Network-based positioning based on frequency

Abstract

In one aspect, a network entity may receive one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device, the self-RFFP measurements being obtained by the target device based on reflections of one or more reference signals transmitted by the target device. The wireless device may determine a location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

Description

Technical Field

[0001]

[0013] Generally speaking, aspects of the present disclosure relate to wireless communications. Background Art

[0002] Wireless communication systems have been developed over many generations, including first generation analog wireless telephone service (1G), second generation (2G) digital wireless telephone service (including transitional 2.5G and 2.75G networks), third generation (3G) high speed data, wireless service with Internet capabilities, and fourth generation (4G) service (e.g., Long Term Evolution (LTE) or WiMax). Currently, there are many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog Advanced Mobile Phone System (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), Global System for Mobile Communications (GSM), etc.

[0003] The fifth generation (5G) wireless standard is called New Radio (NR), which enables higher data transmission speeds, more connections, and better coverage, among other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard is designed to provide higher data rates, more accurate positioning (e.g., based on reference signals for positioning (RS-P), such as downlink, uplink, or sidelink positioning reference signals (PRS)), and other technical enhancements than previous standards. These enhancements, along with the use of higher frequency bands, advances in PRS processes and technologies, and high-density deployments for 5G, enable highly accurate positioning based on 5G. Summary of the invention

[0004] A brief summary of the invention related to one or more aspects disclosed herein is presented below. Therefore, the following summary of the invention should not be regarded as an extensive review related to all covered aspects, nor should the following summary of the invention be regarded as identifying key or important elements related to all covered aspects or describing the scope associated with any particular aspect. Therefore, the sole purpose of the following summary of the invention is to present certain concepts related to one or more aspects related to the mechanisms disclosed herein in a brief form before the detailed embodiments presented below.

[0005] In one aspect, a method of operating a network entity includes: receiving one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device, wherein the self-RFFP measurements are obtained by the target device based on reflections of one or more reference signals sent by the target device; and determining a location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0006] In one aspect, a method of operating a wireless device includes: sending one or more reference signals; obtaining one or more self-RFF fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the wireless device; and sending the one or more self-RFFP measurements to a network entity.

[0007] In one aspect, a network entity includes a memory, at least one transceiver, and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device via the at least one transceiver, the self-RFFP measurements being obtained by the target device based on reflections of one or more reference signals sent by the target device; and determine a location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0008] In one aspect, a wireless device includes a memory, at least one transceiver, and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: send one or more reference signals via the at least one transceiver; obtain one or more self-RFF measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and send the one or more self-RFFP measurement values ​​to a network entity via the at least one transceiver.

[0009] In one aspect, a network entity includes: a unit for receiving one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device, wherein the self-RFFP measurements are obtained by the target device based on reflections of one or more reference signals sent by the target device; and a unit for determining the location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0010] In one aspect, a wireless device includes: a unit for sending one or more reference signals; a unit for obtaining one or more self-radio frequency fingerprint (self-RFFP) measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and a unit for sending the one or more self-RFFP measurement values ​​to a network entity.

[0011] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions, which, when executed by a network entity, cause the network entity to: receive one or more self-RFFP measurements from a target device, wherein the self-RFFP measurements are obtained by the target device based on reflections of one or more reference signals sent by the target device; and determine the location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0012] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions, which, when executed by a wireless device, cause the wireless device to: send one or more reference signals; obtain one or more self-RFF fingerprint (self-RFFP) measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and send the one or more self-RFFP measurement values ​​to a network entity.

[0013] Other objects and advantages associated with the various aspects disclosed herein will be apparent to those skilled in the art based on the drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are presented to aid in describing various aspects of the present disclosure and are provided solely for illustration of the aspects and not limitation thereof.

[0015] Figure 1 An example wireless communication system in accordance with aspects of the present disclosure is shown.

[0016] Figure 2A , Figure 2B and Figure 2C Example wireless network structures in accordance with aspects of the present disclosure are shown.

[0017] Figure 3A , Figure 3B and Figure 3C is a simplified block diagram of several sample aspects of components that may be employed in a user equipment (UE), a base station, and a network entity, respectively, and configured to support communications as taught herein.

[0018] Figure 4 According to aspects of the present disclosure, examples of various positioning methods supported in New Radio (NR) are shown.

[0019] Figure 5 is a diagram illustrating an exemplary radio frequency (RF) channel estimation in accordance with aspects of the present disclosure.

[0020] Figure 6An exemplary neural network in accordance with aspects of the present disclosure is shown.

[0021] Fig. 7A According to aspects of the present disclosure, a mobile device arranged in an ambient environment is shown.

[0022] Figure 7B According to various aspects of the present disclosure, Fig. 7A Graph of the self-RFF measurement of the reflection shown in .

[0023] Fig. 8A According to aspects of the present disclosure, examples of network-based positioning operations based on self-RFFP measurements are shown.

[0024] Figure 8B According to various aspects of the present disclosure, it is shown that Fig. 8A A diagram of the operations performed by network entities in .

[0025] Fig. 9 is a signaling and event diagram illustrating various operations during a position inference phase, in accordance with aspects of the present disclosure.

[0026] Fig.10 is a signaling and event diagram illustrating various operations during a model training phase, according to aspects of the present disclosure.

[0027] Fig.11 According to aspects of the present disclosure, example methods of operating a network entity during a position inference phase are shown.

[0028] Fig.12 According to aspects of the present disclosure, example methods of operating network entities during a model training phase are shown.

[0029] Fig.13 According to aspects of the present disclosure, an example method of operating a wireless device is shown. DETAILED DESCRIPTION

[0030] Various aspects of the present disclosure are provided in the following description and related drawings for various examples provided for illustrative purposes. Alternative aspects may be designed without departing from the scope of the present disclosure. In addition, well-known elements of the present disclosure will not be described in detail or will be omitted to avoid obscuring the relevant details of the present disclosure.

[0031] The words "exemplary" and / or "example" are used herein to mean "serving as an example, instance, or illustration." Any aspect described herein as "exemplary" and / or "example" is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term "aspects of the disclosure" does not require that all aspects of the disclosure include the discussed feature, advantage, or mode of operation.

[0032] Those skilled in the art will appreciate that any of a variety of different techniques and methods may be used to represent the information and signals described below. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the following specification may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof, which depends in part on the specific application, in part on the desired design, in part on the corresponding technology, etc.

[0033] In addition, many aspects are described in terms of a sequence of operations to be performed, for example, by elements of a computing device. It will be appreciated that the various operations described herein may be performed by specific circuits (e.g., an application specific integrated circuit (ASIC)), by program instructions being executed by one or more processors, or by a combination of both. In addition, the sequence of operations described herein may be viewed as being fully embodied within any form of non-transitory computer-readable storage medium having stored therein a corresponding set of computer instructions that, when executed, will cause or instruct an associated processor of a device to perform the functions described herein. Thus, various aspects of the present disclosure may be embodied in a variety of different forms, all of which are considered to be within the scope of the claimed subject matter. In addition, for each aspect described herein, the corresponding form of any such aspect may be described herein as, for example, "logic" that is "configured to" perform the described operations.

[0034] As used herein, unless otherwise specified, the terms "user equipment" (UE) and "base station" are not intended to be specific or otherwise limited to any specific radio access technology (RAT). In general, a UE may be any wireless communication device (e.g., a mobile phone, a router, a tablet computer, a laptop computer, a consumer asset location device, a wearable device (e.g., a smart watch, glasses, an augmented reality (AR) / virtual reality (VR) head-mounted device, etc.), a vehicle (e.g., a car, a motorcycle, a bicycle, etc.), an Internet of Things (IoT) device, etc.) used by a user to communicate over a wireless communication network. A UE may be mobile or may be stationary (e.g., at certain times) and may communicate with a radio access network (RAN). As used herein, the term "UE" may be interchangeably referred to as an "access terminal" or "AT", "client device", "wireless device", "subscribing device", "subscribing terminal", "subscribing station", "user terminal" or UT, "mobile device", "mobile terminal", "mobile station" or variations thereof. Typically, a UE may communicate with a core network via a RAN, and through the core network, the UE may connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for the UE to connect to the core network and / or the Internet are also possible, such as through a wired access network, a wireless local area network (WLAN) network (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 specification, etc.), etc.

[0035] A base station may operate according to one of several RATs for communicating with a UE, depending on the network in which the base station is deployed, and may be optionally referred to as an access point (AP), a network node, a Node B, an evolved Node B (eNB), a next generation eNB (ng-eNB), a new radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be primarily used to support wireless access for a UE, including supporting data, voice, and / or signaling connections for the supported UE. In some systems, a base station may provide only edge node signaling functions, while in other systems, a base station may provide additional control and / or network management functions. The communication link through which a UE can send a signal to a base station is referred to as an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link through which a base station can send a signal to a UE is referred to as a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc.). As used herein, the term traffic channel (TCH) may refer to an uplink / reverse or downlink / forward traffic channel.

[0036] The term "base station" may refer to a single physical transmit-receive point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term "base station" refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term "base station" refers to multiple co-located physical TRPs, the physical TRP may be an antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Optionally, the non-co-located physical TRP may be a serving base station that receives measurement reports from a UE and a neighbor base station whose reference radio frequency (RF) signal the UE is measuring. Because, as used herein, a TRP is a point at which a base station transmits and receives wireless signals, references to transmission from a base station or reception at a base station should be understood to refer to a specific TRP of a base station.

[0037] In some embodiments of supporting positioning of a UE, a base station may not support wireless access by the UE (e.g., may not support data, voice, and / or signaling connections for the UE), but may instead send a reference signal to the UE for measurement by the UE, and / or may receive and measure a signal sent by the UE. Such a base station may be referred to as a positioning beacon (e.g., when sending a signal to the UE) and / or a position measurement unit (e.g., when receiving and measuring a signal from the UE).

[0038] An "RF signal" includes an electromagnetic wave of a given frequency that transmits information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between a transmitter and a receiver may be referred to as a "multipath" RF signal. As used herein, an RF signal may also be referred to as a "wireless signal" or simply a "signal", where it is clear from the context that the term "signal" refers to either a wireless signal or an RF signal.

[0039] Figure 1An example wireless communication system 100 is shown in accordance with various aspects of the present disclosure. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled as "BS") and various UEs 104. The base stations 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In an aspect, the macrocell base stations may include eNBs and / or ng-eNBs where the wireless communication system 100 corresponds to an LTE network, or gNBs where the wireless communication system 100 corresponds to an NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0040] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through a backhaul link 122, and connect to one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) location platform (SLP)) through the core network 170. The location server 172 may be part of the core network 170 or may be external to the core network 170. The location server 172 may be integrated with the base station 102. The UE 104 may communicate with the location server 172 directly or indirectly. For example, the UE 104 may communicate with the location server 172 via the base station 102 currently serving the UE 104. The UE 104 may also communicate with the location server 172 through another path, such as via an application server (not shown), via another network (e.g., via a wireless local area network (WLAN) access point (AP) (e.g., the AP 150 described below), etc.). For signaling purposes, communications between UE 104 and location server 172 may be represented as an indirect connection (e.g., through core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), with intermediate nodes (if any) omitted from the signaling diagram for clarity.

[0041] The base stations 102 may perform, among other functions, functions related to one or more of the following: transmission of user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscription and device tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through EPC / 5GC) over a backhaul link 134, which may be wired or wireless.

[0042] Base station 102 can communicate wirelessly with UE 104. Each base station 102 can provide communication coverage for a respective geographic coverage area 110. In one aspect, one or more cells can be supported by base station 102 in each geographic coverage area 110. A "cell" is a logical communication entity used to communicate with a base station (e.g., on a certain frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.), and can be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (ECI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) used to distinguish cells operating via the same or different carrier frequencies. In some cases, different cells can be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or others) that can provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" can refer to either or both of the logical communication entity and the base station supporting it, depending on the context. In addition, because the TRP is generally the physical transmission point of the cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" can also refer to the geographic coverage area of ​​a base station (e.g., a sector), as long as the carrier frequency can be detected and used for communication within some portion of the geographic coverage area 110.

[0043] Although the geographic coverage areas 110 of neighboring macrocell base stations 102 may partially overlap (e.g., in a handoff region), some of the geographic coverage areas 110 may be substantially overlapped by a larger geographic coverage area 110. For example, a small cell base station 102' (labeled "SC" for "small cell") may have a geographic coverage area 110' that substantially overlaps with the geographic coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB), which may provide services to a restricted group referred to as a closed subscriber group (CSG).

[0044] The communication link 120 between the base station 102 and the UE 104 may include an uplink (also referred to as a reverse link) transmission from the UE 104 to the base station 102 and / or a downlink (DL) (also referred to as a forward link) transmission from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric with respect to the downlink and uplink (e.g., more or fewer carriers may be allocated for the downlink than for the uplink).

[0045] The wireless communication system 100 may also include a wireless local area network (WLAN) access point (AP) 150 that communicates with a WLAN station (STA) 152 in an unlicensed spectrum (e.g., 5 GHz) via a communication link 154. When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or a listen before talk (LBT) procedure prior to communication to determine whether a channel is available.

[0046] The small cell base station 102' can operate in a licensed spectrum and / or an unlicensed spectrum. When operating in an unlicensed spectrum, the small cell base station 102' can adopt LTE or NR technology and use the same 5GHz unlicensed spectrum used by the WLAN AP 150. The small cell base station 102' adopting LTE / 5G in the unlicensed spectrum can improve the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum can be referred to as NR-U. LTE in the unlicensed spectrum can be referred to as LTE-U, Licensed Assisted Access (LAA) or MulteFire.

[0047] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180, which can operate at mmW frequencies and / or near mmW frequencies to communicate with UE 182. Extremely high frequency (EHF) is part of RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz, and a wavelength between 1 mm and 10 mm. Radio waves in this band may be referred to as millimeter waves. Near mmW can be extended down to a frequency of 3 GHz, with a wavelength of 100 mm. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also known as centimeter waves. Communications using mmW / near mmW radio frequency bands have high path loss and relatively short distances. The mmW base station 180 and the UE 182 can utilize beamforming (transmitting and / or receiving) on ​​the mmW communication link 184 to compensate for extremely high path loss and short distances. In addition, it will be understood that in an optional configuration, one or more base stations 102 may also use mmW or near mmW and beamforming for transmission. Therefore, it will be appreciated that the foregoing description is exemplary only and should not be construed as limiting the various aspects disclosed herein.

[0048] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node), and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal to the receiving device. In order to change the directionality of the RF signal when transmitting, the network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that broadcast the RF signal. For example, a network node can use an antenna array (called a "phased array" or "antenna array") that creates an RF beam that can be "steered" to point in different directions without actually moving the antenna. Specifically, the RF current from the transmitter is fed to each antenna in the correct phase relationship so that the radio waves from the separate antennas are superimposed to increase the radiation in the desired direction while canceling to suppress the radiation in the undesired direction.

[0049] The transmit beams can be quasi-co-located, which means that they appear to have the same parameters to the receiver (e.g., UE), regardless of whether the network node's own transmit antenna is physically co-located. In NR, there are four types of quasi-co-located (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters about the second reference RF signal on the second beam can be derived based on information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay and delay spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type D, the receiver can use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0050] In receive beamforming, a receiver uses a receive beam to amplify an RF signal detected on a given channel. For example, the receiver may increase the gain setting and / or adjust the phase setting of the antenna array in a particular direction to amplify the RF signal received from that direction (e.g., increase its gain level). Thus, when a receiver is said to beamform in a certain direction, this means that the beam gain in that direction is high relative to the beam gain along other directions, or that the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), etc.) of the RF signal received from that direction.

[0051] The transmit beam and the receive beam may be spatially correlated. The spatial relationship means that the parameters of the second beam (e.g., transmit beam or receive beam) of the second reference signal may be derived based on information about the first beam (e.g., receive beam or transmit beam) of the first reference signal. For example, the UE may use a specific receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. The UE may then form a transmit beam for sending an uplink reference signal (e.g., a sounding reference signal (SRS)) to the base station based on the parameters of the receive beam.

[0052] Note that a "downlink" beam can be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station is forming a downlink beam to send a reference signal to the UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, it is a receive beam that receives the downlink reference signal. Similarly, an "uplink" beam can be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station is forming an uplink beam, it is an uplink receive beam, while if the UE is forming an uplink beam, it is an uplink transmit beam.

[0053] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc. based on frequency / wavelength. In 5G NR, two initial operating bands have been identified with the frequency range names FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often (interchangeably) referred to as the "Sub-6 GHz" band in various documents and articles. Similar naming issues sometimes arise for FR2, which is often (interchangeably) referred to as the "millimeter wave" band in various documents and articles, although it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) defined as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0054] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has determined the operating bands of these mid-band frequencies as the frequency range name FR3 (7.125GHz-24.25GHz). The frequency bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and can therefore effectively extend the characteristics of FR1 and / or FR2 to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to above 52.6GHz. For example, three higher operating bands have been determined as the frequency range names FR4a or FR4-1 (52.6GHz-71 GHz), FR4 (52.6GHz-114.25GHz), and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls into the EHF band.

[0055] In view of the above, unless otherwise explicitly stated, it should be understood that the term "sub-6 GHz" and the like (if used herein) can broadly represent frequencies less than 6 GHz, can be located within FR1, or can include mid-band frequencies. In addition, unless otherwise explicitly stated, it should be understood that the term "millimeter wave" and the like (if used herein) can broadly represent frequencies that can include mid-band frequencies, can be located within FR2, FR4, FR4-a or FR4-1 and / or FR5, or can be located within the EHF band.

[0056] In a multi-carrier system (e.g., 5G), one of the carrier frequencies is referred to as the "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell", and the remaining carrier frequencies are referred to as "secondary carriers" or "secondary serving cells" or "SCells". In carrier aggregation, the anchor carrier is a carrier operating on the primary frequency (e.g., FR1) used by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection reestablishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured once an RRC connection is established between the UE 104 and the anchor carrier, and can be used to provide additional radio resources. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, since both the primary uplink and downlink carriers are typically UE-specific, those signaling information and signals that are UE-specific may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same is true for the uplink primary carrier. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a "serving cell" (whether PCell or SCell) corresponds to a carrier frequency / component carrier on which a base station is communicating, the terms "cell", "serving cell", "component carrier", "carrier frequency", etc. can be used interchangeably.

[0057] For example, still referring to Figure 1, one of the frequencies used by macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by macrocell base station 102 and / or mmW base station 180 may be secondary carriers ("SCells"). Simultaneous transmission and / or reception of multiple carriers enables UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a two-fold increase in data rate (i.e., 40 MHz) compared to the data rate obtained with a single 20 MHz carrier.

[0058] The wireless communication system 100 may also include a UE 164, which may communicate with the macrocell base station 102 via a communication link 120 and / or communicate with the mmW base station 180 via a mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0059] In some cases, UE 164 and UE 182 are capable of sidelink communications. A sidelink-capable UE (SL-UE) may communicate with base station 102 via communication link 120 using a Uu interface (i.e., an air interface between a UE and a base station). SL-UEs (e.g., UE 164, UE 182) may also communicate directly with each other via a wireless sidelink 162 using a PC5 interface (i.e., an air interface between sidelink-capable UEs). A wireless sidelink (or simply "sidelink") is an adjustment to the core cellular (e.g., LTE, NR) standard that allows direct communication between two or more UEs without communicating through a base station. Sidelink communications may be unicast or multicast and may be used for device-to-device (D2D) media sharing, vehicle-to-vehicle (V2V) communications, vehicle-to-everything (V2X) communications (e.g., cellular V2X (cV2X) communications, enhanced V2X (eV2X) communications, etc.), emergency rescue applications, and the like. One or more of a group of SL-UEs utilizing sidelink communications may be located within the geographic coverage area 110 of the base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of the base station 102, or otherwise unable to receive transmissions from the base station 102. In some cases, a group of SL-UEs communicating via sidelink communications may utilize a one-to-many (1:M) system in which each SL-UE sends a signal to each other SL-UE in the group. In some cases, the base station 102 facilitates scheduling of resources for sidelink communications. In other cases, sidelink communications are performed between SL-UEs without the involvement of the base station 102.

[0060] In one aspect, the sidelink 160 can operate on a wireless communication medium of interest, which can be shared with other wireless communications between other vehicles and / or infrastructure access points and other RATs. A "medium" can consist of one or more time, frequency, and / or spatial communication resources (e.g., covering one or more channels on one or more carriers) associated with wireless communications between one or more transmitter / receiver pairs. In one aspect, the medium of interest can correspond to at least a portion of an unlicensed band shared between various RATs. Although different licensed bands have been reserved for certain communication systems (e.g., by government agencies such as the Federal Communications Commission (FCC) of the United States), these systems (particularly those that employ small cell access points) have recently expanded operations to unlicensed bands, such as the unlicensed national information infrastructure (U-NII) bands used by wireless local area network (WLAN) technologies, most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi". Example systems of this type include different variations of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single carrier FDMA (SC-FDMA) systems, and the like.

[0061] Note that although Figure 1 Only two of the UEs are shown as SL-UEs (i.e., UE 164 and UE 182), but any of the UEs shown may be a SL-UE. In addition, although only UE 182 is described as being capable of beamforming, any of the UEs shown (including UE 164) may be capable of beamforming. In cases where the SL-UEs are capable of beamforming, they may beamform toward each other (i.e., toward other SL-UEs), toward other UEs (e.g., UE 104), toward base stations (e.g., base stations 102, 180, small cells 102', access point 150), and so on. Therefore, in some cases, UE 164 and UE 182 may employ beamforming on the sidelink 160.

[0062] exist Figure 1 In the example of FIG. 1 , any one of the UEs shown (for simplicity, in Figure 1104) can receive a signal 124 from one or more earth-orbiting space vehicles (SVs) 112 (e.g., satellites). In one aspect, the SVs 112 can be part of a satellite positioning system that the UEs 104 can use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g., SVs 112) positioned to enable a receiver (e.g., UE 104) to determine its position on or above the earth based at least in part on a positioning signal (e.g., signal 124) received from the transmitter. Such a transmitter typically transmits a signal marked with a repeating pseudo-random noise (PN) code of a set number of chips. Although typically located in the SVs 112, the transmitters can sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. The UEs 104 can include one or more dedicated receivers specifically designed to receive the signal 124 for deriving geographic location information from the SVs 112.

[0063] In a satellite positioning system, the use of the signal 124 may be enhanced by various satellite-based augmentation systems (SBAS), which may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems. For example, the SBAS may include augmentation systems that provide integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geosynchronous Navigation Overlay Service (EGNOS), the Multifunction Satellite Augmentation System (MSAS), the Global Positioning System (GPS) Assisted Geographic Augmentation Navigation or the GPS and Geographic Augmentation Navigation System (GAGAN), etc. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0064] In one aspect, SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as a modified base station 102 (without a ground antenna) or a network node in a 5GC. This element in turn will provide access to other elements in the 5G network and ultimately provide access to entities outside the 5G network (e.g., Internet web servers and other user devices). In this way, UE 104 can receive communication signals (e.g., signal 124) from SV 112, instead of receiving communication signals from a terrestrial base station 102, or in addition to receiving communication signals from a terrestrial base station 102.

[0065] The wireless communication system 100 may also include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks”). Figure 1 In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity via D2D P2P link 192), and has a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity via D2D P2P link 194). In one example, D2D P2P links 192 and 194 can be supported by any well-known D2D RAT, such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), etc.

[0066] Figure 2A An example wireless network architecture 200 is shown. For example, the 5GC 210 (also referred to as the Next Generation Core (NGC)) can be functionally viewed as a control plane (C-plane) function 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and a user plane (U-plane) function 212 (e.g., UE gateway function, access to data networks, IP routing, etc.), which operate in conjunction to form a core network. The user plane interface (NG-U) 213 and the control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210, and specifically to the user plane function 212 and the control plane function 214, respectively. In another configuration, the ng-eNB 224 can also be connected to the 5GC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. In addition, the ng-eNB 224 can communicate directly with the gNB 222 via the backhaul connection 223. In some configurations, the next generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of ng-eNBs 224 and gNBs 222. Either (or both) the gNB 222 or the ng-eNB 224 may communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0067] Another optional aspect may include a location server 230 that can communicate with the 5GC 210 to provide location assistance for the UE 204. The location server 230 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or optionally each can correspond to a single server. The location server 230 can be configured to support one or more location services for the UE 204, which can be connected to the location server 230 via the core network 5GC 210 and / or via the Internet (not shown). In addition, the location server 230 can be integrated into a component of the core network, or optionally can be located outside the core network (e.g., a third-party server, such as an original equipment manufacturer (OEM) server or a service server).

[0068] Figure 2B Another example wireless network structure 240 is shown. 5GC 260 (which may correspond to Figure 2AThe 5GC 210 in the 5GC 210 can be functionally considered as a control plane function provided by an access and mobility management function (AMF) 264, and a user plane function provided by a user plane function (UPF) 262, which operate in conjunction to form a core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transmission of session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, a transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between the UE 204 and a short message service function (SMSF) (not shown), and a security anchor function (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives an intermediate key established as a result of the UE 204 authentication process. In the case of authentication based on the UMTS (Universal Mobile Telecommunications System) User Identity Module (USIM), AMF264 extracts security materials from AUSF. The functions of AMF 264 also include security context management (SCM). SCM receives keys from SEAF, which use the keys to derive access network-specific keys. The functions of AMF 264 also include location service management for regulatory services, for the transmission of location service messages between UE 204 and location management function (LMF) 270 (which acts as location server 230), for the transmission of location service messages between NG-RAN 220 and LMF 270, for the allocation of evolved packet system (EPS) bearer identifiers for interaction with EPS, and UE 204 mobility event notifications. In addition, AMF 264 also supports functions for non-3GPP (Third Generation Partnership Project) access networks.

[0069] The functions of UPF 262 include acting as an anchor point for intra-RAT / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point for interconnection to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) processing for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in downlink), uplink traffic verification (service data flow (SDF) to QoS flow mapping), transport level packet marking in uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding one or more "end markers" to the source RAN node. UPF 262 can also support the transmission of location service messages between UE 204 and a location server (e.g., SLP 272) on the user plane.

[0070] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at UPF 262 to route traffic to the appropriate destination, control of part of policy enforcement and QoS, and downlink data notification. The interface through which SMF 266 communicates with AMF 264 is called the N11 interface.

[0071] Another optional aspect may include an LMF 270 that can communicate with the 5GC 260 to provide location assistance for the UE 204. The LMF 270 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each can correspond to a single server. The LMF 270 can be configured to support one or more location services for the UE 204, which can be connected to the LMF 270 via the core network 5GC 260 and / or via the Internet (not shown). SLP 272 may support similar functionality as LMF 270, but LMF 270 may communicate with AMF 264, NG-RAN 220, and UE 204 on a control plane (e.g., using interfaces and protocols intended to carry signaling messages rather than voice or data), and SLP 272 may communicate with UE 204 and external clients (e.g., third-party servers 274) on a user plane (e.g., using protocols intended to carry voice and / or data, such as Transmission Control Protocol (TCP) and / or IP).

[0072] Another optional aspect may include a third-party server 274 that can communicate with the LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., location estimate) for the UE 204. Thus, in some cases, the third-party server 274 may be referred to as a location service (LCS) client or external client. The third-party server 274 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or may alternatively each correspond to a single server.

[0073] The user plane interface 263 and the control plane interface 265 connect the 5GC 260 (and specifically, the UPF 262 and the AMF 264) to one or more gNBs 222 and / or ng-eNBs 224 in the NG-RAN 220, respectively. The interface between the gNB 222 and / or ng-eNB 224 and the AMF 264 is referred to as the "N2" interface, and the interface between the gNB 222 and / or ng-eNB 224 and the UPF 262 is referred to as the "N3" interface. The gNBs 222 and / or ng-eNBs 224 of the NG-RAN 220 may communicate directly with each other via a backhaul connection 223 referred to as an "Xn-C" interface. One or more of the gNBs 222 and / or ng-eNBs 224 may communicate with one or more UEs 204 via a wireless interface referred to as a "Uu" interface.

[0074] The functions of the gNB 222 may be divided between a gNB central unit (gNB-CU) 226, one or more gNB distributed units (gNB-DU) 228, and one or more gNB radio units (gNB-RU) 229. The gNB-CU 226 is a logical node that includes base station functions for transmitting user data, mobility control, radio access network sharing, positioning, session management, etc., except for those functions that are specifically assigned to the gNB-DU 228. More specifically, the gNB-CU 226 typically hosts the radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) protocols of the gNB 222. The gNB-DU 228 is a logical node that typically hosts the radio link control (RLC) and media access control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 may support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and one or more gNB-DUs 228 is referred to as the "F1" interface. The physical (PHY) layer functions of the gNB 222 are typically hosted by one or more independent gNB-RUs 229, where one or more independent gNB-RUs 229 perform functions such as power amplification and signal transmission / reception. The interface between the gNB-DU 228 and the gNB-RU 229 is referred to as the "Fx" interface. Thus, the UE 204 communicates with the gNB-CU 226 via the RRC, SDAP, and PDCP layers, communicates with the gNB-DU 228 via the RLC and MAC layers, and communicates with the gNB-RU 229 via the PHY layer.

[0075] The deployment of a communication system (e.g., a 5G NR system) can be arranged in a variety of ways using various components or components. In a 5G NR system or network, a network node, a network entity, a mobile element of a network, a RAN node, a core network node, a network element, or a network device such as a base station, or one or more units (or one or more components) that perform base station functions can be implemented in an aggregated or decomposed architecture. For example, a base station (e.g., a Node B (NB), an evolved NB (eNB), an NR base station, a 5GNB, an access point (AP), a transmit receive point (TRP), or a cell, etc.) can be implemented as an aggregated base station (also called an independent base station or a monolithic base station) or a decomposed base station.

[0076] The aggregated base station can be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. The decomposed base station can be configured to utilize a protocol stack physically or logically distributed between two or more units (e.g., one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some aspects, the CU can be implemented within a RAN node, and one or more DUs can be co-located with the CU, or alternatively, can be geographically or virtually distributed in one or more other RAN nodes. The DU can be implemented to communicate with one or more RUs. Each of the CU, DU, and RU can also be implemented as a virtual unit (i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU)).

[0077] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, decomposed base stations may be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (e.g., a network configuration sponsored by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Decomposition may include: distributed functionality between two or more units at different physical locations, and distributed functionality that is virtualized for at least one unit, which may enable flexibility in network design. Individual units of a decomposed base station or decomposed RAN architecture may be configured to communicate in wired or wireless communication with at least one other unit.

[0078] Figure 2CAccording to aspects of the present disclosure, an exemplary decomposed base station architecture 250 is shown. The decomposed base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CUs 226) that can communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 through one or more decomposed base station units (such as a near real-time (near-RT) RAN intelligent controller (RIC) 259 via an E2 link, or a non-real-time (non-RT) RIC 257 associated with a service management and orchestration (SMO) framework 255, or both). The CU 280 may communicate with one or more distributed units (DUs) 285 (e.g., gNB-DUs 228) via respective mid-haul links, such as via an F1 interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RU 287 may communicate with a corresponding UE 204 via one or more radio frequency (RF) access links. In some implementations, a UE 204 may be served by multiple RUs 287 simultaneously.

[0079] Each unit (i.e., CU 280, DU 285, RU 287) and near-RT RIC 259, non-RT RIC 257, and SMO framework 255 may include one or more interfaces, or be coupled to one or more interfaces, configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each unit, or an associated processor or controller that provides instructions to the communication interface of these units, may be configured to communicate with one or more other units via a transmission medium. For example, these units may include a wired interface that is configured to receive or send signals for one or more of the other units through a wired transmission medium. In addition, these units may include a wireless interface that may include a receiver, a transmitter, or a transceiver (e.g., a radio frequency (RF) transceiver) that is configured to receive or send, or both, signals for one or more other units through a wireless transmission medium.

[0080] In some aspects, CU 280 may host one or more higher-layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), and the like. Each control function may be implemented using an interface configured to transmit signals using other control functions hosted by CU 280. CU 280 may be configured to process user plane functions (i.e., central unit-user plane (CU-UP)), control plane functions (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, CU 280 may be logically divided into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface such as an E1 interface. CU 280 may be implemented to communicate with DU 285 as needed for network control and signaling.

[0081] DU 285 may correspond to a logical unit including one or more base station functions to control the operation of one or more RU287. In some aspects, DU 285 may host one or more of the following items: radio link control (RLC) layer, media access control (MAC) layer, and one or more advanced physical (PHY) layers (e.g., modules for forward error correction coding (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) based at least in part on functional divisions (e.g., those defined by the Third Generation Partnership Project (3GPP)). In some aspects, DU 285 may also host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to transmit signals using other layers (and modules) hosted by DU 285, or to transmit signals using control functions hosted by CU 280.

[0082] One or more RUs 287 may implement lower layer functions. In some deployments, the RUs 287 controlled by the DUs 285 may correspond to logical nodes that host RF processing functions or low PHY layer functions (e.g., performing fast Fourier transforms (FFTs), inverse FFTs (iFFTs), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on functional divisions such as lower layer functional divisions. In such an architecture, the RUs 287 may be implemented to handle over-the-air (OTA) communications with one or more UEs 204. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RUs 287 may be controlled by the corresponding DUs 285. In some scenarios, this configuration may enable the implementation of the DUs 285 and CUs 280 in a cloud-based RAN architecture (e.g., a vRAN architecture).

[0083] The SMO framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, and the RAN coverage requirements may be managed through an operation and maintenance interface (e.g., an O1 interface). For virtualized network elements, the SMO framework 255 may be configured to interact with a cloud computing platform (e.g., an open cloud (O-cloud) platform 269) to perform network element lifecycle management (e.g., instantiate virtualized network elements) via a cloud computing platform interface (e.g., an O2 interface). Such virtualized network elements may include, but are not limited to, CU 280, DU 285, RU 287, and near-RT RIC 259. In some implementations, the SMO framework 255 may communicate with hardware aspects of a 4G RAN such as an open eNB (O-eNB) 261 via an O1 interface. In addition, in some implementations, the SMO framework 255 may communicate directly with one or more RUs 287 via an O1 interface. The SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of the SMO framework 255 .

[0084] The non-RT RIC 257 may be configured to include logic functions that implement non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows (including model training and updating), or policy-based guidance of applications / features in the near-RT RIC 259. The non-RT RIC 257 may be coupled to or in communication with the near-RT RIC 259 (e.g., via an A1 interface). The near-RT RIC 259 may be configured to include logic functions that enable near real-time control and optimization of RAN elements and resources through data collection and actions on interfaces connecting one or more CUs 280, one or more DUs 285, or both, and O-eNBs with the near-RT RIC 259 (e.g., via an E2 interface).

[0085] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 259, the non-RT RIC 257 may receive parameters or external enrichment information from an external server. Such information may be used by the near-RT RIC 259 and may be received from a non-network data source or from a network function at the SMO framework 255 or the non-RT RIC 257. In some examples, the non-RT RIC 257 or the near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 257 may monitor long-term trends and patterns of performance and use the AI / ML model to perform corrective actions through the SMO framework 255 (such as via reconfiguration of O1) or via the creation of a RAN management policy (such as an A1 policy).

[0086] Figure 3A , Figure 3B and Figure 3C 302 (which may correspond to any UE described herein), base station 304 (which may correspond to any base station described herein), and network entity 306 (which may correspond to or embody any network function described herein, including location server 230 and LMF 270), or alternatively may be independent of Figure 2A and Figure 2BThe NG-RAN 220 and / or 5GC 210 / 260 infrastructure (e.g., a dedicated network) depicted in the present disclosure may include several example components (represented by corresponding boxes) to support operations as described herein. It will be understood that these components may be implemented in different embodiments in different types of devices (e.g., in an ASIC, in a system on a chip (SoC), etc.). The components shown may also be incorporated into other devices in the communication system. For example, other devices in the system may include components similar to those described to provide similar functionality. Moreover, a given device may include one or more components. For example, a device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0087] UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, providing means (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for preventing transmitting, etc.) for communicating via one or more wireless communication networks (not shown), such as NR networks, LTE networks, GSM networks, etc. WWAN transceivers 310 and 350 can each be connected to one or more antennas 316 and 356 for communicating with other network nodes (e.g., other UEs, access points, base stations (e.g., eNBs, gNBs), etc.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) on a wireless communication medium of interest (e.g., a certain set of time / frequency resources in a specific spectrum). The WWAN transceivers 310 and 350 may be configured differently to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.) respectively. Specifically, the WWAN transceivers 310 and 350 include one or more transmitters 314 and 354 for transmitting and encoding the signals 318 and 358, respectively, and one or more receivers 312 and 352 for receiving and decoding the signals 318 and 358, respectively.

[0088] At least in some cases, the UE 302 and the base station 304 also each include one or more short-range wireless transceivers 320 and 360. The short-range wireless transceivers 320 and 360 can be connected to one or more antennas 326 and 366, respectively, and provide for communicating over the wireless communication medium of interest via at least one designated RAT (e.g., WiFi, LTE-D, The short-range wireless transceivers 320 and 360 are units (e.g., units for sending, units for receiving, units for measuring, units for tuning, units for preventing sending, etc.) for communicating with other network nodes (such as other UEs, access points, base stations, etc.) using a PC5, dedicated short-range communication (DSRC), wireless access for vehicular environments (WAVE), near field communication (NFC), ultra-wideband (UWB), etc.). The short-range wireless transceivers 320 and 360 can be configured differently to send and encode signals 328 and 368 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, to receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.). Specifically, the short-range wireless transceivers 320 and 360 include one or more transmitters 324 and 364 for sending and encoding signals 328 and 368, respectively, and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368, respectively. As a specific example, the short-range wireless transceivers 320 and 360 may be WiFi transceivers, Transceiver, and / or transceiver, NFC transceiver, UWB transceiver, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceiver.

[0089] At least in some cases, UE 302 and base station 304 also include satellite signal receivers 330 and 370. Satellite signal receivers 330 and 370 can be connected to one or more antennas 336 and 376, respectively, and can provide units 338 and 378 for receiving and / or measuring satellite positioning / communication signals, respectively. Among them, satellite signal receivers 330 and 370 are satellite positioning system receivers, and satellite positioning / communication signals 338 and 378 can be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. In the case where satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, satellite positioning / communication signals 338 and 378 can be communication signals (e.g., carrying control and / or user data) originating from a 5G network. Satellite signal receivers 330 and 370 may include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 330 and 370 may request information and operations from other systems as appropriate, and, at least in some cases, perform calculations to determine UE 302 and base station 304 locations, respectively, using measurements obtained through any suitable satellite positioning system algorithm.

[0090] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, which provide means (e.g., means for sending, means for receiving, etc.) for communicating with other network entities (e.g., other base stations 304, other network entities 306). For example, the base station 304 can use one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 through one or more wired or wireless backhaul links. As another example, the network entity 306 can use one or more network transceivers 390 to communicate with one or more base stations 304 through one or more wired or wireless backhaul links, or communicate with other network entities 306 through one or more wired or wireless core network interfaces.

[0091] The transceiver may be configured to communicate over a wired or wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes a transmitter circuit (e.g., transmitters 314, 324, 354, 364) and a receiver circuit (e.g., receivers 312, 322, 352, 362). In some embodiments, the transceiver may be an integrated device (e.g., embodying the transmitter circuit and the receiver circuit in a single device), may include separate transmitter circuits and separate receiver circuits in some implementations, or may be embodied in other ways in other implementations. The transmitter circuit and the receiver circuit of a wired transceiver (e.g., network transceivers 380 and 390 in some embodiments) may be coupled to one or more wired network interface ports. The wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366) such as antenna arrays, which allow the corresponding devices (e.g., UE 302, base station 304) to perform transmit "beamforming", as described herein. Similarly, the wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366) such as antenna arrays, which allow the corresponding devices (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, the transmitter circuitry and the receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366) so that the respective devices can only receive or transmit at a given time, rather than both receive and transmit at the same time. The wireless transceivers (eg, WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listening module (NLM) or the like for performing various measurements.

[0092] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360, and in some implementations, network transceivers 380 and 390) and wired transceivers (e.g., network transceivers 380 and 390 in some implementations) may be generally described as a "transceiver," "at least one transceiver," or "one or more transceivers." Thus, whether a particular transceiver is a wired or wireless transceiver may be inferred from the type of communication being performed. For example, backhaul communications between network devices or servers will generally involve signaling via a wired transceiver, while wireless communications between a UE (e.g., UE 302) and a base station (e.g., base station 304) will generally involve signaling via a wireless transceiver.

[0093] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operations disclosed herein. UE 302, base station 304, and network entity 306 include one or more processors 332, 384, and 394, respectively, for providing functions related to, for example, wireless communication and providing other processing functions. Thus, processors 332, 384, and 394 can provide means for processing, such as means for determining, means for calculating, means for receiving, means for sending, means for indicating, etc. In one aspect, processors 332, 384, and 394 can include, for example, one or more general-purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), other programmable logic devices or processing circuits, or various combinations thereof.

[0094] UE 302, base station 304, and network entity 306 include memory circuitry for implementing memory 340, 386, and 396 (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.), respectively. Thus, memory 340, 386, and 396 may provide means for storing, means for retrieving, means for maintaining, etc. In some cases, UE 302, base station 304, and network entity 306 may include positioning components 342, 388, and 398, respectively. Positioning components 342, 388, and 398 may be hardware circuitry that is part of or coupled to processors 332, 384, and 394, respectively, which, when executed, causes UE 302, base station 304, and network entity 306 to perform the functions described herein. In other aspects, the positioning components 342, 388, and 398 can be external to the processors 332, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, the positioning components 342, 388, and 398 can be memory modules stored in the memories 340, 386, and 396, respectively, which, when executed by the processors 332, 384, and 394 (or a modem processing system, another processing system, etc.), cause the UE 302, the base station 304, and the network entity 306 to perform the functions described herein. Figure 3A Possible locations for the location component 342 are shown, for example, the location component 342 can be part of one or more WWAN transceivers 310, memory 340, one or more processors 332, or any combination thereof, or can be a stand-alone component. Figure 3B Possible locations for the positioning component 388 are shown, for example, the positioning component 388 can be part of one or more WWAN transceivers 350, memory 386, one or more processors 384, or any combination thereof, or can be a stand-alone component. Figure 3C Possible locations for the location component 398 are shown, for example, the location component 398 can be part of one or more network transceivers 390, memory 396, one or more processors 394, or any combination thereof, or can be a stand-alone component.

[0095] UE 302 may include one or more sensors 344 coupled to one or more processors 332 to provide a unit for sensing or detecting movement and / or orientation information independent of motion data derived from signals received by one or more WWAN transceivers 310, one or more short-range wireless transceivers 320, and / or satellite signal receivers 330. For example, sensor 344 may include an accelerometer (e.g., a microelectromechanical system (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of motion detection sensor. In addition, sensor 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate a position in a two-dimensional (2D) and / or three-dimensional (3D) coordinate system.

[0096] In addition, the UE 302 includes a user interface 346, which provides a means for providing indications to the user (e.g., audible and / or visual indications) and / or for receiving user input (e.g., when the user actuates a sensing device such as a keypad, touch screen, microphone, etc.). Although not shown, the base station 304 and the network entity 306 may also include a user interface.

[0097] Referring to the one or more processors 384 in more detail, in a downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement functions for an RRC layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. One or more processors 384 may provide RRC layer functions associated with broadcasting of system information (e.g., master information block (MIB), system information block (SIB)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functions associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functions associated with transmission of upper layer PDUs, error correction through automatic repeat request (ARQ), concatenation, segmentation and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functions associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0098] The transmitter 354 and the receiver 352 may implement layer 1 (L1) functions associated with various signal processing functions. Layer 1, including the physical (PHY) layer, may include error detection on the transport channel, forward error correction (FEC) encoding / decoding of the transport channel, interleaving, rate matching, mapping to the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. The transmitter 354 processes the mapping to the signal constellation based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be divided into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., a pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator can be used to determine coding and modulation schemes, as well as for spatial processing. Channel estimates can be derived from reference signals and / or channel condition feedback sent by UE 302. Each spatial stream can then be provided to one or more different antennas 356. Transmitter 354 can modulate an RF carrier with a corresponding spatial stream for transmission.

[0099] At the UE 302, the receiver 312 receives the signal through its corresponding antenna 316. The receiver 312 recovers the information modulated onto the RF carrier and provides the information to one or more processors 332. The transmitter 314 and the receiver 312 implement layer 1 functions associated with various signal processing functions. The receiver 312 can perform spatial processing on the information to recover any spatial stream destined for the UE 302. If multiple spatial streams are destined for the UE 302, they can be combined into a single OFDM symbol stream by the receiver 312. The receiver 312 then converts the OFDM symbol stream from the time domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier and the reference signal are recovered and demodulated by determining the most likely signal constellation point sent by the base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally sent on the physical channel by the base station 304. The data and control signals are then provided to one or more processors 332 that implement layer 3 (L3) and layer 2 (L2) functionality.

[0100] In the downlink, one or more processors 332 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets from the core network. One or more processors 332 are also responsible for error detection.

[0101] Similar to the functions described in conjunction with the downlink transmission of the base station 304, one or more processors 332 provide RRC layer functions associated with system information (e.g., MIB, SIB) capture, RRC connection, and measurement reporting; PDCP layer functions associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functions associated with transmission of upper layer PDUs, error correction through ARQ, concatenation, segmentation and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functions associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), priority processing, and logical channel prioritization.

[0102] Channel estimates derived by a channel estimator from a reference signal or feedback sent by base station 304 may be used by transmitter 314 to select appropriate coding and modulation schemes and to facilitate spatial processing. The spatial streams generated by transmitter 314 may be provided to different antennas 316. Transmitter 314 may modulate an RF carrier with a corresponding spatial stream for transmission.

[0103] The uplink transmission is processed at the base station 304 in a manner similar to that described in conjunction with the receiver functionality at the UE 302. The receiver 352 receives the signal through its respective antenna 356. The receiver 352 recovers the information modulated onto the RF carrier and provides the information to one or more processors 384.

[0104] In the uplink, one or more processors 384 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover IP packets from UE 302. The IP packets from one or more processors 384 may be provided to the core network. One or more processors 384 are also responsible for error detection.

[0105] For convenience, UE 302, base station 304 and / or network entity 306 may be configured to: Figure 3A , 3B , Figure 3C1 is shown as including various components that can be configured according to the various examples described herein. However, it will be understood that the components shown may have different functions in different designs. Specifically, FIG. 3A to FIG. 3C The various components in are optional in alternative configurations, and the various aspects include configurations that may vary due to design choice, cost, use of the device, or other considerations. For example, in Figure 3A In the case of , a specific implementation of UE 302 may omit WWAN transceiver 310 (e.g., a wearable device or tablet or PC or laptop may have Wi-Fi and / or Bluetooth capabilities but no cellular capabilities), or may omit short-range wireless transceiver 320 (e.g., only cellular, etc.), or may omit satellite signal receiver 330, or may omit sensor 344, etc. In another example, in Figure 3B In the case of a wireless cellular network, a specific implementation of the base station 304 may omit the WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capabilities), or may omit the short-range wireless transceiver 360 (e.g., cellular only, etc.), or may omit the satellite signal receiver 370, etc. For the sake of brevity, illustrations of various alternative configurations are not provided herein, but this is readily apparent to those of ordinary skill in the art.

[0106] Various components of the UE 302, base station 304, and network entity 306 may be communicatively coupled to one another via data buses 334, 382, ​​and 392, respectively. In one aspect, the data buses 334, 382, ​​and 392 may form or be part of a communication interface for the UE 302, base station 304, and network entity 306, respectively. For example, where different logical entities are embodied in the same device (e.g., gNB and location server functionality are merged into the same base station 304), the data buses 334, 382, ​​and 392 may provide communication therebetween.

[0107] Figure 3A , Figure 3B and Figure 3C The components of can be implemented in various ways. In some embodiments, Figure 3A , Figure 3B and Figure 3CThe components may be implemented in one or more circuits, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or combine at least one memory component to store information or executable code used by the circuit to provide the function. For example, some or all of the functions represented by blocks 310 to 346 may be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate codes and / or by appropriate configuration of the processor components). Similarly, some or all of the functions represented by blocks 350 to 388 may be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate codes and / or by appropriate configuration of the processor components). In addition, some or all of the functions represented by blocks 390 to 398 may be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate codes and / or by appropriate configuration of the processor components). For simplicity, various operations, actions and / or functions are described herein as being performed by "UE", "by base station", "by network entity", etc. However, as will be appreciated, such operations, actions, and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc., such as processors 332, 384, 394, transceivers 310, 320, 350 and 360, memories 340, 386 and 396, positioning components 342, 388 and 398, etc.

[0108] In some designs, network entity 306 may be implemented as a core network component. In other designs, network entity 306 may be operated independently of a network operator or cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, network entity 306 may be a component of a dedicated network that may be configured to communicate with UE 302 via base station 304 or independently of base station 304 (e.g., via a non-cellular communication link such as WiFi).

[0109] NR supports a variety of cellular network-based positioning technologies, including downlink-based, uplink-based, and downlink and uplink-based positioning methods. Downlink-based positioning methods include observed time difference of arrival (OTDOA) in LTE, downlink time difference of arrival (DL-TDOA) in NR, and downlink angle of departure (DL-AoD) in NR. Figure 4Examples of various positioning methods according to various aspects of the present disclosure are shown. In the OTDOA or DL-TDOA positioning process shown in scenario 410, the UE measures the difference between the arrival time (ToA) of the reference signal (e.g., positioning reference signal (PRS)) received from the base station (referred to as reference signal time difference (RSTD) or arrival time difference (TDOA) measurement) and reports them to the positioning entity. More specifically, the UE receives identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in auxiliary data. The UE then measures the RSTD between the reference base station and each non-reference base station. Based on the known positions of the base stations involved and the RSTD measurements, a positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the position of the UE.

[0110] For DL-AoD positioning illustrated by scenario 420, the positioning entity uses measurement reports from the UE regarding received signal strength measurements of multiple downlink transmit beams to determine the angle between the UE and the transmitting base station. The positioning entity can then estimate the position of the UE based on the determined angle and the known location of the transmitting base station.

[0111] Uplink-based positioning methods include uplink time difference of arrival (UL-TDOA) and uplink angle of arrival (UL-AoA). UL-TDOA is similar to DL-TDOA, but is based on uplink reference signals (e.g., sounding reference signals (SRS)) sent by the UE to multiple base stations. Specifically, the UE sends one or more uplink reference signals measured by a reference base station and multiple non-reference base stations. Each base station then reports the reception time of the reference signal (called relative time of arrival (RTOA)) to the positioning entity (e.g., a location server), and the positioning entity knows the location and relative timing of the base stations involved. Based on the received-to-receive (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each non-reference base station, the known location of the base stations, and their known timing offsets, the positioning entity can use TDOA to estimate the position of the UE.

[0112] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurements and the angle of the receive beams to determine the angle between the UE and the base station. Based on the determined angle and the known location of the base station, the positioning entity can then estimate the location of the UE.

[0113] Downlink and uplink based positioning methods include enhanced cell ID (E-CID) positioning and multi-round trip time (RTT) positioning (also known as "multi-cell RTT" and "multi-RTT"). In the RTT process, a first entity (e.g., a base station or UE) sends a first RTT-related signal (e.g., a PRS or SRS) to a second entity (e.g., a UE or a base station), and the second entity (e.g., a UE or a base station) sends a second RTT-related signal (e.g., an SRS or a PRS) back to the first entity. Each entity measures the time difference between the arrival time (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. This time difference is called the received to transmitted (Rx-Tx) time difference. The Rx-Tx time difference measurement can be made or can be adjusted so as to include only the time difference between the nearest time slot boundaries of the received and transmitted signals. The two entities may then send their Rx-Tx time difference measurements to a location server (e.g., LMF 270), which calculates the round trip propagation time (i.e., RTT) between the two entities based on the two Rx-Tx time difference measurements (e.g., as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity may send its Rx-Tx time difference measurements to the other entity, which then calculates the RTT. The distance between the two entities may be determined based on the RTT and a known signal speed (e.g., the speed of light). For multi-RTT positioning as shown in scenario 430, a first entity (e.g., a UE or a base station) performs an RTT positioning process with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined based on the distance to the second entity and the known location of the second entity (e.g., using multi-point measurements). RTT and multi-RTT methods may be combined with other positioning techniques (e.g., UL-AoA and DL-AoD) to improve positioning accuracy as shown in scenario 440.

[0114] The E-CID positioning method is based on radio resource management (RRM) measurements. In E-CID, the UE reports the serving cell ID, timing advance (TA), and the identifiers, estimated timing and signal strength of detected neighbor base stations. The UE's position is then estimated based on this information and the known locations of the base stations.

[0115] To assist the positioning operation, a location server (e.g., location server 230, LMF 270, SLP 272) may provide assistance data to the UE. For example, the assistance data may include an identifier of the base station (or cell / TRP of the base station) from which the reference signal is measured, reference signal configuration parameters (e.g., the number of consecutive time slots including PRS, the period of consecutive time slots including PRS, the mute sequence, the frequency hopping sequence, the reference signal identifier, the reference signal bandwidth, etc.), and / or other parameters applicable to a particular positioning method. Alternatively, the assistance data may be directly derived from the base station itself (e.g., in a periodically broadcast overhead message, etc.). In some cases, the UE may be able to detect neighbor network nodes on its own without the use of assistance data.

[0116] In the case of OTDOA or DL-TDOA positioning process, the auxiliary data may also include the expected RSTD value and the associated uncertainty or search window around the expected RSTD. In some cases, the value range of the expected RSTD may be + / -500 microseconds (μs). In some cases, when any resource used for positioning measurement is located in FR1, the value range of the uncertainty of the expected RSTD may be + / -32μs. In other cases, when all resources used for positioning measurement are in FR2, the value range of the uncertainty of the expected RSTD may be + / -8μs.

[0117] The location estimate may be referred to by other names, such as location estimate, location, position, location fix, fix, etc. The location estimate may be geodetic and include coordinates (e.g., latitude, longitude, and possibly altitude), or may be urban and include a street address, postal address, or some other verbal description of the location. The location estimate may be further defined relative to some other known location, or defined in absolute terms (e.g., using latitude, longitude, and possibly altitude). The location estimate may include expected errors or uncertainties (e.g., by including an area or volume in which the location is expected to be contained with some specified or default confidence level).

[0118] Figure 5500 is a diagram of an exemplary channel estimate for a multipath channel between a receiver device (e.g., any of the UEs or base stations described herein) and a transmitter device (e.g., any of the UEs or base stations described herein) according to aspects of the present disclosure. The channel estimate represents the strength of a radio frequency (RF) signal (e.g., a PRS) received through a multipath channel as a function of time delay and may be referred to as a channel energy response (CER), a channel impulse response (CIR), or a power delay profile of the channel. Thus, the horizontal axis is in units of time (e.g., milliseconds) and the vertical axis is in units of signal strength (e.g., decibels). Note that a multipath channel is a channel in which an RF signal flows through multiple paths or multiple paths between a transmitter and a receiver due to transmission of the RF signal on multiple beams and / or propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0119] exist Figure 5 In the example of , the receiver detects / measures multiple (four) clusters of channel taps. Each channel tap represents a multipath through which an RF signal flows between a transmitter and a receiver. That is, the channel taps represent the arrival of an RF signal on a multipath. Each cluster of channel taps indicates that the corresponding multipath follows substantially the same path. Different clusters may exist due to the RF signal being transmitted on different transmit beams (and therefore at different angles), or due to the propagation characteristics of the RF signal (e.g., possibly along different paths due to reflections), or both.

[0120] The clusters of all channel taps for a given RF signal represent the multipath channels (or simply channels) between the transmitter and the receiver. Figure 5 In the channel shown, the receiver receives a first cluster of two RF signals on the channel taps at time T1, a second cluster of five RF signals on the channel taps at time T2, a third cluster of five RF signals on the channel taps at time T3, and a fourth cluster of four RF signals on the channel taps at time T4. Figure 5 In the example of , because the first cluster of RF signals arrives first at time T1, it is assumed to correspond to RF signals transmitted on a transmit beam aligned with line of sight (LOS) or the shortest path. The third cluster at time T3 consists of the strongest RF signals and may correspond to, for example, RF signals transmitted on a transmit beam aligned with a non-line of sight (NLOS) path. Note that although Figure 5 Clusters having two to five channel taps are shown, but it should be understood that the clusters may have more or fewer channel taps than shown.

[0121] Machine learning can be used to generate models that can be used to facilitate various aspects associated with data processing. One specific application of machine learning involves generating measurement models to process reference signals (e.g., positioning reference signals (PRS)) for positioning, such as feature extraction, reporting reference signal measurements (e.g., selecting which extracted features to report), and the like.

[0122] Machine learning models are generally classified as supervised or unsupervised. Supervised models can be further subdivided into regression models or classification models. Supervised learning involves learning a function that maps inputs to outputs based on exemplary input-output pairs. For example, given a training data set with two variables, age (input) and height (output), a supervised learning model can be generated to predict a person's height based on age. In a regression model, the output is continuous. An example of a regression model is linear regression, which simply tries to find a line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding the best fit plane) and polynomial regression (e.g., finding the best fit curve).

[0123] Another example of a machine learning model is a decision tree model. In a decision tree model, multiple nodes are used to define the tree structure. Decisions are used to move from the root node at the top of the decision tree to the leaf nodes (i.e., nodes that have no other children) at the bottom of the decision tree. Generally, the more nodes there are in a decision tree model, the higher the decision accuracy.

[0124] Another example of a machine learning model is a decision forest. Random forest is an ensemble learning technique based on decision trees. Random forest involves creating multiple decision trees using a bootstrapped dataset of the original data and randomly selecting a subset of the variables at each step of the decision tree. The model then selects the mode of all the predictions of each decision tree. By relying on a "majority wins" model, the risk of error from a single tree is reduced.

[0125] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. A neural network takes one or more input variables and produces one or more output variables through a network of equations. In other words, a neural network receives an input vector and returns an output vector.

[0126] Figure 6An exemplary neural network 600 in accordance with aspects of the present disclosure is shown. The neural network 600 includes an input layer 'i' that receives 'n' input(s) (shown as "input 1", "input 2", and "input n"), one or more hidden layers (shown as hidden layers 'h1', 'h2', and 'h3') for processing the inputs from the input layer, and an output layer 'o' that provides 'm' output(s) (labeled as "output 1" and "output m"). The number of inputs 'n', hidden layers 'h', and outputs 'm' may be the same or different. In some designs, the hidden layer 'h' may include a linear function and / or an activation function according to which the nodes of each successive hidden layer (shown as circles) process the nodes from the previous hidden layer.

[0127] In a classification model, the output is discrete. An example of a classification model is logistic regression. Logistic regression is similar to linear regression but is used to model the probability of a finite number of outcomes (usually two). In essence, a logistic equation is created in such a way that the output value can only be between '0' and '1'. Another example of a classification model is a support vector machine. For example, given two types of data, a support vector machine will find a hyperplane or boundary between the two types of data that maximizes the margin between the two types. There are many planes that can separate the two types, but there is only one plane that maximizes the margin or distance between the two types. Another example of a classification model is the Naive Bayes ( Bayes). Other examples of classification models include decision trees, random forests, and neural networks which are similar to the examples described above, but the outputs are discrete rather than continuous.

[0128] Unlike supervised learning, unsupervised learning is used to make inferences and discover patterns based on input data without reference to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0129] Clustering is an unsupervised technique that involves the grouping or clustering of data points. Clustering is often used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. Simply put, dimensionality reduction is the process of reducing the dimensionality of a set of features (more simply, reducing the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called principal component analysis (PCA). In the simplest case, PCA involves projecting higher dimensional data (e.g., three dimensions) into a smaller space (e.g., two dimensions). This results in lower data dimensionality (e.g., two dimensions instead of three) while retaining all the original variables in the model.

[0130] Regardless of which machine learning model is used, at a high level, the machine learning module (e.g., implemented by a processing system such as processor 332, 384, or 394) can be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and associate the training input data with a set of output data (e.g., a set of possible or potential candidate locations for the various target UEs) so that the same set of output data can be determined later when given similar input data (e.g., other target UEs from the same or similar locations).

[0131] NR supports radio frequency fingerprint (RFFP) based positioning, which is a positioning and location determination technology that uses RF fingerprints captured by mobile devices and / or base stations to determine the location of mobile devices. RFFP measurements can be a histogram of received signal strength indicator (RSSI), CER, CIR, or channel frequency response (CFR). RFFP measurements can represent a single channel received from a transmitter, all channels received from a specific transmitter, or all channels detectable at a receiver. In some aspects, the location of a mobile device can be determined (e.g., triangulated) using the RFFP measurements measured by a mobile device (e.g., UE) and the location of the transmitter associated with these RFFP measurements (i.e., the transmitter that transmits the RF signal measured by the mobile device to determine the RFFP).

[0132] In machine learning RFFP-based positioning, RFFP measurements and their associated locations (i.e., the locations of transmitters associated with the measured RFFPs) are used as features and labels, respectively, to train a machine learning model (e.g., neural network 600) in a supervised manner. After training, the machine learning model can be used to estimate the location of the mobile device by processing RFFPs newly captured by the mobile device.

[0133] In addition, the location of the mobile device can be determined based on radar-like positioning techniques (e.g., positioning techniques based on a single-station sensing mode), or what is called self-anchor positioning, where the mobile device can be located and the mobile device position determined based on reflections (e.g., backscatter reflections) caused by the interaction of signals sent by the mobile device with the surrounding environment. In some aspects, a mobile device that is full-duplex enabled or capable of operating in a pulsed radar manner is able to capture these reflections.

[0134] Fig. 7A According to aspects of the present disclosure, a mobile device 710 is shown arranged in an ambient environment 720. The mobile device 710 includes a transmitter and a receiver. As a non-limiting example, the ambient environment 720 is depicted as an indoor environment. In some aspects, the ambient environment 720 of the mobile device 710 can be indoors, outdoors, or a mixture of indoors and outdoors.

[0135] The transmitter of the mobile device 710 can transmit one or more signals via one or more antennas. As a result of various types of interactions with the surrounding environment 720 (e.g., through reflection, refraction, scattering, attenuation, any combination thereof, etc.), the transmitted one or more signals can be reflected back to the mobile device 710. For example, Fig. 7A Exemplary signal paths 732, 734, 736, and 738 are shown, which can be obtained through ray tracing simulations based on a co-located single transmit antenna and a single receive antenna. The signal transmitted by the transmitter of the mobile device 710 can travel along the signal paths 732, 734, 736, and 738 based on the interaction with objects and obstacles in the surrounding environment 720, and can travel back toward the mobile device 710. In some aspects, the received reflections caused by the interaction of the signal transmitted by the mobile device 710 with the surrounding environment 720 can be considered as a multipath signal, where the transmitter and receiver are co-located. The received reflections can be presented as self-RFFP measurements.

[0136] Figure 7B According to various aspects of the present disclosure, Fig. 7A Graph of self-RFFP measurements 750 of the reflections shown in . In some aspects, these reflections can be presented in the form of RFFP measurements, or also referred to in this disclosure as self-anchored RFFP measurements or self-RFFP measurements. Figure 7B A self-RFFP measurement in the form of CIR of a backscatter channel is shown 750. In some aspects, examples of a UE's self-RFFP measurement may include CFR, CIR, or RSSI corresponding to reflections backscattered from the surrounding environment due to a reference signal (e.g., SRS or SL-PRS) transmitted by the UE.

[0137] In some aspects, for a signal transmitted by the mobile device 710, a backscattered signal (e.g., reflection) caused by the interaction of the transmitted signal and the surrounding environment 720 may arrive at the mobile device 710 at different arrival times and with different signal strengths. Here, the horizontal axis is in units of time (e.g., milliseconds or nanoseconds), and the vertical axis is in units of signal strength (e.g., decibels). Figure 7B In the example of FIG. 7 , each channel tap can represent a path that an RF signal travels through ambient environment 720 and then reflects back to mobile device 710. For example, taps 762, 764, 766, and 768 can correspond to signal reflections along signal paths 732, 734, 736, and 738, respectively. Figure 7B The other taps in can correspond to Fig. 7A Other reflections not shown.

[0138] Thus, in some aspects, self-anchored positioning is a radar-like positioning technique in which the position of a wireless device can be located and determined based on signal reflections (i.e., backscattered reflections) from the surrounding environment. To capture single-station reflections, the wireless device may need to enable full-duplex or may operate in a pulsed radar manner. A machine learning (ML) model may be trained to map RFFP measurements corresponding to reflections and device locations. Self-anchored positioning may also be referred to as "self-anchored RFFP positioning" or "self-RFFP positioning" in the present disclosure.

[0139] Fig. 8A According to various aspects of the present disclosure, examples of network-based positioning operations based on self-RFFP measurements are shown. Fig. 8A As shown in , a wireless device 810 (e.g., any UE described in the present disclosure) is placed in an ambient environment 820 and is communicatively coupled to a base station 830 (e.g., any base station described in the present disclosure). The base station 830 is communicatively coupled to a network entity 840 (e.g., any network entity described in the present disclosure). One or more neighboring base stations (not shown) may be communicatively coupled to the network entity 840, and signals from the wireless device 810 may be detected by the one or more neighboring base stations. In some aspects, the network entity 840 may be a location server, such as Figure 2A and Figure 2B In some aspects, the network entity 840 may be a third-party server providing location services, such as Figure 2B The third party server 274 in.

[0140] The wireless device 810 includes a transmitter 812, a receiver 814, a duplexer 816, and one or more antennas 818. The duplexer 816 can coordinate the operation of the transmitter 812, the receiver 814, and the one or more antennas 818 so that the wireless device 810 is full duplex or can operate in a pulse radar mode. The wireless device 810 can transmit one or more reference signals via the transmitter 812 and the one or more antennas 818, and receive signal reflections via the one or more antennas 818 and the receiver 814. The signal reflection is a reflection of the one or more reference signals generated by the interaction of the one or more reference signals with the surrounding environment 820. In this way, the wireless device 810 can obtain one or more self-RFFP measurements 852 based on the reflection of the one or more reference signals transmitted by the target device 810. In some aspects, the wireless device 810 can report the one or more self-RFFP measurements 852 to the network entity 840, for example, via wireless communication 854 established between the wireless device 810 and the base station 830. In some aspects, the base station 830 and / or neighboring base stations may obtain one or more uplink RFFP (UL-RFFP) measurements 856 based on signals transmitted by the wireless device 810 and send the one or more UL-RFFP measurements 856 to the network entity 840 .

[0141] In operation, during a position inference phase in which the wireless device 810 may be configured as a target device, the network entity 840 may determine the position of the wireless device 810 based on applying the ML model to one or more self-RFFP measurements 852 and / or one or more UL-RFFP measurements 856. During a model training phase in which the wireless device 810 may be configured as an observer device, the network entity 840 may receive one or more training self-RFFP measurements from the wireless device 810 and / or one or more other observer devices, and in some aspects, also receive one or more training UL-RFFP measurements based on signals sent by the wireless device 810 and / or one or more other observer devices from the base station 830 for training the ML model. In some aspects, the one or more observer devices may include dedicated devices for collecting a training data set for the network entity 840.

[0142] In some aspects, one or more self-RFFP measurements, one or more UL-RFFP measurements, one or more trained self-RFFP measurements, and / or one or more trained UL-RFFP measurements may correspond to a particular antenna arrangement (e.g., based on reflections received by a single antenna or multiple antennas).

[0143] Figure 8B According to various aspects of the present disclosure, it is shown that Fig. 8A840 in FIG. 8. These operations can be categorized into a location inference phase 870 and a model training phase 880.

[0144] During the position inference phase 870, the wireless device 810 (configured as a target device) may obtain one or more self-RFFP measurements based on reflections of one or more reference signals transmitted by the wireless device 810. The wireless device 810 may report the one or more self-RFFP measurements to the network entity 840. In some aspects, the network entity 840 may receive from the wireless device 810 the one or more self-RFFP measurements obtained by the wireless device 810 and determine an estimated position 874 of the wireless device 810 based on applying the ML model 872 to the one or more self-RFFP measurements. In some aspects, the ML model 872 may be trained based on one or more training self-RFFP measurements, each of which is obtained by a corresponding observer device based on reflections of a corresponding reference signal transmitted by the corresponding observer device.

[0145] In some aspects, during the position inference phase, the base station 830 and / or one or more neighboring base stations may obtain one or more UL-RFFP measurements based on signals transmitted by the wireless device 810, and send the one or more UL-RFFP measurements to the network entity 840. Thus, the network entity 840 may receive one or more UL-RFFP measurements from the base station 830 and / or one or more neighboring base stations to be fused with one or more self-RFFP measurements obtained by the wireless device 810. The network entity 840 may determine an estimated position 874 of the wireless device 810 based on applying the ML model 872 to the one or more self-RFFP measurements fused with the one or more UL-RFFP measurements. In some aspects, the ML model 872 may be further trained based on one or more training UL-RFFP measurements obtained by the base station 830 and / or one or more neighboring base stations based on signals transmitted by one or more observer devices.

[0146] In some aspects, the one or more reference signals may include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data (including, for example, control information and user data), a sidelink channel reference signal, or a sidelink channel signal carrying data (including, for example, control information and user data). In some aspects, the UL-RFFP measurement value and the reference signal may be based on the same reference signal sent by the target device for position inference. In some aspects, the UL-RFFP measurement value and the reference signal may be based on two different reference signals sent by the target device for position inference, but the transmission timing of the two different reference signals is close enough to ensure the accuracy of the estimated position. In some aspects, the transmission timing of the reference signal may be configured back to back.

[0147] During the model training phase 880, a model training process 882 may be performed to train the ML model 872. In some aspects, the network entity 840 may train the ML model 872 using the model training process 882 based on training input data and reference output data, the training input data may include one or more trained self-RFFP measurements, and the reference output data may include one or more training positions of corresponding observer devices. In some aspects, the ML model 872 may be a NN model, and the training process 882 may train the ML model 872 in a supervised manner.

[0148] In some aspects, the network entity 840 can receive one or more trained self-RFFP measurements to be stored in the self-RFFP database 884, each of which is obtained by a corresponding observer device based on reflections of a corresponding reference signal transmitted by the corresponding observer device. In at least one example, the wireless device 810 can be configured as an observer device among the one or more observer devices.

[0149] In some aspects, the network entity 840 may receive one or more training self-RFFP measurements and obtain one or more training positions of the corresponding one or more observer devices. The training position may also be referred to as the ground truth position or ground truth label of the corresponding training data set. Each training position may be associated with one of the one or more training self-RFFP measurements. In some aspects, the training position represents the position where the observer device sends a reference signal, wherein the corresponding training self-RFFP measurement is based on the reference signal. For example, when the wireless device 810 is used as an observer device for training the ML model 872, the training self-RFFP measurement can be obtained based on the reference signal, and the position (X1, Y1, Z1) of the wireless device 810 when the wireless device 810 sends the reference signal can be used as the training position.

[0150] In some aspects, the network entity 840 can further receive one or more training UL-RFFP measurements to be stored in the UL-RFFP database 886, each of the training UL-RFFP measurements being based on a corresponding reference signal transmitted by a corresponding observer device. In at least one example, the wireless device 810 can be configured as an observer device, and the training UL-RFFP measurements can be obtained based on a reference signal transmitted by the wireless device 810.

[0151] In some aspects, the network entity 840 may receive one or more training UL-RFFP measurements and obtain one or more training locations of the corresponding one or more observer devices. Each training location may be associated with one of the one or more training UL-RFFP measurements. In some aspects, the training location represents the location when the observer device sends a reference signal, wherein the training UL-RFFP measurement is based on the reference signal. In some aspects, the training self-RFFP measurement and the training UL-RFFP measurement may be obtained based on the same reference signal sent by the same observer device. Therefore, the training location for training UL-RFFP measurement may be the same as the training location associated with the corresponding self-RFFP measurement. In some aspects, the network entity 840 may train the ML model 872 based on training input data and reference output data, wherein the training input data includes one or more training self-RFFP measurements and one or more training UL-RFFP measurements, and the reference output data includes the corresponding training location of the corresponding observer device.

[0152] In some aspects, the network entity 840 may receive a training position of an observer device from an observer device, wherein the observer device may determine the training position based on: a downlink time difference of arrival (DL-TDoA), a downlink angle of arrival (DL-AoA), a round trip time (RTT) positioning, operating the observer device at a predetermined reference position, one or more sensors mounted on the observer device, or a global navigation satellite system (GNSS), or a combination thereof. For example, the observer device may be an automated guided vehicle (AGV) equipped with a laser radar or one or more high-precision sensors for obtaining a corresponding training position of the AGV.

[0153] In some aspects, the network entity 840 can determine the training position of the observer device based on one or more measurements provided by the observer device and / or the corresponding base station 830 or a neighboring base station, based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round-trip time (RTT) positioning, or a combination thereof.

[0154] Fig. 9 is a signaling and event diagram illustrating various operations during a position inference phase, in accordance with aspects of the present disclosure. Fig. 9 902 (configured as a target device), a base station 904, one or more neighboring base stations 906, and a network entity 908. The wireless device 902 may correspond to Fig. 8A In the wireless device 810 , the base station 904 and the one or more neighboring base stations 906 may correspond to the base station 830 , and the network entity 908 may correspond to the network entity 840 .

[0155] At 910, the wireless device 902 may send one or more reference signals, such as SRS, SL-PRS, SL-SSB, SLCSI-RS, uplink channel reference signal, uplink channel signal carrying data, sidelink channel reference signal, or sidelink channel signal carrying data, or a combination thereof. At 912, one of the one or more reference signals may interact with the surrounding environment to cause a reflection of the reference signal. At 920, the wireless device 902 may receive the reflection of the reference signal and obtain a self-RFFP measurement value based on the received reflection. In addition, at 930, the base station 904 and / or the neighboring base station 906 may obtain a UL-RFFP measurement value based on the same reference signal or another reference signal in the one or more reference signals. In some aspects, when the self-RFFP measurement value and the UL-RFFP measurement value are based on different reference signals, the transmission timing of the reference signal is close enough to ensure the accuracy of the position estimate. In some aspects, the transmission timing of the reference signal may be configured back to back.

[0156] At 940, the base station 904 and / or the neighboring base station 906 may send the obtained UL-RFFP measurements to the network entity 908. At 950, the wireless device 902 may send the obtained self-RFFP measurements to the network entity 908. At 960, the network entity 908 may determine the location of the wireless device 902 by applying the ML model to the obtained self-RFFP measurements and the obtained UL-RFFP measurements. In some aspects, at 960, the network entity 908 may fuse the obtained self-RFFP measurements and the obtained UL-RFFP measurements and apply the ML model to the fused data set.

[0157] In some aspects, the ML model may be configured without any UL-RFFP measurements. In such a scenario, at 960, the network entity 908 may determine the location of the target device 902 by applying the ML model to the obtained self-RFFP measurements, and events 930 and 940 may be omitted.

[0158] Fig.10 is a signaling and event diagram illustrating various operations during a model training phase, according to aspects of the present disclosure. Fig.10 1002 (configured as an observer device for collecting training data), a base station 1004, one or more neighboring base stations 1006, and a network entity 1008. The wireless device 1002 may correspond to Fig. 8A The wireless device 810 in the embodiment may be configured as an observer device. The base station 1004 and one or more neighboring base stations 1006 may correspond to Fig. 8A , and the network entity 1008 may correspond to the network entity 840.

[0159] At 1010, the wireless device 1002 may transmit one or more training reference signals, such as SRS, SL-PRS, SL-SSB, SL CSI-RS, uplink channel reference signal, uplink channel signal carrying data, sidelink channel reference signal, or sidelink channel signal carrying data, or a combination thereof. At 1012, a training reference signal of the one or more training reference signals may interact with the surrounding environment to cause reflections of the training reference signal. At 1020, the wireless device 1002 may receive the reflections of the training reference signals and obtain training UL-RFFP measurements based on the received reflections. In addition, at 1030, the base station 1004 and / or the neighboring base station 1006 may obtain training UL-RFFP measurements based on the same training reference signal or another training reference signal of the one or more training reference signals sent by the wireless device 1002. In some aspects, when the training self-RFFP measurements and the training UL-RFFP measurements are based on different training reference signals, the transmission timing of the training reference signals is such that one training position can reasonably represent the transmission position for the training reference signal.

[0160] At 1040, the base station 1004 and / or the neighboring base station 1006 may send the obtained training UL-RFFP measurements to the network entity 1008. At 1050, the wireless device 1002 may send the obtained training self-RFFP measurements to the network entity 1008. At 1055, the network entity 1008 may obtain a ground truth label or training position associated with the training self-RFFP measurements and / or the training UL-RFFP measurements. In some aspects, the wireless device 1002 may determine the training position based on DL-TDoA, DL-AoA, RTT positioning, operating an observer device at a predetermined reference position, one or more sensors installed on an observer device, or GNSS, or a combination thereof. In some aspects, the network entity 1008 may determine the training position of the wireless device 1002 based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof.

[0161] At 1060, the network entity 1008 may train an ML model for RFFP-based positioning based on training input data and reference output data, wherein the training input data includes one or more training self-RFFP measurements and one or more training UL-RFFP measurements, and the reference output data includes training positions. In some aspects, at 1060, the network entity 1008 fuses the obtained one or more training self-RFFP measurements and the obtained one or more training UL-RFFP measurements, and trains the ML model based on the fused training data set.

[0162] In some aspects, the ML model may be configured without any UL-RFFP measurements. In such a scenario, at 1060, the network entity 1008 may train the ML model for RFFP-based positioning based on one or more trained self-RFFP measurements and training positions, and events 1030 and 1040 may be omitted.

[0163] Fig.11 According to aspects of the present disclosure, an example method 1100 of operating a network entity during a position inference phase is shown. In some aspects, the method 1100 may be performed by a network entity (e.g., any of the network entities, LMFs, SLPs, or servers described herein). In some aspects, the method 1100 may correspond to operations performed by the network entity 840 and / or 908. In one aspect, the method 1100 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any or all of which may be considered as means for performing one or more of the following operations of the method 1100.

[0164] At 1110, the network entity receives one or more self-RFFP measurements from a target device, which are obtained by the target device based on reflections of one or more reference signals sent by the target device. In some aspects, the one or more reference signals include SRS, SL-PRS, SL-SSB, SL CSI-RS, uplink channel reference signal, uplink channel signal carrying data, sidelink channel reference signal, or sidelink channel signal carrying data. In some aspects, the one or more self-RFFP measurements correspond to reflections received by a single antenna or multiple antennas of the target device.

[0165] At 1120, the network entity obtains one or more UL-RFFP measurements from one or more base stations based on the one or more reference signals or one or more other reference signals sent by the target device. In some aspects, operation 1120 may be omitted if the ML model for RFFP-based positioning does not require any UL-RFFP measurements.

[0166] At 1130, the network entity determines the location of the target device based on applying the ML model to the one or more self-RFFP measurements and the one or more UL-RFFP measurements. In some aspects, if the ML model for RFFP-based positioning does not require any UL-RFFP measurements, the network entity may determine the location of the target device based on applying the ML model to the one or more self-RFFP measurements.

[0167] It should be understood that the technical advantage of method 1100 is to increase diversity to RFFP-based positioning, because the self-RFFP measurement value can further represent the information of the co-location of the transmitter and the receiver. In addition, the network entity can fuse the self-RFFP measurement value with other RFFP (e.g., UL-RFFP) measurement values ​​to enhance positioning accuracy.

[0168] Fig.12 According to aspects of the present disclosure, an example method 1200 of operating a network entity during a model training phase is shown. In some aspects, the method 1200 may be performed by a network entity (e.g., any of the network entities, LMFs, SLPs, or servers described herein). In some aspects, the method 1200 may correspond to operations performed by the network entity 840 and / or 1008. In one aspect, the method 1200 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any or all of which may be considered as means for performing one or more of the following operations of the method 1200.

[0169] At 1210, the network entity receives trained self-RFFP measurements obtained by an observer device based on reflections of one or more reference signals sent by the observer device. In some aspects, the one or more reference signals include an SRS, a SL-PRS, a SL-SSB, a SL CSI-RS, an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data. In some aspects, the one or more trained self-RFFP measurements correspond to reflections received by a single antenna or multiple antennas of the observer device.

[0170] At 1220, the network entity obtains one or more training positions of the observer device, wherein the one or more training positions are associated with one or more trained self-RFFP measurements. In some aspects, the network entity may receive the training positions of the observer device from the observer device. In some aspects, the training positions of the observer device may be determined based on DL-TDoA, DL-AoA, RTT positioning, operating the observer device at a predetermined reference position, one or more sensors installed on the observer device, or GNSS, or a combination thereof.

[0171] In some aspects, the network entity may determine a training location of the observer device. In some aspects, the training location of the observer device may be determined based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof.

[0172] At 1230, the network entity obtains one or more training UL-RFFP measurements based on the one or more reference signals or one or more other reference signals sent by the observer device. In some aspects, operation 1230 may be omitted if the ML model for RFFP-based positioning does not require any UL-RFFP measurements.

[0173] At 1240, the network entity trains the ML model based on the training input data and the reference output data, the training input data may include one or more trained self-RFFP measurements and one or more trained UL-RFFP measurements, and the reference output data includes one or more training positions of the observer device. In some aspects, if the ML model for RFFP-based positioning does not require any UL-RFFP measurements, the training input data may include one or more trained self-RFFP measurements and may not have one or more training UL-RFFP measurements.

[0174] It should be appreciated that the technical advantage of method 1200 is to add diversity to RFFP-based positioning because the self-RFFP measurements can further represent information where the transmitter and receiver are co-located. In addition, the network entity can fuse the self-RFFP measurements with other RFFP (e.g., UL-RFFP) measurements to enhance positioning accuracy. In addition, the network entity can perform training and positioning so that the UE can be configured with more relaxed capability requirements (e.g., capabilities for ML training), which can be helpful when the UE has limited ML capabilities (e.g., redcap UE, IoT UE, etc.).

[0175] Fig.13 According to aspects of the present disclosure, an example method 1300 of operating a wireless device is shown. In some aspects, the method 1300 may be performed by a UE (e.g., any of the UEs described herein). In some aspects, the method 1300 may correspond to operations performed by the wireless device 810, 902, and / or 1002. In one aspect, the method 1300 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any or all of which may be considered as means for performing one or more of the following operations of the method 1300.

[0176] At 1310, the wireless device transmits one or more reference signals. In some aspects, the one or more reference signals may include an SRS, a SL-PRS, a SL-SSB, a SL CSI-RS, an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0177] At 1320, the wireless device obtains one or more self-RFFP measurements based on reflections of one or more reference signals transmitted by the wireless device. In some aspects, the one or more self-RFFP measurements correspond to reflections received by a single antenna or multiple antennas of the wireless device.

[0178] At 1330, the wireless device sends one or more self-RFFP measurements to the network entity. In some aspects, during a position inference phase when the wireless device is configured as a target device, the one or more self-RFFP measurements may enable the network entity to perform RRFP-based positioning based on the one or more self-RFFP measurements. In some aspects, during a model training phase when the wireless device is configured as an observer device, the one or more self-RFFP measurements may enable the network entity to train an ML model for RRFP-based positioning based on training data including the one or more self-RFFP measurements.

[0179] At 1340, during the model training phase in which the wireless device is configured as an observer device, the wireless device may further determine a training position of the wireless device associated with the trained self-RFFP measurements included in the one or more self-RFFP measurements. In some aspects, the training position of the wireless device may be determined based on downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

[0180] At 1350, during a model training phase in which the wireless device is configured as an observer device, the wireless device may send a training location of the wireless device to a network entity for use in training a machine learning model.

[0181] In some aspects, during the position inference phase when the wireless device is configured as a target device, operations 1340 and 1350 may be omitted. In some aspects, during the model training phase when the wireless device is configured as an observer device, in the scenario where the network entity can determine the training position of the wireless device, operations 1340 and 1350 may still be omitted.

[0182] It should be understood that the technical advantage of method 1300 is to add diversity to RFFP-based positioning, because the self-RFFP measurement value can further represent the information in which the transmitter and the receiver are co-located. In addition, the self-RFFP measurement value can be fused with other RFFP (e.g., UL-RFFP) measurement values ​​for RFFP-based positioning, thereby improving positioning accuracy. In addition, by performing position inference and model training at the network entity, the UE can be configured to have more relaxed capability requirements (e.g., capabilities for ML training), and training and positioning can be helpful when the UE has limited ML capabilities (e.g., redcap UE, IoT UE, etc.).

[0183] In the above specific embodiments, it can be seen that different features are brought together in the examples. This disclosure should not be understood as an intention that the example clauses have more features than those explicitly mentioned in each clause. Instead, various aspects of the present disclosure may include fewer features than all the features of the disclosed single example clauses. Therefore, the following clauses should be considered to be incorporated into the specification, wherein each clause itself can be used as a separate example. Although each subordinate clause may refer to a specific combination of one clause with other clauses in a clause, the aspects of the subordinate clause are not limited to the specific combination. It should be understood that other example clauses may also include a combination of the subject matter of the subordinate clause aspect with any other subordinate clause or independent clause, or any feature with other subordinate and independent clauses. Various aspects disclosed herein explicitly include these combinations, unless it is explicitly expressed or can be easily inferred that it is not intended to include a specific combination (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). In addition, it is also intended to be able to include multiple aspects of a clause in any other independent clause, even if the clause is not directly subordinate to the independent clause.

[0184] Examples of implementations are described in the following numbered clauses:

[0185] Clause 1. A method of operating a network entity, comprising: receiving one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device, wherein the self-RFFP measurements are obtained by the target device based on reflections of one or more reference signals sent by the target device; and determining the location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0186] Clause 2. The method according to Clause 1 further includes: obtaining one or more uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more uplink signals sent by the target device, wherein the position of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurement values ​​and the one or more UL-RFFP measurement values.

[0187] Clause 3. A method according to any one of clauses 1 to 2, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0188] Clause 4. The method according to any one of clauses 1 to 3 further includes: receiving one or more trained self-RFFP measurement values ​​obtained by an observer device based on reflections of one or more reference signals sent by the observer device; obtaining one or more training positions of the observer device, the one or more training positions being associated with the one or more trained self-RFFP measurement values; and training the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurement values, and the reference output data including the one or more training positions of the observer device.

[0189] Clause 5. The method according to Clause 4 further includes: obtaining one or more training uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more other reference signals sent by the observer device, wherein the training input data also includes the one or more training UL-RFFP measurement values.

[0190] Clause 6. The method of clause 4, further comprising: receiving one of the one or more training positions of the observer device from the observer device.

[0191] Clause 7. The method according to Clause 4 further includes: determining one of the one or more training positions of the observer device, wherein the one of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round-trip time (RTT) positioning or a combination thereof.

[0192] Clause 8. A method according to clause 1, wherein the machine learning model is trained based on one or more trained RFFP measurement values ​​obtained by one or more observer devices, each of the trained RFFP measurement values ​​being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0193] Clause 9. A method according to clause 8, wherein the machine learning model is further trained based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices.

[0194] Clause 10. The method of any one of clauses 8 to 9, wherein the target device is configured as a watcher device.

[0195] Clause 11. A method of operating a wireless device, comprising: sending one or more reference signals; obtaining one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the wireless device; and sending the one or more self-RFFP measurements to a network entity.

[0196] Clause 12. A method according to clause 11, wherein the one or more self-RFFP measurements include training self-RFFP measurements for training a machine learning model.

[0197] Clause 13. The method according to Clause 12 further includes: determining a training location of the wireless device associated with the trained RFFP measurement value; and sending the training location of the wireless device to the network entity for training the machine learning model.

[0198] Clause 14. A method according to clause 13, wherein the training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round-trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

[0199] Clause 15. A method according to any one of clauses 11 to 14, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0200] Clause 16. The method of any one of clauses 11 to 15, wherein the one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

[0201] Clause 17. A network entity comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive one or more self-radio frequency fingerprint (self-RFFP) measurements from a target device via the at least one transceiver, the self-RFFP measurements being obtained by the target device based on reflections of one or more reference signals sent by the target device; and determine a location of the target device based on applying a machine learning model to the one or more self-RFFP measurements.

[0202] Clause 18. A network entity according to Clause 17, wherein the at least one processor is further configured to obtain one or more uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more uplink signals sent by the target device, wherein the position of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurement values ​​and the one or more UL-RFFP measurement values.

[0203] Clause 19. A network entity according to any one of clauses 17 to 18, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0204] Clause 20. A network entity according to any one of clauses 17 to 19, wherein the at least one processor is further configured to: receive one or more trained self-RFFP measurement values ​​via the at least one transceiver, the one or more trained self-RFFP measurement values ​​being obtained by an observer device based on reflections of one or more reference signals sent by the observer device; obtain one or more training positions of the observer device, the one or more training positions being associated with the one or more trained self-RFFP measurement values; and train the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurement values, and the reference output data including the one or more training positions of the observer device.

[0205] Clause 21. A network entity according to Clause 20, wherein the at least one processor is further configured to obtain one or more training uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more other reference signals sent by the observer device, wherein the training input data also includes the one or more training UL-RFFP measurement values.

[0206] Clause 22. The network entity of clause 20, wherein the at least one processor is further configured to receive, from the observer device via the at least one transceiver, a training location of the one or more training locations of the observer device.

[0207] Clause 23. A network entity according to clause 20, wherein the at least one processor is further configured to: determine one of the one or more training positions of the observer device, wherein the one of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round-trip time (RTT) positioning or a combination thereof.

[0208] Clause 24. A network entity according to Clause 17, wherein the machine learning model is trained based on one or more trained RFFP measurement values ​​obtained by one or more observer devices, each of the trained RFFP measurement values ​​being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0209] Clause 25. A network entity according to clause 24, wherein the machine learning model is further trained based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices.

[0210] Clause 26. A network entity as defined in any one of clauses 24 to 25, wherein the target device is configured as a watcher device.

[0211] Clause 27. A wireless device comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: send one or more reference signals via the at least one transceiver; obtain one or more self-RFF measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and send the one or more self-RFFP measurement values ​​to a network entity via the at least one transceiver.

[0212] Clause 28. A wireless device according to clause 27, wherein the one or more self-RFFP measurements include trained self-RFFP measurements for training a machine learning model.

[0213] Clause 29. A wireless device according to clause 28, wherein the at least one processor is further configured to: determine a training location of the wireless device associated with the trained RFFP measurement value; and send the training location of the wireless device to the network entity via the at least one transceiver for training the machine learning model.

[0214] Clause 30. A wireless device according to clause 29, wherein the training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round-trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

[0215] Clause 31. A wireless device according to any one of clauses 27 to 30, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0216] Clause 32. A wireless device as defined in any one of clauses 27 to 31, wherein the one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

[0217] Clause 33. A network entity comprising: a unit for receiving one or more self-radio frequency fingerprint (self-RFFP) measurement values ​​from a target device, wherein the self-RFFP measurement values ​​are obtained by the target device based on reflections of one or more reference signals sent by the target device; and a unit for determining the location of the target device based on applying a machine learning model to the one or more self-RFFP measurement values.

[0218] Clause 34. The network entity according to clause 33 further includes: a unit for obtaining one or more uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more uplink signals sent by the target device, wherein the position of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurement values ​​and the one or more UL-RFFP measurement values.

[0219] Clause 35. A network entity according to any one of clauses 33 to 34, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0220] Clause 36. A network entity according to any one of clauses 33 to 35, further comprising: a unit for receiving one or more trained self-RFFP measurement values ​​obtained by an observer device based on reflections of one or more reference signals sent by the observer device; a unit for obtaining one or more training positions of the observer device, the one or more training positions being associated with the one or more trained self-RFFP measurement values; and a unit for training the machine learning model based on training input data and reference output data, the training input data comprising the one or more trained self-RFFP measurement values, and the reference output data comprising the one or more training positions of the observer device.

[0221] Clause 37. The network entity according to Clause 36 further includes: a unit for obtaining one or more training uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more other reference signals sent by the observer device, wherein the training input data also includes the one or more training UL-RFFP measurement values.

[0222] Clause 38. The network entity of Clause 36, further comprising: means for receiving, from the observer device, a training position of the one or more training positions of the observer device.

[0223] Clause 39. The network entity according to clause 36 further includes: a unit for determining one of the one or more training positions of the observer device, wherein the one of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

[0224] Clause 40. A network entity according to clause 33, wherein the machine learning model is trained based on one or more trained RFFP measurement values ​​obtained by one or more observer devices, each of the trained RFFP measurement values ​​being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0225] Clause 41. A network entity according to clause 40, wherein the machine learning model is further trained based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices.

[0226] Clause 42. A network entity as defined in any one of clauses 40 to 41, wherein the target device is configured as a watcher device.

[0227] Clause 43. A wireless device comprising: a unit for sending one or more reference signals; a unit for obtaining one or more self-radio frequency fingerprint (self-RFFP) measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and a unit for sending the one or more self-RFFP measurement values ​​to a network entity.

[0228] Clause 44. A wireless device according to clause 43, wherein the one or more self-RFFP measurements include trained self-RFFP measurements for training a machine learning model.

[0229] Clause 45. The wireless device according to clause 44 further includes: a unit for determining a training position of the wireless device associated with the trained RFFP measurement value; and a unit for sending the training position of the wireless device to the network entity for training the machine learning model.

[0230] Clause 46. A wireless device according to clause 45, wherein the training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round-trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

[0231] Clause 47. A wireless device according to any one of clauses 43 to 46, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0232] Clause 48. A wireless device as defined in any one of clauses 43 to 47, wherein the one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

[0233] Item 49. A non-temporary computer-readable medium stores computer-executable instructions which, when executed by a network entity, cause the network entity to perform the following operations: receive one or more self-radio frequency fingerprint (self-RFFP) measurement values ​​from a target device, wherein the self-RFFP measurement values ​​are obtained by the target device based on reflections of one or more reference signals sent by the target device; and determine the location of the target device based on applying a machine learning model to the one or more self-RFFP measurement values.

[0234] Clause 50. The non-temporary computer-readable medium according to clause 49 further includes computer executable instructions that, when executed by the network entity, cause the network entity to perform the following operations: obtain one or more uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more uplink signals sent by the target device, wherein the position of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurement values ​​and the one or more UL-RFFP measurement values.

[0235] Clause 51. A non-temporary computer-readable medium according to any one of clauses 49 to 50, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0236] Clause 52. A non-temporary computer-readable medium according to any one of clauses 49 to 51, further comprising computer executable instructions that, when executed by the network entity, cause the network entity to perform the following operations: receive one or more trained self-RFFP measurement values ​​obtained by an observer device based on reflections of one or more reference signals sent by the observer device; obtain one or more training positions of the observer device, the one or more training positions being associated with the one or more trained self-RFFP measurement values; and train the machine learning model based on training input data and reference output data, the training input data comprising the one or more trained self-RFFP measurement values, and the reference output data comprising the one or more training positions of the observer device.

[0237] Clause 53. The non-temporary computer-readable medium according to Clause 52 further includes computer executable instructions that, when executed by the network entity, cause the network entity to perform the following operations: obtain one or more training uplink RFFP (UL-RFFP) measurement values ​​based on the one or more reference signals or one or more other reference signals sent by the observer device, wherein the training input data also includes the one or more training UL-RFFP measurement values.

[0238] Clause 54. The non-transitory computer-readable medium of clause 52 further comprises computer-executable instructions that, when executed by the network entity, cause the network entity to perform the following operations: receive, from the observer device, one of the one or more training locations of the observer device.

[0239] Clause 55. The non-temporary computer-readable medium according to clause 52 further includes computer executable instructions that, when executed by the network entity, cause the network entity to perform the following operations: determine one of the one or more training positions of the observer device, wherein the training position of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round-trip time (RTT) positioning or a combination thereof.

[0240] Clause 56. A non-temporary computer-readable medium according to clause 49, wherein the machine learning model is trained based on one or more trained RFFP measurement values ​​obtained by one or more observer devices, each of the trained RFFP measurement values ​​being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0241] Clause 57. A non-transitory computer-readable medium according to clause 56, wherein the machine learning model is further trained based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices.

[0242] Clause 58. The non-transitory computer-readable medium of any one of clauses 56 to 57, wherein the target device is configured as a watcher device.

[0243] Clause 59. A non-temporary computer-readable medium storing computer-executable instructions, which, when executed by a wireless device, causes the wireless device to perform the following operations: send one or more reference signals; obtain one or more self-RFF fingerprint (self-RFFP) measurement values ​​based on reflections of the one or more reference signals sent by the wireless device; and send the one or more self-RFFP measurement values ​​to a network entity.

[0244] Clause 60. A non-transitory computer-readable medium as described in clause 59, wherein the one or more self-RFFP measurements include trained self-RFFP measurements for training a machine learning model.

[0245] Clause 61. The non-temporary computer-readable medium according to clause 60 further comprises computer executable instructions which, when executed by the wireless device, cause the wireless device to perform the following operations: determine a training location of the wireless device associated with the trained RFFP measurement value; and send the training location of the wireless device to the network entity for training the machine learning model.

[0246] Clause 62. A non-temporary computer-readable medium according to clause 61, wherein the training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round-trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

[0247] Clause 63. A non-temporary computer-readable medium according to any one of clauses 59 to 62, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0248] Clause 64. The non-transitory computer-readable medium of any one of clauses 59 to 63, wherein the one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

[0249] Those skilled in the art will appreciate that a variety of different technologies and methods may be used to represent information and signals. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0250] In addition, it will be appreciated by those skilled in the art that the various schematic logic blocks, modules, circuits, and algorithm steps described in conjunction with the aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various schematic components, blocks, modules, circuits, and steps have been generally described above in terms of their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. Those skilled in the art may implement the described functions in different ways for each specific application, but these implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0251] The various illustrative logical blocks, modules, and circuits described in conjunction with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration.

[0252] The methods, sequences and / or algorithms described in conjunction with the aspects disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a random access memory (RAM), a flash memory, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. In an alternative, the storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., UE). In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0253] In one or more example aspects, the function can be implemented with hardware, software, firmware or any combination thereof. If implemented with software, the function can be stored on a computer-readable medium or sent via a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, and the communication media include any medium that is convenient for transmitting a computer program from one location to another. Storage media can be any available medium that can be accessed by a computer. By way of example and not limitation, such a computer-readable medium may include: RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage, or other magnetic storage device, or any other medium that can be used to carry or store the desired program code that can be accessed by a computer in the form of an instruction or data structure. In addition, any connection is appropriately referred to as a computer-readable medium. For example, if software is sent from a website, a server or other remote source using a coaxial cable, an optical cable, a twisted pair, a digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwaves, then coaxial cable, optical cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwaves are included in the definition of the medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0254] Although the foregoing disclosure illustrates illustrative aspects of the disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of the disclosure as defined in the appended claims. The functions, steps, and / or actions of the method claims according to aspects of the disclosure described herein do not need to be performed in any particular order. In addition, although elements of the disclosure may be described or claimed in the singular, the plural is contemplated unless expressly stated to be limited to the singular.

Claims

1. A method of operating a network entity, comprising: receiving one or more self radio frequency fingerprint (self RFFP) measurements from a target device, the self RFFP measurements being obtained by the target device based on reflections of one or more reference signals transmitted by the target device; as well as A location of the target device is determined based on applying a machine learning model to the one or more self-RFFP measurements.

2. The method according to claim 1, further comprising: obtaining one or more uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more uplink signals sent by the target device, Wherein, the location of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurements and the one or more UL-RFFP measurements.

3. The method according to claim 1, wherein: The one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

4. The method according to claim 1, further comprising: receiving one or more trained self-RFFP measurements obtained by an observer device based on reflections of one or more reference signals sent by the observer device; obtaining one or more trained positions of the observer device, the one or more trained positions being associated with the one or more trained self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained RFFP measurements and the reference output data includes the one or more training positions of the observer device.

5. The method according to claim 4, further comprising: obtaining one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals sent by the observer device, The training input data further includes the one or more training UL-RFFP measurement values.

6. The method according to claim 4, further comprising: One of the one or more training positions of the Observer device is received from the Observer device.

7. The method according to claim 4, further comprising: determining one of the one or more training positions of the observer device, Wherein, the one training position of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round trip time (RTT) positioning or a combination thereof.

8. The method according to claim 1, wherein: The machine learning model is trained based on one or more training RFFP measurements obtained by one or more observer devices, each of which is obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

9. The method according to claim 8, wherein: The machine learning model is further trained based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices.

10. The method according to claim 8, wherein: The target device is configured as an observer device.

11. A method of operating a wireless device, comprising: sending one or more reference signals; obtaining one or more self radio frequency fingerprint (self RFFP) measurements based on reflections of the one or more reference signals transmitted by the wireless device; as well as The one or more self-RFFP measurements are sent to a network entity.

12. The method according to claim 11, wherein: The one or more self-RFFP measurements include trained self-RFFP measurements used to train a machine learning model.

13. The method according to claim 12, further comprising: determining a trained position of the wireless device associated with the trained RFFP measurements; as well as The training location of the wireless device is sent to the network entity for use in training the machine learning model.

14. The method according to claim 13, wherein: The training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

15. The method according to claim 11, wherein: The one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

16. The method according to claim 11, wherein: The one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

17. A network entity, comprising: Memory; at least one transceiver; as well as at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: receiving, via the at least one transceiver, from a target device one or more self radio frequency fingerprint (self RFFP) measurements, the self RFFP measurements being obtained by the target device based on reflections of one or more reference signals transmitted by the target device; as well as A location of the target device is determined based on applying a machine learning model to the one or more self-RFFP measurements.

18. The network entity according to claim 17, wherein: The at least one processor is further configured to: obtaining one or more uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more uplink signals transmitted by the target device, Wherein, the location of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurements and the one or more UL-RFFP measurements.

19. The network entity according to claim 17, wherein: The one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

20. The network entity according to claim 17, wherein: The at least one processor is further configured to: receiving, via the at least one transceiver, one or more trained self-RFFP measurements obtained by an observer device based on reflections of one or more reference signals sent by the observer device; obtaining one or more trained positions of the observer device, the one or more trained positions being associated with the one or more trained self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained RFFP measurements and the reference output data includes the one or more training positions of the observer device.

21. The network entity according to claim 20, wherein: The at least one processor is further configured to: obtaining one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals sent by the observer device, The training input data further includes the one or more training UL-RFFP measurement values.

22. The network entity according to claim 20, wherein: The at least one processor is further configured to: One of the one or more training positions of the observer device is received from the observer device via the at least one transceiver.

23. The network entity according to claim 20, wherein: The at least one processor is further configured to: determining a training position of the one or more training positions of the observer device, Wherein, the one training position of the one or more training positions of the observer device is determined based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA) or round trip time (RTT) positioning or a combination thereof.

24. The network entity according to claim 17, wherein: The machine learning model is trained based on one or more training RFFP measurements obtained by one or more observer devices, each of which is obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

25. A wireless device comprising: Memory; at least one transceiver; as well as at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: transmitting, via the at least one transceiver, one or more reference signals; obtaining one or more self radio frequency fingerprint (self RFFP) measurements based on reflections of the one or more reference signals transmitted by the wireless device; as well as The one or more self-RFFP measurements are transmitted to a network entity via the at least one transceiver.

26. The wireless device of claim 25, wherein: The one or more self-RFFP measurements include trained self-RFFP measurements used to train a machine learning model.

27. The wireless device of claim 26, wherein: The at least one processor is further configured to: determining a trained position of the wireless device associated with the trained RFFP measurements; as well as The training location of the wireless device is sent to the network entity via the at least one transceiver for use in training the machine learning model.

28. The wireless device of claim 27, wherein: The training position of the wireless device is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), round trip time (RTT) positioning, operating the wireless device at a predetermined reference position, one or more sensors installed on the wireless device, or a global navigation satellite system (GNSS), or a combination thereof.

29. The wireless device of claim 25, wherein: The one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

30. The wireless device of claim 25, wherein: The one or more self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device.

Images