Radio frequency fingerprint localization of transmit-receive points

Abstract

In one aspect, a network node may obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP). The network node may obtain a positioning estimate for the TRP based on applying a positioning model to the plurality of RFFP measurements.

Description

Background Art 1. Technical Field

[0001] Aspects of the present disclosure generally relate to wireless communication.

[0002] 2. Description of the Related Art

[0003] Wireless communication systems have evolved through 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, Internet-capable wireless services, and fourth-generation (4G) services (e.g., Long-Term Evolution (LTE) or WiMax). Currently, many different types of wireless communication systems are in use, including cellular systems and Personal Communication 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.

[0004] The fifth-generation (5G) wireless standard, known as New Radio (NR), enables higher data transfer speeds, a greater number of connections, better coverage, and 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 compared to previous standards. These enhancements, along with the use of higher frequency bands, advancements in PRS processes and technologies, and high-density deployments of 5G, enable high-precision positioning based on 5G. Summary of the Invention

[0005] A simplified summary of one or more aspects related to the present disclosure is presented below. Accordingly, the following summary is neither intended to be an exhaustive overview of all contemplated aspects nor to identify key or critical elements of all contemplated aspects or to delineate the scope associated with any particular aspect. Thus, the sole purpose of the following summary is to present in a concise form certain concepts related to one or more aspects of the mechanisms disclosed herein before the detailed description presented below.

[0006] In one aspect, a method performed by a network node includes: obtaining a plurality of Radio Frequency Fingerprint Positioning (RFFP) measurements associated with a Transmit Receive Point (TRP); and obtaining a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0007] In one aspect, a method performed by a network node includes: obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and training a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0008] In one aspect, a network node 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: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and obtain a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0009] In one aspect, a network node 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: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and train a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0010] In one aspect, a network node includes: means for obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and means for obtaining a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0011] In one aspect, a network node includes: means for obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and means for training a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0012] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network node, cause the network node to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and obtain a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0013] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network node, cause the network node to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known location of a transmit receive point (TRP); and train a positioning model to provide a location estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known location of the TRP.

[0014] Based on the figures and the detailed description, other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings are presented to assist in describing aspects of the present disclosure, and the drawings are provided for illustration only and not to limit the aspects.

[0016] Figure 1 An example wireless communication system in accordance with aspects of the present disclosure is illustrated.

[0017] Figure 2A 、 Figure 2B and Figure 2C An example wireless network structure in accordance with aspects of the present disclosure is illustrated.

[0018] Figure 3A 、 Figure 3B and Figure 3C are simplified block diagrams of several example aspects of components that may be employed in a user equipment (UE), a base station, and a network entity, respectively, and are configured to support communication as taught herein.

[0019] Figure 4 Examples of various positioning methods supported in New Radio (NR) in accordance with aspects of the present disclosure are illustrated.

[0020] Figure 5 An example neural network in accordance with aspects of the present disclosure is illustrated.

[0021] Figure 6 is a graph representing the radio frequency (RF) channel impulse response over time in accordance with aspects of the present disclosure.

[0022] Figure 7 is a diagram illustrating the training and use of a positioning model for radio frequency fingerprint (RFFP)-based positioning in accordance with aspects of the present disclosure.

[0023] Figure 8Depicts an RFFP-based transmit-receive point (TRP) positioning scenario according to aspects of the present disclosure, where multiple UEs measure downlink reference signals (DL-RS) transmitted by the TRP to obtain RFFP, and a trained positioning model can be applied to the RFFP to obtain a positioning estimate of the TRP.

[0024] Figure 9 Depicts an RFFP-based TRP positioning scenario according to aspects of the present disclosure, where the TRP measures uplink reference signals (UL-RS) transmitted by multiple UEs to obtain RFFP, and a trained positioning model can be applied to the RFFP to obtain a positioning estimate of the TRP.

[0025] Figure 10 Illustrates a scenario according to aspects of the present disclosure in which features associated with a sidelink user equipment (SL-UE) are provided to a positioning model to provide a positioning estimate of the TRP.

[0026] Figure 11 Depicts a method that can be performed by a network node according to aspects of the present disclosure.

[0027] Figure 12 Depicts a method that can be performed by a network node according to aspects of the present disclosure. Detailed Description

[0028] Aspects of the present disclosure are provided in the following description of various examples provided for illustrative purposes and the associated drawings. Alternative aspects can be designed without departing from the scope of the present disclosure. Additionally, well-known elements of the present disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the present disclosure.

[0029] 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" need not be construed as superior or better than other aspects. Similarly, the term "aspects of the present disclosure" does not require that all aspects of the present disclosure include the discussed features, advantages, or modes of operation.

[0030] Those skilled in the art should understand that any of a variety of different technologies and methods can be used to represent the information and signals described below. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the following description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof, depending in part on the particular application, in part on the desired design, in part on the corresponding technology, and so on.

[0031] In addition, many aspects are described in terms of sequences of actions to be performed by components of a computing device, for example. It will be recognized that the various actions described herein can be performed by specific circuitry (e.g., an application specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or by a combination of both. Additionally, the sequences of actions described herein can be regarded 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 direct a relevant processor of the device to perform the functionality described herein. Accordingly, the various aspects of the present disclosure can be embodied in many different forms, all of which are contemplated to be within the scope of the claimed subject matter. Additionally, for each of the aspects described herein, a corresponding form of any such aspect can be described herein as, for example, "logic configured to perform the described actions."

[0032] As used herein, unless otherwise specified, the terms "user equipment" (UE) and "base station" are not intended to be specific to or otherwise limited to any particular radio access technology (RAT). In general, a UE can be any wireless communication device used by a user to communicate over a wireless communication network (e.g., a mobile phone, a router, a tablet computer, a laptop computer, a consumer asset tracking device, a wearable device (e.g., a smartwatch, glasses, an augmented reality (AR) / virtual reality (VR) headset, etc.), a vehicle (e.g., a car, a motorcycle, a bicycle, etc.), an Internet of Things (IoT) device, etc.). A UE can be mobile or can be stationary (e.g., at certain times) and can communicate with a radio access network (RAN). As used herein, the term "UE" can be interchangeably referred to as "access terminal" or "AT", "client device", "wireless device", "subscriber equipment", "subscriber terminal", "subscriber station", "user terminal" or "UT", "mobile device", "mobile terminal", "mobile station", or variants thereof. In general, a UE can communicate with a core network via a RAN and, through the core network, a UE can connect to an external network such as the Internet and to other UEs. Of course, other mechanisms for a UE to connect to the core network and / or the Internet are possible, such as via a wired access network, a wireless local area network (WLAN) network (e.g., based on Institute of Electrical and Electronics Engineers (IEEE) 802.11 specifications, etc.).

[0033] A base station can operate according to one of several RATs to communicate with a UE depending on the network in which the base station is deployed, and alternatively can be referred to as an access point (AP), network node, Node B, evolved Node B (eNB), next-generation eNB (ng-eNB), New Radio (NR) Node B (also referred to as gNB or gNodeB), etc. The base station can be mainly used to support the wireless access of the UE, including supporting data, voice, and / or signaling connections for the supported UE. In some systems, the base station can only provide edge node signaling functions, while in other systems, the base station can provide additional control and / or network management functions. The communication link by which the UE can transmit signals to the base station is called the uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication link by which the base station can transmit signals to the UE is called the downlink (DL) or forward link channel (e.g., paging channel, control channel, broadcast channel, forward traffic channel, etc.). As used herein, the term "traffic channel (TCH)" can refer to the uplink / reverse traffic channel or the downlink / forward traffic channel.

[0034] The term "base station" can refer to a single physical transmit-receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, in the case where the term "base station" refers to a single physical TRP, the physical TRP can be the antenna of the base station corresponding to the cell (or several cell sectors) of the base station. In the case where the term "base station" refers to multiple co-located physical TRPs, the physical TRPs can be an antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or in the case where the base station employs beamforming). In the case where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRPs can 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). Alternatively, the non-co-located physical TRPs can be the serving base station that receives measurement reports from the UE and an adjacent base station whose reference radio frequency (RF) signal the UE is measuring. Since, as used herein, the TRP is the point by which the base station sends and receives wireless signals, a reference to sending from or receiving at the base station should be understood to refer to a specific TRP of the base station.

[0035] In some specific implementations that support UE positioning, the base station may not support the wireless access of the UE (e.g., may not support data, voice, and / or signaling connections for the UE), but can alternatively send reference signals to be measured by the UE and / or can receive and measure signals sent by the UE. Such a base station can be called a positioning beacon (e.g., in the case of sending signals to the UE) and / or can be called a position measurement unit (e.g., in the case of receiving and measuring signals from the UE).

[0036] 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 send a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through a multipath channel, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between the transmitter and the receiver may be referred to as a "multipath" RF signal. As used herein, when the context clearly indicates that the term "signal" refers to a wireless signal or an RF signal, the RF signal may also be referred to as a "wireless signal" or simply as a "signal".

[0037] Figure 1 An example wireless communication system 100 in accordance with aspects of the present disclosure is illustrated. 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 "BS") and various UEs 104. The base stations 102 may include macro cell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, the macro cell 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.

[0038] 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)) via a backhaul link 122 and interface with one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) location platform (SLP)) via 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 stations 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 via another path, such as via an application server (not shown), via another network, such as via a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), etc. For signaling purposes, the communication between the UE 104 and the location server 172 may be represented as an indirect connection (e.g., via the core network 170, etc.) or a direct connection (e.g., as shown via a direct connection 128), where intermediate nodes (if any) are omitted from the signaling diagram for clarity.

[0039] In addition to other functions, base station 102 may perform functions related to one or more of the following: delivering 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), subscriber and equipment tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. Base stations 102 may communicate with each other directly or indirectly (e.g., via EPC / 5GC) on a backhaul link 134, which may be wired or wireless.

[0040] Base station 102 may communicate wirelessly with UE 104. Each base station in base station 102 may provide communication coverage for a corresponding geographic coverage area 110. In one aspect, one or more cells may be supported by the base stations 102 in each geographic coverage area 110. A "cell" is a logical communication entity used to communicate with a base station (e.g., via a certain frequency resource, which is referred to as a carrier frequency, component carrier, carrier, frequency band, etc.), and may be associated with an identifier (e.g., physical cell identifier (PCI), enhanced cell identifier (ECI), virtual cell identifier (VCI), cell global identifier (CGI), etc.) for distinguishing cells operating via the same or different carrier frequencies. In some cases, different cells may be configured according to different protocol types that may provide access for different types of UEs (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or other protocol types). Since a cell is supported by a specific base station, the term "cell" may, depending on the context, refer to either or both of the logical communication entity and the base station that supports the logical communication entity. In addition, since the TRP is typically the physical transmission point of a cell, the terms "cell" and "TRP" may be used interchangeably. In some cases, the term "cell" may also refer to the geographic coverage area (e.g., sector) of a base station, as long as a carrier frequency can be detected and used for communication within a certain part of the geographic coverage area 110.

[0041] Although the geographical coverage areas 110 of adjacent macro cell base stations 102 may partially overlap (e.g., in a handover area), some areas within the geographical coverage area 110 may substantially overlap with the larger geographical coverage area 110. For example, a small cell base station 102′ (labeled “SC” for “small cell”) may have a geographical coverage area 110′ that substantially overlaps with the geographical coverage areas 110 of one or more macro cell base stations 102. A network including both small cell base stations and macro cell base stations may be referred to as a heterogeneous network. The heterogeneous network may also include a home eNB (HeNB) that may provide service to a restricted group referred to as a closed subscriber group (CSG).

[0042] 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 for the downlink and the uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink).

[0043] The wireless communication system 100 may also include a WLAN access point (AP) 150 that communicates with a wireless local area network (WLAN) station (STA) 152 via a communication link 154 in an unlicensed spectrum (e.g., 5 GHz). 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 before communication to determine if the channel is available.

[0044] The small cell base station 102′ may operate in licensed and / or unlicensed spectrum. When operating in the unlicensed spectrum, the small cell base station 102′ may employ LTE or NR technology and use the same 5 GHz unlicensed spectrum as used by the WLAN AP 150. The small cell base station 102′ adopting LTE / 5G in the unlicensed spectrum may enhance the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum may be referred to as NR-U. LTE in the unlicensed spectrum may be referred to as LTE-U, licensed-assisted access (LAA), or MulteFire.

[0045] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180, which may operate at mmW frequencies and / or near mmW frequencies to communicate with the UE 182. Extremely high frequency (EHF) is a part of RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. The radio waves in this band may be referred to as millimeter waves. Near mmW may extend down to a frequency of 3 GHz, with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, which is also referred to as centimeter waves. Communication using the mmW / near mmW radio frequency band has high path loss and a relatively short distance. The mmW base station 180 and the UE 182 may utilize beamforming (transmitting and / or receiving) on the mmW communication link 184 to compensate for the extremely high path loss and short distance. In addition, it should be understood that in an alternative configuration, one or more base stations 102 may also use mmW or near mmW and beamforming for transmission. Therefore, it should be understood that the foregoing illustration is only an example and should not be construed as limiting the various aspects disclosed herein.

[0046] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Conventionally, 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 and stronger RF signal (in terms of data rate) for the receiving device. To change the directivity of the RF signal during transmission, the network node can control the phase and relative amplitude of the RF signal at each of one or more transmitters that broadcast the RF signal. For example, the network node may use an array of antennas (referred to as a "phased array" or "antenna array"), which forms an RF beam that can be "manipulated" to point in different directions without actually moving the antennas. Specifically, the RF currents from the transmitters are fed to the individual antennas with the correct phase relationship such that the radio waves from the individual antennas add together in the desired direction to increase radiation, while canceling in the undesired directions to suppress radiation.

[0047] The transmit beams can be quasi - co - located, which means that they appear to have the same parameters to a receiver (e.g., UE), regardless of whether the transmit antennas of the network node are physically co - located. In NR, there are four types of quasi - co - location (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters of a second reference RF signal on a second beam can be derived based on information about a source reference RF signal on a source beam. Thus, if the source reference RF signal is of 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 transmitted on the same channel. If the source reference RF signal is of 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 transmitted on the same channel. If the source reference RF signal is of 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 transmitted on the same channel. If the source reference RF signal is of QCL type D, the receiver can use the source reference RF signal to estimate the spatial reception parameters of the second reference RF signal transmitted on the same channel.

[0048] In receive beamforming, the receiver uses receive beams to amplify the RF signals detected on a given channel. For example, the receiver can increase the gain setting of the antenna array in a specific direction and / or adjust the phase setting of the antenna array in a specific direction to amplify the RF signal received from that direction (e.g., increase its gain level). Thus, when the receiver is described as beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gains in other directions, or the beam gain in that direction is the highest compared to the beam gains of all other receive beams available to the receiver in that direction. This results in a stronger received signal strength for the RF signal received from that direction (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal - to - interference - plus - noise ratio (SINR), etc.).

[0049] The transmit beams and receive beams can be spatially related. The spatial relationship means that the parameters of a second beam (e.g., transmit beam or receive beam) for a second reference signal can be derived based on information about a first beam (e.g., receive beam or transmit beam) of a first reference signal. For example, a UE can use a specific receive beam to receive a reference downlink reference signal (e.g., synchronization signal block (SSB)) from a base station. Then, the UE can form a transmit beam for transmitting an uplink reference signal (e.g., sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0050] Note that depending on the entity forming the "downlink" beam, the beam can be a transmit beam or a receive beam. For example, if the base station is forming a downlink beam to transmit a reference signal to the UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, the downlink beam is a receive beam for receiving the downlink reference signal. Similarly, depending on the entity forming the "uplink" beam, the beam can be a transmit beam or a receive beam. For example, if the base station is forming an uplink beam, the uplink beam is an uplink receive beam, while if the UE is forming an uplink beam, the uplink beam is an uplink transmit beam.

[0051] 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 as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). It should be understood that although part of FR1 is greater than 6 GHz, in various documents and articles, FR1 is often (interchangeably) referred to as the "sub-6 GHz" band. Regarding FR2, a similar naming issue sometimes occurs, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although it is different from the extremely high frequency (EHF) band (30 GHz - 300 GHz) identified by the International Telecommunication Union (ITU) as the "millimeter wave" band.

[0052] The frequencies between FR1 and FR2 are typically referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designations FR3 (7.125 GHz - 24.25 GHz). The bands falling within FR3 can inherit the characteristics of FR1 and / or FR2, and thus can effectively extend the characteristics of FR1 and / or FR2 to the mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz - 71 GHz), FR4 (52.6 GHz - 114.25 GHz), and FR5 (114.25 GHz - 300 GHz). Each of these higher bands falls within the EHF band.

[0053] Taking into account the above aspects, unless otherwise specifically stated, it should be understood that if used herein, terms such as "below 6 GHz" can generally represent frequencies that can be less than 6 GHz, can be within FR1, or can include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if terms such as "millimeter wave" are used herein, they can generally represent frequencies that can include mid-band frequencies, can be within FR2, FR4, FR4-a, or FR4-1 and / or FR5, or can be within the EHF band.

[0054] In a multi-carrier system such as 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 UE104 / 182 and the cell, where the UE104 / 182 performs the initial radio resource control (RRC) connection establishment procedure or initiates the RRC connection re-establishment procedure in this cell. 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), which can be configured and used to provide additional radio resources once an RRC connection is established between the UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier can contain only the necessary signaling information and signals. For example, since the primary uplink carrier and the primary downlink carrier are usually UE-specific, the UE-specific signaling information and signals may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell can have different downlink primary carriers. The same holds true for the primary uplink carrier. The network can change the primary carrier of any UE104 / 182 at any time. This is done, for example, to balance the load on different carriers. Since a "serving cell" (whether PCell or SCell) corresponds to the carrier frequency / component carrier through which a certain base station communicates, terms such as "cell", "serving cell", "component carrier", "carrier frequency", etc. may be used interchangeably.

[0055] For example, still referring to Figure 1, one of the frequencies used by the macro cell base station 102 may be an anchor carrier (or "PCell"), and other frequencies used by the macro cell base station 102 and / or the mmW base station 180 may be secondary carriers ("SCell"). Simultaneous transmission and / or reception of multiple carriers enables the UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, compared to the data rate obtained with a single 20 MHz carrier, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40 MHz).

[0056] The wireless communication system 100 may also include a UE 164, which may communicate with the macro cell base station 102 via the communication link 120 and / or communicate with the mmW base station 180 via the mmW communication link 184. For example, the macro cell base station 102 may support a PCell and one or more SCell for the UE 164, and the mmW base station 180 may support one or more SCell for the UE 164.

[0057] In some cases, the UE 164 and the UE 182 are capable of sidelink communication. A UE with sidelink capabilities (SL-UE) may use the Uu interface (i.e., the air interface between the UE and the base station) to communicate with the base station 102 via the communication link 120. The SL-UE (e.g., UE 164, UE 182) may also use the PC5 interface (i.e., the air interface between UEs with sidelink capabilities) to communicate directly with each other via the wireless sidelink 160. The wireless sidelink (or simply referred to as "sidelink") is an adaptation of the core cellular network (e.g., LTE, NR) standard that allows direct communication between two or more UEs without communicating through a base station. Sidelink communication can be unicast or multicast and can be used for device-to-device (D2D) media sharing, vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X) communication (e.g., cellular V2X (cV2X) communication, enhanced V2X (eV2X) communication, etc.), emergency rescue applications, etc. One or more SL-UEs in a group of SL-UEs that utilize sidelink communication may be located within the geographical coverage area 110 of the base station 102. Other SL-UEs in such a group may be outside the geographical coverage area 110 of the base station 102 or, for other reasons, may not be able to receive transmissions from the base station 102. In some cases, each group of SL-UEs that communicate via sidelink communication may utilize a one-to-many (1:M) system, where each SL-UE transmits to each other SL-UE in the group. In some cases, the base station 102 facilitates the scheduling of resources for sidelink communication. In other cases, sidelink communication is performed between the SL-UEs without involving the base station 102.

[0058] In one aspect, the sidelink 160 may operate on a wireless communication medium of interest, which may be shared with other vehicles and / or infrastructure access points and other wireless communications between other RATs. The "medium" may include one or more time, frequency, and / or spatial communication resources associated with wireless communication between one or more transmitter / receiver pairs (e.g., covering one or more channels across one or more carriers). In one aspect, the medium of interest may 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 entities such as the Federal Communications Commission (FCC) in the United States), these systems (especially those employing small cell access points) have recently extended their operation into 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 technologies commonly referred to as "Wi-Fi"). Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single carrier FDMA (SC-FDMA) systems, etc.

[0059] It should be noted that while Figure 1 only two of these UEs are illustrated as SL-UEs (i.e., UE 164 and 182), any of the illustrated UEs may be an SL-UE. Additionally, although only UE 182 is described as being capable of beamforming, any of the illustrated UEs (including UE 164) are capable of beamforming. In cases where the SL-UEs are capable of beamforming, they may beamform towards each other (i.e., towards other SL-UEs), towards other UEs (e.g., UE 104), towards base stations (e.g., base station 102, 180, small cell 102', access point 150), etc. Thus, in some cases, UE 164 and UE 182 may utilize beamforming over the sidelink 160.

[0060] In Figure 1 the example of, the illustrated UEs (for simplicity, in Figure 1Any UE shown as a single UE 104 in the figure can receive signals 124 from one or more space vehicles (SVs) 112 in Earth orbit (e.g., satellites). In one aspect, the SV 112 can be part of a satellite positioning system where the UE 104 can use it as an independent source of position information. A satellite positioning system generally includes a system of transmitters (e.g., SV 112) that are positioned such that a receiver (e.g., UE 104) can determine its position on or above the Earth at least in part based on positioning signals received from the transmitters (e.g., signal 124). Such transmitters typically send signals marked with a repeating pseudo-random noise (PN) code with a set number of chips. Although typically located in the SV 112, the transmitter can sometimes be located on a ground-based control station, base station 102, and / or other UE 104. The UE 104 can include one or more dedicated receivers that are specifically designed to receive the signal 124 in order to derive geographical location information from the SV 112.

[0061] In a satellite positioning system, the use of the signal 124 can be enhanced by various satellite-based augmentation systems (SBAS) that can be associated with or otherwise enable the use of one or more global and / or regional navigation satellite systems. For example, SBAS can include augmentation systems that provide integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multi-functional Satellite Augmentation System (MSAS), GPS- Aided Geo Augmented Navigation or GPS and Geo Augmented Navigation System (GAGAN), etc. Thus, as used herein, a satellite positioning system can include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0062] In one aspect, the SV 112 can additionally or alternatively be part of one or more non-terrestrial networks (NTN). In an NTN, the SV 112 is connected to an earth station (also known as a ground station, NTN gateway, or gateway) which in turn is connected to elements in a 5G network, such as a modified base station 102 (without a ground antenna) or a network node in the 5GC. This element then provides access to other elements in the 5G network and ultimately provides access to entities external to the 5G network, such as Internet web servers and other user devices. Thus, instead of or in addition to communication signals from the ground base station 102, the UE 104 can receive communication signals (e.g., signal 124) from the SV 112.

[0063] The wireless communication system 100 may also include one or more UEs, such as UE 190, which is 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"). In Figure 1 the example of, UE 190 has a D2D P2P link 192 with one of the UEs in UE 104 connected to one of the base stations in base station 102 (e.g., UE190 can indirectly obtain cellular connectivity through this D2D P2P link), 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 through this D2D P2P link). 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.

[0064] Figure 2A Illustrates an example wireless network structure 200. For example, 5GC 210 (also referred to as Next Generation Core (NGC)) can be functionally regarded 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 cooperate to form the core network. The user plane interface (NG-U) 213 and the control plane interface (NG-C) 215 connect gNB 222 to 5GC 210, and specifically connect to the user plane function 212 and the control plane function 214 respectively. In an additional configuration, ng-eNB 224 can also be connected to 5GC 210 via NG-C 215 to the control plane function 214 and NG-U 213 to the user plane function 212. In addition, ng-eNB 224 can communicate directly with gNB 222 via a backhaul connection 223. In some configurations, the Next Generation RAN (NG-RAN) 220 can have one or more gNB 222, while other configurations include one or more of both ng-eNB 224 and gNB 222. Either (or both) of gNB 222 or ng-eNB 224 can communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0065] Another optional aspect may include a location server 230 that may communicate with the 5GC 210 to provide location assistance for the UE 204. The location server 230 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 alternatively may each correspond to a single server. The location server 230 may be configured to support one or more location services for the UE 204 that may be connected to the location server 230 via the core network, 5GC 210, and / or via the Internet (not illustrated). Additionally, the location server 230 may be integrated into a component of the core network or, alternatively, may be external to the core network (e.g., a third-party server such as an original equipment manufacturer (OEM) server or a service server).

[0066] Figure 2B Another example wireless network structure 240 is illustrated. The 5GC 260 (which may correspond to Figure 2AThe 5GC 210) can be functionally regarded as the control plane function provided by the Access and Mobility Management Function (AMF) 264 and the user plane function provided by the User Plane Function (UPF) 262, which cooperate to form the 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 the Session Management Function (SMF) 266, transparent proxy service for routing SM messages, access authentication and access authorization, transmission of Short Message Service (SMS) messages between the UE 204 and the Short Message Service Function (SMSF) (not shown), and Security Anchor Functionality (SEAF). The AMF 264 also interacts with the Authentication Server Function (AUSF) (not shown) and the UE 204 and receives the intermediate key established as a result of the UE 204 authentication process. In the case of authentication based on a UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM), the AMF 264 retrieves the security material from the AUSF. The functions of the AMF 264 also include Security Context Management (SCM). The SCM receives the key from the SEAF and uses the key to derive the access network-specific key. The functionality of the AMF 264 also includes location service management for regulatory services, transmission of location service messages between the UE 204 and the Location Management Function (LMF) 270 (which acts as the location server 230), transmission of location service messages between the NG-RAN 220 and the LMF 270, allocation of evolved packet system (EPS) bearer identifiers for EPS interoperability, and UE 204 mobility event notification. In addition, the AMF 264 also supports functionality for non-3GPP (Third Generation Partnership Project) access networks.

[0067] 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 the 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) handling for the user plane (e.g., uplink / downlink rate enforcement, reflected QoS marking in the downlink), uplink traffic verification (service data flow (SDF) to QoS flow mapping), transport-level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and transmitting and forwarding one or more "end markers" to the source RAN node. UPF 262 may also support the transfer of location service messages between UE 204 and a location server (such as SLP 272) on the user plane.

[0068] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, traffic steering configuration at UPF 262 for routing traffic to the correct destination, partial control of policy enforcement and QoS, and downlink data notification. The interface through which SMF 266 communicates with AMF 264 is referred to as the N11 interface.

[0069] Another optional aspect may include LMF 270, which may communicate with 5GC 260 to provide location assistance for UE 204. LMF 270 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 alternatively may each correspond to a single server. LMF 270 may be configured to support one or more location services for UE 204, which may be connected to LMF 270 via the core network, 5GC 260, and / or via the Internet (not illustrated). SLP 272 may support similar functions to LMF 270, but LMF 270 may communicate with AMF 264, NG-RAN 220, and UE 204 on the control plane (e.g., using interfaces and protocols designed to carry signaling messages rather than voice or data), and SLP 272 may communicate with UE 204 and an external client (e.g., third-party server 274) on the user plane (e.g., using protocols designed to carry voice and / or data, such as Transmission Control Protocol (TCP) and / or IP).

[0070] Another optional aspect may include a third-party server 274 that may communicate with the LMF 270, SLP 272, 5GC 260 (e.g., via the AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., a location estimate) of the UE 204. Thus, in some cases, the third-party server 274 may be referred to as a location service (LCS) client or an 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 alternatively may each correspond to a single server.

[0071] 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, while 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 the "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 radio interface referred to as the "Uu" interface.

[0072] The functionality of gNB 222 is divided among 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 other than those specifically allocated to the gNB-DU 228, including passing user data, mobility control, radio access network sharing, positioning, session management, etc. 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 gNB 222. The gNB-DU 228 is a logical node that typically hosts the Radio Link Control (RLC) and Medium Access Control (MAC) layers of gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can 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-DU 228 is referred to as the "F1" interface. The physical (PHY) layer functionality of gNB 222 is typically hosted by one or more independent gNB-RU 229, which 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 layer, SDAP layer, and PDCP layer, communicates with the gNB-DU 228 via the RLC layer and MAC layer, and communicates with the gNB-RU 229 via the PHY layer.

[0073] The deployment of a communication system such as a 5G NR system can be arranged in various ways with various components or constituent parts. In a 5G NR system or network, network nodes, network entities, mobility elements of the network, RAN nodes, core network nodes, network elements, or network equipment (such as base stations or one or more units (or one or more components) that perform base station functionality) can be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB), evolved NB (eNB), NR base station, 5G NB, access point (AP), transmit receive point (TRP), or cell, etc.) can be implemented as an aggregated base station (also referred to as a self-standing base station or a monolithic base station) or a disaggregated base station.

[0074] A centralized base station may be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. A split base station may be configured to utilize a protocol stack physically or logically distributed between two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed among one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU may 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).

[0075] Base station type operations or network designs may consider the aggregation characteristics of base station functionality. For example, split base stations may be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN, such as a network configuration advocated by the O-RAN Alliance), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Splitting may include distributing functions across two or more units at various physical locations, as well as virtualizing the distribution of at least one unit, which may enable flexibility in network design. The various units of a split base station or a split RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0076] Figure 2C An example split base station architecture 250 in accordance with aspects of the present disclosure is illustrated. The split base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226), which may 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 split 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-DU 228) via corresponding midhaul links, such as an F1 interface. The DU 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RU 229) via corresponding fronthaul links. The RU 287 may communicate with a corresponding UE 204 via one or more radio frequency (RF) access links. In some embodiments, the UE 204 may be served simultaneously by multiple RUs 287.

[0077] Each of the units (i.e., CU 280, DU 285, RU 287, and the 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 that are configured to receive or transmit signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or the associated processor or controller that provides instructions to the communication interfaces of these units may be configured to communicate with one or more of the other units via the transmission medium. For example, the units may include a wired interface that is configured to receive or transmit signals to one or more of the other units via a wired transmission medium. Additionally, the unit may include a wireless interface that may include a receiver, transmitter, or transceiver (such as a radio frequency (RF) transceiver) that is configured to receive or transmit signals to one or more of the other units via a wireless transmission medium, or both.

[0078] In some aspects, the 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), etc. Each control function may utilize an interface that is configured to convey signals with other control functions hosted by the CU 280. The CU 280 may be configured to handle 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 specific implementations, the CU 280 may be logically partitioned into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP units may communicate bi-directionally with the CU-CP units via an interface (such as the E1 interface). As needed, the CU 280 may be implemented to communicate with the DU 285 for network control and signaling.

[0079] The DU 285 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 287. In some aspects, the DU 285 may host one or more of the radio link control (RLC) layer, the media access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, and demodulation, etc.) at least in part depending on a functional split (such as the functional split defined by the 3rd Generation Partnership Project (3GPP)). In some aspects, the DU 285 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by the DU 285 or with control functions hosted by the CU 280.

[0080] The lower layer functions may be implemented by one or more RUs 287. In some deployments, the RUs 287 controlled by the DU 285 may correspond to logical nodes that host RF processing functions or low PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.) or both at least in part based on a functional split (such as a lower layer functional split). In such an architecture, the RUs 287 may be implemented to handle over-the-air (OTA) communication with one or more UEs 204. In some embodiments, the real-time and non-real-time aspects of the control plane and user plane communication with the RUs 287 may be controlled by the corresponding DU 285. In some scenarios, this configuration may enable the implementation of the DU 285 and the CU 280 in a cloud-based RAN architecture (such as a vRAN architecture).

[0081] The SMO framework 255 can be configured to support the RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 255 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, and these dedicated physical resources can be managed via an operation and maintenance interface (such as the O1 interface). For virtualized network elements, the SMO framework 255 can be configured to interact with a cloud computing platform (such as the Open Cloud (O-Cloud) 269) to perform network element lifecycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as the O2 interface). Such virtualized network elements can include, but are not limited to, the CU 280, DU 285, RU 287, and near RT RIC 259. In some specific implementations, the SMO framework 255 can communicate with the hardware aspect of the 4G RAN (such as the Open eNB (O-eNB) 261) via the O1 interface. Additionally, in some specific implementations, the SMO framework 255 can communicate directly with one or more RUs 287 via the O1 interface. The SMO framework 255 can also include a non-RT RIC 257 configured to support the functions of the SMO framework 255.

[0082] The non-RT RIC 257 can be configured to include logical 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 update, or policy-based guidance of applications / features in the near RT RIC 259. The non-RT RIC 257 can be coupled to or communicate with the near RT RIC 259 (such as via the A1 interface). The near RT RIC 259 can be configured to include logical functions that enable near-real-time control and optimization of RAN elements and resources through an interface (such as via the E2 interface) via data collection and actions, and this interface connects one or more CUs 280, one or more DUs 285, or both, and the O-eNB to the near RT RIC 259.

[0083] 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 utilized 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 employ AI / ML models to perform corrective actions through the SMO framework 255 (such as via reconfiguration of O1) or via the creation of RAN management policies (such as A1 policies).

[0084] Figure 3A , Figure 3B and Figure 3C Several example components (represented by corresponding blocks) are illustrated, which may be incorporated into a UE 302 (which may correspond to any of the UEs described herein), a base station 304 (which may correspond to any of the base stations described herein), and a network entity 306 (which may correspond to or embody any of the network functions described herein, including the location server 230 and the LMF 270, or alternatively may be independent of Figure 2A and Figure 2B The depicted NG-RAN 220 and / or 5GC 210 / 260 infrastructure, such as a dedicated network, is implemented in order to support operations as described herein. It should be understood that these components may be implemented in different types of devices with different specific implementations (e.g., in an ASIC, in a system on a chip (SoC), etc.). The illustrated components 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 as providing similar functionality. In addition, a given device may include one or more of these 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.

[0085] UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively. These wireless wide area network (WWAN) transceivers provide components (e.g., components for transmission, components for reception, components for measurement, components for tuning, components for blocking transmission, etc.) for communication via one or more wireless communication networks (not shown), such as an NR network, an LTE network, a GSM network, etc. WWAN transceivers 310 and 350 can each be respectively connected to one or more antennas 316 and 356 for communicating with other network nodes (such as other UEs, access points, base stations (e.g., eNBs, gNBs), etc.) via at least one specified RAT (e.g., NR, LTE, GSM, etc.) over an interested wireless communication medium (e.g., a set of time / frequency resources in a specific spectrum). WWAN transceivers 310 and 350 can be configured in different ways to respectively transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) according to the specified RAT, and conversely, to respectively receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.). Specifically, WWAN transceivers 310 and 350 respectively include: one or more transmitters 314 and 354 for respectively transmitting and encoding signals 318 and 358, and one or more receivers 312 and 352 for respectively receiving and decoding signals 318 and 358.

[0086] At least in some cases, UE 302 and base station 304 each further include one or more short-range wireless transceivers 320 and 360, respectively. Short-range wireless transceivers 320 and 360 can be respectively connected to one or more antennas 326 and 366, and provide for communication over an interested wireless communication medium via at least one specified RAT (e.g., WiFi, LTE-D, Components (e.g., components for transmitting, receiving, measuring, tuning, blocking transmission, etc.) for a UE 302 to communicate with other network nodes (such as other UEs, access points, base stations, etc.) using various short-range communication technologies (e.g., PC5, dedicated short-range communication (DSRC), wireless access for vehicle environments (WAVE), near-field communication (NFC), ultra-wideband (UWB), etc.). The short-range wireless transceivers 320 and 360 can be configured in different ways to transmit 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 respectively include one or more transmitters 324 and 364 for transmitting and encoding signals 328 and 368, and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368. As a specific example, the short-range wireless transceivers 320 and 360 can be WiFi transceivers, transceivers, and / or transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0087] In at least some cases, the UE 302 and the base station 304 also include satellite signal receivers 330 and 370. The satellite signal receivers 330 and 370 can be respectively connected to one or more antennas 336 and 376, and can provide components for receiving and / or measuring satellite positioning / communication signals 338 and 378. In the case where the satellite signal receivers 330 and 370 are satellite positioning system receivers, the 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 the satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 can be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal receivers 330 and 370 can include any suitable hardware and / or software for receiving and processing the satellite positioning / communication signals 338 and 378. The satellite signal receivers 330 and 370 can request information and operations from other systems as appropriate, and at least in some cases, perform calculations using measurements obtained by any suitable satellite positioning system algorithm to respectively determine the positions of the UE 302 and the base station 304.

[0088] Base station 304 and network entity 306 each include one or more network transceivers 380 and 390 respectively, and the one or more network transceivers provide components (such as components for transmission, components for reception, etc.) for communicating with other network entities (such as other base stations 304, other network entities 306). For example, base station 304 may employ one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 via one or more wired or wireless backhaul links. As another example, network entity 306 may employ one or more network transceivers 390 to communicate with one or more base stations 304 via one or more wired or wireless backhaul links, or communicate with other network entities 306 via one or more wired or wireless core network interfaces.

[0089] The transceiver may be configured to communicate via a wired or wireless link. The transceiver (whether a wired transceiver or a wireless transceiver) includes a transmitter circuit (such as transmitters 314, 324, 354, 364) and a receiver circuit (such as receivers 312, 322, 352, 362). In some specific implementations, the transceiver may be an integrated device (such as implementing the transmitter circuit and the receiver circuit in a single device), in some specific implementations may include separate transmitter circuits and separate receiver circuits, or may be implemented in other ways in other specific implementations. The transmitter circuit and the receiver circuit of a wired transceiver (such as, in some specific implementations, network transceivers 380 and 390) may be coupled to one or more wired network interface ports. The wireless transmitter circuit (such as transmitters 314, 324, 354, 364) may include or be coupled to a plurality of antennas (such as antennas 316, 326, 356, 366), such as an antenna array, which allows the corresponding device (such as UE 302, base station 304) to perform transmission "beamforming" as described herein. Similarly, the wireless receiver circuit (such as receivers 312, 322, 352, 362) may include or be coupled to a plurality of antennas (such as antennas 316, 326, 356, 366), such as an antenna array, which allows the corresponding device (such as UE 302, base station 304) to perform receive beamforming as described herein. In one aspect, the transmitter circuit and the receiver circuit may share the same plurality of antennas (such as antennas 316, 326, 356, 366), such that the corresponding device can only receive or only transmit at a given time, rather than receive and transmit both at the same time. The wireless transceiver (such as WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listening module (NLM) for performing various measurements, etc.

[0090] As used herein, various wireless transceivers (e.g., in some specific embodiments, transceivers 310, 320, 350, and 360, as well as network transceivers 380 and 390) and wired transceivers (e.g., network transceivers 380 and 390 in some specific embodiments) can generally be referred to as "transceiver", "at least one transceiver", or "one or more transceivers". Thus, it can be inferred whether a particular transceiver is a wired transceiver or a wireless transceiver based on the type of communication being performed. For example, backhaul communication between network devices or servers typically involves signaling via a wired transceiver, while wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) will typically involve signaling via a wireless transceiver.

[0091] 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 each include one or more processors 332, 384, and 394 for providing functionality related to, for example, wireless communication, as well as for providing other processing functionality. Thus, processors 332, 384, and 394 can provide components for processing, such as components for determining, for calculating, for receiving, for sending, 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.

[0092] UE 302, base station 304, and network entity 306 each include a memory circuit that implements memories 340, 386, and 396 (e.g., each including a memory device), and the memory circuit is used to maintain information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Thus, memories 340, 386, and 396 can provide components for storage, components for retrieval, components for maintenance, etc. In some cases, UE 302, base station 304, and network entity 306 can each include positioning components 342, 388, and 398. Positioning components 342, 388, and 398 can be hardware circuits that are respectively part of or coupled to processors 332, 384, and 394, and when executed, these hardware circuits cause UE 302, base station 304, and network entity 306 to perform the functionality described herein. In other aspects, positioning components 342, 388, and 398 can be external to processors 332, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, positioning components 342, 388, and 398 can be memory modules respectively stored in memories 340, 386, and 396, and when executed by processors 332, 384, and 394 (or a modem processing system, another processing system, etc.), these memory modules cause UE 302, base station 304, and network entity 306 to perform the functionality described herein. Figure 3A Illustrates possible locations of positioning component 342, which can be part of, for example, one or more WWAN transceivers 310, memory 340, one or more processors 332, or any combination thereof, or can be an independent component. Figure 3B Illustrates possible locations of positioning component 388, which can be part of, for example, one or more WWAN transceivers 350, memory 386, one or more processors 384, or any combination thereof, or can be an independent component. Figure 3C Illustrates possible locations of positioning component 398, which can be part of, for example, one or more network transceivers 390, memory 396, one or more processors 394, or any combination thereof, or can be an independent component.

[0093] The UE 302 may include one or more sensors 344 coupled to one or more processors 332 to provide components for sensing or detecting movement and / or orientation information unrelated to movement 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. By way of example, the sensors 344 may include an accelerometer (e.g., a microelectromechanical systems (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of movement detection sensor. Additionally, the sensors 344 may include multiple different types of devices and combine their outputs to provide movement information. For example, the sensors 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate positioning in a two-dimensional (2D) and / or three-dimensional (3D) coordinate system.

[0094] In addition, the UE 302 includes a user interface 346 that provides components for providing an indication to a user (e.g., an audible and / or visual indication) and / or for receiving user input (e.g., when the user actuates a sensing device such as a keypad, a touch screen, a microphone, etc.). Although not shown, the base station 304 and the network entity 306 may also include a user interface.

[0095] Referring in more detail to one or more processors 384, in the downlink, IP packets from the network entity 306 may be provided to the processor 384. One or more processors 384 may implement functionality for the RRC layer, the packet data convergence protocol (PDCP) layer, the radio link control (RLC) layer, and the medium access control (MAC) layer. One or more processors 384 may provide: RRC layer functionality associated with the broadcast 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 functionality associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the transfer of upper layer PDUs, error correction via automatic repeat request (ARQ), concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and re-ordering of RLC data PDUs; and MAC layer functionality associated with the mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0096] The transmitter 354 and the receiver 352 can implement layer 1 (L1) functionality associated with various signal processing functions. Layer 1, which includes the physical (PHY) layer, can 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 disposes of 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 encoded and modulated symbols can then be split into parallel streams. Each stream can then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., pilot) in the time domain 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 the channel estimator can be used to determine the encoding and modulation schemes and for spatial processing. The channel estimates can be derived from reference signals transmitted by the UE 302 and / or channel state feedback. Each spatial stream can then be provided to one or more different antennas 356. The transmitter 354 can modulate an RF carrier with the respective spatial stream for transmission.

[0097] At the UE 302, the receiver 312 receives signals via its respective antennas 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 functionality associated with various signal processing functions. The receiver 312 can perform spatial processing on the information to recover any spatial streams destined for the UE 302. If there are multiple spatial streams destined for the UE 302, they can be combined by the receiver 312 into a single OFDM symbol stream. The receiver 312 then uses a fast Fourier transform (FFT) to convert the OFDM symbol stream from the time domain to the frequency domain. The frequency-domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols and reference signals on each subcarrier are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 304. These soft decisions can be based on the channel estimates calculated by the channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally transmitted by the base station 304 on the physical channel. The data and control signals are then provided to one or more processors 332, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0098] On the uplink, one or more processors 332 provide demultiplexing between transport channels 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.

[0099] Similar to the functionality described in connection with the downlink transmission performed by base station 304, one or more processors 332 provide: RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connection, and measurement reporting; PDCP layer functionality associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and re-ordering of RLC data PDUs; and MAC layer functionality associated with the 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 via hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0100] Channel estimates derived by a channel estimator from reference signals or feedback transmitted by base station 304 can be used by transmitter 314 to select appropriate decoding and modulation schemes and assist in spatial processing. The spatial streams generated by transmitter 314 can be provided to different antennas 316. Transmitter 314 can modulate an RF carrier with the respective spatial streams for transmission.

[0101] Uplink transmissions are processed at base station 304 in a manner similar to that described in connection with the receiver functionality at UE 302. Receiver 352 receives signals via its respective antennas 356. Receiver 352 recovers the information modulated onto the RF carrier and provides the information to one or more processors 384.

[0102] On the uplink, one or more processors 384 provide demultiplexing between transport channels 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 can be provided to the core network. One or more processors 384 are also responsible for error detection.

[0103] For convenience, UE 302, base station 304, and / or network entity 306 are in Figure 3A , Figure 3B and Figure 3C, are shown as including various components that can be configured according to the various examples described herein. However, it should be understood that the illustrated components may have different functionality in different designs. In particular, Figures 3A to 3C Various components in are optional in alternative configurations, and 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 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 will be readily apparent to those skilled in the art.

[0104] 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 incorporated into the same base station 304), the data buses 334, 382, ​​and 392 may provide for communication between different logical entities.

[0105] Figure 3A , Figure 3B and Figure 3C The components of can be implemented in various ways. In some specific implementations, Figure 3A , Figure 3B and Figure 3CThe components can be implemented in one or more circuits, such as one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit can use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide the functionality. For example, some or all of the functionality represented by blocks 310 to 346 can be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Similarly, some or all of the functionality represented by blocks 350 to 388 can be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Moreover, some or all of the functionality represented by blocks 390 to 398 can be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). For simplicity, various operations, actions, and / or functions are described herein as being "performed by the UE", "performed by the base station", "performed by the network entity", etc. However, it should be understood that such operations, actions, and / or functions can actually be performed by specific components or combinations of components of 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.

[0106] In some designs, network entity 306 can be implemented as a core network component. In other designs, network entity 306 can operate differently from a network operator or a cellular network infrastructure (e.g., NGRAN 220 and / or 5GC 210 / 260). For example, network entity 306 can be a component of a private network that can 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).

[0107] Various frame structures can be used to support downlink and uplink transmissions between network nodes (e.g., base stations and UEs). Some of these transmissions can carry reference (pilot) signals (RS). These reference signals can include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary synchronization signals (PSS), secondary synchronization signals (SSS), synchronization signal blocks (SSB), sounding reference signals (SRS), etc., depending on whether the illustrated frame structure is used for uplink communication or downlink communication.

[0108] Note that the terms "positioning reference signal" and "PRS" generally refer to specific reference signals for positioning in NR and LTE systems. However, as used herein, the terms "positioning reference signal" and "PRS" may also refer to any type of reference signal that can be used for positioning, such as but not limited to: PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, SRS for positioning (SRS-pos), UL-PRS, etc. as defined in LTE and NR. In addition, the terms "positioning reference signal" and "PRS" may refer to a downlink positioning reference signal, an uplink positioning reference signal, or a sidelink positioning reference signal, unless otherwise indicated by the context. If further differentiation of the type of PRS is required, the downlink positioning reference signal may be referred to as "DL-PRS", the uplink positioning reference signal (e.g., SRS for positioning, i.e., PTRS) may be referred to as "UL-PRS", and the sidelink positioning reference signal may be referred to as "SL-PRS". In addition, for signals that can be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), these signals may be prefixed with "DL", "UL", or "SL" to distinguish the direction. For example, "UL-DMRS" may be different from "DL-DMRS".

[0109] NR supports a variety of cellular network-based positioning techniques, including downlink-based positioning methods, uplink-based positioning methods, 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 5 Examples of various positioning methods according to aspects of the present disclosure are illustrated. In the OTDOA or DL-TDOA positioning procedure illustrated by scenario 410, the UE measures the difference in the time of arrival (ToA) of reference signals (e.g., positioning reference signal (PRS)) received from paired base stations (referred to as reference signal time difference (RSTD) or time difference of arrival (TDOA) measurement), and reports these differences to the positioning entity. More specifically, the UE receives the identifiers (IDs) of the reference base station (e.g., serving base station) and multiple non-reference base stations in the assistance data. Then, the UE measures the RSTD between the reference base station and each non-reference base station. Based on the known positions of the involved base stations and the RSTD measurement results, the positioning entity (e.g., the UE for UE-based positioning or the location server for UE-assisted positioning) can estimate the location of the UE.

[0110] For DL-AoD positioning exemplified by scenario 420, the positioning entity uses a measurement report from the UE on the received signal strength measurements of multiple downlink transmission beams to determine the angle between the UE and the transmitting base station. Then, the positioning entity can estimate the location 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)) transmitted by the UE to multiple base stations. Specifically, the UE transmits one or more uplink reference signals, which are measured by a reference base station and multiple non-reference base stations. Then, each base station reports the reception time of the reference signal (referred to as relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the locations and relative timings of the base stations involved. Based on the received-to-received (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 locations of the base stations, and their known timing offsets, the positioning entity can use TDOA to estimate the location 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 reception beams. The positioning entity uses the signal strength measurements and the angles of the reception 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 referred to as "multi-cell RTT" and "multi-RTT"). In the RTT procedure, a first entity (e.g., a base station or a UE) sends a first RTT-related signal (e.g., a PRS or an SRS) to a second entity (e.g., a UE or a base station), and the second entity 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 referred to as the received-to-transmitted (Rx-Tx) time difference. The Rx-Tx time difference measurement can be made or adjusted to include only the time difference between the received signal and the nearest time slot boundary of the transmitted signal. Then, the two entities can transmit their Rx-Tx time difference measurements to a location server (e.g., the LMF 270), which calculates the round-trip propagation time (i.e., the RTT) between the two entities based on these two Rx-Tx time difference measurements (e.g., calculated as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity can transmit its Rx-Tx time difference measurement to the other entity, and then the other entity calculates the RTT. The distance between the two entities can be determined based on the RTT and a known signal speed (e.g., the speed of light). For multi-RTT positioning illustrated by scenario 430, a first entity (e.g., a UE or a base station) performs the RTT positioning procedure with multiple second entities (e.g., multiple base stations or UEs) so that the location of the first entity can be determined (e.g., using multilateration) based on the distances to the second entities and the known locations of the second entities. The RTT and multi-RTT methods can be combined with other positioning techniques (such as UL-AoA and DL-AoD) to improve location accuracy, as illustrated by 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, the timing advance (TA), and the identifiers, the estimated timing, and the signal strengths of the detected neighboring base stations. Then, the location of the UE is estimated based on this information and the known locations of the base stations.

[0115] To assist in 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 a base station (or a cell / TRP of a base station) from which a reference signal is measured, reference signal configuration parameters (e.g., including the number of consecutive time slots including the PRS, the periodicity of consecutive time slots including the PRS, a silence sequence, a frequency hopping sequence, a reference signal identifier, a reference signal bandwidth, etc.), and / or other parameters applicable to a specific positioning method. Alternatively, the assistance data may directly originate from the base station itself (e.g., in a periodically broadcast overhead message, etc.). In some cases, the UE itself may be able to detect adjacent network nodes without using assistance data.

[0116] In the case of the OTDOA or DL-TDOA positioning procedure, the assistance data may further include an expected RSTD value and an 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 of the resources used for positioning measurements are in FR1, the value range of the uncertainty of the expected RSTD may be + / -32 μs. In other cases, when all of the resources used for positioning measurements are in FR2, the value range of the uncertainty of the expected RSTD may be + / -8 μs.

[0117] A location estimate may be referred to by other names, such as positioning estimate, location, positioning, positioning lock, lock, etc. A location estimate may be geodetic and include coordinates (e.g., latitude, longitude, and possibly altitude), or it may be civic and include a street address, a postal address, or some other verbal description of the location. A location estimate may be further defined relative to some other known location or in absolute terms (e.g., using latitude, longitude, and possibly altitude). A location estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be included with a certain specified or default confidence).

[0118] Machine learning can be used to generate models that can be used to facilitate various aspects associated with data processing. A specific application of machine learning involves generating measurement models for processing reference signals used for positioning (e.g., positioning reference signals (PRS)) (such as feature extraction, reporting of reference signal measurements (e.g., selecting which of the extracted features to report), etc.).

[0119] 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 example 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 their age. In a regression model, the output is continuous. An example of a regression model is linear regression, which simply tries to find the line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding the best-fitting plane) and polynomial regression (e.g., finding the best-fitting curve).

[0120] Another example of a machine learning model is the decision tree model. In a decision tree model, a tree structure is defined with multiple nodes. Decisions are used to move from the root node at the top of the decision tree to a leaf node (i.e., a node that does not have additional child nodes) at the bottom of the decision tree. Generally, a higher number of nodes in a decision tree model is associated with higher decision accuracy.

[0121] Another example of a machine learning model is the decision forest. A random forest is an ensemble learning technique built on top of decision trees. Random forest involves using bootstrap data sets of the original data to create multiple decision trees and randomly selecting a subset of variables at each step of the decision tree. Then, the model selects the mode of all the predictions of each decision tree. By relying on a "majority rules" model, the risk of error from individual trees is reduced.

[0122] Another example of a machine learning model is the neural network (NN). A neural network is essentially a network of mathematical equations. A neural network accepts one or more input variables and produces one or more output variables by passing through the network of equations. In other words, a neural network receives a vector of inputs and returns a vector of outputs.

[0123] Figure 5 An example neural network 500 in accordance with aspects of the present disclosure is illustrated. Neural network 500 includes an input layer "i" that receives "n" (one or more) inputs (illustrated as "input 1", "input 2", and "input n"), one or more hidden layers (illustrated as hidden layers "h1", "h2", and "h3") for processing the inputs from the input layer, and an output layer "o" that provides "m" (one or more) outputs (labeled "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" can be the same or different. In some designs, the hidden layer "h" can include linear functions and / or activation functions, and the nodes (illustrated as circles) of each successive hidden layer process the linear functions and / or activation functions of the nodes from the previous hidden layer.

[0124] 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). Substantially, 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, for data with two classes, a support vector machine will find the hyperplane or boundary that maximizes the margin between the two classes of data. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between these classes. Another example of a classification model is Naive Bayes based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the above examples except that the output is discrete rather than continuous.

[0125] Different from supervised learning, unsupervised learning is used to make inferences and find patterns from input data without referring to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0126] Clustering is an unsupervised technique that involves grouping or clustering data points. Clustering is often used in 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. More simply put, dimensionality reduction is the process of reducing the dimensionality of the feature set (even more simply, reducing the number of features). Most dimensionality reduction techniques can be classified as feature elimination or feature extraction. An example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves projecting higher-dimensional data (e.g., three-dimensional) into a smaller space (e.g., two-dimensional). This produces lower-dimensional (e.g., two-dimensional instead of three-dimensional) data while retaining all the original variables in the model.

[0127] Regardless of which machine learning model is used, at a high level, a machine learning module (e.g., implemented by a processing system such as processors 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 this training input data with an output data set (e.g., a set of possible or likely candidate locations for various target UEs), such that the same output data set can be determined later when similar input data (e.g., from other target UEs at the same or similar locations) is provided.

[0128] Figure 6FIG. 600 is a diagram illustrating an example channel estimate of a multipath channel between a receiver device (e.g., any one of the UEs or base stations described herein) and a transmitter device (e.g., any other one of the UEs or base stations described herein) in accordance with aspects of the present disclosure. The channel estimate represents the strength of a radio frequency (RF) signal (e.g., a positioning reference signal (PRS)) received over the multipath channel as a function of time delay, and may be referred to as the channel energy response (CER), channel impulse response (CIR), or power delay profile (PDP) of the channel. Thus, the horizontal axis represents time (e.g., milliseconds), and the vertical axis represents signal strength (e.g., decibels). Note that a multipath channel is a channel between a transmitter and a receiver over which an RF signal travels along multiple paths or multipaths due to the transmission of the RF signal on multiple beams and / or due to the propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0129] In Figure 6 the example of, the receiver detects / measures multiple (four) channel taps of the RF signal. Each channel tap is a cluster of one or more rays and corresponds to a multipath followed by the RF signal between the transmitter and the receiver. Thus, a channel tap represents the arrival time and signal strength of the RF signal on the multipath. Multiple channel taps may exist because the RF signal is transmitted on different transmit beams (and thus at different angles), or because of the propagation characteristics of the RF signal (e.g., may follow different paths due to reflection), or both. Note that although Figure 6 illustrates channel taps with two to five rays, it should be understood that a channel tap may have more or fewer rays than the number of rays illustrated.

[0130] In Figure 6 the example of, the channel tap detected at time T3 consists of stronger rays than the channel tap detected at time T1. This may be due to an obstruction on the LOS path between the transmitter and the receiver. Alternatively or additionally, there may be a strong reflector along the NLOS path corresponding to the channel tap detected at time T3.

[0131] NR supports RF fingerprint (RFFP)-based positioning, which is a positioning and localization technique that uses the RFFP captured by a mobile device to determine the location of the mobile device. The RFFP can be a histogram of received signal strength indicator (RSSI), CER, CIR, PDP, or channel frequency response (CFR). The RFFP can represent a single channel (e.g., PRS) received from a transmitter, all channels received from a specific transmitter, or all channels detectable at the receiver. The RFFP measured by a mobile device (e.g., UE) and the location of the transmitter that transmitted the RF signal measured by the mobile device to determine the RFFP (i.e., the transmitter) can be used to determine (e.g., triangulate) the location of the mobile device.

[0132] Model-based positioning techniques have been shown to provide superior positioning performance compared to classical positioning schemes (e.g., positioning techniques that do not use an ML positioning model). In ML-RFFP-based positioning, an ML model (e.g., neural network 500) takes the RFFP of downlink reference signals (e.g., PRS) and / or uplink reference signals (e.g., UL-PRS) as input and outputs a positioning measurement (e.g., ToA, RSTD) or the device location corresponding to the input RFFP. The ML model (e.g., neural network 500) is trained using "ground truth" (i.e., known) positioning measurements or device locations as the reference (i.e., expected) output of the training set for the RFFP.

[0133] Typically, the location of the TRP in RFFP positioning is not required. An end-to-end ML positioning model is used to determine the UE location, which takes the channel fingerprints from different TRPs as input and outputs the location of the UE. This is in contrast to classical positioning (e.g., positioning techniques that do not employ an ML positioning model), where knowledge of the TRP's positioning is required and incorrect positioning of the TRP can lead to errors in UE location estimation. Thus, some aspects of the present disclosure relate to RFFP-based TRP positioning to obtain an accurate positioning estimate of the TRP. In some aspects, the positioning estimate of the TRP can subsequently be used in classical positioning operations. For example, the TRP location obtained based on applying a positioning model to the RFFP measurements associated with the TRP can be used by the LMF as auxiliary data in future positioning sessions. Some aspects of the present disclosure are applicable to dynamic small cell deployments and / or factories, where the TRP can be repositioned from time to time throughout the positioning environment.

[0134] According to some aspects, RFFP-based TRP positioning can be implemented in an end-to-end positioning model (e.g., a neural network), where the input to the positioning model is the RFFP measurements corresponding to the reference signals associated with the TRP, and the output of the positioning model is the positioning of the TRP (or other measurements from which the positioning of the TRP can be obtained). The

[0135] Figure 7 FIG. 700 is an illustration of the training and use of a positioning model for RFFP-based positioning in accordance with aspects of the present disclosure. In Figure 7 an example, during an "offline" phase, the RFFP (e.g., CER / CIR / CFR) captured by the mobile devices (e.g., UE 1 to UE N) is stored in a database. The database can be located at the mobile device or at a network entity (e.g., a location server, a model management server, etc.).

[0136] For UE-based downlink RFFP (DL-RFFP) positioning, the network (e.g., a location server) can configure the TRPs (e.g., TRP 1 to TRP L) for which the positioning is to be determined to send downlink reference signals (DL-RS) (e.g., PRS) to the mobile devices (e.g., UE 1 to UE N). In such instances, each set of RFFP (e.g., RFFP(1) to RFFP(L)) is the CER / CIR / CFR of the configured downlink reference signal transmitted by the TRP (e.g., TRP 1 to TRP N) and measured by the mobile devices (e.g., UE 1 to UE N). In this example, the first set of RFFP labeled as RFFP(1) is based on the measurement by the UE (e.g., UE 1 to UE N) of the DL-RS transmitted by TRP 1. Similarly, the second set of RFFP labeled as RFFP(2) is based on the measurement of the DL-RS transmitted by TRP 2. The remaining sets of RFFP correspond to the RFFP measurements associated with each of the TRPs, and are obtained in a similar manner. Although the present disclosure describes the application of the ML positioning model to the RFFP measurements, it will be recognized based on the teachings of the present disclosure that other types of positioning models can be used in addition to or in place of the ML positioning model.

[0137] Each measured RFFP is associated with at least one known positioning parameter (used as a label), which is associated with the known location of the TRP when the mobile device obtains the RFFP. In Figure 7Among them, at least one known positioning parameter associated with a known position is shown as the coordinates {x1, y1, z1} corresponding to the positioning of TRP 1. Additionally, or alternatively, at least one known positioning parameter associated with the known positioning of TRP 1 may be a positioning measurement associated with the known positioning of TRP 1. According to some aspects of the present disclosure, a set of RFFP measurements may be obtained by each UE (e.g., UE1 to UE N) for each TRP (TRP 1 to TRP N) at multiple positions (e.g., TRP 1 at coordinates {x1, y1, z1} to TRP L at coordinates {xL, yL, zL}). In one aspect, the positioning of the TRP for training the positioning model may be known via another positioning technique such as that discussed above with reference to Figure 4 as discussed.

[0138] According to some aspects of the present disclosure, a set of RFFP measurements may be based on uplink reference signals (e.g., SRS) transmitted by each UE (e.g., UE 1 to UE N) and measured at the corresponding TRP (e.g., TRP 1 to TRP N). In such instances, each set of RFFP (e.g., RFFP(1) to RFFP(L) UE N) may correspond to RFFP measurements obtained by the TRP at a known positioning (e.g., TRP 1 at coordinates, etc.).

[0139] Based on the information captured during the offline phase, the positioning model (e.g., neural network 500) is trained to provide an estimate of one or more positioning parameters of the TRP corresponding to the RFFP measured by the mobile device and / or the TRP. More specifically, the training set may include RFFP and positioning tags, such as those stored in the database during the offline data acquisition process. The RFFP measurements and the corresponding known positioning parameters associated with the known positioning are used as inputs to the positioning model during training. The known positioning parameters associated with the known position may be used as tags for the corresponding data during the training process. According to various aspects of the present disclosure, the training of the positioning model may be performed at a network server (e.g., a location server, an LMF, a positioning model management server, etc.), at a third-party server (e.g., an over-the-top (OTT) server in the cloud), or any combination thereof.

[0140] Labels for training a positioning model (e.g., the label "{x1, y1, z1}" associated with RFFP measurement RFFP(1) of TRP 1) can be obtained in various ways. According to some aspects of the present disclosure, the label can be based on the coordinates of the TRP (e.g., {x1, y1, z1}). According to some aspects of the present disclosure, the label can be provided by a network operator and include the coordinates of the TRP and a confidence interval or indicator corresponding to the coordinates. Note that only a portion of the ground truth may be known (e.g., only the elevation of the TRP is known, rather than all three-dimensional coordinates), in which case the positioning model is trained to provide a two-dimensional positioning estimate of the TRP. According to some aspects of the present disclosure, the label can be generated using reverse classical positioning techniques with UEs at known locations. As an example, four RTT sessions with four different UEs having known positions can be used to generate a three-dimensional TRP positioning expressed by three-dimensional positioning coordinates (e.g., {x1, y1, z1}).

[0141] After training, during the "online" phase, the trained positioning model can be used to predict (infer) positioning parameters associated with the current position of the TRP based on the RFFP currently measured by the mobile device and / or the TRP (illustrated as "Pos M", which has coordinates {xM, yM, zM} and / or positioning measurements associated with Pos M (e.g., ToA M, TDoA M, RSTDM, AoDM, etc.). For UE-based TRP positioning, the network (e.g., a location server) can provide the trained positioning model to the mobile device and auxiliary data corresponding to the RFFP measured by other UEs in the positioning environment. In such a scenario, the UE can obtain an estimate of the positioning of the TRP and use this positioning estimate and subsequent classical positioning operations.

[0142] For UE-assisted TRP positioning, the mobile device can provide RFFP measurements to the network, where the positioning model is applied to the RFFP measurements to obtain the positioning of the TRP. For TRP UL-RS-based positioning, the TRP can obtain RFFP measurements based on the UL-RS sent by the UE. The positioning model can be applied to the RFFP measurements at the TRP or other network servers (e.g., base stations, LMF, model management servers, etc.). In such a scenario, the network server can use the RFFP measurements to obtain an estimate of the positioning of the TRP, and this estimate of the positioning of the TRP can be reported in the auxiliary data to the UEs participating in the classical positioning techniques. Additionally or alternatively, the network server can use the estimate of the TRP positioning obtained at the network server and use UE-assisted classical positioning techniques to obtain the positioning of the UE.

[0143] Figure 8FIG. 800 depicts an RFFP-based TRP localization scenario in accordance with aspects of the present disclosure, where multiple UEs measure DL-RS transmitted by a TRP to obtain RFFP, and a trained localization model can be applied to the RFFP to obtain a localization estimate of the TRP. In this example, the DL-RS is transmitted by a TRP labeled TRPL. Multiple UEs (″assistant UEs″) (e.g., UE 1 to UE N) measure the transmitted DL-RS to obtain corresponding RFFP measurements. In one aspect, the same DL-RS TRP transmission is measured by multiple UEs. Additionally or in an alternative, a UE can measure multiple DL-RS TRP transmissions from the same TRP, where the multiple DL-RS TRP transmissions occur within a maximum time window threshold T. In accordance with various aspects of the present disclosure, the DL-RS can be PRS, CSI-RS, SSB, etc. In accordance with aspects of the present disclosure, different UEs can observe and measure different DL-RSs with different bandwidths. In accordance with various aspects of the present disclosure, the RFFP measurements can be CIR, CFR, RSRQ, RSRP, delay spread, angular spread, AoA / AoD angles, Doppler spread, etc., or any combination captured at a single or multiple antenna ports.

[0144] The trained localization model 802 is applied to the RFFP measurements obtained by the multiple UEs to provide a localization estimate of TRPL (or other localization parameters from which a localization estimate can be derived). In accordance with certain aspects of the present disclosure, the trained localization model 802 can be applied at a network server based on the DL-RS RFFP measurements reported by the UEs. In accordance with certain aspects of the present disclosure, the DL-RS RFFP measurements can be reported by the UEs to the network server and subsequently provided by the network server as auxiliary data to one or more of the UEs. In such a scenario, the UE can apply the trained localization model 802 at the UE to the RFFP measurements in the auxiliary data to obtain a localization estimate of TRP L However, in each scenario, the UE and / or the network server can use the localization estimate of TRP L when using classical localization techniques to determine the localization of one or more UEs.

[0145] Figure 9Depicts an RFFP-based TRP localization scenario 900 in accordance with aspects of the present disclosure, where a TRP measures UL-RS transmitted by multiple UEs to obtain RFFP, and a trained localization model can be applied to the RFFP to obtain a localization estimate of the TRP. In this example, UL-RS is transmitted by multiple UEs (″assistant UEs″) (e.g., UE 1 to UE N) and measured at the TRP (e.g., TRP L) to obtain corresponding RFFP measurements. In one aspect, the same UL-RS TRP transmission is measured by multiple UEs. In one aspect, multiple UL-RS transmissions measured by TRP L occur within a maximum time window threshold T. In accordance with various aspects of the present disclosure, the UL-RS can be SRS, SRS-p, DMRS, etc. In accordance with various aspects of the present disclosure, the UL-RS measured by TRP L can be different UL-RS and / or UL-RS with different bandwidths. In accordance with various aspects of the present disclosure, the RFFP measurement can be CIR, CFR, RSRQ, RSRP, delay spread, angular spread, AoA / AoD angles, Doppler spread, etc., or any combination captured at a single or multiple antenna ports.

[0146] The trained localization model 902 is applied to the RFFP measurements obtained by TRP L to provide a localization estimate of TRP L (or other localization parameters from which a localization estimate can be derived). In accordance with certain aspects of the present disclosure, the trained localization model 1102 can be applied at a network server based on the UL-RS RFFP measurements at TRP L. In one aspect, the localization estimate of TRP L at the network server can be indicated in the assistance data provided to the UE so that the UE's localization can be determined based on the UE using classical localization techniques. Additionally or in the alternative, the network server can use the localization estimate of TRP L when it uses classical localization techniques assisted by the UE to determine the localization of one or more UEs.

[0147] In accordance with certain aspects of the present disclosure, the localization models described herein can be trained and / or configured with additional inputs and / or outputs. In certain aspects, for flexibility and robustness, the localization model can be trained with a variable number of assistant UEs. In certain aspects, the localization model can be configured to require measurements associated with a minimum number of UEs in order to provide a localization estimate of the TRP. In certain aspects, the localization of the UE can be provided to the localization model as an additional parameter. In such scenarios, the location of each UE can be provided as an input to the localization model with a corresponding uncertainty window or confidence metric. In certain aspects, the localization model can be configured to provide an uncertainty window or confidence metric associated with the TRP localization estimate.

[0148] According to certain aspects of the present disclosure, a positioning model can be trained and / or configured based on features associated with one or more SL-UEs. In one aspect, the SL features can assist the positioning model in differentiating different UE anchor positions when learning the TRP position. In one aspect, RFFP measurements associated with one or more SL-UE measurements and / or features can be provided to the positioning model to supplement the RFFP measurements associated with the assisting UEs.

[0149] Measurements and / or features associated with the SL-UE can be obtained in various ways. In one aspect, RS transmissions between one or more SL-UEs and one or more of the assisting UEs among the one or more assisting UEs can be used to obtain RFFP measurements, which in turn can be used to train the positioning model and infer the positioning estimate of the TRP. In one aspect, one or more of the assisting UEs can be used as SL-UEs relative to one or more other assisting UEs such that the RFFP measurements correspond to RS transmissions between two or more assisting UEs. In each of the foregoing scenarios, the SL RFFP measurements from a UE to another UE can be provided as a CIR, CFR, RSRQ, RSRP, delay spread, angular spread, AoA / AoD angles, Doppler spread, etc., or any combination captured at a single or multiple antenna ports. It will be appreciated based on the teachings of the present disclosure that the SL features and / or measurements can be used in either or both of DL-based and UL-based RFFP-based positioning model training and TRP positioning.

[0150] Figure 10 Scenario 1000 is shown in which features associated with an SL-UE are provided to a positioning model 1002 to provide a positioning estimate of a TRP, in accordance with aspects of the present disclosure. In this example, the positioning model 1002 is applied to RFFP measurements associated with a plurality of UEs (e.g., UE 1 to UE N). Additionally, the positioning model 1002 is applied to the SL RFFP measurements obtained by each of the plurality of UEs. Here, the SL RFFP measurements are illustrated as the SL RFFP measurements at UE 1 to UE N. It will be appreciated based on the teachings of the present disclosure that each SL RFFP measurement input at UE 1 to UE N can include SL RFFP measurements between a single SL UE and the corresponding UE (e.g., UE 1 to UE N) or between multiple UEs and the corresponding UE.

[0151] Figure 11Illustrates method 1100 that can be performed by a network node in accordance with aspects of the present disclosure. At operation 1102, the network node obtains a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP). In one aspect, operation 1102 can be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which can be considered a component for performing this operation. In one aspect, operation 1102 can be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning component 388, any one or all of which can be considered a component for performing this operation. In one aspect, operation 1102 can be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any one or all of which can be considered a component for performing this operation.

[0152] At operation 1104, the network node obtains a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements. In one aspect, operation 1104 can be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which can be considered a component for performing this operation. In one aspect, operation 1104 can be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning component 388, any one or all of which can be considered a component for performing this operation. In one aspect, operation 1104 can be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any one or all of which can be considered a component for performing this operation.

[0153] As will be appreciated, the technical advantage of method 1100 is that the method enables the positioning of the TRP by applying a positioning model to the RFFP measurements associated with the TRP. The RFFP positioning technique provides a high-precision estimate of the positioning of the TRP, which is particularly useful in a positioning environment where the positioning of the TRP changes over time. The positioning estimate of the TRP obtained at the output of the positioning model can subsequently be used in classical positioning techniques. In addition, the disclosed method can be used to estimate the positioning of newly deployed TRPs.

[0154] Figure 12Depicts method 1200 executable by a network node in accordance with aspects of the present disclosure. At operation 1202, the network node obtains a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP). In one aspect, operation 1202 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which may be considered a component for performing this operation. In one aspect, operation 1202 may be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning component 388, any one or all of which may be considered a component for performing this operation. In one aspect, operation 1202 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any one or all of which may be considered a component for performing this operation.

[0155] At operation 1204, the network node trains a positioning model to provide a positioning estimate of the TRP, where the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP. In one aspect, operation 1204 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which may be considered a component for performing this operation. In one aspect, operation 1204 may be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning component 388, any one or all of which may be considered a component for performing this operation. In one aspect, operation 1204 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any one or all of which may be considered a component for performing this operation.

[0156] As will be appreciated, the technical advantage of method 1200 is that the method can be used to train a positioning model to provide an accurate positioning of the TRP using RFFP measurements associated with the TRP. The trained positioning model may be deployed to a network node (e.g., UE, location server, model management server, etc.). The RFFP positioning of the TRP can be particularly useful in a positioning environment where the positioning of the TRP changes over time or in an environment where a new TRP is introduced. The positioning estimate of the TRP obtained at the output of the trained positioning model may subsequently be used in classical positioning techniques.

[0157] In the above specific embodiments, it can be seen that different features are grouped together in each example. This disclosure should not be construed as intending that the example clauses have more features than those explicitly recited in each clause. On the contrary, various aspects of the present disclosure may include fewer features than all of the features of the individual example clauses disclosed. Accordingly, the following clauses are hereby considered incorporated into the description, where each clause by itself may be a separate example. Although each dependent clause may refer in the clause to a particular combination with one of the other clauses, the aspects of that dependent clause are not limited to the particular combination. It should be understood that other example clauses may also include combinations of aspects of the dependent clause with the subject matter of any other dependent clause or independent clause or any features with other dependent clauses and independent clauses. The various aspects disclosed herein expressly include such combinations, unless it is explicitly stated or readily inferred that a particular combination is not intended to be used (e.g., conflicting aspects, such as defining an element as both an electrical insulator and an electrical conductor). Additionally, it is contemplated that aspects of the clauses may be included in any other independent clause, even if that clause does not directly depend on the independent clause.

[0158] Specific implementation examples are described in the following numbered clauses:

[0159] Clause 1. A method performed by a network node, the method comprising: obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and obtaining a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0160] Clause 2. The method according to Clause 1, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0161] Clause 3. The method according to Clause 2, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0162] Clause 4. The method according to any one of Clauses 2 to 3, wherein the DL-RS includes: one or more positioning reference signals (PRSs); one or more channel state information reference signals (CSI-RSs); one or more synchronization signal block (SSB) signals; or any combination thereof.

[0163] Clause 5. The method according to any one of Clauses 2 to 4, the method further comprising: obtaining one or more positioning estimates of the one or more UEs; wherein the positioning estimate of the TRP is further based on applying the positioning model to the one or more positioning estimates of the one or more UEs.

[0164] Clause 6. The method according to clause 5, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further applied to the uncertainty window or confidence metric to obtain the positioning estimate of the TRP.

[0165] Clause 7. The method according to any one of clauses 1 to 6, the method further comprising: obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RS) transmitted by one or more sidelink UEs (SL-UEs); and obtaining the positioning estimate of the TRP based on applying the positioning model to the plurality of RFFP measurements and the one or more SL-RFFP measurements.

[0166] Clause 8. The method according to clause 7, wherein: the one or more SL-UEs are anchor UEs.

[0167] Clause 9. The method according to any one of clauses 1 to 8, wherein: the positioning model further provides an associated uncertainty window or confidence metric associated with the positioning estimate of the TRP.

[0168] Clause 10. The method according to any one of clauses 1 to 9, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs.

[0169] Clause 11. The method according to clause 10, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRS for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0170] Clause 12. The method according to any one of clauses 10 to 11, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0171] Clause 13. The method according to clause 12, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0172] Clause 14. The method according to any one of Clauses 1 to 13, wherein the plurality of RFFP measurements include: channel impulse response (CIR) measurements; channel frequency response (CFR) measurements; reference signal received quality (RSRQ) measurements; reference signal received power (RSRP) measurements; delay spread measurements; angular spread measurements; angle of arrival (AoA) measurements; angle of departure (AoD) measurements; Doppler spread measurements; or any combination thereof.

[0173] Clause 15. The method according to any one of Clauses 1 to 14, wherein the network node includes: a UE; a base station; a location server; or a model management server.

[0174] Clause 16. A method performed by a network node, the method comprising: obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and training a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0175] Clause 17. The method according to Clause 16, wherein: the positioning model is further trained to provide an uncertainty window or a confidence measure associated with the positioning estimate of the TRP.

[0176] Clause 18. The method according to any one of Clauses 16 to 17, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0177] Clause 19. The method according to Clause 18, the method further comprising: obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) received by the one or more UEs from one or more sidelink UEs (SL-UEs); and training the positioning model to provide the positioning estimate of the TRP based on the plurality of RFFP measurements, the known positioning of the TRP, and the one or more SL-RFFP measurements.

[0178] Clause 20. The method according to Clause 19, wherein: the one or more SL-UEs are anchor UEs.

[0179] Clause 21. The method according to any one of Clauses 18 to 20, the method further comprising: obtaining one or more positioning estimates of the one or more UEs; wherein the positioning model is further trained based on the one or more positioning estimates of the one or more UEs to obtain the positioning estimate of the TRP.

[0180] Clause 22. The method according to clause 21, wherein: the one or more location estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the location model is further trained based on the uncertainty windows or confidence metrics to obtain the location estimate of the TRP.

[0181] Clause 23. The method according to any one of clauses 18 to 22, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0182] Clause 24. The method according to any one of clauses 18 to 23, wherein the DL-RS includes: one or more positioning reference signals (PRS); one or more channel state information reference signals (CSI-RS); one or more synchronization signal block (SSB) signals; or any combination thereof.

[0183] Clause 25. The method according to any one of clauses 16 to 24, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs in a positioning environment having the TRP.

[0184] Clause 26. The method according to clause 25, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRSs for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0185] Clause 27. The method according to any one of clauses 25 to 26, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0186] Clause 28. The method according to any one of clauses 25 to 27, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0187] Clause 29. A network node, comprising: a memory; at least one transceiver; and at least one processor, the at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and obtain a location estimate of the TRP based on applying a location model to the plurality of RFFP measurements.

[0188] Clause 30. The network node according to Clause 29, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0189] Clause 31. The network node according to Clause 30, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0190] Clause 32. The network node according to any one of Clauses 30 to 31, wherein the DL-RS includes: one or more positioning reference signals (PRSs); one or more channel state information reference signals (CSI-RSs); one or more synchronization signal block (SSB) signals; or any combination thereof.

[0191] Clause 33. The network node according to any one of Clauses 30 to 32, wherein the at least one processor is further configured to: obtain one or more positioning estimates of the one or more UEs; wherein the positioning estimate of the TRP is further based on applying the positioning model to the one or more positioning estimates of the one or more UEs.

[0192] Clause 34. The network node according to Clause 33, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further applied to the uncertainty windows or confidence metrics to obtain the positioning estimate of the TRP.

[0193] Clause 35. The network node according to any one of Clauses 29 to 34, wherein the at least one processor is further configured to: obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) transmitted by one or more sidelink UEs (SL-UEs); and obtain the positioning estimate of the TRP based on applying the positioning model to the plurality of RFFP measurements and the one or more SL-RFFP measurements.

[0194] Clause 36. The network node according to Clause 35, wherein: the one or more SL-UEs are anchor UEs.

[0195] Clause 37. The network node according to any one of Clauses 29 to 36, wherein: the positioning model further provides an associated uncertainty window or confidence metric associated with the positioning estimate of the TRP.

[0196] Clause 38. The network node according to any one of Clauses 29 to 37, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs.

[0197] Clause 39. The network node according to Clause 38, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRS for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0198] Clause 40. The network node according to any one of Clauses 38 to 39, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0199] Clause 41. The network node according to Clause 40, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0200] Clause 42. The network node according to any one of Clauses 29 to 41, wherein the plurality of RFFP measurements include: channel impulse response (CIR) measurements; channel frequency response (CFR) measurements; reference signal received quality (RSRQ) measurements; reference signal received power (RSRP) measurements; delay spread measurements; angle spread measurements; angle of arrival (AoA) measurements; angle of departure (AoD) measurements; Doppler spread measurements; or any combination thereof.

[0201] Clause 43. The network node according to any one of Clauses 29 to 42, wherein the network node includes: a UE; a base station; a location server; or a model management server.

[0202] Clause 44. A network node, comprising: a memory; at least one transceiver; and at least one processor, the at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and train a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0203] Clause 45. The network node according to Clause 44, wherein: the positioning model is further trained to provide an uncertainty window or a confidence metric associated with the positioning estimate of the TRP.

[0204] Clause 46. The network node according to any one of Clauses 44 to 45, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0205] Clause 47. The network node according to Clause 46, wherein the at least one processor is further configured to: obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) received by the one or more UEs from one or more sidelink UEs (SL-UEs); and train the positioning model to provide the positioning estimate of the TRP based on the plurality of RFFP measurements, the known positioning of the TRP, and the one or more SL-RFFP measurements.

[0206] Clause 48. The network node according to Clause 47, wherein: the one or more SL-UEs are anchor UEs.

[0207] Clause 49. The network node according to any one of Clauses 46 to 48, wherein the at least one processor is further configured to: obtain one or more positioning estimates of the one or more UEs; wherein the positioning model is further trained based on the one or more positioning estimates of the one or more UEs to obtain the positioning estimate of the TRP.

[0208] Clause 50. The network node according to Clause 49, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further trained based on the uncertainty windows or confidence metrics to obtain the positioning estimate of the TRP.

[0209] Clause 51. The network node according to any one of Clauses 46 to 50, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0210] Clause 52. The network node according to any one of Clauses 46 to 51, wherein the DL-RS includes: one or more positioning reference signals (PRSs); one or more channel state information reference signals (CSI-RSs); one or more synchronization signal blocks (SSB) signals; or any combination thereof.

[0211] Clause 53. The network node according to any one of Clauses 44 to 52, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs in a positioning environment having the TRP.

[0212] Clause 54. The network node according to Clause 53, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRSs for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0213] Clause 55. The network node according to any one of Clauses 53 to 54, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0214] Clause 56. The network node according to any one of Clauses 53 to 55, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0215] Clause 57. A network node, the network node comprising: means for obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and means for obtaining a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0216] Clause 58. The network node according to Clause 57, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for downlink reference signals (DL-RS) transmitted by the TRP.

[0217] Clause 59. The network node according to Clause 58, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0218] Clause 60. The network node according to any one of Clauses 58 to 59, wherein the DL-RS includes: one or more positioning reference signals (PRS); one or more channel state information reference signals (CSI-RS); one or more synchronization signal block (SSB) signals; or any combination thereof.

[0219] Clause 61. The network node according to any one of Clauses 58 to 60, the method further comprising: means for obtaining one or more positioning estimates of the one or more UEs; wherein the positioning estimate of the TRP is further based on applying the positioning model to the one or more positioning estimates of the one or more UEs.

[0220] Clause 62. The network node according to Clause 61, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further applied to the uncertainty window or confidence metric to obtain the positioning estimate of the TRP.

[0221] Clause 63. The network node according to any one of Clauses 57 to 62, the network node further comprising: means for obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RS) transmitted by one or more sidelink UEs (SL-UEs); and means for obtaining the positioning estimate of the TRP based on applying the positioning model to the plurality of RFFP measurements and the one or more SL-RFFP measurements.

[0222] Clause 64. The network node according to Clause 63, wherein: the one or more SL-UEs are anchor UEs.

[0223] Clause 65. The network node according to any one of Clauses 57 to 64, wherein: the positioning model further provides an associated uncertainty window or confidence metric associated with the positioning estimate of the TRP.

[0224] Clause 66. The network node according to any one of Clauses 57 to 65, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs.

[0225] Clause 67. The network node according to Clause 66, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRS for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0226] Clause 68. The network node according to any one of Clauses 66 to 67, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0227] Clause 69. The network node according to Clause 68, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0228] Clause 70. The network node according to any one of Clauses 57 to 69, wherein the plurality of RFFP measurements include: channel impulse response (CIR) measurements; channel frequency response (CFR) measurements; reference signal received quality (RSRQ) measurements; reference signal received power (RSRP) measurements; delay spread measurements; angular spread measurements; angle of arrival (AoA) measurements; angle of departure (AoD) measurements; Doppler spread measurements; or any combination thereof.

[0229] Clause 71. The network node according to any one of Clauses 57 to 70, wherein the network node includes: a UE; a base station; a location server; or a model management server.

[0230] Clause 72. A network node, the network node includes: components for obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and components for training a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0231] Clause 73. The network node according to Clause 72, wherein: the positioning model is further trained to provide an uncertainty window or a confidence metric associated with the positioning estimate of the TRP.

[0232] Clause 74. The network node according to any one of Clauses 72 to 73, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0233] Clause 75. The network node according to Clause 74, the network node further includes: components for obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) received by the one or more UEs from one or more sidelink UEs (SL-UEs); and components for training the positioning model to provide the positioning estimate of the TRP based on the plurality of RFFP measurements, the known positioning of the TRP, and the one or more SL-RFFP measurements.

[0234] Clause 76. The network node according to Clause 75, wherein: the one or more SL-UEs are anchor UEs.

[0235] Clause 77. The network node according to any one of Clauses 74 to 76, the method further includes: components for obtaining one or more positioning estimates of the one or more UEs; wherein the positioning model is further trained based on the one or more positioning estimates of the one or more UEs to obtain a positioning estimate of the TRP.

[0236] Clause 78. The network node according to Clause 77, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further trained based on the uncertainty window or confidence metric to obtain the positioning estimate of the TRP.

[0237] Clause 79. The network node according to any one of Clauses 74 to 78, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0238] Clause 80. The network node according to any one of Clauses 74 to 79, wherein the DL-RS includes: one or more positioning reference signals (PRS); one or more channel state information reference signals (CSI-RS); one or more synchronization signal block (SSB) signals; or any combination thereof.

[0239] Clause 81. The network node according to any one of Clauses 72 to 80, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs in a positioning environment having the TRP.

[0240] Clause 82. The network node according to Clause 81, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRS for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0241] Clause 83. The network node according to any one of Clauses 81 to 82, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0242] Clause 84. The network node according to any one of Clauses 81 to 83, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0243] Clause 85. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network node, cause the network node to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit-receive point (TRP); and obtain a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

[0244] Clause 86. The non-transitory computer-readable medium according to Clause 85, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0245] Clause 87. The non-transitory computer-readable medium according to Clause 86, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0246] Clause 88. The non-transitory computer-readable medium according to any one of Clauses 86 to 87, wherein the DL-RS includes: one or more positioning reference signals (PRSs); one or more channel state information reference signals (CSI-RSs); one or more synchronization signal blocks (SSB) signals; or any combination thereof.

[0247] Clause 89. The non-transitory computer-readable medium according to any one of Clauses 86 to 88, the non-transitory computer-readable medium further comprising: computer-executable instructions that, when executed by the network node, cause the network node to: obtain one or more positioning estimates of the one or more UEs; wherein the positioning estimate of the TRP is further based on applying the positioning model to the one or more positioning estimates of the one or more UEs.

[0248] Clause 90. The non-transitory computer-readable medium according to Clause 89, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence measures; and wherein the positioning model is further applied to the uncertainty windows or confidence measures to obtain the positioning estimate of the TRP.

[0249] Clause 91. The non-transitory computer-readable medium according to any one of Clauses 85 to 90, the non-transitory computer-readable medium further comprising: computer-executable instructions that, when executed by the network node, cause the network node to: obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RS) transmitted by one or more sidelink UEs (SL-UEs); and obtain the positioning estimate of the TRP based on applying the positioning model to the plurality of RFFP measurements and the one or more SL-RFFP measurements.

[0250] Clause 92. The non-transitory computer-readable medium according to Clause 91, wherein: the one or more SL-UEs are anchor UEs.

[0251] Clause 93. The non-transitory computer-readable medium according to any one of Clauses 85 to 92, wherein: the positioning model further provides an associated uncertainty window or confidence metric associated with the positioning estimate of the TRP.

[0252] Clause 94. The non-transitory computer-readable medium according to any one of Clauses 85 to 93, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs.

[0253] Clause 95. The non-transitory computer-readable medium according to Clause 94, wherein the UL-RS includes: one or more sounding reference signals (SRS); one or more SRS for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0254] Clause 96. The non-transitory computer-readable medium according to any one of Clauses 94 to 95, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0255] Clause 97. The non-transitory computer-readable medium according to Clause 96, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0256] Clause 98. The non-transitory computer-readable medium according to any one of Clauses 85 to 97, wherein the plurality of RFFP measurements include: channel impulse response (CIR) measurements; channel frequency response (CFR) measurements; reference signal received quality (RSRQ) measurements; reference signal received power (RSRP) measurements; delay spread measurements; angle spread measurements; angle of arrival (AoA) measurements; angle of departure (AoD) measurements; Doppler spread measurements; or any combination thereof.

[0257] Clause 99. The non-transitory computer-readable medium according to any one of Clauses 85 to 98, wherein the network node includes: a UE; a base station; a location server; or a model management server.

[0258] Clause 100. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network node, cause the network node to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and train a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

[0259] Clause 101. The non-transitory computer-readable medium according to Clause 100, wherein: the positioning model is further trained to provide an uncertainty window or a confidence metric associated with the positioning estimate of the TRP.

[0260] Clause 102. The non-transitory computer-readable medium according to any one of Clauses 100 to 101, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

[0261] Clause 103. The non-transitory computer-readable medium according to Clause 102, the non-transitory computer-readable medium further includes: computer-executable instructions that, when executed by the network node, cause the network node to: obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) received by the one or more UEs from one or more sidelink UEs (SL-UEs); and train the positioning model to provide the positioning estimate of the TRP based on the plurality of RFFP measurements, the known positioning of the TRP, and the one or more SL-RFFP measurements.

[0262] Clause 104. The non-transitory computer-readable medium according to Clause 103, wherein: the one or more SL-UEs are anchor UEs.

[0263] Clause 105. The non-transitory computer-readable medium according to any one of Clauses 102 to 104, the non-transitory computer-readable medium further comprising: computer-executable instructions that, when executed by the network node, cause the network node to: obtain one or more positioning estimates of the one or more UEs; wherein the positioning model is further trained based on the one or more positioning estimates of the one or more UEs to obtain a positioning estimate of the TRP.

[0264] Clause 106. The non-transitory computer-readable medium according to Clause 105, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further trained based on the uncertainty windows or confidence metrics to obtain the positioning estimate of the TRP.

[0265] Clause 107. The non-transitory computer-readable medium according to any one of Clauses 102 to 106, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

[0266] Clause 108. The non-transitory computer-readable medium according to any one of Clauses 102 to 107, wherein the DL-RS comprises: one or more positioning reference signals (PRS); one or more channel state information reference signals (CSI-RS); one or more synchronization signal blocks (SSB) signals; or any combination thereof.

[0267] Clause 109. The non-transitory computer-readable medium according to any one of Clauses 100 to 108, wherein: the plurality of RFFP measurements include RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs in a positioning environment having the TRP.

[0268] Clause 110. The non-transitory computer-readable medium according to Clause 109, wherein the UL-RS comprises: one or more sounding reference signals (SRS); one or more SRSs for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

[0269] Clause 111. The non-transitory computer-readable medium according to any one of Clauses 109 to 110, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

[0270] Clause 112. The non-transitory computer-readable medium according to any one of Clauses 109 to 111, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

[0271] Those skilled in the art will understand that information and signals can be represented using any of a variety of different technologies and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to 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.

[0272] In addition, those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each particular application, but such specific implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

[0273] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein can be implemented or executed using 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. The general-purpose processor may be a microprocessor, but in an 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, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0274] The methods, sequences, and / or algorithms described in connection with the various aspects disclosed herein can be embodied directly in hardware, in software modules executed by a processor, or in a combination of both. The software modules can reside in random access memory (RAM), flash memory, read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, 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 the processor such that the processor can read information from, and write information to, the storage medium. In an alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal (e.g., a UE). In an alternative, the processor and the storage medium can reside as discrete components in the user terminal.

[0275] In one or more example aspects, the functions can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored on or transmitted via a computer-readable medium as one or more instructions or code. The computer-readable medium includes both a computer storage medium and a communication medium including any medium that facilitates transfer of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable medium can include RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of the medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, where 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 medium.

[0276] While the foregoing discloses exemplary aspects of the present disclosure, it should be noted that various changes and modifications can be made herein without departing from the scope of the present disclosure as defined by the appended claims. Additionally, the functions, steps, and / or acts of the method claims according to aspects of the present disclosure described herein need not be performed in any particular order. Moreover, although elements of the present disclosure may be described or claimed in the singular, the plural is also contemplated unless explicitly stated to be limited to the singular form.

Claims

1. A method performed by a network node, the method comprising: Obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); And Obtaining a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

2. The method according to claim 1, wherein: The plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

3. The method according to claim 2, wherein the plurality of RFFP measurements are obtained at: A single antenna port of the one or more UEs; Multiple antenna ports of the one or more UEs; or Any combination thereof.

4. The method according to claim 2, wherein the DL-RS includes: One or more positioning reference signals (PRSs); One or more channel state information reference signals (CSI-RSs); One or more synchronization signal blocks (SSB) signals; or Any combination thereof.

5. The method according to claim 2, the method further comprising: Obtaining one or more positioning estimates of the one or more UEs; Wherein the positioning estimate of the TRP is further based on applying the positioning model to the one or more positioning estimates of the one or more UEs.

6. The method according to claim 5, wherein: The one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and Wherein the positioning model is further applied to the uncertainty window or confidence metric to obtain the positioning estimate of the TRP.

7. The method according to claim 1, the method further comprising: Obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) transmitted by one or more sidelink UEs (SL-UEs); and obtaining the positioning estimate of the TRP based on applying the positioning model to the plurality of RFFP measurements and the one or more SL-RFFP measurements.

8. The method according to claim 7, wherein: The one or more SL-UEs are anchor UEs.

9. The method according to claim 1, wherein: The positioning model further provides an associated uncertainty window or confidence metric associated with the positioning estimate of the TRP.

10. The method according to claim 1, wherein: The plurality of RFFP measurements include RFFP measurements obtained by the TRP for an uplink reference signal (UL-RS) transmitted by one or more UEs.

11. The method according to claim 10, wherein the UL-RS includes: One or more sounding reference signals (SRSs); One or more SRSs for positioning (SRS-pos); One or more demodulation reference signals (DMRSs); or Any combination thereof.

12. The method according to claim 10, wherein: The plurality of RFFP measurements are based on a plurality of UL-RSs transmitted by the one or more UEs within a time threshold.

13. The method according to claim 12, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

14. The method according to claim 1, wherein the plurality of RFFP measurements include: channel impulse response (CIR) measurements; channel frequency response (CFR) measurements; reference signal received quality (RSRQ) measurements; reference signal received power (RSRP) measurements; delay spread measurements; angle spread measurements; angle of arrival (AoA) measurements; angle of departure (AoD) measurements; Doppler spread measurements; or any combination thereof.

15. The method according to claim 1, wherein the network node includes: a UE; a base station; a location server; or a model management server.

16. A method performed by a network node, the method comprising: obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and training a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

17. The method according to claim 16, wherein: the positioning model is further trained to provide an uncertainty window or a confidence metric associated with the positioning estimate of the TRP.

18. The method according to claim 16, wherein: the plurality of RFFP measurements include RFFP measurements obtained by one or more user equipments (UEs) for a downlink reference signal (DL-RS) transmitted by the TRP.

19. The method according to claim 18, the method further comprising: obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more reference signals (RSs) received by the one or more UEs from one or more sidelink UEs (SL-UEs); and training the positioning model to provide the positioning estimate of the TRP based on the plurality of RFFP measurements, the known positioning of the TRP, and the one or more SL-RFFP measurements.

20. The method according to claim 19, wherein: the one or more SL-UEs are anchor UEs.

21. The method according to claim 18, the method further comprising: obtaining one or more positioning estimates of the one or more UEs; wherein the positioning model is further trained based on the one or more positioning estimates of the one or more UEs to obtain the positioning estimate of the TRP.

22. The method according to claim 21, wherein: the one or more positioning estimates of the one or more UEs are associated with corresponding uncertainty windows or confidence metrics; and wherein the positioning model is further trained based on the uncertainty window or the confidence metric to obtain the positioning estimate of the TRP.

23. The method according to claim 18, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the one or more UEs; a plurality of antenna ports of the one or more UEs; or any combination thereof.

24. The method according to claim 18, wherein the DL-RS comprises: one or more positioning reference signals (PRS); one or more channel state information reference signals (CSI-RS); one or more synchronization signal blocks (SSB) signals; or any combination thereof.

25. The method according to claim 16, wherein: the plurality of RFFP measurements comprise RFFP measurements obtained by the TRP for uplink reference signals (UL-RS) transmitted by one or more UEs in a positioning environment having the TRP.

26. The method according to claim 25, wherein the UL-RS comprises: one or more sounding reference signals (SRS); one or more SRSs for positioning (SRS-pos); one or more demodulation reference signals (DMRS); or any combination thereof.

27. The method according to claim 25, wherein: the plurality of RFFP measurements are based on a plurality of UL-RS transmitted by the one or more UEs within a time threshold.

28. The method according to claim 25, wherein the plurality of RFFP measurements are obtained at: a single antenna port of the TRP; a plurality of antenna ports of the TRP; or any combination thereof.

29. A network node, the network node comprising: a memory; at least one transceiver; and at least one processor, the at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a transmit receive point (TRP); and obtain a positioning estimate of the TRP based on applying a positioning model to the plurality of RFFP measurements.

30. A network node, the network node comprising: a memory; at least one transceiver; and at least one processor, the at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with a known positioning of a transmit receive point (TRP); and train a positioning model to provide a positioning estimate of the TRP, wherein the training of the positioning model is based on the plurality of RFFP measurements and the known positioning of the TRP.

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