WO2023137711A1

WO2023137711A1 - Methods and apparatuses for artificial intelligence applications

Info

Publication number: WO2023137711A1
Application number: PCT/CN2022/073250
Authority: WO
Inventors: Jianfeng Wang; Bingchao LIU; Haiming Wang
Original assignee: Lenovo (Beijing) Limited
Priority date: 2022-01-21
Filing date: 2022-01-21
Publication date: 2023-07-27

Abstract

Disclosed are methods and apparatuses for artificial intelligence applications in an air interface to enhance the air interface. An embodiment of the subject application provides a user equipment (UE). The UE includes a processor; and a transceiver coupled to the processor, wherein the processor is configured to, via the transceiver: receive a configuration for a set of processing models, wherein the configuration includes an identifier (ID) of each processing model of the set of processing models, and each processing model of the set of processing models can be used to accomplish a wireless network activity; and receive a further configuration indicating association between at least one processing model and at least one report configuration, and each processing model of the at least one processing model can be used to accomplish a wireless network activity.

Description

METHODS AND APPARATUSES FOR ARTIFICIAL INTELLIGENCE APPLICATIONS

TECHNICAL FIELD

The present disclosure generally relates to wireless communication technologies, and especially relates to methods and apparatuses for artificial intelligence (AI) applications in an air interface to enhance the air interface.

BACKGROUND

AI/Machine Learning (ML) is used to learn and perform certain tasks via training neural networks with vast amounts of data, which is successfully applied in computer vison (CV) and nature language processing (NLP) areas. As the subset of ML, Deep Learning (DL) utilizes multi-layered neural networks (NN) as the “AI model” to learn solving problems and optimize performance from vast amounts of data. Because of the promising benefits presented in many academic papers and field test results, the AI/ML-based methods can obtain the better performance than the traditional one if being well trained. Thus, 3GPP (3rd Generation Partnership Project) has been studying the relevant functions to support AI/ML, starting from 2016, including several studies and work items in service and system aspects working group 1 (SA1) , SA2, SA5, RAN3 (Radio Access Network Working Group 3) , and etc. Now, a study to consider introducing AI/ML into physical layer (i.e., RAN1) has been started in 3GPP New Radio (NR) , including the motivations, potential use-cases, methodologies, and framework.

SUMMARY

Various embodiments and methods of the present disclosure provide solutions related to AI applications in an air interface to enhance an air interface.

According to some embodiment of the present disclosure, an exemplary user equipment (UE) is provided. The UE includes: a processor and a transceiver coupled to the processor, wherein the processor is configured to, via the transceiver: receive a first configuration for a set of processing models, wherein the first configuration includes an identifier (ID) of each processing model of the set of processing models, and each processing model of the set of processing models can be used to accomplish a wireless network activity; and receive a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.

In some embodiments, the at least one processing model belongs to the set of processing models.

In some embodiments, the first configuration is received via radio resource control (RRC) signaling.

In some embodiments, the second configuration is received via at least one of a medium access control (MAC) control element (CE) , radio resource configuration (RRC) signaling, or downlink control information (DCI) .

In some embodiments, the at least one report configuration includes at least one of: at least one channel state information (CSI) report configuration; or at least one positioning measurement and report configuration (referred also as positioning report configuration) .

In some embodiments, the second configuration includes association between a subset of processing models within the set of processing models and one or more report configurations.

In some embodiments, the second configuration includes: an ID of each processing model of the at least one processing model; and an ID of the at least one report configuration.

In some embodiments, the ID of each processing model of the set of processing models is a global ID within a global entity.

In some embodiments, each processing model of the set of processing models is associated with: at least one CSI report configuration; or at least one positioning measurement and report configuration.

In some embodiments, an ID of each processing model of the set of processing models is a local ID numbered according to the at least one report configuration.

In some embodiments, all processing models of the set of processing models are associated with at least one CSI report configuration. In some embodiments, the at least one CSI report configuration is for CSI acquisition or for beam management.

In some embodiments, all processing models of the set of processing models are associated with at least one positioning measurement and report configuration.

In some embodiments, the processor of the UE is further configured to: perform inference with the at least one processing model according to the at least one report configuration after receiving the second configuration; and report, via the transceiver, inference results related to the at least one processing model.

In some embodiments, the at least one processing model are selected by a base station (BS) based on at least one of a measurement requirement or a scenario.

According to some embodiment of the present disclosure, an exemplary BS is provided. The BS includes a processor and a transceiver coupled to the processor, wherein the processor is configured to, via the transceiver: transmit a first configuration for a set of processing models, wherein the first configuration includes an ID of each processing model of the set of processing models, and each processing model of the set of processing models can be used to accomplish a wireless network activity; and transmit a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.

In some embodiments, the first configuration is received via RRC signaling.

In some embodiments, the second configuration is transmitted via at least one of an MAC CE, RRC signaling, or DCI.

In some embodiments, the at least one report configuration includes at least one of: at least one CSI report configuration; or at least one positioning measurement and report configuration.

In some embodiments, the second configuration includes at least one of: an ID of each processing model of the at least one processing model; and an ID of the at least one report configuration.

In some embodiments, the ID of each processing model of the set of processing models is a local ID numbered according to the at least one report configuration.

In some embodiments, all processing models of the set of processing models are associated with at least one CSI report configuration.

In some embodiments, the at least one CSI report configuration is for CSI acquisition or for beam management.

In some embodiments, the processor of the BS is further configured to, via the transceiver, receive inference results related to the at least one processing model from a UE.

In some embodiments, the processor of the BS is further configured to select the at least one processing model based on at least one of a measurement requirement or a scenario.

According to some embodiments of the present disclosure, an exemplary method performed by a UE is provided. The method includes: receiving a first configuration for a set of processing models, wherein the first configuration includes an ID of each processing model of the set of processing models, and each processing model of the set of processing models can be used to accomplish a wireless network activity; and receiving a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.

In some embodiments, the first configuration is received via RRC signaling.

In some embodiments, the second configuration is received via at least one of an MAC CE, RRC signaling, or DCI.

In some embodiments, the method further includes: performing inference with the at least one processing model according to the at least one report configuration after receiving the second configuration; and reporting inference results related to the at least one processing model.

In some embodiments, the at least one processing model are selected by a BS based on at least one of a measurement requirement or a scenario.

According to some embodiments of the present disclosure, an exemplary method performed by a BS is provided. The method includes: transmitting a first configuration for a set of processing models, wherein the first configuration includes an ID of each processing model of the set of processing models, and each processing model of the set of processing models can be used to accomplish a wireless network activity; and transmitting a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.

In some embodiments, the first configuration is received via RRC signaling.

In some embodiments, the method further includes receiving inference results related to the at least one processing model from a UE.

In some embodiments, the method further includes selecting the at least one processing model based on at least one of a measurement requirement or a scenario.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which advantages and features of the present disclosure can be obtained, a description of the present disclosure is rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. These drawings depict only exemplary embodiments of the present disclosure and are not therefore intended to limit the scope of the present disclosure.

Figure 1A illustrates a schematic diagram of a wireless communication system according to some embodiments of the present disclosure;

Figure 1 illustrates an exemplary flowchart of a method performed by a UE according to some embodiments of the present disclosure;

Figure 2 illustrates an exemplary signaling flowchart according to some embodiments of the present disclosure;

Figure 3 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure;

Figure 4 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure;

Figure 5 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure;

Figure 6 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure;

Figure 7 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure;

Figure 8 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure;

Figure 9 illustrates an exemplary flowchart of a method performed by a UE according to some embodiments of the present disclosure;

Figure 10 illustrates an exemplary signaling flowchart according to some embodiments of the present disclosure;

Figure 11 illustrates an exemplary flowchart of a method performed by a BS according to some embodiments of the present disclosure;

Figure 12 illustrates an exemplary flowchart of a method performed by a BS according to some embodiments of the present disclosure; and

Figure 13 illustrates a simplified block diagram of an exemplary apparatus according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The detailed description of the appended drawings is intended as a description of some embodiments of the present disclosure and is not intended to represent the only form in which the present disclosure may be practiced. It should be understood that the same or equivalent functions may be accomplished by different embodiments that are intended to be encompassed within the spirit and scope of the present disclosure.

While operations are depicted in the drawings in a particular order, persons skilled in the art will readily recognize that such operations need not be performed in the particular order shown or in sequential order, or that among all illustrated operations be performed, to achieve desirable results, sometimes one or more operations can be skipped. Further, the drawings can schematically depict one or more example processes in the form of a flow diagram. However, other operations that are not depicted can be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the illustrated operations. In certain circumstances, multitasking and parallel processing can be advantageous.

Reference will now be made in detail to some embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. To facilitate understanding, embodiments are provided under specific network architecture and new service scenarios, such as 3GPP 5G NR, 3GPP long-term evolution (LTE) , and so on. It is contemplated that along with the developments of network architectures and new service scenarios, all embodiments in the present disclosure are also applicable to similar technical problems; and moreover, the terminologies recited in the present disclosure may change, which should not affect the principle of the present disclosure.

Figure 1A illustrates a schematic diagram of a wireless communication system according to some embodiments of the present disclosure.

As shown in Figure 1A the wireless communication system 100 includes UE 101 and BS 102. In particular, the wireless communication system 100 includes three UEs 101 and three BSs 102 for illustrative purpose only. Even though a specific number of UEs 101 and BSs 102 are depicted in Figure 1A, one skilled in the art will recognize that any number of UEs 101 and BSs 102 may be included in the wireless communication system 100.

The UEs 101 may include computing devices, such as desktop computers, laptop computers, personal digital assistants (PDAs) , tablet computers, smart televisions (e.g., televisions connected to the Internet) , set-top boxes, game consoles, security systems (including security cameras) , vehicle on-board computers, network devices (e.g., routers, switches, and modems) , or the like. According to an embodiment of the present disclosure, the UEs 101 may include a portable wireless communication device, a smart phone, a cellular telephone, a flip phone, a device having a subscriber identity module, a personal computer, a selective call receiver, or any other device that is capable of sending and receiving communication signals on a wireless network. In some embodiments, the UEs 101 include wearable devices, such as smart watches, fitness bands, optical head-mounted displays, or the like. Moreover, the UEs 101 may be referred to as a subscriber unit, a mobile, a mobile station, a user, a terminal, a mobile terminal, a wireless terminal, a fixed terminal, a subscriber station, a user terminal, or a device, or described using other terminology used in the art. The UEs 101 may communicate directly with the BSs 102 via uplink (UL) communication signals.

The BSs 102 may be distributed over a geographic region. In certain embodiments, each of the BSs 102 may also be referred to as an access point, an access terminal, a base, a macro cell, a Node-B, an enhanced Node B (eNB) , a gNB, a Home Node-B, a relay node, or a device, or described using other terminology used in the art. The BSs 102 are generally part of a radio access network that may include one or more controllers communicably coupled to one or more corresponding BSs 102.

The wireless communication system 100 is compatible with any type of network that is capable of sending and receiving wireless communication signals. For example, the wireless communication system 100 is compatible with a wireless communication network, a cellular telephone network, a Time Division Multiple Access (TDMA) -based network, a Code Division Multiple Access (CDMA) -based network, an Orthogonal Frequency Division Multiple Access (OFDMA) -based network, an LTE network, a 3GPP-based network, a 3GPP 5G network, a satellite communications network, a high altitude platform network, and/or other communications networks.

In one embodiment, the wireless communication system 100 is compatible with the 5G new radio (NR) of the 3GPP protocol, wherein the BSs 102 transmit data using an orthogonal frequency division multiplexing (OFDM) modulation scheme on the downlink and the UEs 101 transmit data on the uplink using Discrete Fourier Transform-Spread-Orthogonal Frequency Division Multiplexing (DFT-S-OFDM) or Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) scheme. More generally, however, the wireless communication system 100 may implement some other open or proprietary communication protocols, for example, WiMAX, among other protocols.

In other embodiments, the BSs 102 may communicate using other communication protocols, such as the IEEE 802.11 family of wireless communication protocols. Further, in some embodiments, the BSs 102 may communicate over licensed spectrums, whereas in other embodiments the BSs 102 may communicate over unlicensed spectrums. The present disclosure is not intended to be limited to the implementation of any particular wireless communication system architecture or protocol. In another embodiment, the BSs 102 may communicate with the UEs 101 using the 3GPP 5G protocols.

Currently, different with the traditional mathematic model-based approach ever used in 3GPP, a problem or an optimization objective is modelled by a mathematic model and solved with well-designed algorithms via mathematic equations, and AI-based approaches do highly rely on the selected models, which are supposed to be well-trained with a large volume of labelled data. Thus, the used model is highly depended on the use cases and deployment scenarios. Therefore, it is necessary to consider supporting such flexible model indication scheme for the air interface enhancement. In general, the concerns are to consider aspects related to, e.g., the specification of the AI Model lifecycle management, and capability indication, configuration and control procedures (training or inference) , and management of data and AI/ML model. Therefore, solutions are needed to provide a signalling design to support the indication to select a target model for any selected use case in different scenarios.

The present disclosure provides various methods and apparatuses for AI applications in an air interface to enhance the air interface. In some embodiments of the present disclosure, a processing model refers to an AI model which learns solving problems and optimizes performance from vast amounts of data and thus can accomplish a wireless network activity. As a processing model is highly depended on the use cases and deployment scenarios, in some embodiments of the present disclosure, a set of processing models may be provided to a UE, the UE may select suitable processing model (s) for a specific object. In other words, some embodiments of the present disclosure relate to flexible processing model indication scheme for air interface enhancement.

More details will be illustrated in the following text in combination with the appended drawings. Persons skilled in the art should well know that the wording "a/the first, " "a/the second" and "a/the third" etc. are only used for clear description, and should not be deemed as any substantial limitation, e.g., sequence limitation.

Figure 1 illustrates an exemplary flowchart of method 100A performed by a UE according to some embodiments of the present disclosure. In some embodiments, a UE in Figure 1 may function as UE 101 in Figure 1A. A UE shown in method 100A or in other methods described as below may not be special UEs. For example, the UE may be a generic device or an apparatus, or a part of a device or an apparatus that uses the technical solution of the present application.

Figure 2 illustrates an exemplary signaling flow chart 200 according to some embodiments of the present disclosure. It would be appreciated that for simplification and concise, Figure 2 only illustrates necessary signaling according to some embodiments of the present disclosure.

Referring to Figure 1 in combination with Figure 2, in operation 110 as shown in Figure 1, a UE receive a configuration for a set of processing models. For example, the UE receives the configuration from a BS (e.g., which may function as BS 102 in Figure 1A) as shown in operation 210 in Figure 2. In some embodiments, the configuration is received via RRC signaling. The configuration received in operation 110 may include an ID of each processing model of the set of processing models. Each processing model of the set of processing models, e.g., an AI model, can be used to accomplish a wireless network activity.

In some embodiments, the set of processing models includes only one processing model. In some embodiments, a processing model within the set of processing models may be a CSI process model or a positioning processing model, and may belong to other kinds of processing models, as long as it does not violate the spirit of the present disclosure.

In some embodiments of the present disclosure, the ID of each processing model of the set of processing models is a global ID within a global entity (e.g., a global unique number) . For example, each processing model of the set of processing models may be configured by using RRC signaling, e.g., in a form of PHY-AI-Config as shown in Figure 3, Figure 4, or Figure 5 as described below.

In some embodiments of the present disclosure, the ID of each processing model of the set of processing models is a local ID (e.g., a unique number for different modules) numbered according to the at least one report configuration associated with the processing model. In other words, all processing models of the set of processing models are associated with the same kind of report configurations. In an embodiment, all processing models are associated with CSI report configurations. In a further embodiment, all processing models of the set of processing models are associated with positioning measurement and report configurations.

According to some embodiments of the present disclosure, a UE in the embodiments of Figure 1 and Figure 2 may receive multiple configurations for the set of processing models. The received configuration (s) may be used for different functions. For example, some of the received configuration (s) may initialize the set of processing models stored in the UE. Some of the received configuration (s) may:

(1) add at least one processing model in the UE;

(2) remove at least one processing model in the UE; and

(3) update at least one processing model stored in the UE.

According to some embodiments, the aforementioned functions of the received configuration (s) may be performed, e.g., via phy-AI-ModelToAddModList and/or phy-AI-ModelToReleaseModList in PHY-AI-Config 300 as shown in Figure 3, via phy-AI-Model-CSIToAddModList and/or phy-AI-Model-CSIToReleaseModList in PHY-AI-Config 400 as shown in Figure 4, or via phy-AI-Model-PositioningToAddModList and/or phy-AI-Model-PositioningToReleaseModList in PHY-AI-Config 500 as shown in Figure 5.

In operation 120 as shown in Figure 1, the UE receive a configuration (e.g., from the BS in operation 220 as shown in Figure 2) indicating association between at least one processing model and at least one report configuration. Each processing model of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity.

In some embodiments, the configuration indicating association in operation 120 may be received via an MAC CE, RRC signaling, and/or a DCI. The configuration may include: an ID of each processing model of the at least one processing model, and an ID of the at least one report configuration.

In some embodiments, a processing model within the at least one processing model is associated with at least one report configuration. All report configurations associated with the at least one processing model may be the same kind of report configurations. For example, all report configurations associated with a CSI processing model are CSI report configurations. All report configurations associated with a positioning processing model are positioning measurement and report configurations.

In some embodiments, the at least one processing model involved in the configuration indicating association in operation 120 belongs to the set of processing models in operation 110. In some further embodiments, the at least one processing model involved in the configuration indicating association in operation 120 does not belong to the set of processing models in operation 110, but belongs to another set of processing models.

In some embodiments of the present disclosure, the configuration received in operation 120 indicates association between “asubset of processing models of the set of processing models in operation 110” and “one or more report configurations” .

According to some embodiments, the configuration indicating association in operation 120 may indicate that a processing model is associated with at least one CSI report configuration. In some embodiments, a CSI report configuration may be for CSI acquisition or for beam management.

According to some other embodiments, the configuration indicating association received in operation 120 may indicate that a processing model is associated with at least one positioning measurement and report configuration. It would be appreciated that according to the present disclosure, a processing mode may be associated with other kinds of report configurations.

According to some embodiments of the present disclosure, a UE in the embodiments of Figure 1 and Figure 2 may receive multiple configurations indicating association between at least one processing model and at least one report configuration. The received configuration (s) indicating association may be used for different functions. For example, some of the received configuration (s) may initialize and/or update association between a set of processing models (or an updated set of processing models) and at least one report configuration. In some embodiments, the received configuration indicating association is included in a measurement request. In such cases, the BS transmits the configuration indicating association to the UE, to request the UE to infer with some selected processing model, e.g., for measuring a target value (e.g., CSI) , and to further report the result to the BS. Upon receiving such measurement request, the UE may perform inference with certain processing models and report the inference result (s) to the BS. A specific example is described in Figures 9 and 10 or Figure 12 as below.

Details described in all other embodiments of the present application (for example, details regarding AI applications) are applicable for the embodiments of Figures 1 and 2. Moreover, details described in the embodiments of Figures 1 and 2 are applicable for all embodiments of Figures 3-13.

Figure 3 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure. In particular, Figure 3 illustrates exemplary PHY-AI-Config 300. In this example, an ID of each processing model of the set of processing models is a global ID within a global entity (e.g., a global unique number) . All the processing models are maintained within a global entity. At least one advantage of this example is that all kinds of the processing models may be maintained and managed uniformly.

As shown in Figure 3, PHY-AI-Config 300 contains parameters related to two processing models, i.e., two sets of parameters “phy-AI-Model : : = SEQUENCE {……} ” . However, it is appreciated that PHY-AI-Config 300 may contain parameters related to more or less processing models. For example, PHY-AI-Config 300 may contain parameters related to each of the set of processing models mentioned in operation 110 in Figure 1. Each processing model contained in PHY-AI-Config 300 may be associated with at least one CSI report configuration, or may be associated with at least one positioning measurement and report configuration, or even other kinds of report configurations.

According to some embodiments, PHY-AI-Config 300 includes the following elements:

(1) phy-AI-ModelToAddModList: the AI model’s ID to be added into the AI model list;

(2) phy-AI-ModelToReleaseModList: the AI model’s ID to be released from the AI model list;

(3) phy-AI-Model: the description on the AI model;

(4) phy-AI-ModelId: AI model ID;

(5) phy-AI-Model-Topo: the topology of the AI model;

(6) phy-AI-Model-Weights: the weights of the AI model; and

(7) maxNrofPHY-AI-Models: the maximum number of PHY-AI-Models configured for a UE according to the UE’s capability.

Figure 4 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure. In particular, Figure 4 illustrates exemplary PHY-AI-Config 400.

Similar to PHY-AI-Config 300 in Figure 3, PHY-AI-Config 400 also contains parameters related to two CSI processing models. However, it is appreciated that PHY-AI-Config 400 may contain parameters related to more or less CSI processing models. Each CSI processing model contained in PHY-AI-Config 400 may be associated with at least one CSI report configuration.

According to some embodiments, PHY-AI-Config 400 includes the following elements:

(1) phy-AI-Model-CSIToAddModList: the AI model’s ID for CSI module to be added into the AI model list;

(2) phy-AI-Model-CSIToReleaseModList: the AI model’s ID for CSI module to be released into the AI model list;

(3) phy-AI-Model-CSI: the description on the AI model for CSI;

(4) phy-AI-Model-CSIId: the AI model’s ID of CSI module;

(5) phy-AI-Model-CSITopo: the topology of the AI model for CSI;

(6) phy-AI-Model--CSIWeights: the weights of the AI model for CSI; and

(7) maxNrofPHY-AI-Model-CSIs: the maximum number of PHY-AI-Model-CSIs configured for a UE according to the UE’s capability, and this type of AI models can only be used for CSI acquisition or beam management.

Figure 5 illustrates an exemplary signaling containing a configuration for a processing model according to some embodiments of the present disclosure. In particular, Figure 5 illustrates exemplary PHY-AI-Config 500.

Similar to PHY-AI-Config 300 and PHY-AI-Config 400 in Figures 3 and 4, PHY-AI-Config 500 also contains parameters related to two CSI processing models. However, it is appreciated that PHY-AI-Config 500 may contain parameters related to more or less CSI processing models. Each positioning processing model contained in PHY-AI-Config 500 may be associated with at least one positioning measurement and report configuration.

According to some embodiments, PHY-AI-Config 500 includes the following elements:

(1) phy-AI-Model-PositioningToAddModList: the AI model’s ID for Positioning module to be added into the AI model list;

(2) phy-AI-Model-PositioningToReleaseModList: the AI model’s ID for Positioning module to be released into the AI model list;

(3) phy-AI-Model-Positioning: the description on the AI model for Positioning;

(4) phy-AI-Model-PositioningId: the AI model’s ID of Positioning module;

(5) phy-AI-Model-PositioningTopo: the topology of the AI model for Positioning;

(6) phy-AI-Model-PositioningWeights: the weights of the AI model for Positioning; and

(7) maxNrofPHY-AI-Model-Positionings: the maximum number of PHY-AI-Model-Positionings configured for a UE according to UE capability, and this type of AI models can only be used for Positioning.

Figure 6 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure. In particular, Figure 6 illustrates a configuration 600 received via an MAC CE. In this example, R means reserved bit (s) . BWP ID is an ID of a bandwidth part (BWP) .

As shown in Figure 6, configuration 600 lists a processing model having an ID of ID ₀ (which may be referred as processing model ID ₀) and a processing model having an ID of ID ₁ (which may be referred as processing model ID ₁) . Processing model ID ₀ is associated with N report configurations: a report configuration having an ID of ID _0, 1 (which may be referred as report configuration ID _0, 1) , ..., a report configuration having an ID of ID _0, N (which may be referred as report configuration ID _0, N) . Processing model ID ₁ is associated with M report configurations: a report configuration having an ID of ID _1, 1 (which may be referred as report configuration ID _1, 1) , ..., a report configuration having an ID of ID _1, M (which may be referred as report configuration ID _1, M) . Herein, M and N are positive integers, i.e., N≥1 and M ≥1.

Configuration 600 illustrates association between two processing models and (N+M) report configurations. As shown in Figure 6, a report configuration ID may be used to identify one CSI processing model or one positioning processing model. The CSI processing model may correspond to a CSI report configuration (e.g., CSI-ReportConfig) for beam management or for CSI acquisition. However, it is appreciated that configuration 600 may illustrate association between more or less processing models and more or less report configurations.

In some embodiments of Figure 6, two processing models involved in configuration 600 may belong to different kinds of processing models. For example, a processing model involved in configuration 600 may be a CSI processing model or a positioning processing model. If a processing model is a CSI processing model, it may be associated with at least one CSI report configuration. Each of the at least one CSI report configuration may be for CSI acquisition or for beam management. If a processing model is a positioning processing model, it may be associated with at least one positioning measurement and report model.

For example, in some embodiments of Figure 6, if processing model ID ₀ is an CSI processing model, report configuration ID _0, 1, ..., report configuration ID _0, N are all CSI report configurations, and each CSI report configuration may be for CSI acquisition or for beam management. In some other embodiments of Figure 6, if processing model ID ₁ is an positioning processing model, then report configuration ID _1, 1, ..., report configuration ID _1, M are all positioning report configurations.

In some other embodiments of Figure 6, all processing models involved in a configuration 600 belong to the same kind of processing models. Specific examples are described in Figures 7 and 8.

Figure 7 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure. In particular, Figure 7 illustrates a configuration 700 received via an MAC CE. Each involved processing model is CSI processing model, and is associated with at least one CSI report configuration. Each CSI report configuration may be for CSI acquisition or for beam management. In this example, R means reserved bit (s) . BWP ID is an ID of a bandwidth part (BWP) .

As shown in Figure 7, configuration 700 lists a CSI processing model having an ID of ID ₀ (which may be referred as CSI processing model ID ₀) and a CSI processing model having an ID of ID ₁ (which may be referred as CSI processing model ID ₁) . CSI processing model ID ₀ is associated with N CSI report configurations: a CSI report configuration having an ID of ID _0, 1 (which may be referred as CSI report configuration ID _0, 1) , ..., a CSI report configuration having an ID of ID _0, N (which may be referred as CSI report configuration ID _0, N) . CSI processing model ID ₁ is associated with M CSI report configurations: a CSI report configuration having an ID of ID _1, 1 (which may be referred as CSI report configuration ID _1, 1) , ..., a CSI report configuration having an ID of ID _1, M (which may be referred as CSI report configuration ID _1, M) . Herein, M and N are positive integers, i.e., N≥1 and M≥1.

Configuration 700 illustrated in Figure 7 illustrates association between two CSI processing models and (N+M) CSI report configurations. As shown in Figure 7, a CSI Report configuration ID is used to identify one CSI processing model. The CSI processing model may correspond to a CSI report configuration (e.g., CSI-ReportConfig) for beam management or for CSI acquisition. However, it is appreciated that configuration 700 may illustrate association between “more or less CSI processing models” and “more or less CSI report configurations” .

Figure 8 illustrates an exemplary signaling containing a configuration indicating association between a processing model and a report configuration according to some embodiments of the present disclosure. In particular, Figure 8 illustrates a configuration 800 received via an MAC CE. Each involved processing model is positioning processing model, and is associated with at least one positioning measurement and report configuration. In this example, R means reserved bit (s) . BWP ID is an ID of a bandwidth part (BWP) .

As shown in Figure 8, configuration 800 lists a positioning processing model having an ID of ID ₀ (which may be referred as positioning processing model ID ₀) and a positioning processing model having an ID of ID ₁ (which may be referred as positioning processing model ID ₁) . Positioning processing model ID ₀ is associated with N positioning measurement and report configurations: a positioning measurement and report configuration having an ID of ID _0, 1 (which may be referred as positioning measurement and report configuration ID _0, 1) , ..., a positioning measurement and report configuration having an ID of ID _0, N (which may be referred as positioning measurement and report configuration ID _0, N) . Positioning processing model ID ₁ is associated with M positioning measurement and report configurations: a positioning measurement and report configuration having an ID of ID _1, 1 (which may be referred as positioning measurement and report configuration ID _1, 1) , ..., a positioning measurement and report configuration having an ID of ID _1, M (which may be referred as positioning measurement and report configuration ID _1, M) . Herein, M and N are positive integers, i.e., N≥1 and M≥1.

Configuration 800 illustrated in Figure 8 illustrates association between two positioning processing models and (N+M) positioning measurement and report configurations. As shown in Figure 8, a positioning measurement and report configuration ID may be used to identify one positioning processing model. However, it is appreciated that in some embodiments, configuration 800 may illustrate association between “more or less positioning processing models” and “more or less positioning measurement and report configurations” .

Details described in all other embodiments of the present application (for example, details regarding AI applications) are applicable for the embodiments of Figures 6-8. Moreover, details described in the embodiments of Figures 6-8 are applicable for all embodiments of Figures 1-5 and 9-13.

Figure 9 illustrates an exemplary flowchart of a method 900 performed by a UE according to some embodiments of the present disclosure. The exemplary method 900 is based on exemplary method 100A as shown in Figure 1, and further includes an operation of performing inference with certain selected processing models according to the received configuration.

Figure 10 illustrates an exemplary signaling flowchart 1000 according to some embodiments of the present disclosure. It would be appreciated that for simplification and concise, Figure 10 only illustrates necessary signaling according to the spirit of the present disclosure.

Referring to Figure 9 in combination with Figure 10, in operation 910 as shown in Figure 9, a UE (which may function as UE 101 in Figure 1A) receives at least one configuration for a set of processing models (e.g., from a BS as shown in operation 1010 in Figure 10, which may function as BS 102 in Figure 1A) . The received at least one configuration may be used to initialize, add, remove, and/or update at least one processing model in the UE. Each of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity.

Furthermore, in operation 1011 as shown in Figure 10, the BS determines a scenario and/or a measurement requirement so as to select at least one processing model. Herein, the selected processing model (s) is already initialized and stored in the UE. The scenarios here may refer to how the processing models are trained via the collected data, for example, if a processing model can be trained either for the cell-centric or the cell-edge UEs.

The BS may include the selected processing model (s) in a configuration (e.g., in operation 1020 as shown in Figure 10) and further indicate association between the at least one selected processing model and at least one report configuration in the configuration (e.g., in operation 1020 as shown in Figure 10) . According to some embodiments, the selected processing model (s) is already listed in the at least one configuration in operation 910 as shown in Figure 9) previously transmitted from the BS to the UE.

In operation 920 as shown in Figure 9, the UE receives a measurement request including the configuration which indicates association between at least one processing model and at least one report configuration (e.g., in operation 1020 as shown in Figure 10) .

In operation 930 as shown in Figure 9, the UE may perform inference with the at least one selected processing model indicated in the configuration included in the measurement request with the at least one associated report configuration.

Then, the UE acquires the inference result (s) (e.g., in operation 1030 as shown in Figure 10) with the at least one selected processing model (e.g., in operation 1021 as shown in Figure 10) and reports the inference result (s) to the BS in operation 940 as shown in Figure 9 (e.g., in operation 1030 as shown in Figure 10) . The inference result (s) may be, e.g., CSI related value, or position related value, or both, or values of more or less kinds of values.

It would be appreciated that according to some embodiments of the present disclosure, the BS performs operations corresponding to the operations performed by a UE. Specific examples are described in Figures 11 and 12 as below.

Figure 11 illustrates an exemplary flowchart of a method 1100 performed by a BS according to some embodiments of the present disclosure. In particular, exemplary method 1100 is based on the embodiments of Figure 2 and corresponds to exemplary method 100A in Figure 1 performed by a UE. In some embodiments, a BS described in method 1100 or in other methods described as below may not be a special BS. For example, the BS may be a generic device or an apparatus, or a part of a device or an apparatus that uses the technical solution of the present application.

Referring to Figure 11 in combination with Figure 2, in operation 1110, a BS (e.g., which may function as BS 102 in Figure 1A) transmits a configuration for a set of processing models (e.g., in operation 210 as shown in Figure 2) . In some embodiments, the configuration is transmitted via RRC signaling. The configuration may include an ID of each processing model of the set of processing models. Each processing model of the set of processing models, e.g., an AI model, can be used to accomplish a wireless network activity. In some embodiments, the set of processing models includes only one processing model.

In operation 1120 in Figure 11, the BS transmits a configuration indicating association between at least one processing model and at least one report configuration (e.g., in operation 220 as shown in Figure 2) . Each of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity. In some embodiments, the configuration indicating association may be transmitted via an MAC CE, RRC signaling, and/or a DCI. In some embodiments, the configuration in operation 1120 includes: an ID of each processing model of the at least one processing model, and an ID of the at least one report configuration.

According to some embodiments, a processing model within at least one processing model may be associated with at least one CSI report configuration, or may be associated with at least one positioning measurement and report configuration, or may be associated with other kinds of report configurations. In some embodiments, the at least one CSI report configuration is for CSI acquisition or for beam management.

Figure 12 illustrates an exemplary flowchart of a method 1200 performed by a BS according to some embodiments of the present disclosure. The method 1200 corresponds to exemplary method 900 as shown in Figure 9 performed by a UE, and is based on exemplary signaling flowchart 1000 in Figure 10.

Referring to Figure 12 in combination with Figure 10, in operation 1210, a BS (e.g., which may function as BS 102 in Figure 1A) transmits at least one configuration for a set of processing models (e.g., in operation 1010 as shown in Figure 10) . The at least one configuration can be used to initialize and/or update at least one processing model in a UE. Each processing model of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity.

In operation 1220 in Figure 12, the BS determines a scenario and/or a measurement requirement so as to select at least one processing model. Herein, the at least one selected processing model is already initialized and stored in the UE. The scenarios here refer to how the processing models are trained via the collected data, for example, if a processing model can be trained either for the cell-centric or the cell-edge UEs.

It would be appreciated that there is no necessary sequence between operation 1210 and operation 1220 in Figure 12. In some embodiments, the BS may determines a scenario and/or a measurement requirement first, and then transmit at least one the at least one configuration for a set of processing models in operation 1210.

In operation 1230 in Figure 12, the BS includes the at least one selected processing model in a configuration (e.g., in operation 1020 as shown in Figure 10) , and further indicates association between the at least one selected processing model and at least one report configurations in the configuration (e.g., in operation 1020 as shown in Figure 10) . According to the present disclosure, the at least one selected processing model is already listed in the at least one configuration for a set of processing models (e.g., in operation 1010 as shown in Figure 10) previously transmitted from the BS to the UE. For instance, in operation 1230 in Figure 12, the BS transmits a measurement request including a configuration, which indicates association between at least one processing model and at least one report configuration, to the UE.

In operation 1240 in Figure 12, the BS receives inference result (s) (e.g., in operation 1030 as shown in Figure 10) from the UE. The inference result may be, e.g., CSI related value, or position related value, or both, or more or less kinds of values.

Details described in all other embodiments of the present application (for example, details regarding AI applications) are applicable for the embodiments of Figures 9-12. Moreover, details described in the embodiments of Figures 9-12 are applicable for all embodiments of Figures 1-8 and 13.

According to some embodiments of the present disclosure, a method and corresponding apparatuses (aUE and a BS) are provided for supporting flexible processing model selection to enhance an air interface. Some embodiments of the present disclosure provide a configuration (i.e., the configuration received in operation 120 in Figure 1) for indicating the selected AI model for a measurement and report configuration.

According to some embodiments of the present disclosure, a BS determines and configures multiple processing models for different purpose for a UE according to UE capability, a scenario, or actual requirements.

According to some embodiments of the present disclosure, a processing model may be a CSI process model or a positioning processing model, and may belong to other kinds of processing models, as long as it does not violate the spirit of the present disclosure. According to some embodiments, a processing model can be initialized, removed, added, and/or updated by a configuration for a set of processing models via a high level signaling, e.g., RRC signaling.

According to some embodiments of the present disclosure, association between processing models and report configurations can be indicated by a configuration, which indicates association between at least one processing model and at least one report configuration, via an MAC CE, DCI, and/or RRC signaling.

According to some embodiments of the present disclosure, a configuration, which indicates association between at least one processing model and at least one report configuration, may be included in a measurement request for a UE to inference at least one selected processing model.

Figure 13 illustrates a simplified block diagram of an exemplary apparatus 700 according to various embodiments of the present disclosure. In some embodiments, apparatus 1300 may be or include at least a part of a UE or similar device having similar functionality. In some other embodiments, apparatus 1300 may be or include at least a part of a BS or similar device that can use the technology of the present disclosure.

As shown in Figure 13, apparatus 1300 may include at least transceiver 1310 and processor 1320, and transceiver 1310 may be coupled to processor 1320. Furthermore, apparatus 1300 may include non-transitory computer-readable medium 1330 with computer-executable instructions 1340 stored thereon, wherein non-transitory computer-readable medium 1330 may be coupled to processor 1320, and computer-executable instructions 1340 may be configured to be executable by processor 1320. In some embodiments, transceiver 1310, non-transitory computer-readable medium 1330, and processor 1320 may be coupled to each other via one or more local buses.

Although in Figure 13, elements such as transceiver 1310, non-transitory computer-readable medium 1330, and processor 1320 are described in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. In some embodiments of the present disclosure, the transceiver 1310 may be configured for wireless communication. In some embodiments of the present disclosure, transceiver 1310 can be integrated into a transceiver. In certain embodiments of the present disclosure, the apparatus 1300 may further include other components for actual usage.

According to some embodiment of the present disclosure, apparatus 1300 is a UE or at least a part of a UE. Processor 1320 is configured to cause the apparatus 1300 at least to perform, with transceiver 1310, any method described above which is performed by a UE according to the present disclosure.

For instance, in some embodiments, processor 1320 of a UE may be configured to receive, via transceiver 1310, a configuration for a set of processing models, which includes an ID of each processing model of the set of processing models. The configuration for the set of processing models may be received via RRC signaling. Each processing model of the set of processing models, e.g., an AI model, can be used to accomplish a wireless network activity. In some embodiments, each processing model of the set of processing models is associated with: at least one CSI report configuration; or at least one positioning measurement and report configuration.

In some embodiments, an ID of each processing model of the set of processing models is a local ID (e.g., a unique number for different modules) numbered according to the at least one report configuration. In some embodiments, all processing models of the set of processing models are associated with at least one CSI report configuration. In some embodiments, the at least one CSI report configuration is for CSI acquisition or for beam management. In some embodiments, all processing models of the set of processing models are associated with at least one positioning measurement and report configuration.

In some embodiments, processor 1320 of a UE may be configured to receive, via transceiver 1310, a configuration indicating association between at least one processing model and at least one report configuration, which may be received via at least one of an MAC CE, RRC signaling, or DCI.

Each processing model of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity. In some embodiments, the at least one processing model belongs to the set of processing models. In some embodiments, the at least one processing model are selected by a BS based on at least one of a measurement requirement or a scenario.

For example, the configuration indicating association includes association between a subset of processing models within the set of processing models and one or more report configurations. In some embodiments, the configuration indicating association includes: an ID of each processing model of the at least one processing model; and an ID of the at least one report configuration. The ID of each processing model of the set of processing models may be a global ID within a global entity (e.g., a global unique number) . In some embodiments, the at least one report configuration includes at least one of: at least one CSI report configuration; or at least one positioning measurement and report configuration.

In some embodiments, processor 1320 of the UE is further configured to: perform inference with the at least one processing model according to the at least one report configuration after receiving the configuration indicating association; and report, via transceiver 1310, inference result (s) related to the at least one processing model.

According to some embodiment of the present disclosure, apparatus 1300 is a BS or at least a part of a BS that can use the technology of the present disclosure. Processor 1320 is configured to cause the apparatus 1300 at least to perform, with transceiver 1310, any method described above which is performed by a BS according to the present disclosure.

For instance, in some embodiments, processor 1320 of a BS may be configured to transmit, via transceiver 1310, a configuration for a set of processing models, which includes an ID of each processing model of the set of processing models. The configuration may be received via RRC signaling. Each processing model of the set of processing models, e.g., an AI model, can be used to accomplish a wireless network activity.

In some embodiments, an ID of each processing model of the set of processing models is a global ID within a global entity (e.g., a global unique number) . In some embodiments, each processing model of the set of processing models is associated with: at least one CSI report configuration; or at least one positioning measurement and report configuration. In some embodiments, the ID of each processing model of the set of processing models is a local ID (e.g., a unique number for different modules) numbered according to the at least one report configuration. In some embodiments, all processing models of the set of processing models are associated with at least one CSI report configuration. In some embodiments, all processing models of the set of processing models are associated with at least one positioning measurement and report configuration.

In some embodiments, processor 1320 of a BS may be configured to transmit, via transceiver 1310, a configuration indicating association between at least one processing model and at least one report configuration. Each processing model of the at least one processing model, e.g., an AI model, can be used to accomplish a wireless network activity. In some embodiments, the at least one processing model belongs to the set of processing models.

In some embodiments, the configuration indicating association is transmitted via at least one of an MAC CE, RRC signaling, or DCI. In some embodiments, the configuration indicating association includes association between a subset of processing models within the set of processing models and one or more report configurations. In some embodiments, the configuration indicating association includes at least one of: an ID of each processing model of the at least one processing model; and an ID of the at least one report configuration.

In some embodiments, the at least one report configuration includes at least one of: at least one CSI report configuration; or at least one positioning measurement and report configuration. In some embodiments, the at least one CSI report configuration is for CSI acquisition or for beam management.

In some embodiments, processor 1320 of the BS is further configured to, via transceiver 1310, receive inference result (s) related to the at least one processing model from a UE. In some embodiments, processor 1320 of the BS is further configured to select the at least one processing model based on at least one of a measurement requirement or a scenario.

In various example embodiments, processor 1320 may include, but is not limited to, at least one hardware processor, including at least one microprocessor such as a CPU, a portion of at least one hardware processor, and any other suitable dedicated processor such as those developed based on for example Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) . Further, processor 1320 may also include at least one other circuitry or element not shown in Figure 13.

In various example embodiments, non-transitory computer-readable medium 1330 may include at least one storage medium in various forms, such as a volatile memory and/or a non-volatile memory. The volatile memory may include, but is not limited to, for example, an RAM, a cache, and so on. The non-volatile memory may include, but is not limited to, for example, an ROM, a hard disk, a flash memory, and so on. Further, non-transitory computer-readable medium 1330 may include, but is not limited to, an electric, a magnetic, an optical, an electromagnetic, an infrared, or a semiconductor system, apparatus, or device or any combination of the above.

Further, in various example embodiments, exemplary apparatus 1300 may also include at least one other circuitry, element, and interface, for example antenna element, and the like.

In various example embodiments, the circuitries, parts, elements, and interfaces in exemplary apparatus 1300, including processor 1320 and non-transitory computer-readable medium 1330, may be coupled together via any suitable connections including, but not limited to, buses, crossbars, wiring and/or wireless lines, in any suitable ways, for example electrically, magnetically, optically, electromagnetically, and the like.

The methods of the present disclosure can be implemented on a programmed processor. However, controllers, flowcharts, and modules may also be implemented on a general purpose or special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an integrated circuit, a hardware electronic or logic circuit such as a discrete element circuit, a programmable logic device, or the like. In general, any device that has a finite state machine capable of implementing the flowcharts shown in the figures may be used to implement the processing functions of the present disclosure.

While the present disclosure has been described with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. For example, various components of the embodiments may be interchanged, added, or substituted in other embodiments. Also, all of the elements shown in each figure are not necessary for operation of the disclosed embodiments. For example, one skilled in the art of the disclosed embodiments would be capable of making and using the teachings of the present disclosure by simply employing the elements of the independent claims. Accordingly, the embodiments of the present disclosure as set forth herein are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit and scope of the present disclosure.

The terms "includes, " "comprising, " "includes, " "including, " or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "a, " "an, " or the like does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element. Also, the term "another" is defined as at least a second or more. The terms "including, " "having, " and the like, as used herein, are defined as "comprising. "

Claims

A user equipment (UE) , comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to, via the transceiver:

receive a first configuration for a set of processing models, wherein the first configuration includes an identifier (ID) of each processing model of the set of processing models, and the each processing model of the set of processing models can be used to accomplish a wireless network activity; and

receive a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.
The UE of Claim 1, wherein the first configuration is received via radio resource control (RRC) signaling.
The UE of Claim 1, wherein the second configuration is received via at least one of a medium access control (MAC) control element (CE) , radio resource configuration (RRC) signaling, or downlink control information (DCI) .
The UE of Claim 1, wherein the at least one report configuration includes at least one of:

at least one channel state information (CSI) report configuration; or

at least one positioning measurement and report configuration.
The UE of Claim 1, wherein:

the second configuration includes association between a subset of processing models within the set of processing models and one or more report configurations.
The UE of Claim 1 or Claim 5, wherein the second configuration includes:

an ID of the each processing model of the at least one processing model; and

an ID of the at least one report configuration.
The UE of Claim 1 or Claim 6, wherein the ID of the each processing model of the set of processing models is a global ID within a global entity.
The UE of Claim 7, wherein the each processing model of the set of processing models is associated with:

at least one channel state information (CSI) report configuration; or

at least one positioning measurement and report configuration.
The UE of Claim 1 or Claim 6, wherein an ID of the each processing model of the set of processing models is a local ID numbered according to the at least one report configuration.
The UE of Claim 1, wherein the processor of the UE is further configured to:

perform inference with the at least one processing model according to the at least one report configuration after receiving the second configuration; and

report, via the transceiver, inference results related to the at least one processing model.
A base station (BS) , comprising:

a processor; and

a transceiver coupled to the processor,

wherein the processor is configured to, via the transceiver:

transmit a first configuration for a set of processing models, wherein the first configuration includes an identifier (ID) of each processing model of the set of processing models, and the each processing model can be used to accomplish a wireless network activity; and

transmit a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.
The BS of Claim 11, wherein the at least one processing model belongs to the set of processing models.
The BS of Claim 11, wherein the processor of the BS is further configured to, via the transceiver:

receive inference results related to the at least one processing model from a user equipment (UE) .
The BS of Claim 13, wherein the processor of the BS is further configured to:

select the at least one processing model based on at least one of a measurement requirement or a scenario.
A method performed by a user equipment (UE) , comprising:

receiving a first configuration for a set of processing models, wherein the first configuration includes an identifier (ID) of each processing model of the set of processing models, and the each processing model can be used to accomplish a wireless network activity; and

receiving a second configuration indicating association between at least one processing model and at least one report configuration, wherein each processing model of the at least one processing model can be used to accomplish a wireless network activity.