RRM layer-3 filter configuration enhancement for time-domain measurement prediction

Abstract

A method includes receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; and reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

Description

RRM LAYER-3 FILTER CONFIGURATION ENHANCEMENT FOR TIME-DOMAIN MEASUREMENT PREDICTIONFIELD

[0001] Various example embodiments relate generally to wireless networking and, more particularly, to configurations for conditional lower-layer triggered mobility (LTM).BACKGROUND

[0002] Wireless networking provides significant advantages for user mobility. A user’s ability to remain connected while on the move provides advantages not only for the user, but also provides greater efficiency and productivity for society as a whole. As user expectations for connection reliability, data speed, and device battery life become more demanding, technology for wireless networking must also keep pace with such expectations. Accordingly, there is continuing interest in improving wireless networking technology.SUMMARY

[0003] According to some aspects, there is provided the subject matter of the independent claims. Some further aspects are defined in the dependent claims.

[0004] In accordance with aspects of the disclosure, a method includes receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; and reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

[0005] In an aspect of the method, the RRC configuration may further comprise information on a rate of measurement reduction and / or prediction.

[0006] In an aspect of the method, the method may further include reporting, prior to the RRC configuration, by the UE to the network apparatus, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction pattern, or (iii) astorage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

[0007] In an aspect of the method, the filtering may be done by a recursive filter.

[0008] In an aspect of the method, the radio measurement prior to filtering may be a layer 1(LI) measurement.

[0009] In an aspect of the method, the radio measurement prior to filtering may be an L3 measurement.

[0010] In an aspect of the method, the RRC configuration method may further include information for configuring the UE to collect an L3 measurement based on a physical device or predict an L3 measurement based on a machine learning model.

[0011] In an aspect of the method, the information may further include an indication of a measurement reduction method and an associated configuration, including at least one of a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LI measurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where a measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain.

[0012] In an aspect of the method, a processor-readable medium storing instructions which, when executed by at least one processor of a UE apparatus, may cause the UE apparatus at least to perform a method as in any one of the foregoing methods.

[0013] In accordance with aspects of the disclosure, a user equipment (UE) apparatus includes at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, causes the UE apparatus at least to perform receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radiomeasurement based on the first coefficient and the second coefficient; and reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

[0014] In accordance with aspects of the disclosure, the RRC configuration may further comprise information on a rate of measurement reduction and / or prediction.

[0015] In accordance with aspects of the disclosure, the instructions, when executed by the processor, may further cause the apparatus to at least perform reporting, prior to the RRC configuration, by the UE to the network apparatus, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction pattern, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

[0016] In accordance with aspects of the disclosure, the filtering may be done by a recursive filter.

[0017] In accordance with aspects of the disclosure, the radio measurement prior to filtering may be a layer 1 (LI) measurement.

[0018] In accordance with aspects of the disclosure, the radio measurement prior to filtering may be an L3 measurement.

[0019] In accordance with aspects of the disclosure, the RRC configuration message may further include information for configuring the UE to collect an L3 measurement based on a physical device or predict an L3 measurement based on a machine learning model.

[0020] In accordance with aspects of the disclosure, the information may further include an indication of a measurement reduction method and an associated configuration, including at least one of a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LI measurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where a measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain.

[0021] In accordance with aspects of the disclosure, a method includes transmitting, by a network apparatus to a user equipment (UE), a radio resource control (RRC) configurationmessage, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement, wherein the predicted L3 radio measurement and real L3 radio measurement are filtered based on the first coefficient and the second coefficient; and receiving, by the network apparatus from the UE, the filtered L3 radio measurements.

[0022] In an aspect of the method, the RRC configuration may further comprise information on a rate of measurement reduction and / or prediction.

[0023] In an aspect of the method, the method may further include receiving a report, prior to the RRC configuration, by a network apparatus from the UE, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction patterns, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

[0024] In an aspect of the method, the filtering may be done by a recursive filter.

[0025] In an aspect of the method, the radio measurement prior to filtering may be a layer 1(LI) measurement.

[0026] In an aspect of the method, the radio measurement prior to filtering may be an L3 measurement.

[0027] In accordance with aspects of the disclosure, a network apparatus includes at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, causes the network apparatus at least to perform a method as in any one of the foregoing methods.

[0028] In accordance with aspects of the disclosure, a processor-readable medium storing instructions which, when executed by at least one processor of a network apparatus, cause the network apparatus at least to perform a method as in any one of the foregoing methods.

[0029] According to some aspects, there is provided the subject matter of the independent claims. Some further aspects are defined in the dependent claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0030] S ome example embodiments will now be described with reference to the accompanying drawings.

[0031] FIG. 1 is a diagram of an example embodiment of wireless networking between a network system and a user equipment (UE), according to one illustrated aspect of the disclosure;

[0032] FIG. 2 is a diagram of example components of a network system, according to one illustrated aspect of the disclosure;

[0033] FIG. 3 is a diagram of an example embodiment of signals and operations among a UE and a wireless station or node (e.g., network node (such as gNodeB (gNB))) according to one illustrated aspect of the disclosure;

[0034] FIG. 4 is a diagram of an example embodiment of signals and operations among a UE, and a wireless station or node (e.g., network node (such as gNodeB (gNB)) according to one illustrated aspect of the disclosure;

[0035] FIG. 5 is a diagram of an example block diagram of a wireless station or node (e.g., network node (such as gNodeB (gNB)), user node or UE, relay node, or other node), according to one illustrated aspect of the disclosure;

[0036] FIG. 6 is an example measurement prediction pattern, according to one illustrated aspect of the disclosure;

[0037] FIG. 7 is another example measurement prediction pattern, according to one illustrated aspect of the disclosure;

[0038] FIG. 8 is a diagram of a simulation experiment to evaluate the enhanced filter of the system of FIG. 1, according to one illustrated aspect of the disclosure; and

[0039] FIG. 9 is a graph illustrating a mean absolute error between the ground truth filtered measurements and the predicted measurements after applying the enhanced filter of the system of FIG. 1 , according to one illustrated aspect of the disclosure.DETAILED DESCRIPTION

[0040] In the following description, certain specific details are set forth in order to provide a thorough understanding of the disclosed aspects. However, one skilled in the relevant art will recognize that aspects may be practiced without one or more of these specific details or with other methods, components, materials, etc. In other instances, well-known structures associated with transmitters, receivers, or transceivers have not been shown or described in detail to avoid unnecessarily obscuring descriptions of the aspects.

[0041] Reference throughout this specification to “one aspect” or “an aspect” means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, the appearances of the phrases “in one aspect” or “in an aspect” in various places throughout this specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more aspects.

[0042] Embodiments described in the present disclosure may be implemented in wireless networking apparatuses, such as, without limitation, apparatuses utilizing Worldwide Interoperability for Microwave Access (WiMAX), Global System for Mobile communications (GSM, 2G), GSM EDGE radio access Network (GERAN), General Packet Radio Service (GRPS), Universal Mobile Telecommunication System (UMTS, 3G) based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), Long Term Evolution (LTE), LTE-Advanced, enhanced LTE (eLTE), 5G New Radio (5G NR), 5G Advance, 6G (and beyond) and 802.1 lax (Wi-Fi 6), among other wireless networking systems. The term ‘eLTE’ here denotes the LTE evolution that connects to a 5G core. LTE is also known as evolved UMTS terrestrial radio access (EUTRA) or as evolved UMTS terrestrial radio access network (EUTRAN).

[0043] The present disclosure may use the term “serving network device” to refer to a network node or network device (or a portion thereof) that services a UE. As used herein, the terms “transmit to,” “receive from,” and “cooperate with,” (and their variations) include communications that may or may not involve communications through one or more intermediate devices or nodes. The term “acquire” (and its variations) includes acquiring in the first instance or reacquiring after the first instance. The term “connection” may mean a physical connection or a logical connection.

[0044] The present disclosure uses 5G NR as an example of a wireless network and may use smartphones and / or extended reality headsets as an example of user equipments (UEs). It is intended and shall be understood that such examples are merely illustrative, and the present disclosure is applicable to other wireless networks and user equipment.

[0045] FIG. 1 is a diagram depicting an example of wireless networking between a network system 100 and a user equipment (UE) 150. The network system 100 may include one or more network nodes 120, one or more servers 110, and / or one or more network equipment 130 (e.g., test equipment). The network nodes 120 will be described in more detail below. As used herein, the term “network apparatus” may refer to any component of the network system 100, such as theserver 110, the network node 120, the network equipment 130, any component(s) of the foregoing, and / or any other component(s) of the network system 100. Examples of network apparatuses include, without limitation, apparatuses implementing aspects of 5G NR, among others. The present disclosure describes embodiments related to 5G NR and embodiments that involve aspects defined by 3rd Generation Partnership Project (3 GPP). However, it is contemplated that embodiments relating to other wireless networking technologies are encompassed within the scope of the present disclosure.

[0046] The following description provides further details of examples of network nodes. In a 5G NR network, a gNodeB (also known as gNB) may include, e.g., a node that provides new radio (NR) user plane and control plane protocol terminations towards the UE and that is connected via a NG interface to the 5Gcore (5GC), e.g., according to 3GPP TS 38.300 VI 6.6.0 (2021-06) section 3.2, which is hereby incorporated by reference herein.

[0047] A gNB supports various protocol layers, e.g., Layer 1 (LI) - physical layer, Layer 2 (L2), and Layer 3 (L3).

[0048] The layer 2 (L2) of NR is split into the following sublayers: Media Access Control (MAC), Radio Link Control (RLC), Packet Data Convergence Protocol (PDCP) and Service Data Adaptation Protocol (SDAP), where, e.g.: o The physical layer offers to the MAC sublayer transport channels; o The MAC sublayer offers to the RLC sublayer logical channels; o The RLC sublayer offers to the PDCP sublayer RLC channels; o The PDCP sublayer offers to the SDAP sublayer radio bearers; o The SDAP sublayer offers to 5GC quality of service (QoS) flows; o Control channels include broadcast control channel (BCCH) and physical control channel (PCCH).

[0049] Layer 3 (L3) includes, e.g., radio resource control (RRC), e.g., according to 3GPP TS 38.300 V16.6.0 (2021-06) section 6, which is hereby incorporated by reference herein.

[0050] A gNB central unit (gNB-CU) includes, e.g., a logical node hosting, e.g., radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) protocols of the gNB or RRC and PDCP protocols of the en-gNB, that controls the operation of one or more gNB distributed units (gNB-DUs). The gNB-CU terminates the Plinterface connected with the gNB-DU. A gNB-CU may also be referred to herein as a CU, a central unit, a centralized unit, or a control unit.

[0051] A gNB Distributed Unit (gNB-DU) includes, e.g., a logical node hosting, e.g., radio link control (RLC), media access control (MAC), and physical (PHY) layers of the gNB or en- gNB, and its operation is partly controlled by the gNB-CU. One gNB-DU supports one or multiple cells. One cell is supported by only one gNB-DU. The gNB-DU terminates the Fl interface connected with the gNB-CU. A gNB-DU may also be referred to herein as DU or a distributed unit.

[0052] As used herein, the term “network node” may refer to any of a gNB, a gNB-CU, or a gNB-DU, or any combination of them. A RAN (radio access network) node or network node such as, e.g., a gNB, gNB-CU, or gNB-DU, or parts thereof, may be implemented using, e.g., an apparatus with at least one processor and / or at least one memory with processor-readable instructions (“program”) configured to support and / or provision and / or process CU and / or DU related functionality and / or features, and / or at least one protocol (sub-)layer of a RAN (radio access network), e.g., layer 2 and / or layer 3. Different functional splits between the central and distributed units are possible. An example of such an apparatus and components will be described in connection with FIG. 5 below.

[0053] The gNB-CU and gNB-DU parts may, e.g., be co-located or physically separated. The gNB-DU may even be split further, e.g., into two parts, e.g., one including processing equipment and one including an antenna. A central unit (CU) may also be called baseband unit / radio equipment controller / cloud-RAN / virtual-RAN (BBU / REC / C-RAN / V-RAN), open-RAN (O- RAN), or part thereof. A distributed unit (DU) may also be called remote radio head / remote radio unit / radio equipment / radio unit (RRH / RRU / RE / RU), or part thereof. Hereinafter, in various example embodiments of the present disclosure, a network node, which supports at least one of central unit functionality or a layer 3 protocol of a radio access network, may be, e.g., a gNB-CU. Similarly, a network node, which supports at least one of distributed unit functionality or a layer 2 protocol of the radio access network, may be, e.g., a gNB-DU.

[0054] A gNB-CU may support one or multiple gNB-DUs. A gNB-DU may support one or multiple cells and, thus, could support a serving cell for a user equipment (UE) or support a candidate cell for handover, dual connectivity, and / or carrier aggregation, among other procedures.

[0055] The user equipment (UE) 150 may be or include a wireless or mobile device, an apparatus with a radio interface to interact with a RAN (radio access network), a smartphone, an in-vehicle apparatus, an loT device, or a machine-to-machine (M2M) device, among other types of user equipment. Such UE 150 may include: at least one processor; and at least one memory including program code; where the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform certain operations, such as, e.g., RRC connection to the RAN. An example of components of a UE will be described in connection with FIG. 5. In embodiments, the UE 150 may be configured to generate a message (e.g., including a cell ID) to be transmitted via radio towards a RAN (e.g., to reach and communicate with a serving cell). In embodiments, the UE 150 may generate and transmit and receive RRC messages containing one or more RRC PDUs (packet data units). Persons skilled in the art will understand RRC protocol as well as other procedures a UE may perform.

[0056] With continuing reference to FIG. 1, in the example of a 5G NR network, the network system 100 provides one or more cells, which define a coverage area of the network system 100. As described above, the network system 100 may include a gNB of a 5G NR network or may include any other apparatus configured to control radio communication and manage radio resources within a cell. As used herein, the term “resource” may refer to radio resources, such as a resource block (RB), a physical resource block (PRB), a radio frame, a subframe, a time slot, a sub-band, a frequency region, a sub-carrier, a beam, etc. In embodiments, the network node 120 may be called a base station.

[0057] FIG. 1 provides an example and is merely illustrative of a network system 100 and a UE 150. Persons skilled in the art will understand that the network system 100 includes components not illustrated in FIG. 1 and will understand that other user equipment may be in communication with the network system 100.

[0058] FIG. 2 is a block diagram of example components of the network system 100 of FIG. 1. A 5G NR network may be described as an example of the network system 100, and it is intended that aspects of the following description shall be applicable to other types of network systems, as well. The network system may operate in accordance with the signals and connections shown in FIG. 1 such that the UE 150 is in communication with the network system 100 through the radio access network 225. Additionally, the network system may be divided into user plane components and functions and control plane components and functions, as shown and described herein. Unlessindicated otherwise, the terms “component,” “function,” and “service” may be used interchangeably herein, and they may refer to and be implemented by instructions executed by one or more processors.

[0059] Example functions of the components are described below. The example functions are merely illustrative, and it shall be understood that additional operations and functions may be performed by the components described herein. Additionally, the connections between components may be virtual connections over service-based interfaces such that any component may communicate with any other component. In this manner, any component may act as a service “producer,” for any other component that is a service “consumer,” to provide services for network functions.

[0060] For example, a core network 210 is described in the control plane of the network system. The core network 210 may include an authentication server function (AUSF) 211, an access and mobility function (AMF) 212, and a session management function (SMF) 213. The core network 210 may also include a network slice selection function (NSSF) 214, a network exposure function (NEF) 215, a network repository function (NRF) 216, and a unified data management function (UDM) 217, which may include a uniform data repository (UDR) 224.

[0061] Additional components and functions of the core network 210 may include an application function 218, policy control function (PCF) 219, network data analytics function (NWDAF) 220, analytics data repository function (ADRF) 221, management data analytics function (MDAF) 222, and operations and management function (0AM) 223.

[0062] The user plane includes the UE 150, a radio access network (RAN) 225, a user plane function (UPF) 226, and a data network (DN) 227. The RAN 225 may include one or more components described in connection with FIG. 1, such as one or more network nodes. However, the RAN 225 may not be limited to such components. The UPF 226 provides connection for data being transmitted over the RAN 225. The DN 226 identifies services from service providers, Internet access, and third-party services, for example.

[0063] The AMF 212 processes connection and mobility tasks. The AUSF 211 receives authentication requests from the AMF 212 and interacts with UDM 217 to authenticate and validate network responses to determine successful authentication. The SMF 213 conducts packet data unit (PDU) session management and manages session context with the UPF 226.

[0064] The NSSF 214 may select a network slicing instance (NSI) and determine the allowed network slice selection assistance information (NSSAI). This selection and determination are utilized to set the AMF 212 to provide service to the UE 150. The NEF 215 secures access to network services for third parties to create specialized network services. The NRF 216 acts as a repository to store network functions to allow the functions to register with and discover each other.

[0065] The UDM 217 generates authentication vectors for use by the AUSF 211 and ADM 212 and provides user identification handling. The UDM 217 may be connected to the UDR 224 which stores data associated with authentication, applications, or the like. The AF 218 provides application services to a user (e.g., streaming services, etc.). The PCF 219 provides policy control functionality. For example, the PCF 219 may assist in network slicing and mobility management, as well as provide quality of service (QoS) and charging functionality.

[0066] The NWDAF 220 collects data (e.g., from the UE 150 and the network system) to perform network analytics and provide insight to functions that utilize the analytics in the providing of services. The ADRF 221 allows the storage, retrieval, and removal of data and analytics by consumers. The MDAF 222 provides additional data analytics services for network functions. The 0AM 223 provides provisioning and management processing functions to manage elements in or connected to the network (e.g., UE 150, network nodes, etc.).

[0067] FIG. 2 is merely an example of components of a network system, and variations are contemplated to be within the scope of the present disclosure. In embodiments, the network system may include other components not illustrated in FIG. 2. In embodiments, the network system may not include every component illustrated in FIG. 2. In embodiments, the components and connections may be implemented with different connections than those illustrated in FIG. 2. Such and other embodiments are contemplated to be within the scope of the present disclosure.

[0068] FIG. 3 is a diagram of an example embodiment of signals and operations among a UE, a serving cell and a target cell according to one illustrated aspect of the disclosure. In various embodiments, FIG. 3 shows an example method of deactivating, by a user equipment (UE), at least one candidate transmission configuration indicator (TCI) state associated with at least one reference signal (RS) according to one illustrated aspect of the disclosure. In various embodiments, the components depicted in FIG. 3 may correspond to similar components described above inFIGS. 1 and 2. It will be understood that a described signal may have associated operations, and a described operation may have associated signals.

[0069] When a UE is capable of performing radio resource management (RRM) measurement prediction, the UE uses past measurements to predict future measurements, whether for the purpose of improving handover performance or measurement reduction. The UE can use, for example, past LI RSRP measurements to predict future LI RSRP measurements (and then calculate L3 measurements) or it can use past L3 RSRP measurements to predict future L3 RSRP measurements. In the case of L3 to L3 measurement prediction, either the filtered or unfiltered measurements can be predicted. In both cases, the following “legacy” L3 filter is used:

[0070] Fn= (1 — n)Fn-i + aMn

[0071] Problems may arise when either the previous filtered measurement Fn_i or the current unfiltered measurement Mnis coming from a machine learning model’s prediction rather than measured by the physical device. Since predictions are always subject to a certain amount of error, that error can “propagate” or “leak” into future filtered measurements, because future filtered measurements are derived from past measurements according to the above formula. If the error is large enough, the error can significantly corrupt future filtered measurements, and negatively affect the UE’s mobility or handover performance due to possibly inaccurate L3 measurements.

[0072] The present disclosure provides the benefit of providing an enhanced L3 filtering mechanism and associated configuration and signaling schemes to enable capable UEs to mitigate the effect of erroneously predicted measurements when applying measurement prediction and / or measurement reduction methods.

[0073] FIG. 3 is a diagram of an example embodiment of signals and operations among a UE, and a gNB according to one illustrated aspect of the disclosure. In various embodiments, FIG. 3 shows an example method of conditional configuration execution according to one illustrated aspect of the disclosure. In various embodiments, the components depicted in FIG. 3 may correspond to similar components described above in FIGS. 1 and 2. It will be understood that a described signal may have associated operations, and a described operation may have associated signals.

[0074] At operation 300, the UE and gNB exchange (e.g., the UE transmits and the gNB receives) capability information. The capability information (e.g., a measurement reduction feature) may include the capability of measurement predictions, a measurement prediction pattern,and / or storage and / or buffering capability of real and / or predicted measurements. In aspects, the information may further include an indication of a measurement reduction method and an associated configuration, including at least one of: a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LI measurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where a measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain. In aspects, the information may include a rate of measurement reduction and / or measurement prediction.

[0075] At operation 301, the gNB configures the UE (through RRC signaling or MAC control elements) with at least the measurement reduction rate, the radio measurement filtering mode and its parameters, and / or a quantity configuration.

[0076] At operation 302, the UE collects or determines the radio measurements (e.g. , LI and / or L3), whether by measuring them using the physical device or predicting them using a machine learning model, according to the configuration.

[0077] In aspects, the UE may collect or determine the L3 measurements, whether by measuring them using the physical device or predicting them using a machine learning model, according to the configuration.

[0078] At operation 303, the UE applies the filter according to the configured radio measurement filtering mode. The filtering mode may be the legacy radio measurement filter (e.g., L3 legacy filter) using a fixed filter coefficient for all samples or the enhanced radio measurement filter (e.g., enhanced L3 filter) with a different coefficient for the predicted radio measurement(s) than for the real radio measurement(s). The filtering mode may include at least one of: a legacy mode, an enhanced mode, an exponentially weighted moving average mode, or a moving window mode.

[0079] Rather than using the same weight a for every time instance, a different filter weight is used according to whether the previous or current time instance is measured (i.e., coming from the physical device) or predicted (i.e., coming from an ML model). There is a benefit in assigning alower weight to predicted measurements than real measurements (i.e., samples), because there is a higher confidence in the latter than in the former. That way, the errors in predicted samples will be weighted less in the calculation of future filtered measurements. FIG. 6 illustrates an example measurement prediction pattern. The arrows 602, 603 represent the weights which will be multiplied by the previous filtered sample Fn-1and the current unfiltered sample Mnto obtain the current filtered sample Fn. This enhanced filtering mechanism is then enabled by an RRC signaling method to configure capable UEs to use the appropriate filtering scheme.

[0080] At operation 304, the UE reports or transmits to the gNB an RRM measurement prediction report. The RRM measurement prediction report includes the L3 filtering mode used and the filtered measurements using that mode.

[0081] For example, a UE capable of performing a 50% measurement reduction on the L3 RSRP measurements in the time domain. In other words, the UE can predict every other L3 RSRP measurement in the time domain, as illustrated in FIG. 6. When the measurement reduction feature is not activated, the UE may use the legacy L3 filter, where the gNB configures the UE with a fixed value for the filter coefficient, which will be used for all measured samples. When the measurement reduction feature is activated (e.g., by gNB configuration through RRC signaling), the gNB may signal to the UE at least one additional filter coefficient, which can be used to weight the predicted and measured samples differently. Let aPbe the additional filter coefficient to use when the time domain measurement reduction feature is enabled, which will typically have a smaller value than the legacy filter coefficient. Following the example of FIG. 6, this coefficient can be used as follows: for every L3 RSRP sample, there can be two cases: The current unfiltered sample Mnis predicted, while the previous filtered sample Fn-1is measured. In this case, the current filtered sample can be calculated as: Fn= (1 — af>)Fn-1+ aPMn. The current unfiltered sample Mnis measured, while the previous filtered sample Fn-1is predicted. In this case, the current filtered sample may be calculated as: Fn= a^Fn^ + (1 — aP~)Mn. In both cases, the net effect of using the enhanced filtering scheme is to assign a lower weight to the predicted sample compared to the measured sample when aP< 0.5.

[0082] The operations of FIG. 3 are merely illustrative, and variations are contemplated to be within the scope of the present disclosure. In embodiments, the operations may include other operations not illustrated in FIG. 3. In embodiments, the operations may be implemented in a different order than that illustrated in FIG. 3. Such and other embodiments are contemplated to bewithin the scope of the present disclosure. Persons of skill in the art will appreciate that, although various example components are described as performing various functions, other components may perform those functions described in FIG. 3.

[0083] FIG. 4 is a diagram of an example embodiment of signals and operations among a UE, and a gNB according to one illustrated aspect of the disclosure. In various embodiments, FIG. 4 shows an example method of conditional configuration execution according to one illustrated aspect of the disclosure. In various embodiments, the components depicted in FIG. 4 may correspond to similar components described above in FIGS. 1 and 2. It will be understood that a described signal may have associated operations, and a described operation may have associated signals.

[0084] Operation 400 includes a function similar to operation 300 of FIG. 3, described above.

[0085] At operation 401, the gNB transmits to the UE an RRC configuration. The RRC configuration may include one or more of: the monitoring conditions on the measured quantity, the measurement reduction rate, the L3 filtering mode and its parameters, or the quantity configuration. The monitoring conditions may include, for example, the prediction error exceeding a certain threshold, the UE invalidates or changing its measurement prediction method or its configuration, the gNB configuring the UE to do the same, the mobility state of the UE changes, and / or the handover performance of the UE changes.

[0086] Operations 402 and 403 include functions similar to operations 302 and 303 of FIG. 3, described above.

[0087] At operation 404, the UE determines that one of the monitoring conditions that were signaled to the UE was satisfied.

[0088] At operation 405, the UE reports or transmits to the gNB the satisfied monitoring condition. This report can include the buffered measurements or buffered predictions.

[0089] At operation 406, the gNB responds to the UE with the corresponding reconfiguration for the filtering mode.

[0090] The operations of FIG. 4 are merely illustrative, and variations are contemplated to be within the scope of the present disclosure. In embodiments, the operations may include other operations not illustrated in FIG. 4. In embodiments, the operations may be implemented in a different order than that illustrated in FIG. 4. Such and other embodiments are contemplated to be within the scope of the present disclosure. Persons of skill in the art will appreciate that, althoughvarious example components are described as performing various functions, other components may perform those functions described in FIG. 4.

[0091] The following describes operations from the perspective of a UE. From such a perspective, a method may include: receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message including information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; and reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

[0092] FIG. 5 is a block diagram of a wireless station or node (e.g., UE, user device, AP, BS, eNB, gNB, RAN node, network node, TRP, or other node) 500, according to one illustrated aspect of the present disclosure. The wireless station 500 may include, for example, one or more (e.g., two as shown in FIG. 5) RF (radio frequency) or wireless transceivers 502A, 502B, where each wireless transceiver includes a transmitter to transmit signals and a receiver to receive signals. The wireless station also includes a processor or control unit / entity (controller) 504 to execute instructions or software and control transmission and receptions of signals, and a memory 506 to store data and / or instructions.

[0093] Processor 504 may also make decisions or determinations, generate frames, packets or messages for transmission, decode received frames or messages for further processing, and other tasks or functions described herein. Processor 504, which may be a baseband processor, for example, may generate messages, packets, frames, or other signals for transmission via wireless transceiver 502 (502A or 502B). Processor 504 may control transmission of signals or messages over a wireless network and may control the reception of signals or messages, etc., via a wireless network (e.g., after being down-converted by wireless transceiver 502, for example). Processor 504 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform the various tasks and functions described above, such as one or more of the tasks or methods described above. Processor 504 may be (or may include), for example, hardware, programmable logic, a programmable processor that executes software or firmware, and / or any combination of these. Using other terminology, processor 504 and transceiver 502 together may be considered as a wireless transmitter / receiver system, forexample.

[0094] In addition, referring to FIG. 5, a controller (or processor) 508 may execute software and instructions, and may provide overall control for the station 500, and may provide control for other systems not shown in FIG. 5, such as controlling input / output devices (e.g., display, keypad), and / or may execute software for one or more applications that may be provided on wireless station 500, such as, for example, an email program, audio / video applications, a word processor, a Voice over IP application, or other application or software.

[0095] In addition, a storage medium may be provided that includes stored instructions, which when executed by a controller or processor may result in the processor 504, or other controller or processor, performing one or more of the functions or tasks described above.

[0096] According to another example embodiment, RF or wireless transceiver(s) 502A / 502B may receive signals or data and / or transmit or send signals or data. Processor 504 (and possibly transceivers 502A / 502B) may control the RF or wireless transceiver 502A or 502B to receive, send, broadcast or transmit signals or data.

[0097] Example embodiments are provided or described for each of the example methods, including: An apparatus (e.g., 500, FIG. 5) including means (e.g., processor 504, RF transceivers 502A and / or 502B, and / or memory 506, in FIG. 5) for carrying out any of the methods; a non- transitory computer-readable storage medium (e.g., memory 506, FIG. 5) comprising instructions stored thereon that, when executed by at least one processor (processor 504, FIG. 5), are configured to cause a computing system (e.g., 500, FIG. 5) to perform any of the example methods; and an apparatus (e.g., 500, FIG. 5) including at least one processor (e.g., processor 504, FIG. 5), and at least one memory (e.g., memory 506, FIG. 5) including computer program code, the at least one memory (506) and the computer program code configured to, with the at least one processor (504), cause the apparatus (e.g., 500) at least to perform any of the example methods.

[0098] FIG. 6 illustrates an example measurement prediction pattern. The unfiltered measurements Mninclude measured samples 601 and predicted samples 600. The arrows 602, 603 represent the weights for predicted samples 600 and the filter weight for measured samples 601, which will be multiplied by the previous filtered sample Fn-1and the current unfiltered sample Mnto obtain the current filtered sample Fn. In this example measurement prediction pattern the measured samples 601 and predicted samples 600 alternate.

[0099] FIG. 7 illustrates another example measurement prediction pattern. In this example measurement prediction pattern the measured samples 601 and predicted samples 600 alternate, two at a time. The example patterns shown in FIGS. 6 and 7 are exemplary only and are meant to be non-limiting, other patterns are contemplated.

[0100] To evaluate the influence of the enhanced filtering scheme on mitigating the prediction errors, an experiment is depicted in FIG. 8. A dataset of time series of a UE’s unfiltered L3 RSRP measurements for a cell is processed from a simulation in the following manner: each unfiltered time series is filtered using the legacy L3 filter with a = 0.5 to obtain the ground truth filtered measurements. The same unfiltered time series is then corrupted by adding random Gaussian noise of mean 0 and standard deviation of 2 to every other sample to simulate the effect of prediction errors. This time series is then filtered using the enhanced L3 filter described above, with several values for aPranging from 0 to 0.5. This filtered time series is then compared to the ground truth time series to calculate the absolute error between each corresponding sample and find the mean absolute error across the entire time series.

[0101] FIG. 9 is a graph showing the mean absolute error obtained for several values of aPafter applying the enhanced L3 filter to the real measurements and the predicted measurements. A value of 0.5 corresponds to using the legacy filter, while smaller values correspond to using the enhanced filter. Using a smaller value decreases the error significantly compared to using the legacy filter, which is due to the enhanced filter preventing the prediction errors from propagating into future filtered measurements by assigning them a smaller weight.

[0102] Further embodiments of the present disclosure include the following examples.

[0103] Example 1.1. A user equipment (UE), comprising: means for receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; means for filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; and means for reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

[0104] Example 1.2. The UE of Example 1.1, wherein the RRC configuration further comprises information on a rate of measurement reduction and / or prediction.

[0105] Example 1.3. The UE of any of Example 1.1 to 1.2, further comprising: means for reporting, prior to the RRC configuration, by the UE to the network apparatus, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction pattern, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

[0106] Example 1.4. The UE of any of Examples 1.1 to 1.3, wherein the filtering is done by a recursive filter.

[0107] Example 1.5. The UE of any of Examples 1.1 to 1.3, wherein the radio measurement prior to filtering is a layer 1 (LI) measurement.

[0108] Example 1.6. The UE of any of Examples 1.1 to 1.3, wherein the radio measurement prior to filtering is an L3 measurement.

[0109] Example 1.7. The UE of Example 1.1, wherein the RRC configuration message further includes information for configuring the UE to collect an L3 measurement based on a physical device or predict an L3 measurement based on a machine learning model.

[0110] Example 1.8. The UE of Example 1.1, wherein the information further includes an indication of a measurement reduction method and an associated configuration, including at least one of: a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LI measurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where the measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain.

[0111] Example 1.18. A network apparatus, comprising:means for transmitting, by the network apparatus to a user equipment (UE), a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement, wherein the predicted L3 radio measurement and real L3 radio measurement are filtered based on the first coefficient and the second coefficient; and means for receiving, by the network apparatus from the UE, the filtered L3 radio measurements.

[0112] Example 1.19. The network apparatus of Example 1.18, wherein the RRC configuration further comprises information on a rate of measurement reduction and / or prediction.

[0113] Example 1.20. The network apparatus of any of Examples 1.18 to 1.19, further comprising: means for receiving a report, prior to the RRC configuration, by a network apparatus from the UE, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction patterns, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

[0114] Example 1.21. The network apparatus of any of Examples 1.18 to 1.20, wherein the filtering is done by a recursive filter.

[0115] Example 1.22. The network apparatus of any of Examples 1.18 to 1.20, wherein the radio measurement prior to filtering is a layer 1 (LI) measurement.

[0116] Example 1.23. The network apparatus of any of Examples 1.18 to 1.20, wherein the radio measurement prior to filtering is an L3 measurement.

[0117] The embodiments and aspects disclosed herein are examples of the present disclosure and may be embodied in various forms. For instance, although certain embodiments herein are described as separate embodiments, each of the embodiments herein may be combined with one or more of the other embodiments herein. Specific structural and functional details disclosed herein are not to be interpreted as limiting, but as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure. Like reference numerals may refer to similar or identical elements throughout the description of the figures.

[0118] The phrases “in an aspect,” “in aspects,” “in various aspects,” “in some aspects,” or “in other aspects” may each refer to one or more of the same or different aspects in accordance with this present disclosure. The phrase “a plurality of’ may refer to two or more.

[0119] In various embodiments, the terms “first message” and “second message,” as well as any subsequent messages may refer to any messages that are transmitted or received in an order and are not necessarily limited to any particular message.

[0120] The phrases “in an embodiment,” “in embodiments,” “in various embodiments,” “in some embodiments,” or “in other embodiments” may each refer to one or more of the same or different embodiments in accordance with the present disclosure. A phrase in the form “A or B” means “(A), (B), or (A and B).” A phrase in the form “at least one of A, B, or C” means “(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C) ”

[0121] Any of the herein described methods, programs, algorithms or codes may be converted to, or expressed in, a programming language or computer program. The terms “programming language” and “computer program,” as used herein, each include any language used to specify instructions to a computer, and include (but is not limited to) the following languages and their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, Python, scripting languages, Visual Basic, metalanguages which themselves specify programs, and all first, second, third, fourth, fifth, or further generation computer languages. Also included are database and other data schemas, and any other meta-languages. No distinction is made between languages which are interpreted, compiled, or use both compiled and interpreted approaches. No distinction is made between compiled and source versions of a program. Thus, reference to a program, where the programming language could exist in more than one state (such as source, compiled, object, or linked) is a reference to any and all such states. Reference to a program may encompass the actual instructions and / or the intent of those instructions.

[0122] While aspects of the present disclosure have been shown in the drawings, it is not intended that the present disclosure be limited thereto, as it is intended that the present disclosure be as broad in scope as the art will allow and that the specification be read likewise. Therefore, the above description should not be construed as limiting, but merely as exemplifications of particular aspects. Those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto.

Claims

WHAT IS CLAIMED IS:

1. A method, comprising: receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message including information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; and reporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

2. The method of claim 1, wherein the RRC configuration message further comprises information on a rate of measurement reduction and / or prediction.

3. The method of any of claims 1 to 2, further comprising: reporting, prior to the RRC configuration message, by the UE to the network apparatus, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction pattern, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

4. The method of any of claims 1 to 3, wherein the filtering is done by a recursive filter.

5. The method of any of claims 1 to 3, wherein the radio measurement prior to filtering is a layer 1 (LI) measurement.

6. The method of any of claims 1 to 3, wherein the radio measurement prior to filtering is an L3 measurement.

7. The method of claim 1 , wherein the RRC configuration message further includes information for configuring the UE to collect an L3 measurement based on a physical device or predict an L3 measurement based on a machine learning model.

8. The method of claim 1, wherein the information further includes an indication of a measurement reduction method and an associated configuration, including at least one of: a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LI measurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where a measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain.

9. A processor-readable medium storing instructions which, when executed by at least one processor of a UE apparatus, cause the UE apparatus at least to perform a method as in any one of claims 1 to 8.

10. A user equipment (UE) apparatus, comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, causes the UE apparatus at least to perform: receiving, by a user equipment (UE) from a network apparatus, a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement; filtering the predicted L3 radio measurement and the real L3 radio measurement based on the first coefficient and the second coefficient; andreporting, by the UE to the network apparatus, the predicted L3 radio measurement and the real L3 radio measurement.

11. The UE apparatus of claim 10, wherein the RRC configuration message further comprises information on a rate of measurement reduction and / or prediction.

12. The UE apparatus of any of claims 10 to 11, wherein the instruction, when executed by the processor cause at least the performance of: reporting, prior to the RRC configuration message, by the UE to the network apparatus, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction pattern, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

13. The UE apparatus of any of claims 10 to 12, wherein the filtering is done by a recursive filter.

14. The UE apparatus of any of claims 10 to 12, wherein the radio measurement prior to filtering is a layer 1 (LI) measurement.

15. The UE apparatus of any of claims 10 to 12, wherein the radio measurement prior to filtering is an L3 measurement.

16. The UE apparatus of claim 10, wherein the RRC configuration message further includes information for configuring the UE to collect an L3 measurement based on a physical device or predict an L3 measurement based on a machine learning model.

17. The UE apparatus of claim 10, wherein the information further includes an indication of a measurement reduction method and an associated configuration, including at least one of: a first associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered LI measurements from past LImeasurements and derive a future L3 measurement from the predicted future filtered and / or unfiltered LI measurements from past LI measurements; a second associated configuration of the measurement reduction method where the UE is configured to predict future filtered and / or unfiltered L3 measurements from past L3 measurements; or a third associated configuration of the measurement reduction method where a measurement reduction rate to be applied by the UE, is applied in a case where the UE supports measurement reduction in a time domain.

18. A method, comprising: transmitting, by a network apparatus to a user equipment (UE), a radio resource control (RRC) configuration message, the RRC configuration message includes information regarding a first coefficient corresponding to a predicted Layer 3 (L3) radio measurement and a second coefficient corresponding to a real L3 radio measurement, wherein the predicted L3 radio measurement and real L3 radio measurement are filtered based on the first coefficient and the second coefficient; and receiving, by the network apparatus from the UE, the filtered L3 radio measurements.

19. The method of claim 18, wherein the RRC configuration message further comprises information on a rate of measurement reduction and / or prediction.

20. The method of any of claims 18 to 19, further comprising: receiving a report, prior to the RRC configuration message, by a network apparatus from the UE, information on a capability of the UE of at least one of: (i) a time domain measurement prediction, (ii) a measurement prediction patterns, or (iii) a storage and / or buffering capability of the real L3 radio measurement and / or the predicted L3 radio measurement.

21. The method of any of claims 18 to 20, wherein the filtering is done by a recursive filter.

22. The method of any of claims 18 to 20, wherein the radio measurement prior to filtering is a layer 1 (LI) measurement.

23. The method of any of claims 18 to 20, wherein the radio measurement prior to filtering is an L3 measurement.

24. A network apparatus, comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, causes the network apparatus at least to perform a method as in any one of claims 18 to 23.

25. A processor-readable medium storing instructions which, when executed by at least one processor of a network apparatus, cause the network apparatus at least to perform a method as in any one of claims 18 to 23.

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