Signaling of scaled 3D position information

Abstract

Systems and methods for signaling information related to wireless device positioning in a cellular communications network are disclosed. In some embodiments, a method of operation of a first network node for signaling information related to wireless device positioning in a cellular communications network comprises signaling, from the first network node to a second node, information comprising information that relates to a new vertical surface model for a plurality of cells in a cellular communications network. The new vertical surface model is a translated and scaled version of an initial vertical surface model.

Claims

1. A method of operation of a first network node for signaling information related to wireless device positioning in a cellular communications network, comprising: generating a new vertical surface model fora plurality of cells in a cellular communications network, the new vertical surface model being a translated and scaled version of an initial vertical surface model; and signaling, from the first network node to a second node, information that represents the new vertical surface model for the plurality of cells in the cellular communications network, wherein the information that represents the new vertical surface model comprises: information that defines the initial vertical surface model for the plurality of cells in the cellular communications network, where for each cell of the plurality of cells, the initial vertical surface model: is based on a polygon that represents the cell where the polygon comprises plurality of corners augmented with altitude values; and defines altitudes for positions at points within an interior of the polygon; and information that defines a transformation of the initial vertical surface model into a new vertical surface model, wherein, for each cell of the plurality of cells, the information that defines the transformation of the initial vertical surface model into the new vertical surface model comprises: a translation vector that defines a translation of augmented corners in three-dimensional space; and a three-dimensional scaling matrix; wherein, together, the translation vector and the three-dimensional scaling matrix define the transformation of the initial vertical surface model into the new vertical surface model.

2. The method of claim 1 wherein the information that defines the initial vertical surface model comprises, for each cell of the plurality of cells: information that defines the polygon that represents the cell in a horizontal dimension, the information that defines the polygon comprising information that defines a latitude and longitude of each of the plurality of corners of the polygon; and information that defines a plurality of altitude values for the plurality of corners of the polygon, respectively, where together the plurality of altitude values and the plurality of corners form augmented corners of the polygon.

3. The method of claim 2 wherein the information that relates to the new vertical surface model further comprises a new format indicator.

4. The method of claim 2 wherein the information that relates to the new vertical surface model further comprises: a model degree of an x-coordinate; a model degree of a y-coordinate; and an indicator of whether the information comprises information regarding virtual boundary points in addition to the plurality of corners of the polygon that represent the cell.

5. The method of claim 1 wherein the information that defines the initial vertical surface model comprises, for each cell of the plurality of cells: information that defines the polygon that represents the cell in a horizontal dimension, the information that defines the polygon comprising information that defines a latitude and longitude of each of the plurality of corners of the polygon; and a bi-polynomial parameter vector that comprises a plurality of unknowns in the initial vertical surface model.

6. The method of claim 5 wherein the information that relates to the new vertical surface model further comprises a new format indicator.

7. The method of claim 5 wherein the information that relates to the new vertical surface model further comprises: a model degree of an x-coordinate; a model degree of a y-coordinate; and an indicator of whether the information comprises information regarding virtual boundary points in addition to the plurality of corners of the polygon that represent the cell.

8. The method of claim 1 wherein the first network node is a positioning node and the second node is a wireless device or a radio access node, and wherein if the second node is a wireless device, signaling the information comprises signaling the information via a Long Term Evolution Positioning Protocol, LPP, interface or its Fifth Generation, 5G, counterpart, and wherein if the second node is a radio access node, signaling the information via a Long Term Evolution Positioning Protocol Annex, LPPa, interface or its Fifth Generation, 5G, counterpart.

9. The method of claim 1 wherein the first network node is a positioning node and the second node is an end user, and wherein signaling the information comprises signaling the information via an end user interface.

10. The method of claim 1 wherein the first network node is a positioning node and the second node is a Public Safety Answering Point, PSAP, or other emergency center, and wherein signaling the information comprises signaling the information via one or more PSAP interfaces for E-911 signaling.

11. The method of claim 1 wherein the first network node is a positioning node and the second node is a positioning database in a Radio Access Network, RAN, or Core Network, CN, of the cellular communications network, and wherein signaling the information comprises signaling the information via a proprietary interface.

12. The method of claim 1 wherein the first network node is a positioning node and the second node is a positioning database in a cloud, and wherein signaling the information comprises signaling the information via an interface to the cloud.

13. The method of claim 1 wherein the first network node is a positioning node located in a cloud and the second node is a positioning database in a Radio Access Network, RAN, or Core Network, CN, of the cellular communications network, and wherein signaling the information comprises signaling the information via an interface from the cloud.

14. The method of claim 1 wherein the first network node is a positioning node located in a cloud or a cloud based positioning database and the second node is an end user, and wherein signaling the information comprises signaling the information via an interface from the cloud to the end user.

15. The method of claim 1 wherein the first network node is a positioning node located in a cloud or a cloud based positioning database and the second node is a Public Safety Answering Point, PSAP, or other emergency center, and wherein signaling the information comprises signaling the information via an interface from the cloud to the PSAP or other emergency center.

16. The method of claim 1 wherein the first network node is a positioning node located in a cloud and the second node is a cloud based positioning database, or wherein the first network node is a cloud based positioning database and the second node is a positioning node located in the cloud, and wherein signaling the information comprises signaling the information via a cloud internal interface.

17. The method of claim 1 wherein the first network node is a cloud based positioning database and the second node is positioning node of a Radio Access Network, RAN, or Core Network, CN, and wherein signaling the information comprises signaling the information via an interface from the cloud to RAN or CN.

18. A first network node for signaling information related to wireless device positioning in a cellular communications network, the first network node adapted to: generate a new vertical surface model fora plurality of cells in a cellular communications network, the new vertical surface model being a translated and scaled version of an initial vertical surface model; and signal, from the first network node to a second node, information that represents the new vertical surface model for the plurality of cells in the cellular communications network, wherein the information that represents the new vertical surface model comprises: information that defines the initial vertical surface model for the plurality of cells in the cellular communications network, where for each cell of the plurality of cells, the initial vertical surface model: is based on a polygon that represents the cell where the polygon comprises plurality of corners augmented with altitude values; and defines altitudes for positions at points within an interior of the polygon; and information that defines a transformation of the initial vertical surface model into a new vertical surface model, wherein, for each cell of the plurality of cells, the information that defines the transformation of the initial vertical surface model into the new vertical surface model comprises: a translation vector that defines a translation of augmented corners in three-dimensional space; and a three-dimensional scaling matrix; wherein, together, the translation vector and the three-dimensional scaling matrix define the transformation of the initial vertical surface model into the new vertical surface model.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

(2) FIG. 1 illustrates the Long Term Evolution (LTE) positioning architecture for the control plane;

(3) FIG. 2 illustrates the LTE user plane positioning architecture;

(4) FIG. 3 illustrates an example part of the time-frequency grid of the LTE downlink that depicts the locations of Positioning Reference Signals (PRSs);

(5) FIG. 4 illustrates an example cell polygon format that gives a geographical extension of a cell;

(6) FIG. 5 illustrates an example data structure for representing a cell polygon;

(7) FIG. 6 illustrates Observed Time Difference of Arrival (OTDOA);

(8) FIG. 7 illustrates one example of a cellular communications network in which embodiments of the present disclosure may be implemented;

(9) FIGS. 8 through 11 provide an illustration of the performance of the new scaled and translated surface model as disclosed herein;

(10) FIGS. 12 through 25 illustrate various example signaling architectures for signaling of information related to the new scaled and translated surface model in accordance with some example embodiments of the present disclosure;

(11) FIGS. 26 through 28 illustrate example embodiments of a radio access node; and

(12) FIGS. 29 and 30 illustrate example embodiments of a User Equipment (UE).

DETAILED DESCRIPTION

(13) The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

(14) Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features, and advantages of the enclosed embodiments will be apparent from the following description.

(15) Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

(16) Radio Node: As used herein, a “radio node” is either a radio access node or a wireless device.

(17) Radio Access Node: As used herein, a “radio access node” or “radio network node” is any node in a radio access network of a cellular communications network that operates to wirelessly transmit and/or receive signals. Some examples of a radio access node include, but are not limited to, a base station (e.g., a New Radio (NR) base station (gNB) in a Third Generation Partnership Project (3GPP) Fifth Generation (5G) NR network or an eNB in a 3GPP Long Term Evolution (LTE) network), a high-power or macro base station, a low-power base station (e.g., a micro base station, a pico base station, a home eNB, or the like), and a relay node.

(18) Core Network Node: As used herein, a “core network node” is any type of node in a core network. Some examples of a core network node include, e.g., a Mobility Management Entity (MME), a Packet Data Network Gateway (P-GW), a Service Capability Exposure Function (SCEF), or the like.

(19) Wireless Device: As used herein, a “wireless device” is any type of device that has access to (i.e., is served by) a cellular communications network by wirelessly transmitting and/or receiving signals to a radio access node(s). Some examples of a wireless device include, but are not limited to, a User Equipment device (UE) in a 3GPP network and a Machine Type Communication (MTC) device.

(20) Network Node: As used herein, a “network node” is any node that is either part of the radio access network or the core network of a cellular communications network/system.

(21) Note that the description given herein focuses on a 3GPP cellular communications system and, as such, 3GPP terminology or terminology similar to 3GPP terminology is oftentimes used. However, the concepts disclosed herein are not limited to a 3GPP system.

(22) Note that, in the description herein, reference may be made to the term “cell;” however, particularly with respect to 5G NR concepts, beams may be used instead of cells and, as such, it is important to note that the concepts described herein are equally applicable to both cells and beams.

(23) There currently exist certain challenge(s) with respect to positioning in a cellular communications system. In particular, there is no known Time Difference of Arrival (TDOA) positioning method that can provide three-dimensional accuracies meeting E-911 requirements in large rural cells covering hilly terrain, where the sites are coarsely distributed. This also impacts the lawful intercept possibilities since Assisted Global Navigation Satellite System (A-GNSS) can be turned off in mobiles, which is well known to criminals. The hybrid TDOA method proposed in the present disclosure discloses a positioning method with the potential to solve this problem.

(24) In addition, the hybrid positioning method using Geographical Information System (GIS) information of U.S. Pat. No. 7,203,499 does not account for scaling or translation of the lateral variables, the reason for this being listed below, nor does any other such method exist. The method of U.S. Pat. No. 7,203,499 can therefore not be used in a straightforward manner, together with the disclosed surface modeling technique of the present disclosure.

(25) In addition, the surface modeling solution of U.S. Pat. No. 7,676,232 is based on a bi-polynomial model of the surface, in the lateral x and y coordinates. The coefficients of this model determine the vertical model inside of a polygon. However, the solution of the Least Squares (LS) problem that determine the parameters become ill-conditioned, close to singular and produces very poor solutions whenever the variation of x, y, and z is far from 0. In addition, if the mean value of the polygon corner is large, this problem is amplified, rendering severe accuracy problems in practical systems, where said variation is far from zero. This problem is not mentioned, or solved by known solutions.

(26) Still further, the surface modeling solution of U.S. Pat. No. 7,676,232 is obtaining the altitude information of the corners of the polygon from maps, i.e. a GIS system. In case no GIS system is available, the disclosed surface modeling method can therefore not be used.

(27) As shown by the present disclosure, a solution to the problem is to translate and scale the position variables to a suitable range before the problem is solved. To further improve, additional points on the boundary of the polygon may be introduced. In order to signal all needed information to nodes that, e.g., compute UE positions in the radio access network, core network, and the end user, the information about the scaling, translation, and additional points needs to be signaled as well. However, no standardized reporting format or proposed augmented reporting format support this need.

(28) In 5G, it is likely that the position calculation may be moved to the cloud and distributed within the cloud. However, there is no signaling defined in known solutions that allow for signaling of the polygon information, augmented with altitude, together with scaling, translation and additional point parameters.

(29) Certain aspects of the present disclosure and their embodiments may provide solutions to the aforementioned or other challenges. The present disclosure discloses the surface modeling method and the signaling means that solve parts of the above problems. There are actually two problems, namely, (i) a lack of hybrid TDOA methods with needed functionality and (ii) lack of support for preferred signaling. The first problem is addressed by U.S. Provisional Patent Application No. 62/633,287 which is the priority document of PCT Application No. [P74280 WO1], entitled SCALED TDOA 3D POSITIONING, while the second problem is treated in this disclosure. Note that the solutions to the first and second problems addressed by the present disclosure and the parallel disclosure may be used separately (i.e., each in a standalone manner) or used in combination.

(30) In detail, the present disclosure discloses: 1. A new surface modeling method for modeling of the vertical coordinate of the interior of a polygon, the method being based on polygon corners in the horizontal plane augmented with vertical information for each polygon corner, a translation vector and a scaling matrix, and virtual points on the three dimensional (3D) line segment between the augmented corner points. 2. Signaling means and formats for signaling of the original horizontal 3GPP polygon, augmented with altitudes for each corner, a translation vector, a scaling matrix, and an indicator for selection of additional points on the line segments between augmented polygon corners. The signaling means cover the needed signaling between the UE the base station, the positioning node, the end user, and a distribution of the positioning node in the cloud.

(31) FIG. 7 illustrates one example of a cellular communications network 700 in which embodiments of the present disclosure may be implemented. In the embodiments described herein, the cellular communications network 700 is an LTE or 5G NR network, but the present disclosure is not limited thereto. In this example, the cellular communications network 700 includes base stations 702-1 and 702-2, which in LTE are referred to as eNBs and in 5G NR are referred to as gNBs, controlling corresponding macro cells 704-1 and 704-2. The base stations 702-1 and 702-2 are generally referred to herein collectively as base stations 702 and individually as base station 702. Likewise, the macro cells 704-1 and 704-2 are generally referred to herein collectively as macro cells 704 and individually as macro cell 704. The cellular communications network 700 may also include a number of low power nodes 706-1 through 706-4 controlling corresponding small cells 708-1 through 708-4. The low power nodes 706-1 through 706-4 can be small base stations (such as pico or femto base stations) or Remote Radio Heads (RRHs), or the like. Notably, while not illustrated, one or more of the small cells 708-1 through 708-4 may alternatively be provided by the base stations 702. The low power nodes 706-1 through 706-4 are generally referred to herein collectively as low power nodes 706 and individually as low power node 706. Likewise, the small cells 708-1 through 708-4 are generally referred to herein collectively as small cells 708 and individually as small cell 708. The base stations 702 (and optionally the low power nodes 706) are connected to a core network 710.

(32) The base stations 702 and the low power nodes 706 provide service to wireless devices 712-1 through 712-5 in the corresponding cells 704 and 708. The wireless devices 712-1 through 712-5 are generally referred to herein collectively as wireless devices 712 and individually as wireless device 712. The wireless devices 712 are also sometimes referred to herein as UEs.

(33) While not illustrated, the cellular communications network 700 includes positioning nodes (e.g., Evolved Serving Mobile Location Center (E-SMLC) in LTE or similar positioning node in 5G) and, in some embodiments, other nodes such as, e.g., nodes hosting a positioning database in the radio access network or core network. Further, in some embodiments, the cellular communications network 700 may be connected to a Public Safety Answering Point (PSAP) or other emergency center, a cloud-based positioning database, where these nodes may or may not be considered part of the cellular communications network 700.

(34) As described below, system and methods related to positioning of a UE (e.g., a wireless device 712) are disclosed herein. Notably, some aspects of the present disclosure may be performed by or implemented in a network node, which may be referred to herein as a positioning node. While 5G positioning standardization is not close to finalization at the time of writing of the present disclosure, the positioning architecture in 5G may, for example, be similar to that used in 3GPP LTE.

I. NEW SCALED AND TRANSLATED SURFACE MODEL

(35) A. Description of the New Scaled and Translated Surface Model

(36) In some embodiments, a method of generating a new (scaled and translated) surface model is provided. The description of the method first defines the unscaled surface modeling algorithm, after which translation, scaling, and additional points are introduced. The following unscaled bi-polynomial vertical surface model is first selected,

(37) $z=\underset{l=0}{\stackrel{P\left(j\right)}{.Math.}}\underset{m=0}{\stackrel{Q\left(j\right)}{.Math.}}{d}_{l,m,j}{x}^l{y}^m,\left(\begin{array}{ccc}x& y& 0\end{array}\right)\in \mathrm{cell}j$
where z is altitude, P(j) is a binomial degree, Q(j) is another binomial degree, d.sub.l,m,j are the unknowns in the surface model, and x and y are the two dimensional (2D) position variables. Note that the model is here assumed to be Cartesian, i.e. transformed from the latitude-longitude system to a local Cartesian coordinate system, e.g. with the x-axis pointing east, the y-axis pointing north, and the z-axis pointing up, and with the origin somewhere on the global ellipsoidal Earth model used to define latitude and longitude. The above model is linear in the unknowns d.sub.l,m,j that determine the surface. These unknowns will be determined from the points that define the corners of the polygon. Towards that end, note that the surface model above can be written as
z=f.sup.T(x,y)d.sub.j,
f(x,y)=(1 . . . y.sup.Q(j)xxy . . . xy.sup.Q(j). . . x.sup.P(j)y.sup.Q(j)).sup.T,
d.sub.j=(d.sub.0,0,j. . . d.sub.0,Q(j),jd.sub.1,0,j. . . d.sub.1,Q(j),j. . . d.sub.P(j),Q(j),j).sup.T
These equations are in linear regression form and LS modeling is therefore applicable. Assume that the number of points of the polygon fulfills M(j)≥(P(j)+1)(Q(j)+1), where M(j) is the number of corners of the polygon. Then, there are at least as many data points as there are unknowns and the LS problem is normally non-singular. To formulate the LS solution in detail, define the LS modeling error as

(38) $\stackrel{\_}{E}\left(d_j\right)=\underset{i=1}{\overset{M\left(j\right)}{.Math.}}{\left(z_{i,j}-f^T\left(x_{i,j},y_{i,j}\right)d_j\right)}^2.$
Differentiation of the error with respect to d(j), and equating the result to 0, results in the following LS estimate

(39) ${\hat{d}}_j=R_j^{-1}{r}_j,R_j=\underset{i=1}{\overset{M\left(j\right)}{.Math.}}f\left(x_{i,j},y_{i,j}\right)f^T\left(x_{i,j},y_{i,j}\right),r_j=\underset{i=1}{\overset{M\left(j\right)}{.Math.}}{z}_{i,j}f\left(x_{i,j},y_{i,j}\right).$
The altitudes in interior points of the cell are obtained from
z=f.sup.T(x,y){circumflex over (d)}.sub.j,(xy0).sup.T∈cell j.

(40) As stated above, the LS algorithm does work well close to the origin of the Cartesian coordinate system, but it does not work well when the numerical values of x, y, and z become large.

(41) One solution, disclosed here, is to apply a change of coordinates for each cell j, thereby translating the surface modeling problem back to a situation where the numerical properties are good. To do so, a transformation could, e.g., rescale from m to km, or use the maximum distance between two polygon corners in each of the dimensions. As a first step, a subtraction of the 3D mean value of the polygon corners is needed to translate the cell polygon to be centered around zero. The resulting scaling method can then be defined in terms of the scaled corner variables
{tilde over (P)}.sub.i,j.sup.C=({tilde over (x)}.sub.i,j{tilde over (y)}.sub.i,j{tilde over (z)}.sub.i,j).sup.T.
The scaling transformation then becomes

(42) $\left(\begin{array}{c}{\tilde{x}}_{i,j}\\ {\tilde{y}}_{i,j}\\ {\tilde{z}}_{i,j}\end{array}\right)=\left(\begin{array}{ccc}{\sigma }_{x,j}^{-1}& 0& 0\\ 0& {\sigma }_{x,j}^{-1}& 0\\ 0& 0& {\sigma }_{x,j}^{-1}\end{array}\right)\left(\begin{array}{c}{x}_{i,j}-.Math.x_j.Math.\\ y_{i,j}-.Math.y_j.Math.\\ z_{i,j}-.Math.z_j.Math.\end{array}\right),$
where: {tilde over (x)}.sub.i,j is the Cartesian x-coordinate of the i-th scaled and translated polygon corner of the polygon that represents cell j in the vertical surface model; {tilde over (y)}.sub.i,j is the Cartesian y-coordinate value of the i-th scaled and translated polygon corner of the polygon that represents cell j in the vertical surface model; {tilde over (z)}.sub.i,j is the altitude value of the i-th scaled and translated polygon corner of the polygon that represents cell j in the vertical surface model; x.sub.i,j is a Cartesian x-coordinate value of the i-th polygon corner of the polygon that represents cell j in the initial vertical surface model; y.sub.i,j is a Cartesian y-coordinate value of the i-th polygon corner of the polygon that represents cell j in the initial vertical surface model; z.sub.i,j is an altitude value of the i-th polygon corner of the polygon that represents cell j in the initial vertical surface model; <x.sub.j> is a translation value applied to x.sub.i,j; <y.sub.j> is a translation value applied to y.sub.i,j; <z.sub.j> is a translation value applied to z.sub.i,j; σ.sub.x,j.sup.−1 is a scaling factor applied to x.sub.i,j; σ.sub.y,j.sup.−1 is a scaling factor applied to y.sub.i,j; and σ.sub.z,j.sup.−1 is a scaling factor applied to z.sub.i,j.
Further, the translation values and scaling factors are defined as:

(43) $\left(\begin{array}{c}.Math.x_j.Math.\\ .Math.y_j.Math.\\ .Math.z_j.Math.\end{array}\right)=\frac{1}{M\left(j\right)}\underset{i=1}{\overset{M\left(j\right)}{.Math.}}\left(\begin{array}{c}{x}_{i,j}\\ y_{i,j}\\ z_{i,j}\end{array}\right),{\sigma }_{x,j}=c_x\underset{i_1,i_2}{\max }.Math.x_{i_1,j}-x_{i_2,j}.Math.,{\sigma }_{y,j}=c_y\underset{i_1,i_2}{\max }.Math.y_{i_1,j}-y_{i_2,j}.Math.,{\sigma }_{z,j}=c_z\underset{i_1,i_2}{\max }.Math.z_{i_1,j}-z_{i_2,j}.Math..$
Here, M(j) is a number of corners for cell j, c.sub.x, c.sub.y, c.sub.z denote scaling constants that may be used for tuning, max.sub.i.sub.1.sub.,i.sub.2|x.sub.i.sub.1.sub.,j−x.sub.i.sub.2.sub.,j| denotes a search for a maximum distance between any two corner points in the Cartesian x-coordinate plane, max.sub.i.sub.1.sub.,i.sub.2|y.sub.i.sub.1.sub.,j−y.sub.i.sub.2.sub.,j| denotes a search for a maximum distance between any two corner points in the Cartesian y-coordinate plane, and max.sub.i.sub.1.sub.,i.sub.2|z.sub.i.sub.1.sub.,j−z.sub.i.sub.2.sub.,j| denotes a search for a maximum distance between any two corner points in the Cartesian z-coordinate plane. The LS problem defined above is then solved for the scaled and translated corner points. This results in the following scaled and translated surface model
{tilde over (z)}=f.sup.T({tilde over (x)},{tilde over (y)}){tilde over ({circumflex over (d)})}.sub.j,({tilde over (x)} {tilde over (y)} 0).sup.T∈j
where (as naturally follows from the LS problem defined above): {tilde over (x)}, {tilde over (y)}, and {tilde over (z)} are Cartesian x-coordinate, Cartesian y-coordinate, and altitude value, respectively, in the scaled and translated surface model; f({tilde over (x)},{tilde over (y)}) is defined as:
f({tilde over (x)},{tilde over (y)})=(1 . . . {tilde over (y)}.sup.Q(j){tilde over (x)}{tilde over (x)}{tilde over (y)}. . . {tilde over (x)}{tilde over (y)}.sup.Q(j). . . {tilde over (x)}.sup.P(j){tilde over (y)}.sup.Q(j)).sup.T {tilde over ({circumflex over (d)})}.sub.j is defined as:

(44) ${\hat{\tilde{d}}}_j={\tilde{R}}_j^{-1}{\tilde{r}}_j,{\tilde{R}}_j=\underset{i=1}{\overset{M\left(j\right)}{.Math.}}f\left({\tilde{x}}_{i,j},{\tilde{y}}_{i,j}\right)f^T\left({\tilde{x}}_{i,j},{\tilde{y}}_{i,j}\right),{\tilde{r}}_j=\underset{i=1}{\overset{M\left(j\right)}{.Math.}}{\tilde{z}}_{i,j}f\left({\tilde{x}}_{i,j},{\tilde{y}}_{i,j}\right).$

(45) In order to use the result in original coordinates, a back transformation results in the following interior surface model of the original augmented polygon

(46) $z=.Math.z_j.Math.+{\sigma }_{z,j}{f}^T\left(\frac{x-.Math.x_j.Math.}{{\sigma }_{x,j}},\frac{y-.Math.y_j.Math.}{{\sigma }_{y,j}}\right){\hat{\tilde{d}}}_j,{\left(\begin{array}{ccc}x& y& 0\end{array}\right)}^T\in \mathrm{cell}j.$

(47) This end result is useful to solve practical problems in a cellular network. In case P(j)=Q(j)=3, 16 corners are needed to avoid a singular LS problem, leading to a need to add points on the polygon boundary. Numerical experiments also show that an augmentation of the problem with additional polygon boundary points on the 3D line segments in between polygon corners can provide significantly improved interior surface models. It is therefore recommended to introduce one or several equidistant additional points for every 3D line segment of the augmented polygon. Note that these points are uniquely defined by the information encoded by the augmented polygon format and no additional signaling is therefore required.

(48) It needs to be kept in mind that also other schemes for addition of points on the boundary can be used. This is treated in the parallel disclosure.

(49) B. Performance Illustration

(50) In order to obtain an indication of the accuracy of the proposed method given a rural 10 cell geometry was set up on the artificial topography of FIG. 8. The topography is intended to model a coastal region, with zero altitude representing the sea level. This represents an additional modeling difficulty, since some of the cells cover both the sea and the hilly terrain. The cells are depicted with overlaid 3D lines. The LS algorithm was then used to compute the 3D surfaces of each cell, applying scaling according to the above description. A simplified approach was used, with horizontal down scaling a factor of 1000 and vertical down scaling a factor of 100. Additional boundary points, centered on the connecting line segments between corners, were used for regularization and the bipolynomial degrees were selected as P(j)=Q(j)=3,j=1, . . . , 10. The promising result is depicted in FIG. 9. To further visualize the performance, vertical errors were computed on a grid covering the topography of FIG. 8. The vertical errors can be obtained directly by a comparison between the true altitude and that obtained by the surface model. The results are depicted in FIG. 10. The statistical result depicted by the Cumulative Distribution Function (CDF) of FIG. 11 was computed only for points in the interior of each polygon. As can be seen, the proposed modeling method is remarkably accurate and reduces the maximal vertical errors with about 80%. For the treated topography the vertical Minimum Mean Square Error (MMSE) is less than 3 meters in almost 50% of the coverage of the cells. This indicates that the Federal Communications Commission (FCC) E-911 positioning requirements can be met by the new method, also in quite difficult scenarios.

II. NEW SIGNALING

(51) A. New Signaling Formats

(52) It is first noted that the new 3D surface model, defined on the interior support of the original 3GPP 2D (horizontal) Geographical Area Description (GAD) polygon, is uniquely defined by any of the following combinations of additional parameters, where (M) stands for mandatory for the format and (O) stands for optional for the format:

(53) New 3D interior Polygon format 1: New format indicator ‘Polygon with 3D interior surface’ (M) Latitude, longitude list, encoded as in ‘Polygon’ format of 3GPP Technical Specification (TS) 23.032 (M) Altitude list, for each corner of the ‘Polygon’ format (see 3GPP TS 23.032), encoded as ‘altitude’ of 3GPP TS 23.032 (M) Model degree of x-coordinate, new integer format (M) Model degree of y-coordinate, new integer format (M) Translation vector, (x y z).sup.T, new encoding (M) 3D scaling matrix S, new encoding (M) Virtual boundary point addition principle indicator, new encoding (M) Confidence, new encoding (0) Vertical inaccuracy. New encoding (0). New 3D interior Polygon format 2: New format indicator ‘Polygon with 3D interior surface’ (M) Latitude, longitude list, encoded as in ‘Polygon’ format of 3GPP TS 23.032 (M) Model degree of x-coordinate, new integer format (M) Model degree of y-coordinate, new integer format (M) Bi-polynomial parameter vector {tilde over ({circumflex over (d)})}.sub.j (M) Translation vector, (x y z).sup.T, new encoding (M) 3D scaling matrix S, new encoding (M) Virtual boundary point addition principle indicator, new encoding (M) Confidence, new encoding (0) Vertical inaccuracy. New encoding (0).

(54) Mandatory means that the information is needed to uniquely define the interior surface model; however, it needs to be noted that some of the information may also be implicitly defined. As an example that can be done for ‘Model Degree x’ and ‘Model Degree y’ by defining a structure for {tilde over ({circumflex over (d)})}.sub.i in the standard, such that ‘Model Degree x’ and ‘Model degree y’ follow from the filled in/not filled in elements of said structure.

(55) Optional means that said information is not needed to uniquely define the surface model; however, the optional information rather carries additional useful information.

(56) The confidence is a statistical quantity, defined as the probability that the UE is actually found in the reported region.

(57) Finally note that the above two formats ‘Polygon with 3D interior surface’ could be combined in other ways than the ones listed here. Other combinations obtained with other models than a bipolynomial model could also be developed. However, as is understood by anyone skilled in the art, such variations are merely variations of the signaling means disclosed by the present disclosure and are therefore to be considered as covered by the present disclosure.

(58) B. New Signaling Architectures

(59) The surface modeling is implementable in limited form in a positioning node like the E-SMLC of LTE. However, there are a number of situations that may require standardization, to enable signaling of ‘Polygon with 3D interior surface’ over cellular related interfaces. These are listed in the following sub-sections, mainly using figures.

(60) The involved nodes are: The Fourth Generation (4G) or 5G positioning node in the Radio Access Network (RAN) or core network, e.g. the LTE E-SMLC. The 4G or 5G positioning database in the RAN or core network, e.g. associated with the LTE E-SMLC The UE PSAP node or a general emergency center The end user—can be any kind of positioning application The cloud based 4G or 5G positioning node—in case 5G or 4G positioning nodes would be located in the cloud. The cloud based 4G or 5G positioning data base—in case positioning information would be stored in the cloud. This data base would contain at least all information for generation of the surface model for all cells handled by said data base.

(61) Finally note that although the present disclosure is described with 4G and 5G nodes and interfaces, it is directly applicable to WiFi and future 3GPP cellular systems. It is also applicable for the Internet of Things (IoT) positioning standards.

(62) i. Signaling Over LTE Positioning Protocol (LPP) in LTE and the Corresponding Interface in 5G

(63) By signaling to the UE, further distribution, e.g. to Google maps via UE proprietary interfaces, become possible, as may Secure User Plane Location (SUPL) E-911 reporting from the UE, see FIG. 12. In this example, information that defines the new 3D surface model is signaled from a positioning node 1200 to a UE 1202 (e.g., a wireless device 712) using the LPP interface or its 5G counterpart. The positioning node 1200 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(64) ii. Signaling Over LPP Annex (LPPa) in LTE and the Corresponding Interface in 5G

(65) See FIG. 13. In this example, information that defines the new 3D surface model is signaled from a positioning node 1300 to an eNB or gNB 1302 using the LPPa interface or its 5G counterpart. The positioning node 1300 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(66) iii. Signaling from 4G or 5G Positioning Node in RAN or Core Network (CN) to End User

(67) See FIG. 14. In this example, information that defines the new 3D surface model is signaled from a positioning node 1400 to an end user 1402 via an end user interface. The positioning node 1400 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. The end user 1402 is, e.g., any type of positioning application operating on, e.g., a UE. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(68) iv. Signaling from 4G or 5G Positioning Node in RAN or CN to PSAP Node (Emergency Center)

(69) See FIG. 15. In this example, information that defines the new 3D surface model is signaled from a positioning node 1500 to a PSAP or other emergency center 1502 using the PSAP interfaces for E-911 signaling. The positioning node 1500 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(70) v. Signaling from 4G or 5G Positioning Node in RAN or CN to a Positioning Database in the RAN or the CN

(71) See FIG. 16. In this example, information that defines the new 3D surface model is signaled from a positioning node 1600 to another node 1602 which in this example is a positioning database (e.g., in 4G), a 5G RAN node, or a 5G CN node using a proprietary interface. The positioning node 1600 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(72) vi. Signaling from 4G or 5G Positioning Node in RAN or CN to Positioning Database in Cloud

(73) See FIG. 17. In this example, information that defines the new 3D surface model is signaled from a positioning node 1700 to a positioning database 1702 in the cloud using an interface to the cloud. The positioning node 1700 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(74) vii. Signaling from 4G or 5G Positioning Node Located in the Cloud to Database Associated with Positioning Node in RAN or CN

(75) See FIG. 18. In this example, information that defines the new 3D surface model is signaled from a positioning node 1800 to a positioning database 1802 in a 4G or 5G RAN or CN using an interface to a cloud. The positioning node 1800 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(76) viii. Signaling from Cloud Based 4G or 5G Positioning Node to End User

(77) See FIG. 19. In this example, information that defines the new 3D surface model is signaled from a positioning node 1900 located in the cloud to an end user 1902 using an interface from the cloud to the end user. The positioning node 1900 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. The end user 1902 is, e.g., any type of positioning application operating on, e.g., a UE. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(78) ix. Signaling from Cloud Based 4G or 5G Positioning Node to PSAP

(79) See FIG. 20. In this example, information that defines the new 3D surface model is signaled from a positioning node 2000 located in the cloud to a PSAP or emergency center 2002 via an interface from the cloud to the PSAP or emergency center. The positioning node 2000 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(80) x. Signaling from Cloud Based 4G or 5G Positioning Node to Cloud Based Positioning Database

(81) See FIG. 21. In this example, information that defines the new 3D surface model is signaled from a positioning node 2100 located in the cloud to a cloud based positioning database 2102 using a cloud internal interface. The positioning node 2100 is, in this example, a 4G positioning node (e.g., E-SMLC of LTE) or 5G positioning node. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(82) xi. Signaling from Cloud Based Positioning Database to Positioning Node in RAN or CN

(83) See FIG. 22. In this example, information that defines the new 3D surface model is signaled from a cloud based positioning database 2200 to a positioning node 2202 located in the 4G or 5G RAN or CN using an interface to the cloud. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(84) xii. Signaling from Cloud Based Positioning Database to Positioning Node in Cloud

(85) See FIG. 23. In this example, information that defines the new 3D surface model is signaled from a cloud based positioning database 2300 to a 4G or 5G positioning node 2302 located in the cloud using a cloud internal interface. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(86) xiii. Signaling from Cloud Based Positioning Database to End User

(87) See FIG. 24. In this example, information that defines the new 3D surface model is signaled from a cloud based positioning database 2400 to an end user 2402 using an end user interface. The end user 2402 is, e.g., any type of positioning application operating on, e.g., a UE. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

(88) xiv. Signaling from Cloud Based Positioning Database to PSAP Node (Emergency Center)

(89) See FIG. 25. In this example, information that defines the new 3D surface model is signaled from a cloud based positioning database 2500 to a PSAP or emergency center 2502 using an interface from the cloud to the PSAP or emergency center. In general, the signaling includes any information related to the new 3D surface model. In some particular embodiments, the signaling uses either format 1 or format 2 described above.

III. ADDITIONAL IMPLEMENTATION ASPECTS

(90) FIG. 26 is a schematic block diagram of a radio access node 2600 according to some embodiments of the present disclosure. The radio access node 2600 may be, for example, a base station 702 or 706. As illustrated, the radio access node 2600 includes a control system 2602 that includes one or more processors 2604 (e.g., Central Processing Units (CPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and/or the like), memory 2606, and a network interface 2608. In addition, the radio access node 2600 includes one or more radio units 2610 that each includes one or more transmitters 2612 and one or more receivers 2614 coupled to one or more antennas 2616. In some embodiments, the radio unit(s) 2610 is external to the control system 2602 and connected to the control system 2602 via, e.g., a wired connection (e.g., an optical cable). However, in some other embodiments, the radio unit(s) 2610 and potentially the antenna(s) 2616 are integrated together with the control system 2602. The one or more processors 2604 operate to provide one or more functions of a radio access node 2600 as described herein. In some embodiments, the function(s) are implemented in software that is stored, e.g., in the memory 2606 and executed by the one or more processors 2604.

(91) FIG. 27 is a schematic block diagram that illustrates a virtualized embodiment of the radio access node 2600 according to some embodiments of the present disclosure. This discussion is equally applicable to other types of network nodes. Further, other types of network nodes may have similar virtualized architectures.

(92) As used herein, a “virtualized” radio access node is an implementation of the radio access node 2600 in which at least a portion of the functionality of the radio access node 2600 is implemented as a virtual component(s) (e.g., via a virtual machine(s) executing on a physical processing node(s) in a network(s)). As illustrated, in this example, the radio access node 2600 includes the control system 2602 that includes the one or more processors 2604 (e.g., CPUs, ASICs, FPGAs, and/or the like), the memory 2606, and the network interface 2608 and the one or more radio units 2610 that each includes the one or more transmitters 2612 and the one or more receivers 2614 coupled to the one or more antennas 2616, as described above. The control system 2602 is connected to the radio unit(s) 2610 via, for example, an optical cable or the like. The control system 2602 is connected to one or more processing nodes 2700 coupled to or included as part of a network(s) 2702 via the network interface 2608. Each processing node 2700 includes one or more processors 2704 (e.g., CPUs, ASICs, FPGAs, and/or the like), memory 2706, and a network interface 2708.

(93) In this example, functions 2710 of the radio access node 2600 described herein are implemented at the one or more processing nodes 2700 or distributed across the control system 2602 and the one or more processing nodes 2700 in any desired manner. In some particular embodiments, some or all of the functions 2710 of the radio access node 2600 described herein are implemented as virtual components executed by one or more virtual machines implemented in a virtual environment(s) hosted by the processing node(s) 2700. As will be appreciated by one of ordinary skill in the art, additional signaling or communication between the processing node(s) 2700 and the control system 2602 is used in order to carry out at least some of the desired functions 2710. Notably, in some embodiments, the control system 2602 may not be included, in which case the radio unit(s) 2610 communicate directly with the processing node(s) 2700 via an appropriate network interface(s).

(94) In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of radio access node 2600 or a node (e.g., a processing node 2700) implementing one or more of the functions 2710 of the radio access node 2600 in a virtual environment according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

(95) FIG. 28 is a schematic block diagram of the radio access node 2600 according to some other embodiments of the present disclosure. The radio access node 2600 includes one or more modules 2800, each of which is implemented in software. The module(s) 2800 provide the functionality of the radio access node 2600 described herein. This discussion is equally applicable to the processing node 2700 of FIG. 27 where the modules 2800 may be implemented at one of the processing nodes 2700 or distributed across multiple processing nodes 2700 and/or distributed across the processing node(s) 2700 and the control system 2602.

(96) FIG. 29 is a schematic block diagram of a UE 2900 according to some embodiments of the present disclosure. As illustrated, the UE 2900 includes one or more processors 2902 (e.g., CPUs, ASICs, FPGAs, and/or the like), memory 2904, and one or more transceivers 2906 each including one or more transmitters 2908 and one or more receivers 2910 coupled to one or more antennas 2912. In some embodiments, the functionality of the UE 2900 described above may be fully or partially implemented in software that is, e.g., stored in the memory 2904 and executed by the processor(s) 2902.

(97) In some embodiments, a computer program including instructions which, when executed by at least one processor, causes the at least one processor to carry out the functionality of the UE 2900 according to any of the embodiments described herein is provided. In some embodiments, a carrier comprising the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium (e.g., a non-transitory computer readable medium such as memory).

(98) FIG. 30 is a schematic block diagram of the UE 2900 according to some other embodiments of the present disclosure. The UE 2900 includes one or more modules 3000, each of which is implemented in software. The module(s) 3000 provide the functionality of the UE 2900 described herein.

(99) While FIGS. 26 through 30 focus on the radio access node 2600 and the UE 2900, other types of network nodes (e.g., positioning nodes, node implementing a positioning database, etc.) may have a similar architecture. For instance, in general, a network node includes processing circuitry (e.g., similar to the processor(s) of the radio access node 2600 and the UE 2900) configured to cause the network node to perform the functionality of that network node as described herein, e.g., via execution of respective software instructions that are stored in memory. In addition, the network node includes one or more communication interfaces (e.g., a network interface(s) and/or a radio interface(s)). For example, the positioning node 1200 includes processing circuitry that causes the positioning node 1200 to provide signaling to the UE 1202 (e.g., via a respective network interface) as described herein. The same is true of the other network nodes illustrated in FIGS. 13 through 25.

(100) Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and/or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according one or more embodiments of the present disclosure.

(101) While processes in the figures may show a particular order of operations performed by certain embodiments of the present disclosure, it should be understood that such order is exemplary (e.g., alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, etc.).

IV. EXAMPLE EMBODIMENTS

(102) Some example embodiments are as follows.

(103) Embodiment 1: A method of operation of a first network node for signaling information related to wireless device positioning in a cellular communications network, comprising: signaling, from the first network node to a second node, information comprising information that relates to a new vertical surface model for a plurality of cells in a cellular communications network, the new vertical surface model being a translated and scaled version of an initial vertical surface model.

(104) Embodiment 2: The method of embodiment 1 wherein the information that relates to the new vertical surface model comprises: information that defines the initial vertical surface model for the plurality of cells in the cellular communications network, where for each cell of the plurality of cells, the initial vertical surface model: is based on a polygon that represent the cell where the polygon comprises a plurality of corners augmented with altitude values; and defines altitudes for positions at points within the interior of the polygon; and information that defines a transformation of the initial vertical surface model into a new vertical surface model.

(105) Embodiment 3: The method of embodiment 2 wherein, for each cell of the plurality of cells, the information that defines the transformation of the initial vertical surface model into the new vertical surface model comprises: a translation vector that defines a translation of the augmented corners in three-dimensional space; and a three-dimensional scaling matrix; wherein, together, the translation vector and the three-dimensional scaling matrix define the transformation of the initial vertical surface model into the new vertical surface model.

(106) Embodiment 4: The method of embodiment 2 or 3 wherein the information that defines the initial vertical surface model comprises, for each cell of the plurality of cells: information that defines the polygon that represent the cell in a horizontal dimension, the information that defines the polygon comprising information that defines a latitude and longitude of each of the plurality of corners of the polygon; and information that defines a plurality of altitude values for the plurality of corners of the polygon, respectively, where together the plurality of altitude values and the plurality of corners form the augmented corners of the polygon.

(107) Embodiment 5: The method of embodiment 4 wherein the information that relates to the new vertical surface model further comprises a new format indicator.

(108) Embodiment 6: The method of embodiment 4 or 5 wherein the information that relates to the new vertical surface model further comprises: a model degree of x-coordinate; a model degree of y-coordinate; and an indicator of whether the information comprises information regarding virtual boundary points in addition to the corners of the polygons that represent the cells.

(109) Embodiment 7: The method of embodiment 2 or 3 wherein the information that defines the initial vertical surface model comprises, for each cell of the plurality of cells: information that defines the polygon that represent the cell in a horizontal dimension, the information that defines the polygon comprising information that defines a latitude and longitude of each of the plurality of corners of the polygon; and a bi-polynomial parameter vector.

(110) Embodiment 8: The method of embodiment 7 wherein the information that relates to the new vertical surface model further comprises a new format indicator.

(111) Embodiment 9: The method of embodiment 7 or 8 wherein the information that relates to the new vertical surface model further comprises: a model degree of x-coordinate; a model degree of y-coordinate; and an indicator of whether the information comprises information regarding virtual boundary points in addition to the corners of the polygons that represent the cells.

(112) Embodiment 10: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is the wireless device.

(113) Embodiment 11: The method of embodiment 10 wherein signaling the information comprises signaling the information via a LPP interface or its 5G counterpart.

(114) Embodiment 12: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is a radio access node (e.g., a base station).

(115) Embodiment 13: The method of embodiment 12 wherein signaling the information comprises signaling the information via a LPPa interface or its 5G counterpart.

(116) Embodiment 14: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is an end user.

(117) Embodiment 15: The method of embodiment 14 wherein signaling the information comprises signaling the information via an end user interface.

(118) Embodiment 16: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is a PSAP or other emergency center.

(119) Embodiment 17: The method of embodiment 16 wherein signaling the information comprises signaling the information via one or more PSAP interfaces for E-911 signaling.

(120) Embodiment 18: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is a positioning database in a RAN or CN of the cellular communications network.

(121) Embodiment 19: The method of embodiment 18 wherein signaling the information comprises signaling the information via a proprietary interface.

(122) Embodiment 20: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node and the second node is a positioning database in the cloud.

(123) Embodiment 21: The method of embodiment 20 wherein signaling the information comprises signaling the information via an interface to the cloud.

(124) Embodiment 22: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node located in the cloud and the second node is a positioning database in a RAN or CN of the cellular communications network.

(125) Embodiment 23: The method of embodiment 22 wherein signaling the information comprises signaling the information via an interface from the cloud.

(126) Embodiment 24: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node located in the cloud and the second node is an end user.

(127) Embodiment 25: The method of embodiment 24 wherein signaling the information comprises signaling the information via an interface from the cloud to the end user.

(128) Embodiment 26: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node located in the cloud and the second node is a PSAP or other emergency center.

(129) Embodiment 27: The method of embodiment 26 wherein signaling the information comprises signaling the information via an interface from the cloud to the PSAP or other emergency center.

(130) Embodiment 28: The method of any one of embodiments 1 to 9 wherein the first network node is a positioning node located in the cloud and the second node is a cloud based positioning database.

(131) Embodiment 29: The method of embodiment 28 wherein signaling the information comprises signaling the information via a cloud internal interface.

(132) Embodiment 30: The method of any one of embodiments 1 to 9 wherein the first network node is a cloud based positioning database and the second node is positioning node of a RAN or CN.

(133) Embodiment 31: The method of embodiment 30 wherein signaling the information comprises signaling the information via an interface from the cloud to RAN or CN.

(134) Embodiment 32: The method of any one of embodiments 1 to 9 wherein the first network node is cloud based positioning database and the second node is a positioning node located in the cloud.

(135) Embodiment 33: The method of embodiment 32 wherein signaling the information comprises signaling the information via a cloud internal interface.

(136) Embodiment 34: The method of any one of embodiments 1 to 9 wherein the first network node is a cloud based positioning database and the second node is an end user.

(137) Embodiment 35: The method of embodiment 34 wherein signaling the information comprises signaling the information via an interface from the cloud to the end user.

(138) Embodiment 36: The method of any one of embodiments 1 to 9 wherein the first network node is cloud based positioning database and the second node is a PSAP or other emergency center.

(139) Embodiment 37: The method of embodiment 36 wherein signaling the information comprises signaling the information via an interface from the cloud to the PSAP or other emergency center.

(140) Embodiment 38: A first network node for signaling information related to wireless device positioning in a cellular communications network, the first network node adapted to perform the method of any one of embodiments 1 to 37.

(141) At least some of the following abbreviations may be used in this disclosure. If there is an inconsistency between abbreviations, preference should be given to how it is used above. If listed multiple times below, the first listing should be preferred over any subsequent listing(s). 2D Two Dimensional 3D Three Dimensional 3GPP Third Generation Partnership Project 4G Fourth Generation 5G Fifth Generation AECID Adaptive Enhanced Cell Identity A-GNSS Assisted Global Navigation Satellite System A-GPS Assisted Global Positioning System AoA Angle of Arrival AP Access Point ASIC Application Specific Integrated Circuit CDF Cumulative Distribution Function CID Cell Identifier CN Core Network CPU Central Processing Unit DSP Digital Signal Processor E-CID Enhanced Cell Identifier (positioning method) eNB Enhanced or Evolved Node B E-SMLC Evolved Serving Mobile Location Center FCC Federal Communications Commission FPGA Field Programmable Gate Array GAD Geographical Area Description GIS Geographical Information System GLONASS Global Navigation Satellite System GMLC Gateway Mobile Location Center gNB New Radio Base Station GNSS Global Navigation Satellite System GPS Global Positioning System ID Identifier IoT Internet of Things IP Internet Protocol kHz Kilohertz LBS Location Based Services LCS Location Service LMU Location Measurement Unit LPP Long Term Evolution Positioning Protocol LPPa Long Term Evolution Positioning Protocol Annex LS Least Squares LTE Long Term Evolution MBS Metropolitan Beacon System MME Mobility Management Entity MMSE Minimum Mean Square Error mmw Millimeter Wave ms Millisecond MSC Mobile Switching Center MTC Machine Type Communication NR New Radio O&M Operation and Maintenance OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access OTDOA Observed Time Difference of Arrival P-GW Packet Data Network Gateway PRS Positioning Reference Signal PSAP Public Safety Answering Point RAM Random Access Memory RAN Radio Access Network ROM Read Only Memory RRC Radio Resource Control RRH Remote Radio Head SCEF Service Capability Exposure Function SET Secure User Plane Location Enabled Terminal SLC Secure User Plane Location Center SLP Secure User Plane Location Platform SON Self-Organizing Network SPC Secure User Plane Location Positioning Center SRS Sounding Reference Signal SUPL Secure User Plane Location TA Timing Advance TBS Terrestrial Beacon System TCP Transmission Control Protocol TDOA Time Difference of Arrival TOA Time of Arrival TS Technical Specification UE User Equipment U-TDOA Uplink Time Difference of Arrival WGS World Geodetic System

(142) Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.