposed LOCUS architecture [19] integrates localization and analytics func-
tions within a 5G Core architectural approach, as it is defined in 5G. LOCUS provides a
platform for localization analytics to be offered and exposed to smart network manage-
ment and new services and/or 3rd party applications, and makes use of 3GPP technolo-
gies combined with analytics and Machine Learning (ML)/Artificial Intelligence (AI)
techniques.

Localization enablers are responsible for providing the user device location data (e.g. coordinates, velocity, direction) needed by the other LOCUS functions. They utilize data such as network data or user device outside the LOCUS system, providing an estimation about user’s device location. This function employs analytics, data fusion and ML mechanisms, to achieve high accuracy as well as to ensure data reliability for the detection and mitigation of attacks against location data and for correction of the aforementioned data. The information acquired from the external sources, as well as the expected positioning-related output, is stored in the LOCUS Persistence Entity. This Entity is responsible for the storage of all relevant data, as well as analytics/ML results and metadata.

Localization & Analytics for Smart Network Management (SNM) block, as it is depicted in Fig. 2 is responsible for the use of Analytics and ML mechanisms in con-junction with the position-related information provided by the Localization Enablers, so as to offer localization analytics services related to KPIs’ (Key Performance Indicators) monitoring and knowledge building, network planning, management, optimization and diagnostics purposes. The results are exposed outside of LOCUS through the LOCUS APIs (Application Programming Interfaces) and can be used by other external SNM applications and, given availability of the appropriate APIs, can be applied towards OSS (Operations Support Systems) / BSS (Business Support Systems). Intermediate results can be stored internally within the LOCUS platform and re-fed to the various subcomponents, enabling the internal communication of the various subcomponents.

Analytics functions and ML models such as clustering, anomaly detection, classifica-
tion, regression and time-series modelling are employed for the derivation of localization
analytics. Data privacy and security are of high importance in the context of the LOCUS
architecture. Thus, functions related to these aspects are applied horizontally to all data
and externally as well as internal interfaces for ensuring privacy regulations and for
triggering alarm functionalities.

All the above-mentioned LOCUS functions are subject to automated lifecycle man-
agement through dedicated LOCUS MANO (Management and Orchestration) features
which enable automated instantiation, configuration, and operation of the various local-
ization and analytics; the latter are deployed in the form of virtualized network func-
tions (VNFs) on top of the virtualized infrastructure, following the ETSI NFV (Network
Functions Virtualization) principles for functions virtualization and their management.
<figure><img src="image_5.png" /><figcaption>Fig. 2. LOCUS high level architecture</figcaption></figure>

The virtualized platform is implemented as a 5G-enabled edge and core virtualized infrastructure, capable to offer isolated running environments for the various LOCUS functions.

# 6 Privacy in Location Based Services

In 5G networks, localization accuracy is unprecedented, especially in mmWave networks. 5G includes protocols for estimating, elaborating, and distributing the location information [21]. During the past four generations of mobile networks, LBSs were applied especially in cases of emergencies. Information related to user's location was retained only by telecom operators and could not be shared to any third parties. In 5G networks, location information can be disclosed to OTT market players, causing extra threat in already existing privacy concerns. As it is obvious, location privacy is a serious concern not only in current but in future networks as well, like 6G networks [22].

Location services by themselves are not the actual problem. The information/data that they can potentially store and provide to network operators and/or the third parties are the key concern for privacy. When mobile networks locate millions of UEs automatically on an ongoing basis, they generate an enormous amount of sensitive data. The control of access to this sensitive information is a high level, but deeply accurate, definition of privacy in LBSs.

The knowledge of the serving cell of a mobile user can disclose information about the end-user's exact location, especially in urban 5G environments with dense deployment. So, the main focus is given on the management of this location information. Threats such as semantic information attacks [23] (i.e., the use of incorrect information to cause
harm) often target the location data of users. Location data can also be leaked by access point selection algorithms in 5G mobile networks [24, 25].

Since 5G networks radio planning requires many smaller antennas (small cells) and base stations, compared to 4G/LTE, which are densely deployed in order to ensure adequate coverage, the main issue in privacy is location tracking, identity, and personal data safeguarding. However, with 5G, although users will be able to achieve faster and easier communication as well as location addresses, this implicates that privacy may be affected and potentially be disrupted.

Privacy in 5G networks brings huge transformation in daily life applications and access modes of digital services, which is of high importance for both users and Mobile Network Operators (MNOs). New enhancements in terms of architectural and service requirements are brought and the need for strong privacy policies and regulations should be considered as indebtedness.

User privacy of 5G mobile network can be divided into three main categories namely: data, location and identity privacy [26]. The main focus is upon the context of “personal data”, according to the General Data Protection Regulation (as in GDPR article 4, §1 [27]). With the fast deployment of the 5G infrastructures, the amount of data will rapidly increase more than ever, due to its technical characteristics; thus, 5G networks are expected to serve a wide range of applications and sectors supporting 100 times more devices if compared to the prior 4G/LTE networks [28].

As of the case of location privacy, with the advent of 5G technology, LBSs are increasingly expanded as users demand services based on their location [13]. So, UEs—and consequently the involved users— can continuously be tracked not only from the networks owners but from the service providers as well, causing serious concerns regarding their privacy, in general.

Recently, the promotion of LBSs has significantly been increased in several verticals, for instance, government, entertainment, transportation, healthcare, food delivery and others. Indeed, any corresponding LBSs make users’ life easier and more enjoyable but also bring a plethora of privacy-related issues. In some cases, the individuals may be unaware of the potential risks graveled, the implications of how their location is being determined and to whom is permitted access to that information. In addition, service providers would like not to share the information gathered from their application to the providers of other services.

As far LBSs are concerned, two types of location privacy are acknowledged: personal subscriber level privacy and corporate level privacy [29]. Personal subscriber-level privacy is subjected to GDPR, giving also options to users related to their location control and the use of their location data as well as the option to unsubscribe from applications that use their UE location. Corporate level privacy refers to the use of data from service providers, in order to gain benefit from it.

With respect to identity, International Mobile Subscriber Identity (IMSI) catching attacks can reveal the identity of mobile subscribers. By seizing the IMSI of the subscriber's device, an attacker intercepts mobile traffic in a defined area to monitor an individual's activity. While an attacker can see the number of outgoing calls or text messages sent, they still cannot see the contents of those messages. However, even after an
individual has left the attack area, the attacker can still monitor the number of past and future calls or messages.

Data collection is another major concern for 5G users. Virtually all smartphone applications require users' personal information before or during installation. App developers rarely mention how and where they store that data and what they will use it for. 5G networks have no physical boundaries and use cloud-based data storage. Subsequently, 5G operators cannot protect or control user data stored in cloud environments. As each country has different levels of privacy measures and enforcement, user privacy is seriously challenged if and when the data is stored in the cloud of a different country.

In order to protect mobile user's privacy several techniques that prevent disclosure of sensitive location information have been proposed such as privacy policies [30], anonymity-based techniques and systems where the users' true identity can be hidden, with the use of a pseudonym. Also differential privacy [31] or k-anonymity [32], or even the introduction of mix zones [33], meaning the interference a middle-ware between UE and LBS will enhance the anonymization of location information.

## 7 Conclusion

5G will provide super-high data rates, better quality of service and very low latency through dense base station deployments, serving increased huge number—compared to current networks—of connected devices. To fulfill these increased demands in terms of location based services, that benefit from precise and accurate location information, this paper proposed a novel 5G based architecture with integrated localization and analytics functions, which at the same time fulfill adequate privacy standards. This ecosystem, implemented during the lifetime of LOCUS project, incorporates advanced localization techniques and provides the potential for addressing 5G challenges related to location awareness, offering thus the opportunity of new applications development based on seamless and ubiquitous localization information.

**Acknowledgments.** This paper has been based on the context of the LOCUS ("LOCalization and analytics on-demand embedded in the 5G ecosystem, for Ubiquitous vertical applicationS") Project, and has been supported by the Commission of the European Communities / H2020, Grant Agreement No.871249.

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