Radio access network node, network node and methods in a wireless communications network

Abstract

A method performed by a Radio Access Network (RAN) node. The method is for assisting a network node in locating a position of a User Equipment (UE) in a wireless communications network. The RAN node comprises a Multiple-Input Multiple-Output (MIMO) antenna system capable of performing beamforming. The RAN node receives (502), on the MIMO antenna system, one or more uplink signals from the UE. The one or more uplink signals comprises one or more out of: a Line of Sight (LoS) signal and one or more non LoS (nLoS) multipath reflections. The RAN node determines (503) an Angles- of-Arrival (AoA) and a Time-of-Arrival (ToA) related to the received LoS signal and each of the nLoS multipath reflections. The RAN node extracts (504), based on a temporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals. Additionally, the RAN node sends (505) information to the network node. The information comprises one or more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections.

Description

[0001]RADIO ACCESS NETWORK NODE, NETWORK NODE AND METHODS IN AWIRELESS COMMUNICATIONS NETWORK TECHNICAL FIELD Embodiments herein relate to a Radio Access Network (RAN) node and methodstherein. In some aspects, embodiments relate to assisting a network node in locating aposition of a User Equipment (UE) in a wireless communications network. Embodiments herein further relate to a network node and methods therein. In some aspects, embodiments relate to locating a position of a UE in a wireless communications network. BACKGROUND In a typical wireless communication network, wireless devices, also known as wireless communication devices, mobile stations, stations (STA) and / or User Equipment (UE), communicate via a Wide Area Network or a Local Area Network such as a Wi-Fi network or a cellular network comprising a Radio Access Network (RAN) part and a Core Network (CN) part. The RAN covers a geographical area which is divided into service areas or cell areas, which may also be referred to as a beam or a beam group, with each service area or cell area being served by a radio network node such as a radio accessnode e.g., a Wi-Fi access point, a Base Station (BS) or a radio base station (RBS), whichin some networks may also be denoted, for example, a Base Station (BS), a NodeB,eNodeB (eNB), or gNodeB (gNB) as denoted in Fifth Generation (5G)telecommunications. A service area or cell area is a geographical area where radio coverage is provided by the radio network node. The radio network node communicatesover an air interface operating on a radio frequency with the wireless devices within therange of the radio network node. 3rd Generation Partnership Project (3GPP) is the standardization body forspecifying the standards for the cellular system evolution, e.g., including 3G, 4G, 5G andthe future evolutions. Specifications for Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Packet System (EPS) have been completed within the 3GPP. In 4Galso called a Fourth Generation (4G) network, EPS is core network and E-UTRA is radio access network. In 5G, 5G Core (5GC) is core network, NR is radio access network. As a continued network evolution, the new release of 3GPP specifies a 5G network alsoreferred to as 5G New Radio (NR) and 5GC. Frequency bands for 5G NR are being separated into two different frequency ranges, Frequency Range 1 (FR1) and Frequency Range 2 (FR2). FR1 comprises sub-6 GHz frequency bands. Some of these bands are bands traditionally used by legacy standards but have been extended to cover potential new spectrum offerings from 410 MHz to 7125 MHz. FR2 comprises frequency bands from 24.25 GHz to 52.6 GHz. Bands in this millimeter wave range have shorter range but higher available bandwidth than bands in the FR1. Multi-antenna techniques may significantly increase the data rates and reliability of a wireless communication system. For a wireless connection between a single user, suchas UE, and a base station (BS), the performance is in particular improved if both thetransmitter and the receiver are equipped with multiple antennas, which results in a Multiple-Input Multiple-Output (MIMO) communication channel. This may be referred to as Single-User (SU)-MIMO. In the scenario where MIMO techniques is used for the wireless connection between multiple users and the base station, MIMO enables the users to communicate with the base station simultaneously using the same time-frequency resources by spatially separating the users, which increases further the cell capacity. This may be referred to as Multi-User (MU)-MIMO. Note that MU-MIMO may benefit when each UE only has one antenna. The cell capacity can be increased linearly with respect to the number of antennas at the BS side. Due to that, more and more antennas are employed in BS. Such systems and / or related techniques are commonly referred to as massive MIMO. In the transition to 6G, UE positioning is expected to become a global macrocapability, driven by increased automation, massive IoT deployments, prevalence ofautonomous guided vehicles, coupled with heightened security concerns.3GPPstandardization efforts are underway for RAN assisted positioning strategies, which is well known to suffer from large errors as manufacturers are reticent to accept stringentmeasurement reporting requirements. Regardless, network-based strategies will enhancepositioning accuracy by over determination of uplink (UL) measurements in cases wheremultiple Transmit / Receive Points, or alternatively Transmission / Reception Points (TRPs),are visible. A TRP may e.g., be defined as part of the gNB that is transmitting andreceiving radio signals to / from UE according to physical layer properties and parametersinherent to that element. A TRP may e.g., refer to the location of the gNB AntennaReference Point (ARP) defined e.g., as the Global Positioning System (GPS) location ofthe physical antenna element. UE location calculations require absolute or relative timingmeasurements referenced to the antenna element location. In some cases, where UEsare configured to bypass UE assisted positioning procedures, network assistedpositioning becomes the only fallback. UE Line-of-Sight (LoS) visibility to multiple cellssites is uncommon, as those who are experts in the field understand that in urban environments, such as e.g., London England, fewer than 20% of UE locations have LoSvisibility to two or more cells sites. This simple statistic indicates that all currenttriangulation technologies such as Round-Trip-Time (RTT) or Uplink Time Difference ofArrival (UTDoA) that require LoS path delays, will fail to find the UE location. Regardlessof whether calculations are performed by the UE or in the network, insufficient LoS measurements from spatially separated TRPs makes it impossible to locate the UE. Time-based positioning algorithms may be divided into categories of relative andtrue ranging or timing. Relative ranging algorithms, such as Observed Time Difference ofArrival (OTDoA) measure Time of Arrival (ToA) which includes an unknown delay constant (C), common to all cell sites. On calculating the Time Difference of Arrival(TDoA) between two cell sites, where TDoA = (ToA1 + C) – (ToA2 + C), the unknownconstant delay falls out of the calculation, defining a fixed time delay between the two cellsites resulting in TDoA = ToA1 – ToA2. This difference defines a hyperbolic curvebetween the two cell sites along which the UE may reside. Additional TDoA measurements from other gNB radios gives multiple hyperbolic curves, the intersection ofwhich defines the UE location. OTDoA is a relative ranging algorithm used to determinethe UE location. True ranging algorithms, such as the use of RTT or Timing Advance (TA) provide an indication of the true Time-of-Flight (ToF) distance to the UE. TA is used to control the timing of the UL transmission of individual UEs to ensure that the UL transmissions from all attached UEs in the cell, regardless of their distance to the gNB, arrive within the same time window for OFDM symbol processing. In essence, TA synchronizes the UL transmissions, avoiding interference to receptions in adjacent timeslots received at thegNB. TAs purpose is aligning UL transmissions from different UEs, to adjust for RFpropagation delays. As such, TA provides an approximate distance metric of attachedUEs. In these cases, the ToF provides ranging information drawing a circle of possiblelocations of the UE. In the case of RTT, ToF is determined at three or more cell sites resulting in three circles, the intersection of which is the location of the UE. TA may becombined with Angles-of-Arrival (AoA) to locate the UE, eliminating the cost of makingmeasurements at different cell sites. AoA and high precision TA provide high qualitypositioning estimates from a single TRP or cell site. This technology calculates UEpositions as a polar or spherical coordinate with known angles and radius (r) determinedby TA. The TA has accuracies in nanoseconds. However, this technology isfundamentally limited by the UE where TA reports are loosely defined, resulting in mean errors of ±60ns (±20m), with typical worst-case errors of ±120ns (±40m). UE errors are driven by the following two main factors. The resetting of Application Specific IntegratedCircuit (ASIC) clock trees after micro-sleep cycles introduces tens of nanoseconds oferrors. UEs have small and uncontrolled antenna geometries leading to poor multipathdiscrimination. As such, UEs’ small antenna geometries make it impossible to extract LoSpaths from near-in non-LoS (nLos) paths, as UE processing is unable to leverage angulardiscrimination to separate multipath signals. Network based timing is significantly more accurate than UE clocks and the ultra-high gain massive MIMO antennas enable robust angular discrimination of LoS fromnLoS. It is possible to estimate the Azimuth and ToA based on 2D Multiple SignalClassification (MUSIC) considering Joint Angle and Delay Estimation (JADE) for multipath signals arriving at an antenna array. In this method, the Azimuth and ToA are consideredas a joint problem to be solved, to leverage the method to discern the LoS from nLoS.Still, AoA / TA technologies offer a significant improvement in positioning accuracies than UE based methods, but do not meet 3GPP established 6G targets of <2m accuracy for automated guided vehicle, AGVs, and many of the next generation mobility positioningapplications. GPS has become the dominant macro and / or outdoor positioningtechnology, with reported accuracies of ±5m. However, these cases are often much worsein urban environments, and suffer from poor LoS to satellites, most of which are along theskyline. In locations with good visibility to satellites, Real-Time Kinematic (RTK)positioning is often offered as a means to further improve performance, with the potentialto reach centimeter accuracies. Currently, such high accuracies are typically onlyavailable to high end GPS transceivers, while most active mobile devices are not capableof this accuracy. Apart from network and UE based positioning technologies, and satellite-based technologies such as GPS, Global Navigation Satellite System (GNSS), GLObalnaya NAvigatsionnaya Sputnikovaya Sistema (Russian GPS) (GLONAS), etc., noother technologies are known. All positioning methods suffer from measurement errors, asit is related to the precise intersection of all curves described above. Absolute error, ormore commonly, least squared error is calculated, and a solver may then be employed to minimize the errors. Gauss Newton methods are often employed to minimize these errors, and to calculate the estimated UE location. Figure 1 shows a cloud implementation of the UE positioning services using the 5GLocation Services (LS) according to prior art.5G LS may e.g., be a network provided enabling technology consisting of standardised service capabilities, which enable theprovision of location applications. This is to say that LS is a core network functionalitywhich uses standardized network interfaces and services to enable location applications. The Location Requests are received from a Location Client Services (LCS) and are directed to the LS through the Network Location Gateway (NLG). The NLG accesses the core nodes comprising the Home Subscriber Server (HSS) and Home Location Register (HLR) databases for client subscriber and location information, as well as with the Mobile Services Switching Center (MSC), Mobile Management Entity (MME) and Servicegateway GPRS Support Node (SGSN) for other functions. The NLG directs the UELocation Requests to the Network Location Server (NLS) located in the region of the Radio Nodes via the 5G Baseband (BB) and gNB Remote Radio Units (RRU) to which the UE is attached. The RAN in the figure 1 may be implemented using a split architecture with Open- RAN (O-RAN) capabilities thereby comprising an Open-Distributed Unit (O-DU) acting as a computation component in the RAN, an Open-Radio Unit (O-RU) responsible for transmission and reception of radio signals, and an Open-Centralized Unit (O-CU)controlling the O-DU and O-RU. Figure 2 shows an O-RAN with 5G LS interfaces. The Leinterface as seen in figure 2 interfaces the LCS Client to the NLG, which sends NL2messages to the Access and Mobility Management Function (AMF) for the target UE,which are sent to the NLS via NL1 messages. The communication from the O-RU / RRU toO-DU is via the Open FH M-Plane, which includes N2 messages. Note that the O-RANNG-c interface is also referred as N2. The NLS thus communicates with the O-RAN via the control plane functions such as User Plane Function (UPF) and AMF to determine thelocation of the position of the UE. The O-RAN may be split with two variations - CategoryA and Category B. Category A may e.g., refer to the 7-2x Cat-A no-BF-RU split and may e.g., be defined as the standard for RRU. Category A places the Lower Layer Split (LLS) close to the RRU to keep the radio unit functionality as simple as possible. The Category A LLS limits the total number of precoded streams of up to 8, which supports 4G LTE radios and 5G RRU radios with up to 8 branches. In the Category A LLS, antenna branch information is processed in the O-DU. Category A radio positioning with up to 8 branches does not have the same level of accuracy as large antenna arrays for massive MIMO which forearly 5G radios has 64 branches, for 6G may have hundreds to a thousand or morebranch counts. Category B may e.g., refer to 7-2x Cat-B and may e.g., be defined as the standard for massive MIMO (M-MIMO) radios. Category B places the LLS higher in the physical layer, moving beamforming processing and functionality into the M-MIMO O-RU. While Category A passes signal measurements for up to 8 antenna branches into the O-DU, this is not viable for massive MIMO radios. Category B split reduces the 7.2x interfacethroughput relative to Category A but results in a more complex O-RU. In Category B,mapping to frequency resources is performed at O-DU, and mapping to antenna ports isperformed in the O-RU. Since positioning functionality processes the received signals atthe antenna port, before mapping into frequency resources or MIMO layers, Category Bradios perform positioning signal processing in the O-RU. SUMMARY As part of developing embodiments herein, the inventors identified some problemsthat first will be described. Existing technologies have timing inaccuracies, due to reliance on UE reportingschemes. Accuracies are up to ±40m from TA reports, plus AoA errors which depend onthe resolution and calibration of the macro radio base station array antenna. Moreover,since 80% of radio base stations may not have LoS to the UE, positioning calculationsbased on nLoS TA and AoA measurements will naturally have greater errors. Asdiscussed, GPS is the main industry method, but suffers greatly in urban areas where thevisibility of the satellites is obscured by buildings. Satellite reflections from tall buildingsfurther hampers accuracy, often displacing GPS calculated locations by tens of metersmaking the UE appear as if on a different street. Integration of velocity and accelerationsensor data is used by mobile industry to increase location robustness. Automotive manufacturers leverage camera systems to improve vehicular location accuracy and leverage machine learning from collected datasets of connected vehicles to improveoverall accuracy. While machine learning and image processing may help the automotiveindustry achieve their location accuracy requirements, it is not a viable or cost-effectivemethod for the expected tens of billions of 5G and 6G IoT and mobile applications.Existing technologies do not consider the use of 6G Massive Array Radio System(MARS) Radio Base Station (RBS) with their massive arrays of azimuth and elevationantenna elements leading to a 3-dimensional method to UE positioning. They also fail tosuggest the use of virtual TRPs (vTRPs), which may e.g., be physical locations in spacethat are not TRPs but are reflection points of the received signal from the UE that act asvirtual TRPs. They do not exploit the ability to include predefined location dataset such ase.g., point cloud dataset as a means to consolidate LoS and nLoS measurements to solvefor UE location. Predefined location dataset is a dataset comprising a discrete set of data pointswhere each point position has a Cartesian (x, y, z) or GPS (longitude, latitude, altitude)coordinate representing points on the external surfaces and possibly internal surfaces ofbuildings, roads, trees, and all objects which may interact with radio frequency signalswithin the sector coverage area of a gNB. While locations datasets are described asCartesian and most typically would be described using relative (x, y, z) or absolute(longitude, latitude, altitude) coordinates, the dataset might use any dataset system – forexample spherical or cylindrical or more advanced canonical systems, which may determine unique locations for the vTRP associated with radio frequency reflecting objects relative to the location and orientation of the gNB. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D Computer- Aided Design (CAD) or Geographic Information Systems (GIS) models for manufactured parts, for metrology and quality inspection, and for a multitude of visualizing, animating,rendering, and mass customization applications. One example of a predefined locationdataset may be a point cloud dataset which combines image and positional (x, y, z)information for visualization, animation, and application specific purposes. Data is often collected with specialized camera systems using infrared lasers capable of measuringdistances to surfaces or objects to sub-meter resolution. High resolution predefinedlocation datasets are formed by stitching together multiple images taken at different locations, each with associated positional coordinates. Such datasets are becoming more commonly available and where unavailable, is readily orderable as a service offering RTK GPS centimeter accuracy drone-based services to provide high resolution predefinedlocation datasets for all cell sites. Other datasets may e.g., be CityGML, CityJSON, DXF,2D or 3D Shape, DWG, 3DS, Sketchup, OBJ, AutoCAD or many other specializedmodelling software formats and packages. Existing technologies for positioning the UE also do not employ the benefits of polarization to separate the LoS path signals from the nLoS multipath signals, and todetermine which surface in the dataset model is generating the reflection and thereforewhere the location of the vTRP should be placed. For example, deciduous vegetationtends to generate random polarizations, while the outer walls of the building tend to reflectvertical polarizations, so that a predominantly vertically polarized multipath signal may be assumed to have been reflected from the building behind the tree. It is well known that most multipath signals in an outdoor macro network are constrained to a limited angularspread. Figure 3a shows the UMa-6GHz Averaged Angular Delay Spread Across allVenders. The channel models show a 50th percentile of angular spreads of 20° as seen inFigure 3a. Therefore, the angles of incidence of reflections approach the Brewster anglewhere the Transverse Magnetic (TM) reflection coefficient is minimized. Figure 3b showsthe reflection coefficient amplitude for air / concrete interface at 1 GHz. At the Brewster angle, vertically polarized signals experience minimal reflection from horizontal surfaces, and horizontally polarized signals experience minimal reflection from vertical surfaces.This physical phenomenon enables polarization to be used to determine the reflectivesource of the multipath reflections. Poles, building corners and windows will typically show a dominant vertical polarization, while reflections from streets, parking lots, or building rooftops and parapets would be predominantly horizontal polarization. An object of embodiments herein is to improve the way of locating a position of aUE in a wireless communications network. According to an aspect of embodiments herein, the object is achieved by a methodperformed by a Radio Access Network (RAN) node. The method is for assisting a networknode in locating a position of a User Equipment (UE) in a wireless communications network. The RAN node comprises a Multiple-Input Multiple-Output (MIMO) antenna system capable of performing beamforming. The RAN node receives, on the MIMO antenna system, one or more uplink signals from the UE. The one or more uplink signals comprises one or more out of: a Line of Sight (LoS) signal and one or more non LoS (nLoS) multipath reflections. The RAN node determines an Angles-of-Arrival (AoA) and aTime-of-Arrival (ToA) related to the received LoS signal and each of the nLoS multipathreflections. The RAN node extracts, based on a temporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more receiveduplink signals. Additionally, the RAN node sends information to the network node. Theinformation comprises one or more out of one or more: determined AoA, determined ToA,extracted LoS signal and extracted nLoS multipath reflections. According to an aspect of embodiments herein, the object is achieved by a methodperformed by a network node. The method is for locating a position of a User Equipment (UE) in a wireless communications network. The network node receives information from a RAN node. The information comprises one or more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections. The information is related to a LoS signal and one or more nLoS multipath reflections comprised in one or more uplink signals received from the UE. Based on one or more out of one or more: determined AoA, determined ToA, and predefined location dataset, the network node locates a position of one or more virtual Transmission Reference Points (vTRPs) related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections. The respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node and LoS visibility to the UE and apoint of transmission. The network node determines a relative ToA (rToA) at each of theone or more vTRPs, by calculating a propagation time, of the LoS signal and each of the nLoS multipath reflections out of the one or more received uplink signals, from the RANnode (111) to each of the respective vTRPs. The rToA is related to the uplink signal fromthe UE (121) to the respective vTRP. Additionally, the network node computes theposition of the UE (121) based on the located one or more vTRPs and the corresponding determined rToA. According to another aspect of embodiments herein, the object is achieved by aRAN node. The RAN node is configured to assist a network node in locating a position of a UE in a wireless communications network. The RAN node comprises a MIMO antenna system capable of performing beamforming. The RAN node is further being configured toreceive, on the MIMO antenna system, one or more uplink signals from the UE. The oneor more uplink signals comprises one or more out of: a LoS signal and one or more nLoS multipath reflections. The RAN node is further being configured to determine an AoA and a ToA related to the received LoS signal and each of the nLoS multipath reflections. The RAN node is further being configured to extract, based on a temporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals. Additionally, the RAN node is further being configured tosend information to the network node. The information comprises one or more out of oneor more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections. According to an aspect of embodiments herein, the object is achieved by a networknode. The network node is configured to locate a position of a UE in a wireless communications network. The network node is further being configured to receive information from a RAN node. The information is adapted to comprise one or more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections. The information is adapted to be related to a LoS signal and one or more nLoS multipath reflections comprised in one or more uplink signals received from the UE. The network node is further being configured to, based on one or more out of one or more: determined AoA, determined ToA, and predefined location dataset, locate a position of one or more vTRPs related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections. The respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node and LoS visibility to the UE, and a point of transmission. The network node is further being configured to determine a rToA at each of the one or more vTRPs, by further being configured to calculate a propagation time, of the LoS signal and each of the nLoS multipath reflections out of the one or more received uplink signals, from the RAN node (111) to each of the respective vTRPs. The rToA is related to the uplink signal from the UE (121) to the respective vTRP. Additionally, the network node is further being configured to compute the position of the UE based on the located one or more vTRPs and the corresponding determined rToA. Thanks to that the RAN node is able to extract the LoS signals and the nLoSmultipath reflections more efficiently using temporal beamforming algorithms, the networknode may use them together with the predefined location dataset to improve the locatingof the position of the UE in the wireless communications network. This is because the network node is able to utilize the nLoS multipath reflections in determining the position ofthe UE by determining the locations of the point of reflection i.e., the vTRPs related to theextracted nLoS multipath reflections and using the signals from the vTRPs as LoS signals.This enables the use of multiple rToAs related to the multiple vTRPs within a single cell todetermine the position of the UE thereby improving the accuracy of the positioning of theUE. Embodiments herein may provide one or more of the following advantages: They are network node based and UE agnostic thereby providing consistentperformance that is independent of the type of the UE. AoA and rToA measurements aremade by the RAN node and the network node, respectively, ensuring that all UE, whetherexpensive or cheap, well, or poorly designed, may be located to the same level ofaccuracy. This ensures that lower cost Internet-of-Things (IoT) devices may be located aswell as vehicular devices, or mobile phones. Provides greater insight of the UE position by using the predefined location dataset to provide location information together with visual indications of the UE location therebyimproving the interpretation of the provided location information. For example, if the UE islocated at a bus-stop, example embodiments herein provide a location and an image of the bus stop, or even a sub-set of the predefined location dataset around the bus stop in the UE location response. High accuracy rToA measurements with expected accuracy of tens of picosecondsis achieved rather than current TA methods that provide accuracy upwards of a hundrednanoseconds due to UE measurement errors. This reduces the estimated UE locationerror from tens of meters to an expected sub 1m due to the improved accuracies.Suitable for operation in urban areas with <20% LoS visibility to UEs.Enables locating the position of the UE from a single cell by using spatiallyseparated vTRPs to replace the need for neighbor cell involvement in UE positioning. Enables to locate the UE using the resources of a single cell or together with multiple neighboring cells to provide a larger set of measurements to improve UE location accuracy. Thus, instead of each neighbor cell providing a single rToA and / or AoA, eachneighbor cell may provide rToA from the many vTRP points determined by each neighborcell. The number of measurement points used to determine the position of the UE is increased. For example, rather than basing the UE position on a small set of e.g., three or four LoS rToA measurements from the neighbor cells, example embodiments herein provide access to possibly ten times that number of measurements, enabling the possibility to employ discrimination algorithms to improve UE location results. Enables to be used in combination with other currently available UE positioningmethods. Possibility to measure a carrier phase to estimate fine motion and angular motion.Estimation of velocity of the UE using differential measurements across a plurality ofsymbols using one vTRP or across multiple vTRPs. Differential measurements maycompare an estimated carrier phase of a received symbol, or an estimated rToA of thesignal and / or reflection. High angular selectivity enables to isolate and reject multipath components arrivingfrom different directions i.e., enabling selective isolation of the LoS signals and nLoSmultipath reflections, thereby improving rToA and carrier phase measurements, andoverall robustness.BRIEF DESCRIPTION OF THE DRAWINGSExamples of embodiments herein are described in more detail with reference toattached drawings in which:Figure 1 is a schematic block diagram illustrating prior art.Figure 2 is a schematic block diagram illustrating prior art.Figure 3a is a diagram illustrating prior art.Figure 3b is a diagram illustrating prior art.Figure 4 is a schematic block diagram illustrating embodiments of a wirelesscommunication network.Figure 5 is a flowchart depicting an embodiment of a method in a RAN node.Figure 6 is a flowchart depicting an embodiment of a method in a network node.Figure 7a is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 7b is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 8a is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 8b is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 8c is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 9 is a diagram illustrating an example embodiment of a method herein.Figure 10a is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 10a is a schematic block diagram illustrating an example embodiment of a methodherein.Figure 11 is a diagram illustrating an example embodiment of a method herein.Figure 12 is a schematic block diagram illustrating embodiments of a RAN node.Figure 13 is a schematic block diagram illustrating embodiments of a network node.Figure 14 schematically illustrates embodiments of a communication system.Figure 15 is a generalized block diagram of embodiments of a UE.Figure 16 is a generalized block diagram of embodiments of a network node.Figure 17 is a generalized block diagram of embodiments of a virtualizationenvironment. DETAILED DESCRIPTION Example embodiments herein extend a Line-of-Sight (LoS) based User Equipment(UE) positioning algorithms by using a temporal beamforming algorithm in the RAN node.The temporal beamforming algorithm when used herein defines an algorithm that maximizes signal power both spatially and temporally. More specifically, the temporal beamforming algorithm maximizes the signal power of the earliest detectable time correlation of the signal of individual spectral reflections. It is different from beamforming, for example 3GPP defined PMI or Reciprocity beamforming which generate one or morebeam directions to maximize signal power received by UE. Rather temporal beamformingseeks to maximize the signal power of each of the spectral reflections to discern the earliest signal correlation or arrival time in that specific direction, so that the individualspectral reflections most closely represent LoS paths to the UE. For this reason, it iscalled temporal beamforming. It is beamforming to maximize the earliest temporalcorrelations of each vTRP spectral reflection location, to achieve the highest probability ofa direct LoS path from the vTRP to the UE. As an example, assuming that a M-MIMOgNB TRP receives signal data from the UE which is physically located behind a building,and is therefore not directly LoS to the TRP. The temporal beamforming algorithm maydetect spectral reflections from adjacent buildings which have LoS or near LoS fromdiffraction around the corner of a building to the UE. The temporal beamforming algorithmmay maximize the coherent beam power to the earliest LoS spectral reflection in anyspecific direction, thereby maximizing the probability of the LoS path to the UE.Example embodiments herein use virtual antenna beamforming to maximize thepower of the earliest and highest spatially separated temporal correlations. According toexample embodiments herein, the temporal beamforming algorithm employs a spatial andtime-based processing to extract LoS signals and non LoS (nLoS) multipath reflections togenerate a rich set of LoS multipath reflection points. The multipath reflection points are inembodiments herein called virtual Transmit Reference Points (vTRPs) and are LoS to the UE and LoS to the RAN node. This may significantly increase the number of viable LoS measurements from 20% as described earlier for the case of e.g., London urbanenvironment to an expected 90%. This significant improvement becomes readily apparentwhen considering that the spectral reflection vTRP locations represent points in spacewhich might have significantly improved radio frequency propagation path to the UE,which typically would be LoS. While most often true, perhaps 10% of the time, vTRPlocations may have non-LoS visibility to the UE. In the majority of cases, however thevTRP may have LoS visibility to a second order spectral reflection with direct LoS visibilityto the UE. This problem is similar to the six-degrees of separation but much more limited.In terms of RF propagation, we may consider one-degree of separation to represent directLoS to the UE, two-degrees of separation to represent direct LoS to a spectral reflectioni.e., vTRP that has direct LoS to the UE, and so on. Given the limited range of the cellulargNB / TRP with typical sector coverage distances of 500m to 1.5km, the majority of RFpropagation paths to the UE would have zero i.e., one-degree of separation, one i.e., two-degrees of separation, or possibly two i.e., three degrees of separation spectral reflectionpaths. Regardless, the end goal of these calculations according to example embodimentsherein is to determine the UE location, and therefore, employing vTRPs enables two-degrees of separation to the UE and might provide measurements with a very goodestimate of the UE location. According to example embodiments herein, phase alignment calibration performed by the RAN node is recommended to ensure that temporal beam directions match ground truth locations. This calibration process addresses rotational offsets in the orientation of the macro-cell on the cell tower, building top, or mounting apparatus, as well as small inaccuracies in the alignment of the predefined location dataset to the phase center of the antenna at the RAN node. The calibration may be performed only one-time and may be as simple as locating a known high accuracy georeferenced fixed or mobile device and applying the location error as a correction vector. Examples of embodiments herein employ a predefined location dataset such ase.g., a point cloud dataset to translate high precision AoA measurements into LoS vTRPlocations. The predefined location dataset may be used by the network node incombination with the multiple LoS and nLoS AoA measurements received from the RAN node to determine the locations of vTRPs. The location of the vTRPs may be defined asthe point of reflection of these multipath reflections visible on the predefined locationdatasets. According to some example embodiments herein, the predefined locationdataset comprises both non-polarized images and polarized images. As described earlier,the polarization data may be used to better discriminate the LoS signal and the nLoSreflections, and to correctly match them to the predefined location dataset. Thecomputation of the position of the UE may e.g., be overdetermined and may e.g., beformulated as a least squared error problem and solved using e.g., the Gauss-Newtonalgorithm. The computation might be performed in the RAN network or in the corenetwork. Figure 4 is a schematic overview depicting a wireless communications network100 wherein embodiments herein may be implemented. The wireless communicationsnetwork 100 comprises one or more RANs such as RAN 110, and one or more CNs suchas CN 106. The wireless communications network 100 may use 5G NR but may furtheruse a number of other different technologies, such as, 6G, Wi-Fi, Long Term Evolution (LTE), LTE-Advanced, Wideband Code Division Multiple Access (WCDMA), Global System for Mobile communications / enhanced Data rate for GSM Evolution (GSM / EDGE), Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), just to mention a few possible implementations. RAN nodes, such as a RAN node 111 and a RAN node 112, operate in the RAN110 of the wireless communications network 100. The RAN nodes 111, 112, may each bea transmission and reception point e.g. a radio access network node such as a base station, e.g. a radio base station such as a NodeB, an evolved Node B (eNB, eNode B), an NR Node B (gNB), a base transceiver station, a radio remote unit, an Access Point Base Station, a base station router, a transmission arrangement of a radio base station, a stand-alone access point, a Wireless Local Area Network (WLAN) access point or an Access Point Station (AP STA), an access controller, or any other network unit capable ofcommunicating with UEs, such as a UE 121, within a cell, served by the respective basestation 111, 112. The respective base station 111, 112 may be referred to as a servingradio network node and may communicate with the UE 121 with Downlink (DL)transmissions to the UE 121 and Uplink (UL) transmissions from the UE 121.According to some embodiments herein, the RAN nodes 111 and 112 may comprise: a Remote Radio Unit (RRU) and a Baseband Unit (BBU). In these embodiments, the RRU comprises a Multiple-Input-Multiple-Output (MIMO) antennasystem. In some embodiments, the RAN nodes 111 and 112 may be Open-RAN split intoOpen-Radio Unit (O-RU), Open-Distributed Unit (O-DU) and Open-Centralized Unit (O- CU). RAN nodes, such as e.g., RAN node 111 may provide radio coverage over a geographical area, a service area 11 or a first cell. RAN nodes, such as e.g., RAN node 112 may provide radio coverage over a geographical area, a service area 12 or a second cell. One or more UEs operate in the wireless communication network 100, such as e.g.the UE 121. The UE 121 may e.g. be 5G-RG, a remote UE, a wireless device, an NRdevice, a mobile station, a wireless terminal, an NB-IoT device, an MTC device, an eMTC device, a CAT-M device, a WiFi device, an LTE device and an a non-access point (non- AP) STA, a STA, that communicates via a RAN node such as e.g. RAN node 111 and / or RAN node 112, one or more Access Networks (AN), e.g. a RAN 110, to one or more corenetwork (CN) nodes, in one or more CNs. It should be understood by the skilled in the artthat “UE” is a non-limiting term which means any terminal, client, mobile client, IMS client, wireless communication terminal, user equipment, Device to Device (D2D) terminal, or node e.g. smart phone, laptop, mobile phone, sensor, relay, mobile tablets or even a car or any small base station communicating within a cell. Network nodes, such as e.g., network node 151, operate in the wirelesscommunications network 100. In some embodiments, the network node 151 is operating in the RAN such as e.g., RAN 110. In these embodiments, the network node 151 is represented by a BBU. In some other embodiments, the network node 151 is operating in the CN such as e.g., CN 106. In these embodiments, the network node 151 isrepresented by one or more of: a 4G Mobile Positioning Solution (MPS) function whichcomprises a Serving Mobile Positioning Centre (SMPC) with associated servers, aGateway Mobile Positioning Centre (GMPC) with associated servers; or a 5G EricssonNetwork Location (ENL) method comprising Network Location Gateway (NLG), orNetwork Location Server (NLS) with Location Management Function (LMF) servers.Methods according to embodiments herein are performed by the RAN node 111 andthe network node 151. These nodes may be Distributed Nodes (DN)s and functionality,e.g. comprised in a cloud 170 as shown in Figure 4.Datasets, such as e.g., predefined location dataset 190 is a dataset comprised inthe wireless communication network 100. The predefined location dataset 190 may be comprised in one or more out of: the RAN 110, the CN 106 and the cloud 170 and be accessible to the RAN nodes 111 and 112 and the network node 151. Examples of embodiments herein process the UE uplink signals received by theRAN node 111 to extract multiple channel impulse signals such as e.g., LoS signal andreflections such as e.g., nLoS multipath reflections, each with an associated ToA and oneor more AoA.Examples of embodiments herein may use the AoA to each of the LoS signals andnLos multipath reflections to determine their (x, y, z) locations i.e., the location of thevTRPs using the predefined location dataset 190.Examples of embodiments herein calculate the propagation time from the RANnode 111 to each of locations of the vTRPs related to the LoS signals and nLoS multipathreflections to assign a rToA to each vTRP. Examples of embodiments herein then calculate the UE location using the set of vTRP locations and rToA. Therefore, examples of embodiments herein provide a method performed by thenetwork node 151 in using the predefined location dataset 190 to determine the physicallocations of the LoS signals and nLoS multipath reflections in the processed channelimpulse response measurements received from the RAN node 111. The network node151 generates a set of vTRP locations, each with unique rToA to solve for the location ofthe UE. A number of embodiments will now be described, some of which may be seen as alternatives, while some may be used in combination. A method according to embodiments will first be described as seen from the view ofthe RAN node 111 together with Figure 5, and then as seen from the view of the networknode 151 together with Figure 6. Figure 5 shows exemplary embodiments of a method performed by the RAN node111. The method is for assisting the network node 151 in locating the position of the UE 121 in the wireless communications network 100. The RAN node 111 comprises a MIMO antenna system capable of performing beamforming. According to an example scenario, the RAN node 111 may receive a request from acore network node such as e.g., Access and Mobility Management Function (AMF) or aNLS to provide information related to a position of a UE such as e.g., UE 121. These UElocationing requests initiate from a Location Client Services (LCS) by messaging theLocation Services (LS) and specifically the Network Location Gateway (NLG). The NLGinterfaces to core network nodes such as e.g., the Home Location register (HLR), NSC,MME to locate the UE to a cell site and to determine its reference ID. NLG uses NRPositioning Protocol (NRPP) messages to the RAN node 111 to initiate cellular positioningfunctions in the NLS and thereby in RAN node 111 to locate the UE 121.The method comprises the following actions, which actions may be taken in anysuitable order. Optional actions are referred to as dashed boxes in Figure 5.Action 501. The RAN node 111 may calibrate the phase alignment of the predefined location dataset 190 such as e.g., point cloud dataset to a phase centre of the MIMO antenna system of the RAN node 111. Action 502. The RAN node 111 receives, on the MIMO antenna system, one ormore uplink signals from the UE 121. The one or more uplink signals comprises one or more out of: a LoS signal and one or more nLoS multipath reflections. In some embodiments the LoS between the RAN node 111 and the UE 121 is blocked, resulting in no LoS signal received in the received one or more uplink signals. In these embodiments, the received uplink signals comprise of only one or more nLoS multipath reflections. If there is LoS, then there is a LoS signal received in the one or more received uplink signals. In some embodiments, the receiving of the one or more uplink signals isperformed by the RRU comprising the MIMO antenna system in the RAN node 111. In thecase where the RAN node 111 is operating in an O-RAN architecture, then the receivingof the one or more uplink signals may be performed by the O-RU. Action 503. The RAN node 111 determines the AoA and the ToA related to thereceived LoS signal and each of the nLoS multipath reflections. In some embodiments,the determination is performed at the RRU of the RAN node 111. In some otherembodiments, the determination is performed at the BBU of the RAN node 111. In some other embodiments the RAN node 111 is operating in an O-RAN architecture. In these embodiments, the determination is performed in the O-DU for Category A radios and in the O-RU for Category B radios. Based on the described example scenario, the RAN node 111 may start a UE positioning procedure related to the UE 121 for which the Location Request was received from the core network node or the NLS. With respect tothis procedure, the RAN node 111 may use one or more algorithms such as e.g., MUSIC,ESPIRIT, and Beam space algorithms to determine the AoA and ToA of the uplink signals specific to the UE 121 of interest. Action 504. The RAN node 111 extracts, based on a temporal beamforming algorithm, the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals. In some embodiments, the one or more nLoS multipath reflections are selected for extraction based on one or more criteria of their suitability forlocating the position of the UE 121. The criteria may be one or more of: the signal-to-interference-and-noise-ratio (SINR) of the extracted vTRP position, the AoA of the vTRPlocation where it is desirable to have a wide angular spread of measurements to mitigatethe effects of dilution of precision, a confidence indicator or estimate of correctness thatthe extracted vTRP location represents a valid reflection point, a relative comparison ofthe measured ToA of the various vTRP enabling outliers to be rejected. The criteria may e.g., be that at least one of the reflection points of the nLoS multipath reflection has LoS visibility to both the RAN node 111 and the UE 121. The temporal beamforming algorithms may be functionally located in the O-DU for Category A radios, and in the O- RU for Category B radios. In some embodiments, the extracting is performed by using one or more out of: sub-space algorithms comprising one or more out of: MUltiple SIgnal Classification (MUSIC) algorithm, and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm, and sequential algorithms comprising one or more out of: Orthogonal Matching Pursuit (OMP) algorithm. In some embodiments, the extracting is based on one or more out of: a correlated power specific to a UE 121, angular spread, number of antennas, and a Signal-to-Noise Ratio (SNR) of the one or more received uplink signals, number of multipath components, dimension of signal subspace, dimension of one or more out of: noise, interference and clutter subspace. Theextracting may be performed by one or more RAN nodes 111, 112 in the wirelesscommunication network 100. In some embodiments, the UE 121 is covered by the servicearea 11 of RAN node 111 and by the service area 12 of the RAN node 112. In these embodiments, according to the example scenario, the Location Request may be sent to both RAN node 111 and RAN node 112 to start UE positioning procedure. Action 505. The RAN node 111 sends information to the network node 151. Theinformation comprises one or more out of one or more: determined AoA, determined ToA,extracted LoS signal and extracted nLoS multipath reflections. In this way by using the methods above, the RAN node 111 is able to assist thenetwork node 151 for locating the position of the UE 121 with higher accuracy. This isbecause of the increased number of measurement points generated by the RAN node 111 consisting of zero or one LoS signals and one or more nLoS multipath reflections withLoS visibility to the RAN node 111 and the UE 121 providing an overdetermined set ofmeasurements which enable the network node 151 to locate the UE 121 more efficiently.This is achieved by the RAN node 111 using the temporal beamforming algorithm.Figure 6 shows exemplary embodiments of a method performed by the networknode 151. In some embodiments, the network node 151 is a distributed node in the cloud170 as described earlier. In these embodiments, the network node 151 is represented byan NLS. In some embodiments, the customers may require that the functionality of thenetwork node 151 be performed in other core network servers, where for geopoliticalreasons, UE Positioning might be performed by government managed servers. Themethod is for locating a position of the the UE 121 in the wireless communications network 100. According to an example scenario, the network node 151 may receive a requestfrom a LCS or a NLS to provide information related to a position of a UE such as e.g., UE121. In some embodiments, the network node 151 is represented by an NLS. In these embodiments, the request is received from the LCS through the Network Location Gateway (NLG). In some other embodiments, the network node 151 is represented by a BBU. In these embodiments, the request is received from the NLS. The method comprises the following actions, which actions may be taken in anysuitable order. Optional actions are referred to as dashed boxes in Figure 6. Action 601. The network node 151 receives information from a RAN node 111. Insome embodiments, the information is received from one or more RAN nodes 111, 112. In some embodiments, the UE 121 is covered by the service area 11 of RAN node 111 and by the service area 12 of the RAN node 112. In these embodiments, according to the example scenario, the Location Request may be sent to both RAN node 111 and RAN node 112 to start UE positioning procedure and provide relevant information to the network node 151. The information comprises one or more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections. The information is related to a LoS signal and one or more nLoS multipathreflections comprised in one or more uplink signals received from the UE 121. The RANnode 111 may use one or more temporal beamforming algorithms such as e.g., MUSIC,ESPIRIT, and Beam Space algorithms to determine the AoA and ToA and extract LoS signals and nLoS multipath reflections of the uplink signals specific to the UE 121 ofinterest. Based on the described example scenario, the network node 151 may start a UEpositioning procedure related to the UE 121 for which the Location Request was receivedfrom the LCS or the NLS. With respect to this procedure, the network node 151 mayrequest the RAN node 111 to provide location related information specific to the UE 121 of interest. Action 602. Based on one or more out of one or more: determined AoA, determinedToA, and predefined location dataset 190 e.g., point cloud dataset, the network node 151locates a position of one or more vTRPs related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections. The respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node 111 and LoS visibility to the UE 121, and a point of transmission. For the nLoS multipath reflection, the vTRP may be the point of reflection of the uplink signal from the UE 121. For a LoS signal, the vTRP may be the point of transmission e.g., UE 121. According to the example scenario, the predefined location dataset 190 might be available to the NLS or NLG servers based on whichever is acting as the network node 151. In some embodiments, the predefined location dataset 190 comprises polarized images and non- polarized images, and the locating of the position of the one or more vTRPs is performed based on the polarized images. In some embodiments, the locating of the position of the one or more vTRPs is performed by one or more network nodes 151 in the wireless communication network 100. Action 603. The network node 151 calculates a propagation time of the LoS signal and each of the nLoS multipath reflections out of the one or more received uplink signals. The propagation time is calculated from the RAN node 111 to each of the respective vTRPs. Action 604. The network node 151 determines the rToA at each of the one or morevTRPs. The rToA is related to the uplink signal from the UE 121 to the respective vTRP.In some embodiments, the determining of the rToA is performed by one or more network nodes 151 in the wireless communication network 100. Action 605. The network node 151 computes the position of the UE 121 based onthe located one or more vTRPs and the corresponding determined rToA. The computationmay be performed by the network node 151 based on algorithms such as UTDoA bysolving for the intersection of hyperbolic curves defined as the Time Difference of Arrival (TDoA) of two rToA measurements. The computation may also be performed by thenetwork node 151 based on algorithms that process round-trip-time (RTT) measurements,where the RTT is determined at each spectral reflection point vTRP. In these algorithms, subtracting twice the ToF from the gNB to the vTRP may result in RTT measurements ateach of the vTRP locations. UE positioning may then be determined as the intersection ofthe circles of radius of the rToA_RTT from each of the vTRP location.In some embodiments, the computing of the position of the UE 121 may be performed by using a machine-learning model. In these embodiments, the machine- learning model is pre-trained with simulated data by using the 3D datasets in ray tracingsimulators. The simulator may use the 3D datasets to generate vectors comprising UElocation, and a set of resulting AoA and corresponding relative Time Error (rTE) values.For each UE location, the simulator may generate multiple AoA and rTE values, and thismay be used to train the machine learning model. The machine learning is not algorithmicand therefore may not require explicit calculations of vTRP locations, which may beimplicit in the training vectors. The process of machine learning training might generatehundreds or thousands of UE position vectors, at locations throughout the cellularcoverage, and for each would generate multiple AoA and rTE measurements. Themachine learning system might then use this training data to compute UE 121 locations atpoints which were not part of the training sets. Action 606. The network node 151 may provide information related to the located position of the UE 121. The information related to the located position comprises one or more out of: a co-ordinate, and an image from the predefined location dataset 190. According to the above-mentioned example scenario, the network node 151 may respond to the Location request received from the LCS or the NLS by providing the co-ordinate of the location of the UE 121 and an image from the predefined location dataset 190. The image may be of the environment in which the UE 121 is located e.g., a bus stop. Action 607. The network node 151 may estimate a velocity of the UE 121 by performing differential measurements for the LoS signal and each of the nLoS multipath reflections related to the UE 121. In some embodiments, a differential phase measurement is performed which provides one velocity for the LoS signal and one velocity for each of the one or more nLoS multipath reflections. In these embodiments, the individual velocities of the LoS signal and each of the nLoS multipath reflections are combined to estimate the true velocity of the UE 121. In this way by using the methods above, the network node 151 is able to locate the position of the UE 121 with higher accuracy. This is because of the increased number of measurement points i.e., vTRPs generated from nLoS multipath reflections by the network node 151 which have LoS visibility to the RAN node 111 and the UE 121. This is achieved by using the predefined location dataset 190 and the AoA and ToA received form the RANnode 111. The network node 151 is also able to reduce measurement noise by usingdifferential measurements, by optimally combining current and historical measurement data. Applying a simple model for UE 121 motion, such as constant velocity, advancedtools such as e.g., Kalman Filters may be employed to optimally combine historical andnew measurement data, proving significant improvements in positioning accuracy. Embodiments herein such as the embodiments mentioned above will now be further described and exemplified. The text below is applicable to and may be combined with any suitable embodiment described above. The nLoS multipath reflections may be extracted from the LoS signal by the RANnode 111 from the uplink signals of the UE 121 using temporal beamforming algorithmsas mentioned above. Figures 7a and 7b shows the view from the RAN node 111 and thetop-down view, respectively of the dominant nLoS multipath reflections. The dominantnLoS multipath reflections may be selected based on one or more criteria such as e.g., nLoS reflections with a single point of reflection, and nLoS reflections with at least onepoint of reflection that has LoS visibility to both the RAN node 111 and the UE 121. The nLoS multipath reflections may be referred to herein as nLoS reflections, nLoS signals, multipath reflections, nLoS multipath reflection signals, and nLoS. The nLoS multipathreflections are calculated using sub-space algorithms such as e.g., MUSIC, ESPRIT, orsequential algorithms such as e.g., OMP algorithms, or beam space algorithms. Theextraction may be performed by using any variations and / or modifications of the above-mentioned algorithms. In this view, the UE 121 is shown to be emitting signals from anoffice building. Four dominant nLoS multipath reflections are visible with two from the corners of nearby buildings, one ground reflection, and one reflection from a street light pole. Figure 7b highlights the azimuth angles of the nLoS multipath reflection signals inrelation to the UE located in the office building. This view captures the physical locations(x, y, z) of the vTRPs (vTRP1, vTRP2, vTRP3, vTRP4) each of which has LoS visibility tothe RAN node 111. Since the location (x, y, z) of the RAN node 111 is known, thedistances from the RAN node 111 to each vTRP (d1, d2, d3, d4) may be determined.Then the (rToA1, rToA2, rToA3, rToA4) from the UE 121 to each (vTRP1, vTRP2, vTRP3,vTRP4) respectively may be determined as described in Action 604. The location of theUE 121 may be computed as described in Action 605 using the vTRP locationmeasurements. Examples of embodiments herein involve one or more steps which are described in detail below. Processing Uplink Signals from UE 121:This section corresponds to Actions 502 to 504 as described earlier. Exampleembodiments herein process uplink signals related to UE 121 received at the RAN node111 to extract multiple channel impulse signals and reflections, each with an associatedToA, and one or two AoA. The channel impulse signals and reflections when used hereinrefers to correlated signal power from the UE 121. Many algorithms may be used for thistask, comprising but not limited to MUSIC and ESPRIT. Figure 8a shows an example of2D MUSIC angular delay spread of the LoS signal and nLoS multipath reflections relatedto the UE 121 located in the office building as shown in Figure 7. This is for highresolution using 6G Massive Array Radio Systems (MARS). These next generation 6GMARS enable ultra-high precision necessary for Multi-User MIMO (MU-MIMO),Distributed MIMO (D-MIMO), Radar, and Positioning functions. A much larger set of lower power reflections have been removed from this example to enable discussion of five multipath reflections visible above. AoA and AoA Accuracy: Angles-of-Arrival (AoA) null pointing, has extremely high relative accuracy indetermining the angles. Null pointing when used herein means e.g., the process ofdetermining a set of beamweight angles and power levels in a MIMO radio whichminimize a multipath spectral reflection. The null pointing resulting AoA direction whichminimizes the spectral reflection power determines the UE 121 AoA. Null pointing, with itssteep gradient has an advantage to more easily discern the optimal direction, whilebeamforming with the intent to maximize the multipath spectral reflections has a minimal gradient and is sensitive to noise. Simply put, nulls are pointed while beams are rounded, and it is much easier to precisely point a null than a beam, especially in the presence ofnoise. Null pointing is often used in tracking applications. With 6G radios capable ofachieving peak to null ratios approaching 40 dB, relative AoA may be determined to lessthan 0.1°. To achieve high accuracy AoA measurements, examples of embodiments use an ultra-high gain array antenna such as a 6G MARS. The RAN node 111 comprising a MIMO antenna system which e.g., is a MARS may be characterized as having on the order of 1000 antenna elements, with array dimensions such as 24Hx16Vx3V where 3V represents subarray elements. Many MARS configurations have antenna gains of 40 dBi, consisting of 10 dBi element gains plus 30 dB of beamforming processing gain. At such high gains, and with tightly controlled antenna calibration algorithms as described in Action 501, the example of embodiments herein delivers peak-to-null ratios approaching 40 dB, the resulting relative angular resolution generally better than ±0.1°. Upon completion of the MUSIC (or alternate) algorithm, as described in Action 504, a set of nLoS multipath reflections, each with an associated ToA and AoA (θi, ^i) are extracted. It is left as an implementation detail to determine appropriate thresholds and selection criteria to extract a sufficient number of multipath reflections over a wide range of AoA to mitigate dilution-of-precision issues. One or more criteria to determine the dominant nLoS multipath reflection suitable for accurate positioning of the UE 121 is described earlier in Action 504. The set of nLoS multipath reflections should be greater than or equal to 4 and ideally greater than or equal to 10 to enable post processingdiscrimination, while also targeting an angular spread greater than ±45° within the typicalcell coverage of ±60°. Small angular spreads of the extracted multipath reflections will result in dilution-of-precision issues, decreasing the accuracy of the estimated UE location. Determining vTRP Locations: This section corresponds to Action 602 mentioned above. After completing a one-time calibration as described in Action 501, MARS 6G radios nulls may azimuthally locatea vTRP at 500m distance to sub-meter accuracy. The highly accurate AoA determinedusing methods as described in the previous section for each of these vTRPs, enable toaccurately locate the absolute position of each vTRP. These AoA are used to locate eachof the LoS signals and nLoS reflections within a predefined location dataset 190. Theintersection of the AoA vector to a physical location in the predefined location dataset 190becomes the vTRP. For example, the first multipath reflection nLoS1 as shown in figure 7is extracted from the MUSIC processed data with a determined ToA1 with azimuth and elevation AoA1 (θ1, ^1) respectively. This angle intersects multipath reflection vTRP1 in the predefined location dataset 190 at location (x1, y1, z1) and is defined as the first physical encountered object. It is for this reason that all vTRPs have, by definition, LoSvisibility to the RAN node 111. Figure 8b shows an overlay of delay spread onto thepredefined location dataset 190. This figure 8b is the overlay from the perspective of theRAN node 111, onto the calculated angular delay spread for a 6G MARS RAN node 111as shown in Figure 8a. The LoS signal and nLoS multipath reflections become visible,aligning with physical locations of surfaces and structures which reflect the signal from theUE 121. Examples of these reflection locations are described in Figure 8c showing themultipath reflection locations processed using MARS. Figure 8c comprises the buildingsurfaces, ground, and light poles. These locations of the LoS signal and nLoS reflectionsaligned with the physical locations from the predefined location dataset 190 are thenlabelled as vTRPs as seen in Figure 7. Each of these locations is assigned a determinedrToA described in the following section. The determination of the rToA is performed asdescribed in Action 604. As mentioned earlier, in urban environments such as London England, less than 20% of connected UEs such as UE 121 have direct LoS visibility to the RAN node 111,while more than 80% of connected UEs such as UE 121 are behind buildings, or otherstructures and are nLoS. Examples of embodiments herein provide a method for locatingthe position of the UE 121 without the requirement of LoS visibility of the UE 121 from theMARS of the RAN node 111. However, according to example embodiments herein atleast a few of the extracted nLoS multipath reflection locations have LoS visibility to theUE 121 and LoS visibility to the RAN node 111. The determination of the location of theUE 121 is performed by first using temporal beamforming algorithms such as e.g., MUSIC, ESPRIT, or others to extract a set of nLoS multipath reflection points with LoSvisibility to the RAN node 111 with at least a few of those points with LoS visibility to theUE 121. The location of the UE 121 is then computed using Time Difference of Arrival(TDoA) calculations from the located nLoS multipath reflection vTRP coordinates withinthe predefined location dataset 190. Predefined location dataset 190 may herein bereferred to as dataset 190. The use of predefined location dataset 190 such as e.g., point cloud datasets may enable accurate representation of the visible 3D environment of the cell site such as e.g.,service area 11 of the RAN node 111 in which the nLoS multipath reflections aregenerated. For example, Creo is a popular CAD application software tool by Parametric Technology Corporation (PTC) that performs 3D modeling. A PTC Creo model may be used to determine the locations of vTRPs within the service area 11 of the RAN node 111.While the examples of point cloud dataset and PTC Creo have been given for predefinedlocation dataset 190, they are only two of in excess of a hundred available 3D modellingapplications, most of which have unique datasets suitable for examples of embodimentsherein. At minimum, the selected 3D dataset must be sufficient to model the environmentas a shell or boundary, representing surfaces of objects such as e.g., buildings, trees,roads, and lights. As is well known, radio wave propagation reflections are largely off ofthese surface structures and therefore, a shell model is sufficient for most outdoorraytracing to determine the locations of the vTRPs as described in Action 602. A morecomprehensive predefined location dataset 190 which enables solid or internal 3D structures to be modelled may also be used. Such a dataset 190 would not only model the visible outdoor shell or surfaces, but may incorporate available information, such as e.g.,internal building structure comprising e.g., floors, internal walls, and other structures.Stadiums are a prime example where detailed 3D CAD models would comprise internalstructures, but 3D models of office and apartment buildings have high value in simulation. Emergency services and Low-latency personal care service require locating the position of the UE 121 with at least 50m horizontal accuracy and less than 3m floor level accuracy.This high level of accuracy may be achieved from methods provided by exampleembodiments herein of using temporal beamforming positioning and high-resolutionpredefined location dataset 190. To achieve the required high accuracy, a morecomprehensive dataset 190 models that comprises not only the external shell or surfacestructures, but also the internal 3D structures may be required to compute the location ofthe UE 121 if situated inside one of these structures. Given a sufficiently large set of vTRP location measurements, the computation ofthe UE 121 position may typically be overdetermined and may leverage discriminationcombined with Gauss-Newton techniques to solve for the location of UE 121. Determining rToA:This section corresponds to the Action 604 described above. The location of the RAN node 111 is (x0, y0, z0) and represents the phase center of the antenna systemsuch as e.g., MIMO, MARS of the RAN node 111. The location of the RAN node 111 is aconfigured network parameter determined during the installation process using GPS surveying methods. The relative time-of-arrival rToA1 of vTRP1 is defined as the relative time-of-arrivalfrom the UE 121 to the vTRP1 location (x1, y1, z1). It is therefore calculated bysubtracting the LoS delay from the RAN node 111 to vTRP1 from ToA1 as given in theequation below, where c is the velocity of light: With expected null precision resolutions of much lesser than 1 degree, and rToA measurement accuracies of much lesser than 1ns, the examples of embodiments herein extract the multipath reflections with a wide range of angular spreads, determine theassociated dataset 190 locations, and calculate the rToA, to obtain a comprehensive setof vTRP that may be used to estimate the location of the UE 121 using Gauss-Newtonsolvers. Estimating the Location of UE 121:Examples of embodiments herein calculate the propagation time from the RANnode 111 to the vTRP locations of the LoS signal and each of the nLoS reflections toassign an rToA to each vTRP. It is well known to those in the field that Gauss-Newton (G-N) methods may be employed to determine the location of the UE given a set of ToA measurements. Examples embodiments herein employ Iterative Least-Squares (ILS) methods and use TDoA, but other G-N solvers may be used which are more stable opting to minimize absolute differences rather than squared differences. Maximum likelihood estimators may also be used as alternate methods. Alternative metrics such as the Hubernorm may also be used which is quadratic for small input arguments but has slowergrowth e.g. linear for larger input arguments and by that deemphasizes outliers. In theseembodiments, as with all G-N solvers, a seed location for the UE 121 is necessary. In 6GUE positioning methods, a high accuracy seed may be determined from traditional 5G UEpositioning methods which combine AoA and UE TA feedback. While generally accurateonly to tens of meters, these estimates are more than sufficient for use as an initial seed, leading to a rapid convergence on the true UE location. The description below provides details related to the temporal beamforming algorithms according to example embodiments herein. MUSIC / ESPRIT / Beam Space Algorithms Examples of embodiments herein utilizes an algorithm to determine the direction ofLoS signal and their nLoS reflections covering LoS and nLoS paths to the UE 121.Temporal beamforming algorithms such as e.g., sub-space algorithms such as e.g., MUSIC, ESPIRIT and sequential algorithms such as e.g., OMP such as e.g., Beam space may be used. Examples of embodiments herein consider advanced antenna systems such as e.g., MIMO, 6G MARS with azimuth and elevation measurement capabilities. Thus, example embodiments herein extend the used one or more temporal beamformingalgorithms out of those mentioned above to include this additional degree of freedom.They also extend the algorithm, which is normally based on power measurements, to usecell specific correlated power as described in Action 504 and therefore is based on signalto noise ratio of the signal from the UE 121 rather than absolute power. While traditionalalgorithms base all calculations on spatial correlations of received power, exampleembodiments herein correlate with expected reference signals to target strictly the UE 121in the cell such as e.g., service area 11 related to RAN node 111 rather than targeting theUEs in neighbouring cells such as e.g., service area 12 related to RAN node 112. Thus,the resulting measurements are specific to the cell 11 of the RAN node 111 and immuneto interference from neighbouring cells such as e.g., cell 12.The algorithm may generate an output comprising a set of beam directions and ToAof the LoS signal and its nLoS reflections. The size of the set is determined by the number of reflections in the environment, limited by the signal to noise level in each of the beam optimized signal reflections. In highly reflective environments, the set may include 20 or more beam directions and in less reflective environments, the set may include only a fewbeam directions of discernible energy. In some embodiments, the RAN node 111 sendsthe LoS and all of the nLoS multipath beam directions to an external server such as e.g.,the NLS or to the network node 151 for processing and extracting the dominant nLoSreflections suitable for UE positioning. In some other embodiments, the RAN node 111 sends a precursor such as e.g., the Channel Impulse Response (CIR) may be passed tothe network node 151. The CIR may comprise multiple cross-correlation peaks of which the earliest is the desired ToA. In these embodiments, the extraction of the nLoS reflections is performed by the network node 151. This may have the benefit of flexibility of processing, as programming at the RAN node 111 may be more complex than at the network node 151 due to the multicore DSP architecture at the RAN node 111, and also due to interactions with many other software features. Machine learning systems are capable of determining the UE location using vTRP locations plus rTE values, but equallycapable of determining the UE location using vTRP locations and CIR data. Examples ofembodiments herein may select beam directions where reflections are from highly preciselocations in the predefined location dataset 190. As an example, if the location of thenLoS reflection determined from the predefined location dataset 190 is from a corner of abuilding, or a structure such as a stop sign with a small diameter, these reflections may receive greater weightings than from reflections off of a windowed surface of a building. Examples of embodiments herein optimize the LoS and nLoS beam angles basedon beam polarizations as described earlier and return the polarization optimized AoA. Theuse of polarization may further improve the SNR of weak signals and enable the algorithmto remove in-line clutter effects by isolating reflective surfaces where expected polarization is known, such as vertical building windows, or horizontal roadways orparking lots. According to embodiments herein, the multipath reflection data determinedby MUSIC, ESPRIT, or using Beam Space methods comprises polarization information toenable this differentiation during the extraction process.MUSIC Algorithm High Level Equations: Figure 9 shows a receiver antenna array and the incident signal with AoA inelevation and azimuth from a vTRP e.g., vTRP1. Let us assume that there are ^ multipathsignals arriving to the receiver antenna array. For a single polarization, the received signalat the ^th row and the ^th column of the ^ × ^ antenna array is given by: where ^ corresponds to the ^th OFDM subcarrier and ^ corresponds to the ^th timesymbol, ^(^, ^) is the transmitted signal which is assumed to be a scalar at the ^thsubcarrier and the ^th time symbol, ^^,^(^, ^) is the additive noise at the ^th subcarrier andthe ^th time symbol, ℎ^,^(θ, ^, ^) is the channel between the ^ vTRPs and the receiverantenna element (^, ^) dependent on all the ^ angles in azimuth = [^^ , … , ^^] andelevation ^ = [^^ , … , ^^] and ^ = [^^, … , ^^] is the vector of time-delays.The received signal matrix for the ^ × ^ antenna array is given by:^(^, ^) = ^(θ, ^, ^)^(^, ^) + ^(^, ^)∊ℂ^×^and the vectorized received signal vector is given by:^(^, ^) = ℎ(θ, ^, ^)^(^, ^) + ^(^, ^) ∊ ℂ^^×^where ^(^, ^) = vect(^(^, ^)) ∊ℂ^^×^, ℎ(θ, ^, ^) = vect(^(θ, ^, ^)) ∊ℂ^^×^, and^(^, ^) = vect(^(^, ^)) ∊ℂ^^×^.The channel vector may be written as a function of the elevation and azimuth anglesand the delay for each multipath component ^ = 1, … , where ^ is the total number ofmultipath components, for a given polarization: where, ^^ is the baseband equivalent channel gain, ^^is the channel delay, ^^is thecarrier frequency, ^^^^, ^^^ denotes the steering vector of path injected from azimuthangle ^^and elevation angle ^^. The steering vector with the antenna spacings of azimuth and elevation, denoted by ^^^and ^^^, respectively, is defined as: ^(θ, ^) = ^^^(θ, ^) ⊗ ^^^(^) ∊ ℂ^^×^ where ^^^(θ, ^) is the steering vector in horizontal domain and ^^^(^) is thesteering vector in vertical domain respectively defined as The steering vector at the particular angle is defined by Kronecker productas follows: where the first M elements are obtained by multiplying the first element of ^^^, denoted by ^^^,1 ^^^, ^^^, by the entire vector ^^^ and so on untilthe last element The first step consists of estimating the CIR from the vTRPs to the antenna array.This may be performed by using reference signals or in a blind manner.If reference signals are used, the estimated channel vector is given by: where ^(^, ^) is a reference signal known at the receiver side.Let ^ be the number of the symbol-spaced samples of the channel impulseresponses. The channel sample covariance matrix estimated at the receiver by averaging over^ subcarriers at a given time ^ is given by: Let ^1, … , ^^^ be the eigen vectors of ^ corresponding to the eigenvalues^1 > … > ^2^^ ordered in a decreasing order.Let us assume that the angles in azimuth and elevation and the corresponding time-delays of the ^ multipath components need to the estimated. The noise eigen-subspacemay be denoted The MUSIC localization function is given by: The estimated angles of arrival in azimuth and elevation and the corresponding time-delay are denoted respectively by ^^and and ^^, which are the estimator of thetrue values ^^, ^^, ^^. These estimates correspond to the P maximums of the MUSIClocalization function: ^, ^) = for ^ = 1, … ^. In practice, a grid of values for θ , ^, ^ belonging to some intervals isconsidered in which the estimated angles and the delay corresponding to all the Pmultipath components of the received signal are expected.Beam Space Algorithm High Level Equations: MUSIC algorithm mentioned above has high resolution in the estimation of ToA andAoA, however it also suffers several issues occurring in the applications, such as degraded resolution due to correlation across propagation paths, or high computationalcomplexity in the matrix decomposition. This section provides an alternate means thatmay solve these issues according to some embodiments herein. It is seen that the propagation channel comprises P multiple paths, wherein one path may be LoS path and other paths may be nLoS multipath reflections. It is also possible that there is no LoS path, but all are nLoS multipath reflections. Iterative method may be used to estimate the parameters of each path to gradually get the parameters for all paths. Step 1 may comprise finding out the AoA of one path that has the maximumcontribution. Normally the first path is the LoS if it does exist, or the strongest nLoS.Starting with ^ = 1, the processing may be expressed as^^^, ^^^ = arg min |^(^, ^)^ℎ(^, ^, ^)|2^,^ Step 2 may comprise finding out the ToA of the path with AoA of Thechannel is projected to the path with AoA of ^^^, and the ToA is estimated on theprojected channel. Working on the path p, the processing may be expressed as = ^^^^ exp^−^2^^^^^^The ToA estimation may be obtained from ℎ^(^, ^, ^) by linear regression, wherein^^ is the phase offset, and is the slope. Step 3 may comprise removing the contribution of the path ^ and the residualchannel may be expressed as where, ^^ is estimated by Step 4 may comprise repeating step 1 to 3 for ^ = 1, … ^, until the power of residualchannel ‖ℎ^(^, ^, ^)‖2 is smaller than a threshold. The threshold may be determined e.g.,by estimating noise floor and applying an offset of e.g., 6 dB. That means all paths havingnon-trivial contributions are all found. Then, the result ^^, ^^, ^^ for further processing maybe outputted. Temporal Beamforming Positioning Across a Plurality of Cell Sites. As described above in Action 504, the examples of embodiments herein may beperformed by one or more RAN nodes 111, 112 in the wireless communication network 100. In some embodiments, the UE 121 is covered by the service area 11 of RAN node 111 and by the service area 12 of the RAN node 112. In these embodiments, according to the example scenario, the Location Request may be sent to both RAN node 111 and RANnode 112 to start UE positioning procedure. Examples of embodiments herein enable 2Dand 3D UE positioning using a set of vTRP multipath reflections with good angularspread. In some embodiments, measurements from a plurality of cells such as e.g.,service area 11 and 12 may be used to augment traditional positioning technologies.Thus, rather than employing a single ToA measurement from each cell site, torespectively derive a set of circular or hyperbolic UE curves, the intersection of whichdetermines the UE location, example embodiments herein provide a method to usemultiple vTRPs from each cell site to determine the location of the UE 121. Figure 10illustrates the use of vTRPs from different cell sites. Figure 10a shows RBS Cell sites #1,#2 and #3 each with measured ToA, allowing the location of UE 121 to be found at theintersection of TDoA3,1 and TDoA2,1. These are just two curves, each of which hasimplicit measurement error. Figure 10b shows the benefit of adding vTRP measurementsvTRP A, vTRP B, and vTRP C. Only two additional TDoA curves are highlighted out of amuch larger set which would significantly improve the location accuracy of UE 121.Velocity Estimation with Differential Measurements: As described above in Action 607, the velocity of the UE 121 may be estimatedusing examples of embodiments herein. By measuring the Doppler-induced frequencyshift of the UL signal of a moving UE 121, the UE 121 velocity may be measured. TheDoppler-induced frequency shift of the received UL signal of a moving UE 121 may bedetermined by measuring the phase difference between receptions of subsequentlytransmitted pulses by the UE 121. In Figure 11 a pulse train with two pulses separated by^^^^ seconds is shown. Assuming phase and frequency synchronization betweenstationary UE 121 and the RAN node 111 the distance between the UE 121 and vTRPand the antenna at the RAN node 111 does not change. Each received pulse has thesame phase shift relative to its transmitted copy, i.e., no phase difference is observedbetween subsequently received pulses. With the moving UE 121, distance between theUE 121 and the vTRP and the RAN node 111 changes over time which manifests itself ina phase change between subsequently received pulses which may be used to determinethe Doppler shift. The determination of the Doppler shift may be performed at the networknode 151 using the received UL signals from the RAN node 111. The phase change maybe determined as Δ^ = 2^ ∙ ^^ ∙ ^^^^ with ^^ the Doppler shift induced by the moving UE121. The Doppler shift depends on the velocity of the UE 121 as well as the directionrelative to the vTRP. For example, UE 121 moving with velocity v towards a vTRP such ase.g., vTRP1 leads to a Doppler shift of ^^= ^^^⁄ ^0 with ^^, the speed of light and ^^ thecarrier frequency. For an angle ^ between the velocity vector and the connection linebetween the UE 121 and the vTRP, the Doppler shift becomes ^^= ^ cos ^ ^^⁄ ^0. Bymeasuring the phase changes for LoS component and each relevant nLoS path at eachrespective vTRPs, the network node 151 may determine a set of Doppler shifts i.e.,velocities, one for each relevant path. The Doppler shift observed for each path dependson the common UE 121 speed but also its direction relative to the vTRP. By combiningDoppler shifts observed from multiple paths such as the LoS signal and nLoS multipathreflection i.e., vTRPs, it is possible to determine the total velocity value and direction ofthe UE 121. This determination is not only a projection as observed by a single TRP but atrue total velocity value. Examples of embodiments herein may be implemented on cloud 170 as described earlier. Figure 1 shows the cloud implementation of the UE positioning services.According to example embodiments herein, a predefined location dataset 190 may belocated in the proximity of the NLS or NLG servers to expedite the access. The predefinedlocation dataset 190 may e.g., be a Point Cloud Data Base (PCDB) comprising thenecessary cell site specific point cloud information. The NLS server may employ thisinformation in performing the UE positioning function. The PCDB may be functionallysimilar to an Advanced Enhanced Cell ID (AECID) database, which provides geolocationdata with measurements of UE reported cell site RSRP levels. The AECID database isaccessible from the NLS via a TCP-IP interface. Both AECID and PCDB are largedatabases containing location specific information. In the case of AECID, the database contains UE reported visible cells and their associated downlink power levels measured at high resolution. The PCDB database contains E57 or similar files which encompass both image and positional information. It is not unexpected for each sector point cloud E57 file to be several gigabytes in size, given the necessity to derive better than 1m point cloud resolution. Examples of embodiments herein operate over O-RAN defined interfaces as shownin Figure 2, with positioning functionalities split as: processing of UL signals to extract ToAand AoA of detected multipaths and calculating of the location of the UE 121 using thisextracted data, the location of the RAN node 111, and the predefined location dataset190. UE location calculations are performed by the NLS shown in Figure 2 which islocated above the O-RAN O-DU interface by using PCDS database connected to the NLSaccording to embodiments herein. Processing of UE signals to extract the ToA and AoAfor the detected multipaths requires MUSIC or Beam space algorithms to process received signals from all antenna branches. The availability of antenna branch information depends on the variation of the Lower Layer Split (LLS) between O-DU and O-RU / RRU. Eight and fewer branch RU / RRU radios are low cost and constitute a significant portion ofnetwork cell sites, and even with just 8 antenna branches, may extract multipath azimuthAoA to good accuracy, and ToA to high accuracy for useful vTRP measurement. Unlike previous generations of radios, 6G radios have significantly higher measurement data flows from the O-RU (Cat. B) and O-DU (Cat A) than previousgeneration. A100% positioning of all connected UEs, may result in a 100-fold increase inmeasurement data, coupled with a 10-fold increase in measurements for each positioning request, due to the transport of not only a single estimated LoS and AoA representing the UE location, but generally a set of 10 multipath measurements each with a nLoS and AoAindication. Example embodiments herein use multipath measurements including ToA andAoA and their application to predefined location datasets 190 to improve the estimated UE 121 position. This may result in a 1000-fold increase in measurements information passed from the O-RU and O-DU to the core network positioning servers such as e.g., NLS. The6G positioning data flow thereby may have increased by an order of 1000-fold,necessitating a high-speed interface for these measurements. The same measurementsmay be transferred for 6G radar applications but augmented to comprise a spectral viewof the LoS and nLoS reflections and generally across two or more 6G symbols or sample times. In some embodiments, this larger data set will be passed to machine learning functions in the positioning server, which may be labelled as e.g., a positioning and radarsensor server or a positioning and radar Sensing Management Function (SeMF), toassess Radar Cross Sectional (RCS) amplitude and doppler changes and frequencies to determine characteristic modulation or harmonics of the return signal. Assuming that the AoA of the LoS, as well as the AoA of each nLoS measurement includes a grid of 32x32points representing the spectral reflection view, the measurement rate may increase anadditional 1000-fold. The many radar reflection images might be processed in the SeMFby a ML functional block that has been extensively trained with similar reflection datarepresenting a vast number of known objects. Radar measurements therefore have thepotential to increase the 6G positioning measurement data flow, already a 1000-fold increase over 5G systems, to a million or more, demanding a dedicated high-speed interface. However, while positioning is expected to be performed on all connecteddevices, radar processing would likely only be conducted on a very limited set of objects,such as active aerial delivery drones and air taxis where such information is useful in managing these services. According to example embodiments herein, the SeMF supporting ML functionality for positioning and radar processing, will reside in the NLS, and will demand a very highspeed N2 i.e., NG-c interface. However, this interface may be insufficient for the allocatedpositioning and radar bandwidth, requiring a dedicated data interface between the O- RU / RRU or O-DU and the NLS. Examples of embodiments herein provide an interface N3u from the O-RU / RRU to the NLS to carry dedicated user plane N3 or NG-u high-rate positioning and radar measurement flows from the O-RU / RRU to NLS.To perform the method actions above, the RAN node 111 is configured to assist anetwork node 151 in locating a position of a User Equipment, UE, 121 in a wireless communications network 100. The RAN node 111 comprises a Multiple-Input Multiple- Output, MIMO, antenna system capable of performing beamforming. The RAN node 111 may comprise an arrangement depicted in Figure 12. The RANnode 111 may comprise an input and output interface 1200 configured to communicatein the communications network 100, e.g., with the network node 151. The input and outputinterface 1200 may comprise a wireless receiver not shown, and a wireless transmitter not shown. The RAN node 111 is further configured to receive, on the MIMO antenna system,one or more uplink signals from the UE 121. The one or more uplink signals comprisesone or more out of: a Line of Sight, LoS, signal and one or more non LoS, nLoS, multipathreflections. The RAN node 111 is further configured to determine an Angles-of-Arrival,AoA, and a Time-of-Arrival, ToA, related to the received LoS signal and each of the nLoSmultipath reflections. The RAN node 111 is further configured to extract, based ontemporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals. Additionally, the RAN node 111is further configured to send information to the network node 151. The informationcomprises one or more out of one or more: determined AoA, determined ToA, extractedLoS signal and extracted nLoS multipath reflections. In some embodiments, the RAN node 111 is being configured to extract by using one or more out of: sub-space algorithms comprising one or more out of: MUltiple SIgnal Classification, MUSIC, algorithm, and Estimation of Signal Parameters via Rotational Invariance Techniques, ESPRIT, algorithm, and sequential algorithms comprising one or more out of: Orthogonal Matching Pursuit, OMP, algorithms. In some embodiments, the RAN node 111 is being configured to extract based on one or more out of: a correlated power specific to a UE 121, angular spread, number of antennas, and a Signal-to-Noise Ratio, SNR, of the one or more received uplink signals,number of multipath components, dimension of signal subspace, dimension of one ormore out of: noise, interference and clutter subspace. In some embodiments, the RAN node 111 is further being configured to calibratethe phase alignment of a predefined location dataset 190 to a phase centre of the MIMO antenna system of the RAN node 111.To perform the method actions above, the network node 151 is configured to locatea position of a User Equipment, UE, 121 in a wireless communications network 100. The network node 151 may comprise an arrangement depicted in Figure 13. Thenetwork node 151 may comprise an input and output interface 1300 configured tocommunicate in the communications network 100, e.g., with the RAN node 111. The inputand output interface 1300 may comprise a wireless receiver not shown, and a wireless transmitter not shown. The network node 151 is further configured to receive information from a RAN node111. The information is adapted to comprise one or more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections. The information is adapted to be related to a LoS signal and one or more nLoS multipath reflections comprised in one or more uplink signals received from the UE 121. The network node 151 is further configured to locate a position of one or more virtual Transmission Reference Points, vTRPs, related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections based on one or more out of one or more: determined AoA, determined ToA, and predefined location dataset 190. The respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node 111 and LoS visibility to the UE 121, and a point of transmission. The network node 151 is further configured to determine a relative ToA, rToA, at each of the one or more vTRPs, by further being configured to calculate a propagation time, of the LoS signal and each of the nLoS multipath reflections out of the one or more received uplink signals, from the RAN node 111 to each of the respectivevTRPs. The rToA is related to the uplink signal from the UE 121 to the respective vTRP.Additionally, the network node 151 is further configured to compute the position of the UE 121 based on the located one or more vTRPs and the corresponding determined rToA. In some embodiments, the network node 151 is further being configured to compute by using a machine-learning model. In some embodiments, the network node 151 is further being configured to provide information related to the located position of the UE 121. The information may be relatedto the located position is adapted to comprise one or more out of: a co-ordinate, and animage from the predefined location dataset 190. In some embodiments, the network node 151 is further being configured to estimate a velocity of the UE 121 by performing differential measurements for the LoS signal and each of the nLoS multipath reflections related to the UE 121. In some embodiments, the predefined location dataset 190 is adapted to comprise polarized images and non-polarized images, and the network node 151 is further being configured to locate the position of the one or more vTRPs based on the polarized images. Embodiments herein may be implemented through a respective processor or one or more processors, such as the respective processor 1210 of a processing circuitry in theRAN node 111 depicted in Figure 12, and processor 1310 of a processing circuitry in thenetwork node 151 depicted in Figure 13 together with respective computer program codefor performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing theembodiments herein when being loaded into the respective RAN node 111 and networknode 151. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the respective RAN node 111 and network node 151. The RAN node 111 and network node 151 may further comprise a respective memory 1220 and memory 1320 comprising one or more memory units. The respective memory 1220 and memory 1320 comprises instructions executable by the processor in the respective RAN node 111 and network node 151. The respective memory 1220 andmemory 1320 are arranged to be used to store e.g., media functions, indications, tags,information, data, configurations, communication data, and applications to perform the methods herein when being executed in the respective RAN node 111 and network node 151. In some embodiments, a respective computer program 1230 and computerprogram 1330 comprises instructions, which when executed by the respective at leastone processor 1210 and processor 1310, cause the at least one processor of respective RAN node 111 and network node 151 to perform the actions above. In some embodiments, a respective carrier 1240 and carrier 1340 comprises therespective computer program 1230 and computer program 1330, wherein the respective carrier 1240 and carrier 1340 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium. Those skilled in the art will appreciate that units in the respective RAN node 111and network node 151 described above may refer to a combination of analog and digitalcircuits, and / or one or more processors configured with software and / or firmware, e.g.stored in the respective RAN node 111 and network node 151, that when executed by therespective one or more processors such as the processors described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry ASIC, or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC). Figure 14 shows an example of a communication system QQ100 in accordancewith some embodiments. In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108. Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network QQ102. In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more host computing systems, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF). The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server. As a whole, the communication system QQ100 of 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to differentdevices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive IoT services to yet further UEs. In some examples, the UEs QQ112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the accessnetwork QQ104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio AccessNetwork) New Radio – Dual Connectivity (EN-DC).In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices. The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection.In some embodiments, the hub QQ114 may be a dedicated hub – that is, a hub whoseprimary function is to route communications to / from the UEs from / to the network nodeQQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub – that is, adevice which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Figure 15 shows a UE QQ200 in accordance with some embodiments. The UEQQ200 presents additional details of some embodiments of the UE QQ112 of Figure 14 and of the UE 121 of Figure 4 as described in example embodiments herein. As used herein, a UE refers to a device capable, configured, arranged and / or operable tocommunicate wirelessly with network nodes such as e.g., RAN node 111 and networknode 151. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in 10. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc. The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. Theprocessing circuitry QQ202 may be implemented as one or more hardware-implementedstate machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs). In the example, the input / output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device. In some embodiments, the power source QQ208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied. The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic disks, optical disks, hard disks,removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems. The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatiledisc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive,holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium. The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g.,once every 15 minutes if it reports the sensed temperature), random (e.g., to even out theload from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient). As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input. A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a watersprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, anindustrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE QQ200 shown in Figure 15. As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UEmay in this case be an M2M device, which may in a 3GPP context be referred to as anMTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators. Figure 16 shows a network node QQ300 in accordance with some embodiments.The network node QQ300 presents additional details of some embodiments of the RANnode 111 and the network node 151 of Figure 4 as described in example embodimentsherein. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O- CU). Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS). Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi- cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs). The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300. The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality. In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units. The memory QQ304 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory(RAM), read-only memory (ROM), mass storage media (for example, a hard disk),removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device- readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated. The communication interface QQ306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown). The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port. The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment. The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail. Embodiments of the network node QQ300 may include additional componentsbeyond those shown in Figure 16 for providing certain aspects of the network node’sfunctionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. In some embodiments providing acore network node, such as core network node 108 of Figure 14, some components, suchas the radio front-end circuitry QQ318 and the RF transceiver circuitry QQ312 may be omitted. Figure 17 is a block diagram illustrating a virtualization environment QQ400 inwhich functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment QQ400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host. Applications QQ402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware QQ404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ408a and QQ408b (one or more of which may be generally referred to as VMs QQ408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ406 may present a virtual operating platform that appears like networking hardware to the VMs QQ408. The VMs QQ408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ406. Different embodiments of the instance of a virtual appliance QQ402 may be implemented on one or more of VMs QQ408, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment. In the context of NFV, a VM QQ408 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs QQ408, and that part of hardware QQ404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ408 on top of the hardware QQ404 and corresponds to the application QQ402. Hardware QQ404 may be implemented in a standalone network node with generic or specific components. Hardware QQ404 may implement some functions via virtualization. Alternatively, hardware QQ404 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together andare managed via management and orchestration QQ410, which, among others, overseeslifecycle management of applications QQ402. In some embodiments, hardware QQ404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ412 which may alternatively be used for communication between hardware nodes and radio units. Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware. In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally. When using the word "comprise" or “comprising” it shall be interpreted as non- limiting, i.e. meaning "consist at least of". The embodiments herein are not limited to the preferred embodiments described above. Various alternatives, modifications and equivalents may be used. 

Claims

CLAIMS 1. A method performed by a Radio Access Network, RAN, node (111) for assisting anetwork node (151) in locating a position of a User Equipment, UE, (121) in a wireless communications network (100), wherein the RAN node (111) comprises a Multiple-Input Multiple-Output, MIMO, antenna system capable of performing beamforming, the method comprising: receiving (502), on the MIMO antenna system, one or more uplink signals fromthe UE (121), which one or more uplink signals comprises one or more out of: a Line of Sight, LoS, signal and one or more non LoS, nLoS, multipath reflections, determining (503) an Angles-of-Arrival, AoA, and a Time-of-Arrival, ToA, related to the received LoS signal and each of the nLoS multipath reflections, extracting (504), based on a temporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals, and sending (505) information to the network node (151), which informationcomprises one or more out of one or more: determined AoA, determined ToA,extracted LoS signal and extracted nLoS multipath reflections.

2. The method according to claim 1, wherein the extracting (504) is performed byusing one or more out of: sub-space algorithms comprising one or more out of: MUltiple SIgnal Classification, MUSIC, algorithm, and Estimation of Signal Parameters via Rotational Invariance Techniques, ESPRIT, algorithm, and sequential algorithms comprising one or more out of: Orthogonal Matching Pursuit, OMP, algorithms.

3. The method according to any of claims 1-2, wherein the extracting (504) is basedon one or more out of: a correlated power specific to a UE (121), angular spread, number of antennas, and a Signal-to-Noise Ratio, SNR, of the one or more received uplink signals, number of multipath components, dimension of signal subspace, dimension of one or more out of: noise, interference and clutter subspace.

4. The method according to any of claims 1-3, further comprising:calibrating (501) the phase alignment of a predefined location dataset (190) to a phase centre of the MIMO antenna system of the RAN node (111).

5. The method according to any of claims 1-4, wherein the extracting (504) isperformed by one or more RAN nodes (111, 112) in the wireless communication network (100).

6. A computer program comprising instructions, which when executed by aprocessor, causes the processor to perform actions according to any of the claims 1-5.

7. A carrier comprising the computer program of claim 6, wherein the carrier is one ofan electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer- readable storage medium.

8. A method performed by a network node (151) for locating a position of a UserEquipment, UE, (121) in a wireless communications network (100), the methodcomprising: receiving (601) information from a RAN node (111), which information comprises one or more out of one or more: determined Angles-of-Arrival, AoA, determined Time-of-Arrival, ToA, extracted Line of Sight, LoS, signal and extractednon LoS, nLoS, multipath reflections, wherein the information is related to a LoSsignal and one or more nLoS multipath reflections comprised in one or more uplink signals received from the UE (121), based on one or more out of one or more: determined AoA, determined ToA, and predefined location dataset (190), locating (602) a position of one or more virtual Transmission Reference Points, vTRPs, related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections, wherein the respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node (111) and LoS visibility to the UE (121), and a point of transmission, determining (604) a relative ToA, rToA, at each of the one or more vTRPs, by calculating (603) a propagation time, of the LoS signal and each of the nLoSmultipath reflections out of the one or more received uplink signals, from the RAN node (111) to each of the respective vTRPs, which rToA is related to the uplink signal from the UE (121) to the respective vTRP, and computing (605) the position of the UE (121) based on the located one or more vTRPs and the corresponding determined rToA.

9. The method according to claim 8, wherein the computing (605) of the position ofthe UE (121) is performed by using a machine-learning model.

10. The method according to any of claims 8-9, wherein the method further comprises:providing (606) information related to the located position of the UE (121), which information related to the located position comprises one or more out of: a co-ordinate, and an image from the predefined location dataset (190).

11. The method according to any of claims 8-10, wherein the locating (602) of theposition of the one or more vTRPs and the determining (604) of the rToA are performed by one or more network nodes (151) in the wireless communication network (100).

12. The method according to any of claims 8-11, further comprising:estimating (607) a velocity of the UE (121) by performing differential measurements for the LoS signal and each of the nLoS multipath reflections related to the UE (121).

13. The method according to any of claims 8-12, wherein the predefined locationdataset (190) comprises polarized images and non-polarized images, and the locating (602) of the position of the one or more vTRPs is performed based on the polarized images.

14. A computer program comprising instructions, which when executed by aprocessor, causes the processor to perform actions according to any of the claims 8-13.

15. A carrier comprising the computer program of claim 14, wherein the carrier is oneof an electronic signal, an optical signal, an electromagnetic signal, a magneticsignal, an electric signal, a radio signal, a microwave signal, or a computer- readable storage medium.

16. A Radio Access Network, RAN, node (111) configured to assist a network node(151) in locating a position of a User Equipment, UE, (121) in a wireless communications network (100), wherein the RAN node (111) comprises a Multiple- Input Multiple-Output, MIMO, antenna system capable of performing beamforming, the RAN node (111) further being configured to: Receive, on the MIMO antenna system, one or more uplink signals from theUE (121), which one or more uplink signals comprises one or more out of: a Line of Sight, LoS, signal and one or more non LoS, nLoS, multipath reflections, determine an Angles-of-Arrival, AoA, and a Time-of-Arrival, ToA, related to the received LoS signal and each of the nLoS multipath reflections, extract, based on a temporal beamforming algorithm the LoS signal and the one or more nLoS multipath reflections, from the one or more received uplink signals, and send information to the network node (151), which information comprises oneor more out of one or more: determined AoA, determined ToA, extracted LoS signal and extracted nLoS multipath reflections.

17. The RAN node (111) according to claim 16, wherein is being configured to extractby using one or more out of: sub-space algorithms comprising one or more out of: MUltiple SIgnal Classification, MUSIC, algorithm, and Estimation of Signal Parameters via Rotational Invariance Techniques, ESPRIT, algorithm, and sequential algorithms comprising one or more out of: Orthogonal Matching Pursuit, OMP, algorithms.

18. The RAN node (111) according to any of claims 16-17, wherein is beingconfigured to extract based on one or more out of: a correlated power specific to a UE (121), angular spread, number of antennas, and a Signal-to-Noise Ratio, SNR, of the one or more received uplink signals, number of multipath components, dimension of signal subspace, dimension of one or more out of: noise, interference and clutter subspace.

19. The RAN node (111) according to any of claims 16-18, further being configured to:calibrate the phase alignment of a predefined location dataset (190) to a phase centre of the MIMO antenna system of the RAN node (111).

20. A network node (151) configured to locate a position of a User Equipment, UE,(121) in a wireless communications network (100), the network node (151) further being configured to: receive information from a RAN node (111), which information is adapted to comprise one or more out of one or more: determined Angles-of-Arrival, AoA, determined Time-of-Arrival, ToA, extracted Line of Sight, LoS, signal and extractednon LoS, nLoS, multipath reflections, wherein the information is adapted to berelated to a LoS signal and one or more nLoS multipath reflections comprised in one or more uplink signals received from the UE (121), based on one or more out of one or more: determined AoA, determined ToA, and predefined location dataset (190), locate a position of one or more virtual Transmission Reference Points, vTRPs, related to the received extracted LoS signal and the received extracted one or more nLoS multipath reflections, wherein the respective one or more vTRPs is any one out of: a point of reflection with LoS visibility to the RAN node (111) and LoS visibility to the UE (121), and a point of transmission, determine a relative ToA, rToA, at each of the one or more vTRPs, by further being configured to calculate a propagation time, of the LoS signal and each of the nLoS multipath reflections out of the one or more received uplink signals, from the RAN node (111) to each of the respective vTRPs, which rToA is related to the uplink signal from the UE (121) to the respective vTRP, and compute the position of the UE (121) based on the located one or more vTRPs and the corresponding determined rToA.

21. The network node (151) according to claim 20, wherein is further being configuredto compute by using a machine-learning model.

22. The network node (151) according to any of claims 20-21, wherein the networknode (151) is further being configured to: provide information related to the located position of the UE (121), which information related to the located position is adapted to comprise one or more out of: a co-ordinate, and an image from the predefined location dataset (190).

23. The network node (151) according to any of claims 20-22, wherein the networknode (151) is further being configured to: estimate a velocity of the UE (121) by performing differential measurements for the LoS signal and each of the nLoS multipath reflections related to the UE (121).

24. The network node (151) according to any of claims 20-23, wherein the predefinedlocation dataset (190) is adapted to comprise polarized images and non-polarized images, and the network node (151) is further being configured to locate the position of the one or more vTRPs based on the polarized images.

Images