Machine learning model monitoring for sensing

Abstract

In some aspects, a sensing node can obtain one or more sensing measurements of one or more sensing reference signals. The sensing node can apply a machine learning model to the one or more sensing measurements to obtain first sensing results associated with one or more target objects. The sensing node can obtain second sensing results associated with the one or more target objects based on one or more non-machine learning sensing methods. The sensing node can transmit, to a sensing entity, a model monitoring report indicating a result of a comparison between the first sensing results and the second sensing results.

Description

Technical Field

[0001] All aspects of this disclosure relate to wireless technologies in general, and more specifically to radio frequency (RF) sensing involving machine learning. Background Technology

[0002] Wireless communication systems have evolved through many generations, including first-generation analog radiotelephone service (1G), second-generation (2G) digital radiotelephone service (including transitional 2.5G and 2.75G networks), third-generation (3G) high-speed data, wireless services with internet capabilities, 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 Services (PCS) systems. Known examples of cellular systems include cellular analog Advanced Mobile Phone Systems (AMPS), as well as 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), and others.

[0003] The fifth-generation (5G) wireless standard, known as New Radio (NR), delivers higher data speeds, more connections, better coverage, and other improvements. According to the Next Generation Mobile Networks Alliance, compared to previous standards, 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)), radio frequency (RF) sensing, and other technological enhancements. These enhancements, along with the use of higher frequency bands, advancements in PRS processes and technologies, and the high-density deployment of 5G, enable high-accuracy positioning and RF sensing based on 5G. Summary of the Invention

[0004] The following is a simplified summary of the invention relating to one or more aspects disclosed herein. Therefore, this summary should not be considered an exhaustive overview relating to all conceived aspects, nor should it be considered to identify key or decisive elements relating to all conceived aspects or to depict the scope associated with any particular aspect. Thus, the sole purpose of this summary is to present, in a simplified form, certain concepts relating to one or more aspects involving the mechanisms disclosed herein, prior to the detailed description presented below.

[0005] In one aspect, a method for wireless sensing performed by a sensing node includes: obtaining one or more sensing measurements of one or more sensing reference signals; applying a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; obtaining a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; and sending a model monitoring report to a sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0006] In one aspect, a sensing node includes: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being configured individually or in combination to: acquire one or more sensing measurements of one or more sensing reference signals; apply a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; acquire a second sensing result associated with one or more target objects based on one or more non-machine learning sensing methods; and send a model monitoring report indicating the result of a comparison between the first sensing result and the second sensing result to the sensing entity via the one or more transceivers.

[0007] In one aspect, a sensing node includes: components for acquiring one or more sensing measurements of one or more sensing reference signals; components for applying a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; components for acquiring a second sensing result associated with one or more target objects based on one or more non-machine learning sensing methods; and components for sending a model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0008] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a sensing node, cause the sensing node to: acquire one or more sensing measurements of one or more sensing reference signals; apply a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; acquire a second sensing result associated with one or more target objects based on one or more non-machine learning sensing methods; and send a model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0009] Based on the accompanying drawings and detailed description, other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art. Attached Figure Description

[0010] The accompanying drawings are provided to help describe various aspects of this disclosure, and are provided for illustrative purposes only and not to limit the aspects.

[0011] Figure 1 Example wireless communication systems according to various aspects of this disclosure are illustrated.

[0012] Figure 2A , Figure 2B and Figure 2C Example wireless network architectures based on various aspects of this disclosure are illustrated.

[0013] Figure 3A , Figure 3B and Figure 3C It is a simplified block diagram of several examples of components that can be used in user equipment (UE), base stations and network entities and configured to support communications as taught herein.

[0014] Figure 4 Examples of various positioning methods supported in new radios (NR) according to various aspects of this disclosure are illustrated.

[0015] Figure 5A and Figure 5B Different types of wireless sensing according to various aspects of this disclosure are illustrated.

[0016] Figure 6 Example machine learning models based on various aspects of this disclosure are illustrated.

[0017] Figure 7A This is an illustration of an example of direct artificial intelligence / machine learning (AI / ML) localization according to various aspects of this disclosure.

[0018] Figure 7B This is an illustration illustrating examples of AI / ML-assisted localization according to various aspects of this disclosure.

[0019] Figure 8 Various AI / ML positioning scenarios based on aspects of this disclosure are illustrated.

[0020] Figure 9A and Figure 9B An example system for model monitoring based on a comparison of results from AI / ML-based sensing methods and other reference sensing methods is illustrated according to various aspects of this disclosure.

[0021] Figure 10 This is a diagram illustrating an example signaling flow for model monitoring based on a comparison of results from AI / ML-based sensing methods with other reference sensing methods, according to various aspects of this disclosure.

[0022] Figure 11Example methods of wireless sensing according to various aspects of this disclosure are illustrated. Detailed Implementation

[0023] Various aspects of this disclosure are provided below in the description and accompanying drawings of various examples provided for illustrative purposes. Alternative aspects may be devised without departing from the scope of this disclosure. Additionally, well-known elements of this disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of this disclosure.

[0024] Wireless sensing is involved in various aspects overall. Some aspects are more specifically related to wireless sensing using machine learning models. In some examples, a network or sensing node may perform model monitoring based on a comparison of the results of a machine learning approach with those of other reference sensing methods. The sensing node may receive a configuration to perform model monitoring (including reference sensing methods and weights / priorities) and then report back the results of the comparison (e.g., the variance of the results based on the machine learning model compared to the results of the reference sensing method).

[0025] Specific aspects of the subject matter described in this disclosure can be implemented to achieve one or more of the following potential advantages. In some examples, the described techniques can be used to determine the performance of a deployed machine learning model by performing model monitoring after the model has been deployed. If the performance of the machine learning model meets expectations, the deployed machine learning model can continue to be used. Otherwise, the model needs to be disabled or updated.

[0026] The terms “exemplary” and / or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and / or “example” is not necessarily to be construed as superior to or better than other aspects. Similarly, the term “aspects of this disclosure” does not require that all aspects of this disclosure include the features, advantages, or modes of operation discussed.

[0027] Those skilled in the art will understand that any of a variety of different techniques and skills 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 mentioned throughout the following description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, and so on.

[0028] Furthermore, many aspects are described according to a sequence of actions to be performed by elements of, for example, a computing device. It will be appreciated 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 sequence of actions described herein can be considered to be entirely embodied in any form of non-transitory computer-readable storage medium storing a corresponding set of computer instructions that, when executed, will cause or command the associated processor of the device to perform the functionality described herein. Therefore, various aspects of this disclosure can be embodied in a variety of different forms, all of which are contemplated within the scope of the claimed subject matter. Furthermore, for each aspect described herein, the corresponding form of any such aspect can be described herein as, for example, "logic configured to perform the described actions."

[0029] As used herein, unless otherwise stated, the terms “User Equipment” (UE) and “Base Station” are not intended to be specific 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., mobile phone, router, tablet computer, laptop computer, consumer asset positioning device, wearable device (e.g., smartwatch, glasses, augmented reality (AR) / virtual reality (VR) headset, etc.), vehicle (e.g., car, motorcycle, bicycle, etc.), Internet of Things (IoT) device, etc.). A UE can be mobile or can (e.g., at certain times) be stationary 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 Equipment,” “Wireless Equipment,” “Subscriber Equipment,” “Subscriber Terminal,” “Subscriber Station,” “User Terminal” or “UT,” “Mobile Equipment,” “Mobile Terminal,” “Mobile Station,” or variations thereof. Overall, a UE can communicate with a core network via the RAN, and through the core network, a UE can connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as through wired access networks, wireless local area network (WLAN) networks (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, etc.).

[0030] A base station may operate according to one of several RATs to communicate with the UE, depending on the network in which it is deployed, and may alternatively be referred to as an Access Point (AP), Network Node, NodeB, Evolved NodeB (eNB), Next Generation eNB (ng-eNB), New Radio (NR) NodeB (also referred to as gNB or gNodeB), etc. The base station may primarily be used to support the UE's radio access, including supporting data, voice, and / or signaling connections for the supported UE. In some systems, the base station may only provide edge node signaling functions, while in others, it may provide additional control and / or network management functions. The communication link through which the UE can transmit signals to the base station is called an uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication link through which the base station can transmit signals to the UE is called a 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)" may refer to an uplink / reverse traffic channel or a downlink / forward traffic channel.

[0031] 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, when the term "base station" refers to a single physical TRP, the physical TRP can be the antenna of a base station corresponding to a cell (or several cell sectors) of the base station. When the term "base station" refers to multiple co-located physical TRPs, the physical TRP can be the antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). When the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP can be a distributed antenna system (DAS) (a network of spatially separated antennas connected via a transmission medium to a common source) or a remote radio headend (RRH) (a remote base station connected to a serving base station). Alternatively, a non-co-located physical TRP can be the serving base station from which the UE receives measurement reports and a neighboring base station where the UE is measuring its reference radio frequency (RF) signal. Because, as used herein, a TRP is the point by which a base station transmits and receives radio signals, references to transmitting from or receiving at a base station should be understood to refer to a specific TRP of the base station.

[0032] In some specific implementations supporting UE positioning, the base station may not support the UE's radio access (e.g., it may not support data, voice, and / or signaling connections for the UE), but may instead transmit reference signals to the UE for measurement and / or receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning beacon (e.g., in the case of transmitting signals to the UE) and / or as a location measurement unit (e.g., in the case of receiving and measuring signals from the UE).

[0033] An “RF signal” refers to an electromagnetic wave of a given frequency that transmits information across 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 multipath channels, 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 receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where the context clearly indicates that the term “signal” refers to a wireless signal or an RF signal.

[0034] Figure 1 An example wireless communication system 100 according to various aspects of this 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. Base station 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 station may include an eNB and / or an ng-eNB (wherein the wireless communication system 100 corresponds to an LTE network), or a gNB (wherein the wireless communication system 100 corresponds to an NR network), or a combination of both, and the small cell base station may include femtocells, picocells, microcells, etc.

[0035] Base station 102 can collectively form a RAN and interface with core network 170 (e.g., evolved packet core (EPC) or 5G core (5GC)) via backhaul link 122, and interface with one or more location servers 172 (e.g., location management function (LMF) or secure user plane location (SUPL) location platform (SLP)) via core network 170. Location server 172 can be part of core network 170 or can be external to core network 170. Location server 172 can be integrated with base station 102. UE 104 can communicate with location server 172 directly or indirectly. For example, UE 104 can communicate with location server 172 via base station 102 currently serving UE 104. UE 104 can also communicate with location server 172 via another path, such as via application server (not shown), via another network, such as via wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), etc. For signaling purposes, communication between UE 104 and location server 172 can be represented as an indirect connection (e.g., via core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), wherein intermediate nodes (if present) are omitted from the signaling diagram for clarity.

[0036] In addition to other functions, base station 102 may perform functions associated with one or more of the following: transmitting 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, location, and delivery of warning messages. Base stations 102 may communicate with each other directly or indirectly (e.g., via EPC / 5GC) on backhaul link 134, which may be wired or wireless.

[0037] Base station 102 can wirelessly communicate with UE 104. Each base station in base station 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, one or more cells can be supported by base station 102 in each geographic coverage area 110. A “cell” is a logical communication entity used to communicate with a base station (e.g., via a frequency resource, which is referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., Physical Cell Identifier (PCI), Enhanced Cell Identifier (ECI), Virtual Cell Identifier (VCI), Cell Global Identifier (CGI), etc.) used to distinguish cells operating via the same or different carrier frequencies. In some cases, different cells can be configured according to different protocol types that can 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). Because a cell is supported by a specific base station, the term “cell” can refer to either or both of the logical communication entity and the base station supporting the logical communication entity, depending on the context. Furthermore, since the TRP is typically the physical transmission point of a cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" can also refer to the geographical coverage area of ​​a base station (e.g., a sector), as long as the carrier frequency can be detected and used for communication within a portion of the geographical coverage area 110.

[0038] While the geographic coverage areas 110 of adjacent macro cell base stations 102 may partially overlap (e.g., in handover areas), some areas within geographic coverage areas 110 may substantially overlap with larger geographic coverage areas 110. For example, a small cell base station 102' (labeled "SC" for "small cell") may have a geographic coverage area 110' that substantially overlaps with the geographic coverage areas 110 of one or more macro cell base stations 102. A network that includes both small cell base stations and macro cell base stations can be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB) that can provide service to a restricted group referred to as a Closed Subscriber Group (CSG).

[0039] The communication link 120 between base station 102 and UE 104 may include uplink (also known as reverse link) transmission from UE 104 to base station 102 and / or downlink (DL) (also known as forward link) transmission from base station 102 to UE 104. The communication link 120 may use MIMO antenna techniques, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may use one or more carrier frequencies. Carrier allocation may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink).

[0040] 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 unlicensed spectrum (e.g., 5 GHz). When communicating in unlicensed spectrum, the WLAN STA 152 and / or WLAN AP 150 may perform a free channel assessment (CCA) or listen-before-talk (LBT) process before communication to determine whether the channel is available.

[0041] Small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in unlicensed spectrum, small cell base station 102' can employ LTE or NR technology and use the same 5GHz unlicensed spectrum as WLAN AP 150. Small cell base station 102' employing LTE / 5G in unlicensed spectrum can improve the coverage and / or increase the capacity of the access network. NR in unlicensed spectrum may be referred to as NR-U. LTE in unlicensed spectrum may be referred to as LTE-U, Licensed Assisted Access (LAA), or MULTEFIRE. ® .

[0042] The wireless communication system 100 may also include a millimeter-wave (mmW) base station 180, which can operate at mmW and / or near-mmW frequencies to communicate with the UE 182. Extremely high frequency (EHF) is a portion of the electromagnetic spectrum that contains radio frequency (RF). EHF has a range of 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this band are referred to as millimeter waves. Near-mmW extends down to frequencies of 3 GHz with wavelengths of 100 mm. Ultra-high frequency (SHF) bands extend between 3 GHz and 30 GHz, and are also referred to as centimeter waves. Communication using mmW / near-mmW radio bands has high path loss and relatively short range. The mmW base station 180 and the UE 182 can utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short range. Furthermore, it should be understood that, in alternative configurations, 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 examples are merely illustrative and should not be construed as limiting the various aspects disclosed herein.

[0043] Transmit beamforming is a technique used to focus RF signals in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). Using 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, thus providing the receiving device with a faster and stronger RF signal (in terms of data rate). 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 broadcasting the RF signal. For example, the network node can use an array of antennas (called a "phased array" or "antenna array") that forms an RF beam that can be "manipulated" to be pointed in different directions without actually moving the antennas. Specifically, RF currents from the transmitters are fed to individual antennas with the correct phase relationship, such that radio waves from the individual antennas add up in the desired direction to increase radiation, while canceling out in the undesired direction to suppress radiation.

[0044] Transmit beams can be quasi-co-located, meaning they appear to the receiver (e.g., the UE) as having the same parameters regardless of whether the network node's own transmit antennas are physically co-located. In NR, there are four types of quasi-co-located (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters of a second reference RF signal on a second beam can be derived based on information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the second reference RF signal transmitted on the same channel. If the source reference RF signal is of type QCL D, the receiver can use the source reference RF signal to estimate the spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0045] In receive beamforming, a receiver uses a receive beam to amplify an RF signal detected on a given channel. For example, the receiver may increase the gain setting of an antenna array in a particular direction and / or adjust the phase setting of the antenna array in a particular direction to amplify the RF signal received from that direction (e.g., increase its gain level). Therefore, when a receiver is described as performing beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gain along other directions, or that the beam gain in that direction is the highest compared to the beam gain of all other receive beams available to the receiver in that direction. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), etc.) of the RF signal received from that direction.

[0046] The transmit and receive beams can be spatially correlated. Spatial correlation means that parameters for a second beam (e.g., transmit or receive beam) for a second reference signal can be derived based on information about a first beam (e.g., receive or transmit beam) for a first reference signal. For example, a UE can use a specific receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. The UE can then form a transmit beam for transmitting an uplink reference signal (e.g., a sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0047] It is important to note that, depending on the entity forming the "downlink" beam, the beam can be either 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 either 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.

[0048] 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 designated as frequency ranges FR1 (410MHz to 7.125GHz) and FR2 (24.25GHz to 52.6GHz). It should be understood that although a portion of FR1 is greater than 6GHz, in various documents and articles, FR1 is often (interchangeably) referred to as the "sub-6GHz" band. A similar naming issue sometimes occurs with FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although this differs from the designation used by the International Telecommunication Union. ® Extremely high frequency (EHF) bands (30 GHz to 300 GHz) are designated as “millimeter wave” bands.

[0049] The frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR studies have identified the operating bands used for these mid-band frequencies as the frequency range designation FR3 (7.125 GHz to 24.25 GHz). Bands falling within FR3 can inherit FR1 and / or FR2 characteristics, thus effectively extending the features of FR1 and / or FR2 to mid-band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating frequency bands have been identified as the frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0050] In light of the foregoing, unless otherwise specifically stated, it should be understood that, as used herein, the term "below 6 GHz" and the like can broadly refer to frequencies less than 6 GHz, within FR1, or including intermediate frequency band frequencies. Furthermore, unless otherwise specifically stated, it should be understood that, as used herein, the term "millimeter wave" and the like can broadly refer to frequencies that can include intermediate frequency band frequencies, within FR2, FR4, FR4-a or FR4-1 and / or FR5, or within the EHF band.

[0051] In multi-carrier systems such as 5G, one of the carrier frequencies is referred to as the "primary carrier," "anchor carrier," "primary serving cell," or "PCell," and the remaining carrier frequencies are referred to as "secondary carriers," "secondary serving cells," or "SCell." In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) used by UE 104 / 182 and the cell, where UE 104 / 182 performs an initial Radio Resource Control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier on a licensed frequency (however, this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier on an unlicensed frequency. Secondary carriers may contain only the necessary signaling information and signals. For example, since the primary uplink and primary downlink carriers are typically UE-specific, the UE-specific signaling information and signals may not be present in the secondary carrier. This means that different UEs 104 / 182 within a cell can have different downlink primary carriers. The same applies to the uplink primary carrier. The network can change the primary carrier of any UE 104 / 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 base station communicates, the terms "cell," "serving cell," "component carrier," and "carrier frequency" can be used interchangeably.

[0052] For example, still refer to Figure 1One of the frequencies used by macro cell base station 102 can be an anchor carrier (or "PCell"), and the other frequencies used by macro cell base station 102 and / or mmW base station 180 can be secondary carriers ("SCell"). Simultaneous transmission and / or reception on multiple carriers allows UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, compared to the data rate obtained by a single 20MHz carrier, two aggregated 20MHz carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40MHz).

[0053] The wireless communication system 100 may also include a UE 164, which can communicate with the macro cell base station 102 via communication link 120 and / or with the mmW base station 180 via mmW communication link 184. For example, the macro cell base station 102 may support PCells and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0054] In some cases, UE 164 and UE 182 may be able to communicate via sidelink. A sidelink-capable UE (SL-UE) can communicate with base station 102 via communication link 120 using the Uu interface (i.e., the air interface between the UE and the base station). SL-UEs (e.g., UE 164, UE 182) can also communicate directly with each other via radio sidelink 160 using the PC5 interface (i.e., the air interface between sidelink-capable UEs). Radio sidelink (or simply "sidelink") is an adaptation of core cellular network (e.g., LTE, NR) standards that allows direct communication between two or more UEs without the need for communication 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, emergency rescue applications, etc. One or more SL-UEs in a group of SL-UEs utilizing sidelink communication may be located within the geographical coverage area 110 of base station 102. Other SL-UEs in this group may be outside the geographical coverage area 110 of base station 102, or may be unable to receive transmissions from base station 102 for other reasons. In some cases, the groups of SL-UEs communicating via sidelink communication may utilize a one-to-many (1:M) system, where each SL-UE transmits to every other SL-UE in the group. In some cases, base station 102 facilitates the scheduling of resources used for sidelink communication. In other cases, sidelink communication is performed between the individual SL-UEs without involving base station 102.

[0055] On one hand, the sidelink 160 can operate via a wireless communication medium of interest that can be shared with other vehicles and / or infrastructure access points and other RATs for wireless communication. "Medium" can include one or more time, frequency, and / or space communication resources (e.g., covering one or more channels across one or more carriers) associated with wireless communication between one or more transmitter / receiver pairs. On another hand, the medium of interest may correspond to at least a portion of unlicensed frequency bands shared among various RATs. While different licensed frequency bands have been reserved for certain communication systems (e.g., by government entities such as the U.S. Federal Communications Commission (FCC), these systems (particularly those employing small cell access points) have recently extended their operation to unlicensed National Information Infrastructure (U-NII) bands used by Wireless Local Area Network (WLAN) technologies (most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi"). Example systems of this type include various variants of CDMA, TDMA, FDMA, Orthogonal FDMA (OFDMA), Single-Carrier FDMA (SC-FDMA), and so on.

[0056] It should be noted that, although Figure 1 Only two of these UEs are exemplified as SL-UEs (i.e., UE 164 and UE 182), but any UE exemplified can be an SL-UE. Furthermore, although only UE 182 is described as capable of beamforming, any UE exemplified (including UE 164) can be capable of beamforming. When SL-UEs are capable of beamforming, they can 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, base station 180, small cell 102', access point 150), etc. Therefore, in some cases, UE 164 and UE 182 can utilize beamforming via sidelink 160.

[0057] exist Figure 1 In the example, the UE shown (for simplicity, in) Figure 1Any UE (shown as a single UE 104) can receive signal 124 from one or more Earth-orbiting spacecraft (SV) 112 (e.g., satellites). In one aspect, SV 112 may be part of a satellite positioning system that allows UE 104 to use as an independent source of location information. Satellite positioning systems typically include a system of transmitters (e.g., SV 112) positioned such that a receiver (e.g., UE 104) can determine its location on or above the Earth based at least in part on positioning signals (e.g., signal 124) received from the transmitters. Such transmitters typically transmit signals marked with a set number of repeating pseudo-random noise (PN) codes. While typically located in SV 112, transmitters may sometimes be located at ground-based control stations, base stations 102, and / or other UEs 104. UE 104 may include one or more dedicated receivers specifically designed to receive signal 124 in order to derive geographic location information from SV 112.

[0058] In a satellite positioning system, the use of signal 124 can be enhanced by various satellite-based augmentation systems (SBAS), which may be associated with or otherwise made capable of being used with one or more global and / or regional navigation satellite systems. For example, SBAS may include augmentation systems that provide integrity information, differential correction, etc., such as Wide Area Augmentation System (WAAS), European Geostationary Navigation Overlap Service (EGNOS), Multifunctional Satellite Augmentation System (MSAS), GPS-assisted geographic augmentation navigation, or GPS and geographic augmentation navigation system (GAGAN). Therefore, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0059] On one hand, SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, SV 112 connects to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn connects to elements in the 5G network, such as a modified base station 102 (without a ground antenna) or a network node in a 5GC. This element, in turn, provides access to other elements in the 5G network and ultimately to entities outside the 5G network, such as internet web servers and other user equipment. Thus, as a replacement or supplement to communication signals from ground base station 102, UE 104 can receive communication signals (e.g., signal 124) from SV 112.

[0060] The wireless communication system 100 may also include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as "side links"). Figure 1 In one example, UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of the base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity through this D2D P2P link), and has a D2D P2P link 194 with a WLAN STA 152 connected to a 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 utilize any known D2D RAT (such as LTE Direct (LTE-D), Wi-Fi Direct). ® ,Bluetooth ® (etc.) to support.

[0061] Figure 2A An example wireless network architecture 200 is illustrated. For instance, the 5GC 210 (also referred to as the Next Generation Core (NGC)) can be functionally viewed as control plane (C-plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U-plane) functions 212 (e.g., UE gateway functions, access to data networks, IP routing, etc.), which work together to form the core network. The user plane interface (NG-U) 213 and the control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210, specifically to user plane functions 212 and control plane functions 214, respectively. In an additional configuration, the ng-eNB 224 can also connect to the 5GC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. Furthermore, the ng-eNB 224 can communicate directly with the gNB 222 via a backhaul connection 223. In some configurations, the next-generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either or both of the gNBs 222 or ng-eNBs 224 can communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0062] Another optional aspect may include a location server 230, which can communicate with the 5GC 210 to provide location assistance to 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, each may correspond to a single server. The location server 230 may be configured to support one or more location services for the UE 204, which may be connected to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not illustrated). Furthermore, the location server 230 may be integrated into a component of the core network, or alternatively, may be located outside the core network (e.g., a third-party server, such as an original equipment manufacturer (OEM) server or a service server).

[0063] Figure 2B Another example wireless network architecture 240.5GC 260 is illustrated (which can be used with...). Figure 2AThe 5GC 210 (corresponding to 5GC 210) can be functionally considered as a control plane function provided by the Access and Mobility Management Function (AMF) 264 and a user plane function provided by the User Plane Function (UPF) 262, which work together to form the core network (i.e., 5GC 260). The functions of 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, a transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between UE 204 and the Short Message Service Function (SMSF) (not shown), and Secure Anchoring Functionality (SEAF). AMF 264 also interacts with the Authentication Server Function (AUSF) (not shown) and UE 204 and receives an intermediate key established as a result of the UE 204's authentication process. In the case of UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM) authentication, AMF 264 retrieves security material from the AMF. AMF 264 also includes Security Context Management (SCM). The SCM receives a key from the SEAF and uses this key to derive an access network-specific key. AMF 264 functionality also includes location service management for regulatory services, transmission of location service messages between UE 204 and Location Management Function (LMF) 270 (which acts as location server 230), transmission of location service messages between NG-RAN 220 and LMF 270, Evolved Packet System (EPS) bearer identifier allocation for EPS interoperability, and UE 204 mobility event notification. Furthermore, AMF 264 also supports non-3GPP... ® (Third Generation Partner Program) Access network functionality.

[0064] The functions of UPF 262 include: acting as an anchor point for intra-RAT / inter-RAT mobility (where applicable), acting as an external Protocol Data Unit (PDU) session point interconnecting to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., strobing, redirection, traffic steering), lawful eavesdropping (user plane collection), traffic usage reporting, quality of service (QoS) processing for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in 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 delivering and forwarding one or more "end markers" to the source RAN node. UPF 262 can also support the delivery of location service messages between UE 204 and location servers (such as SLP 272) on the user plane.

[0065] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, service orientation configuration at UPF 262 for routing services 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 called the N11 interface.

[0066] Another optional aspect may include an LMF 270, which can communicate with the 5GC 260 to provide location assistance to the UE 204. The LMF 270 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each can correspond to a single server. The LMF 270 can be configured to support one or more location services for the UE 204, which can connect to the LMF 270 via the core network, the 5GC 260, and / or via the Internet (not illustrated). SLP 272 can support similar functions to LMF 270, but while LMF 270 can communicate with AMF 264, NG-RAN 220, and UE 204 on the control plane (e.g., using interfaces and protocols designed to transmit signaling messages rather than voice or data), SLP 272 can communicate with UE 204 and external clients (e.g., third-party server 274) on the user plane (e.g., using protocols designed to carry voice and / or data, such as Transmit Control Protocol (TCP) and / or IP).

[0067] Another optional aspect may include a third-party server 274, which can communicate with LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., location estimation) of UE 204. Therefore, in some cases, the third-party server 274 may be referred to as a Location Services (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, each may correspond to a single server.

[0068] User plane interface 263 and control plane interface 265 connect 5GC 260, and specifically connect UPF 262 and AMF 264 to one or more gNB 222 and / or ng-eNB 224 in NG-RAN 220. The interface between gNB 222 and / or ng-eNB 224 and AMF 264 is referred to as the "N2" interface, while the interface between gNB 222 and / or ng-eNB 224 and UPF 262 is referred to as the "N3" interface. The gNB 222 and / or ng-eNB 224 of NG-RAN 220 can communicate directly with each other via backhaul connection 223, referred to as the "Xn-C" interface. One or more of gNB 222 and / or ng-eNB 224 can communicate with one or more UEs 204 via a radio interface referred to as the "Uu" interface.

[0069] The functionality of the gNB 222 is divided among the 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 user data delivery, mobility control, radio access network sharing, location, session management, etc. More specifically, the gNB-CU 226 typically hosts the Radio Resource Control (RRC), Serving Data Adaptation Protocol (SDAP), and Packet Data Convergence Protocol (PDCP) protocols of the gNB 222. The gNB-DU 228 is a logical node that typically hosts the Radio Link Control (RLC) and Media Access Control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and a cell is supported by only one gNB-DU 228. The interface 232 between 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 managed by one or more independent gNB-RU 229s, which perform functions such as power amplification and signal transmission / reception. The interface between gNB-DU 228 and gNB-RU 229 is referred to as the "Fx" interface. Therefore, UE 204 communicates with gNB-CU 226 via the RRC, SDAP, and PDCP layers, with gNB-DU 228 via the RLC and MAC layers, and with gNB-RU 229 via the PHY layer.

[0070] Communication systems (such as 5G NR systems) can be deployed in various ways with a variety of components or parts. In a 5G NR system or network, network nodes, network entities, network mobility elements, RAN nodes, core network nodes, network elements, or network equipment (such as base stations or one or more units (or components) that perform base station functions) can be implemented in aggregated or decomposed architectures. For example, base stations (such as Node B (NB), evolved NB (eNB), NR base stations, 5GNB, AP, TRP, cells, etc.) can be implemented as aggregated base stations (also known as standalone base stations or monolithic base stations) or decomposed base stations.

[0071] Aggregated base stations can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. Decentralized base stations can be configured to utilize a protocol stack that is physically or logically distributed across 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 respects, the CU may be implemented within a RAN node, and one or more DUs may co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other RAN nodes. DUs may be implemented to communicate with one or more RUs. Each of the CUs, DUs, and RUs may also be implemented as a virtual unit, namely a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0072] Base station type operation or network design can consider the aggregation characteristics of base station functionality. For example, decomposed base stations can be used in Integrated Access Backhaul (IAB) networks, Open Radio Access Networks (O-RAN) (such as those developed by the O-RAN Alliance), etc. ® This can be used in proposed network configurations or virtualized radio access networks (vRAN, also known as cloud radio access networks (C-RAN)). Decomposition can include distributing functionality across two or more units in various physical locations, as well as virtually distributing the functionality of at least one unit, which allows for flexibility in network design. Various units in a decomposed base station or decomposed RAN architecture can be configured to communicate wirelessly with at least one other unit.

[0073] Figure 2C An example disaggregated base station architecture 250 according to various aspects of this disclosure is illustrated. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with the core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 via one or more disaggregated 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). CUs 280 may communicate with one or more duplex units (DUs) 285 (e.g., gNB-DU 228) via a corresponding midhaul link (e.g., an F1 interface). DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RU 229) via a corresponding fronthaul link. RU 287 can communicate with the corresponding UE 204 via one or more radio frequency (RF) access links. In some implementations, UE 204 can be served by multiple RU 287s simultaneously.

[0074] Each of these units (i.e., CU 280, DU 285, RU 287, and near-RT RIC 259, non-RT RIC 257, and SMO frame 255) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each of these units, or an associated processor or controller providing instructions to the communication interfaces of these units, may be configured to communicate with one or more other units via transmission media. For example, these units may include wired interfaces configured to receive signals or transmit signals to one or more other units via wired transmission media. Additionally, these units may include wireless interfaces that may include receivers, transmitters, or transceivers (such as RF transceivers) configured to receive signals or transmit signals to one or more other units, or both, via wireless transmission media.

[0075] In some aspects, the CU 280 can host one or more higher-level control functions. Such control functions may include RRC, PDCP, Serving Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to communicate signaling with other control functions hosted by the CU 280. The CU 280 can be configured to handle user plane functionality (i.e., Central Unit-User Plane (CU-UP)), control plane functionality (i.e., Central Unit-Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically split 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 can communicate bidirectionally with the CU-CP units via an interface such as an E1 interface. The CU 280 can be implemented to communicate with the DU 285 for network control and signaling purposes, as needed.

[0076] DU 285 may correspond to a logic unit that includes one or more base station functions for controlling the operation of one or more RU 287s. In some aspects, DU 285 may be at least partially based on functional partitioning (such as that provided by the 3rd Generation Partnership Project (3GPP)). ®The DU285 is functionally partitioned to host one or more of the RLC layer, MAC layer, and one or more high-PHY layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, and demodulation). In some respects, the DU285 may further host one or more low-PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by the DU285 or with control functions hosted by the CU280.

[0077] Lower-layer functionality can be implemented by one or more RU 287s. In some deployments, an RU287 controlled by a DU 285 may correspond to a logical node that hosts 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, or both, based at least in part on functional decomposition (such as lower-layer functional decomposition). In this architecture, the RU 287 can be implemented to handle over-the-air (OTA) communications with one or more UE 204s. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 287 may be controlled by the corresponding DU 285. In some scenarios, this configuration allows the DU285 and CU 280 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0078] SMO framework 255 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, SMO framework 255 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, SMO framework 255 can be configured to interact with cloud computing platforms such as Open Cloud (O-Cloud) 269 to perform network element lifecycle management (such as instantiating virtualized network elements) via cloud computing platform interfaces such as the O2 interface. Such virtualized network elements may include, but are not limited to, CU 280, DU 285, RU 287, and near-RT RIC 259. In some implementations, SMO framework 255 can communicate with the hardware aspects of the 4G RAN (such as Open eNB (O-eNB) 261) via the O1 interface. Additionally, in some implementations, SMO framework 255 can communicate directly with one or more RU 287s via the O1 interface. SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of SMO framework 255.

[0079] The non-RT RIC 257 can be configured to include logical functions enabling non-real-time control and optimization of RAN elements and resources, including artificial intelligence / machine learning (AI / ML) workflows for model training and updates, or policy-based guidance for 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 an A1 interface. The near-RT RIC 259 can be configured to include logical functions enabling near real-time control and optimization of RAN elements and resources via data collection and actions through an interface such as an E2 interface, connecting one or more CU 280s, one or more DU 285s, or both, and O-eNBs to the near-RT RIC 259.

[0080] In some implementations, to generate AI / ML models 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. This information can be utilized by the near-RT RIC 259 and may be received from non-network data sources or network functions 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 in performance and employ AI / ML models to perform corrective actions via the SMO framework 255 (such as reconfiguration via O1) or by creating RAN management policies (such as A1 policies).

[0081] Figure 3A , Figure 3B and Figure 3C Several example components (represented by corresponding boxes) are illustrated, which can be incorporated into UE 302 (which may correspond to any UE described herein), base station 304 (which may correspond to any base station described herein), and network entity 306 (which may correspond to or embody any network function described herein, including location server 230 and LMF 270, or alternatively may be independent of UE 302). Figure 2A and Figure 2BThe NG-RAN 220 and / or 5GC 210 / 260 infrastructures depicted herein (such as dedicated networks) are used to support the operations described herein. It should be understood that these components can be implemented in different specific implementations in different types of devices (e.g., in ASICs, in System-on-Chip (SoCs), etc.). The illustrated components can 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. Furthermore, a given device may contain 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.

[0082] UE 302 and base station 304 each include one or more Wireless Wide Area Network (WWAN) transceivers 310 and 350, which provide components (e.g., components for transmitting, components for receiving, components for measuring, components for tuning, components for blocking transmission, etc.) for communication via one or more wireless communication networks (not shown), such as NR networks, LTE networks, GSM networks, etc. WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356 for communication with other network nodes (such as other UEs, access points, base stations (e.g., eNB, gNB), etc.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) through a wireless communication medium of interest (e.g., a time / frequency resource set in a specific spectrum). WWAN transceivers 310 and 350 can be configured in different ways to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, to receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.). Specifically, WWAN transceivers 310 and 350 each include: one or more transmitters 314 and 354 for transmitting and encoding signals 318 and 358, respectively; and one or more receivers 312 and 352 for receiving and decoding signals 318 and 358, respectively.

[0083] In at least some cases, UE 302 and base station 304 each further include one or more short-range radio transceivers 320 and 360, respectively. Short-range radio transceivers 320 and 360 can be connected to one or more antennas 326 and 366, respectively, and provide access over a wireless communication medium of interest via at least one designated RAT (e.g., Wi-Fi, LTE Direct, Bluetooth). ® ZIGBEE ® Z-WAVE ® Components (e.g., components for transmitting, components for receiving, components for measuring, components for tuning, components for blocking transmission, etc.) that enable communication between PC5, Dedicated Short Range Communication (DSRC), Wireless Access for Vehicle Environments (WAVE), Near Field Communication (NFC), Ultra Wideband (UWB), etc., and other network nodes (such as other UEs, access points, base stations, etc.). Short-range transceivers 320 and 360 can be configured in different ways to transmit and encode signals 328 and 368 (e.g., messages, indications, information, etc.) respectively according to a specified RAT, and conversely, to receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.) respectively. Specifically, short-range wireless transceivers 320 and 360 each include: one or more transmitters 324 and 364 for transmitting and encoding signals 328 and 368, respectively; and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368, respectively. As a specific example, short-range wireless transceivers 320 and 360 can be Wi-Fi transceivers, Bluetooth transceivers, etc. ® Transceiver, Zigbee ® and / or Z-WAVE ® Transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0084] In at least some cases, UE 302 and base station 304 also include satellite signal interfaces 330 and 370, each satellite signal interface including one or more satellite signal receivers 332 and 372, and optionally including one or more satellite signal transmitters 334 and 374, respectively. In some cases, base station 304 may be a terrestrial base station that can communicate with a spacecraft (e.g., spacecraft 112) via satellite signal interface 370. In other cases, base station 304 may be a spacecraft (or other non-terrestrial entity) that uses satellite signal interface 370 to communicate with terrestrial networks and / or other spacecraft.

[0085] Satellite signal receivers 332 and 372 can be connected to one or more antennas 336 and 376, respectively, and can provide components for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. When satellite signal receivers 332 and 372 are satellite positioning system receivers, 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) signals, etc. When satellite signal receivers 332 and 372 are non-terrestrial network (NTN) receivers, satellite positioning / communication signals 338 and 378 can be communication signals originating from a 5G network (e.g., carrying control and / or user data). Satellite signal receivers 332 and 372 can include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 332 and 372 may request appropriate information and operations from other systems, and in at least some cases, use measurements obtained by any suitable satellite positioning system algorithm to perform calculations to determine the locations of UE 302 and base station 304, respectively.

[0086] Optional satellite signal transmitters 334 and 374 (when present) can be connected to one or more antennas 336 and 376, respectively, and can be provided with components for transmitting satellite positioning / communication signals 338 and 378, respectively. When satellite signal transmitter 374 is a satellite positioning system transmitter, the satellite positioning / communication signal 378 can be a GPS signal, GLONASS signal, etc. ® Signals include Galileo signals, BeiDou signals, NAVIC signals, and QZSS signals. When satellite signal transmitters 334 and 374 are NTN transmitters, satellite positioning / communication signals 338 and 378 can be communication signals originating from a 5G network (e.g., carrying control and / or user data). Satellite signal transmitters 334 and 374 can include any suitable hardware and / or software for transmitting satellite positioning / communication signals 338 and 378, respectively. Satellite signal transmitters 334 and 374 can request appropriate information and operations from other systems.

[0087] Base station 304 and network entity 306 each include one or more network transceivers 380 and 390, which provide components (e.g., transmitting components, receiving components, etc.) for communicating with other network entities (e.g., other base stations 304, other network entities 306). For example, base station 304 may use 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. Similarly, network entity 306 may use 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 to communicate with other network entities 306 via one or more wired or wireless core network interfaces.

[0088] Transceivers can be configured to communicate via wired or wireless links. A transceiver (whether wired or wireless) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362). In some embodiments, the transceiver may be an integrated device (e.g., implementing transmitter and receiver circuitry in a single device), in some embodiments it may include separate transmitter and receiver circuitry, or in other embodiments it may be implemented in a different manner. The transmitter and receiver circuitry of a wired transceiver (e.g., network transceiver 380 and network transceiver 390 in some embodiments) may be coupled to one or more wired network interface ports. Wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, which allows the corresponding device (e.g., UE 302, base station 304) to perform transmit beamforming, as described herein. Similarly, wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, which allows the corresponding device (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, the transmitter and receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366), such that the corresponding device may perform only receive or only transmit at a given time, rather than both receive and transmit simultaneously. Wireless transceivers (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include network listening modules (NLMs) for performing various measurements.

[0089] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360 in some specific embodiments, and network transceivers 380 and 390) and wired transceivers (e.g., network transceivers 380 and 390 in some specific embodiments) may generally be described as "transceiver," "at least one transceiver," or "one or more transceivers." Therefore, whether a particular transceiver is a wired or wireless transceiver can be inferred from the type of communication 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.

[0090] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operation disclosed herein. UE 302, base station 304, and network entity 306 each include one or more processors 342, 384, and 394 for providing functionality related to, for example, wireless communication, and for providing other processing functionality. Thus, processors 342, 384, and 394 may provide components for processing, such as components for determining, components for calculating, components for receiving, components for transmitting, components for indicating, etc. In one aspect, processors 342, 384, and 394 may 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.

[0091] UE 302, base station 304, and network entity 306 each include memory circuitry implementing memories 340, 386, and 396 (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Therefore, memories 340, 386, and 396 can provide components for storage, retrieval, maintenance, etc. In some cases, UE 302, base station 304, and network entity 306 may each include machine learning components 348, 388, and 398. Machine learning components 348, 388, and 398 may be hardware circuitry that is part of or coupled to processors 342, 384, and 394, respectively, which, when executed, enable UE 302, base station 304, and network entity 306 to perform the functionality described herein. In other respects, machine learning components 348, 388, and 398 may be external to processors 342, 384, and 394 (e.g., as part of a modem processing system, integrated with another processing system, etc.). Alternatively, machine learning components 348, 388, and 398 may be memory modules stored in memories 340, 386, and 396, respectively, which, when executed by processors 342, 384, and 394 (or the modem processing system, another processing system, etc.), enable UE 302, base station 304, and network entity 306 to perform the functionality described herein. Figure 3A Possible locations for the machine learning component 348 are illustrated. This machine learning component may be part of, for example, one or more WWAN transceivers 310, memory 340, one or more processors 342, or any combination thereof, or it may be a standalone component. Figure 3B The possible locations of the machine learning component 388 are illustrated. The machine learning component may be part of, for example, one or more WWAN transceivers 350, memory 386, one or more processors 384 or any combination thereof, or it may be a standalone component. Figure 3C The possible locations of the machine learning component 398 are illustrated. The machine learning component may be part of, for example, one or more network transceivers 390, memory 396, one or more processors 394 or any combination thereof, or it may be a standalone component.

[0092] UE 302 may include one or more sensors 344 coupled to one or more processors 342 to provide components for sensing or detecting motion and / or orientation information independent of motion data derived from signals received by one or more WWAN transceivers 310, one or more short-range wireless transceivers 320, and / or satellite signal interfaces 330. By way of example, sensor 344 may include accelerometers (e.g., microelectromechanical systems (MEMS) devices), gyroscopes, geomagnetic sensors (e.g., compasses), altimeters (e.g., barometric altimeters), and / or any other type of motion detection sensor. Furthermore, sensor 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of multi-axis accelerometers and orientation sensors to provide the ability to calculate positioning in two-dimensional (2D) and / or three-dimensional (3D) coordinate systems.

[0093] In addition, UE 302 includes a user interface 346 that provides components for providing instructions to a user (e.g., audible and / or visual instructions) and / or for receiving user input (e.g., when the user actuates a sensing device such as a keypad, touchscreen, microphone, etc.). Although not shown, base station 304 and network entity 306 may also include user interfaces.

[0094] Referring more specifically to one or more processors 384, in the downlink, IP packets from network entity 306 can be provided to processor 384. One or more processors 384 can implement functionality for the RRC layer, Packet Data Convergence Protocol (PDCP) layer, Radio Link Control (RLC) layer, and Media Access Control (MAC) layer. One or more processors 384 may provide: RRC layer functionality associated with broadcasting 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 delivery of upper-layer PDUs, error correction via Automatic Repeat Request (ARQ), concatenation, segmentation, and reassembly of RLC Service Data Units (SDUs), resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority processing, and logical channel priority ordering.

[0095] Transmitter 354 and receiver 352 implement Layer 1 (L1) functionality associated with various signal processing functions. Layer 1, including the physical (PHY) layer, may include: error detection on the transport channel, forward error correction (FEC) decoding / decoding of the transport channel, interleaving, rate matching, mapping to the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. Transmitter 354 processes the mapping to the signal constellation based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The decoded and modulated symbols can then be split into parallel streams. Each stream can then be mapped to orthogonal frequency division multiplexing (OFDM) subcarriers, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domains, and then combined using inverse fast Fourier transform (IFFT) to produce a physical channel carrying a stream of time-domain OFDM symbols. The OFDM symbol stream is spatially pre-decoded to generate multiple spatial streams. Channel estimates from the channel estimator can be used to determine the decoding and modulation scheme, as well as for spatial processing. Channel estimates can be derived from reference signals transmitted by UE 302 and / or channel condition feedback. Each spatial stream can then be provided to one or more different antennas 356. The transmitter 354 can use the corresponding spatial stream to modulate an RF carrier for transmission.

[0096] At UE 302, receiver 312 receives signals via its corresponding antenna 316. Receiver 312 recovers the information modulated onto the RF carrier and provides this information to one or more processors 342. Transmitter 314 and receiver 312 implement Layer 1 functionality associated with various signal processing functions. Receiver 312 can perform spatial processing on the information to recover any spatial streams destined for UE 302. If multiple spatial streams are destined for UE 302, they can be combined by receiver 312 into a single OFDM symbol stream. Receiver 312 then uses a Fast Fourier Transform (FFT) to transform 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. Symbols on each subcarrier, along with a reference signal, are recovered and demodulated by determining the signal constellation points most likely to be transmitted by base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally transmitted by base station 304 on the physical channel. Then, data and control signals are provided to one or more processors 342, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0097] In the downlink, one or more processors 342 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transport and logical channels to recover IP packets from the core network. One or more processors 342 are also responsible for error detection.

[0098] Similar to the functionality described in conjunction with downlink transmissions performed by base station 304, one or more processors 342 provide: RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connectivity, 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 delivery of upper-layer PDUs, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via Hybrid Automatic Repeat Request (HARQ), priority processing, and logical channel priority ordering.

[0099] The channel estimate derived by the channel estimator from the reference signal or feedback transmitted by the base station 304 can be used by the transmitter 314 to select an appropriate decoding and modulation scheme and facilitate spatial processing. The spatial stream generated by the transmitter 314 can be provided to different antennas 316. The transmitter 314 can use the corresponding spatial stream to modulate the RF carrier for transmission.

[0100] Uplink transmissions are processed at base station 304 in a manner similar to that described in conjunction with the receiver function at UE 302. Receiver 352 receives signals via its corresponding antenna 356. Receiver 352 recovers the information modulated onto the RF carrier and provides this information to one or more processors 384.

[0101] In the uplink, one or more processors 384 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transport channel and the logical channel to recover IP packets from UE 302. 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.

[0102] For convenience, UE 302, base station 304 and / or network entity 306 are in Figure 3A , Figure 3B and Figure 3CThe document is 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 functionalities in different designs. In particular, Figures 3A to 3C Various components are optional in alternative configurations, and various aspects include configurations that can vary due to design choices, cost, equipment usage, or other considerations. For example, in Figure 3A In certain cases, specific implementations of UE 302 may omit WWAN transceiver 310 (e.g., wearable devices, tablets, personal computers (PCs), or laptops may have Wi-Fi and / or Bluetooth). ® The short-range wireless transceiver 320 can be omitted (e.g., cellular only), or the satellite signal interface 330 can be omitted, or the sensor 344 can be omitted, etc. In another example, in Figure 3B In certain cases, specific implementations of base station 304 may omit WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capabilities), or short-range wireless transceiver 360 (e.g., cellular only), or satellite signal interface 370, etc. For the sake of brevity, examples of various alternative configurations are not provided herein, but will be readily understood by those skilled in the art.

[0103] Various components of UE 302, base station 304, and network entity 306 can be communicatively coupled to each other via data buses 308, 382, ​​and 392, respectively. In one aspect, data buses 308, 382, ​​and 392 can form or be part of the communication interfaces of UE 302, base station 304, and network entity 306, respectively. For example, in cases where different logical entities are embodied in the same device (e.g., gNB and location server functionality integrated into the same base station 304), data buses 308, 382, ​​and 392 can provide communication between these logical entities.

[0104] Figure 3A , Figure 3B and Figure 3C The components 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, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or combine at least one memory component for storing information or executable code used by the circuit to provide that functionality. For example, some or all of the functionalities represented by blocks 310 to 346 may 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 functionalities represented by blocks 350 to 388 may 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 functionalities represented by blocks 390 to 398 may 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," "by the base station," "by the network entity," etc. However, as will be understood, such operations, actions and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc. (such as processors 342, 384, 394, transceivers 310, 320, 350 and 360, memory 340, 386 and 396, machine learning components 348, 388 and 398, etc.).

[0105] In some designs, network entity 306 may be implemented as a core network component. In other designs, network entity 306 may operate differently from the network operator or cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, network entity 306 may be a component of a 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 Wi-Fi).

[0106] NR supports various cellular network-based positioning technologies, including downlink-based positioning methods, uplink-based positioning methods, and positioning methods based on both downlink and uplink. Downlink-based positioning methods include: Observed Time Difference of Arrival (OTDOA) in LTE, Downlink Time Difference of Arrival (DL-TDOA) in NR, and Downlink Angle of Departure (DL-AoD) in NR. Figure 4Examples of various positioning methods according to aspects of this disclosure are illustrated. In the OTDOA or DL-TDOA positioning process illustrated in scenario 410, the UE measures the difference between the times of arrival (ToA) of reference signals (e.g., positioning reference signals (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 identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in auxiliary data. The UE then measures the RSTD between the reference base station and each non-reference base station. Based on the known locations of the base stations involved and the RSTD measurement, the positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the UE's location.

[0107] For the DL-AoD positioning illustrated in scenario 420, the positioning entity uses measurement reports of received signal strength measurements from multiple downlink transmitted beams of the UE to determine the angle between the UE and the transmitting base station. The positioning entity can then estimate the UE's position based on the determined angle and the known location of the transmitting base station.

[0108] 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. Each base station then 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 location and relative timing of the base stations involved. Based on the received-receive (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each non-reference base station, the known location of the base stations, and their known timing offsets, the positioning entity can use the TDOA to estimate the UE's location.

[0109] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurement and the angle of the receive beam 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.

[0110] Downlink and uplink-based positioning methods include Enhanced Cell ID (E-CID) positioning and Multiple Round-Trip Time (RTT) positioning (also known as "Multi-Cell RTT" and "Multi-RTT"). During RTT, a first entity (e.g., a base station or a UE) sends a first RTT-related signal (e.g., PRS or SRS) to a second entity (e.g., a UE or a base station), which then sends a second RTT-related signal (e.g., SRS or PRS) back to the first entity. Each entity measures the time difference between the time of arrival (ToA) of the received RTT-related signal and the time of transmission of the transmitted RTT-related signal. This time difference is called the receive-to-transmit (Rx-Tx) time difference. The Rx-Tx time difference measurement can be performed or adjusted to include only the time difference between the nearest time slot boundary of the received signal and the transmitted signal. The two entities can then transmit their Rx-Tx time difference measurements to a location server (e.g., LMF 270), which calculates the round-trip time (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 another entity, which then 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 the multi-RTT positioning illustrated in scenario 430, a first entity (e.g., a UE or base station) performs an RTT positioning process with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined based on the distance to the second entities and the known location of the second entities (e.g., using multilateral positioning). RTT and multi-RTT methods can be combined with other positioning technologies (such as UL-AoA and DL-AoD) to improve location accuracy, as illustrated in scenario 440.

[0111] The E-CID positioning method is based on Radio Resource Management (RRM) measurements. In E-CID, the UE reports the serving cell ID, timing advance (TA), identifiers of detected neighboring base stations, estimated timing, and signal strength. The UE's location is then estimated based on this information and the known locations of the base stations.

[0112] To assist in positioning operations, a location server (e.g., location server 230, LMF 270, SLP 272) may provide auxiliary data to the UE. For example, auxiliary data may include: the identifier of the base station (or the cell / TRP of the base station) from which the reference signal is measured, reference signal configuration parameters (e.g., including the number of consecutive time slots of the PRS, the periodicity of consecutive time slots of the PRS, silence sequences, frequency hopping sequences, reference signal identifier, reference signal bandwidth, etc.), and / or other parameters applicable to a particular positioning method. Alternatively, auxiliary data may be derived directly from the base station itself (e.g., in periodically broadcast overhead messages, etc.). In some cases, the UE may be able to detect neighboring network nodes without using auxiliary data.

[0113] In the case of OTDOA or DL-TDOA positioning procedures, auxiliary data may also include the expected RSTD value and the associated uncertainty or search window around the expected RSTD. In some cases, the expected RSTD value may range from + / - 500 microseconds (µs). In some cases, when any of the resources used for positioning measurements is in FR1, the uncertainty of the expected RSTD may range from + / - 32 µs. In other cases, when all resources used for positioning measurements are in FR2, the uncertainty of the expected RSTD may range from + / - 8 µs.

[0114] Location estimates can be referred to by other names, such as location estimation, location, positioning, fixed location, etc. Location estimates can be geodesic and include coordinates (e.g., latitude, longitude, and possible elevation), or they can be municipal and include street addresses, postal addresses, or some other verbal description of the location. Location estimates can be further defined relative to some other known location or in absolute terms (e.g., using latitude, longitude, and possible elevation). Location estimates can include expected errors or uncertainties (e.g., by including the area or volume that the location is expected to include with a specified or default confidence level).

[0115] Wireless communication signals transmitted between the UE and the base station (e.g., radio frequency (RF) signals configured to carry orthogonal frequency division multiplexing (OFDM) symbols according to wireless communication standards such as LTE, NR, etc.) can be used for environmental sensing (also known as "wireless sensing," "RF sensing," or "radar"). Environmental sensing using wireless communication signals can be considered as consumer-grade radar with advanced detection capabilities, enabling contactless / device-free interaction with devices / systems, etc. Wireless communication signals can be cellular communication signals, such as LTE or NR signals, WLAN signals such as Wi-Fi signals, etc. As a specific example, wireless communication signals can be OFDM waveforms as utilized in LTE and NR. High-frequency communication signals, such as millimeter-wave (mmW) RF signals, are particularly advantageous for use as sensing signals because higher frequencies provide at least more accurate ranging (distance) detection.

[0116] Possible use cases for RF sensing include: health monitoring use cases, such as heart rate detection and respiratory rate monitoring; gesture recognition use cases, such as human activity recognition, keystroke detection, and sign language recognition; context information acquisition use cases, such as location detection / tracking, direction finding, and distance estimation; and automotive sensing use cases, such as intelligent cruise control and collision avoidance.

[0117] There are different types of sensing, including single-station sensing (also known as "active sensing") and dual-station sensing (also known as "passive sensing"). Figure 5A and Figure 5B These different types of sensing are illustrated. Specifically, Figure 5A This is illustration 500 illustrating a single-station sensing scenario, and Figure 5B This is illustration 530, illustrating a dual-station sensing scenario. Figure 5A In this configuration, the transmitter (Tx) and receiver (Rx) are co-located in the same sensing device 504 (e.g., a UE). The sensing device 504 transmits one or more RF sensing signals 534 (e.g., uplink or sidelink positioning reference signals (PRS) in the case of a UE), and some of the RF sensing signals 534 are reflected from a target object 506 (e.g., an unmanned aerial vehicle (UAV)). The sensing device 504 can measure various properties of the reflected 536 of the RF sensing signals 534 (e.g., time of arrival (ToA), angle of arrival (AoA), phase shift, etc.) to determine characteristics of the target object 506 (e.g., size, shape, speed, motion state, etc.).

[0118] exist Figure 5B In this architecture, the transmitter (Tx) and receiver (Rx) are not co-located; that is, they are separate devices (e.g., the UE and the base station). It should be noted that although... Figure 5BAn example is shown using a downlink RF signal as the RF sensing signal 532, but uplink or sidelink RF signals can also be used as the RF sensing signal 532. In the downlink scenario, as shown, the transmitter sensing node 502 is a base station (e.g., a gNB), and the receiver sensing node 508 is a UE (e.g., a mobile phone, a V2X-enabled vehicle, a roadside unit (RSU), etc.), while in the uplink scenario, the transmitter sensing node 502 is the UE, and the receiver sensing node 508 is the base station. When the transmitter sensing node 502 is the base station and the receiver sensing node 508 is the UE, the sensing is referred to as UE-assisted sensing. In UE-assisted sensing, the location of the receiver sensing node 508 should be known by the network (e.g., via GPS or other UE positioning methods).

[0119] For more detailed information, please refer to [link / reference]. Figure 5B Transmitter sensing node 502 sends RF sensing signals 532 and 534 (e.g., a positioning reference signal (PRS)) to receiver sensing node 508, but some of the RF sensing signals 534 are reflected from the target object 506. Receiver sensing node 508 (also referred to as a "sensing device") can measure the time of arrival (ToA) of the RF sensing signal 532 received directly from transmitter sensing node 502 and the time of reflection 536 of the RF sensing signal 534 reflected from the target object 506.

[0120] More specifically, as described above, a transmitter device (e.g., a base station) may transmit a single RF signal or multiple RF signals to a receiver device (e.g., a UE). However, due to the propagation characteristics of RF signals through multipath channels, the receiver may receive multiple RF signals corresponding to each transmitted RF signal. Each path may be associated with a cluster of one or more channel taps. Typically, the time when the receiver detects the first channel tap cluster is considered to be the ToA of the RF signal on the site line (LOS) path (i.e., the shortest path between the transmitter and receiver). Subsequent channel tap clusters are considered to have been reflected by objects between the transmitter and receiver, and therefore have been along a non-LOS (NLOS) path between the transmitter and receiver.

[0121] Therefore, return to the reference. Figure 5BRF sensing signal 532 follows the LOS path between transmitter sensing node 502 and receiver sensing node 508, while RF sensing signal 534 follows the NLOS path between transmitter sensing node 502 and receiver sensing node 508 due to reflection from target object 506. Transmitter sensing node 502 may have transmitted multiple RF sensing signals 532 and 534, some of which follow the LOS path and others follow the NLOS path. Alternatively, transmitter sensing node 502 may have transmitted a single RF sensing signal in a sufficiently wide beam such that a portion of the RF sensing signal follows the LOS path (RF sensing signal 532) and a portion follows the NLOS path (RF sensing signal 534).

[0122] Based on the ToA of the LOS path, the ToA of the NLOS path, and the speed of light, the receiver sensing node 508 can determine the distance to the target object. For example, the receiver sensing node 508 can calculate the distance to the target object as the difference between the ToA of the LOS path and the ToA of the NLOS path multiplied by the speed of light. Furthermore, if the receiver sensing node 508 is capable of receiving beamforming, it can determine the approximate direction to the target object 506 as the direction (angle) of the received beam that receives the RF sensing signal following the NLOS path. That is, the receiver sensing node 508 can determine the direction to the target object 506 as the AoA of the RF sensing signal, which is the angle of the received beam used to receive the RF sensing signal. The receiver sensing node 508 can then optionally report this information to the transmitter sensing node 502, its serving base station, an application server associated with the core network, an external client, a third-party application, or another sensing entity. Alternatively, receiver sensing node 508 may report the ToA measurement to transmitter sensing node 502 or other sensing entities (e.g., if receiver sensing node 508 itself does not have the processing capability to perform calculations), and transmitter sensing node 502 may determine the distance to target object 506 and optionally determine the direction to the target object.

[0123] It should be noted that if the RF sensing signal is an uplink RF signal sent by the UE to the base station, the base station will perform object detection based on the uplink RF signal, just as the UE does based on the downlink RF signal.

[0124] Similar to conventional radar, wireless communication-based sensing signals can be used to estimate the range (distance), velocity (Doppler), and angle (AoA) of a target. However, performance (e.g., resolution and maximum values ​​of range, velocity, and angle) can depend on the design of the reference signal.

[0125] Machine learning can be used to generate models that can facilitate various aspects associated with data processing. A specific application of machine learning involves generating measurement models for processing reference signals (e.g., PRS) used for sensing or localization (such as feature extraction, reporting of reference signal measurement results (e.g., selecting which extracted features to report)).

[0126] Machine learning models are generally categorized 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 dataset 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 regression models, the output is continuous. An example of a regression model is linear regression, which simply attempts to find a line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding a best-fitting plane) and multinomial regression (e.g., finding a best-fitting curve).

[0127] Another example of a machine learning model is the decision tree model. In a decision tree model, the tree structure is defined as having multiple nodes. Decisions are made to move from the root node at the top of the decision tree to a leaf node at the bottom (i.e., a node that has no other children). Generally, a higher number of nodes in a decision tree model is associated with higher decision accuracy.

[0128] Another example of a machine learning model is the decision forest. Random forests are an ensemble learning technique built on top of decision trees. Random forests involve creating multiple decision trees using a bootstrap dataset of the original data and randomly selecting a subset of variables at each step of the decision trees. The model then selects the pattern of all predictions from each decision tree. By relying on a "majority decision" model, the risk of errors from individual trees is reduced.

[0129] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. It takes one or more input variables and produces one or more output variables by passing them through the network of equations. In other words, a neural network receives a vector of inputs and returns a vector of outputs.

[0130] Figure 6An example neural network 600 according to various aspects of this disclosure is illustrated. The neural network 600 includes an input layer "i" that receives "n" (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" (or more) outputs (labeled as "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" may be the same or different. In some designs, hidden layers "h" may include linear functions and / or activation functions, with each node of a successive hidden layer (illustrated as a circle) processing the linear function and / or activation function from the node of the previous hidden layer.

[0131] In classification models, the output is discrete. An example of a classification model is logistic regression. Logistic regression is similar to linear regression, but it's used to model the probabilities of a finite number of outcomes (usually two). Essentially, it's a logistic equation created in a way that ensures the output value can only be between "0" and "1". Another example of a classification model is a support vector machine (SVM). For example, given data from two classes, an SVM will find a hyperplane, or boundary, that maximizes the margin between the two classes. Many hyperplanes can separate the two classes, but only one hyperplane maximizes the margin or distance between them. 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 examples described above, except that the output is discrete rather than continuous.

[0132] Unlike supervised learning, unsupervised learning is used to derive inferences and find patterns from input data without referring to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0133] Clustering is an unsupervised technique involving the grouping or clustering of data points. Clustering is commonly used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean-shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of main variables. More simply, dimensionality reduction is the process of reducing the dimension of a feature set (or, even more simply, reducing the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called Principal Component Analysis (PCA). In its simplest sense, PCA involves projecting higher-dimensional data (e.g., three-dimensional) onto a smaller space (e.g., two-dimensional). This produces lower-dimensional (e.g., two-dimensional instead of three-dimensional) data while preserving all the original variables in the model.

[0134] Regardless of the machine learning model used, at a high level, the machine learning module (e.g., implemented by a processing system such as processor 342, 384, or 394) can be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs / target objects) and correlate that training input data with an output dataset (e.g., a set of possible or highly probable candidate locations for various UEs / target objects), thereby enabling the same output dataset to be determined later when similar input data (e.g., from other UEs / target objects at the same or similar locations) is provided.

[0135] AI / ML localization provided by artificial intelligence / machine learning (AI / ML) localization and / or sensing models can be direct AI / ML localization / sensing or AI / ML-assisted localization / sensing. Figure 7A This is a diagram 700 illustrating examples of direct AI / ML positioning / sensing according to various aspects of this disclosure. For example... Figure 7A As shown, direct AI / ML positioning / sensing refers to the measurement of one or more reference signals (e.g., DL-PRS or SRS) for the input features (or simply "inputs" or "features") of the AI / ML positioning / sensing model, and the output label (or simply "output" or "label") of the AI / ML positioning / sensing model is the target (estimated) location of the UE or target object. Measurements of the reference signals may include the channel energy response (CER), channel impulse response (CIR), channel frequency response (CFR), received signal strength indicator (RSSI), reference signal received power (RSRP), path RSRP (RSRPP), reference signal received quality (RSRQ), time of arrival (ToA), relative ToA (RTOA), reference signal time difference (RSTD), angle of departure (AoD), angle of arrival (AoA), etc.

[0136] Figure 7B Figure 750 is an illustration of examples of AI / ML-assisted localization and / or sensing according to various aspects of this disclosure. Figure 7BAs shown, AI / ML-assisted localization / sensing involves the input features of an AI / ML localization / sensing model being measurements of one or more reference signals (e.g., DL-PRS, SRS), and the output label of the AI / ML localization / sensing model being intermediate measurements (or quantities) of the reference signals. Non-AI techniques (e.g., Chan algorithm, Kalman filtering, etc.) or different machine learning models are then used to determine the location of the UE or target object. In this context, the measurements of the reference signals may include CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angular spectrum, etc. Intermediate measurements of the reference signals may include ToA, RTOA, RSTD, AoD, AoA, line-of-sight (LOS) indication, etc.

[0137] Figure 8 Various artificial intelligence / machine learning (AI / ML) localization and / or sensing scenarios according to various aspects of this disclosure are illustrated. As shown in Figure 800, there are three AI / ML localization / sensing deployment scenarios based on downlink reference signals (e.g., DL-PRS). The first deployment scenario (labeled "Scenario 1") is a UE-based localization / sensing scenario with a UE-side AI / ML localization / sensing model (labeled "AI / ML"). In this case, the UE applies the AI / ML localization / sensing model (or simply "AI / ML model") to determine the location of the UE or a target object and reports that location to the network (e.g., LMF 270). The AI / ML localization / sensing provided by the AI / ML localization / sensing model can be direct AI / ML (D-AI / ML) localization / sensing or AI / ML-assisted (A-AI / ML) localization / sensing.

[0138] The second deployment scenario (labeled "Scenario 2a") is UE-assisted / network-based positioning / sensing with a UE-side AI / ML positioning / sensing model that provides AI / ML-assisted positioning / sensing. That is, the UE inputs measurements of downlink reference signals (e.g., DL-PRS) received from one or more TRPs into the AI / ML positioning / sensing model to obtain intermediate measurements (or quantities) of the downlink reference signals. The UE then reports these intermediate measurements to the network (e.g., LMF 270). As noted above, for AI / ML-assisted positioning / sensing, measurements of the downlink reference signals may include CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angular spectrum (which shows the intensity or probability of each value (e.g., the x-axis is the delay and the y-axis is the probability of the delay)), etc. Intermediate measurements of the downlink reference signals may include ToA, RSTD, AoD, AoA, line-of-sight (LOS) indication, etc.

[0139] The third deployment scenario (labeled "Scenario 2b") is UE-assisted / network-based positioning / sensing with a network-side model that provides direct AI / ML positioning / sensing. That is, the UE reports measurements of downlink reference signals received from one or more TRPs to the core network (e.g., LMF 270). The core network then applies the AI / ML positioning / sensing model to the measurements to determine the location of the UE or a target object. In this case, the measurements of the downlink reference signals reported to the network may include CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, ToA, RSTD, AoD, AoA, LOS indication, delay, Doppler, angular spectrum, etc.

[0140] As shown in Figure 850, there are two AI / ML positioning / sensing deployment scenarios based on uplink reference signals (e.g., SRS). The first deployment scenario (labeled "Scenario 3a") is NG-RAN node-assisted positioning / sensing with an NG-RAN side model that provides AI / ML-assisted positioning / sensing. In this case, the NG-RAN applies the AI / ML positioning / sensing model to the TRP measurement of one or more uplink reference signals (e.g., SRS) transmitted by the UE to obtain intermediate measurements of the received uplink reference signals. The NG-RAN then reports the intermediate measurements to the core network (e.g., LMF 270). As noted above, for AI / ML-assisted positioning / sensing, the measurements of the uplink reference signals may include CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angular spectrum, etc. of the uplink reference signals. Intermediate measurements of the uplink reference signals may include RTOA, RSTD, AoD, AoA, LOS indication, etc.

[0141] The second deployment scenario (labeled "Scenario 3b") involves NG-RAN nodes providing network-side AI / ML positioning / sensing models for direct AI / ML positioning / sensing. In this scenario, the NG-RAN reports measurements of one or more uplink reference signals received from the UE to the core network (e.g., LMF270). The core network then applies the AI / ML positioning / sensing model to the uplink reference signal measurements to obtain the UE's target location. The uplink reference signal measurements may include CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, RTOA, RSTD, AoD, AoA, LOS indication, delay, Doppler, angular spectrum, etc.

[0142] It should be noted that there may be other deployment scenarios in which the UE, NG-RAN, or core network uses AI / ML positioning / sensing models to calculate or report positioning / sensing estimates (target location), but these are implementation-specific and do not necessarily involve signaling between the UE, NG-RAN, and / or core network.

[0143] Model monitoring is a consideration in AI / ML lifecycle management (LCM). In AI / ML positioning, model monitoring is more straightforward because the target UE can report its Global Navigation Satellite System (GNSS) positioning to verify the accuracy of model inference. However, in sensing scenarios, because the target object is device-free (i.e., lacking wireless communication capabilities, or at least not the ability to communicate with the sensing node), the baseline reality (i.e., the actual location of the object) is unknown to the sensing node performing model inference. Therefore, a model monitoring technique is needed for sensing.

[0144] This disclosure provides a technique for performing model monitoring for a network or UE by comparing the performance of an AI / ML model used to sense a target object with the performance of one or more non-AI / ML reference methods used to sense the same target object.

[0145] Figure 9A and Figure 9B An example system for model monitoring based on a comparison of results from AI / ML-based sensing methods and other reference sensing methods, according to various aspects of this disclosure, is illustrated. Specifically, Figure 9A Figure 900 illustrates an example scenario in which a network entity 910 (e.g., a sensing management function (SnMF), a sensing server, a base station, or other type of sensing entity) configures a receiver (Rx) sensing node 508 (e.g., a UE, a base station, a sensing reference unit (SRU), or other type of sensing node) to compare the results of an AI / ML-based sensing method with one or more reference sensing methods and report the results of the comparison. Figure 9B Figure 950 illustrates an example scenario in which receiver sensing node 508 receives the location of reference node 920 (e.g., UE, SRU, base station) and uses this location to determine the performance of the AI / ML model when sensing reference node 920 and target object 506. In some cases, communication between network entity 910 and receiver sensing node 508 may be relayed by another network entity (e.g., base station), which is in Figure 9A and Figure 9B The middle part is omitted.

[0146] Figure 10This is a diagram 1000 illustrating an example signaling flow for model monitoring based on a comparison of results from an AI / ML-based sensing method with other reference sensing methods, according to various aspects of this disclosure. The signaling can... Figure 9A and Figure 9B Between the receiver sensing node 508 and the sensing entity 910. Stages 1010 and 1020 can be... Figure 9A The illustrated system executes the commands, and stages 1030 to 1060 are available. Figure 9B Executed in the illustrated system.

[0147] At stage 1010, sensing entity 910 sends a message to receiver sensing node 508 instructing the receiver sensing node to perform model monitoring. This message may specify the sensing reference methods and weights used for comparison with the AI / ML sensing model inference on K measurements. That is, the AI / ML sensing model will be applied to K sensing measurements, and the model's output will then be compared with the sensing results of multiple sensing reference methods using the received weights.

[0148] Reference methods for comparison with AI / ML-based sensing methods may include: (1) non-AI / ML sensing algorithms, such as compressed sensing, multiple signal classification (MUSIC), maximum likelihood (ML), least squares (LS), minimum mean square error (MMSE), etc.; (2) NR positioning for sensing one or more reference UEs as targets; (3) sensing one or more reference base stations as targets; (4) sensor measurements (wherein receiver sensing node 508 uses one or more equipped sensors (e.g., camera, radar sensor, lidar sensor, etc.) to measure the target); and / or (5) auxiliary information from a server or other network function (e.g., the reference true location of the target). It should be noted that for the second option, NR positioning may be based on RF measurements or GNSS positioning of the reference UEs. The reference UEs may report not only their positioning but also their velocity.

[0149] The message at stage 1010 may indicate which reference method(s) to use, as well as the accuracy and weight (priority) of the indicated reference method. Alternatively, the message may indicate the criteria / priority / conditions used to determine the reference method.

[0150] At stage 1020, receiver sensing node 508 reports the difference in results between AI / ML sensing methods and non-AI / ML sensing methods, or a model performance decision based on the difference in results and method accuracy. Figure 10 In the example, the report indicates that for K measurements, the variance between AI / ML sensing results is greater than or less than the weighted variance of compressed sensing and the MUSIC algorithm.

[0151] As a concrete example, sensing entity 910 can (at stage 1010) indicate that the accuracy of the AI / ML-based sensing method should be 0.1 meters (m), and the accuracy of the non-AI / ML method should be 1 meter (m). Although the non-AI / ML method has lower accuracy than the AI / ML-based sensing method in this example, it has higher reliability. Therefore, the result of the non-AI / ML method can be considered more reliable. In this case, if the result based on AI / ML is 100 meters and the result based on non-AI / ML is 120 meters, the model inference performance might be considered unacceptable. That is, a difference of 20 meters between their results is much larger than a difference of 1 meter in their accuracy. Based on the non-AI / ML-based result, the true result should be in the range of 119 meters to 121 meters.

[0152] In some cases, if the reference method is determined by the receiver sensing node 508 itself, the receiver sensing node 508 may indicate the reference method (and weights) used in the report at stage 1020.

[0153] In some cases, the report at stage 1020 may include results based on multiple measurements (i.e., measurement timing) to indicate trends and statistical characteristics. Sensing entity 910 may configure the receiver sensing node 508 to specify the number (K) of measurements for comparison. For example, if AI / ML-based and non-AI / ML-based results have the same trend or statistical characteristics, the model inference performance may be considered acceptable even if their results differ significantly. As another example, AI / ML-based results may be considered unacceptable when they are unstable (with large variance) while non-AI / ML-based results are stable (with small variance).

[0154] In some cases, the thresholds used to determine model performance may differ between AI / ML methods and other reference methods. For example, using GNSS-based NR positioning to sense a reference UE may have stricter thresholds than those used for non-AI / ML algorithms, because in some cases, GNSS can be considered the baseline reality.

[0155] Model monitoring (i.e., result comparison) can be triggered in different ways. As a first option, sensing entity 910 can instruct receiver sensing node 508 to perform a result comparison between the AI / ML method and a reference method. As a second option, receiver sensing node 508 can perform the result comparison based on a period of model inference time (i.e., the receiver sensing node 508 has been configured to use the AI / ML model for a duration greater than a threshold) or a periodic timer (i.e., periodically), which can be pre-configured by the network entity. As a third option, receiver sensing node 508 can perform the result comparison after the model is passed / delivered to it or after a local model update.

[0156] Further referring to non-AI / ML sensing algorithms, although their accuracy may be lower than that of AI / ML sensing models in some cases, they have been extensively evaluated and may have higher robustness. Therefore, the receiver sensing node 508 can compare the AI / ML sensing results with multiple non-AI / ML sensing algorithms. In this case, if the difference between the results of the non-AI / ML algorithms is smaller than the difference between the results of the AI / ML model and the results of the non-AI / ML algorithms, the AI / ML model can be considered to have unacceptable performance.

[0157] For example, an AI / ML-based model can be determined to be inaccurate when the result is 100m, while the results from non-AI / ML algorithms are 120m, 121m, and 119m. As another example, for K consecutive measurements, an AI / ML-based model can be determined to be inaccurate when the results increase while most results from non-AI / ML algorithms decrease. As yet another example, an AI / ML model can be determined to be inaccurate when the expected / variance of multiple AI / ML-based results is 10m / s, while the expected / variance of multiple higher-accuracy non-AI / ML-based results is 20m / s, 21m / s, and 19m / s.

[0158] In some cases, different non-AI / ML algorithms may have different weights in comparisons based on their accuracy and reliability. In such cases, sensing entity 910 may assign weights to receiver sensing node 508 (at stage 1010), or sensing entity 910 may assign accuracy levels to receiver sensing node 508, and receiver sensing node 508 may determine the weights.

[0159] In some cases, during model monitoring, the network can allocate more (in time and / or frequency) sensing resources for measurements to be processed using non-AI / ML algorithms to improve the accuracy and / or reliability of the non-AI / ML algorithms. In this case, sensing entity 910 can indicate a higher weight for the non-AI / ML algorithm to receiver sensing node 508 (at stage 1010) than in other cases.

[0160] Referring now to the option of using NR positioning to determine the performance of an AI / ML sensing model, at stage 1030, receiver sensing node 508 requests NR positioning of one or more reference nodes 920 in its target sensing area (i.e., the geographical area where receiver sensing node 508 can receive sensing reference signals from transmitter sensing node 502), and may indicate a reference node of a desired type (e.g., a telephone, unmanned aerial vehicle (UAV), car, etc.). In response, sensing entity 910 selects one or more reference nodes 920 and obtains the NR positioning results (i.e., estimated locations) of the reference nodes 920. For example, sensing entity 910 may communicate with reference node 920 to request NR positioning results, in which case reference node 920 may initiate an NR positioning process, such as... Figure 4 As illustrated, the results are reported to sensing entity 910. Alternatively, sensing entity 910 may send a request to a location server (e.g., LMF 270) or other positioning entities associated with reference node 920 to request NR positioning results from reference node 920. The location server may then trigger reference node 920 to perform NR positioning procedures, such as... Figure 4 As illustrated, the results are reported to sensing entity 910.

[0161] At stage 1040, sensing entity 910 provides the NR positioning result to receiver sensing node 508. At stage 1050, based on the NR positioning result, receiver sensing node 508 senses reference node 920. In some cases, transmitter sensing node 502 may configure its transmit beam direction and receiver sensing node 508 may configure its receive beam in the direction indicated by the NR positioning result (i.e., the estimated position of reference node 920). Receiver sensing node 508 then compares the sensing result obtained by applying an AI / ML sensing model to the sensing measurement of the reflection from reference node 920 with the NR positioning result of reference node 920.

[0162] At stage 1060, receiver sensing node 508 sends a model monitoring report to sensing entity 910, which indicates the model performance level based on a weighted comparison with NR positioning results (and optionally other non-AI / ML algorithms).

[0163] When using reference node 920 to monitor model inference performance, there are different options for finding reference node 920. For example, receiver sensing node 508 can find one or more reference nodes 920 via sidelink discovery. As a second option, the base station can broadcast an indication of available reference nodes 920, or the base station can select one or more registered sensing nodes 920 in the same cell as receiver sensing node 508 or in nearby (e.g., adjacent) cells. As a third option, sensing entity 910 can select one or more reference nodes 920 in the sensing area of ​​receiver sensing node 508.

[0164] In some cases, where the reference node 920 is a base station or other access point, the receiver sensing node 508 can use an AI / ML model to sense the reference node 920 and monitor the AI / ML-based results based on the actual location of the reference node 920. In this scenario, if the receiver sensing node 508 is a UE, for UE-based sensing (where the UE estimates the target object location), the location of the reference node 920 (base station) can be signaled to the receiver sensing node 508 (UE) via auxiliary data. For UE-assisted sensing (where the UE reports sensing measurements to a sensing entity, which then estimates the target object location), the location of the reference node 920 may not be known to the receiver sensing node 508 (UE), therefore the receiver sensing node 508 will need to request the reference node's location from an appropriate location server or other positioning entity.

[0165] In some cases, where the receiver sensing node 508 is a base station, the receiver sensing node 508 can be a reference node 920, and another device can transmit a sensing reference signal.

[0166] Now, referring to the behavior after the model is monitored, if the difference in results between the AI / ML model and the non-AI / ML method or the confidence level of the model performance meets a (pre-)configured threshold (i.e., the model performance is lower than expected), the receiver sensing node 508 can also report the sensing results of the non-AI / ML algorithm and the indicated method type.

[0167] In some cases, if the model performance is lower than expected based on a comparison with a non-AI / ML method, the receiver sensing node 508 may request comparisons with the results of one or more other non-AI / ML methods as further checks in a (pre-)configured order. For example, if the difference between the results of an AI / ML-based method and a non-AI / ML-based method is greater than a threshold, the receiver sensing node 508 may request model monitoring using NR positioning. In this example and others, the sensing entity 910 and the receiver sensing node 508 will execute stages 1010 to 1060 instead of stages 1010 and 1020 or stages 1030 to 1060.

[0168] Different types of signaling protocols can be used Figure 10 The illustrated message sending and receiving. When sensing entity 910 is a location server or other server and receiver sensing node 508 is a UE, the signaling can be LTE Location Protocol (LPP) signaling or a dedicated sensing protocol. When sensing entity 910 is a location server or other server and receiver sensing node 508 is a base station, the signaling can be New Radio Positioning Protocol Type A (NRPPa) signaling or a dedicated sensing protocol.

[0169] When sensing entity 910 is a core network entity and receiver sensing node 508 is a UE, the signaling can be NAS signaling. When sensing entity 910 is a core network entity and receiver sensing node 508 is a base station, the signaling can be Next Generation Application Protocol (NGAP) signaling.

[0170] When sensing entity 910 is a base station and receiver sensing node 508 is a UE, the signaling can be RRC signaling, MAC control element (MAC-CE) signaling, or downlink control information (DCI) signaling. When sensing entity 910 is the first UE and receiver sensing node 508 is the second UE, the signaling can be RRC signaling, MAC-CE signaling, sidelink control information (SCI) signaling, physical sidelink control channel (PSCCH) signaling, or physical sidelink shared channel (PSSCH) signaling. When sensing entity 910 is a UE and receiver sensing node 508 is a base station, the signaling can be RRC signaling, MAC-CE signaling, or uplink control information (UCI) signaling.

[0171] Figure 11 An example method 1100 for wireless sensing according to various aspects of this disclosure is illustrated. In one aspect, method 1100 may be performed by a sensing node (e.g., any of the UE, SRU, or base station described herein).

[0172] At operation 1110, the sensing node can obtain one or more sensing measurements of one or more sensing reference signals.

[0173] In one aspect, when the sensing node is a UE, operation 1110 may be performed by one or more WWAN transceivers 310, one or more short-range radio transceivers 320, one or more processors 342, memory 340, and / or machine learning components 348, any one or all of which can be considered as components for performing the operation. In another aspect, when the sensing node is a base station or base station component, operation 1110 may be performed by one or more WWAN transceivers 350, one or more short-range radio transceivers 360, one or more network transceivers 380, one or more processors 384, memory 386, and / or machine learning components 388, any one or all of which can be considered as components for performing the operation.

[0174] At operation 1120, the sensing node can apply a machine learning model to one or more sensing measurements to obtain a first sensing result associated with one or more target objects.

[0175] In one aspect, when the sensing node is a UE, operation 1120 may be performed by one or more WWAN transceivers 310, one or more short-range radio transceivers 320, one or more processors 342, memory 340, and / or machine learning components 348, any or all of which may be considered as components for performing the operation. In another aspect, when the sensing node is a base station or base station component, operation 1120 may be performed by one or more WWAN transceivers 350, one or more short-range radio transceivers 360, one or more network transceivers 380, one or more processors 384, memory 386, and / or machine learning components 388, any or all of which may be considered as components for performing the operation.

[0176] At operation 1130, the sensing node can obtain a second sensing result associated with one or more target objects based on one or more non-machine learning sensing methods.

[0177] In one aspect, when the sensing node is a UE, operation 1130 may be performed by one or more WWAN transceivers 310, one or more short-range radio transceivers 320, one or more processors 342, memory 340, and / or machine learning components 348, any one or all of which can be considered as components for performing the operation. In another aspect, when the sensing node is a base station or base station component, operation 1130 may be performed by one or more WWAN transceivers 350, one or more short-range radio transceivers 360, one or more network transceivers 380, one or more processors 384, memory 386, and / or machine learning components 388, any one or all of which can be considered as components for performing the operation.

[0178] At operation 1140, the sensing node may send a model monitoring report indicating the results of a comparison between the first sensing result and the second sensing result to the sensing entity (e.g., another UE, base station, SnMF, LMF, etc.).

[0179] In one aspect, when the sensing node is a UE, operation 1140 may be performed by one or more WWAN transceivers 310, one or more short-range radio transceivers 320, one or more processors 342, memory 340, and / or machine learning components 348, any one or all of which can be considered as components for performing the operation. In another aspect, when the sensing node is a base station or base station component, operation 1140 may be performed by one or more WWAN transceivers 350, one or more short-range radio transceivers 360, one or more network transceivers 380, one or more processors 384, memory 386, and / or machine learning components 388, any one or all of which can be considered as components for performing the operation.

[0180] As will be understood, the technical advantage of method 1100 is that it enables sensing nodes to determine the performance of the deployed machine learning model. Based on the determined performance, network entities can further determine better sensing methods at the sensing nodes (such as disabling AI / ML-based methods and enabling non-AI / ML-based methods, or upgrading the AI / ML model) to improve sensing performance.

[0181] As can be seen in the detailed description above, different features are grouped together in the examples. This manner of disclosure should not be construed as an intention to have more features than those explicitly mentioned in each clause. Rather, the various aspects of this disclosure may include fewer features than those in the individual example clauses disclosed. Therefore, the following clauses should be regarded accordingly as incorporated into the description, where each clause may serve as a separate example. Although each dependent clause may refer in the clause to a specific combination with one of the other clauses, the aspect of that dependent clause is not limited to that specific combination. It should be understood that other example clauses may also include combinations of aspects of a dependent clause with the subject matter of any other dependent or independent clause, or combinations of any feature with other dependent and independent clauses. The various aspects disclosed herein explicitly include these combinations unless explicitly stated or readily inferred that a particular combination is not intended for use (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). Furthermore, it is contemplated that aspects of a clause may be included in any other independent clause, even if that clause does not directly depend on the independent clause.

[0182] Specific implementation examples are described in the following numbered clauses:

[0183] Clause 1. A method for wireless sensing performed by a sensing node, the method comprising: obtaining one or more sensing measurements of one or more sensing reference signals; applying a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; obtaining a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; and sending a model monitoring report to a sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0184] Clause 2. The method described in Clause 1, wherein the one or more non-machine learning sensing methods include: compressed sensing algorithms, multi-signal classification (MUSIC) algorithms, maximum likelihood (ML) algorithms, least squares (LS) algorithms, minimum mean square error (MMSE) algorithms, or any combination thereof.

[0185] Clause 3. The method according to Clause 2, wherein obtaining the second sensing result comprises: applying the one or more non-machine learning sensing methods to the one or more sensing measurements to obtain the second sensing result.

[0186] Clause 4. The method according to any one of Clauses 1 to 3, wherein: the one or more target objects are one or more reference nodes, and the second sensing result is the localization of the one or more reference nodes.

[0187] Clause 5. The method according to Clause 4, wherein obtaining the second sensing result includes: receiving the positioning of the one or more reference nodes.

[0188] Clause 6. The method according to Clause 5, the method further comprising: sending a request to the sensing entity for the positioning of the one or more reference nodes.

[0189] Clause 7. The method described in Clause 6, wherein the request indicates one or more types of the one or more reference nodes.

[0190] Clause 8. The method according to any one of Clauses 4 to 7, the method further comprising: selecting the one or more reference nodes based on: sidelink discovery; broadcast information indicating that the one or more reference nodes are in the same cell or adjacent cells as the sensing node; sensing assistance data from the sensing entity indicating that the one or more reference nodes are in the sensing area of ​​the sensing node.

[0191] Clause 9. The method according to any one of Clauses 4 to 8, wherein: the one or more reference nodes include one or more user equipment (UE), one or more sensing reference units (SRU), or any combination thereof, and the positioning of the one or more UEs, the one or more SRUs, or the combination thereof is obtained based on one or more cellular-based positioning procedures involving the one or more reference nodes.

[0192] Clause 10. The method according to any one of Clauses 4 to 9, wherein the one or more reference nodes include one or more base stations.

[0193] Clause 11. The method according to any one of Clauses 1 to 10, wherein the one or more non-machine learning sensing methods comprise: one or more cameras of the sensing node, one or more radar sensors of the sensing node, one or more lidar sensors of the sensing node, or any combination thereof.

[0194] Clause 12. The method according to any one of Clauses 1 to 11, wherein: the one or more sensing measurements include a plurality of sensing measurements obtained at a plurality of measurement times, the first sensing result includes a first sensing result for each of the plurality of measurement times, the second sensing result includes a second sensing result for each of the plurality of measurement times, and the comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

[0195] Clause 13. The method according to any one of Clauses 1 to 12, wherein the one or more non-machine learning sensing methods have different accuracy thresholds, different reliability thresholds, different priorities, different weights, or any combination thereof.

[0196] Clause 14. The method according to Clause 13, the method further comprising: receiving from the sensing entity a configuration for performing the comparison between the first sensing result and the second sensing result, wherein the configuration indicates the different accuracy thresholds, the different reliability thresholds, the different priorities, the different weights, or combinations thereof.

[0197] Clause 15. The method according to any one of Clauses 13 to 14, wherein: the one or more non-machine learning sensing methods are weighted higher than the machine learning model, and the resource patterns of the one or more sensing reference signals have increased resource density in time, frequency, or both based on the one or more non-machine learning sensing methods being weighted higher than the machine learning model.

[0198] Clause 16. The method according to any one of Clauses 1 to 15, wherein the model monitoring report is triggered based on: a request for the model monitoring report from the sensing entity, the sensing node being configured to use the machine learning model for a period of time greater than a threshold, a periodic timer, upon receiving the machine learning model, upon updating the machine learning model, or any combination thereof.

[0199] Clause 17. The model monitoring report, according to any one of Clauses 1 to 16, indicates the type of the one or more non-machine learning sensing methods.

[0200] Clause 18. The method according to any one of Clauses 1 to 17, the method further comprising: sending a request to the sensing entity for configuration of one or more second non-machine learning sensing methods based on the fact that the result of the comparison between the first sensing result and the second sensing result is below a performance threshold; and receiving the configuration of the one or more second non-machine learning sensing methods from the sensing entity in response to the request.

[0201] Clause 19. The method according to Clause 18, further comprising: obtaining a third sensing result associated with the one or more target objects based on the one or more second non-machine learning sensing methods; and sending a second model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the third sensing result.

[0202] Clause 20. The method according to any one of Clauses 1 to 19, wherein the sensing node is: user equipment (UE) or base station.

[0203] Clause 21. The method according to any one of Clauses 1 to 20, wherein the sensing entity is: user equipment (UE), base station or server.

[0204] Clause 22. A sensing node comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being individually or in combination configured to: acquire one or more sensing measurements of one or more sensing reference signals; apply a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; acquire a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; and transmit a model monitoring report indicating a comparison between the first sensing result and the second sensing result to a sensing entity via the one or more transceivers.

[0205] Clause 23. The sensing node as described in Clause 22, wherein the one or more non-machine learning sensing methods include: compressed sensing algorithms, multi-signal classification (MUSIC) algorithms, maximum likelihood (ML) algorithms, least squares (LS) algorithms, minimum mean square error (MMSE) algorithms, or any combination thereof.

[0206] Clause 24. The sensing node according to Clause 23, wherein the one or more processors configured to obtain the second sensing result include the one or more processors configured individually or in combination to perform the following: applying the one or more non-machine learning sensing methods to the one or more sensing measurements to obtain the second sensing result.

[0207] Clause 25. The sensing node according to any one of Clauses 22 to 24, wherein: the one or more target objects are one or more reference nodes, and the second sensing result is the localization of the one or more reference nodes.

[0208] Clause 26. The sensing node according to Clause 25, wherein the one or more processors configured to obtain the second sensing result includes the one or more processors configured individually or in combination to perform the following: receiving the positioning of the one or more reference nodes via the one or more transceivers.

[0209] Clause 27. The sensing node according to Clause 26, wherein the one or more processors are further configured individually or in combination to send a request for the location of the one or more reference nodes to the sensing entity via the one or more transceivers.

[0210] Clause 28. The sensing node as described in Clause 27, wherein the request indicates one or more types of the one or more reference nodes.

[0211] Clause 29. A sensing node according to any one of Clauses 25 to 28, wherein the one or more processors are further configured individually or in combination to select the one or more reference nodes based on: sidelink discovery; broadcast information indicating that the one or more reference nodes are in the same cell or adjacent cells as the sensing node; sensing assistance data from the sensing entity indicating that the one or more reference nodes are in the sensing area of ​​the sensing node.

[0212] Clause 30. A sensing node according to any one of Clauses 25 to 29, wherein: the one or more reference nodes include one or more user equipment (UE), one or more sensing reference units (SRU), or any combination thereof, and the location of the one or more UEs, the one or more SRUs, or the combination thereof is obtained based on one or more cellular-based positioning procedures involving the one or more reference nodes.

[0213] Clause 31. A sensing node according to any one of Clauses 25 to 30, wherein the one or more reference nodes include one or more base stations.

[0214] Clause 32. A sensing node according to any one of Clauses 22 to 31, wherein the one or more non-machine learning sensing methods comprise: one or more cameras of the sensing node, one or more radar sensors of the sensing node, one or more lidar sensors of the sensing node, or any combination thereof.

[0215] Clause 33. A sensing node according to any one of Clauses 22 to 32, wherein: the one or more sensing measurements include a plurality of sensing measurements obtained at a plurality of measurement times, the first sensing result includes a first sensing result for each of the plurality of measurement times, the second sensing result includes a second sensing result for each of the plurality of measurement times, and the comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

[0216] Clause 34. A sensing node pursuant to any one of Clauses 22 to 33, wherein the one or more non-machine learning sensing methods have different accuracy thresholds, different reliability thresholds, different priorities, different weights, or any combination thereof.

[0217] Clause 35. The sensing node according to Clause 34, wherein the one or more processors are further configured individually or in combination to receive from the sensing entity via the one or more transceivers a configuration for performing the comparison between the first sensing result and the second sensing result, wherein the configuration indicates the different accuracy thresholds, the different reliability thresholds, the different priorities, the different weights, or combinations thereof.

[0218] Clause 36. A sensing node according to any one of Clauses 34 to 35, wherein: the one or more non-machine learning sensing methods are weighted higher than the machine learning model, and the resource pattern of the one or more sensing reference signals has increased resource density in time, frequency, or both based on the one or more non-machine learning sensing methods being weighted higher than the machine learning model.

[0219] Clause 37. A sensing node pursuant to any one of Clauses 22 to 36, wherein the model monitoring report is triggered based on: a request for the model monitoring report from the sensing entity, the sensing node being configured to use the machine learning model for a duration greater than a threshold, a periodic timer, upon receiving the machine learning model, upon updating the machine learning model, or any combination thereof.

[0220] Clause 38. For any of the sensing nodes pursuant to Clauses 22 to 37, the model monitoring report indicates the type of the one or more non-machine learning sensing methods.

[0221] Clause 39. A sensing node according to any one of Clauses 22 to 38, wherein the one or more processors are further configured individually or in combination to: send a request for configuration of one or more second non-machine learning sensing methods to the sensing entity via the one or more transceivers based on the result of the comparison between the first sensing result and the second sensing result being below a performance threshold; and in response to the request, receive the configuration of the one or more second non-machine learning sensing methods from the sensing entity via the one or more transceivers.

[0222] Clause 40. The sensing node according to Clause 39, wherein the one or more processors are further configured individually or in combination to: obtain a third sensing result associated with the one or more target objects based on the one or more second non-machine learning sensing methods; and send a second model monitoring report to the sensing entity via the one or more transceivers, indicating the result of a comparison between the first sensing result and the third sensing result.

[0223] Clause 41. A sensing node according to any one of Clauses 22 to 40, wherein the sensing node is: a user equipment (UE) or a base station.

[0224] Clause 42. A sensing node according to any one of Clauses 22 to 41, wherein the sensing entity is: a user equipment (UE), a base station, or a server.

[0225] Clause 43. A sensing node comprising: components for acquiring one or more sensing measurements of one or more sensing reference signals; components for applying a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; components for acquiring a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; and components for sending a model monitoring report to a sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0226] Clause 44. The sensing node as described in Clause 43, wherein the one or more non-machine learning sensing methods include: compressed sensing algorithms, multi-signal classification (MUSIC) algorithms, maximum likelihood (ML) algorithms, least squares (LS) algorithms, minimum mean square error (MMSE) algorithms, or any combination thereof.

[0227] Clause 45. The sensing node according to Clause 44, wherein the component for obtaining the second sensing result includes: a component for applying the one or more non-machine learning sensing methods to the one or more sensing measurements to obtain the second sensing result.

[0228] Clause 46. The sensing node according to any one of Clauses 43 to 45, wherein: the one or more target objects are one or more reference nodes, and the second sensing result is the localization of the one or more reference nodes.

[0229] Clause 47. The sensing node according to Clause 46, wherein the component for obtaining the second sensing result includes: a component for receiving the positioning of the one or more reference nodes.

[0230] Clause 48. The sensing node according to Clause 47, the sensing node further includes: a component for sending a request to the sensing entity for the positioning of the one or more reference nodes.

[0231] Clause 49. The sensing node as described in Clause 48, wherein the request indicates one or more types of the one or more reference nodes.

[0232] Clause 50. The sensing node according to any one of Clauses 46 to 49, the sensing node further comprising: components for selecting the one or more reference nodes based on: sidelink discovery; broadcast information indicating that the one or more reference nodes are in the same cell or adjacent cells as the sensing node; sensing assistance data from the sensing entity indicating that the one or more reference nodes are in the sensing area of ​​the sensing node.

[0233] Clause 51. A sensing node according to any one of Clauses 46 to 50, wherein: the one or more reference nodes include one or more user equipment (UE), one or more sensing reference units (SRU), or any combination thereof, and the location of the one or more UEs, the one or more SRUs, or the combination thereof is obtained based on one or more cellular-based positioning procedures involving the one or more reference nodes.

[0234] Clause 52. A sensing node according to any one of Clauses 46 to 51, wherein the one or more reference nodes include one or more base stations.

[0235] Clause 53. A sensing node according to any one of Clauses 43 to 52, wherein the one or more non-machine learning sensing methods comprise: one or more cameras of the sensing node, one or more radar sensors of the sensing node, one or more lidar sensors of the sensing node, or any combination thereof.

[0236] Clause 54. A sensing node according to any one of Clauses 43 to 53, wherein: the one or more sensing measurements include a plurality of sensing measurements obtained at a plurality of measurement times, the first sensing result includes a first sensing result for each of the plurality of measurement times, the second sensing result includes a second sensing result for each of the plurality of measurement times, and the comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

[0237] Clause 55. A sensing node pursuant to any one of Clauses 43 to 54, wherein the one or more non-machine learning sensing methods have different accuracy thresholds, different reliability thresholds, different priorities, different weights, or any combination thereof.

[0238] Clause 56. The sensing node according to Clause 55, the sensing node further comprising: a component for receiving from the sensing entity a configuration for performing the comparison between the first sensing result and the second sensing result, wherein the configuration indicates the different accuracy thresholds, the different reliability thresholds, the different priorities, the different weights, or combinations thereof.

[0239] Clause 57. A sensing node according to any one of Clauses 55 to 56, wherein: the one or more non-machine learning sensing methods are weighted higher than the machine learning model, and the resource pattern of the one or more sensing reference signals has increased resource density in time, frequency, or both based on the one or more non-machine learning sensing methods being weighted higher than the machine learning model.

[0240] Clause 58. A sensing node pursuant to any one of Clauses 43 to 57, wherein the model monitoring report is triggered based on: a request for the model monitoring report from the sensing entity, the sensing node being configured to use the machine learning model for a duration greater than a threshold, a periodic timer, upon receiving the machine learning model, upon updating the machine learning model, or any combination thereof.

[0241] Clause 59. For any of the sensing nodes pursuant to any one of Clauses 43 to 58, the model monitoring report indicates the type of the one or more non-machine learning sensing methods.

[0242] Clause 60. The sensing node according to any one of Clauses 43 to 59, the sensing node further comprising: a component for sending a request to the sensing entity for a configuration of one or more second non-machine learning sensing methods based on the result of the comparison between the first sensing result and the second sensing result being lower than a performance threshold; and a component for receiving the configuration of the one or more second non-machine learning sensing methods from the sensing entity in response to the request.

[0243] Clause 61. The sensing node according to Clause 60, the sensing node further comprising: a component for obtaining a third sensing result associated with the one or more target objects based on the one or more second non-machine learning sensing methods; and a component for sending a second model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the third sensing result.

[0244] Clause 62. A sensing node according to any one of Clauses 43 to 61, wherein the sensing node is: a user equipment (UE) or a base station.

[0245] Clause 63. A sensing node according to any one of Clauses 43 to 62, wherein the sensing entity is: a user equipment (UE), a base station, or a server.

[0246] Clause 64. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a sensing node, cause the sensing node to: acquire one or more sensing measurements of one or more sensing reference signals; apply a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; acquire a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; and send a model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the second sensing result.

[0247] Clause 65. The non-transitory computer-readable medium as described in Clause 64, wherein the one or more non-machine learning sensing methods include: compressed sensing algorithms, multiple signal classification (MUSIC) algorithms, maximum likelihood (ML) algorithms, least squares (LS) algorithms, minimum mean square error (MMSE) algorithms, or any combination thereof.

[0248] Clause 66. The non-transitory computer-readable medium according to Clause 65, wherein the computer-executable instructions that, when executed by the sensing node, cause the sensing node to obtain the second sensing result include, when executed by the sensing node, causing the sensing node to perform the following computer-executable instructions: apply the one or more non-machine learning sensing methods to the one or more sensing measurements to obtain the second sensing result.

[0249] Clause 67. A non-transitory computer-readable medium according to any one of Clauses 64 to 66, wherein: the one or more target objects are one or more reference nodes, and the second sensing result is the positioning of the one or more reference nodes.

[0250] Clause 68. The non-transitory computer-readable medium according to Clause 67, wherein the computer-executable instructions that, when executed by the sensing node, cause the sensing node to obtain the second sensing result include, when executed by the sensing node, causing the sensing node to perform the following computer-executable instructions: receive the positioning of the one or more reference nodes.

[0251] Clause 69. The non-transitory computer-readable medium according to Clause 68 further includes computer-executable instructions that, when executed by the sensing node, cause the sensing node to: send a request to the sensing entity for the location of the one or more reference nodes.

[0252] Clause 70. A non-transitory computer-readable medium as described in Clause 69, wherein the request indicates one or more types of the one or more reference nodes.

[0253] Clause 71. A nontransitory computer-readable medium according to any one of Clauses 67 to 70, the nontransitory computer-readable medium further comprising computer-executable instructions, which, when executed by the sensing node, cause the sensing node to: select the one or more reference nodes based on: sidelink discovery; broadcast information indicating that the one or more reference nodes are in the same cell or adjacent cells as the sensing node; and sensing assistance data from the sensing entity indicating that the one or more reference nodes are in the sensing area of ​​the sensing node.

[0254] Clause 72. A nontransitory computer-readable medium according to any one of Clauses 67 to 71, wherein: the one or more reference nodes comprise one or more user equipment (UE), one or more sensing reference units (SRU), or any combination thereof, and the location of the one or more UEs, the one or more SRUs, or the combination thereof is obtained based on one or more cellular-based location procedures involving the one or more reference nodes.

[0255] Clause 73. A non-transitory computer-readable medium pursuant to any one of Clauses 67 to 72, wherein the one or more reference nodes comprise one or more base stations.

[0256] Clause 74. A non-transitory computer-readable medium according to any one of Clauses 64 to 73, wherein the one or more non-machine learning sensing methods comprise: one or more cameras of the sensing node, one or more radar sensors of the sensing node, one or more lidar sensors of the sensing node, or any combination thereof.

[0257] Clause 75. A non-transitory computer-readable medium according to any one of Clauses 64 to 74, wherein: the one or more sensing measurements include a plurality of sensing measurements obtained at a plurality of measurement times, the first sensing result includes a first sensing result for each of the plurality of measurement times, the second sensing result includes a second sensing result for each of the plurality of measurement times, and the comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

[0258] Clause 76. A non-transitory computer-readable medium pursuant to any one of Clauses 64 to 75, wherein the one or more non-machine learning sensing methods have different accuracy thresholds, different reliability thresholds, different priorities, different weights, or any combination thereof.

[0259] Clause 77. The non-transitory computer-readable medium according to Clause 76 further includes computer-executable instructions that, when executed by the sensing node, cause the sensing node to: receive from the sensing entity a configuration for performing the comparison between the first sensing result and the second sensing result, wherein the configuration indicates the different accuracy thresholds, the different reliability thresholds, the different priorities, the different weights, or combinations thereof.

[0260] Clause 78. A non-transitory computer-readable medium according to any one of Clauses 76 to 77, wherein: the one or more non-machine learning sensing methods are weighted higher than the machine learning model, and the resource patterns of the one or more sensing reference signals have increased resource density in time, frequency, or both based on the one or more non-machine learning sensing methods being weighted higher than the machine learning model.

[0261] Clause 79. A non-transitory computer-readable medium pursuant to any one of Clauses 64 to 78, wherein the model monitoring report is triggered based on: a request for the model monitoring report from the sensing entity, the sensing node being configured to use the machine learning model for a period of time greater than a threshold, a periodic timer, upon receiving the machine learning model, upon updating the machine learning model, or any combination thereof.

[0262] Clause 80. A non-transitory computer-readable medium pursuant to any one of Clauses 64 to 79, wherein the model monitoring report indicates the type of the one or more non-machine learning sensing methods.

[0263] Clause 81. A non-transitory computer-readable medium according to any one of Clauses 64 to 80, the non-transitory computer-readable medium further comprising computer-executable instructions, which, when executed by the sensing node, cause the sensing node to: send a request to the sensing entity for configuration of one or more second non-machine learning sensing methods based on the result of the comparison between the first sensing result and the second sensing result being below a performance threshold; and, in response to the request, receive the configuration of the one or more second non-machine learning sensing methods from the sensing entity.

[0264] Clause 82. The non-transitory computer-readable medium according to Clause 81 further includes computer-executable instructions that, when executed by the sensing node, cause the sensing node to: obtain a third sensing result associated with the one or more target objects based on the one or more second non-machine learning sensing methods; and send a second model monitoring report to the sensing entity indicating the result of a comparison between the first sensing result and the third sensing result.

[0265] Clause 83. A non-transitory computer-readable medium according to any one of Clauses 64 to 82, wherein the sensing node is: a user equipment (UE) or a base station.

[0266] Clause 84. A non-transitory computer-readable medium according to any one of Clauses 64 to 83, wherein the sensing entity is: user equipment (UE), base station, or server.

[0267] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and arts. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0268] Furthermore, those skilled in the art will understand that the various exemplary logic 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 between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above in general terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific 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 specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

[0269] The various exemplary logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein may be implemented or performed 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 components, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternative embodiments, 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 combined with a DSP core, or any other such configuration.

[0270] The methods, sequences, and / or algorithms described in conjunction with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or a combination of both. The software module may reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. Example storage media are coupled to a processor such that the processor can read information from and write information to the storage medium. Alternatively, the storage medium may be integral with the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., a UE). Alternatively, the processor and storage medium may reside as discrete components in the user terminal.

[0271] In one or more examples, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality may be stored as one or more instructions or code on or transmitted via a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, which includes any medium that facilitates the transfer of a computer program from one place to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and is accessible to a computer. Furthermore, any connection is appropriately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. As used herein, disks and optical discs include: compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0272] While the foregoing disclosure illustrates exemplary aspects of this disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of this disclosure as defined by the appended claims. For example, the functions, steps, and / or actions of the method claims according to aspects of this disclosure described herein need not be performed in any particular order. Furthermore, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly stated otherwise. Additionally, as used herein, the terms “set,” “group,” etc., are intended to include one or more of the stated elements. Furthermore, as used herein, the terms “having,” “comprising,” “including,” etc., do not exclude the presence of one or more additional elements (e.g., element “having” A may also have B). Furthermore, the phrase “based on” is intended to mean “at least partially based on” unless otherwise explicitly stated. Furthermore, as used herein, the term “or” is intended to be open-ended when used in a series and is interchangeable with “and / or” unless otherwise explicitly stated (e.g., if used in conjunction with “any” or “only one”), or these alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more”). Additionally, although components, functions, actions, and instructions may be described or claimed in the singular, plural forms may also be considered unless explicitly stated to be limited to the singular. Therefore, as used herein, the articles “a,” “an,” “the,” and “described” are intended to include one or more of the stated elements. Additionally, as used herein, the terms “at least one” and “one or more” include “one” component, function, action, or instruction that performs or is capable of performing the described or claimed functionality, and also include “two or more” components, functions, actions, or instructions that perform or are capable of performing the described or claimed functionality in combination.

Claims

1. A sensing node, the sensing node comprising: One or more memory units; One or more transceivers; and One or more processors, communicatively coupled to one or more memories and one or more transceivers, wherein the one or more processors are configured individually or in combination to: One or more sensing measurements to obtain one or more sensing reference signals; A machine learning model is applied to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; A second sensing result associated with the one or more target objects is obtained based on one or more non-machine learning sensing methods; as well as A model monitoring report indicating the comparison between the first sensing result and the second sensing result is sent to the sensing entity via the one or more transceivers.

2. The sensing node according to claim 1, wherein the one or more non-machine learning sensing methods include: Compressed sensing algorithm, Multi-signal classification (MUSIC) algorithm, Maximum likelihood (ML) algorithm, Least Squares (LS) algorithm The Least Mean Square Error (MMSE) algorithm, or Any combination of them.

3. The sensing node according to claim 1, wherein: The one or more target objects are one or more reference nodes, and The second sensing result is the location of the one or more reference nodes.

4. The sensing node of claim 3, wherein the one or more processors configured to obtain the second sensing result comprises processors individually or in combination configured to perform the following: The location of the one or more reference nodes is received via the one or more transceivers.

5. The sensing node of claim 4, wherein the one or more processors are further configured individually or in combination to: The one or more transceivers send a request for the location of the one or more reference nodes to the sensing entity.

6. The sensing node of claim 5, wherein the request indicates one or more types of the one or more reference nodes.

7. The sensing node of claim 3, wherein the one or more processors are further configured individually or in combination to: The one or more reference nodes are selected based on the following: sidelink discovery; broadcast information indicating that the one or more reference nodes are in the same cell or adjacent cells as the sensing node; and sensing assistance data from the sensing entity indicating that the one or more reference nodes are in the sensing area of ​​the sensing node.

8. The sensing node according to claim 3, wherein: The one or more reference nodes include one or more user equipment (UE), one or more sensing reference units (SRU), or any combination thereof, and The location of the one or more UEs, the one or more SRUs, or the combination thereof is obtained based on one or more cellular-based location procedures involving the one or more reference nodes.

9. The sensing node according to claim 1, wherein the one or more non-machine learning sensing methods include: One or more cameras of the sensing node, One or more radar sensors of the sensing node One or more lidar sensors of the sensing node, or Any combination of them.

10. The sensing node according to claim 1, wherein: The one or more sensing measurements include multiple sensing measurements obtained at multiple measurement times. The first sensing result includes a first sensing result for each of the plurality of measurement times. The second sensing result includes a second sensing result for each of the plurality of measurement times, and The comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

11. The sensing node of claim 1, wherein the one or more non-machine learning sensing methods have different accuracy thresholds, different reliability thresholds, different priorities, different weights, or any combination thereof.

12. The sensing node of claim 11, wherein the one or more processors are further configured individually or in combination to: The configuration for performing the comparison between the first sensing result and the second sensing result is received from the sensing entity via the one or more transceivers, wherein the configuration indicates the different accuracy thresholds, the different reliability thresholds, the different priorities, the different weights, or combinations thereof.

13. The sensing node according to claim 11, wherein: The one or more non-machine learning sensing methods are weighted higher than the machine learning model, and The resource patterns of the one or more sensed reference signals are weighted higher than the machine learning model based on the one or more non-machine learning sensing methods to have increased resource density in time, frequency, or both.

14. The sensing node of claim 1, wherein the model monitoring report is triggered based on: A request for a model monitoring report from the sensing entity. The sensing node has been configured to use the machine learning model for a duration exceeding a threshold. Periodic timer Upon receiving the machine learning model When updating the machine learning model, or Any combination of them.

15. The sensing node of claim 1, wherein the one or more processors are further configured individually or in combination to: Based on the fact that the result of the comparison between the first sensing result and the second sensing result is lower than a performance threshold, a request for configuration of one or more second non-machine learning sensing methods is sent to the sensing entity via the one or more transceivers; and In response to the request, the configuration of the one or more second non-machine learning sensing methods is received from the sensing entity via the one or more transceivers.

16. The sensing node of claim 15, wherein the one or more processors are further configured individually or in combination to: A third sensing result associated with the one or more target objects is obtained based on the aforementioned one or more second non-machine learning sensing methods; and A second model monitoring report, indicating the result of a comparison between the first sensing result and the third sensing result, is sent to the sensing entity via the one or more transceivers.

17. A method for wireless sensing performed by a sensing node, the method comprising: One or more sensing measurements to obtain one or more sensing reference signals; A machine learning model is applied to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; A second sensing result associated with the one or more target objects is obtained based on one or more non-machine learning sensing methods; as well as A model monitoring report is sent to the sensing entity, indicating the results of the comparison between the first sensing result and the second sensing result.

18. The method of claim 17, wherein: The one or more sensing measurements include multiple sensing measurements obtained at multiple measurement times. The first sensing result includes a first sensing result for each of the plurality of measurement times. The second sensing result includes a second sensing result for each of the plurality of measurement times, and The comparison between the first sensing result and the second sensing result indicates a comparison between the first sensing result and the second sensing result at the plurality of measurement times.

19. The method of claim 17, further comprising: If the result of the comparison between the first sensing result and the second sensing result is lower than a performance threshold, a request for configuration of one or more second non-machine learning sensing methods is sent to the sensing entity. as well as In response to the request, the configuration of the one or more second non-machine learning sensing methods is received from the sensing entity.

20. A sensing node, the sensing node comprising: One or more sensing measurements are used to obtain one or more sensing reference signals; A component for applying a machine learning model to the one or more sensing measurements to obtain a first sensing result associated with one or more target objects; A component for obtaining a second sensing result associated with the one or more target objects based on one or more non-machine learning sensing methods; as well as A component for sending a model monitoring report to the sensing entity, indicating the result of a comparison between the first sensing result and the second sensing result.

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