Methods and systems for positioning wireless devices

Abstract

A method performed by a communication device is described, alongside corresponding methods for a communications network. The method for a communication device comprises obtaining one or more radio measurements, obtaining one or more audio measurements, and determining a position of a wireless device using the audio measurements and the radio measurements.

Description

[0001] METHODS AND SYSTEMS FOR POSITIONING WIRELESS DEVICES

[0002] Technical Field

[0003] Embodiments of the present disclosure relate to methods and apparatus in communication networks, and particular methods and apparatus for location verification in communication networks.

[0004] Background

[0005] Sixth Generation (6G), Fifth Generation (5G) and Fourth Generation (4G) New Radio (NR) cellular networks (for example 3rd Generation Partnership Project (3GPP) networks) may require positioning methods to compute and verify the position of elements such as wireless devices in the network. For example, positioning methods may be required to meet regulatory requirements such as emergency call positioning. Such positioning techniques may include, for example, techniques relying on an interaction between a wireless device such as a user equipment (UE) and a network.

[0006] Figure 1 depicts a reference NR positioning architecture. As shown in Figure 1 , the network may comprise the following components:

[0007] • a wireless device 104; in the specific example of Figure 1 , the wireless device 104 is a UE. The wireless device 104 may be configured to carry out measurements on downlink (DL) signals from a Next-Generation Radio Access Network (NG-RAN) 122 or may send reference signals in uplink such as Long-Term Evolution (LTE)-Uu 101 and NR-Uu 103 to the NG-RAN 122;

[0008] • a Next-Generation Radio Access Network (NG-RAN) 122. The NG-RAN 122 may comprise a base station (gNB) 106, which includes one or more Transmission and Reception Points (TRP). The NG-RAN 122 may further comprise a next generation- evolved NodeB (ng-eNB) 108, which includes a Transmission Point (TP). The gNB 106 and ng-eNB 108 forming the NG-RAN 122 may interact through a connection Xn 105. The gNB 106 and / or the ng-eNB may be configured to receive signals such as LTE-Uu 101 and NR-Uu 103 transmitted from a wireless device 104. The gNB 106 and / or the ng-eNB may be further configured to interact with an Access and Mobility Management Function (AMF) 110 through one or more Control Plane Interfaces (NG-C) 107. In the architecture of Figure 1 , the interactions between the gNB 106 and wireless device 104 may be supported by the Radio Resource Control (RRC) protocol and so are NR-llu interactions 103. In contrast, the ng-eNB acts as a location node and interfaces with the UE via the LTE Positioning Protocol (LPP) using LTE-llu interactions 101 ;

[0009] • an Access and Mobility Management Function (AMF) 110 which may be configured for handling connection and mobility management tasks for a target wireless device;

[0010] • a Location Management Function (LMF) 102, which may be configured to manage location services for a target wireless device, including computing and verifying a location for a target wireless device. The LMF 102 may be configured to communicate with the AMF 110 via one or more Network Links (NLs) 109. Accordingly, in the architecture of Figure 1 the LMF 102 may be configured to interact with the NG-RAN 122 via the AMF 110, for example using the NR Positioning Protocol-Annex (NRPPa) protocol;

[0011] • an Enhanced Serving Mobile Location Centre (E-SMLC) 112 which may communicate with the LMF 102 in order to provide the LMF 102 with location information from older networks; and

[0012] • a Secure User Plane Location (SUPL) Location Platform (SLP) 114 which may communicate with the LMF 102 in order to provide secure and accurate location services.

[0013] LPP is a point-to-point communication protocol between an LMF and a target wireless device. Since Release-15 and the introduction of NR communications networks, LPP protocols have been agreed to be reused in both NR and LTE networks. As detailed above, the LMF 102 may be the main server responsible for computing the wireless device position, based on one or more of NR, E-UTRA, or both Radio Access Technologies (RATs) specific positioning methods. For example, NRPPa may be the communication protocol used between an NG- RAN and LMF.

[0014] Networks such as that depicted in Figure 1 may have employed positioning methods such as network triangulation methods, wherein the network comprises a plurality of base stations and the base station locations are assumed to be known. An example of such a network is depicted in Figure 2, which comprises a plurality of base stations 206 located at (xi, yi), (X2, y2) , and (X3, ya), and a wireless device 204 located at (xm, ym). Measures of a delay of a signal from each base station to the wireless device can be obtained, and the measured delay may be used to calculate a distance between the wireless device and each associated base station (di, d2, and da). These distances may then act as path lengths, and a comparison of these path lengths may be used to compute the position of the wireless device 204 relative to the known locations of the base stations 206. Alternatively or additionally, networks such as that depicted in Figure 1 may have employed positioning methods such as fingerprinting. Fingerprinting may be used, for example, in indoor positioning technology to determine a position estimation. Fingerprinting may rely on signal strength data or Received Signal Strength (RSS), which represents a distance between the wireless device and each beacon, sensor, and / or indoor radio node. As indoor propagation environments may be complex, such as signals having large numbers of reflection and diffraction interactions, the simplistic equations used in triangulation methods may not be applicable. Accordingly, a large number of measurements may be conducted for fingerprinting methods; these methods may then store the path strengths for each measurement and consider the relative strengths of multiple links as a fingerprint to calculate the position of the wireless device.

[0015] 5G and potential 6G positioning use cases may have performance requirements including accuracy, latency, availability, and reliability requirements. 3GPP communication networks presently heavily rely on radio based positioning methods. Radio based positioning is based on the reception of radio signals which are transmitted by satellites (e.g. Global Navigation Satellite System (GNSS) methods), gNBs, or dedicated positioning systems.

[0016] However, as the complexity of communication networks increases, such radio based positioning methods may not provide suitable performance requirements. For example, indoor scenarios are typically very rich in scattering and may lack strong line-of-sight components, rending radio positioning difficult. Satellite-based positioning may provide particularly poor results for indoor positioning, due to the complexity of indoor propagation environments impacting performance requirements such as accuracy, latency, availability, and reliability requirements. The use of additional positioning systems such as cameras, radio, and Light Detection and Ranging (LiDAR) systems may be expensive to operate; many such systems also may not be deployed densely enough to allow for high positioning accuracy, especially in indoor environments such as homes, shopping malls, and factories. Summary

[0017] It is an object of the present disclosure to provide positioning methods with improved accuracy, for example in indoor environments. Embodiments of the disclosure aim to provide apparatuses and methods that alleviate some or all of the problems identified.

[0018] A first embodiment of the present disclosure provides a method performed by a communication device. The method comprises obtaining one or more radio measurements, obtaining one or more audio measurements, and determining a position of a wireless device using the audio measurements and the radio measurements.

[0019] A second embodiment of the present disclosure provides a method performed by a communications network. The network comprises a communication device and a wireless device. The communication device is configured to obtain one or more radio measurements, obtain one or more audio measurements, and determine a position of the wireless device using the audio measurements and radio measurements. The UE is configured to record at least one of the one or more audio measurements and transmit the recorded measurements to the communication device . Alternatively, the UE is configured to emit a reference signal.

[0020] A third embodiment of the present disclosure provides a communication device . The communication device comprises processing circuitry and a memory containing instructions executable by the processing circuitry. The communication device is configured to obtain one or more radio measurements, obtain one or more audio measurements, and determine a position of a wireless device using the audio measurements and the radio measurements.

[0021] A fourth embodiment of the present disclosure provides a communications network. The network comprises a communication device comprising processing circuitry and a memory containing instructions executable by the processing circuitry. The communication device is configured to obtain one or more radio measurements, obtain one or more audio measurements, and determine a position of the wireless device using the audio measurements and radio measurements. The network further comprises a wireless device comprising processing circuitry and a memory containing instructions executable by the processing circuitry. The wireless device is configured to record at least one of the one or more audio measurements and transmit the recorded measurements to the communication device. Alternatively, the wireless device is configured to emit a reference signal. Further embodiments provide methods, communication devices, and networks as discussed herein.

[0022] Brief Description of Drawings

[0023] For a better understanding of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0024] Figure 1 is a diagram of a positioning reference architecture;

[0025] Figure 2 is a diagram of a network triangulation reference architecture;

[0026] Figure 3 is a flowchart of a method for a communication device, in accordance with embodiments;

[0027] Figure 4 is a flowchart of a method for an audio recording device, in accordance with embodiments;

[0028] Figure 5 is a flowchart of a method for a signal emitting device, in accordance with embodiments;

[0029] Figure 6 is a schematic diagram of a communications network, in accordance with embodiments.

[0030] Figure 7 is a schematic diagram of a communication device, in accordance with embodiments.

[0031] Figure 8 is a schematic diagram of a wireless device, in accordance with embodiments.

[0032] Figure 9 is a diagram of a first embodiment of the present disclosure; and

[0033] Figure 10 is a diagram of a second embodiment of the present disclosure. Detailed Description

[0034] For the purpose of explanation, details are set forth in the following description in order to provide a thorough understanding of the embodiments disclosed. It will be apparent, however, to those skilled in the art that the embodiments may be implemented without these specific details or with an equivalent arrangement.

[0035] The following sets forth specific details, such as particular embodiments for purposes of explanation and not limitation. It will be appreciated by one skilled in the art that other embodiments may be employed apart from these specific details. In some instances, detailed descriptions of well-known methods, nodes, interfaces, circuits, and devices are omitted so as to not obscure the description with unnecessary detail. Those skilled in the art will appreciate that the functions described may be implemented in one or more nodes using hardware circuitry (e.g., analog and / or discrete logic gates interconnected to perform a specialized function, ASICs, PLAs, etc.) and / or using software programs and data in conjunction with one or more digital microprocessors or general purpose computers that are specially adapted to carry out the processing disclosed herein, based on the execution of such programs. Nodes that communicate using the air interface also have suitable radio communications circuitry. Moreover, the technology may additionally be considered to be embodied entirely within any form of computer-readable memory, such as solid-state memory, magnetic disk, or optical disk containing an appropriate set of computer instructions that would cause a processor to carry out the techniques described herein.

[0036] Hardware implementation may include or encompass, without limitation, digital signal processor (DSP) hardware, a reduced instruction set processor, hardware (e.g., digital or analog) circuitry including but not limited to application specific integrated circuit(s) (ASIC) and / or field programmable gate array(s) (FPGA(s)), and (where appropriate) state machines capable of performing such functions.

[0037] In terms of computer implementation, a computer is generally understood to comprise one or more processors, one or more processing modules or one or more controllers, and the terms computer, processor, processing module and controller may be employed interchangeably. When provided by a computer, processor, or controller, the functions may be provided by a single dedicated computer or processor or controller, by a single shared computer or processor or controller, or by a plurality of individual computers or processors or controllers, some of which may be shared or distributed. Moreover, the term “processor” or “controller” also refers to other hardware capable of performing such functions and / or executing software, such as the example hardware recited above.

[0038] Figure 3 is a flowchart of a method for a communication device, in accordance with embodiments. As shown in Figure 3, the method may comprise obtaining one or more radio measurements (Step S302). These radio measurements may for example be received from a wireless device, for example the target wireless device or wireless device to be positioned. Alternatively or additionally, these radio measurements may be provided by a network node, SLP and / or a E-SMLC. In embodiments, the communication device may receive radio measurements via an AMF. Additionally or alternatively, the communication device may receive radio measurements using NRPPA. Radio measurements may be based on NR, E- LITRA, or both RATs specific positioning methods.

[0039] As shown in Figure 3, the method may further comprise obtaining one or more audio measurements (Step S304). In an embodiment, these audio measurements may be recorded by and / or received from a wireless device. Alternatively or additionally, in an embodiment these audio measurements may be recorded by and / or received from one or more microphones with known locations. These embodiments will be discussed in greater detail below. Further, the method may comprise determining the position of the wireless device using the obtained audio measurements and radio measurements (Step S306).

[0040] Figure 4 is a flowchart of a method for an audio recording device, in accordance with embodiments. In some embodiments, an audio recording device may be a wireless device and accordingly the method of Figure 4 may be performed by a wireless device. Alternatively or additionally, in some embodiments an audio recording device may be a microphone and accordingly the method of Figure 4 may be performed by a microphone. Furthermore, in some embodiments multiple audio recording devices may be present including both a wireless device and a microphone and accordingly the method of Figure 4 may be performed by both a wireless device and a microphone.

[0041] As shown in Figure 4, the method may comprise recording one or more audio measurements (Step S402) and transmitting the recorded one or more audio measurements to the communication device (Step S404).

[0042] Figure 5 is a flowchart of a method for a signal emitting device, in accordance with embodiments. In some embodiments, a signal emitting device may be a wireless device and accordingly the method of Figure 5 may be performed by a wireless device. Alternatively or additionally, in some embodiments a signal emitting device may be a speaker and accordingly the method of Figure 4 may be performed by a speaker. Furthermore, in some embodiments multiple a signal emitting device may be present including both a wireless device and a speaker and accordingly the method of Figure 5 may be performed by both a wireless device and a speaker.

[0043] As shown in Figure 5, the method may comprise emitting a reference signal (Step S502).

[0044] The methods of Figure 3, Figure 4, and Figure 5 may be performed by any suitable apparatus, for example a communications network. In particular, the method of Figure 3 is performed by a communication device. The communication device may be a network node, for example an LMF. Alternatively, the communication device may be a wireless device and / or integrated in a wireless device. For example, the method of Figure 3 may be performed by the wireless device which is being positioned. That is, the communication device may be integrated in the wireless device to be positioned. The following discussion and example embodiments may involve a network node undertaking the positioning method, however it will be understood that present embodiments are not limited thereto. An example of a suitable network is depicted in Figure 6.

[0045] As depicted in Figure 6, the communications network 600 performing the methods may comprise a network node 602 and wireless device 604. The communications network may further comprise a speaker 616 and / or a microphone 618. The positioning method of Figure 3 may be undertaken by the network node 602, in order to position the wireless device 604. Alternatively, the positioning method may be undertaken by the wireless device 604 itself.

[0046] An example of a communication device suitable for use in the communications network 600 is depicted in Figure 7. As depicted in Figure 7, the communication device 702 may comprise a processor 702A, interfaces 702B, and a memory 702C storing a computer program 702D. The steps of the method, for example as depicted in Figure 3, may be performed in accordance with the computer program 702D stored on the memory 702C, and may be executed by the processor 702A in conjunction with one or more interfaces 702B.

[0047] An example of a wireless device suitable for use in the communications network 600 is depicted in Figure 8. As depicted in Figure 8, the wireless device 804 may comprise a processor 804A, interfaces 804B, and a memory 804C storing a computer program 804D. The steps of the method, for example as depicted in either Figure 4 or Figure 5, may be performed in accordance with the computer program 804D stored on the memory 804C, and may be executed by the processor 804A in conjunction with one or more interfaces 804B.

[0048] In present embodiments, the method may further comprise receiving the one or more audio measurements from one or more microphones, wherein each of the one or more audio measurements corresponds to one of the one or more microphones. The microphones may record, collect, or otherwise measure the audio measurements. The microphones may then transmit the audio measurements to the network node. The microphones may transmit the audio measurements to the network node directly, or via further network node.

[0049] In specific embodiments, the method may further comprise receiving the one or more audio measurements from one or more microphones wherein at least one of the one or more microphones is integrated in the wireless device. An example of such an embodiment is depicted in Figure 9. Figure 9 depicts an indoor environment for example an office, factory, or shopping mall; however, it will be appreciated that other environments are also envisaged.

[0050] As shown in Figure 9, a wireless device 904 is present in an environment which may comprise multiple characteristic sound sources 920. These characteristic sound sources 920 may include one or more of the following: washing machines, clothes drying machines, microwaves, fridges, freezers, and dish washers. In both internal and external environments, for example, characteristic sound sources may include human voices. Each of these appliances or machines may emit a sound with unique characteristics. That is, the one or more audio measurements measured by the wireless device may comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, and the method performed by the positioning network node may further comprise obtaining source location information associated with the characteristic sound source. The unique characteristics associated with the characteristic sound may be one or more of: frequency spread of audio measurement, phase of audio measurement, periodicity of audio measurement, and volume of audio measurement. This unique characteristic may allow for the characteristic sound source to act as a fixed reference for computing the position of the wireless device as detailed below.

[0051] Alternatively or additionally, the environment may include one or more speakers 906 (depicted as speaker-1 , speaker-2, and speaker-3). That is, speakers 906 may be used to generate suitable noise for positioning a wireless device in addition to or alternatively to characteristic sound sources 920 which, for example in indoor environments where no characteristic sound sources are available. In this way, the speaker 906 may be considered to be characteristic sound sources, as the speakers 906 are configured to provide a characteristic sound. It will be understood that features of the characteristic sound sources 920 may also be applicable to speakers 906 in the environment which provide characteristic sounds. Thus, the speakers 906 may be at fixed locations and have associated source location information. This source location information may be received by the network node from a further network node and / or from a user input. Alternatively or additionally, this source location information may be determined by obtaining characteristic sound information from a database and determining the sound location information by processing the characteristic sound information using an ML algorithm.

[0052] The speakers 906 and / or the characteristic sound sources 920 may play or emit noises that are recorded by the wireless device 904 as an audio measurement. The wireless device 904 may then transmit the recorded measurements to a communication device, so that the communication device may determine the position of the wireless device. The wireless device may transmit the audio measurements to the communication device directly, or via a further communication device.

[0053] As shown in Figure 9, the characteristic sound sources 920 and speakers 906 form fixed sources, from which sound is emitted. The fixed sources have fixed geographical locations with known coordinates. Accordingly, in present embodiments the characteristic sound source may have a fixed location and the method performed by the positioning communication device may further comprise receiving the source location information from a further communication device and / or a user input. The characteristic sound source therefore may act as a fixed reference for computing the position of the wireless device; the unique characteristics of the characteristic sound emitted by the characteristic sound source allows the characteristic sound source to be identified and monitored, while the fixed location associated with the characteristic sound source provides a fixed reference from which the position of the wireless device may be calculated. Audio measurements recorded by the wireless device may be used to position the wireless device using one or more of: measuring the time of arrival (TOA) of the characteristic sound at the wireless device, by fingerprinting the audio measurements, and / or by measuring the phase of the audio measurements and / or the signal power spectrum of the audio measurements. Accordingly, the method performed by the communication device may further comprise determining the position of the wireless device by processing the radio measurements using triangulation and / or fingerprinting in addition to processing the audio measurements as described with reference to any of the present embodiments. Alternatively or additionally, in specific embodiments the network node may learn the fixed reference positions of the characteristic sound sources 920 and / or speakers 906. For example, the network node may comprise a Machine Learning (ML) algorithm, and the method performed by the network node may further comprise obtaining characteristic sound information from a database, and determining the sound location information by processing the characteristic sound information using the ML algorithm.

[0054] That is, in a specific embodiment where characteristic sound sources 920 are used, there may be a variety of sound sources with characteristic sounds associated with them. For example, notification sounds made by a dishwasher upon completion of a cycle may differ from notification noises made by a fridge when left open for a set length of time. Similarly, the background noise generated by a dishwasher during a cycle may differ from background noise generated by a fridge during operation or a washing machine during a cycle. These characteristic sounds may be learnt and associated with fixed location equipment for positioning. For example, the network node may access or generate a fingerprinting database on various sound sources in an environment. This fingerprinting database may comprise characteristic sound information that allows for the identification of characteristic sound sources present in the environment. The network node may then identify characteristic sound sources present in an environment based on the received audio measurements and the characteristic sound information. The sound location information associated with the identified characteristic sound sources may then be determined by the network node. For example, the sound location information may be received from a further network node. Alternatively or additionally, the sound location information may be received from a user input.

[0055] In further specific embodiments, the method performed by the network node may comprise receiving one or more audio measurements from one or more microphones wherein at least one of the one or more microphones is a reference microphone. An example of such an embodiment is depicted in Figure 10. As with Figure 9, Figure 10 depicts an indoor environment for example an office, factory, or shopping mall; however, it will be appreciated that other environments are also envisaged.

[0056] As shown in Figure 10, a wireless device 1004 is present in an environment which may comprise one or more reference microphones 1018 (depicted as Mic 1 , Mic 2, Mic 3, and Mic 4). These microphones may have fixed locations associated with them. Accordingly, the method performed by the network node may additionally comprise obtaining microphone location information for a reference microphone 1018, with the microphone location information associated with the fixed location of the reference microphone 1018. The network node may obtain microphone location for each reference microphone 1018, or for a subset of reference microphones 1018. This microphone location information may be received from a further network node. Alternatively or additionally, this microphone location information may be received from a user input.

[0057] In specific embodiments the network node may learn the fixed location(s) of the one or more reference microphones 1018. For example, the network node may comprise a Machine Learning (ML) algorithm, and the method performed by the network node may further comprise obtaining characteristic sound information from a database, and determining the microphone location information by processing the characteristic sound information using the ML algorithm. The characteristic sound information obtained from a database may be associated with a characteristic sound source 1020.

[0058] That is, and as depicted in Figure 10, the wireless device 1004 may be present in an environment which comprises multiple characteristic sound sources 1020. These characteristic sound sources 1020 may include one or more of the following: washing machines, clothes drying machines, microwaves, fridges, freezers, and dish washers. In both internal and external environments, for example, characteristic sound sources may include human voices. Each of these appliances or machines may emit a sound with unique characteristics. That is, the one or more audio measurements measured by the wireless device may comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, and the method performed by the positioning network node may further comprise obtaining source location information associated with the characteristic sound source. The unique characteristics associated with the characteristic sound may be one or more of: frequency spread of audio measurement, phase of audio measurement, periodicity of audio measurement, and volume of audio measurement. This unique characteristic may allow for the characteristic sound source to act as a fixed reference for computing the position of the one or more reference microphones, for example using an ML algorithm. As with previous embodiments, the environment may additionally comprise speakers 1006. These speakers 1006 may act as characteristic sound sources 1020.

[0059] That is, in a specific embodiment where characteristic sound sources 1020 are used, there may be a variety of sound sources with characteristic sounds associated with them. For example, notification sounds made by a dishwasher upon completion of a cycle may differ from notification noises made by a fridge when left open for a set length of time. Similarly, the background noise generated by a dishwasher during a cycle may differ from background noise generated by a fridge during operation or a washing machine during a cycle. These characteristic sounds may be learnt and associated with fixed location equipment for positioning. For example, the network node may access or generate a fingerprinting database on various sound sources in an environment. This fingerprinting database may comprise characteristic sound information that allows for the identification of characteristic sound sources present in the environment. The network node may then identify characteristic sound sources present in an environment based on the received audio measurements and the characteristic sound information. In embodiments where the fixed location of characteristic sound sources is known, the sound location information associated with the reference microphones may then be determined by the network node.

[0060] In embodiments such as that of Figure 10, the reference microphones have fixed locations with known or determined coordinates. A multitude of sound sources are distributed in the environment which may have unique characteristics of the sound they emit, as discussed above. Accordingly, each reference microphone may collect a different combination of characteristic sounds (for example, different sound weights / volumes, different sound directions, and the like) and the combination of characteristic sounds may be unique for each microphone location. Although Figure 10 depicts a domestic indoor environment, further environments such as factory and office environments with different characteristic sound sources (for example, manufacturing machinery, printers, and other office hardware equipment) are envisaged.

[0061] In such a system, the wireless device 1004 may also record audio measurements and transmit said audio measurements to the network node. This may be performed, for example, using an integrated microphone at the wireless device 1004. Accordingly, the audio measurements recorded by the wireless device 1004 may be compared to the audio measurements recorded by the one or more reference microphones 1018. For example, a presence of characteristic sounds in the recording taken by the wireless device 1004 may be compared to a presence of characteristic sounds in the recordings taken by the one or more reference microphones 1018. This comparison may be used to determine the location of the wireless device 1004 with reference to the reference microphones 1018. In the specific example of Figure 10, such a comparison would result in the audio measurement of Mic 3 being determined as a best match to the audio measurement of the wireless device 1004. Accordingly, the network node may determine that the wireless device 1004 is located near Mic 3 and / or is located closer to Mic 3 than to any of Mic 1 , Mic 2, and Mic 4. In this way, the method performed by the network node may further comprise determining the position of the wireless device by processing the radio measurements using triangulation and / or fingerprinting in addition to processing the audio measurements as described with reference to any of the present embodiments. Alternatively, the wireless device may emit a reference signal. A comparison of a presence of the reference signal in to the audio measurements recorded by the one or more reference microphones 1018 may be made. This comparison may be used to determine the location of the wireless device 1004 with reference to the reference microphones 1018.

[0062] In present embodiments, comparisons of audio measurements recorded by the wireless device 1004 and reference microphone(s) 1018 and / or multiple reference microphones 1018 may be performed using trough correlation of the audio measurements. Alternatively or additionally, such comparisons may be made based on spectral analysis of the audio measurements. For example, the audio measurements may be filtered such that a desired key characteristic is isolated and compared. Accordingly, the method performed by the network node may further comprise extracting a key characteristic from each audio measurement, and determining the position of the wireless device based on the key characteristic. The key characteristic may comprise one or more of the following: time of arrival, signal amplitude, signal phase, and / or signal power spectrum. For example, the signal amplitude associated with a characteristic sound may be used when comparing multiple audio measurements comprising the same characteristic sound. Alternatively or additionally, time of arrival of a signal may be used when determining if a reference signal is present in an audio measurement.

[0063] The key characteristic may relate to and / or allow the identification of a characteristic sound source, for example where the characteristic sound source is a human voice. In such cases, the recording of audio measurements by the wireless device 1004 and / or reference microphone 1018 may be periodic or otherwise may not be continuous. That is, the recording of audio measurements by the wireless device 1004 and / or reference microphone 1018 may be timed based on the desired key characteristic, including for example expected changes to the desired key characteristic. Alternatively, the recording of audio measurements by the wireless device 1004 and / or reference microphone 1018 may be continuous.

[0064] In present embodiments, the reference microphones may be integrated microphones present in existing devices present in the environment. For example, in indoor environments devices such as smart assistants, voice-controlled devices, and the like may already be present. This may include for example televisions, speakers, smart assistants, tablets, and laptops. Over time, these devices may also become more ubiquitous. Such devices have significant processing capabilities, and may be able to provide the processing necessary for present embodiments without the need for further apparatus to be installed or maintained. Similarly, common devices in households and indoor environments such as home appliances, factory machines, distributed speakers, and the like may be able to provide the characteristic sounds necessary for present embodiments without the need for further apparatus to be installed or maintained.

[0065] Accordingly, it will be understood that in the embodiment depicted in Figure 9 the location of the sound sources is known or calculated and used to determine the position of a wireless device. In the embodiment depicted in Figure 10 the location of the reference microphones is known or calculated and used to determine the position of a wireless device. These embodiments may be combined into a single environment where either the location of the sound sources or the reference microphones is known or calculated and used to determine the position of a wireless device, depending on the needs of the wider network. For example, in an environment there may be both reference microphones with fixed locations and sound sources with fixed locations present. The fixed locations of both the reference microphones and the sound sources may be known, allowing for either the method discussed with reference to Figure 9 or the method discussed with reference to Figure 10 to be performed. Alternatively or additionally, both methods may be performed in order to verify the accuracy of any determined wireless device position.

[0066] In present embodiments, the method performed by the network node may further comprise determining an estimated location of the wireless device using the one or more radio measurements, selecting a subset of the one or more audio measurements based on the estimated location, and determining the position of the wireless device using the subset of audio measurements and the radio measurements. Accordingly, a rough positioning or estimated location of the wireless device may be obtained through network radio-based positioning that allows for the selecting of a subset of audio measurements to be processed by the network node. This may reduce the processing needed for determining the location of the wireless device, which may include sophisticated sound / radio fusing algorithms.

[0067] As detailed above, speakers 906 / 1006 may be used as characteristic sound sources. That is, speakers may form fixed reference points within the environment. In such embodiments, at least one of the one or more audio measurements may comprise a reference signal, the reference signal being emitted by a speaker. In some embodiments, the reference signal may be a sound known to be emitted by speakers in the environment. As detailed above, the speaker may be integrated in the wireless device.

[0068] In embodiments, the reference signal may be injected into the speaker signal such as an existing sound to be emitted by the speaker. Alternatively or additionally, one or more speakers may be triggered for emission of a reference signal. This may be useful in environments such as train stations, where triggered emission by speakers in the environment is already used for other purposes. The triggering of emission by the speakers and / or the injection of a specific reference signal into the speaker signal may be done continuously, may be done periodically, may be event driven, and / or may be based on a request. The selection of the triggering method for emission by the speakers may be based on the needs of the environment.

[0069] In embodiments, reference signals may be injected into speaker signals at low volumes to avoid disturbing humans in the environment. This may be done on top of existing emitted sounds, or when there is no further sound present. For example, in a factory or shopping mall environment, there may be an existing deployment of speakers (and potentially microphones associated with the wireless device(s) to be positioned in the environment) with a wide frequency range such that infra sound or ultra sound may be emitted and recorded in frequency ranges where the human ear is not sensitive. Alternatively, an injected reference signal may be a broadband signal with properties resembling ordinary noise to the human ear, to avoid disturbing humans in the environment. Accordingly, in embodiments the reference signal may meet one of the following criteria: the reference signal is of an infra sound frequency, the reference signal is of an ultra sound frequency , or the reference signal is configured to correspond with the environment of the speaker.

[0070] In further embodiments, the audio measurements obtained by the network node may be used to classify the environment in which the wireless device is located. For example, in embodiments where characteristic sound sources are identified, the type and / or nature of the characteristic sound sources may allow for the identification of the environment. Example identifications may include: indoor environments and outdoor environments, and / or specific indoor environment types such as offices, factories, shopping malls, public transport stations and airports, domestic environments, and the like. In specific embodiments, the content of the audio measurement may be analysed to further identify the environment. For example, in an embodiment here it is determined that an announcement of a next bus stop is present in an audio measurement, it may be determined that the wireless device is located on a bus or at a bus stop. Such classifications may be processed using a ML algorithm. For example, the network node performing the positioning of the wireless device may comprise a ML algorithm configured to process characteristic sounds and / or characteristic sound sources and associate said sounds / sources with a particular identified environment or environment type. Accordingly, the audio measurements may be used as a classifier of the environment in which the wireless device is located. In a specific embodiment, the wireless device may trigger a determination of the wireless device position from a network node. That is, the wireless device may provide a trigger for its own position to be determined. For example, this trigger may be in response to contacting emergency services. The trigger from the wireless device may also trigger additional computation from the network node such as analysis of the content of audio measurements, the determination of whether a relevant characteristic sound source (for example, a human voice) is present, and / or the analysis of the content of audio measurements when it is determined that a relevant characteristic sound source is present. In a case where a wireless device has been used to contact emergency services, such analysis may determine whether the user is at home or in a public space, travelling, or the like which may reduce the response time of emergency services to the user’s location.

[0071] In specific embodiments, further positioning information (for example, GNSS information and / or information provided by a further network node) may be used to determine the position of the wireless device.

[0072] In accordance with the above, present embodiments may be implemented in a communications network comprising a network node and a wireless device. The network node may be configured to obtain one or more radio measurements, obtain one or more audio measurements, and determine a position of the wireless device using the audio measurements and radio measurements. The wireless device may be configured to record at least one of the one or more audio measurements and transmit the recorded measurements to the network node. Alternatively, the wireless device may be configured to emit a reference signal. The wireless device may further transmit radio measurements to the network node, for example where the wireless device is configured to transmit radio measurements.

[0073] The communications network may further comprise one or more speakers. The speakers may be configured to emit a reference signal. For example, the speakers may be configured to emit a reference signal continuously and / or in response to a trigger.

[0074] The communications network may further comprise one or more reference microphones located at a fixed location. The reference microphones may be configured to record at least one of the one or more audio measurements and transmit the recorded audio measurements to the network node. The reference microphones may be configured to transmit the recorded audio measurements to the network node via the wireless device. It will be appreciated that the embodiments of Figure 9 and Figure 10 include the positioning method being undertaken by the network node 904 / 1004 of the communication network. However, present embodiments are not limited thereto, as the positioning method may be undertaken by any suitable communication device. For example, the positioning method may be undertaken by a network node. In present embodiments, the network node may be a Location Management Function (LMF). Alternatively, the positioning method may be undertaken by a wireless device. In present embodiments, the wireless device may be any of: a User Equipment (UE), a sensor, and / or and Internet of Things (loT) device.

[0075] Accordingly, sound information such as audio measurements may be used in the positioning of the wireless device. Sound information may carry information complementary to radio signals, and may be collected at a lower cost than other complementary solutions in particular as suitable microphones commonly form a part of wireless devices to be positioned. If a wireless device without a microphone is to be positioned, a microphone may be added to the wireless device at a low cost, in particular when compared to other sensors such as 3GPP radios, LiDAR, and the like. Microphones may also already be present in the environment, for example as part of voice-controlled hardware, and may be suitable for use in present embodiments.

[0076] Present embodiments may use audio signals or audio measurements, for example in indoor environments, to retrieve position information for establishing a position or location of a wireless device. Such audio measurements may provide a variety of positioning information; for example, the positioning information provided by the audio measurement may be determined by the method in which the audio measurement is collected or measured.

[0077] In accordance with the above, present embodiments may provide methods and apparatus for positioning a wireless device in a network with improved accuracy, in particular in indoor environments, by combining information from radio based positioning with audio measurements. That is, the positioning based on audio measurements may be used to complement and add to the radio based positioning.

[0078] In present embodiments, the audio measurements may be measured or recorded by the wireless device itself. These audio measurements may be compared with known characteristic sounds which are associated with characteristic sound sources. By correlating the audio measurements with known characteristic sounds, location precision may be improved. Alternatively or additionally, present embodiments may include using audio measurements collected by one or several distributed reference microphones, together with radio signal information, in the positioning and sensing solutions. By correlating audio measurements captured by the reference microphones, which have known locations, with audio recorded by the wireless device, for which location is to be determined, the location precision may be improved. Alternatively or additionally, by correlating audio measurements captured by the reference microphones, which have known locations, with a reference signal emitted by the wireless device, for which location is to be determined, the location precision may be improved.

[0079] Present embodiments may also allow for the use of existing hardware in the environment, such as hardware deployed for other use cases, thus providing methods and apparatus for positioning a wireless device with reduced costs. For example, in indoor environments devices such as smart assistants, voice-controlled devices, and the like may already be present. This may include for example televisions, speakers, smart assistants, tablets, and laptops. Over time, these devices may also become more ubiquitous. Such devices have significant processing capabilities, and may be able to provide the processing necessary for present embodiments without the need for further apparatus to be installed or maintained. Similarly, common devices in households and indoor environments such as home appliances, factory machines, distributed speakers, and the like may be able to provide the characteristic sounds necessary for present embodiments without the need for further apparatus to be installed or maintained.

[0080] The methods of the present disclosure may be implemented in hardware, or as software modules running on one or more processors. The methods may also be carried out according to the instructions of a computer program, and the present disclosure also provides a computer readable medium having stored thereon a program for carrying out any of the methods described herein. A computer program embodying the disclosure may be stored on a computer readable medium, or it could, for example, be in the form of a signal such as a downloadable data signal provided from an Internet website, or it could be in any other form.

[0081] In general, the various exemplary embodiments may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the disclosure is not limited thereto. While various aspects of the exemplary embodiments of this disclosure may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0082] As such, it should be appreciated that at least some aspects of the exemplary embodiments of the disclosure may be practiced in various components such as integrated circuit chips and modules. It should thus be appreciated that the exemplary embodiments of this disclosure may be realized in an apparatus that is embodied as an integrated circuit, where the integrated circuit may comprise circuitry (as well as possibly firmware) for embodying at least one or more of a data processor, a digital signal processor, baseband circuitry and radio frequency circuitry that are configurable so as to operate in accordance with the exemplary embodiments of this disclosure.

[0083] It should be appreciated that at least some aspects of the exemplary embodiments of the disclosure may be embodied in computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The computer executable instructions may be stored on a computer readable medium such as a hard disk, optical disk, removable storage media, solid state memory, RAM, etc. As will be appreciated by one of skill in the art, the function of the program modules may be combined or distributed as desired in various embodiments. In addition, the function may be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits, field programmable gate arrays (FPGA), and the like.

[0084] References in the present disclosure to “one embodiment”, “an embodiment” and so on, indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0085] It should be understood that, although the terms “first”, “second” and so on may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of the disclosure. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed terms.

[0086] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “has”, “having”, “includes” and / or “including”, when used herein, specify the presence of stated features, elements, and / or components, but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. The terms “connect”, “connects”, “connecting” and / or “connected” used herein cover the direct and / or indirect connection between two elements.

[0087] The present disclosure includes any novel feature or combination of features disclosed herein either explicitly or any generalization thereof. Various modifications and adaptations to the foregoing exemplary embodiments of this disclosure may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings. However, any and all modifications will still fall within the scope of the non-limiting and exemplary embodiments of this disclosure. For the avoidance of doubt, the scope of the disclosure is defined by the claims.

Claims

CLAIMS1. A method performed by a communication device, the method comprising: obtaining one or more radio measurements; obtaining one or more audio measurements; and determining a position of a wireless device using the audio measurements and the radio measurements.

2. The method as claimed in Claim 1 , wherein the method further comprises: receiving the one or more audio measurements from one or more microphones, wherein each of the one or more audio measurements corresponds to one of the one or more microphones.

3. The method as claimed in Claim 2, wherein at least one of the one or more microphones is integrated in the wireless device.

4. The method as claimed in Claim 3, wherein the one or more audio measurements comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, and wherein the method further comprises: obtaining source location information associated with the characteristic sound source.

5. The method as claimed in Claim 4, wherein the characteristic sound source has a fixed location and wherein the method further comprises: receiving the source location information from a further communication device and / or a user input.

6. The method as claimed in any of Claims 4 and 5, wherein the communication device comprises a Machine Learning (ML) algorithm, and wherein method further comprises: obtaining characteristic sound information from a database; and determining the sound location information by processing the characteristic sound information using the ML algorithm.

7. The method as claimed in any of Claims 2 to 6, wherein at least one of the one or more microphones is a reference microphone.

238. The method as claimed in Claim 7, wherein the reference microphone has a fixed location and wherein the method further comprises: obtaining microphone location information associated with the fixed location.

9. The method as claimed in Claim 8, wherein the method further comprises: receiving the microphone location information from a further communication device and / or a user input.

10. The method as claimed in any of Claims 8 and 9, wherein the one or more audio measurements comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, the communication device comprises a ML algorithm, and wherein method further comprises: obtaining characteristic sound information from a database; and determining the microphone location information by processing the characteristic sound information using the ML algorithm.

11. The method as claimed in any preceding claim, wherein the method further comprises: extracting a key characteristic from each audio measurement, and determining the position of the wireless device based on the key characteristic.

12. The method as claimed in Claim 11 , wherein the key characteristic comprises one or more of the following: time of arrival, signal amplitude, signal phase, and / or signal power spectrum.

13. The method as claimed in any preceding claim, wherein the method further comprises: determining the position of the wireless device by processing the radio measurements using triangulation and / or fingerprinting.

14. The method as claimed in any preceding claim, wherein the method further comprises: determining an estimated location of the wireless device using the one or more radio measurements; selecting a subset of the one or more audio measurements based on the estimated location; and determining the position of the wireless device using the subset of audio measurements and the radio measurements.

15. The method as claimed in any preceding claim, wherein at least one of the one or more audio measurements comprises a reference signal, the reference signal being emitted by a speaker.

16. The method as claimed in Claim 12, wherein the reference signal is injected into the speaker signal.

17. The method as claimed in any of Claims 15 and 16, wherein the speaker is integrated in the wireless device.

18. A method as claimed in any of Claims 15 to 17, wherein the reference signal meets one of the following criteria: the reference signal is of an infra sound frequency, the reference signal is of an ultra sound frequency, or the reference signal is configured to correspond with the environment of the speaker.

19. The method as claimed in any preceding claim, wherein the communication device is a network node and / or a Location Management Function (LMF).

20. The method as claimed in any preceding claim, wherein the communication device is integrated in a wireless device.

21. The method as claimed in Claim 20, wherein the wireless device is one or more of: a User Equipment (UE), a sensor, and / or an Internet of Things (loT) device.

22. A method performed by a communications network, the network comprising a communication device and a wireless device, wherein the communication device is configured to: obtain one or more radio measurements; obtain one or more audio measurements; and determine a position of the wireless device using the audio measurements and radio measurements; and wherein the wireless device is configured to: record at least one of the one or more audio measurements and transmit the recorded measurements to the communication device; or emit a reference signal.

23. The method as claimed in Claim 22, wherein the network further comprises one or more speakers and wherein the speakers are configured to: emit a reference signal.

24. The method as claimed in any of Claims 22 and 23, wherein the speakers are configured to emit a reference signal continuously and / or in response to a trigger.

25. The method as claimed in any of Claims 22 to 24, the network additionally comprising one or more reference microphones located at a fixed location, wherein the reference microphones are configured to: record at least one of the one or more audio measurements; and transmit the recorded audio measurements to the communication device.

26. A communication device comprising processing circuitry and a memory containing instructions executable by the processing circuitry, wherein the communication device is configured to: obtain one or more radio measurements; obtain one or more audio measurements; and determine a position of a wireless device using the audio measurements and the radio measurements.

27. The communication device as claimed in Claim 26, wherein the communication device is further configured to: receive the one or more audio measurements from one or more microphones, wherein each of the one or more audio measurements corresponds to one of the one or more microphones.

28. The communication device as claimed in Claim 27, wherein at least one of the one or more microphones is integrated in the wireless device.

29. The communication device as claimed in Claim 28, wherein the one or more audio measurements comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, and wherein the communication device is further configured to: obtain source location information associated with the characteristic sound source.

30. The communication device as claimed in Claim 29, wherein the characteristic sound source has a fixed location and wherein the communication device is further configured to: receive the source location information from a further communication device and / or a user input.

31. The communication device as claimed in any of Claims 29 and 30, wherein the communication device comprises a Machine Learning (ML) algorithm, and wherein the communication device is further configured to: obtain characteristic sound information from a database; and determine the sound location information by processing the characteristic sound information using the ML algorithm.

32. The communication device as claimed in any of Claims 27 to 31 , wherein at least one of the one or more microphones is a reference microphone.

33. The communication device as claimed in Claim 32, wherein the reference microphone has a fixed location and wherein the communication device is further configured to: obtain microphone location information associated with the fixed location.

34. The communication device as claimed in Claim 33, wherein the communication device is further configured to: receive the microphone location information from a further communication device and / or a user input.

35. The communication device as claimed in any of Claims 33 and 34, wherein the one or more audio measurements comprise a characteristic sound, the characteristic sound being associated with a characteristic sound source, the communication device comprises a ML algorithm, and wherein the communication device is further configured to: obtain characteristic sound information from a database; and determine the microphone location information by processing the characteristic sound information using the ML algorithm.

36. The communication device as claimed in any of Claims 26 to 35, wherein the communication device is further configured to: extract a key characteristic from each audio measurement, and determine the position of the wireless device based on the key characteristic.

37. The communication device as claimed in Claim 36, wherein the key characteristic comprises one or more of the following: time of arrival, signal amplitude, signal phase, and / or signal power spectrum.

38. The communication device as claimed in any one of Claims 26 to 37, wherein the communication device is further configured to: determine the position of the wireless device by processing the radio measurements using triangulation and / or fingerprinting.

39. The communication device as claimed in any one of Claims 26 to 38, wherein the communication device is further configured to: determine an estimated location of the wireless device using the one or more radio measurements; select a subset of the one or more audio measurements based on the estimated location; and determine the position of the wireless device using the subset of audio measurements and the radio measurements.

40. The communication device as claimed in any one of Claims 26 to 39, wherein at least one of the one or more audio measurements comprises a reference signal, the reference signal being emitted by a speaker.

41. The communication device as claimed in Claim 40, wherein the reference signal is injected into the speaker signal.

42. The communication device as claimed in any of Claims 40 and 41 , wherein the speaker is integrated in the wireless device.

43. The communication device as claimed in any of Claims 40 to 42, wherein the reference signal meets one of the following criteria: the reference signal is of an infra sound frequency, the reference signal is of an ultra sound frequency, or the reference signal is configured to correspond with the environment of the speaker.

44. The communication device as claimed in any of Claims 26 to 43, wherein the communication device is a network node and / or a Location Management Function (LMF).

45. The communication device as claimed in any of Claims 26 to 44, wherein the communication device is integrated in a wireless device.

46. The communication device as claimed in Claim 45, wherein the wireless device is one or more of: a User Equipment (UE), a sensor, and / or an Internet of Things (loT) device.

47. A communications network, the network comprising: a communication device comprising processing circuitry and a memory containing instructions executable by the processing circuitry, wherein the communication device is configured to: obtain one or more radio measurements; obtain one or more audio measurements; and determine a position of the wireless device using the audio measurements and radio measurements; and a wireless device comprising processing circuitry and a memory containing instructions executable by the processing circuitry, wherein the wireless device is configured to: record at least one of the one or more audio measurements and transmit the recorded measurements to the communication device; or emit a reference signal.

48. The communications network as claimed in Claim 47, wherein the network further comprises: one or more speakers comprising processing circuitry and a memory containing instructions executable by the processing circuitry, wherein the speakers are configured to: emit a reference signal.

49. The communications network as claimed in any of Claims 47 and 48, wherein the speakers are configured to emit a reference signal continuously and / or in response to a trigger.

50. The communications network as claimed in any of Claims 47 to 49, wherein the network further comprises: one or more reference microphones located at a fixed location, the reference microphones comprising processing circuitry and a memory containing instructions executable by the processing circuitry, wherein the reference microphones are configured to: record at least one of the one or more audio measurements; andtransmit the recorded audio measurements to the communication device.30

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