Patent Publication Number: US-2023137760-A1

Title: Multipath Estimation and Multi-Line-of-Sight Detection for Localization

Description:
TECHNICAL FIELD 
     The present invention relates to a method and apparatus for multipath estimation and multi-line-of-sight detection for localization. 
     BACKGROUND 
     This section is intended to provide a background or context to the invention that is recited in the claims. The description herein may include concepts that could be pursued, but are not necessarily ones that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, what is described in this section is not prior art to the description and claims in this application and is not admitted to be prior art by inclusion in this section. 
     5G-NR (5 th  generation New Radio) is a new radio access technology which has been developed by the 3 rd  generation partnership project (3GPP) for the 5 th  generation mobile networks. 5G-NR has been specified within 3GPP to be able to coexist with 4G-LTE (Long Term Evolution) within the same spectrum. 5G supports 5G NR positioning, specifically network-based positioning, where the calculation of the location estimate of a mobile communication device, which may also be called as a user equipment (UE), is carried out at the network, at a location management function (LMF). 
     A special aspect of positioning in multipath propagation environments is that multiple signal paths can be used in estimating UE&#39;s location. However, the use of multiple paths is not trivial, since they are associated with reflections generated by landmarks with unknown locations. If the locations of such landmarks are obtained then the network may be able to successfully employ multipath measurement reports in refining the UE position beyond what the standard time-of-arrival/angle-of-arrival (TOA/AOA) methods can produce. 
     For localization purposes, a user equipment measures non-exclusively a metric called time-of-arrival (TOA). TOA is the shortest time a signal takes to travel the distance between a transmitter and a receiver. If TOA is measured correctly, then the distance can be accurately obtained as d=TOA×c, where c=speed of light in vacuum. To compute TOA, many user equipment estimate a power-delay profile (PDP) of the wireless propagation channel and select as the TOA the delay at which the PDP shows a peak as depicted in  FIG.  7   a   . A problem with this approach is however that the strongest component does not always correspond to the line-of-sight LOS path (i.e. the direct path), as depicted in the example of  FIG.  7   b    in which the tree attenuates the direct line-of-sight signal so that the signal reflected by the surrounding building is stronger at the location of the receiving user equipment. The user equipment observing this situation may select the time or arrival as the delay of the non-line-of-sight (NLOS) component since its power is the highest in this example. This selection will bias the overall location estimate since this component does not reflect the shortest distance between the gNB and the UE. 
     Furthermore, the radio environment is very dynamic, especially in cm wave and mm wave frequency bands, and any movement of the UE and/or radio obstacles in the environment can cause transitions from LOS to NLOS conditions. In practice, therefore the UE will rarely experience a pure LOS or pure NLOS propagation conditions and determining which of these conditions is dominant, and for how long, in the received signals can become a computationally intensive task. 
     One approach is to perform cross-correlation between the received and transmitted signal and search for the first energy peak of the resulting signal&#39;s envelope. Such detection approaches are limited however in resolution by the signal bandwidth and noise levels. In addition, the first tap may not have the strongest energy and can be smudged into other taps depending on channel/system conditions. Other solution categories for LOS detection use either hypothesis testing or machine learning methodology by evaluating channel metrics such as: mean excess delay, RMS delay spread, amplitude kurtosis, total received power, rise time, TOA of the first multipath component, maximum signal amplitude. These features are extracted after estimating channel impulse response and require extensive domain knowledge for a meaningful selection. 
     Extracting a set of input features from a noisy channel impulse response (CIR) estimate, obtained from band-limited measurements may be suboptimal since the selected features are inherently noisy and thus they exhibit misleading dependencies and limit the input set to only tens of observations and cannot ensure that the channel characteristics are fully captured. 
     Therefore, there is a need for a mechanism to improve the reliability and accuracy of location applications. 
     SUMMARY 
     Some embodiments provide a method and apparatus for localization. 
     Some embodiments provide a positioning-related method of multipath reconstruction and multi-LOS detection that is based on applying tools from the mean-field theory (MFT). The method poses the channel reconstruction problem as that of estimating the approximate probability density functions (pdf) for each of the variables in the set (delay, phase, amplitude, LOS-indicator) characterizing each reconstructed multipath component, using downlink positioning reference signals. 
     To enable the method, several choices have been made to pose an estimation model in which a channel is approximated as a sum of components lying on a fine delay grid, where each channel tap is assigned a LOS probability. The delay grid has flexible resolution, i.e., not limited to the sampling time of the system and allows for a flexible implementation, trading complexity for performance. The sampling time may be, for example, a basic time unit for NR (Tc); or a basic time unit for LTE (Ts). 
     In accordance with an embodiment of the disclosure, each tap is characterized by a line-of-sight probability, a complex gain and a delay, each modeled as a random variable characterized by a selected prior probability density function. The taps are considered to be independent and identically distributed (i.i.d.). The noise precision and/or noise variance pdf is also estimated, assuming an improper noise prior. 
     A method has also been derived that applies tools from mean-field theory (MFT) onto the above described model to estimate 1) a channel impulse response and subsequently to return the delay and phase of the most likely LOS component, but also those of other relevant components; 2) the LOS probability of each detected channel component; and 3) a level of a signal-to-noise ratio (SNR). However, also other than the mean-field theory may be used. 
     In accordance with an embodiment of the disclosure the method models and estimates a LOS probability/indicator per detected component, applies mean field theory tools to jointly estimate multiple LOS indicators, noise levels and detect multipath components from which the delay and phase can be subsequently extracted. Furthermore, the method enables LOS TOA selection based on the outcome of the estimation problem. 
     According to a first aspect there is provided an apparatus comprising: 
     means for receiving positioning reference signals from a positioning signal transmitter; 
     means for forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimation model of the channel; 
     means for estimating a noise precision for the channel taps of a noise process corrupting the signals; 
     means for estimating channel gains for the channel taps; and 
     means for estimating a probability of line of sight signal for each channel tap. 
     According to a second aspect there is provided a method comprising: 
     receiving positioning reference signals from a positioning signal transmitter; 
     forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimation model of the channel; 
     estimating a noise precision for the channel taps of a noise process corrupting the signals; 
     estimating channel gains for the channel taps; and 
     estimating a probability of line of sight signal for each channel tap. 
     According to a third aspect there is provided an apparatus comprising at least one processor; and at least one memory including computer program code the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: 
     receive positioning reference signals from a positioning signal transmitter; 
     form a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimation model of the channel; 
     estimate a noise precision for the channel taps of a noise process corrupting the signals; 
     estimate channel gains for the channel taps; and 
     estimate a probability of line of sight signal for each channel tap. 
     According to a fourth aspect there is provided a computer program comprising computer readable program code which, when executed by at least one processor; cause the apparatus to perform at least the following: 
     receive positioning reference signals from a positioning signal transmitter; 
     form a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimation model of the channel; 
     estimate a noise precision for the channel taps of a noise process corrupting the signals; 
     estimate channel gains for the channel taps; and 
     estimate a probability of line of sight signal for each channel tap. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of example embodiments of the present invention, reference is now made to the following descriptions taken in connection with the accompanying drawings in which: 
         FIG.  1    shows a block diagram of one possible and non-limiting example in which the examples may be practiced; 
         FIG.  2    illustrates an example situation in which a signal transmitted by a base station arrives at a receiving communication device as a direct line of sight signal and as signals reflected from obstacles; 
         FIG.  3    illustrates an architecture implementation according to an example embodiment of the disclosure; 
         FIG.  4    shows as a flow diagram a method, in accordance with an embodiment of the disclosure; 
         FIG.  5    illustrates a time-domain structure of a frame of an OFDM scheme in downlink direction according to an approach; 
         FIG.  6    shows a part of an exemplifying wireless communications access network in accordance with at least some embodiments; 
         FIG.  7   a    shows an example of an estimated power-delay profile of a wireless propagation channel; and 
         FIG.  7   b    shows an example in which a tree attenuates a direct line-of-sight signal so that the signal reflected by a surrounding building is stronger at a location of a receiving user equipment. 
     
    
    
     DETAILED DESCRIPTON OF SOME EXAMPLE EMBODIMENTS 
     The following embodiments are exemplary. Although the specification may refer to “an”, “one”, or “some” embodiment(s) in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. 
     It should be noted here that in this specification, the term ‘base station’ refers to a logical element containing logical communication system layers (e.g. L 1 , L 2 , L 3 ). The base stations of different RATs may be implemented in the same hardware or at separate hardware. It should also be mentioned that although the expressions “each base station” and “each mobile station” or “each user equipment” may be used, these terms need not mean every existing base station, mobile station or user equipment but base stations, mobile stations or user equipment in a certain area or set. For example, each base station may mean all base stations within a certain geographical area or all base stations of an operator of a wireless communication network or a sub-set of base stations of an operator of a wireless communication network. 
       FIG.  1    shows a block diagram of one possible and non-limiting example in which the examples may be practiced. A user equipment (UE)  110 , radio access network (RAN) node  170 , and network element(s)  190  are illustrated. In the example of  FIG.  1   , the user equipment  110  is in wireless communication with a wireless network  100 . A user equipment is a wireless device that can access the wireless network  100 . The user equipment  110  includes one or more processors  120 , one or more memories  125 , and one or more transceivers  130  interconnected through one or more buses  127 . Each of the one or more transceivers  130  includes a receiver, Rx,  132  and a transmitter, Tx,  133 . The one or more buses  127  may be address, data, or control buses, and may include any interconnection mechanism, such as a series of lines on a motherboard or integrated circuit, fibre optics or other optical communication equipment, and the like. The one or more transceivers  130  are connected to one or more antennas  128 . The one or more memories  125  include computer program code  123 . The user equipment  110  includes a module  140 , which may be implemented in a number of ways. The module  140  may be implemented in hardware as module  140 - 1 , such as being implemented as part of the one or more processors  120 . The module  140 - 1  may also be implemented as an integrated circuit or through other hardware such as a programmable gate array. In another example, the module  140  may be implemented as module  140 - 2 , which is implemented as computer program code  123  and is executed by the one or more processors  120 . For instance, the one or more memories  125  and the computer program code  123  may be configured to, with the one or more processors  120 , cause the user equipment  110  to perform one or more of the operations as described herein. The user equipment  110  communicates with RAN node  170  via a wireless link  111 . The modules  140 - 1  and  140 - 2  may be configured to implement the functionality of the user equipment as described herein. 
     For example, the module  140 - 1  and/or  140 - 2  may comprise units (e.g. as a computer code) for performing different operations related to the reception and analyses of positioning signals as will be explained later. 
     The RAN node  170  in this example is a base station that provides access by wireless devices such as the user equipment  110  to the wireless network  100 . Thus, the RAN node  170  (and the base station) may also be called as an access point of a wireless communication network). The RAN node  170  may be, for example, a base station for 5G, also called New Radio (NR). In 5G, the RAN node  170  may be a NG-RAN node, which is defined as either a gNB or an ng-eNB. A gNB is a node providing NR user plane and control plane protocol terminations towards the UE and connected via the NG interface to a 5GC (such as, for example, the network element(s)  190 ). The ng-eNB is a node providing E-UTRA user plane and control plane protocol terminations towards the UE and connected via the NG interface to the 5GC. The NG-RAN node may include multiple gNBs, which may also include a central unit (CU) (gNB-CU)  196  and distributed unit(s) (DUs) (gNB-DUs), of which DU  195  is shown. Note that the DU  195  may include or be coupled to and control a radio unit (RU). The gNB-CU  196  is a logical node hosting radio resource control (RRC), SDAP and PDCP protocols of the gNB or RRC and PDCP protocols of the en-gNB that controls the operation of one or more gNB-DUs. The gNB-CU  196  terminates the F1 interface connected with the gNB-DU  195 . The F 1  interface is illustrated as reference  198 , although reference  198  also illustrates a link between remote elements of the RAN node  170  and centralized elements of the RAN node  170 , such as between the gNB-CU  196  and the gNB-DU  195 . The gNB-DU  195  is a logical node hosting RLC, MAC and PHY layers of the gNB or en-gNB, and its operation is partly controlled by gNB-CU  196 . One gNB-CU  196  supports one or multiple cells. One cell is supported by one gNB-DU  195 . The gNB-DU  195  terminates the F1 interface  198  connected with the gNB-CU  196 . Note that the DU  195  is considered to include the transceiver  160 , e.g., as part of a RU, but some examples of this may have the transceiver  160  as part of a separate RU, e.g., under control of and connected to the DU  195 . The RAN node  170  may also be an eNB (evolved NodeB) base station, for LTE (long term evolution), or any other suitable base station or node. 
     The RAN node  170  includes one or more processors  152 , one or more memories  155 , one or more network interfaces (N/W I/F(s))  161 , and one or more transceivers  160  interconnected through one or more buses  157 . Each of the one or more transceivers  160  includes a receiver, Rx,  162  and a transmitter, Tx,  163 . The one or more transceivers  160  are connected to one or more antennas  158 . The one or more memories  155  include computer program code  153 . The CU  196  may include the processor(s)  152 , memory(ies)  155 , and network interfaces  161 . Note that the DU  195  may also contain its own memory/memories and processor(s), and/or other hardware, but these are not shown. 
     The RAN node  170  includes a module  150 , comprising one of or both parts  150 - 1  and/or  150 - 2 , which may be implemented in a number of ways. The module  150  may be implemented in hardware as module  150 - 1 , such as being implemented as part of the one or more processors  152 . The module  150 - 1  may also be implemented as an integrated circuit or through other hardware such as a programmable gate array. In another example, the module  150  may be implemented as module  150 - 2 , which is implemented as computer program code  153  and is executed by the one or more processors  152 . For instance, the one or more memories  155  and the computer program code  153  are configured to, with the one or more processors  152 , cause the RAN node  170  to perform one or more of the operations as described herein. Note that the functionality of the module  150  may be distributed, such as being distributed between the DU  195  and the CU  196 , or be implemented solely in the DU  195 . The modules  150 - 1  and  150 - 2  may be configured to implement the functionality of the base station described herein. Such functionality of the base station may include a location management function (LMF) implemented based on functionality of the LMF described herein. Such LMF may also be implemented within the RAN node  170  as a location management component (LMC). 
     The one or more network interfaces  161  communicate over a network such as via the links  176  and  131 . Two or more gNBs  170  may communicate using, e.g., link  176 . The link  176  may be wired or wireless or both and may implement, for example, an Xn interface for 5G, an X2 interface for LTE, or other suitable interface for other standards. 
     The one or more buses  157  may be address, data, or control buses, and may include any interconnection mechanism, such as a series of lines on a motherboard or integrated circuit, fiber optics or other optical communication equipment, wireless channels, and the like. For example, the one or more transceivers  160  may be implemented as a remote radio head (RRH)  195  for LTE or a distributed unit (DU)  195  for gNB implementation for 5G, with the other elements of the RAN node  170  possibly being physically in a different location from the RRH/DU  195 , and the one or more buses  157  could be implemented in part as, for example, fiber optic cable or other suitable network connection to connect the other elements (e.g., a central unit (CU), gNB-CU) of the RAN node  170  to the RRH/DU  195 . Reference  198  also indicates those suitable network link(s). 
     It is noted that description herein indicates that “cells” perform functions, but it should be clear that equipment which forms the cell may perform the functions. The cell makes up part of a base station. That is, there can be multiple cells per base station. For example, there could be three cells for a single carrier frequency and associated bandwidth, each cell covering one-third of a 360 degree area so that the single base station&#39;s coverage area covers an approximate oval or circle. Furthermore, each cell can correspond to a single carrier and a base station may use multiple carriers. So, if there are three 120 degree cells per carrier and two carriers, then the base station has a total of 6 cells. 
     The wireless network  100  may include a network element or elements  190  that may include core network functionality, and which provides connectivity via a link or links  181  with a further network, such as a telephone network and/or a data communications network (e.g., the Internet). Such core network functionality for 5G may include location management functions (LMF(s)) and/or access and mobility management function(s) (AMF(S)) and/or user plane functions (UPF(s)) and/or session management function(s) (SMF(s)). Such core network functionality for LTE may include MME (Mobility Management Entity)/SGW (Serving Gateway) functionality. These are merely example functions that may be supported by the network element(s)  190 , and note that both 5G and LTE functions might be supported. The RAN node  170  is coupled via a link  131  to the network element  190 . The link  131  may be implemented as, e.g., an NG interface for 5G, or an S1 interface for LTE, or other suitable interface for other standards. The network element  190  includes one or more processors  175 , one or more memories  171 , and one or more network interfaces (N/W I/F(s))  180 , interconnected through one or more buses  185 . The one or more memories  171  include computer program code  173 . The one or more memories  171  and the computer program code  173  are configured to, with the one or more processors  175 , cause the network element  190  to perform one or more operations such as functionality of an LMF as described herein. In some examples, a single LMF could serve a large region covered by hundreds of base stations. 
     The wireless network  100  may implement network virtualization, which is the process of combining hardware and software network resources and network functionality into a single, software-based administrative entity, a virtual network. Network virtualization involves platform virtualization, often combined with resource virtualization. Network virtualization is categorized as either external, combining many networks, or parts of networks, into a virtual unit, or internal, providing network-like functionality to software containers on a single system. Note that the virtualized entities that result from the network virtualization are still implemented, at some level, using hardware such as processors  152  or  175  and memories  155  and  171 , and also such virtualized entities create technical effects. 
     The computer readable memories  125 ,  155 , and  171  may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The computer readable memories  125 ,  155 , and  171  may be means for performing storage functions. The processors  120 ,  152 , and  175  may be of any type suitable to the local technical environment, and may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on a multi-core processor architecture, as non-limiting examples. The processors  120 ,  152 , and  175  may be means for performing functions, such as controlling the UE  110 , RAN node  170 , network element(s)  190 , and other functions as described herein. 
     A memory may be a computer readable medium that may be non-transitory. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The data processors may be of any type suitable to the local technical environment, and may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multi-core processor architecture, as non-limiting examples. 
     In general, the various embodiments of the user equipment  110  can include, but are not limited to, cellular telephones such as smart phones, tablets, personal digital assistants (PDAs) having wireless communication capabilities, portable computers having wireless communication capabilities, image capture devices such as digital cameras having wireless communication capabilities, gaming devices having wireless communication capabilities, music storage and playback appliances having wireless communication capabilities, Internet appliances permitting wireless Internet access and browsing, tablets with wireless communication capabilities, as well as portable units or terminals that incorporate combinations of such functions. 
     Module  150 - 1  and/or module  150 - 2  may implement the functionalities and signaling of the gNB or radio node as herein described. Computer program code  173  may implement the functionalities and signaling of the AMF or network element as herein described. 
       FIG.  2    illustrates an example situation in which a signal transmitted by a base station  170  arrives at a receiving communication device  110  as a direct, line-of-sight (LoS) signal  203  and as signals  204  reflected from obstacles  201 . In other words, the receiving communication device  110  receives the transmitted signal via multiple signal paths. In this example the signal is reflected from two buildings  201  and arrives at the receiving communication device  110  via the paths  204  illustrated in  FIG.  2   . In addition to reflecting signals an object may scatter a signal and also such a scattered signal may arrive at the receiving communication device  110 . In this disclosure the reflected and scattered signals are also called as multipath propagated signals. 
     Because the reflected and scattered signals travel a longer path than the line-of-sight signal, they arrive later at the receiving communication device  110  than the line-of-sight signal but they represent the same information content. Each multipath propagated signal can be considered as a signal from a virtual transmission reception point (V-TRP)  170 ′ and can be utilized in a UE positioning procedure. However, to do that the location of the object  201  reflecting and/or scattering the signal should be known by some accuracy in order to utilize it in the UE positioning procedure. 
     In the following, some embodiments are presented which mainly focus on the problem of effects of multi-path propagation to the calculation of the time-of-arrival. 
     The method in accordance with an embodiment of the disclosure is depicted in  FIG.  4    and will be described in the following. It is assumed that the network comprises a location management function (LMF)  400 , for example in a base station  170  or in some other network element and the user equipment  110  is used as the signal receiving entity, which obtains information related to the multipath propagated signals. 
     In 4G LTE and 5G NR the OFDM (Orthogonal Frequency Division Multiplexing) modulation scheme is used.  FIG.  5    illustrates the time-domain structure of a frame (Type I) of the OFDM scheme in downlink direction according to an approach. The length of one frame is Tframe and is divided to ten subframes of length Tsubframe. Each subframe comprises two slots of length Tsiot. comprising a cyclic prefix CP # 1 , CP # 2 , . . . , CP # 7  and OFDM symbols # 1 , . . . , # 7 . 
     In the following a positioning signal transmission (PRS) and reception is described, assuming that the location management function  400  or/and another entity (a positioning signal transmission unit, a positioning transmitter) is capable of transmitting positioning signals and that the location of the entity is known. It should be noted that in practical positioning systems there are a plurality of positioning signal transmission units, but in the following only one such unit is considered. Furthermore, the user equipment  110  is assumed to be the receiving unit i.e. the device which receives the transmitted positioning signals. 
     To enable a method according to an example of the disclosure, several choices have been made to create an estimation model in which a channel is approximated based on received positioning signals as a sum of components lying on a fine delay grid, where each channel tap is assigned a LOS probability. The delay grid has flexible resolution, i.e., not limited to the sampling time of the system and allows for a flexible implementation, trading complexity for performance. The sampling time may be, for example, a basic time unit for NR (Tc); or a basic time unit for LTE (Ts). 
     It is assumed that the positioning transmitter  400  generates K reference symbols u=[u 1 , . . . , u K ] K , which are OFDM modulated (Orthogonal Frequency Division Multiplex) with a cyclic prefix of N cp  samples. These samples are sent over a channel, which can be assumed to have an impulse response α(t)=pα 1 δ(t−τ 1 )+Σ l=2   L α l δ(t−τ l ), where (α l , τ l ) are a complex gain and delay of an l-th tap. Without loss of generality, it can be assumed that α 1  is the gain of the LOS component which arrives at a receiver placed r meters away from the transmitter, with a delay 
     
       
         
           
             
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     This delay can be called as a time of arrival (TOA). A variable q indicates LOS presence and is a Bernoulli distributed variable pr(p=1)=s, pr(p=0)=1−s. In other words, the Bernoulli distributed variable for the LOS can be expressed as pr(LOS present)=s, pr(LOS not present)=1−s. 
     In the following, an estimation problem is derived that is built using the following underlying modelling choices. A delay search space of variable (fine) resolution is derived by selecting a variable oversampling factor G and each channel tap lying on the above-mentioned delay grid is defined as a random variable composed of a complex gain part and a LOS indication part. 
     Prior probability density functions are selected for the postulated channel model defined above and the noise variance. 
     The receiver  132  of the user equipment  110  receives the OFDM modulated transmission and performs a corresponding OFDM demodulation. After the OFDM demodulation an approximation unit  140 - 3  of the user equipment  110  performs the following approximations 
     
       
         
           
             
               
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     An approximate model of the received signal can be created so that the model consists of K samples y=[y 1 , . . . , y K ] T : 
         y=Γ   toa   F   tail α+ξ  (3)
 
     where ξ is additive white Gaussian noise (AWGN) with variance 1/λ, and G is the selected oversampling delay factor. 
     The matrix Γ toa =D (r) O (d)  is the perceived TOA matrix modelling on a per-subcarrier basis the contributions of the TOA τ 1  and the potential clock offset between the transmitter of the positioning signal transmitter and the receiver  132  of the user equipment dT s , where T s  is the sampling time of the system. 
     Matrices D (r) , O (d)  ∈ C K×K  are each diagonal, with diagonal entries: 
     
       
         
           
             
               
                 
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     and model the TOA contribution and the offset contributions across subcarriers respectively. 
     The matrix 
     
       
         
           
             
               
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     models the contributions of the remaining taps, i.e. a tail of the channel impulse response (CIR) vector. 
     The CIR vector is α=[q 1 α 1 , q 2 α 2 , . . . , q S α S ] T  where a LOS probability may be assigned to each of the taps. Then, the LOS vector is q=[q 1 , . . . , q S ] illustrating LOS probabilities of different taps. 
     When the clock offset dT s  is unknown by the user equipment, the user equipment aims at estimating the perceived TOA, i.e. t p =(τ 1 +d), and reports the estimated, perceived TOA t p  to the network, e.g. to the gNB or TRP node. Hence, the user equipment leaves the task of decoupling d and τ 1  to the network side. In such a situation, the matrix Γ toa  can be re-written as 
     
       
         
           
             
               
                 
                   Γ 
                   toa 
                 
                 ( 
                 
                   k 
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                   k 
                 
                 ) 
               
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                 ⁡ 
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                       ⁢ 
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                       ⁢ 
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                       ⁢ 
                       
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                 ) 
               
             
             , 
             
               
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                 p 
               
               = 
               
                 
                   
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                     p 
                   
                   
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                     s 
                   
                 
                 ∈ 
                 
                   
                     ℝ 
                     + 
                   
                   . 
                 
               
             
           
         
       
     
     This re-written Γ toa  can now be used on the equation (3) to estimate a probability of LOS expressed through vector q, a perceived discrete TOA d p , and a gain of LOS tap α 1 . 
     To do that, the probability density functions of α, q, λ are estimated using the equation (3) for which the joint probability density function is proportional to: 
       p(α, q, λ, s, y)∝p(y|a, q, λ, s)p(λ)p(α)p(q|s)p(s)   (4)
 
     It should be noted that by estimating all the channel gains â the corresponding delays can be obtained as 
     
       
         
           
             
               
                 
                   τ 
                   ^ 
                 
                 l 
               
               = 
               
                 
                   
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                     ϵ 
                   
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     where ∈ is a chosen error floor. In equation (4) the factors in the right-hand side product are: 
     p(y| α,q)=CN(Γ toa F tail a, 1/λI), CN( ) is the complex Gaussian distribution, 
     
       
         
           
             
               P 
               ⁡ 
               ( 
               λ 
               ) 
             
             = 
             
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               λ 
             
           
         
       
     
     p(q)= 529  p(q l ), p(q l )=U(0,1), where U( ) is the uniform distribution, 
     p(α)=CN(0, C), C is known covariance matrix, e.g. C=I. 
     To estimate the unknowns in the equation (4), the mean field theory methodology can be applied and approximate probability density functions of (α, q, λ) called beliefs are estimated and denoted by v( ). Then, the following expressions are obtained. 
     The noise precision estimate ({circumflex over (λ)}) reads: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             λ 
                             ^ 
                           
                           = 
                           
                             K 
                             
                               
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                                     ⁢ 
                                     
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                         = 
                         
                           
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                                   ^ 
                                 
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                             ] 
                           
                           T 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     The l-th channel gains belief is v(α l )=CN({circumflex over (α)} l , V l ) with: 
     
       
         
           
             
               
                 
                   
                     
                       
                         α 
                         ^ 
                       
                       l 
                     
                     = 
                     
                       
                         ( 
                         
                           
                             λ 
                             ^ 
                           
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                               q 
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     Ω l  is the l-th column of a Ω=(Γ toa F tail ), J k,l =Ω l   H Ω k , V l =C(l, l) −1 +{circumflex over (λ)}|f l | 2 J l,l . 
     Lastly, the LOS indicator per tap is computed as 
     
       
         
           
             
               
                 
                   
                     
                       
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                           max 
                           
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     where q ¬l =[  . . . , q l ,   . . . , q S ] T  and A=diag(α). 
     In the following an architecture implementation according to an example embodiment of the disclosure will be described in more detail with reference to  FIG.  3   . and a method applying the equations (3)-(7) with reference to  FIG.  4   . The sample collector  310  collects 403 samples obtained from the received signal by demodulation. 
     The method is initialized by defining  401  a delay grid by an initializes  302  with chosen resolution, e.g. 5 ns. The matrix Γ toa F tail  can then be generated  402  by a matrix generator  304  and input, together with the received samples, to a noise estimator  306  and a channel impulse response reconstructor  308 . 
     In accordance with an embodiment of the disclosure, the method then continues with a noise update by the noise estimator  306 , implementing the equation (5) to calculate  404  the noise precision estimate value {circumflex over (λ)}. The channel impulse response reconstructor  308  receives the noise precision estimate value and calculates  405  the channel gains beliefs v(α l ) for 1 channels and LOS indicators   per each tap by implementing an inner loop, ping-ponging between updates according to the equations (6) and (7) for a number of N_inner times  406 . In other words, the channel impulse response reconstructor  308  estimates 312 channel gains with the equation (6) and LOS indicators  314  for each tap q with the equation (7). This is repeated N_inner times. One sequence of updates of the equation (5) and N_inner repetitions (updates) of {(6, (7))} can be called as a turbo loop in this description. The method can implement N_outer turbo loops  407 . After the N_outer turbo loops (i.e. N_outer repetitions of the equation (5) and N_inner repetitions of {(6, (7))}), the CIR reconstructor  316  outputs  408  a channel impulse response (CIR) vector characterized by (delay, amplitude, phase), where the amplitude is the real-valued gain multiplied by the LOS indicator q. 
     In accordance with an embodiment, the updates of the equations (6) and (7) (the inner loop  406 ) and/or the turbo loop  407  may be performed sequentially and/or in parallel until a predetermined criteria is fulfilled. Such criteria may be, for example, that the output of one or both of the equations (i.e. an estimate of one or more of the random variables) has converged towards or to a value which does not change during further repetitions or the change is less than a threshold. 
     According to some embodiments of the disclosure some advantages may be obtained. For example, joint delay and LOS probability indication per tap may be obtained and flexibility may be achieved due to trading complexity for performance via the model selection, e.g. delay grid resolution, method convergence criteria. Increased accuracy positioning via multipath gain and LOS indication reporting may also follow from the implementation of the method. 
       FIG.  6    depicts examples of simplified system architectures only showing some elements and functional entities, all being logical units, whose implementation may differ from what is shown. The connections shown in  FIG.  6    are logical connections; the actual physical connections may be different. It is apparent to a person skilled in the art that the system typically comprises also other functions and structures than those shown in  FIG.  6   . 
     The embodiments are not, however, restricted to the system given as an example but a person skilled in the art may apply the solution to other communication systems provided with necessary properties. 
     The example of  FIG.  6    shows a part of an exemplifying radio access network. 
       FIG.  6    shows user equipments  110   a  and  110   b  configured to be in a wireless connection on one or more communication channels in a cell with an access node (such as (e/g)NodeB)  104  providing the cell. The physical link from a user equipment to a (e/g)NodeB is called uplink (UL) or reverse link and the physical link from the (e/g)NodeB to the user equipment is called downlink (DL) or forward link. It should be appreciated that (e/g)NodeBs or their functionalities may be implemented by using any node, host, server or access point etc. entity suitable for such a usage. 
     A communication system typically comprises more than one (e/g)NodeB in which case the (e/g)NodeBs may also be configured to communicate with one another over links, wired or wireless, designed for the purpose. These links may be used for signaling purposes. The (e/g)NodeB is a computing device configured to control the radio resources of communication system it is coupled to. The NodeB may also be referred to as a base station, an access point or any other type of interfacing device including a relay station capable of operating in a wireless environment. The (e/g)NodeB includes or is coupled to transceivers. From the transceivers of the (e/g)NodeB, a connection is provided to an antenna unit that establishes bi-directional radio links to user equipments. The antenna unit may comprise a plurality of antennas or antenna elements. The (e/g)NodeB is further connected to core network  109  (CN or next generation core NGC). Depending on the system, the counterpart on the CN side can be a serving gateway (S-GW, routing and forwarding user data packets), packet data network gateway (P-GW), for providing connectivity of user equipments (UEs) to external packet data networks, or mobile management entity (MME), etc. The CN may comprise network entities or nodes that may be referred to as management entities. Examples of the network entities comprise at least an Access management Function (AMF). 
     The user equipment (also called a user device, a user terminal, a terminal device, a wireless device, a mobile station (MS) etc.) illustrates one type of an apparatus to which resources on the air interface are allocated and assigned, and thus any feature described herein with a user equipment may be implemented with a corresponding network apparatus, such as a relay node, an eNB, and an gNB. An example of such a relay node is a layer  3  relay (self-backhauling relay) towards the base station. 
     The user equipment typically refers to a portable computing device that includes wireless mobile communication devices operating with or without a subscriber identification module (SIM), including, but not limited to, the following types of devices: a mobile station (mobile phone), smartphone, personal digital assistant (PDA), handset, device using a wireless modem (alarm or measurement device, etc.), laptop and/or touch screen computer, tablet, game console, notebook, and multimedia device. It should be appreciated that a user equipment may also be a nearly exclusive uplink only device, of which an example is a camera or video camera loading images or video clips to a network. A user equipment may also be a device having capability to operate in Internet of Things (IoT) network which is a scenario in which objects are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. The user equipment may also utilize cloud. In some applications, a user equipment may comprise a small portable device with radio parts (such as a watch, earphones or eyeglasses) and the computation is carried out in the cloud. The user equipment (or in some embodiments a layer  3  relay node) is configured to perform one or more of user equipment functionalities. The user equipment may also be called a subscriber unit, mobile station, remote terminal, access terminal, user terminal or user equipment (UE) just to mention but a few names or apparatuses. 
     Various techniques described herein may also be applied to a cyber-physical system (CPS) (a system of collaborating computational elements controlling physical entities). CPS may enable the implementation and exploitation of massive amounts of interconnected ICT devices (sensors, actuators, processors microcontrollers, etc.) embedded in physical objects at different locations. Mobile cyber physical systems, in which the physical system in question has inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals. 
     Additionally, although the apparatuses have been depicted as single entities, different units, processors and/or memory units (not all shown in  FIG.  6   ) may be implemented. 
     5G enables using multiple input-multiple output (MIMO) antennas, many more base stations or nodes than the LTE (a so-called small cell concept), including macro sites operating in co-operation with smaller stations and employing a variety of radio technologies depending on service needs, use cases and/or spectrum available. 5G mobile communications support a wide range of use cases and related applications including video streaming, augmented reality, different ways of data sharing and various forms of machine type applications (such as (massive) machine-type communications (mMTC), including vehicular safety, different sensors and real-time control. 5G is expected to have multiple radio interfaces, namely below 6 GHz, cmWave and mmWave, and also capable of being integrated with existing legacy radio access technologies, such as the LTE. Integration with the LTE may be implemented, at least in the early phase, as a system, where macro coverage is provided by the LTE and 5G radio interface access comes from small cells by aggregation to the LTE. In other words, 5G is planned to support both inter-RAT operability (such as LTE-5G) and inter-RI operability (inter-radio interface operability, such as below 6 GHz-cmWave, below 6 GHz-cmWave-mmWave). One of the concepts considered to be used in 5G networks is network slicing in which multiple independent and dedicated virtual sub-networks (network instances) may be created within the same infrastructure to run services that have different requirements on latency, reliability, throughput and mobility. 
     The current architecture in LTE networks is fully distributed in the radio and fully centralized in the core network. The low latency applications and services in 5G require to bring the content close to the radio which leads to local break out and multi-access edge computing (MEC). 5G enables analytics and knowledge generation to occur at the source of the data. This approach requires leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets and sensors. MEC provides a distributed computing environment for application and service hosting. It also has the ability to store and process content in close proximity to cellular subscribers for faster response time. Edge computing covers a wide range of technologies such as wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing also classifiable as local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented and virtual reality, data caching, Internet of Things (massive connectivity and/or latency critical), critical communications (autonomous vehicles, traffic safety, real-time analytics, time-critical control, healthcare applications). 
     The communication system is also able to communicate with other networks, such as a public switched telephone network or the Internet  102 , or utilize services provided by them. The communication network may also be able to support the usage of cloud services, for example at least part of core network operations may be carried out as a cloud service (this is depicted in  FIG.  6    by “cloud”  102 ). The communication system may also comprise a central control entity, or a like, providing facilities for networks of different operators to cooperate for example in spectrum sharing. 
     Edge cloud may be brought into radio access network (RAN) by utilizing network function virtualization (NFV) and software defined networking (SDN). Using edge cloud may mean access node operations to be carried out, at least partly, in a server, host or node operationally coupled to a remote radio head or base station comprising radio parts. It is also possible that node operations will be distributed among a plurality of servers, nodes or hosts. Application of cloudRAN architecture enables RAN real time functions being carried out at the RAN side (in a distributed unit, DU  104 ) and non-real time functions being carried out in a centralized manner (in a centralized unit, CU  108 ). 
     It should also be understood that the distribution of labor between core network operations and base station operations may differ from that of the LTE or even be non-existent. Some other technology advancements probably to be used are Big Data and all-IP, which may change the way networks are being constructed and managed. 5G (or new radio, NR) networks are being designed to support multiple hierarchies, where MEC servers can be placed between the core and the base station or nodeB (gNB). It should be appreciated that MEC can be applied in 4G networks as well. The gNB is a next generation Node B (or, new Node B) supporting the 5G network (i.e., the NR). 
     5G may also utilize satellite communication to enhance or complement the coverage of 5G service, for example by providing backhauling. Possible use cases are providing service continuity for machine-to-machine (M2M) or Internet of Things (IoT) devices or for passengers on board of vehicles, or ensuring service availability for critical communications, and future railway/maritime/aeronautical communications. Satellite communication may utilize geostationary earth orbit (GEO) satellite systems, but also low earth orbit (LEO) satellite systems, in particular mega-constellations (systems in which hundreds of (nano)satellites are deployed). Each satellite  106  in the mega-constellation may cover several satellite-enabled network entities that create on-ground cells. The on-ground cells may be created through an on-ground relay node  104  or by a gNB located on-ground or in a satellite. 
     It is obvious for a person skilled in the art that the depicted system is only an example of a part of a radio access system and in practice, the system may comprise a plurality of (e/g)NodeBs, the user equipment may have an access to a plurality of radio cells and the system may comprise also other apparatuses, such as physical layer relay nodes or other network elements, etc. At least one of the (e/g)NodeBs or may be a Home(e/g)nodeB. Additionally, in a geographical area of a radio communication system a plurality of different kinds of radio cells as well as a plurality of radio cells may be provided. Radio cells may be macro cells (or umbrella cells) which are large cells, usually having a diameter of up to tens of kilometers, or smaller cells such as micro-, femto- or picocells. The (e/g)NodeBs of  FIG.  6    may provide any kind of these cells. A cellular radio system may be implemented as a multilayer network including several kinds of cells. Typically, in multilayer networks, one access node provides one kind of a cell or cells, and thus a plurality of (e/g)NodeBs are required to provide such a network structure. 
     For fulfilling the need for improving the deployment and performance of communication systems, the concept of “plug-and-play” (e/g)NodeBs has been introduced. Typically, a network which is able to use “plug-and-play” (e/g)Node Bs, includes, in addition to Home (e/g)NodeBs (H(e/g)nodeBs), a home node B gateway, or HNB-GW (not shown in  FIG.  6   ). A HNB Gateway (HNB-GW), which is typically installed within an operator&#39;s network may aggregate traffic from a large number of HNBs back to a core network. 
     Embodiments may be implemented in software, hardware, application logic or a combination of software, hardware and application logic. The software, application logic and/or hardware may reside on memory, or any computer media. In an example embodiment, the application logic, software or an instruction set is maintained on any one of various conventional computer-readable media. In the context of this document, a “memory” or “computer-readable medium” may be any media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. 
     Reference to, where relevant, “computer-readable storage medium”, “computer program product”, “tangibly embodied computer program” etc., or a “processor” or “processing circuitry” etc. should be understood to encompass not only computers having differing architectures such as single/multi-processor architectures and sequencers/parallel architectures, but also specialized circuits such as field programmable gate arrays FPGA, application specify circuits ASIC, signal processing devices and other devices. References to computer readable program code means, computer program, computer instructions, computer code etc. should be understood to express software for a programmable processor firmware such as the programmable content of a hardware device as instructions for a processor or configured or configuration settings for a fixed function device, gate array, programmable logic device, etc. 
     Although the above examples describe embodiments of the invention operating within a wireless device or a gNB, it would be appreciated that the invention as described above may be implemented as a part of any apparatus comprising a circuitry in which radio frequency signals are transmitted and/or received. Thus, for example, embodiments of the invention may be implemented in a mobile phone, in a base station, in a computer such as a desktop computer or a tablet computer comprising radio frequency communication means (e.g. wireless local area network, cellular radio, etc.). 
     In general, the various embodiments of the invention may be implemented in hardware or special purpose circuits or any combination thereof. While various aspects of the invention may be illustrated and described as block diagrams or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof. 
     Embodiments of the inventions may be practiced in various components such as integrated circuit modules, field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), microcontrollers, microprocessors, a combination of such modules. The design of integrated circuits is by and large a highly automated process. Complex and powerful software tools are available for converting a logic level design into a semiconductor circuit design ready to be etched and formed on a semiconductor substrate. 
     Programs, such as those provided by Synopsys, Inc. of Mountain View, California and Cadence Design, of San Jose, California automatically route conductors and locate components on a semiconductor chip using well established rules of design as well as libraries of pre stored design modules. Once the design for a semiconductor circuit has been completed, the resultant design, in a standardized electronic format (e.g., Opus, GDSII, or the like) may be transmitted to a semiconductor fabrication facility or “fab” for fabrication. 
     As used in this application, the term “circuitry” may refer to one or more or all of the following:
         (a) hardware-only circuit implementations (such as implementations in only analogue and/or digital circuitry) and   (b) combinations of hardware circuits and software, such as (as applicable):
           (i) a combination of analogue and/or digital hardware circuit(s) with software/firmware and   (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and   
           (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.       

     This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device. 
     In the following, a list of acronyms used in this specification are provided: 
     3GPP—3 rd  Generation Partnership Project 
     4G-LTE—Long Term Evolution 
     5G—5th Generation 
     AOA Angle of Arrival 
     AOD Angle of Departure 
     BS Base station 
     BW Bandwidth 
     CFR—Channel Frequency Response 
     CU—Central Unit 
     DL—Downlink 
     DSP—Digital Signal Processor 
     DU—Distributed Unit 
     eNB—evolved NodeB 
     FPGA—Field-programmable gate array 
     gNB—evolved NodeB 
     GEO—Geostationary Earth Orbit 
     HNB-GW—Home Node B Gateway 
     IoT—Internet of Things 
     LEO—Low Earth Orbit 
     LMC—Location Management Component 
     LMF—Location Management Function 
     LOS—Line of Sight 
     LOS—Long Time Evolution 
     M2M—Machine-to-Machine 
     MFT—Mean Field Theory 
     MIMO—Multiple Input-Multiple Output 
     MME—Mobility Management Entity 
     mMTC—(massive) Machine-type Communications 
     MSE—Mean Square Error 
     NGC—Next Generation Core 
     NLOS—Non-Line of Sight 
     NR—New Radio 
     OFDM—Orthogonal Frequency Division Multiplexing 
     pdf—Probability Density Function 
     PDP—Power Delay Profile 
     PRS Positioning Reference Signals 
     RAN—Radio Access Network 
     RAT—Radio Access Technology 
     RRC—Radio Resource Control 
     RRH—Remote Radio Head 
     RU—Radio Unit 
     SGW—Serving Gateway 
     SIM—Subscriber Identification Module 
     SMF—Session Management Function 
     SNR—Signal to Noise Ratio 
     TDOA—Time Difference of Arrival 
     TOA—Time of Arrival 
     TRP—Transmission Reception Point 
     UE—User Equipment 
     UL—Uplink 
     UPF—User Plane Function 
     V-TRP Virtual Transmission/Reception Point 
     The foregoing description has provided by way of exemplary and non-limiting examples a full and informative description of the exemplary embodiment of this invention. However, various modifications and adaptations may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings and the appended claims. However, all such and similar modifications of the teachings of this invention will still fall within the scope of this invention.