LOCATION DATA TRANSFORMED BY 5G More Precise, or More Dangerous?
Small Cell Architecture, Centimetre-Level Positioning and the Privacy Paradox An Interview with a Telecommunications Engineer and a Privacy Law Specialist Interview Keywords: 5G • Location Determination • Small Cell • Geofence • Privacy ABSTRACT In fourth-generation (4G) cellular networks, base station records permitted only coarse location determination, because coverage areas ranged from hundreds of metres to tens of kilometres. Fifth-generation (5G) networks change this picture fundamentally: through dense small cell deployment, millimeter-wave bands and the advanced positioning technologies defined in 3GPP Releases 16–18, 5G can deliver location accuracy at the centimetre level indoors and within a few metres outdoors. While this transformation offers a revolutionary opportunity for forensic investigations, it constitutes an unprecedented threat to individual privacy. This interview addresses the technical foundations of 5G positioning technology, a comparative accuracy analysis against 4G, opportunities and limitations in forensic cases, the legal debate over geofence search warrants, operator data policies, privacy assessment under the GDPR and ECHR frameworks, and the perspective extending toward 6G.
Keywords: 5G positioning, small cell, PRS, TDOA, RTT, AoA, AoD, geofence, location privacy, 3GPP Release 16, forensic telecommunications, GDPR, Fourth Amendment ABSTRACT In fourth-generation (4G) cellular networks, base station records allowed only coarse location determination due to coverage areas spanning from hundreds of meters to tens of kilometers. Fifth-generation (5G) networks are radically transforming this landscape: through dense small cell deployment, millimeterwave bands, and advanced positioning technologies defined in 3GPP Releases 16–18, 5G can deliver centimeter-level accuracy indoors and a few meters outdoors. This transformation presents a revolutionary opportunity for forensic investigations while simultaneously creating an unprecedented threat to individual privacy. This interview addresses the technical foundations of 5G positioning technology, comparative accuracy analysis with 4G, opportunities and limitations in forensic cases, the legal debate over geofence search warrants, operator data policies, privacy assessment under the GDPR and ECHR frameworks, and the perspective extending toward 6G.
EDITOR'S NOTE: This interview addresses the location-data accuracy brought by 5G networks in both its technical and its legal dimensions. No direct reference is made to any case, person or institution; the aim is to create technical and legal awareness. I. 5G Positioning Technology: Technical Foundations QUESTION: Compared with 4G, how different a picture do 5G networks actually offer in location determination?
The difference is not one of degree but one of kind. In 4G LTE, when a user's location is determined through the base station record (Cell-ID), there is a margin of uncertainty ranging from hundreds of metres to tens of kilometres. This wide area may amount to a few hundred metres in urban environments and tens of kilometres in rural areas. Although the Timing Advance (TA) parameter can narrow this area into a ring segment, the result is still a fairly coarse estimate.
5G approaches this picture with three fundamental innovations. The first is physical architecture: 5G small cells serve far smaller geographical regions than the wide areas covered by macro base stations. Because there is a small cell every few hundred metres in urban environments, the area indicated by the Cell-ID record narrows dramatically. This alone significantly increases location accuracy.
The second is new positioning signals: with 3GPP Release 16, 5G NR defined new reference signals called Positioning Reference Signals (PRS), designed for native positioning support. Thanks to wide bandwidth (up to 100 MHz in FR1 and 400 MHz in mmWave FR2), beamforming capability and Massive MIMO antenna arrays, these signals offer a location resolution far beyond that of LTE. The third is new measurement methods: Dwivedi et al. (2021) analysed comprehensively the positioning techniques brought to 5G with 3GPP Rel-16: Round-Trip Time (RTT) measures the round-trip signal duration between the terminal and the base station; Time Difference of Arrival (TDOA) uses the differences in arrival times of signals reaching the terminal from multiple base stations; Angle of Arrival (AoA) and Angle of Departure (AoD) triangulate the position by determining the angle from which the signal arrives, using Massive MIMO antenna arrays. The combination of these techniques enables an accuracy that no single method could achieve.
ACCURACY TABLE: 3GPP Rel-16: 3 m indoors, 10 m outdoors. Rel-17: 20 cm horizontal / 1 m vertical in indoor IIoT settings. Rel-18: centimetre level with carrier phase measurement. Compared with the 'hundreds of metres' uncertainty of GSM, these values point to an accuracy approaching that of GPS.
QUESTION: What does centimetre-level location accuracy mean in concrete terms? Which technology makes it possible?
To answer this question, the concept of 'location accuracy' must first be defined operationally. Location accuracy is the distance within which the error between the terminal's actual position and the calculated position estimate remains at a given probability level (usually 90%). An accuracy of 3 metres at 90% means that 90% of estimates fall no more than 3 metres from the true position.
Foremost among the technologies delivering this accuracy are the carrier phase (CP) measurements introduced with 3GPP Release 18. Whereas conventional methods measure the strength or arrival time of the signal, CP measurement determines the phase of the radio wave — at which point in the wave it is. Because phase length is on the millimetre scale, this method offers a far finer positional granularity. The technical documents of 5G HUB Technologies (2024) and Nokia (undated) record that this technique delivers centimetre-level accuracy in IIoT (Industrial Internet of Things) scenarios. Another critical factor is the widening of the bands. Bandwidth, capped at 20 MHz in LTE, rises to 100 MHz in FR1 and to 400 MHz in the millimeter-wave FR2 bands of 5G NR. In time-based methods such as TDOA, bandwidth directly determines time resolution; wider bands mean shorter time segments and therefore more accurate distance estimation. The Rel-17 positioning enhancements document published by 3GPP (2022) sets out this relationship mathematically. QUESTION: How does small cell architecture affect location accuracy? Is this merely a technical detail, or a paradigm change?
A paradigm change. Macro base stations serve areas with a radius of several kilometres, and any user within those areas can connect to that station. The coverage area of small cells — micro, pico and femto cells — is between 50 and 300 metres. This narrows the location estimate provided by the Cell-ID record alone by tens of times compared with a macro base station.
The comprehensive study published in the ISACA Journal (2024) summarises the situation: 'The degree of geographical precision offered by 5G and the high volume of data that can be collected may be used for surveillance purposes without users' consent.' This observation draws attention to the fact that small cell density simultaneously increases both location resolution and potential surveillance capacity.
5G cells operating in millimeter-wave (mmWave) bands in particular cover extremely small geographical areas with a radius of 50–200 metres. In such environments the Cell-ID record may point to a particular street, the façade of a particular building, or even, in large indoor spaces, a particular floor. What LTE could state as 'within the neighbourhood', 5G becomes able to state at the level of 'in front of which building'.
Table 1. The Evolution of Location Accuracy from GSM to 5G Generation Technique Typical Accuracy Mechanism Standard 2G (GSM) Cell-ID 100 m – 35 km Base station coordinate ETSI GSM 2G+ (GSM) Cell-ID + TA ~550 m (1 TA unit) Round-trip time ring 3GPP TS 04.08 3G (UMTS) Cell-ID + RTT 50 m – 5 km Round-trip time 3GPP TS 25 4G (LTE) E-CID, OTDOA 40 m – 2 km Enhanced Cell-ID, TDOA 3GPP Rel. 9 5G Rel-16 PRS, RTT, TDOA, AoA/AoD Indoor: 3 m / Outdoor: 10 m Wide band + multi-antenna 3GPP Rel-16 5G Rel-17 PRS + CP IIoT: 20 cm / Outdoor: <5 m CP + enhanced PRS 3GPP Rel-17 5G Rel-18 Carrier phase (CP) Centimetre level Phase measurement + band aggregation 3GPP Rel-18 + LPHAP Note. Source: compiled on the basis of Dwivedi et al. (2021); 3GPP (2022); RCR Wireless (2022); 5G HUB Technologies (2024); Nokia (undated). IIoT: Industrial Internet of Things; CP: Carrier Phase; LPHAP: Low Power High Accuracy Positioning.
II. The Forensic Opportunity: What Does 5G Location Data Offer Investigations? QUESTION: What concrete new possibilities does this increased accuracy open up in forensic investigations?
From a forensic standpoint, the meaning of this change can be made concrete as follows: in the 4G era one could say that a suspect was in that neighbourhood on that day; in the 5G era it may become possible to determine whether the person was in front of a particular building, inside it, or tens of metres away. This makes a decisive difference particularly in the following scenarios: Homicide and serious crime investigations: determining whether a victim or a suspect genuinely coincided with the scene at a given moment can clarify with 5G a large number of cases that remained uncertain under 4G. Being able to say 'he was 30 metres from the entrance of that building' rather than 'he was within that cell area' constitutes qualitatively different evidence in court proceedings.
Organised crime and terrorism: the simultaneous, high-accuracy tracking of the movements of multiple suspects offers a far stronger analytical basis, thanks to 5G data density and accuracy, for identifying meeting points, documenting coordinated movement and reconstructing reconnaissance activity.
Reconstruction of events: where a fight, a traffic accident or an incident occurring in a crowded environment is concerned, the dense small cell structure of a 5G network makes it possible to reconstruct in far greater detail the physical distance between those involved and their movements. The study of 5G network forensics published through Springer Nature (Arshad et al., 2018) stresses that the quality of location evidence obtainable in these networks has reached a level incomparable with earlier generations. QUESTION: What are the limitations of this increase in accuracy for forensic proceedings? Does everything look this promising?
No, there are significant limitations, and stating them honestly is a methodological necessity. The expression 'centimetre accuracy' reflects values obtained under controlled experimental conditions or in industrial factory (IIoT) environments; reaching these values in the chaotic radio environment of a real city is far harder. First limitation: standard operator records do not contain this accuracy. Although 5G networks can technically offer centimetre accuracy, what level of data actually enters an operator's standard HTS (Historical Traffic Signalling) record varies from country to country and from operator to operator. Operators generally store data recorded for billing and network management purposes; location information is mostly limited to the base station identity. This constraint means that the theoretical accuracy of 5G is not always usable in practice.
Second limitation: the retrospective analysis paradox. The high accuracy of 5G positioning rests on real-time measurements. Where a retrospective forensic investigation is concerned, the network conditions at that moment — cell load, obstructing objects, atmosphere — cannot be reconstructed. The methodological framework of Hargreaves and Chivers (2016) in HTS analysis retains its validity here too: retrospective measurement verification is not possible.
Third limitation: multipath propagation and urban obstacles. Millimeter-wave bands are seriously affected by concrete walls, rain and even dense crowds of people. These conditions complicate the signal path and can produce errors in position estimation far above theoretical values. The accuracy valid in an IIoT factory environment is not automatically valid in a crowded outdoor urban setting.
FORENSIC WARNING: There is a significant gulf between the theoretical location accuracy of 5G, the data actually reflected in the operator's standard record, and the information usable in retrospective forensic analysis. Ignoring this gulf reproduces, in an upgraded form, the errors found in narrowed base station analysis.
QUESTION: Does the location data offered by 5G solve the narrowed base station problem we encounter in 4G HTS analysis?
This is an extremely important question, and the answer has two layers. At the theoretical layer: yes, 5G networks can provide much smaller coverage areas and much higher accuracy than 4G. If the operator records real-time positioning data and that data is usable in forensic proceedings, one can go far beyond the coarse estimates of 4G. At the practical layer: no, the fundamental problems of 4G HTS analysis persist in 5G. The standard operator record still contains primarily the base station identity; combined with a 5G small cell this narrows the coverage area but does not give an exact position. The impossibility of retrospective verification continues. The problem of identifying the phone with the user has not been solved. 5G therefore does not legitimise narrowed base station analysis; the logical errors of that analysis in the 4G era retain their validity in 5G. The real opportunities offered by 5G are different: the real-time location services and geofence applications I will describe below.
III. The Privacy Threat: How Does 5G Transform Surveillance Capacity? QUESTION: Why does the location accuracy of 5G constitute such a large privacy threat?
To answer this, let me first recall the following: unlike other categories of personal data, location data means the mapping of behaviour in the real world. Knowing where someone goes, when they go there and who is in the same place at the same time indirectly reveals which hospital they attend when ill, which religious sites they visit, which political meetings they attend and whom they meet.
In 4G this information was coarse and uncertain: it was known that a person was in that neighbourhood, but not which building they were in. With 5G this uncertainty is dramatically reduced. An actor that owns a city's 5G infrastructure — or that has access to this data — may become able to track individuals' daily movements at building and floor level.
The ISACA Journal (2024) frames the situation in these words: 'The degree of geographical precision offered by 5G and its high-volume data collection capacity may be used for surveillance purposes without users' consent.' Canzittu et al. (2021), in their study published in PMC, identify three new dimensions in which 5G directly threatens location privacy: more accurate positioning signals, the transfer of location information to OTT (over-the-top) service providers, and AI-based location inference tools. QUESTION: What do you mean by 'transfer of location information to OTT service providers'? How does this work?
This is a rarely discussed but extremely important dimension. The network APIs offered by the 5G architecture technically enable operators to provide real-time or delayed location data to certain authorised third parties — application developers, advertising companies, smart city platforms. This is a feature not available as standard in 4G.
The Location Management Function (LMF) architecture defined with 3GPP Release 16, and the 5G core APIs, can expose in-network positioning data to external systems. This integration may, for example, technically enable a shopping centre application to learn a user's exact indoor position from the 5G network with centimetre accuracy. The legal framework of course limits this; but the technical capacity exists, which means there is a risk that the user is unaware of this data flow.
Canzittu et al. (2021) describe this risk as the 'OTT threat vector' and argue that existing privacy regulations are insufficient to police this transfer chain. Assessed in the GDPR context, such transfer requires one of the grounds of explicit consent, legitimate interest or performance of a contract, and makes it mandatory that the scope and purpose of the data transfer be explained transparently to the user. QUESTION: What are geofence search warrants, and how does 5G inflame this debate? A geofence warrant is an investigative technique in which law enforcement requests from third parties — Google, Apple, operators — the location data of all devices present in a given geographical area during a given time window. In essence it seeks an answer to the question: 'Which phones were on this street at this hour?'
The technique came into use in the 4G era through Google's Sensorvault database. Its legal legitimacy is contested: in the United States, the Fourth and Fifth Circuit Courts issued conflicting rulings in 2024. According to a Columbia University Law School review (2025), United States v. Chatrie (4th Circuit, 2024) held that a geofence warrant does not constitute a search under the Fourth Amendment, while the 5th Circuit ruled to the contrary. The case received certiorari in 2026 for review by the US Supreme Court.
5G inflames this debate on two levels. First, it magnifies the area problem: 4G geofencing, because of coverage areas hundreds of metres wide, swept in large numbers of people with no connection whatsoever to the crime. With the small cell structure of 5G, the target area can be narrowed — which is what advocates want — while far more accurate data can also be obtained, which is what worries critics.
Second, it creates new actors: at the end of 2024 Google implemented its decision to move location history from Sensorvault to the device. This decision effectively curtailed its capacity to respond to geofence requests. However, the policy report published by the think tank TLPC in June 2025 notes that operators and other location data providers may fill the gap created by Google's move. 5G operators, with richer and more accurate location data, may find themselves at the centre of this new picture. THE GEOFENCE PARADOX: Geofence warrants draw innocent people into criminal investigations; their data is seized until they are proven not to be suspects. The accuracy of 5G may reduce this risk, but by increasing data richness it also prepares the ground for privacy violations. A double-edged sword. Table 2. Forensic Opportunities and Privacy Threats of 5G Location Data Dimension Forensic Opportunity Privacy Threat Location accuracy Determining at centimetre level who occupied the scene Tracking visits to religious, medical and political sites at building level Small cell density Reconstruction of movement patterns at far finer granularity Minute-by-minute documentation of every movement within the city OTT data transfer Enrichment of third-party location data within a legal framework Location data reaching third-party companies without consent Real-time tracking Instantaneous location updates in suspect surveillance Instantaneous surveillance capacity for unauthorised actors High data volume Rich data for communication network and social pattern analysis Raw material for large-scale mass surveillance AI-based location inference Analysis of implicit patterns in existing data Prediction of behaviour that has not been disclosed Note. Source: compiled on the basis of ISACA (2024); Canzittu et al. (2021); TLPC (2025); Davidson & Byers (2026).
IV. The Legal Framework: GDPR, the ECHR and Turkish Law QUESTION: How does European law address this situation? Does the GDPR offer adequate protection for 5G location data?
The GDPR (General Data Protection Regulation) explicitly defines location data as personal data. Sensitive location data — pointing to a religious site, a health institution or a political meeting area — may fall within the status of special category data and be subject to a higher protection threshold. Within this framework the location data collected by 5G operators cannot be processed or shared with third parties without a legitimate purpose, explicit consent or a legal obligation.
Even so, the GDPR's fit with the reality of 5G is contested. Canzittu et al. (2021) argue that the existing regulation cannot fully police the OTT transfer chain — from operator through API to application developer and from there to advertising networks. Establishing and auditing responsibility at every link at which sensitive location data is processed is a complexity that strains regulatory capacity. ECHR case law also offers a clear framework in this area. The judgments in Zakharov v. Russia (2015) and Szabó and Vissy v. Hungary (2016) held that bulk and untargeted access to telecommunications data violates the right to respect for private life under Article 8 of the Convention. This case law fundamentally questions the legitimacy of bulk access mechanisms directed at the high-accuracy location data offered by 5G. Interference must be necessary, proportionate and based on a concrete suspicion of crime. QUESTION: What framework does Turkish law offer for the collection, processing and forensic use of 5G location data?
Law No. 6698 on the Protection of Personal Data (KVKK) covers location information as personal data. Under the KVKK, location data may be processed on one of the grounds of explicit consent, legal obligation or legitimate interest. For sensitive data categories explicit consent is mandatory. The question of the accuracy level at which location data acquires 'sensitive' character is, in the 5G context, a matter of interpretation not yet settled. As regards forensic use, Article 135 of the Turkish Code of Criminal Procedure (CMK) governs the interception of electronic communications, while Article 138 governs the use of evidence obtained incidentally. Within this framework the real-time, high-accuracy location data offered by 5G will either be requested in advance by court order or extracted retrospectively from the operator's existing HTS record. Whether existing HTS records will fully reflect the theoretical accuracy of 5G is technically a matter of operator policy.
An important gap should be noted: commercial 5G deployment in Türkiye begins in the 2025–2026 period. Case law on how 5G location data will be assessed under the KVKK and the CMK has yet to form. Monitoring this period is of great importance for forensic practitioners, legal academics and regulators alike.
V. Google's Policy Change and the Future of Geofencing QUESTION: How did Google's removal of Sensorvault at the end of 2024 change the geofence debate?
This development is one of the most important turning points in the geofence debate. For years Google stored location history in the central Sensorvault database and allowed law enforcement to access that database through geofence warrants. With the policy change that took effect in December 2024, Google began storing users' location history directly on their devices rather than in Sensorvault, and shortened the default retention period. An analysis in the Suffolk University Law School journal (2025) predicts that in practice this change will render geofence requests largely ineffective: data stored on the device cannot be obtained through a court order directed at a Google server; that requires direct physical access to the device.
However, the TLPC policy report (2025) issues a critical warning: other actors will fill the gap created by Google's move. 5G operators, smart city platforms, advertising networks and location-based service providers may become new focal points of location data accumulation, with different retention policies and different thresholds for legal cooperation. The work of Davidson and Byers (2026) demonstrates mathematically that this problem — dynamic spatial sampling and determining an appropriate geofence radius — remains unsolved.
QUESTION: Where is the geofence debate heading in the coming period? Will the Supreme Court be able to resolve this question?
The US Supreme Court's grant of certiorari in Chatrie v. United States in January 2026 suggests that this question may finally be resolved. But a Supreme Court ruling binds only US law; the international picture will continue to differ from country to country.
The fundamental legal tension lies between two principles. The third-party doctrine holds that an individual can have no expectation of privacy in information voluntarily given to a third party; under this doctrine location data is deemed to have been voluntarily transferred to the operator or to Google. The mosaic theory, which began to take shape in Carpenter v. United States (2018), argues that long-term and comprehensive digital surveillance constitutes as a whole a meaningful account of a person's location and must be treated as a search. 5G provides strong ground for this second theory: continuously updated location data at centimetre accuracy makes concrete the point that a surveillance capacity far beyond the ordinary data sharing envisaged by the third-party doctrine has come into being. How the Supreme Court addresses this point will be one of the most critical privacy decisions of the digital age.
VI. 6G and Beyond: Prediction, or Real-Time Tracking?
QUESTION: What new dimensions does 6G research bring in terms of location data? 6G is being built around the ISAC (Integrated Sensing and Communication) concept, which unites positioning and communication in a single system. In this approach radio waves will be used both for communication and for environmental sensing — much like radar. Canzittu et al. (2021) state this development plainly: 'Location privacy will become a far greater risk in 6G networks, where sub-millimeter wave transmission will enable much more accurate location inference.' What this means is: 6G may determine not only the location of the user's device but the user's physical position, their movement, and even to a certain extent the position of objects and people within a room. A 6G base station may become able to 'see' how many people are in a room within its coverage area and where they are standing — including through walls. This is a revolutionary tool for forensic investigations and an unparalleled threat to privacy.
AI-based location inference adds to this picture: machine learning models that learn from past movement patterns can predict not only current position but probable future location. Although this 'predictive location' capacity may look attractive from a crime prevention perspective, it also raises the question of individuals being surveilled on the basis of acts they have not yet committed.
QUESTION: Finally: faced with this picture, what can be done as a citizen? And what should regulators do?
At the individual level, practical steps are limited but meaningful:
• Keep location services active only while in use; restrict background location access.
• Audit regularly which applications hold location permission; revoke those that are not necessary.
• VPN use hides the IP address but cannot prevent the base station record at operator level. Relying on it excessively as a privacy tool is a mistake. • Privacy-focused mobile operating systems that restrict sensor access (such as GrapheneOS) offer more comprehensive protection, but come with usability difficulties.
• Evaluate location history retention options carefully at application and platform level; review features such as Google Timeline and Apple Significant Locations.
At the regulatory level, the following steps should be priorities. First, keeping location data retention standards for 5G operators to a minimum and tying them to a clear legal framework. Second, strengthening transparency obligations regarding the OTT transfer chain, so that the user can see in real time which data is transferred to whom. Third, introducing criteria of proportionality, scope limitation and necessity for geofence search warrants. Fourth, and perhaps most importantly: the positioning capacity offered by 5G and 6G has been developed ahead of existing legal frameworks, producing a technology that regulators are perpetually 'trying to catch up with'. Closing this gap requires continuous dialogue between academia, industry, civil society and regulators.
— End of Interview — References 3GPP. (2022). 5G NR positioning enhancements, Release 17 (TR 38.857). 3rd Generation Partnership Project.
Arshad, J., Khan, M. A., Abbas, A., Iqbal, W., Amjad, M. F., Abbas, H., & Rashid, I. (2018). Towards 5G cellular network forensics. EURASIP Journal on Information Security, 2018(1), 14. https://doi.org/10.1186/s13635-018-0078-7 Canzittu, M., Karray, M., Maruta, S., & Vaton, S. (2021). Location-privacy leakage and integrated solutions for 5G cellular networks and beyond. Sensors (MDPI), 21(15), 5135. https://doi.org/10.3390/s21155135 Columbia Undergraduate Law Review. (2025). Mapping the future of surveillance: Geofence warrants and the risks of Chatrie. https://www.culawreview.org/journal/mapping-thefuture-of-surveillance-geofence-warrants-and-the-risks-of-chatrie Congress.gov. (2026, January 22). Geofence warrants and the Fourth Amendment. CRS Legal Sidebar LSB11274. https://www.congress.gov/crs-product/LSB11274 Dahlman, E., Parkvall, S., & Sköld, J. (2018). 5G NR: The next generation wireless access technology. Academic Press.
Davidson, M., & Byers, J. (2026). The problem of dynamic spatial sampling and geofence surveillance. arXiv preprint arXiv:2603.28958. https://arxiv.org/pdf/2603.28958 Dwivedi, S., Shreevastav, R., Munier, F., Nygren, J., Siomina, I., Lyazidi, Y., Shrestha, D., Lindmark, G., Ernström, P., Stare, E., Razavi, S. M., Muruganathan, S., Masini, G., Busin, Å., & Gunnarsson, F. (2021). Positioning in 5G networks. IEEE Communications Magazine, 59(11), 38–44. https://arxiv.org/pdf/2102.03361 5G HUB Technologies. (2024, March 17). Precision and power: The evolution of 5G NR positioning from Release 16 to 18. https://5ghub.us/precision-and-power-theevolution-of-5g-nr-positioning-from-release-16-to-18/ Hargreaves, C., & Chivers, H. (2016). Recovery of mobile device forensic evidence from telecommunication networks. Digital Investigation, 17, 72–83. https://doi.org/10.1016/j.diin.2016.03.002 ISACA. (2024, Volume 6). The impact of 5G: Unpacking security and privacy concerns. ISACA Journal. https://www.isaca.org/resources/isaca-journal/issues/2024/volume- 6/the-impact-of-5g-unpacking-security-and-privacy-concerns Nokia. (undated). The evolution of 5G New Radio positioning technologies [White paper]. https://d1p0gxnqcu0lvz.cloudfront.net/documents/Nokia_The_Evolution_of_5G_New _Radio_Positioning_Technologies_White_Paper_EN.pdf RCR Wireless. (2022, April 27). What 5G NR positioning enhancements are in Release 17? https://www.rcrwireless.com/20220427/featured/5g-nr-positioning-enhancements-inrel-17 Rangan, S., Rappaport, T. S., & Erkip, E. (2014). Millimeter-wave cellular wireless networks: Potentials and challenges. Proceedings of the IEEE, 102(3), 366–385. https://doi.org/10.1109/JPROC.2014.2299397 Sensors (MDPI). (2024). Survey on 5G physical layer security threats and countermeasures. Sensors, 24(17), 5523. https://doi.org/10.3390/s24175523 TLPC (Technology Law & Policy Clinic). (2025, June). Geofence warrants: A model state policy. University of Colorado. https://tlpc.colorado.edu/wpcontent/uploads/2025/06/TLPC-Geofence-Report-June-2025.pdf
