Multipath estimation and multiosculation detection for positioning

Abstract

Apparatuses, methods, and computer program products are provided. According to one embodiment, a method is disclosed that includes receiving a positioning reference signal from a positioning signal transmitter, forming a delay search space from the received positioning reference signal to obtain a plurality of channel taps, estimating a noise precision of the channel taps, estimating a channel gain of the channel taps, and estimating a probability of a line of sight signal of the channel taps.

Description

TECHNICAL FIELD

[0001] The present invention relates to a method and apparatus for multi-path estimation and multi-line-of-sight detection for positioning. BACKGROUND

[0002] This section is intended to provide background information to facilitate a better understanding of the invention as set forth in the following claims. The description herein can include concepts that can be pursued, but are not necessarily ones that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the description in this section is not prior art to the claims and is not admitted to be prior art by its inclusion in this section.

[0003] 5G-NR (Fifth Generation New Radio) is a new radio access technology developed by the Third Generation Partnership Project (3GPP) for the fifth generation of mobile networks. 5G-NR has been specified within 3GPP to be able to coexist in the same spectrum as 4G-LTE (Long Term Evolution). 5G supports 5G NR positioning, in particular network-based positioning, where the computation of the position estimate of a mobile communication device, which can also be referred to as a user equipment (UE), is performed at the network, at a location management function (LMF).

[0004] One particular aspect of positioning in a multi-path propagation environment is that multiple signal paths can be used in estimating the position of a UE. However, using multiple paths is not straightforward because they are associated with reflections generated by landmarks with unknown positions. If the position of such landmarks is obtained, then the network can be able to successfully employ multi-path measurement reports to improve the position of the UE beyond what standard time of arrival / angle of arrival (TOA / AOA) methods can produce.

[0005] For the purpose of positioning, user equipment non-specifically measures an indicator called time of arrival (TOA). TOA is the shortest time it takes for a signal 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 x c, where c = the speed of light in a vacuum. To compute TOA, many user equipment estimate the power delay profile (PDP) of the wireless propagation channel and select the delay at which the PDP shows a peak as the TOA, as depicted in Figure 7a However, one problem with this approach is that the strongest component does not always correspond to the line-of-sight LOS path (i.e. the direct path), as depicted in Figure 7bIn the example depicted in FIG. 1, the tree attenuates the direct line-of-sight signal, so that the signal reflected by the surrounding buildings is stronger at the location of the receiving user equipment. A user equipment observing this situation can choose the time of arrival as the delay of the non-line-of-sight (NLOS) component, as its power is the highest in this example. This choice will bias the overall position estimate, as this component does not reflect the shortest distance between the gNB and the UE.

[0006] Furthermore, the radio environment is very dynamic, especially in centimeter and millimeter wave bands, and any movement of the UE and / or radio obstacles in the environment will cause a transition from LOS to NLOS conditions. Therefore, in practice, a UE will rarely experience pure LOS or pure NLOS propagation conditions, and determining which of these conditions is dominant in the received signal and for how long can become a computationally intensive task.

[0007] One approach is to cross-correlate between the received and transmitted signals and search for the first energy peak of the resulting signal envelope. However, this detection method is limited in resolution by the signal bandwidth and the noise level. Furthermore, the first tap can not have the strongest energy and, depending on the channel / system conditions, can be mixed in with other taps. Other solution classes for LOS detection use hypothesis testing or machine learning methods 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 the channel impulse response and require extensive domain knowledge for a meaningful selection.

[0008] Extracting an input feature set from the noisy channel impulse response (CIR) estimate obtained from band-limited measurements can be suboptimal, as the selected features are inherently noisy and, therefore, they exhibit misleading dependencies, and limiting the input set to only a few tens of observations does not guarantee that the channel characteristics are fully captured.

[0009] Therefore, there is a need for a mechanism to improve the reliability and accuracy of positioning applications. SUMMARY

[0010] Some embodiments provide a method and apparatus for positioning.

[0011] Some embodiments provide a positioning related method of multi-path reconstruction and multi-LOS detection based on the application of tools from Mean Field Theory (MFT). The method formulates the channel reconstruction problem as estimating the approximate probability density function (pdf) for each variable in the set characterizing each reconstructed multi-path component (delay, phase, amplitude, LOS indicator) using downlink positioning reference signals.

[0012] To implement this method, several choices have been made to formulate an estimation model in which the channel is approximated as a sum of components located on a fine delay grid, where each channel tap is assigned a LOS probability. The delay grid has a 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 can be, for example, the basic time unit of NR (Tc); or the basic time unit of LTE (Ts).

[0013] According to embodiments of the disclosure, each tap is characterized by a line-of-sight probability, a complex gain, and a delay, each tap being modeled as a random variable characterized by a chosen prior probability density function. The taps are assumed to be independently and identically distributed (i.i.d.). Noise precision and / or noise variance pdfs are also estimated assuming an improper noise prior.

[0014] A method has also been derived that applies tools from Mean Field Theory (MFT) on the above model to estimate 1) the channel impulse response, and subsequently the delay and phase of the most probable LOS component, as well as the delays and phases of other relevant components; 2) the LOS probability of each detected channel component; and 3) the level of signal-to-noise ratio (SNR). However, other theories than Mean Field Theory can also be used.

[0015] According to embodiments of the disclosure, the method models and estimates the LOS probability / indicator of each detected component, applies Mean Field Theory tools to jointly estimate multiple LOS indicators, noise levels, and detect multi-path components, from which delays and phases can subsequently be extracted. Furthermore, the method enables the selection of the LOS TOA based on the results of the estimation problem.

[0016] According to a first aspect, there is provided an apparatus comprising:

[0017] means for receiving positioning reference signals from a positioning signal transmitter;

[0018] means for forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps of an estimation model representing a channel;

[0019] means for estimating a noise precision of a channel tap of a noise process of the disrupting signal;

[0020] means for estimating a channel gain of the channel taps; and

[0021] means for estimating a probability of a line of sight signal for each channel tap.

[0022] According to a second aspect, there is provided a method comprising:

[0023] receiving positioning reference signals from a positioning signal transmitter;

[0024] forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimated model of a channel;

[0025] estimating a noise precision of the channel taps of a noise process of a corrupting signal;

[0026] estimating a channel gain of the channel taps; and

[0027] estimating a probability of a line of sight signal for each channel tap.

[0028] 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 at least to perform the following:

[0029] receiving positioning reference signals from a positioning signal transmitter;

[0030] forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimated model of a channel;

[0031] estimating a noise precision of the channel taps of a noise process of a corrupting signal;

[0032] estimating a channel gain of the channel taps; and

[0033] estimating a probability of a line of sight signal for each channel tap.

[0034] According to a fourth aspect, there is provided a computer program comprising computer readable program code which, when executed by at least one processor; causes the apparatus at least to perform the following:

[0035] receiving positioning reference signals from a positioning signal transmitter;

[0036] forming a delay search space from the received positioning reference signals to obtain a plurality of channel taps representing an estimated model of a channel;

[0037] estimating a noise precision of the channel taps of a noise process of a corrupting signal;

[0038] estimating channel gains of the channel taps; and

[0039] estimating a probability of a line of sight signal for each channel tap. BRIEF DESCRIPTION OF DRAWINGS

[0040] For a more complete understanding of example embodiments of the present application, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:

[0041] Figure 1 shows a block diagram in which an example, possible and non-limiting, example can be practiced;

[0042] Figure 2 illustrates an example scenario in which a signal transmitted by a base station arrives at a receiving communication device as a direct line of sight signal and as a signal reflected from an obstacle;

[0043] Figure 3 illustrates an architecture implementation in accordance with example embodiments of the present disclosure;

[0044] Figure 4 shows a method as a flowchart in accordance with embodiments of the present disclosure;

[0045] Figure 5 illustrates a time domain structure of a frame of an OFDM scheme in the downlink direction in accordance with a method;

[0046] Figure 6 shows a portion of an exemplary wireless communication access network in accordance with at least some embodiments;

[0047] Figure 7a shows an example of an estimated power delay profile of a wireless propagation channel; and

[0048] Figure 7b shows an example in which a tree attenuates a direct line of sight signal so that a signal reflected by surrounding buildings is stronger at the location of a receiving user equipment. DETAILED DESCRIPTION

[0049] The following examples are illustrative. Although this description can in places refer to "an" or "one" implementation, or discloses only one implementation, this does not mean every implementation includes the particular feature, or that the feature is important only to a single implementation. Different embodiments can include different features or the same feature at different levels of detail. Not all features of each implementation are necessarily described. Concepts described in this disclosure can be used as elements of the described implementations and / or any number of variations and / or permutations of the described systems. As such, particular embodiments can take a variety of forms.

[0050] It should be noted here that in this specification the term "base station" refers to a logical element that comprises logical communication system layers (e.g. LI, L2, L3). Base stations of different RATs can 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" can be used, these terms do not necessarily mean each existing base station, mobile station or user equipment, but a base station, mobile station or user equipment of a certain area or set. For example, each base station can mean all base stations within a certain geographical area or all base stations of an operator of a wireless communication network or a subset of base stations of an operator of a wireless communication network.

[0051] Figure 1 A block diagram of one possible and non-limiting example in which examples can be practiced is shown. A user equipment (UE) 110, a radio access network (RAN) node 170, and network element(s) 190 are illustrated. In Figure 1 In the example of FIG. 1, a user equipment 110 is in wireless communication with a wireless network 100. The 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 can be address, data, or control buses, and can include any interconnection mechanism, such as a series of lines on a motherboard or integrated circuit, fiber optics or other optical communication devices, 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 can be implemented in a number of ways. The module 140 can be implemented as a module 140-1 in hardware, such as part of the one or more processors 120. The module 140-1 can also be implemented as an integrated circuit or through other hardware, such as a programmable gate array. In another example, the module 140 can be implemented as a module 140-2, which is implemented as computer program code 123 and executed by the one or more processors 120. For example, the one or more memories 125 and the computer program code 123 can 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 the RAN node 170 via a wireless link 111. The modules 140-1 and 140-2 can be configured to implement the functionality of a user equipment as described herein.

[0052] For example, the modules 140-1 and / or 140-2 can comprise units (e.g., as computer code) for performing different operations related to the reception and analysis of positioning signals, which will be explained later.

[0053] The RAN node 170 in the present example is a base station that provides access to wireless devices, such as the user equipment 110, to the wireless network 100. Thus, the RAN node 170 (and base station) can also be referred to as an access point of the wireless communication network. The RAN node 170 can be, for example, a base station of 5G (also referred to as New Radio (NR)). In 5G, the RAN node 170 can be an NG-RAN node, which is defined as a gNB or ng-eNB. A gNB is a node that terminates the NR user plane and control plane protocol and connects via an NG interface to a 5GC (e.g., network element(s) 190). An ng-eNB is a node that terminates the E-UTRA user plane and control plane protocol and connects via an NG interface to a 5GC. The NG-RAN node can comprise a plurality of gNBs, which can also include a central unit (CU) (gNB-CU) 196 and distributed unit(s) (DU) (gNB-DU), of which a DU 195 is shown. Note that the DU 195 can include or be coupled to and control a radio unit (RU). The gNB-CU 196 is a logical node hosting the radio resource control (RRC), SDAP, and PDCP protocols of a gNB or the RRC and PDCP protocols of an en-gNB, which controls the operation of one or more gNB-DUs. The gNB-CU 196 terminates the F1 interface with the gNB-DU 195. The F1 interface is illustrated by reference sign 198, although the reference sign 198 also illustrates links 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 the RLC, MAC, and PHY layers of a gNB or en-gNB, and whose operation is controlled in part by the gNB-CU 196. One gNB-CU 196 supports one or more cells. One cell is supported by one gNB-DU 195. The gNB-DU 195 terminates the F1 interface 198 with the gNB-CU 196. Note that the DU 195 is considered to include the transceiver 160, e.g., as part of a RU, although some examples thereof can 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 can also be an eNB (evolved Node B) base station for LTE (Long Term Evolution), or any other suitable base station or node.

[0054] 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 with one or more antennas 158. The one or more memories 155 include computer program code 153. The CU 196 can include the processor(s) 152, the memory(ies) 155, and the network interface 161. Note that the DU 195 can also contain one or more memories and processor(s) and / or other hardware of its own, but these are not shown.

[0055] The RAN node 170 includes a module 150, which can be implemented in a variety of ways, including one or both of artifacts 150-1 and / or 150-2. The module 150 can be implemented in hardware as module 150-1, such as part of the processor(s) 152. The module 150-1 can also be implemented as an integrated circuit or through other hardware such as a programmable gate array. In another example, the module 150 can be implemented as module 150-2, which is implemented as computer program code 153 and executed by the processor(s) 152. For example, the memory(ies) 155 and the computer program code 153 are configured to, with the processor(s) 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 can be distributed, such as between the DU 195 and the CU 196, or implemented only in the DU 195. The module 150-1 and 150-2 can be configured to implement the functionality of the base station as described herein. Such functionality of the base station can include a location management function (LMF) implemented based on the functionality of the LMF described herein. Such an LMF can also be implemented within the RAN node 170 as a location management component (LMC).

[0056] The one or more network interfaces 161 communicate through a network, such as via links 176 and 131. Two or more gNBs 170 can communicate using, for example, link 176. The link 176 can be wired or wireless or both, and can implement, for example, an Xn interface for 5G, an X2 interface for LTE, or other suitable interface for other standards.

[0057] The one or more buses 157 can be address, data, or control buses, and can 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 can be implemented as a remote radio head (RRH) 195 for LTE or a distributed unit (DU) 195 for a gNB implementation for 5G, other elements of the RAN node 170 can be physically located remote from the RRH / DU 195, and the one or more buses 157 can be implemented, in part, as, for example, an optical cable or other suitable network connection to connect the other elements of the RAN node 170 (e.g., central unit (CU), gNB-CU) to the RRH / DU 195. Reference 198 also indicates those suitable network link(s).

[0058] It is noted that the description herein indicates that a“cell” performs functions, but it should be clear that the device forming the cell can perform the functions. A cell constitutes a part of a base station. That is, each base station can have multiple cells. For example, a single carrier frequency and associated bandwidth can have three cells, each covering one third of a 360 degree area, so that the coverage area of a single base station covers an approximate oval or circular shape. Further, each cell can correspond to a single carrier, and a base station can use multiple carriers. Thus, if each carrier has three 120 degree cells, then two carriers, the base station has a total of six cells.

[0059] The wireless network 100 can include one or more network elements 190 that can include core network functionality and provide connectivity to additional networks such as a telephone network and / or a data network (e.g., the Internet) via one or more links 181. Such core network functionality for 5G can include Location Management Function(s) (LMF) and / or Access and Mobility Management Function(s) (AMF) and / or User Plane Function(s) (UPF) and / or Session Management Function(s) (SMF). Such core network functionality for LTE can include MME (Mobility Management Entity) / SGW (Serving Gateway) functionality. These are merely example functions that can be supported by the network element(s) 190, and note that both 5G and LTE functionality can be supported. The RAN nodes 170 are coupled to the network elements 190 via links 131. The links 131 may, for example, be implemented as an NG interface for 5G, or an SI interface for LTE, or other suitable interface for other standards. The network elements 190 include one or more processors 175, one or more memories 171, and one or more network interfaces (N / W I / F) 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 elements 190 to perform one or more operations such as the functionality of an LMF described herein. In some examples, a single LMF can serve a large area covered by hundreds of base stations.

[0060] The wireless network 100 can implement network virtualization, which is the process of combining hardware and software network resources and network functionality into a single, software-based management entity (virtual network). Network virtualization involves platform virtualization, often combined with resource virtualization. Network virtualization can be categorized into two cases: one is external, combining many networks or parts of networks into a virtual unit; the other is internal, providing network-like functionality for software containers on a single system. Note that the virtualized entities that result from network virtualization are still implemented using hardware such as the processors 152 or 175 and memories 155 and 171 to some extent, and such virtualized entities also produce technical effects.

[0061] The computer-readable memories 125, 155, and 171 can be of any type suitable to the local technical environment and can 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 can be means for performing the storage functions described herein. The processors 120, 152, and 175 can be of any type suitable to the local technical environment, and can include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), and processors based on multi-core processor architectures, as non-limiting examples. The processors 120, 152, and 175 can be means for performing the functions described herein and other functions indicated as being performed by a UE 110, RAN node 170, network element 190, and the like.

[0062] The memory can be a computer-readable medium that can be non-transitory. The memory can be of any type suitable to the local technical environment and can 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 processor can be of any type suitable to the local technical environment, and can include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), and processors based on multi-core processor architectures, as non-limiting examples.

[0063] In general, the various embodiments of the user equipment 110 can include, but are not limited to, mobile telephones, tablet computers, 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, tablet computers having wireless communication capabilities, as well as combinations of these and other functions.

[0064] The modules 150-1 and / or 150-2 can implement the functionality and signaling of a gNB or radio node described herein. The computer program code 173 can implement the functionality and signaling of an AMF or network element described herein.

[0065] Figure 2Fig. 1 illustrates an example scenario 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 as well as a signal 204 reflected from an obstacle 201. In other words, the receiving communication device 110 receives the transmitted signal via multiple signal paths. In this example, the signal is reflected by two buildings 201 and arrives at the receiving communication device 110 via Figure 2 the paths 204 illustrated in the middle. In addition to reflected signals, objects can scatter signals, and such scattered signals can also arrive at the receiving communication device 110. In this disclosure, reflected and scattered signals are also referred to as multipath propagated signals.

[0066] Since reflected and scattered signals travel a longer path than the LoS signal, they arrive later at the receiving communication device 110 than the LoS 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 exploited in a UE positioning procedure. However, in order to do so, the position of the obstacle 201 of the reflected and / or scattered signal should be known with a certain accuracy in order to exploit it in the UE positioning procedure.

[0067] In the following, some embodiments are presented which mainly focus on the problem of the influence of multipath propagation on the calculation of the time of arrival.

[0068] The method according to embodiments of the present disclosure is depicted in Figure 4 and will be described in the following. It is assumed that the network comprises a location management function (LMF) 400 - e.g. in a base station 170 or some other network element, and a user equipment 110 is used as a signal receiving entity which obtains information related to multipath propagated signals.

[0069] In 4G LTE and 5G NR, an OFDM (orthogonal frequency division multiplexing) modulation scheme is used. Figure 5 Fig. 2 illustrates the time domain structure of one frame (type I) of an OFDM scheme in the downlink direction according to one approach. The length of one frame is T 帧 and is divided into ten subframes of length T 子帧 Each subframe comprises two slots of length T 时隙 including cyclic prefixes CP#1, CP#2,..., CP#7 and OFDM symbols #1,..., #7.

[0070] In the following description, the location signal transmission (PRS) and reception are described, assuming that the location management function 400 and / or another entity (location signal transmitting unit, location transmitter) are capable of transmitting a location signal and that the location of that entity is known. It should be noted that in a real positioning system, there are multiple location signal transmitting units, but only one such unit is considered in the following description. Furthermore, user equipment 110 is assumed to be the receiving unit, i.e., the device that receives the transmitted location signal.

[0071] To implement the method according to the examples of this disclosure, several choices have been made to create an estimation model in which the channel is approximated as the sum of components located on a fine delay grid based on the received positioning signal, wherein each channel tap is assigned a LOS probability. The delay grid offers flexible resolution, i.e., it is not limited to the system's sampling time, and allows for flexible implementation, trading complexity for performance. The sampling time can, for example, be the basic time unit of NR (T). c ); or the basic time unit of LTE (Ts).

[0072] Assume that the positioning transmitter 400 generates K reference symbols u = [u1, ..., u2]. K ] K These symbols are OFDM modulated (Orthogonal Frequency Division Multiplexing) and have N cp The cyclic prefix of each sample. These samples are transmitted through the channel, and it can be assumed that the channel has an impulse response. Where (α) l , τ l Let α1 be the composite gain and delay of the l-th tap. Without loss of generality, we can assume that α1 is the gain of the LOS component reaching a receiver placed r meters away from the transmitter, and its delay is... This delay can be called the Time of Arrival (TOA). The variable q indicates the existence of LOS and is a Bernoulli distribution variable pr(p=1)=s, pr(p=0)=1-s. In other words, the Bernoulli distribution variable of LOS can be expressed as pr(LOS present)=s, pr(LOS not present)=1-s.

[0073] The estimation problem, which is established using the following basic modeling choices, is presented below. By choosing variable oversampling coefficients G, a variable (fine) resolution delay search space is derived, and each channel tap on the aforementioned delay grid is defined as a random variable consisting of a composite gain component and a LOS indication component.

[0074] Choose a prior probability density function for the assumed channel model and noise variance defined above.

[0075] The receiver 132 of the user equipment 110 receives the OFDM modulated transmission and performs the corresponding OFDM demodulation. After OFDM demodulation, the approximation unit 140-3 of the user equipment 110 performs the following approximation L ~ N cp An approximate model of the received signal can be created such that the model consists of K samples y = [yl,..., y K ] T

[0076] y = Γ toa F tail a + ξ (3)

[0077] where ξ is an additive white Gaussian noise (AWGN) with variance 1 / λ, and G is a chosen oversampling delay factor.

[0078] The matrix Γ toa = D (r) O (d) is a perceived TOA matrix modeling the TOA contribution τ1, and a potential clock offset dT s between the transmitter of the positioning signal transmitter and the receiver 132 of the user equipment on a per subcarrier basis, where T s is the sampling time of the system.

[0079] The matrices D (r) , O (d) ∈ C K×K are diagonal with diagonal entries: modeling the TOA contribution and the offset contribution on a subcarrier, respectively.

[0080] The matrix F tail ∈ C K×S , S = (N cp T s - τ1) / G + 1 models the contribution of the remaining taps (i.e., the tail of the channel impulse response (CIR) vector).

[0081] The CIR vector is a = [q1α1, q2α2,..., q S α S ] f where one can assign a LOS probability to each tap. Then, the LOS vector is q = [q1,..., q S ] stating the LOS probability of the different taps.

[0082] When the user equipment does not know the clock offset dT s , the user equipment aims to estimate the perceived TOA, i.e., t p ​= (t1+ d), and the estimated perceived TOA t p is reported to the network, e.g., to a gNB or TRP node. Thus, the user equipment leaves the task of decoupling d and t1to the network side. In this case, the matrix G toa can be rewritten as This rewritten G toa can now be used in equation (3) to estimate the LOS probability expressed by the vector q, the perceived discrete TOA d p , and the gain of the LOS tap a1.

[0083] To this end, the probability density function of a, q, l is estimated with equation (3), for which the joint probability density function is proportional to:

[0084] p(a, q, l, s, y) a p(y|a, q, l, s) p(l) p(a) p(q|s) p(s) (4)

[0085] It should be noted that by estimating all the channel gains one obtains the corresponding delays if where e is a chosen error floor. In equation (4), the factors in the right-hand side product are:

[0086] p(y|a, q) = CN(G toa F tail a, 1 / l I), CN() is the complex Gaussian distribution,

[0087]

[0088] p(q) = Π p(q l ), p(q l ) = U(0, 1), where U() is the uniform distribution,

[0089] p(a) = CN(0, C), C is a known covariance matrix, e.g., C = I.

[0090] To estimate the unknowns in equation (4), one can apply the mean field theory method, estimating an approximate probability density function of (a, q, l) called belief, and denoted by v(). Then, one can obtain the following expressions.

[0091] Noise precision estimation is:

[0092]

[0093] The 1st channel gain belief is where:

[0094]

[0095] Ω l is the l-th column of the matrix Ω

[0096]

[0097] Finally, the LOS indicator for each tap is computed as

[0098]

[0099] where and A = diag(a).

[0100] In the following, the architecture implementation of the example embodiments according to the present disclosure will be described in more detail with reference to Figure 3 The method of applying equations (3)-(7) will be described with reference to Figure 4 The sample collector 310 collects 403 the samples obtained from the received signal by demodulation.

[0101] The method is initialized by defining 401 a delay grid using a selected resolution (e.g. 5 ns) by the initializer 302. Then, the matrix Γ toa F tail can be generated 402 by the matrix generator 304 and input together with the received samples to the noise estimator 306 and the channel impulse response reconstructor 308.

[0102] According to embodiments of the present disclosure, the method then proceeds with noise updating by the noise estimator 306, implementing equation (5) to compute 404 the noise precision estimate The channel impulse response reconstructor 308 receives the noise precision estimate and computes 405 the channel gain trust v(a l ) and the LOS indicator for each tap for l channels by implementing the inner loop according to equations (6) and (7), ping-ponging N_inner times 406 between updates. In other words, the channel impulse response reconstructor 308 estimates the channel gain 312 with equation (6) and the LOS indicator 314 for each tap q with equation (7). This is repeated N_inner times. One update sequence of equation (5) and N_inner repetitions (updates) of {(6, (7))} can be referred to in this specification as a turbo cycle. This method can implement N_outer turbo cycles 407. After N_outer turbo cycles (i.e. N_outer repetitions of 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 a real-valued gain multiplied by the LOS indicator q.

[0103] According to embodiments, the updates of equations (6) and (7) (inner loop 406) and / or turbo cycles 407 can be performed sequentially and / or in parallel until a predetermined criterion is met. Such criterion can be, for example, that the output of one or both equations (i.e. the estimate of one or more random variables) has converged towards a value that does not change, or changes less than a threshold, during further repetitions.

[0104] According to some embodiments of the disclosure, some advantages can be obtained. For example, a joint delay and LOS probability indication per tap can be obtained, and flexibility can be achieved due to the complexity for performance trade-off via model selection (e.g. delay grid resolution), method convergence criteria. Increased accuracy positioning via multipath gain and LOS indication reporting can also result from implementation of this method.

[0105] Figure 6 An example of a simplified system architecture is depicted, only some elements and functional entities being shown, all being logical units, whose implementation can differ from what is shown. Figure 6 The connections in are logical connections; actual physical connections can be different.It will be apparent to those skilled in the art that the system generally includes further functions and structures than those shown in Figure 6

[0106] However, the present embodiments are not limited to the system given as an example, but a person skilled in the art can apply the solution to other communication systems providing the necessary properties.

[0107] Figure 6 The example of Fig. 1 shows a part of an exemplary radio access network.

[0108] Figure 6User equipments 110a and 110b are shown, configured to be in wireless connection with an access node (e.g. (e / g)NodeB) 104 providing a cell on one or more communication channels in the cell. The physical link from the user equipment to the (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 the (e / g)NodeB or functionality thereof can be implemented by using any node, host, server or access point etc. entity suitable for such a use.

[0109] The communication system typically includes more than one (e / g)NodeB, in which case the (e / g)NodeBs can also be configured to communicate with one another over links designed for the purpose, which can be wired or wireless links. These links can be used for signaling purposes. The (e / g)NodeB is a computing device configured to control the radio resources of the communication system it is coupled to. The NodeB can also be called a base station, an access point or any other type of interfacing device including a relay station capable for operating in a wireless environment. The (e / g)NodeB includes or is coupled to a transceiver. From the transceiver of the (e / g)NodeB, a connection is provided to an antenna unit that establishes the bi- directional radio link with the user equipment. The antenna unit can include multiple antennas or antenna elements. The (e / g)NodeB is further connected to a 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), a Packet Data Network Gateway (P-GW) to provide connectivity of the user equipment (UE) to external packet data networks, or a Mobility Management Entity (MME), etc. The CN can include network entities or nodes that can be referred to as management entities. Examples of network entities include at least an Access Management Function (AMF).

[0110] User equipment (also known as user terminal, terminal device, wireless device, mobile station (MS), etc.) illustrates one type of apparatus to which resources on the air interface are allocated and assigned, and thus any features described herein for a user equipment can be implemented for a corresponding network equipment, such as a relay node, eNB and gNB. An example of such a relay node is a layer 3 relay towards a base station (self-backhauled relay).

[0111] User equipment typically refers to a portable computing device, including 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), a smartphone, a personal digital assistant (PDA), a handset, a device using a wireless modem (alarm or measurement device, etc.), a laptop and / or touch screen computer, a tablet, a game console, a notebook, and a multimedia device. It should be appreciated that a user equipment can also be a nearly uplink-only device, an example of which is a camera or video camera that loads images or video clips to a network. A user equipment can also be a device with capability to operate in an Internet of Things (IoT) network, which is a scenario where objects are provided with the ability to transfer data over a network, without the need for human-to- human or human-to-computer interaction. A user equipment can also utilize cloud. In some applications, a user equipment can include a small portable device (such as a watch, earpiece, or glasses) with a radio part, and the computing is done in the cloud. A user equipment (or in some embodiments a third layer relay node) is configured to perform one or more of the user equipment functionalities. A user equipment can also be referred to as a subscriber unit, mobile station, remote terminal, access terminal, user terminal, or user equipment (UE), to name but a few names or apparatuses.

[0112] The various techniques described herein can also be applied to cyber-physical systems (CPS) (systems of collaborating computational elements controlling physical entities). CPS can enable the implementation and exploitation of a vast number 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 robots and electronics transported by humans or animals.

[0113] Furthermore, although the apparatuses have been depicted as single entities, different units, processors and / or memory units (not all shown in Figure 6 Fig. 1) can be implemented.

[0114] 5G supports the use of multiple input multiple output (MIMO) antennas, many more base stations or nodes than LTE (the so-called concept of small cell), including macro sites operating in co-operation with smaller stations and employing various radio technologies depending on service needs, use cases and / or available spectrum. 5G mobile communications supports 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 communication (such as (massive) machine type communications, mMTC including vehicle safety, different sensors and real-time control. 5G is expected to have multiple radio interfaces namely below 6GHz, cmWave and mmWave and can also be integrated with existing legacy radio access technologies, such as LTE. At least in the early phase, the integration with LTE can be implemented as a system where macro coverage is provided by LTE and 5G wireless access is accessed from small cells that are aggregated to LTE. In other words, 5G plans to support both inter-RAT operability (such as LTE-5G) and inter-RI operability (inter-radio interface operability, such as below 6GHz-cmWave, below 6GHz-cmWave-mmWave). One of the concepts considered for use in 5G networks is network slicing, where multiple independent and dedicated virtual sub-networks (network instances) can be created within the same infrastructure to run services that have different requirements on latency, reliability, throughput and mobility.

[0115] The current architecture in LTE networks is fully distributed in radio and fully centralized in the core network. The low latency applications and services requirements in 5G require bringing the content close to the radio, which leads to local breakouts and multi-access edge computing (MEC). 5G enables analytics and knowledge generation to happen at the data source. This approach requires leveraging resources that can not have persistent connectivity to the 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 the cell user to speed up response times. 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 networks and processing, which can also be categorized as local cloud / fog computing and grid / mesh computing, dew computing, mobile edge computing, cloudlets, distributed data storage and retrieval, 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).

[0116] 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 system can also be able to support the usage of cloud services, wherein for example at least a part of core network operations can be carried out as a cloud service (this is depicted in Figure 6 by "cloud" 102). The communication system can also comprise a central control entity etc. providing facilities for networks of different operators to cooperate for example in spectrum sharing.

[0117] Edge cloud can be brought closer to the Radio Access Network (RAN) by utilizing Network Function Virtualization (NFV) and Software-Defined Networking (SDN). Using edge cloud can mean that the access node operations are executed, at least partly, in a server, host or node that is operationally coupled to a remote radio head or base station that comprises the radio part. Node operations can also be distributed among a plurality of servers, nodes, or hosts. The application of cloud RAN architecture enables RAN real-time functions to be executed in the RAN side (in a Distributed Unit, DU 104) while non-real-time functions can be executed in a centralized manner (in a Centralized Unit, CU 108).

[0118] It should also be understood that the distribution of work between core network operations and base station operations can differ from that of the LTE or even be non-existent. Some other technology advancements that can be used are Big Data and All-IP, which can change the way networks are built 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 also be applied to 4G networks. The gNB is the next generation NodeB (or New NodeB) that supports 5G networks (i.e. NR).

[0119] 5G can also utilize satellite communication to enhance or complement the 5G service coverage, for example by providing backhauling. Possible use cases are providing service continuity for machine-to-machine (M2M) or Internet of Things (IoT) devices or passengers on board of vehicles, or ensuring service availability in cases of network failure. Satellite communication can be utilized by reusing existing space to ground infrastructure, such as GEO satellites, but also by utilizing low earth orbit (LEO) satellite systems and especially mega-constellations (systems that deploy hundreds of (nano)satellites). Each satellite 106 in a mega-constellation can cover several satellite supported network entities that create ground cells. Ground cells can be created by ground relay nodes 104 or by gNBs located in the ground or in the satellite.

[0120] It will be apparent to those skilled in the art that the described system is merely an example of a portion of a wireless access system, and in practice, the system may include multiple (e / g) Node Bs, user equipment may access multiple radio cells, and the system may also include other devices such as physical layer relay nodes or other network elements. At least one of the (e / g) Node Bs may be a home (e / g) Node B. Furthermore, multiple different types of radio cells and multiple radio cells may be provided within the geographical area of ​​the radio communication system. Radio cells may be macrocells (or umbrella cells), which are large cells, typically with diameters up to tens of kilometers, or they may be smaller cells, such as microcells, femtocells, or picocells. Figure 6 The (e / g) nodes B in the network can provide any type of these cells. Cellular radio systems can be implemented as multi-layer networks comprising various types of cells. Typically, in a multi-layer network, one access node provides one or more cells of a particular type, thus requiring multiple (e / g) nodes B to provide such a network structure.

[0121] To meet the needs of improving communication system deployment and performance, the concept of "plug and play" (e / g) node B was introduced. Typically, a network capable of using "plug and play" (e / g) node B includes, in addition to the home (e / g) node B (H(e / g) node B), a home node B gateway or HNB-GW (Host Node-Gateway). Figure 6 (Not shown in the image). HNB gateways (HNB-GWs), typically installed within a carrier's network, can aggregate traffic from a large number of HNBs and bring it back to the core network.

[0122] Implementations may be carried out using 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 medium. In example embodiments, the application logic, software, or instruction set is maintained on any of a variety of conventional computer-readable media. In the context of this document, "memory" or "computer-readable medium" can be any medium or component that can contain, store, communicate, propagate, or transmit instructions for use by or associated with an instruction execution system, apparatus, or device (such as a computer).

[0123] In related cases, references to "computer- readable storage media," "computer program products," "tangibly embodied computer programs," 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 FPGAs, ASICs, signal processing devices, and other devices and circuits. References to computer-readable program code, computer program instructions, computer code, etc. should be understood to express software for a programmable processor, firmware such as, for example, that used in an FPGA, ASIC, etc., or microcode for a truly fixed function device or other hardware, etc.

[0124] While the above examples describe embodiments of the application operating within a wireless device or gNB, it should be appreciated that the application as described above can be implemented as part of any apparatus that includes circuitry that transmits and / or receives radio frequency signals. Thus, for example, embodiments of the application can be implemented in a mobile phone, a base station, a computer that includes radio frequency communication components (e.g., wireless local area network, cellular radio, etc.), such as a desktop computer or a tablet computer.

[0125] In general, the various embodiments of the application can be implemented in hardware or special-purpose circuits or any combination thereof. While various aspects of the application can 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 can be implemented in hardware, software, firmware, special-purpose circuits or logic, general purpose hardware or control units, other computing devices, or some combination thereof.

[0126] Embodiments of the application can be practiced in a variety of components such as integrated circuit modules, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), microcontrollers, microprocessors, and other modules. The design of integrated circuits is by and large a highly automated process. Complex and powerful software tools are available for conversion of a logic level design into a semiconductor circuit design ready to be fabricated on semiconductor wafers. 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 the geometric data to generate data, including file sets for photolithography, etching, grooming and other mask tools.

[0127] 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 the geometric data to generate data, including file sets for photolithography, etching, grooming and other mask tools. 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) can be transmitted to a semiconductor fabrication facility or "fab" for fabrication.

[0128] As used in this application, the term "circuitry" can refer to one or more or all of the following:

[0129] (a) hardware-only circuitry (e.g., in an implementation that is solely an analog and / or digital circuit implementation), and

[0130] (b) combinations of hardware circuits and software, such as (as applicable):

[0131] (i) combinations of analog and / or digital hardware circuit(s) with software / firmware

[0132] (ii) any portions of hardware processor(s) with software (including digital signal processors), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions and

[0133] (c) hardware circuit(s) that can be a part of a larger physical entity created to perform particular operations and / or can require software or firmware for operation, but are not physical entities in and of themselves, such as a microprocessor or a portion thereof.

[0134] This definition of circuitry applies to all uses of this term in this application, including all uses in any claims. As a further example, as used in this application, the term circuitry also encompasses an implementation that is a hardware-only circuit or processor (or multiple processors) or hardware-only circuit or processor portion and it's (or their) accompanying software and / or firmware. As an example and where applicable, the term circuitry includes, but is not limited to, a baseband integrated circuit and / or processor integrated circuit for a mobile device, or similar integrated circuits in server, cellular network device, or other computing or network device.

[0135] The following is a list of acronyms used in this specification:

[0136] 3GPP - Third Generation Partnership Project

[0137] 4G - LTE - Long Term Evolution

[0138] 5G - Fifth Generation

[0139] AOA - Angle of Arrival

[0140] AOD - Angle of Departure

[0141] BS - Base Station

[0142] BW - Bandwidth

[0143] CFR - Channel Frequency Response

[0144] CU - Central Unit

[0145] DL - Downlink

[0146] DSP - Digital Signal Processor

[0147] DU - Distributed Unit

[0148] eNB - Evolved Node B

[0149] FPGA - Field Programmable Gate Array

[0150] gNB - Evolved Node B

[0151] GEO - Geostationary Earth Orbit

[0152] HNB-GW - Home Node B Gateway

[0153] IoT - Internet of Things

[0154] LEO - Low Earth Orbit

[0155] LMC - Location Management Component

[0156] LMF - Location Management Function

[0157] LOS - Line of Sight

[0158] LTE - Long Term Evolution

[0159] M2M - Machine to Machine

[0160] MFT - Mean Field Theory

[0161] MIMO - Multiple Input Multiple Output

[0162] MME - Mobility Management Entity

[0163] mMTC - (Massive) Machine Type Communication

[0164] MSE - Mean Square Error

[0165] NGC - Next Generation Core

[0166] NLOS - Non Line of Sight

[0167] NR - New Radio

[0168] OFDM - Orthogonal Frequency Division Multiplexing

[0169] pdf - Probability Density Function

[0170] PDP - Power Delay Profile

[0171] PRS Positioning Reference Signal

[0172] RAN - Radio Access Network

[0173] RAT - Radio Access Technology

[0174] RRC - Radio Resource Control

[0175] RRH - Remote Radio Head

[0176] RU - Radio Unit

[0177] SGW - Serving Gateway

[0178] SIM card - Subscriber Identity Module

[0179] SMF - Session Management Function

[0180] SNR - Signal to Noise Ratio

[0181] Time difference - Time Difference of Arrival

[0182] TOA - Time of Arrival

[0183] TRP - Transmission Reception Point

[0184] UE - User Equipment

[0185] UL - Uplink

[0186] UPF - User Plane Function

[0187] V-TRP Virtual Transmission / Reception Point

[0188] The foregoing description provides a complete and informative description of exemplary embodiments of the application based on specific examples. However, various modifications and adaptations to those embodiments might occur to those skilled in the relevant art in view of the foregoing description, when taken in conjunction with the accompanying drawings and the appended claims. However, all such and similar modifications will still fall within the scope of the application.

Claims

1. A positioning device, comprising: A component used to receive positioning reference signals from a positioning signal transmitter from a single base station; Components for forming a delay search space from the received positioning reference signal to obtain a plurality of channel taps representing an estimated model of the channel between the device and the single base station; Components for estimating noise accuracy for the plurality of channel taps in response to a noise process that disrupts the positioning reference signal received from the individual base station; A component for estimating channel gain for each of the plurality of channel taps; A component for estimating the probability of a line-of-sight signal for each of the plurality of channel taps; A component for determining the gaze signal for each channel tap using the estimated probability of the gaze signal for each channel tap; as well as Components for using the line-of-sight signal for each of the plurality of channel taps at least during the positioning process performed by the device.

2. The apparatus of claim 1, wherein the component for forming the delay search space is configured to select a variable resolution and length of the delay search space.

3. The apparatus of claim 1 or 2, wherein each channel tap located in the delay search space is defined as a random variable consisting of a composite gain portion and a line-of-sight identifier portion.

4. The apparatus of claim 1 or 2, wherein the apparatus is configured to perform parallel or serial estimation of noise accuracy, channel gain, and line-of-sight signal probability until a predetermined condition has been met.

5. The apparatus of claim 4, wherein the predetermined condition is: one or more of the estimates have converged toward a value or have converged to a value that has not changed during further repetitions, or has changed less than a threshold.

6. The apparatus of claim 1 or 2, configured to map samples of the received positioning reference signal to the estimation model of the channel, which includes the tail of the channel impulse response vector.

7. The apparatus according to claim 1 or 2, configured as follows: Multipath components are detected based on the estimation model; and Determine the delay and phase of the detected multipath components.

8. The apparatus according to claim 1 or 2, comprising: A component for estimating the delay of the plurality of channel taps based on the estimated channel gain.

9. A positioning method, comprising: At the user equipment, a positioning reference signal is received from a positioning signal transmitter from a single base station; A delay search space is formed from the received positioning reference signal to obtain multiple channel taps representing an estimation model of the channel between the user equipment and the single base station; The noise accuracy is estimated for the multiple channel taps of the noise process, which disrupts the positioning reference signal received from the single base station; Channel gain is estimated for the multiple channel taps; as well as The probability of the line-of-sight signal is estimated for each of the plurality of channel taps; The gaze signal for each channel tap is determined using the estimated probability of the gaze signal for each channel tap. as well as The line-of-sight signal for each channel tap is used at least during the positioning process performed by the user equipment.

10. The method of claim 9, comprising: Choose the variable resolution and length of the delayed search space.

11. The method according to claim 9 or 10, comprising: Each channel tap in the delay search space is defined as a random variable consisting of a composite gain component and a line-of-sight identifier component.

12. The method according to claim 9 or 10, comprising: Parallel or serial estimations are performed on noise accuracy, channel gain, and line-of-sight signal probability until predetermined conditions are met.

13. The method of claim 12, wherein the predetermined condition is: one or more of the estimates have converged toward a value or have converged to a value that has not changed during further repetitions, or has changed less than a threshold.

14. A positioning device, comprising at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code being configured, together with the at least one processor, to cause the device to perform at least the following operations: Receive positioning reference signals from a positioning signal transmitter from a single base station; A delay search space is formed from the received positioning reference signal to obtain multiple channel taps representing an estimation model of the channel between the device and the single base station; The noise accuracy is estimated for the multiple channel taps of the noise process, which disrupts the positioning reference signal received from the single base station; The channel gain is estimated for each of the multiple channel taps. The probability of the line-of-sight signal is estimated for each of the plurality of channel taps; The gaze signal for each channel tap is determined using the estimated probability of the gaze signal for each channel tap. as well as The line-of-sight signal for each of the plurality of channel taps is used at least during the positioning process performed by the device.

Images