Task-Specific Models for Wireless Networks

Abstract

According to an example embodiment, the method may include: user equipment receiving, from a network node, a request for a subtask-specific model including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; validating the request for the subtask-specific model; user equipment modifying the subtask-specific model based on the trained generic model and the configuration parameters of the subtask or subtask-specific model; user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and user equipment transmitting, to the network node, a subtask-specific model output based on or using the modified subtask-specific model for performing or executing the machine-learnable subtask.

Description

[Technical Field]

[0001] The present description relates to wireless communications. [Background technology]

[0002] A communication system may be a facility that enables communication between two or more nodes or devices, such as fixed or mobile communication devices. The signals may be carried over wired or wireless carriers.

[0003] An example of a cellular communication system is the architecture being standardized by the 3rd Generation Partnership Project (3GPP). Recent developments in this field are often referred to as the Long Term Evolution (LTE) of Universal Mobile Telecommunications System (UMTS) radio access technology. E-UTRA (Evolved UMTS Terrestrial Radio Access) is the air interface of 3GPP's Long Term Evolution (LTE) upgrade path for mobile networks. In LTE, base stations or access points (APs) are called enhanced Node APs (eNBs) and provide wireless access within a coverage area or cell. In LTE, mobile devices or mobile stations are called user equipment (UE). LTE has included many improvements or developments. Aspects of LTE continue to improve.

[0004] The development of 5G New Radio (NR) is part of the continuing mobile broadband evolution process to meet 5G requirements, similar to the previous evolution of 3G and 4G wireless networks. Additionally, 5G targets new and emerging use cases beyond mobile broadband. The goal of 5G is to provide significant improvements in wireless performance, which may include new levels of data speed, latency, reliability, and security. 5G NR can also scale to efficiently connect vast numbers of Internet of Things (IoT) devices and deliver new types of mission-critical services. For example, Ultra-Reliable Low-Latency Communications (URLLC) devices may require high reliability and very low latency. Summary of the Invention [Means for solving the problem]

[0005] According to an example embodiment, the method may include: a first model training unit receiving, from a second model training unit, a trigger instruction for triggering or causing modification of the subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; the first model training unit receiving training data for the subtask-specific model from one or more subtask-specific model collectors; the first model training unit modifying the subtask-specific model based on the one or more parameters of the generic model, the subtask, or the subtask-specific model and the training data received from the one or more subtask-specific model collectors; and the first model training unit transmitting the modified subtask-specific model to the first wireless node.

[0006] According to an exemplary embodiment, an apparatus may include at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code may be configured to, by the at least one processor, cause the apparatus to receive at least: a trigger instruction for triggering or causing a first model training unit to modify a subtask-specific model from a second model training unit; an instruction for a generic model trained for the generic task by the second model training unit; and one or more parameters of a subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model. the first model training unit receives training data for the subtask-specific model from one or more subtask-specific model collectors; the first model training unit modifies the subtask-specific model based on the one or more parameters of the generic model, the subtask, or the subtask-specific model and the training data received from the one or more subtask-specific model collectors; and the first model training unit transmits the modified subtask-specific model to the first wireless node.

[0007] According to an example embodiment, an apparatus may include means for receiving, from a second model training unit, a trigger instruction for triggering or causing a first model training unit to modify a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; means for the first model training unit to receive training data for the subtask-specific model from one or more subtask-specific model collectors; means for the first model training unit to modify the subtask-specific model based on the one or more parameters of the generic model, the subtask, or the subtask-specific model, and the training data received from the one or more subtask-specific model collectors; and means for the first model training unit to transmit the modified subtask-specific model to a first wireless node.

[0008] According to an example embodiment, a non-transitory computer-readable storage medium may include stored instructions that, when executed by at least one processor, are configured to cause the computing system to: receive, from a second model training unit, a trigger instruction to trigger or cause a first model training unit to modify a subtask-specific model, an indication of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; receive, from one or more subtask-specific model collectors, training data for the subtask-specific model; modify, based on the one or more parameters of the generic model, the subtask, or the subtask-specific model, and the training data received from the one or more subtask-specific model collectors; and transmit, to the first wireless node, the modified subtask-specific model.

[0009] According to an example embodiment, a method may include receiving a request to modify a subtask-specific model for a first wireless node based on a generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; a second model training unit transmitting to the first model training unit a trigger instruction to trigger or cause the first model training unit to modify the subtask-specific model, an indication of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; the second model training unit configuring one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and the second model training unit receiving from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0010] The apparatus may include at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code cause the apparatus, by the at least one processor, to at least: receive a request to modify a subtask-specific model for the first wireless node based on the generic model; or determine a need to modify a subtask-specific model for the first wireless node based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; and transmit the subtask-specific model to the first model training unit and the subtask-specific model to the second model training unit. the second model training unit is configured to transmit a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; the second model training unit is configured to configure one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and the second model training unit is configured to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0011] According to an example embodiment, the apparatus may include means for receiving a request to modify a subtask-specific model for a first wireless node based on a generic model or means for determining a need to modify a subtask-specific model for the first wireless node based on a generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; means for a second model training unit to transmit to the first model training unit a trigger instruction to trigger or cause the first model training unit to modify the subtask-specific model, an indication of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; means for the second model training unit to configure one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and means for the second model training unit to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0012] According to an example embodiment, a non-transitory computer-readable storage medium may include stored instructions that, when executed by at least one processor, cause a computing system to: receive a request to modify a subtask-specific model for a first wireless node based on a generic model; or determine a need to modify a subtask-specific model for the first wireless node based on a generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; and transmit a second model training unit to the first model training unit; and the first model training unit modifies the subtask-specific model. the second model training unit is configured to transmit a trigger instruction to trigger or cause the first model training unit to perform the above-described processing, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; the second model training unit is configured to configure one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and the second model training unit is configured to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0013] According to an example embodiment, a method includes: determining, by a user equipment, a trained generic model for performing or assisting in performing a machine-learnable generic task; determining, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; transmitting, by the user equipment, the one or more generic model-based outputs to a network node; and receiving, by the user equipment, from the network node, a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, wherein the subtask-specific model is configured to perform or assist in performing a machine-learnable subtask. validating the request for the subtask-specific model; the user equipment modifying the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; the user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and the user equipment transmitting to the network node the subtask-specific model output based on or using the modified subtask-specific model performing or executing the machine-learnable subtask.

[0014] The apparatus may include at least one processor and at least one memory containing computer program code, the at least one memory and the computer program code causing the apparatus to at least: determine, by the at least one processor, a trained generic model for performing or assisting in performing a generic machine-learnable task; determine, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; transmit, by the user equipment, the one or more generic model-based outputs to a network node; and receive, by the user equipment, a request for a subtask-specific model from the network node based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model. the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; validating a request for the subtask-specific model; the user equipment modifying the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; the user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and the user equipment transmitting to the network node a subtask-specific model output based on or using the trained subtask-specific model performing or executing the machine-learnable subtask.

[0015] According to an exemplary embodiment, an apparatus includes means for determining, by a user equipment, a trained generic model for performing or assisting in performing a machine-learnable generic task; means for determining, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; means for the user equipment to transmit the one or more generic model-based outputs to a network node; and means for the user equipment to receive from the network node a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, wherein the subtask-specific model performs or assists in performing a machine-learnable subtask. means for validating a request for a subtask-specific model; means for the user equipment to modify the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; means for the user equipment to perform or execute a machine-learnable subtask based on or using the modified subtask-specific model; and means for the user equipment to transmit a subtask-specific model output to a network node based on performing or executing the machine-learnable subtask based on or using the modified subtask-specific model.

[0016] According to an example embodiment, a non-transitory computer-readable storage medium may include stored instructions that, when executed by at least one processor, cause a computing system to: determine, by a user equipment, a trained generic model for performing or assisting in performing a generic machine-learnable task; determine, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; transmit, by the user equipment, the one or more generic model-based outputs to a network node; and receive, by the user equipment, a request for a subtask-specific model from the network node based at least in part on the one or more generic model-based outputs, the request including configuration parameters for the subtask-specific model, the task-specific model is for performing or assisting in performing the machine-learnable subtask; validating the request for the subtask-specific model; the user equipment modifying the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; the user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and the user equipment transmitting to the network node the subtask-specific model output based on or using the trained subtask-specific model performing or executing the machine-learnable subtask.

[0017] According to an example embodiment, the method may include a network node determining a trained generic model for performing or assisting in performing the machine-learning enabled generic task; the network node providing the trained generic model to a user equipment; the network node receiving a request for a subtask-specific model from the user equipment; validating the request for the subtask-specific model; the network node transmitting a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; the network node receiving from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and the network node transmitting the modified subtask-specific model to the user equipment.

[0018] The apparatus may include at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code are configured, by the at least one processor, to cause the apparatus to at least: determine, by a network node, a trained generic model for performing or assisting in performing the machine-learning enabled generic task; provide, by the network node, the trained generic model to a user equipment; receive, by the network node, a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit, by the network node, a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive, by the network node, from the user equipment, the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify, by the network node, the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit, by the network node, the modified subtask-specific model to the user equipment.

[0019] According to an example embodiment, an apparatus may comprise: means for a network node to determine a trained generic model for performing or assisting in performing a machine-learning enabled generic task; means for the network node to provide the trained generic model to a user equipment; means for the network node to receive a request for a subtask-specific model from the user equipment; means for validating the request for the subtask-specific model; means for the network node to transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; means for the network node to receive from the user equipment the subtask-specific model configuration or constraints and / or subtask-specific model training data; means for the network node to modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and means for the network node to transmit the modified subtask-specific model to the user equipment.

[0020] According to an example embodiment, a non-transitory computer-readable storage medium may include stored instructions that, when executed by at least one processor, are configured to cause the computing system to: determine a trained generic model for performing or assisting in performing the machine-learning enabled generic task; provide the trained generic model to a user equipment; receive a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit the modified subtask-specific model to the user equipment.

[0021] According to an example embodiment, the method may include a network node determining a trained generic model for performing or assisting in performing the machine-learning enabled generic task; the network node providing the trained generic model to a user equipment; the network node receiving a request for a subtask-specific model from the user equipment; validating the request for the subtask-specific model; the network node transmitting a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; the network node receiving from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and the network node transmitting the modified subtask-specific model to the user equipment.

[0022] The apparatus may include at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code are configured, by the at least one processor, to cause the apparatus to at least: determine, by a network node, a trained generic model for performing or assisting in performing the machine-learning enabled generic task; provide, by the network node, the trained generic model to a user equipment; receive, by the network node, a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit, by the network node, a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive, by the network node, from the user equipment, the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify, by the network node, the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit, by the network node, the modified subtask-specific model to the user equipment.

[0023] According to an example embodiment, an apparatus may comprise: means for a network node to determine a trained generic model for performing or assisting in performing a machine-learning enabled generic task; means for the network node to provide the trained generic model to a user equipment; means for the network node to receive a request for a subtask-specific model from the user equipment; means for validating the request for the subtask-specific model; means for the network node to transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; means for the network node to receive from the user equipment the subtask-specific model configuration or constraints and / or subtask-specific model training data; means for the network node to modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and means for the network node to transmit the modified subtask-specific model to the user equipment.

[0024] According to an example embodiment, a non-transitory computer-readable storage medium may include stored instructions that, when executed by at least one processor, are configured to cause the computing system to: determine a trained generic model for performing or assisting in performing the machine-learning enabled generic task; provide the trained generic model to a user equipment; receive a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit the modified subtask-specific model to the user equipment.

[0025] The details of one or more example embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a block diagram of a wireless network in accordance with an example embodiment. [Figure 2] 10 is a flowchart illustrating the operation of a model training unit in accordance with an exemplary embodiment. [Figure 3] 10 is a flowchart illustrating the operation of a second model training unit according to an exemplary embodiment. [Figure 4] 10 is a flowchart illustrating the operation of a user equipment according to an exemplary embodiment. [Figure 5]1 is a flowchart illustrating the operation of a network node (e.g., a gNB) according to an example embodiment. [Figure 6] FIG. 1 illustrates the operation of a generic model training unit (GMTU) and a meta-learning model training unit (MMTU) or specific model training unit according to an exemplary embodiment. [Figure 7] FIG. 1 is a diagram of a network in which generic model (GM) training and subtask-specific model (SSM) training are performed at network nodes, according to an example embodiment. [Figure 8] 1 is a diagram of a network in which generic model (GM) training and subtask-specific model (SSM) training are performed at a UE or user device. [Figure 9] 1 is a block diagram of a wireless station or node (e.g., a network node, a user node or UE, a relay node, or other node). DETAILED DESCRIPTION OF THE INVENTION

[0027] 1 is a block diagram of a wireless network 130 according to an example embodiment. In the wireless network 130 of FIG. 1, user devices 131, 132, 133, and 135 can connect to (and communicate with) a base station (BS) 134, which may also be referred to as a mobile station (MS) or user equipment (UE), and which may also be referred to as an access point (AP), enhanced Node B (eNB), gNB, or network node. The terms user device and user equipment (UE) can be used interchangeably. A BS (or network node) may also include or be referred to as a RAN (Radio Access Network) node, and may include a portion of a BS or a portion of a RAN node (e.g., a centralized unit (CU) and / or a distributed unit (DU) in the case of a split BS or split gNB). At least a portion of the functionality of a BS (e.g., access point (AP), base station (BS) or (e)Node B (eNB), gNB, RAN node) may be performed by any node, server, or host that can be operatively coupled to a transceiver, such as a remote radio head. BS (or AP) 134 provides wireless coverage within cell 136, which includes user devices (or UEs) 131, 132, 133, and 135. While only four user devices (or UEs) are shown connected or attached to BS 134, any number of user devices may be provided. BS 134 is also connected to core network 150 via S1 interface 151. This is just one simple example of a wireless network; other wireless networks may be used. Wireless nodes may include, for example, BSs, gNBs, eNBs, APs, RAN nodes, CUs, and / or DUs (or other network nodes), relay nodes, user devices, UEs, or other nodes with wireless communication capabilities.

[0028] A base station (e.g., BS 134, etc.) is an example of a Radio Access Network (RAN) node in a wireless network. A BS (or RAN node) may be or include (or may alternatively be referred to as) an access point (AP), a gNB, an eNB, or a portion thereof (e.g., a centralized unit (CU) and / or a distributed unit (DU) in the case of a split BS or split gNB), or other network node.

[0029] According to an exemplary example, a BS node or other network node (e.g., BS, eNB, gNB, CU / DU, transmit / receive point (TRP),...), or radio access network (RAN) can be part of a mobile telecommunications system. The RAN (radio access network) can include one or more BS or RAN nodes implementing radio access technologies, e.g., to enable one or more UEs to access a network or core network. Thus, for example, a RAN (RAN node such as a BS or gNB) can reside between one or more user devices or UEs and the core network. According to an exemplary embodiment, each RAN node (e.g., BS, eNB, gNB, CU / DU,...) or BS can provide one or more wireless communication services to one or more UEs or user devices, e.g., to enable the UEs to wirelessly access the network via the RAN node. Each RAN node or BS can implement or provide wireless communication services, e.g., to enable the UEs or user devices to establish a wireless connection to the RAN node and transmit data to and / or receive data from one or more of the UEs. For example, after establishing a connection to a UE, a RAN node or a network node (e.g., BS, eNB, gNB, CU / DU...) may forward data received from the network or core network to the UE and / or may forward data received from the UE to the network or core network.A RAN node or network node (e.g., BS, eNB, gNB, CU / DU...) may perform a wide variety of other wireless functions or services, such as broadcasting control information (e.g., system information or on-demand system information, etc.) to UEs, paging UEs when there is data to deliver to the UE, assisting in handover of UEs between cells, scheduling resources for uplink and downlink data transmissions from and to the UE, sending control information to configure one or more UEs, etc. These are just a few examples of one or more functions that a RAN node or BS may perform.

[0030] A user device or user node (user terminal, user equipment (UE), mobile terminal, portable wireless device, etc.) may refer to a portable computing device, including wireless mobile communication devices that operate with or without a subscriber identity module (SIM), including, by way of example and without limitation, the following types of devices: a mobile station (MS), a mobile phone, a mobile phone, a smartphone, a personal digital assistant (PDA), a handset, a device that uses a wireless modem (such as an alarm or measurement device), a laptop and / or touchscreen computer, a tablet, a phablet, a game console, a notebook, a vehicle, a sensor, and a multimedia device, or any other wireless device. It should be understood that a user device can also be (or include) a device that is substantially dedicated solely to the uplink. An example is a camera or video camera that loads images or video clips into the network. A user node may also include user equipment (UE), a user device, a user terminal, a mobile terminal, a mobile station, a mobile node, a subscriber device, a subscriber node, a subscriber terminal, or another user node. For example, a user node, regardless of its technology or radio access technology (RAT), may be used for wireless communication with one or more network nodes (e.g., gNB, eNB, BS, AP, CU, DU, CU / DU) and / or one or more other user nodes. In LTE (an illustrative example), the core network 150 may be referred to as an Evolved Packet Core (EPC) and may include a mobility management entity (MME) that can handle or assist mobility / handover of user devices between BSs, one or more gateways that can transfer data and control signaling between the BSs and a packet data network or the Internet, and other control functions or blocks. Other types of wireless networks, such as 5G (which may be referred to as New Radio (NR)), may also include a core network.

[0031] Additionally, the techniques described herein may be applied to various types of user devices or data service types, or to user devices running multiple applications, potentially of different data service types. New Radio (5G) developments may accommodate multiple different applications or multiple different data service types, such as machine type communications (MTC), enhanced machine type communications (eMTC), Internet of Things (IoT), and / or narrowband IoT user devices, enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communications (URLLC). Many of these new 5G (NR) related applications may require generally higher performance than traditional wireless networks.

[0032] The IoT can refer to a growing group of objects that can have internet or network connectivity, and thus can send information to and receive information from other network devices. For example, many sensor-type applications or devices can monitor physical conditions or states and, for example, send reports to a server or other network devices when an event occurs. Machine-type communication (MTC or machine-to-machine communication) can be characterized, for example, by fully automatic data generation, exchange, processing, and action between intelligent machines, with or without human intervention. Enhanced mobile broadband (eMBB) can support data speeds much faster than those currently available with LTE.

[0033] Ultra-reliable and low-latency communications (URLLC) is a new data service type or new usage scenario that can be supported by New Radio (5G) systems. This will enable the emergence of new applications and services such as industrial automation, autonomous driving, vehicle safety, and e-health services. 3GPP has proposed the 10 -5 The goal is to provide reliable connectivity with a Block Error Rate (BLER) of up to 1 ms and a U-Plane (user / data plane) delay of up to 1 ms. Thus, for example, a URLLC user device / UE may require a significantly lower Block Error Rate and lower delay than other types of user devices / UE (with or without a concurrent requirement for high reliability). Thus, for example, a URLLC UE (or a URLLC application on a UE) may require a much lower delay than an eMBB UE (or an eMBB application running on a UE).

[0034] The techniques described herein may be applied to a wide variety of wireless technologies or wireless networks, such as LTE, LTE-A, 5G (New Radio (NR)), cmWave and / or mmWave band networks, 6G, IoT, MTC, eMTC, eMBB, URLLC, etc., or any other wireless network or wireless technology. These example networks, technologies, or data service types are provided by way of example only.

[0035] According to example embodiments, machine learning (ML) models may be used to perform (or assist in performing) one or more tasks within a wireless network. Generally, one or more nodes (e.g., BS, gNB, eNB, RAN node, user node, UE, user device, relay node, or other wireless node) within a wireless network may use or utilize an ML model, such as, for example, a neural network model (which may be referred to as a neural network, an artificial intelligence (AI) neural network, an AI neural network model, an AI model, a machine learning (ML) model or algorithm, a model, or other terminology), to perform or assist in performing one or more ML-enabled tasks. Other types of models may also be used. An ML-enabled task may include a task that can be performed (or assists in performing) by an ML model or a task that an ML model has been trained to perform (or assists in performing).

[0036] ML-based algorithms or ML models may be used to perform and / or assist in performing various wireless and / or radio resource management (RRM) functions or tasks to improve network performance, e.g., for antenna panel or beam control at a UE, RRM measurements and feedback (Channel State Information (CSI) feedback), link monitoring, Transmit Power Control (TPC), etc. In some instances, the use of ML models may be used to improve performance of a wireless network in one or more aspects or as measured by one or more performance indicators or metrics.

[0037] A model (e.g., a neural network or ML model) can be or include, for example, a computational model used in machine learning composed of nodes organized in layers. These nodes, also called artificial neurons or simply neurons, perform a function on provided inputs to produce some output value. A neural network or ML model may typically require a training period to learn the parameters, or weights, used to map inputs to desired outputs. The mapping occurs through this function. The weights are therefore weights for the neural network's mapping function. Each neural network model or ML model can be trained for a specific task.

[0038] To provide an output for an input, a neural network model or an ML model must be trained, which may involve learning appropriate values ​​for a number of parameters (e.g., weights) for the mapping function. These parameters are also called weights because they are commonly used to weight the terms of the mapping function. This training can be an iterative process, where the values ​​of the weights are fine-tuned over many (e.g., thousands) of training rounds until an optimal or most accurate value (or weighting) is reached. In the context of a neural network (neural network model) or an ML model, the parameters can often be initialized with random values, and a training optimizer iteratively updates the parameters (weights) of the neural network to minimize errors in the mapping function. In other words, during each round or step of iterative training, the network updates the values ​​of the parameters, so that the values ​​of the parameters eventually converge to optimal values.

[0039] Neural network models or ML models, for example, are trained using either supervised learning or unsupervised learning. In supervised learning, training examples are provided to a neural network model or other machine learning algorithm. The training examples include inputs and desired or previously observed outputs. Training examples are also called labeled data because the inputs are labeled with the desired or observed outputs. In the case of neural networks, the network learns the weighting values ​​used in the mapping function that result in the most desirable output given the training inputs. In unsupervised learning, the neural network model learns to identify structures or patterns in the inputs provided. In other words, the model identifies implicit relationships in the data. Unsupervised learning is used in many machine learning problems and typically requires large amounts of unlabeled data.

[0040] According to exemplary embodiments, the learning or training of neural network or ML models can be classified into (or include) two broad categories: supervised and unsupervised, depending on whether or not a learning "signal" or "feedback" is available to the model. Thus, for example, in the field of machine learning, there can be two main types of model learning or training: supervised and unsupervised. The main difference between the two types is that supervised learning is performed using known or prior knowledge regarding what the output value should be for a particular data sample. Thus, the goal of supervised learning is to learn a function that best approximates the relationship between inputs and outputs observable in the data, given the data sample and the desired output. On the other hand, unsupervised learning does not have labeled outputs, and therefore its goal is to infer natural structures present within multiple data points.

[0041] Supervised learning: Example inputs and desired outputs are presented to a computer, and the goal can be to learn general rules that map inputs to outputs. Supervised learning can be performed, for example, in the context of classification, where a computer or learning algorithm attempts to map inputs to output labels, or in the context of regression, where a computer or algorithm attempts to map inputs to continuous outputs. Common algorithms in supervised learning can include, for example, logistic regression, naive Bayes, support vector machines, artificial neural networks, and random forests. In both regression and classification, the goal may be to find unique relationships or structures in the input data that can effectively yield the correct output data. In special cases, the input signal may only be partially available or may be limited by special feedback. Semi-supervised learning: Only incomplete training signals are given to the computer, and some (possibly many) of the target outputs in the training set are missing. Active learning: The computer can obtain only training labels for limited instances (based on a budget) and can optimize the selection of objects that acquire labels. When used interactively, these can be presented to the user for labeling. Reinforcement learning: In a dynamic environment, for example using live data, training data (in the form of rewards and punishments) is given only as feedback to the program's actions.

[0042] Unsupervised learning: A learning algorithm is not given labels and must independently discover structure in its input. Some example tasks within unsupervised learning include clustering, representation learning, and density estimation. In these cases, the computer or learning algorithm attempts to learn the inherent structure of the data without the use of explicitly provided labels. Some common algorithms include k-means clustering, principal component analysis, and autoencoders. Because no labels are provided, most unsupervised learning methods do not provide a specific way to compare model performance.

[0043] It may be advantageous to provide techniques that can enable the use of ML models for various wireless network-related tasks, coordinate the use of ML models, enable the configuration of ML models, enable the modification or training of ML models, and / or facilitate the distribution or communication of ML models or ML-enabled functions across a wireless network, e.g., between gNB or RAN nodes and UEs. Some example tasks for which ML models may be used include, for example, enhancing channel state information (CSI) feedback, e.g., reducing overhead, improving accuracy, prediction, and beam management, e.g., beam prediction in the time and / or spatial domain for reduced overhead and delay, improving beam selection accuracy, and / or enhancing UE positioning accuracy for different scenarios, including, e.g., those with severe NLOS (non-line-of-sight) conditions. These are just a few examples, and ML models may be applied or used to perform or assist in the performance of a wide variety of tasks within a wireless network.

[0044] In general, meta-learning can include or refer to modifying, training, or tuning a generic model (e.g., one trained using features or data extracted from heterogeneous sources, or from different UEs, or from different wireless nodes) for a specific type of entity and / or a specific task. For example, meta-learning can include the process of setting the knobs (or adjustable parameters) of the learning procedure and / or modifying the weights or training of the model for a specific task. Meta-learning can also include instances in which an ML model is trained for a general (or more general) task, and then this trained ML model (trained for this general task) is trained or retrained (in part, possibly using transfer learning) for a specific subtask. In some instances, meta-learning can also include the process in which the machine learning algorithm itself proposes its own task distribution and / or sets the knobs of the learning procedure (e.g., adjusts weights or performs other modifications or training) through optimization. An algorithm that automatically performs this optimization and / or training of an ML model can be referred to as a meta-learning algorithm. A meta-learning algorithm can consider data and / or algorithms for many tasks. In wireless communications, meta-learning (which may involve, for example, modifying or training ML models for specific tasks) can be used to create, modify, and / or train ML models for various use cases or applications, such as, for example, radio resource management and / or receiver design (illustrative examples).

[0045] Various example embodiments are described, for example, where a model (e.g., an ML model) is trained for a first task (e.g., a general-purpose task) and communicated or transmitted from a first wireless node to a second wireless node, and the ML model can then be modified, trained, or tuned by the second wireless node for a second task (e.g., which may be a task different from the general-purpose task, but in some aspects is a sub-task that may be related to the general-purpose task, e.g., where a sub-task can be a sub-task within the same task category, e.g., both the general-purpose task and the sub-task may be positioning-related, or both the general-purpose task and the sub-task may be CSI-RS measurement-related).

[0046] 2 is a flowchart illustrating the operation of a model training unit according to an example embodiment. Operation 210 includes a first model training unit (e.g., a meta-learning model training unit or a specific model training unit) receiving, from a second model training unit (e.g., a generic model training unit), a trigger instruction for triggering or causing modification (e.g., training) of a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of a subtask or subtask-specific model to be used to modify (e.g., train) the subtask-specific model based on the generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask. Operation 220 includes the first model training unit receiving training data for the subtask-specific model from one or more subtask-specific model collectors (e.g., UEs, gNBs, or other wireless nodes). Operation 230 includes the first model training unit modifying (e.g., training or retraining, adjusting weights, configuring or updating, or otherwise modifying the subtask-specific model) the subtask-specific model based on one or more parameters of the generic model, the subtask, or the subtask-specific model and the training data received from the one or more subtask-specific model collectors. Further, operation 240 includes transmitting, by the first model training unit, the modified (e.g., trained) subtask-specific model to the first wireless node (e.g., UE or gNB).

[0047] In an example embodiment of the method of FIG. 2 , modifying may include at least one of modifying one or more weightings of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weightings or parameters of the subtask-specific model, and / or upgrading (e.g., increasing the complexity, or increasing the number of inputs and / or outputs, or other upgrading) or downgrading (e.g., decreasing the complexity, decreasing the number of inputs and / or outputs, or other downgrading) the subtask-specific model.

[0048] According to the exemplary embodiment of FIG. 2, receiving the trigger instruction may include receiving, by the first model training unit from the second model training unit, one or more of: generic model information including one or more of the architecture of the generic model, weights of the generic model, a loss function and / or an activation function of the generic model, and / or a type of output of the generic model; subtask parameterization including constraints of the subtask-specific model or subtask-specific cost functions of the subtask-specific model; and / or identifiers of one or more subtask-specific model collectors.

[0049] According to the exemplary embodiment of FIG. 2 , modifying the subtask-specific models by the first model training unit may include performing one or more of: pruning or reducing the size of the generic model based on the one or more constraints of the generic model and the subtask-specific model so that the subtask-specific model falls within a maximum allowable range of the subtask-specific model constraints; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format size or depth of the training data received from the one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0050] According to the exemplary embodiment of FIG. 2, the method may further include transmitting the modified subtask-specific model from the first model training unit to a second model training unit.

[0051] According to the exemplary embodiment of FIG. 2, the method may further include transmitting the modified subtask-specific model from the first model training unit to at least one of the one or more subtask-specific model collectors.

[0052] According to the exemplary embodiment of FIG. 2, the first model training unit and the second model training unit are provided in a second wireless node, or the first model training unit is provided in a second wireless node and the second model training unit is provided in a third wireless node.

[0053] According to the exemplary embodiment of FIG. 2, the method may further include transmitting a request for subtask-specific model training or meta-learning for the subtask from the first model training unit to the second model training unit.

[0054] According to the exemplary embodiment of FIG. 2, the second model training unit may comprise a generic model training unit configured to modify a generic model for a generic task, and the first model training unit may comprise a meta-learning model training unit or a specific model training unit configured to modify (e.g., train) a model specific to a subtask or a subtask-specific model.

[0055] According to the example embodiment of FIG. 2, one or more of the first wireless node, the second wireless node, or the third wireless node may comprise at least one of a user equipment, a user device, a base station, or a gNB.

[0056] 3 is a flowchart illustrating the operation of a second model training unit according to an exemplary embodiment. Operation 310 includes receiving a request to modify a subtask-specific model for a first wireless node based on a generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask. Operation 320 includes the second model training unit transmitting to the first model training unit a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an indication of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model. Operation 330 includes the second model training unit configuring one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model. Additionally, operation 340 includes the second model training unit receiving, from the first model training unit, the modified subtask-specific model modified by the first model training unit.

[0057] According to an example embodiment of the method of FIG. 3, the transmitting may include the second model training unit transmitting to the first model training unit one or more of: generic model information including one or more of: an architecture of the generic model, weights of the generic model, a loss function and / or an activation function of the generic model, and / or a type of output of the generic model; subtask parameterization including constraints of the subtask-specific model or subtask-specific cost functions of the subtask-specific model; and / or identifiers of one or more subtask-specific model collectors.

[0058] According to an exemplary embodiment of the method of FIG. 3, the first model training unit and the second model training unit are provided in a second wireless node, or the first model training unit is provided in a second wireless node and the second model training unit is provided in a third wireless node.

[0059] According to an example embodiment of the method of FIG. 3 , receiving a request for modification of the subtask-specific model or determining the need for modification of the subtask-specific model may include the second model training unit receiving, from the first model training unit, a request for modifying or training a subtask-specific model for the subtask, and verifying the request for modifying or training the subtask-specific model for the subtask.

[0060] According to an exemplary embodiment of the method of FIG. 3, the second model training unit may comprise a generic model training unit configured to modify or train a generic model for a generic task, and the first model training unit may comprise a meta-learning model training unit or a specific model training unit configured to modify or train a specific model for a subtask or a subtask-specific model.

[0061] According to an exemplary embodiment of the method of FIG. 3, one or more of the first wireless node, the second wireless node, or the third wireless node comprises at least one of user equipment, a user device, a base station, or a gNB.

[0062] 4 is a flowchart illustrating user equipment (UE) operation according to an exemplary embodiment. Operation 410 includes the user equipment (e.g., a UE or user device) determining a trained generic model for performing or assisting in the performance of a machine-learning enabled generic task. Operation 420 includes determining, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs. Operation 430 includes the user equipment transmitting the one or more generic model-based outputs to a network node (e.g., a gNB). Operation 440 includes the user equipment receiving, from the network node, a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the request including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in the performance of a machine-learning enabled subtask. Operation 450 includes validating the request for the subtask-specific model. Operation 460 includes the user equipment modifying the subtask-specific model based on the trained generic model and the configuration parameters of the subtask or subtask-specific model. Operation 470 includes the user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model. Further, operation 480 includes the user equipment transmitting to the network node a subtask-specific model output based on or using the modified subtask-specific model performing or executing the machine-learnable subtask.

[0063] According to an example embodiment of the method of FIG. 4 , modifying may include at least one of modifying one or more weightings of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weightings or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0064] According to an example embodiment of the method of FIG. 4 , validating the request for the subtask-specific model may include verifying against the subtask-specific model at least one of that the requested subtask-specific model is on a list of allowed subtask-specific models, that a threshold amount of training data and / or input signals is available for training the subtask-specific model, and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0065] According to an example embodiment of the method of FIG. 4 , receiving a request for a subtask-specific model including configuration parameters for the subtask-specific model may include receiving a trigger instruction for triggering or causing meta-learning or training of the subtask-specific model, and one or more parameters of the subtask or subtask-specific model to be used to train the subtask-specific model based on the generic model, which may include receiving a parameterization of the subtask including constraints of the subtask-specific model or a subtask-specific cost function of the subtask-specific model.

[0066] According to an example embodiment of the method of FIG. 4 , the configuration parameters of the subtask or subtask-specific model may include one or more constraints for the subtask-specific model, and modifying, by the user equipment, the subtask-specific model based on the generic model may include performing one or more of: pruning or reducing the size of the generic model based on the one or more constraints of the generic model and the subtask-specific model so that the subtask-specific model falls within a maximum allowed subtask-specific model depth or size; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model conform to the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0067] According to an exemplary embodiment of the method of FIG. 4, determining one or more generic model-based outputs may include the user equipment receiving a request from a network node to train a generic model for a machine-learnable generic task, the user equipment training the generic model based on configurations or inputs received from the network node, and performing or executing the machine-learnable generic task using the trained generic model to obtain one or more generic model-based outputs.

[0068] According to an exemplary embodiment of the method of FIG. 4, a request for a subtask-specific model is received by the user equipment in response to the user equipment transmitting one or more generic model-based outputs of the trained generic model to a network node.

[0069] FIG. 5 is a flowchart illustrating operation of a network node (e.g., a gNB) according to an example embodiment. Operation 510 includes the network node determining a trained generic model for performing or assisting in performing a machine-learning enabled generic task. Operation 520 includes the network node providing the trained generic model to a user equipment (e.g., a UE or user device). Operation 530 includes the network node receiving a request for a subtask-specific model from the user equipment. Operation 540 includes validating the request for the subtask-specific model. Operation 550 includes the network node transmitting a request for at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data to the user equipment. Operation 560 includes the network node receiving at least one of the subtask-specific model configurations or constraints and / or subtask-specific model training data from the user equipment. Operation 570 includes the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configurations or constraints and / or the subtask-specific model training data. Further, operation 580 includes the network node transmitting the modified subtask-specific model to the user equipment.

[0070] According to an example embodiment of the method of FIG. 5 , modifying may include at least one of modifying one or more weightings of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weightings or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0071] According to an example embodiment of the method of FIG. 5 , validating the request for the subtask-specific model may include verifying for the subtask-specific model at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0072] According to an example embodiment of the method of FIG. 5 , modifying the subtask-specific models by the network node may include performing one or more of: pruning or reducing the size of the generic model so that the subtask-specific models are within a maximum allowed subtask-specific model depth or size based on one or more constraints of the generic model and the subtask-specific models; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing a generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0073] According to example embodiments, techniques are provided for using meta-learning (e.g., modifying or training) of ML models for ML-enabled function(s) or task(s), such as radio resource management (RRM)-related tasks for wireless networks. A framework for meta-learning of ML-enabled (RRM) functions is described. The framework may include and / or describe one or more operations and / or steps that can be used to port (i.e., generate and signal or transmit) the generic ML model to another wireless node and then use meta-learning (e.g., training and / or modifying) the generic ML model (trained for a generic task) to subtask-specific models trained to perform or assist in performing specific subtasks. New training data can be received and used to modify or train the subtask-specific models (e.g., to create subtask-specific models based on the generic ML model and new training data or subtask-specific training data). Various example embodiments may provide or describe wireless domain-specific methods for i) modifying and / or training (e.g., tuning / refinement) a (e.g., general-purpose) ML model (which may be trained to perform a general-purpose task or a first task) into a subtask-specific model (which may be trained to perform a second task or a specific subtask, which may differ from the general-purpose task or the first task), and ii) forwarding signals between network nodes associated therewith.

[0074] The described framework, messages, or signaling may enable the identification, communication, and implementation of decomposition of a generic task into subtasks, and / or modification and / or training of a generic model to create subtask-specific models that can be used to implement specific subtasks.

[0075] Exemplary definition: Definition 1: A Subtask Specific Model (SSM) can include or be defined as follows:

[0076] 1) A model that solves the same task as a generic model, but is implemented by a different type of NR (new radio) element (e.g., wireless node) (compared to the NR element used to train the generic model). In one embodiment, the training data for the generic model does not include training observations collected from a specific element type (e.g., a specific type of wireless node). For example, the generic task can be optimization of UL (uplink) power control parameters for generic UEs, and a subtask can be adjustment of UL power control for a different UE mobility class (or specific UE mobility class) (e.g., pedestrian UEs vs. vehicular UEs identified by a different algorithm). Thus, for example, a subtask can be the same as or similar to the generic task, but can apply to a different type of wireless node (compared to the generic task or generic ML model), or a subtask-specific model can apply. Thus, for example, the generic task can be optimization of UL power control parameters for UEs in general, but the specific subtask (for which the subtask-specific model is trained) is UL power control for a specific class (or subclass) of UEs, e.g., pedestrian UEs, vehicular UEs, or aeronautical UEs. In another example, a general task for UE power saving can be performed by an NR UE, and a subtask can be UE power saving performed by a low capability (RedCap) UE.

[0077] 2) A model that solves a similar (or different) task from the generic model (hence, the subtask is similar to the generic task but differs in at least one respect). Thus, for example, the subtask may be different from the generic (or general) task but related to the generic task; e.g., the subtask may be within the same task category (e.g., UL power control) as the generic (or general) task (e.g., the generic task may be for the general task, and the subtask is for a different task within the same task category (e.g., UL power control) as the generic task); and / or the subtask may be for or applicable to a subset (or different set) of conditions or devices compared to the generic task. For example, the generic task may be best beam selection for the UE, and a similar (but different in at least one respect) subtask may be best panel (or best antenna) selection. Also, in some cases, the subtask-specific model may be implemented, for example, by a different / specific type of NR element (or a different type of NR element) (compared to the NR element used to train the generic model).

[0078] Definition 2: A Generic Model Training Unit (GMTU) is a network (NW) node-resident entity or a UE-resident entity that trains a model to solve a generic task (e.g., a generic Radio Resource Management (RRM) task). Thus, a GMTU is an ML model training unit that trains a generic model to solve or perform a generic task.

[0079] Definition 3: A Meta-Learning Model Training Unit (MMTU) is a network (NW) node-resident or UE-resident entity that trains models to solve subtasks. Thus, the MMTU trains subtask-specific models, and the MMTU and GMTU may reside in different entities (e.g., UE or network node) or the same entity (e.g., the same network node or UE). The MMTU and GMTU may require or use (or rely on) processing and data collection sources. The MMTU can be configured by the GMTU, which can transfer GM (generic model) and SSM (subtask-specific model) characteristics to the MMTU. Both the MMTU and GMTU can reside in the same or different NR elements. For example, the GMTU-MMTU interface can be Xn or F1 when both reside on the NW side, or RRC / MAC when either resides in the NR UE.

[0080] Definition 4: To train a subtask-specific model, the MMTU may require the assistance of other NR elements (resident on the UE side and / or NW side) called SSM (Subtask-Specific Model) Collectors, which collect and then provide additional (or new or subtask-specific) training data to refine the model for the specific subtask. The MMTU can interact with the SSM Collectors directly or indirectly via the GMTU. This interaction is responsible for refining the subtask model. For example, the MMTU can receive training data from an SSM Collector (e.g., a UE or other wireless node) activated by the GMTU / MMTU to collect and provide training data. The MMTU then trains a new subtask-specific model. The MMTU can first receive the SSM Collector's constraints related to data acquisition and preprocessing (such as sampling resolution, periodicity of feature extraction and reporting), or model constraints related to the size of the trained SSM that the SSM Collector can introduce (after the MMTU has trained). Note that the MMTU and SSM collectors can reside within the same NR element, e.g., an NR UE can be responsible for (or perform) both collecting training data and training SSMs (subtask-specific models). When the MMTU and SSM collectors are not co-located, their coordination can be achieved via Xn / F1 (e.g., when both are NW elements or different wireless nodes), or when the MMTU and SSM collectors are co-located (located within the same wireless node, e.g., the same UE or the same network node), the RRC (Radio Resource Control) / MAC (Medium Access Control) interface can be used.

[0081] 6 illustrates the operation of a generic model training unit (GMTU) 610 and a meta-learning model training unit (MMTU) 612 or specific model training units in accordance with an example embodiment. The GMTU and MMTU may be located within the same wireless node (e.g., located within a UE or network node) or may be located within different wireless nodes. A new radio element 616 (which may be an NR element or a wireless node, e.g., a UE or a network node) and an SSM collector 614 are also shown in the network of FIG. 6.

[0082] In step 1 of Figure 6, the GMTU 610 collects features from different NR element types (different types of UEs or devices that can use the model being trained, e.g., UE, TRP (Transmit / Receive Point), RSU Roadside Unit) and trains a generic model (GM) for a given RRM task (e.g., a generic task). The GMTU 610 can maintain a list of RRM subtasks that are candidates for meta-learning (for which subtask-specific ML models can be trained).

[0083] In step 2 of FIG. 6, the correction or learning / refinement of the subtask-specific model (SSM) can be 1) triggered on-demand / reactively by the NR element(s) (e.g., when performance degradation is observed at such NR element(s); or 2) triggered periodically and / or proactively by the GMTU 610 or MMTU 612. The request can also contain or include the reason for the SSM training or refinement. For example, the gNB may have a generic model for calculating the AoA (angle of arrival used to position the UE), and the UE would like to use a subtask-specific model for determining the ToA (time of arrival used to position the UE). Thus, the subtask (e.g., determining the ToA) and the generic task (e.g., determining the AoA) can be similar, e.g., because they may be within the same task category (e.g., positioning or determining arrival information for positioning), but the subtask is slightly different from the generic task (in this example, ToA compared to AoA).

[0084] In step 3 of Figure 6, the GMTU authenticates / verifies the SSM learning request (e.g., without necessarily verifying or authenticating the SSM model itself). For example, validating a request for a subtask-specific model (or a request for meta-learning or training of a subtask-specific model) may include, for example, verifying for the subtask-specific model at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model. Also, for example, to validate an SSM learning or training request, the GMTU 610 may check the age (or date stamp compared to the current date and time) of recent SSMs, the number, reason, and source of SSM requests. Then, upon authentication, the GMTU 610 can select a host MMTU 612 (a meta-learning model training unit for modifying or training subtask-specific models) to manage model tuning, modification, or training, generate a list of NR elements (e.g., a list of UEs, network nodes, or other wireless nodes) to act as SSM collectors. Each NR element functioning as an SSM collector can have (and / or be identified by) a network identifier (ID). Using such ID, each SSM collector can contact (e.g., be contacted by) other network elements. In one example, the network ID can be an IP (Internet Protocol) address and port number. The SSM collector can collect data (e.g., for or related to a specific subtask, or for a subtask-specific model) upon request and share the data generated thereon and / or data obtained from other sources via standardized interfaces (e.g., RRC, F1) and / or implementation-specific interfaces.If authentication / verification fails, the GMTU 610 indicates to the requesting node that it cannot generate the SSM.

[0085] In step 4 of Figure 6, the GMTU 610 triggers the adjustment, modification, or training of the subtask-specific model in the MMTU 612. The trigger message can include, for example, the trained generic model (model architecture, e.g., neural network, loss function, activation function weighting, type of output the GM produces) and subtask parameterization—additional constraints—where the activation functions for the subtasks may be different and the inputs may be different (e.g., only a subset of the GM's inputs may be available, requiring new labels for the training data). The trigger message can include an indication / identification of the trained generic model (GM), subtask-specific parameterization, e.g., additional model constraints and subtask-specific cost functions, and / or an SSM collector ID. For example, the MMTU 612 can receive a trigger instruction to trigger or cause the MMTU to modify or train the subtask-specific model. The trigger message may include a trigger instruction, an instruction for the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, where the subtask-specific model is for performing or assisting in performing the machine-learnable subtask.

[0086] Thus, for example, modifying or training the subtask-specific models based on the generic model by the first MMTU may include performing one or more of: pruning or reducing the size of the generic model based on one or more constraints of the generic model and the subtask-specific model so that the subtask-specific model falls within a maximum allowable subtask-specific model constraint; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0087] Also, for example, modifying or training the subtask-specific model may include performing at least one of modifying one or more weights of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weights or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0088] In step 5 of Figure 6, the GMTU 610 configures the SSM collector 614 to listen for MMTU requests and forward any combination of the following: 1) their training data. The SSM collector can collect and send measurements or other training data to be used to be processed by the MMTU to extract input features, or to be processed by the SSM collector and forwarded to the MMTU, or it can simply send measurements to the MMTU 612, and this training data can enable the MMTU 612 to train subtask-specific models. The type of training data can be defined by the GMTU 610 or can be explicitly requested if needed by the GMTU 610 or MMTU 612. The type of training data can be defined by any combination of feature definitions, feature cleaning and normalization processes required, data collection periodicity (i.e., how frequently training data is acquired), and reporting frequency (i.e., how frequently training data is cleaned and forwarded). 2) ML model limitations, e.g., maximum depth of NN (neural network or ML model), and 3) other SSM collector-specific constraints, e.g., power limitations, mobility levels, etc.

[0089] In step 6 of Figure 6, the MMTU 610 may send a request to the SSM collector 614 to activate the selected SSM collector 614. The activation may include or involve 1) transferring training data at a time instance characterized by an offset relative to receipt of the activation signal, and 2) sending a request to begin collecting, cleaning, and then transferring the training data.

[0090] In step 7 of FIG. 6, the SSM collector 614 can respond to the MMTU with what was requested in step 5 (including the information described above).

[0091] In step 8 of Figure 6, the subtask-specific models are refined, modified, or trained by MMTU 612. During this step, MMTU 612 initializes SSMs (subtask-specific models) using the GMs (generic models) and SSM-related constraints. For example, MMTU 612 may prune the GM model to fit within the maximum allowed SSM depth, deactivate some / all of the GM's inputs to fit the training data format that the SSM collector can provide, replace the GM activation function with an SSM-specific activation function, e.g., change the regression problem solved by the GM to a classification problem to be solved by the SSM, define an SSM-specific cost function, e.g., by constraining the initial function with the SSM-specific constraints, etc.

[0092] In step 9 of FIG. 6, the subtask-specific model (SSM) may be transferred (eg, transmitted or communicated) by the MMTU 612 to the GMTU 610 .

[0093] In step 10 of FIG. 6, the SSMs can be simultaneously or later transferred to the subtask-specific models and transferred to the SSM collector.

[0094] In step 11 of FIG. 6, the SSM can be forwarded (transmitted or communicated) to the NR element 616 for use or inference, e.g., so that the NR element (e.g., UE or gNB) can apply the subtask-specific model (SSM) to solve or perform or assist in performing the subtask.

[0095] Based on the generic signaling flow of FIG. 6, FIGS. 7 and 8 are diagrams illustrating the operation of a network, where FIG. 7 is a diagram of a network in which training of a generic model (GM) and training of a subtask-specific model (SSM) are performed at a network node (e.g., gNB, AP, BS, core network node, CU and / or DU, RAN node, TRP (transmission / reception point)), and FIG. 8 is a diagram of a network in which training of a generic model (GM) and training of a subtask-specific model (SSM) are performed at a UE or user device.

[0096] FIG. 7 is a diagram of a network in which training of generic models (GMs) and training of subtask-specific models (SSMs) are performed at network nodes (e.g., gNBs, APs, BSs, core network nodes, CUs and / or DUs, RAN nodes, TRPs (transmission / reception points)). Thus, for example, both the GMTU and MMTU can reside within the network node 710 of FIG. 7 or can be provided within the network node 710 of FIG. 7. Referring to FIG. 7, the network node 710 can communicate with a UE 712. Meta-learning or training / modification of the subtask-specific models (e.g., based on the generic model, training data, and / or SSM constraints or parameters) can be performed by the network node 710. Thus, in FIG. 7, the network node 710 can manage ML models (e.g., generic models and subtask-specific models), which can be used, applied, or implemented by the UE 712.

[0097] In step 1 of Figure 7, when a new generic model needs to be introduced (or provided for use in a UE or other node), the network node 710 requests corresponding training data from one or more (selected) UEs. Thus, in step 1 of Figure 7, the network node 710 can send a request for task-specific training data and constraints to the UE 712 (and possibly other UEs).

[0098] In step 2 of FIG. 7, the UE 712 (which received the request in step 1) may provide the requested task-specific training data and / or constraints to the network node 710.

[0099] In step 3 of FIG. 7, the network node 710 modifies or trains an ML model, which may be a generic task model or generic model (GM) designed or trained to perform or solve a task, such as a generic task. Some descriptions and examples regarding generic (or general) task models and subtask-specific models are provided above. For example, in general mathematical terms, a generic ML model (or generic task model) may find or obtain a solution x to a function f(x) = 0, and a subtask-specific model finds a solution x to a function g(x) = 0, where a known relationship exists between functions g() and f(), e.g., g(x) = α * f(x) + x (possibly one or more) constraints on the value of x. Also, for example, the subtask-specific model may have a set of inputs, hence g(x'), that are a subset of the inputs used by the generic task model x. Similarly, for example, the subtask-specific model may have a set of outputs that are a subset of the outputs of the generic task model.

[0100] In step 4 of FIG. 7, the network node 710 deploys (eg, transmits or communicates) a Generic Model (GM) to selected UE(s), such as UE 712.

[0101] In step 5 of FIG. 7, the UE 712 uses the deployed GM (generic ML model) to perform or execute an ML-enabled task or function based on the GM. During use or implementation of the GM, the UE 712 can identify or determine that adaptation of the generic ML model is beneficial or required, for example, due to a change in input distribution, a decrease in accuracy, a decrease in performance, or the like, or due to the need to apply this model to a slightly different (e.g., but related) task (e.g., subtask). Note that this capability may require the UE to obtain details about the contents of the generic model, i.e., how the GM training dataset was generated, whether the dataset is balanced (e.g., label distribution), etc. Alternatively, the network node can also detect the need for ML model adaptation in the UE, for example, the need to adapt (retrain) the generic model to perform a different task or subtask, based on feedback (traditional or ML-related) from the UE.

[0102] In step 6 of FIG. 7, if the UE detects a need for ML application (or retraining) of a generic model in step 6 (e.g., to apply or retrain a GM to perform a different task, e.g., a subtask), then in step 6 the UE 712 may generate and send a request to the network node 710 including instructions for the subtask-specific changes, or instructions for the requested subtask-specific model (SSM), or instructions or requests for retraining / application of the generic model to perform the new task or subtask. Also, for example, when the need for ML application of a generic model is determined or detected by a network node, e.g., a gNB, this request (from the gNB) may still be needed in some cases to indicate the need for a subtask-specific model to the GMTU (in the network node 710 in the case of FIG. 7).

[0103] In step 7 of FIG. 7, a network node (e.g., a GMTU in network node 710) may validate the request received in step 6 (e.g., validate a request to apply or retrain a generic model to perform a different task or subtask, or a request for a subtask-specific model based on the generic model). This validation may be performed based on the provided subtask description, an existing database of possible subtasks, and / or available computing power for such training tasks. Thus, for example, validating the request for a subtask-specific model may include verifying, for the subtask-specific model, at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that at least a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that at least a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0104] In step 8 of Figure 7, the network node may trigger (or cause) the generation of a subtask-specific model (SSM) and its required configuration parameters (such as training data type / source). The task of generating / training the SSM may be passed to the MMTU. Thus, in step 8 of Figure 7, the network node 710 may trigger (or cause) subtask-specific meta-learning or training of the subtask-specific model by the MMTU and the transfer of the subtask parameterization (within the network node 710 from the GMTU to the MMTU).

[0105] At step 9 of FIG. 7, the network node 710 (e.g., which may be or may include one or both of a GMTU and / or an MMTU) may request SSM training data and / or constraints from the UE 712 (or one or more UEs including the UE 712). Note that the SSM training data need not be strictly identical to the GM training data but may be a different set of data or a subset of the GM training data. For example, the SSM training data may be a subtask-specific subset of the GM training data, and the SSM and GM training data may have different granularity, reporting frequency, etc. Thus, for example, at step 9, the network node 710 may transmit a request for at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data to the UE 712.

[0106] In step 10 of FIG. 7, the UE 712 may provide (or transmit to the network node 710) training data specific to the SSM along with any UE-specific constraints (e.g., limitations on the size of ML models supported by the UE, or specific features or capabilities supported or not supported by the UE 712 (e.g., may be related to the ML model or the RRM task for which the ML model may be used), or other constraints).

[0107] In step 11 of FIG. 7, the network node 710 (which may include, for example, a GMTU and / or an MMTU) performs training of the SSM based, for example, on the generic model, the received subtask-specific training data, and / or any UE-specific constraints provided by the UE 712.

[0108] In step 12 of FIG. 7, the network node 710 may deploy (provide or transmit) the trained SSM model to UEs, including UE 712.

[0109] In step 13 of FIG. 7, the UE (including, for example, UE 712) uses or applies the new SSM model to solve the problem or perform the subtask.

[0110] As mentioned above, FIG. 8 is a diagram of a network in which training of a generic model (GM) and training of a subtask-specific model (SSM) are performed at a UE or user device. In FIG. 8, a network node 810 can communicate with a UE 812. In FIG. 8, training of the generic model and the subtask-specific model can be performed by the UE 812. The SSM can be trained or modified to perform the same task or a different task (e.g., a subtask) for which the generic model was used. Training of the GM and / or SSM can be performed by the UE 812 (e.g., a GMTU and / or MMTU provided within the UE 812), and the training can be controlled and / or configured by the network node 810. Also, in the network of FIG. 8, the network node 810 can configure and / or test ML-enabled functions implemented within the UE 812 or performed by the SSM at the UE 812. The UE may, for example, perform training of the ML model without the network node 810 necessarily being aware of the implementation details of the SSM model in the UE 812 (which the network node may or may not be aware of). Also, according to an example embodiment, in FIG. 8, for example, the GMTU may be located in the network node 810 and the MMTU may be located in the UE 812.

[0111] In step 1 of Figure 8, the network node 812 may transmit a request to the UE to train a model for the selected ML-enabled function, including one or more configuration parameters (e.g., transmit a request to the UE 812 to train a generic ML model for a generic task). The network node does not need to indicate to the UE that this is a generic meta-model, thus avoiding the possibility that the UE may perform certain "tricks" when generating the ML model.

[0112] 8, the UE 812 trains its ML model to generate a generic model for performing a generic task, e.g., using input signals, data, configuration parameters, etc. provided by the network node 810. Details of the input signals / data and / or configuration parameters required for the generic model may be provided by the network node 810. The UE 812 does not need to be aware that its model will be treated as a generic model by the network node 810.

[0113] In step 3 of FIG. 8, the UE 812 sends an indication to a network node that the ML model has been trained to perform a general purpose task or solve a problem and is ready to be used.

[0114] 8, the network node 810 triggers or causes the UE 812 to use or apply the generic model, for example, by sending or transmitting a request for the network node 810 to use or apply the generic model to the UE 812. Required input signals / data may also be provided by the network node 810 to the UE 812.

[0115] In step 5 of FIG. 8, the UE 812 uses or applies the generic trained ML model to perform the generic ML-enabled function using the provided input signal / data.

[0116] In step 6 of Figure 8, the UE 812 provides configured feedback to the network node 810 when using the ML-enabled function (GM-based). This step can occur periodically or on a one-off basis (one-time) depending on the ML-enabled function (generic task) under test. As part of this step, the UE 812 can provide or send generic model-based outputs (e.g., outputs of a generic trained model) to the network node 810 so that the network node 810 can verify or confirm that the outputs are correct and that the generic model is operating properly or as expected.

[0117] In step 7 of Figure 8, the network node 810 validates the UE output by comparing the provided feedback (e.g., generic model-based output provided by the UE) with the expected feedback generated by its own generic model (trained in the NW with the same data, configuration, etc.). If this step is declared PASSED (the expected output is consistent with the actual UE output / feedback), the NW proceeds to step 8 for a new subtask-specific test. ELSE (if this step is not declared passed), the network node can return to step 1 to perform the same or a new task (potentially using a different set of input conditions, parameters, etc.).

[0118] 8, the network node 810 may request the UE 812 to modify the ML model for subtask-specific use (e.g., to generate a subtask-specific model based on retraining of the generic mode). The network node 810 may provide configuration parameters, a description of the subtask, etc. to the UE 812.

[0119] In step 9 of FIG. 8, the UE 812 validates the request from the network node to modify the generic ML model for a particular subtask (e.g., the UE validates the request to generate a subtask-specific model based on the trained generic model and other information provided by the network node 810). This validation may be performed based on the provided description of the subtask, an existing database of possible subtasks, and / or available computing power for such training task. Thus, validating may include, for example, verifying for the subtask-specific model at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0120] In step 10 of FIG. 8, the UE 812 trains the SSM (subtask-specific model) with input signals / data from the network according to the received configuration (e.g., based on the trained generic model).

[0121] In step 11 of FIG. 8, the UE 812 indicates to the network node (GMTU) that the ML model (SSM) is trained and ready for use.

[0122] In step 12 of FIG. 8, the network node 810 sends a message or signal to the UE 812 to cause or trigger the UE 812 to use or apply SSM to perform the subtask.

[0123] In step 12 of FIG. 8, the UE 812 uses the subtask-specific ML model (SSM) to execute the ML-enabled function (to perform the subtask) using the provided input signal / data.

[0124] In step 13 of Figure 8, the UE 812 provides configured feedback when using an ML-capable function (SSM-based), for example, the UE 812 can provide an SSM-based output (or an SSM output) to the network node 810. This step 13 can be performed periodically or only once (or one-off) depending on the ML-capable function (e.g., subtask) being tested.

[0125] In step 14 of Figure 8, the network node 810 validates the UE output by comparing the provided feedback (e.g., SSM-based output) with the expected feedback generated by its own SSM (trained in the NW with the same data and configuration, etc.). If this step is declared PASSED (e.g., the provided feedback matches sufficiently within a threshold with the SSM-based output generated by the network node), the network node 810 proceeds to step 8 for a new subtask-specific test or to step 1 for a new task. ELSE (if this step is not passed, e.g., the feedback does not match the expected feedback), the network node 810 proceeds to step 8 of the same subtask (potentially using a different set of input conditions).

[0126] Example use case when the NR entity is a UE: An ML-based model that is part of one or more UE ML-enabled functions can be constructed to perform general-purpose tasks within the UE, such as using a DNN (Deep Neural Network)-based model to output categories / labels and their probabilities, which are then used to trigger the operation of UE beam selection.

[0127] For the same task: The same task can be executed in two different (sets) of UEs using different numbers of beams: the ML-based model is assumed to be initially trained based on only one UE type, e.g., with X=3 beams, or alternatively, only X beams out of Y beams are enabled for each UE during training (X beam can be selected by the UE or network node). Training of the generic model (including testing / certification, etc.) can be performed or implemented within the network node or GMTU based on appropriate feedback / measurement results from the selected UE. The network node or GMTU deploys (or configures) the generic model to the UE (e.g., sends the generic model configuration, constraints, or other parameters to be trained by the UE). After receiving and using the generic model, a specific UE in the network indicates the ability or need to use a subtask-specific model (SSM) for controlling Z beams instead of X (Z<>X). The network node or GMTU initiates training or tuning of the subtask-specific (UE-specific) version of the generic model for the corresponding UE (in addition, other UE-specific information can also be used if available in the network). The subtask-specific model can be generated in the MMTU or a network node, which deploys (or configures) the specific model (SSM) for the UE that requested the subtask-specific model.

[0128] For a similar (e.g., but slightly different) task: A similar (e.g., but slightly different) task of the ML-based model could be (for example) selecting an antenna panel rather than an antenna beam within the same or different UE: the ML-based model is assumed to be initially trained based on only one UE type, e.g., with X=3 beams. Or alternatively, only X beams out of Y beams are enabled for each UE during training (X beam can be selected by the UE or the NW). The model training is performed within the NW / GMTU based on appropriate feedback / measurement results from the selected UE. The network node or GMTU can deploy a generic model (or configure or transmit the model and configuration information) to the UE. After receiving the generic model, a specific UE in the NW indicates the need to use only ML-enabled antenna panel selection rather than beam selection. The NW generates subtask-specific (UE-specific) training or tuning of the generic model for the corresponding UE (in addition, other UE-specific information can be used if available in the NW). The NW deploys (or configures) a specific model for the UE that requested the subtask-specific model.

[0129] From the above illustrative example, the availability of the proposed mechanism to deploy the same ML model for the same task on different UEs (different number of beams) can be directly utilized in adapting the UE ML-enabled functionality of beam selection. For example, the choice and advantage of using meta-learning can be on the network side. The diagram in Figure 8 shows some example signaling for this use case.

[0130] Conformance testing using the proposed approach can also be extended to cases where the ML model is not fully trained within the network node but training is at least partially configurable by the network node (which may be a 3GPP prerequisite for UE-gNB cooperation, for example). The UE ML model can be trained within the UE using published configuration parameter settings received from the network node and test signals provided by the network node. A generic task could, for example, be to use only X=2 beams (out of Y>X supported by the UE). After training and functional verification for X=2, the network node can reconfigure (e.g., modify or retrain) the UE ML-capable capabilities (e.g., modify or retrain the generic model to become an SSM or generate an SSM) to request the use of Y>=Z>2 beams. In response to this request, the UE can indicate the need for further training and test signals from the network. In this case, the choice and advantage of using meta-learning is purely on the UE side. However, its use can be detected by measuring the time spent adapting a generic / initial model for X beams to the new requirements for Z beams. If additional "retraining time" constraints were imposed on the UE, then meta-learning would almost have to be implemented within the UE in order to be conformant. The diagram in Figure 8 shows an example of signaling that can be used for these types of UE conformance testing use cases.

[0131] Example when the NR element is a gNB or network node: An example of a case where the NR element performing the ML-capable function is a gNB may be the optimization of UL (uplink) power control parameters to determine OLPC (open loop power control) P0 and / or α and / or to determine CLPC (closed loop power control) adjustment steps. For Figure 7, the following implementation may be used or possible: GMTU / MMTU resides on the NW side. The NR element is a gNB. The SSM collector is a selected cell edge / cell center UE.

[0132] OLPC can be an exemplary use case. Typically, OLPC (open loop power control, a specific usage function or subtask for the SSM model) parameters (P and α) are configured at the cell level, e.g., UEs working within a cell can use the same values. However, this specification supports per-UE (UE-specific) signaling of these parameters, so more optimized approaches can also be used or implemented. One such approach is to use UE clusters based on their radio proximity (DL (downlink) RSRP (Reference Signal Received Power)) to serving or neighboring cells. In this case, there are at least two UE clusters ("cell edge" and "cell center") with different sets of OLPC parameters, which can be controlled separately by ML-enabled functions. The proposed meta-learning approach described herein allows for training a generic model using available radio measurements from one or more cells (within a selected geographic area) as inputs for the purpose of controlling both P and / or α of OLPC. This generic model can then be deployed to each gNB or network node, potentially including network nodes or gNBs for which training data was not used. During operation, some of the network nodes or gNBs identify a third UE cluster that may be beneficial to control separately from the “cell edge” and “cell center” clusters they already contain, e.g., clusters of high-speed UEs. These gNBs or network nodes can then request subtask-specific models (e.g., for the same task) from the GMTU, and after authentication or verification of the request, SSMs can be trained (by the UE) and redistributed or retransmitted to the corresponding network nodes or gNBs. SSMs can also be differentiated based on which OLPCs (similar tasks) are being controlled. For example, the system can also include an identification algorithm that determines the need for subtask-specific models and the provision and / or collection of input training data.

[0133] Some further examples are provided.

[0134] Example 1. A method may include: a first model training unit receiving, from a second model training unit, a trigger instruction for triggering or causing modification of a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; the first model training unit receiving training data for the subtask-specific model from one or more subtask-specific model collectors; the first model training unit modifying the subtask-specific model based on the one or more parameters of the generic model, the subtask, or the subtask-specific model, and the training data received from the one or more subtask-specific model collectors; and the first model training unit transmitting the modified subtask-specific model to a first wireless node.

[0135] Example 2. The method of Example 1, wherein modifying includes at least one of modifying one or more weights of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weights or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0136] Example 3. The method of Example 1 or 2, wherein receiving the trigger instruction includes the first model training unit receiving from the second model training unit one or more of: generic model information including one or more of the following: an architecture of the generic model, weights of the generic model, a loss function and / or activation function of the generic model, and / or a type of output of the generic model; subtask parameterization including constraints of the subtask-specific model or subtask-specific cost functions of the subtask-specific model; and / or identifiers of one or more subtask-specific model collectors.

[0137] Example 4. The method of any of Examples 1 to 3, wherein the first model training unit modifying the subtask-specific models based on the generic model includes performing one or more of: pruning or reducing the size of the generic model based on one or more constraints of the generic model and the subtask-specific model so that the subtask-specific models fall within a maximum allowable subtask-specific model constraint; deactivating one or more inputs of the generic model so that the subtask-specific model depth or size inputs match the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0138] Example 5. The method of any of Examples 1 to 4, further comprising transmitting the modified subtask-specific model from the first model training unit to a second model training unit.

[0139] Example 6. The method of any of Examples 1 to 5, further comprising transmitting the modified subtask-specific model from the first model training unit to at least one of the one or more subtask-specific model collectors.

[0140] Example 7. The method of any of Examples 1 to 6, wherein either the first model training unit and the second model training unit are provided on a second wireless node, or the first model training unit is provided on a second wireless node and the second model training unit is provided on a third wireless node.

[0141] Example 8. The method of any of Examples 1 to 7, further comprising the first model training unit transmitting to the second model training unit a request for training or meta-learning a subtask-specific model for the subtask.

[0142] Example 9. The method of any of Examples 1 to 8, wherein the second model training unit comprises a generic model training unit configured to modify a generic model for a generic task, and the first model training unit is a meta-learning model training unit or a specific model training unit configured to modify a specific model for a subtask or a subtask-specific model.

[0143] Example 10. The method of any of Examples 1 to 8, wherein one or more of the first wireless node, the second wireless node, or the third wireless node comprises at least one of a user equipment, a user device, a base station, or a gNB.

[0144] Example 11. An apparatus comprising at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code are configured to cause the apparatus to receive, by the at least one processor, at least a trigger instruction for triggering or causing a first model training unit to modify a subtask-specific model from a second model training unit, an instruction for a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model. the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; a first model training unit receives training data for the subtask-specific model from one or more subtask-specific model collectors; the first model training unit modifies the subtask-specific model based on one or more parameters of the generic model, the subtask, or the subtask-specific model and the training data received from the one or more subtask-specific model collectors; and the first model training unit transmits the modified subtask-specific model to the first wireless node.

[0145] Example 12. An apparatus comprising: means for a first model training unit to receive from a second model training unit a trigger instruction for triggering or causing modification of a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; means for the first model training unit to receive training data for the subtask-specific model from one or more subtask-specific model collectors; means for the first model training unit to modify the subtask-specific model based on the one or more parameters of the generic model, the subtask, or the subtask-specific model and the training data received from the one or more subtask-specific model collectors; and means for the first model training unit to transmit the modified subtask-specific model to a first wireless node.

[0146] Example 13. A non-transitory computer-readable storage medium including stored instructions that, when executed by at least one processor, are configured to cause a computing system to: receive, from a second model training unit, a trigger instruction to trigger or cause a first model training unit to modify a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; receive, from one or more subtask-specific model collectors, training data for the subtask-specific model; modify, based on the one or more parameters of the generic model, the subtask, or the subtask-specific model, and the training data received from the one or more subtask-specific model collectors; and transmit, to a first wireless node, the modified subtask-specific model.

[0147] Example 14. A method including receiving a request to modify a subtask-specific model for a first wireless node based on a generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, where the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; transmitting, by a second model training unit to the first model training unit, a trigger instruction to trigger or cause the first model training unit to modify the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configuring, by the second model training unit, one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and receiving, by the second model training unit, from the first model training unit, the modified subtask-specific model modified by the first model training unit.

[0148] Example 15. The method of Example 14, wherein the transmitting includes the second model training unit transmitting to the first model training unit one or more of: generic model information including one or more of the generic model architecture, the generic model weights, the generic model loss function and / or activation function, and / or the generic model output type; subtask parameterization including subtask-specific model constraints or subtask-specific cost functions for the subtask-specific model; and / or identifiers of one or more subtask-specific model collectors.

[0149] Example 16. The method of Example 14 or 15, wherein either the first model training unit and the second model training unit are provided in a second wireless node, or the first model training unit is provided in the second wireless node and the second model training unit is provided in a third wireless node.

[0150] Example 17. The method of any of Examples 14 to 16, wherein receiving a request to modify the subtask-specific model or determining the need to modify the subtask-specific model includes: a second model training unit receiving a request to modify or train a subtask-specific model for the subtask from the first model training unit; and validating the request to modify or train the subtask-specific model for the subtask.

[0151] Example 18. The method of any of Examples 14 to 17, wherein the second model training unit comprises a generic model training unit configured to correct or train a generic model for a generic task, and the first model training unit is a meta-learning model training unit or a specific model training unit configured to correct or train a specific model for a subtask or a subtask-specific model.

[0152] Example 19. The method of any of Examples 14 to 18, wherein one or more of the first wireless node, the second wireless node, or the third wireless node comprises at least one of a user equipment, a user device, a base station, or a gNB.

[0153] Example 20. An apparatus comprising at least one processor and at least one memory containing computer program code, the at least one memory and the computer program code causing the apparatus, by the at least one processor, to at least: receive a request to modify a subtask-specific model for a first wireless node based on a generic model; or determine a need to modify a subtask-specific model for the first wireless node based on the generic model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; and a second model training unit to train the first model; The second model training unit is configured to transmit a trigger instruction for triggering or causing the modification of the task-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configure one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and cause the second model training unit to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0154] Example 21. An apparatus comprising: means for receiving a request to modify a subtask-specific model for a first wireless node based on a generic model or determining a need to modify a subtask-specific model for the first wireless node based on a generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; means for a second model training unit to transmit to the first model training unit a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; means for the second model training unit to configure one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and means for the second model training unit to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0155] Example 22. A non-transitory computer-readable storage medium including stored instructions, the instructions, when executed by at least one processor, causing a computing system to: receive a request to modify a subtask-specific model for a first wireless node based on a generic model; or determine a need to modify a subtask-specific model for the first wireless node based on a generic model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; and trigger a second model training unit to the first model training unit to modify the subtask-specific model. and transmitting a trigger instruction to cause the first model training unit to perform or cause the first model training unit to perform a subtask-specific model modification based on the generic model, an instruction for the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configuring the one or more subtask-specific model collectors to provide training data to the first model training unit for modifying the subtask-specific model; and causing the second model training unit to receive from the first model training unit the modified subtask-specific model modified by the first model training unit.

[0156] Example 23. A method for implementing or assisting in the implementation of a machine-learnable generic task, comprising: a user equipment determining a trained generic model for implementing or assisting in the implementation of the machine-learnable generic task; determining, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; the user equipment transmitting, to a network node, the one or more generic model-based outputs; and the user equipment receiving, from the network node, a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the request including configuration parameters for the subtask-specific model, the subtask-specific model for implementing or assisting in the implementation of the machine-learnable subtask. validating the request for the subtask-specific model; user equipment modifying the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and user equipment transmitting to the network node a subtask-specific model output based on or using the modified subtask-specific model performing or executing the machine-learnable subtask.

[0157] Example 24. The method of Example 23, wherein modifying includes at least one of modifying one or more weights of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weights or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0158] Example 25. The method of Example 23 or 24, wherein validating the request for the subtask-specific model includes verifying, for the subtask-specific model, at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0159] Example 26. The method of any of Examples 23 to 25, wherein receiving a request for a subtask-specific model including configuration parameters for the subtask-specific model includes receiving trigger instructions for triggering or causing meta-learning or training of the subtask-specific model, and one or more parameters of the subtask or subtask-specific model to be used to train the subtask-specific model based on the generic model, and receiving a parameterization of the subtask including constraints for the subtask-specific model or a subtask-specific cost function for the subtask-specific model.

[0160] Example 27. The method of any of Examples 23 to 26, wherein the configuration parameters of the subtask or subtask-specific model include one or more constraints for the subtask-specific model, and wherein modifying the subtask-specific model based on the generic model includes performing one or more of: pruning or reducing the size of the generic model based on the one or more constraints of the generic model and the subtask-specific model so that the subtask-specific model falls within a maximum allowable subtask-specific model depth or size; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model conform to the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0161] Example 28. The method of any of Examples 23 to 27, wherein determining the one or more generic model-based outputs includes: user equipment receiving a request from a network node to train a generic model for the machine-learnable generic task; user equipment training the generic model based on configurations or inputs received from the network node; and performing or executing the machine-learnable generic task using the trained generic model to obtain one or more generic model-based outputs.

[0162] Example 29. The method of Example 28, wherein the request for the subtask-specific model is received by the user equipment in response to the user equipment transmitting to the network node one or more generic model-based outputs of the trained generic model.

[0163] Example 30. An apparatus comprising at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code are configured to cause the apparatus, by the at least one processor, to at least: determine a trained generic model for performing or assisting in performing a generic machine-learnable task; determine, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; transmit, by the user equipment, the one or more generic model-based outputs to a network node; and receive, by the user equipment, a request for a subtask-specific model from the network node, based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model. the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; validating a request for the subtask-specific model; the user equipment modifying the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; the user equipment performing or executing the machine-learnable subtask based on or using the modified subtask-specific model; and the user equipment transmitting to the network node a subtask-specific model output based on or using the trained subtask-specific model performing or executing the machine-learnable subtask.

[0164] Example 31. An apparatus, comprising: means for a user equipment to determine a trained generic model for performing or assisting in performing a generic machine-learnable subtask; means for determining, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; means for the user equipment to transmit the one or more generic model-based outputs to a network node; and means for the user equipment to receive from the network node a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, wherein the subtask-specific model is configured to perform or assist in performing a machine-learnable subtask. means for validating a request for a subtask-specific model; means for the user equipment to modify the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; means for the user equipment to perform or execute a machine-learnable subtask based on or using the modified subtask-specific model; and means for the user equipment to transmit a subtask-specific model output to a network node based on performing or executing the machine-learnable subtask based on or using the trained subtask-specific model.

[0165] Example 32. A non-transitory computer-readable storage medium including stored instructions, the instructions, when executed by at least one processor, causing a computing system to: determine a trained generic model for performing or assisting in performing a generic machine-learnable task; determine, based on the trained generic model, one or more generic model-based outputs based on one or more signals or inputs; transmit, by the user equipment, the one or more generic model-based outputs to a network node; and receive, by the user equipment, from the network node, a request for a subtask-specific model based at least in part on the one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, wherein the subtask-specific model is the user equipment is configured to perform or assist in performing a machine-learnable subtask; validate a request for a subtask-specific model; modify the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; perform or execute the machine-learnable subtask based on or using the modified subtask-specific model; and transmit a subtask-specific model output to a network node based on the user equipment performing or executing the machine-learnable subtask based on or using the trained subtask-specific model.

[0166] Example 33. A method including: a network node determining a trained generic model for performing or assisting in performing a machine-learning enabled generic task; the network node providing the trained generic model to a user equipment; the network node receiving a request for a subtask-specific model from the user equipment; validating the request for the subtask-specific model; the network node transmitting a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; the network node receiving from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and the network node transmitting the modified subtask-specific model to the user equipment.

[0167] Example 34. The method of Example 33, wherein modifying includes at least one of modifying one or more weights of the subtask-specific model, training the subtask-specific model, retraining the subtask-specific model, configuring or updating one or more weights or parameters of the subtask-specific model, and / or upgrading or downgrading the subtask-specific model.

[0168] Example 35. The method of Example 33 or 34, wherein validating the request for the subtask-specific model includes verifying, for the subtask-specific model, at least one of: that the requested subtask-specific model is on a list of allowed subtask-specific models; that a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or that a threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific model.

[0169] Example 36. The method of any of Examples 33 to 35, wherein modifying, by the network node, the subtask-specific models based on the generic model includes performing one or more of: pruning or reducing the size of the generic model based on one or more constraints of the generic model and the subtask-specific model so that the subtask-specific models fall within a maximum allowable subtask-specific model depth or size; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from one or more subtask-specific model collectors; replacing the generic model activation function with a subtask-specific model-specific activation function; or defining a subtask-specific model-specific cost function.

[0170] Example 37. An apparatus comprising at least one processor and at least one memory containing computer program code, wherein the at least one memory and the computer program code are configured, by the at least one processor, to cause the apparatus to at least: determine a trained generic model for performing or assisting in performing a machine-learning enabled generic task; provide the trained generic model to a user equipment; receive a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive from the user equipment the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit the modified subtask-specific model to the user equipment.

[0171] Example 38. An apparatus comprising: means for a network node to determine a trained generic model for performing or assisting in performing a machine-learning enabled generic task; means for the network node to provide the trained generic model to a user equipment; means for the network node to receive a request for a subtask-specific model from the user equipment; means for validating the request for the subtask-specific model; means for the network node to transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; means for the network node to receive from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; means for the network node to modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and means for the network node to transmit the modified subtask-specific model to the user equipment.

[0172] Example 39. A non-transitory computer-readable storage medium including stored instructions that, when executed by at least one processor, are configured to cause a computing system to: determine a trained generic model for performing or assisting in performing a machine-learnable generic task; provide the trained generic model to a user equipment; receive a request for a subtask-specific model from the user equipment; validate the request for the subtask-specific model; transmit a request for at least one of a subtask-specific model configuration or constraints and / or subtask-specific model training data to the user equipment; receive from the user equipment the at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configuration or constraints and / or subtask-specific model training data; and transmit the modified subtask-specific model to the user equipment.

[0173] 9 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) 1200 according to an example embodiment. The wireless station 1200 may, for example, comprise one or more (e.g., two as shown in FIG. 9) RF (radio frequency) or wireless transceivers 1202A, 1202B, each comprising a transmitter for transmitting signals and a receiver for receiving signals. The wireless station also comprises a processor or control unit / entity (controller) 1204 for executing instructions or software and controlling transmission and reception of signals, and a memory 1206 for storing data and / or instructions.

[0174] The processor 1204 may also make decisions or determinations, generate frames, packets, or messages for transmission, decode received frames or messages for further processing, and perform other tasks or functions described herein. The processor 1204 may be a baseband processor, e.g., generate messages, packets, frames, or other signals for transmission via the wireless transceiver 1202 (1202A or 1202B). The processor 1204 may control the transmission of signals or messages over a wireless network and may control the reception of signals, messages, etc. over a wireless network (e.g., after being downconverted by the wireless transceiver 1202). The processor 1204 may be programmable and capable of executing software or other instructions stored in memory or on other computer media to perform various tasks and functions described above, such as one or more of the tasks or methods described above. The processor 1204 may be (or include) hardware, programmable logic, a programmable processor executing software or firmware, and / or any combination thereof. Using other terminology, the processor 1204 and the transceiver 1202 together may be considered, for example, a wireless transmit / receive system.

[0175] Further, with reference to FIG. 9, controller (or processor) 1208 may execute software and instructions and may provide overall control over station 1200, may provide control over other systems not shown in FIG. 9, such as control of input / output devices (e.g., display, keypad), and / or may execute software for one or more applications that may be provided on wireless station 1200, such as, for example, an email program, an audio / video application, a word processor, a voice-over-IP application, or other application or software.

[0176] Additionally, a storage medium may be provided containing stored instructions that, when executed by a controller or processor, will cause the processor 1204 or other controllers or processors to perform one or more of the functions or tasks described above.

[0177] According to another exemplary embodiment, the RF or wireless transceiver(s) 1202A / 1202B can receive signals or data and / or transmit or transmit signals or data. The processor 1204 (and possibly the transceiver 1202A / 1202B) can control the RF or wireless transceiver 1202A or 1202B to receive, transmit, broadcast, or transmit signals or data.

[0178] Embodiments of the various technologies described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or combinations of them. Embodiments may also be implemented as a computer program product, such as a computer program tangibly embodied in an information carrier, for example, a machine-readable storage device or a propagated signal, for execution by or to control the operation of a data processing apparatus, for example, a programmable processor, a computer, or multiple computers. Embodiments may also be provided on a computer-readable medium or a computer-readable storage medium, where the storage medium may be a non-transitory medium. Embodiments of the various technologies may also include embodiments provided via a transitory signal or medium and / or embodiments of programs and / or software downloadable via the Internet or other network, where the network may be either a wired network and / or a wireless network. Furthermore, embodiments may be provided via machine-type communications (MTC) and via the Internet of Things (IoT).

[0179] The computer program may be in source code form, object code form, or some intermediate form, and may be stored on some kind of carrier, distribution medium, or computer-readable medium, which may be any entity or device capable of transmitting a program. Such carriers include, for example, recording media, computer memory, read-only memory, optical and / or electrical carrier signals, telecommunications signals, and software distribution packages. Depending on the processing power required, the computer program may be executed in a single electronic digital computer or distributed across several computers.

[0180] Furthermore, various embodiments of the technologies described herein may employ cyber-physical systems (CPSs)—systems that coordinate computational elements to control physical entities. CPSs may enable the realization and utilization of vast amounts of interconnected ICT devices (sensors, actuators, processors, microcontrollers, etc.) embedded within physical objects in different locations. Mobile cyber-physical systems, where the physical system has inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robots and electronic devices carried by humans or animals. The increasing penetration of smartphones has led to increased interest in the field of mobile cyber-physical systems. Thus, various embodiments of the technologies described herein may be provided via one or more of these technologies.

[0181] Computer programs such as those described above can be written in any form of programming language, including compiled or interpreted languages, and can be implemented in any form, such as a stand-alone program, or as a module, component, subroutine, or other unit or portion thereof suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at a single location, or distributed across multiple locations and interconnected by a communication network.

[0182] The method steps may be performed by one or more programmable processors executing computer programs or portions of computer programs to perform functions by operating on input data and generating output. The method steps may also be performed by, and an apparatus may be implemented as, special purpose logic circuitry, for example an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0183] Processors suitable for executing a computer program include, by way of example, general-purpose and special-purpose microprocessors, as well as one or more processors of any kind of digital computer, chip, or chipnet. Typically, a processor receives instructions and data from a read-only memory or a random-access memory, or both. Elements of a computer may include at least one processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer may also include one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data, or may be operatively coupled to receive data from or transfer data to such mass storage devices, or both. Information carriers suitable for embodying computer program instructions and data all include forms of non-volatile memory, including, by way of example, semiconductor memory devices, such as EPROMs, EEPROMs, and flash memory devices, magnetic disks, such as internal or removable hard disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special-purpose logic circuitry.

[0184] To enable user interaction, embodiments may be implemented on a computer that includes a display device, e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor, for displaying information to the user, and a user interface, such as a keyboard and pointing device, e.g., a mouse or trackball, through which the user can provide input to the computer. Other types of devices may be used to similarly enable user interaction; for example, feedback provided to the user may be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback, and input from the user may be received in any form, including acoustic, speech, or tactile input.

[0185] Embodiments may be implemented within a computing system that includes back-end components, e.g., a data server, or middleware components, e.g., an application server, or front-end components, e.g., a client computer having a graphical user interface or web browser through which a user can interact with the embodiments, or any combination of such back-end, middleware, or front-end components. The components may be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include local area networks (LANs) and wide area networks (WANs), e.g., the Internet.

[0186] While certain features of the embodiments described herein have been shown as described herein, many modifications, substitutions, changes, and equivalents will occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and variations that fall within the true spirit of the various embodiments.

Claims

1. 1. A method comprising: the first model training unit receiving from the second model training unit a trigger instruction for triggering or causing modification of the subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing the machine-learnable subtask; a first model training unit receiving training data for the subtask-specific models from one or more subtask-specific model collectors; a first model training unit modifying the subtask-specific model based on one or more parameters of the generic model, the subtask, or the subtask-specific model, and training data received from one or more subtask-specific model collectors; the first model training unit transmitting the modified subtask-specific model to the first wireless node; A method comprising:

2. To correct, modifying one or more weightings of the subtask-specific models; training subtask-specific models; retraining subtask-specific models; Configuring or updating one or more weights or parameters of the subtask-specific model; and / or Upgrading or downgrading subtask-specific models The method of claim 1 , comprising at least one of:

3. receiving a trigger instruction, The first model training unit receives from the second model training unit: information about the generic model, including one or more of the following information: an architecture of the generic model, weights of the generic model, a loss function and / or activation function of the generic model, and / or a type of output of the generic model; a parameterization of the subtask, including constraints of the subtask-specific model or a subtask-specific cost function of the subtask-specific model; and / or Identifiers of one or more subtask-specific model collectors receiving one or more of 3. The method of claim 1 or 2, comprising:

4. The first model training unit modifies the subtask-specific models based on the generic model based on one or more constraints of the generic model and the subtask-specific models; pruning or reducing the size of the generic model so that the subtask-specific models fall within the maximum allowable subtask-specific model constraints; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from the one or more subtask-specific model collectors; Replacing the generic model activation function with a subtask-specific model-specific activation function, or Defining subtask-specific model-specific cost functions The method of any of claims 1 to 3, comprising performing one or more of the following:

5. The first model training unit transmits the modified subtask-specific model to the second model training unit. The method of any one of claims 1 to 4, further comprising:

6. the first model training unit transmitting the modified subtask-specific model to at least one of the one or more subtask-specific model collectors. The method of any one of claims 1 to 5, further comprising:

7. the first model training unit and the second model training unit are provided in a second wireless node; or the first model training unit is provided in a second wireless node and the second model training unit is provided in a third wireless node; 7. The method according to any one of claims 1 to 6.

8. The first model training unit transmits to the second model training unit a request for training or meta-learning a subtask-specific model for the subtask. The method of any of claims 1 to 7, further comprising:

9. the second model training unit comprises a generic model training unit configured to modify a generic model for a generic task; the first model training unit is a meta-learning model training unit or a specific model training unit configured to modify a specific model for a subtask or a subtask-specific model; 9. The method according to any one of claims 1 to 8.

10. one or more of the first wireless node, the second wireless node, or the third wireless node; 9. The method of claim 1, comprising at least one of a user equipment, a user device, a base station, or a gNB.

11. at least one processor; at least one memory containing computer program code; An apparatus comprising: At least one memory and computer program code are configured by at least one processor to cause the device to at least: a first model training unit receiving from a second model training unit a trigger instruction for triggering or causing modification of a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; a first model training unit receiving training data for the subtask-specific models from one or more subtask-specific model collectors; a first model training unit modifying the subtask-specific model based on one or more parameters of the generic model, the subtask, or the subtask-specific model, and training data received from one or more subtask-specific model collectors; the first model training unit transmitting the modified subtask-specific model to the first wireless node; An apparatus configured to cause

12. 1. An apparatus comprising: means for the first model training unit to receive from the second model training unit a trigger instruction for triggering or causing modification of the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing the machine-learnable subtask; and means for the first model training unit to receive training data for the subtask-specific models from one or more subtask-specific model collectors; means for the first model training unit to modify the subtask-specific models based on one or more parameters of the generic model, the subtask, or the subtask-specific models, and training data received from one or more subtask-specific model collectors; means for transmitting the modified subtask-specific model by the first model training unit to the first wireless node; An apparatus comprising:

13. A non-transitory computer-readable storage medium containing stored instructions, the instructions, when executed by at least one processor, causing a computing system to: a first model training unit receiving from a second model training unit a trigger instruction for triggering or causing modification of a subtask-specific model, an instruction of a generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; a first model training unit receiving training data for the subtask-specific models from one or more subtask-specific model collectors; a first model training unit modifying the subtask-specific model based on one or more parameters of the generic model, the subtask, or the subtask-specific model, and training data received from one or more subtask-specific model collectors; the first model training unit transmitting the modified subtask-specific model to the first wireless node; 1. A non-transitory computer-readable storage medium configured to cause

14. 1. A method comprising: receiving a request to modify a subtask-specific model for the first wireless node based on the generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; transmitting, by the second model training unit to the first model training unit, a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configuring one or more subtask-specific model collectors such that the second model training unit provides training data to the first model training unit for modifying the subtask-specific models; the second model training unit receiving from the first model training unit the modified subtask-specific model modified by the first model training unit; A method comprising:

15. To transmit, the second model training unit to the first model training unit; information about the generic model, including one or more of the following information: an architecture of the generic model, weights of the generic model, a loss function and / or activation function of the generic model, and / or a type of output of the generic model; a parameterization of the subtask, including constraints of the subtask-specific model or a subtask-specific cost function of the subtask-specific model; and / or Identifiers of one or more subtask-specific model collectors 15. The method of claim 14, comprising transmitting one or more of:

16. the first model training unit and the second model training unit are provided in a second wireless node; or the first model training unit is provided in a second wireless node and the second model training unit is provided in a third wireless node; 16. The method of claim 14 or 15.

17. Receiving a request to modify the subtask-specific model or determining the need to modify the subtask-specific model includes: receiving, by the second model training unit, a request from the first model training unit to modify or train a subtask-specific model for the subtask; Verifying the requirement to modify or train a subtask-specific model for the subtask; 17. The method of any of claims 14 to 16, comprising:

18. the second model training unit comprises a generic model training unit configured to modify or train a generic model for a generic task; the first model training unit is a meta-learning model training unit or a specific model training unit configured to correct or train a specific model for the subtask or a subtask-specific model; 18. The method according to any one of claims 14 to 17.

19. one or more of the first wireless node, the second wireless node, or the third wireless node; 19. The method of any of claims 14 to 18, comprising at least one of a user equipment, a user device, a base station, or a gNB.

20. at least one processor; at least one memory containing computer program code; An apparatus comprising: At least one memory and computer program code are configured by at least one processor to cause the device to at least: receiving a request to modify a subtask-specific model for the first wireless node based on the generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; transmitting, by the second model training unit to the first model training unit, a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an indication of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configuring one or more subtask-specific model collectors such that the second model training unit provides training data to the first model training unit for modifying the subtask-specific models; the second model training unit receiving from the first model training unit the modified subtask-specific model modified by the first model training unit; An apparatus configured to cause

21. 1. An apparatus comprising: means for receiving a request to modify a subtask-specific model for the first wireless node based on the generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; and means for the second model training unit to transmit to the first model training unit a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an instruction of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; means for configuring one or more subtask-specific model collectors such that the second model training unit provides training data to the first model training unit for modifying the subtask-specific models; means for the second model training unit to receive from the first model training unit the modified subtask-specific model modified by the first model training unit; An apparatus comprising:

22. A non-transitory computer-readable storage medium containing stored instructions, the instructions, when executed by at least one processor, causing a computing system to: receiving a request to modify a subtask-specific model for the first wireless node based on the generic model or determining a need to modify a subtask-specific model for the first wireless node based on the generic model, wherein the subtask-specific model is for performing or assisting in performing a machine-learnable subtask; transmitting, by the second model training unit to the first model training unit, a trigger instruction for triggering or causing the first model training unit to modify the subtask-specific model, an indication of the generic model trained for the generic task by the second model training unit, and one or more parameters of the subtask or subtask-specific model to be used to modify the subtask-specific model based on the generic model; configuring one or more subtask-specific model collectors such that the second model training unit provides training data to the first model training unit for modifying the subtask-specific models; the second model training unit receiving from the first model training unit the modified subtask-specific model modified by the first model training unit; 1. A non-transitory computer-readable storage medium configured to cause

23. 1. A method comprising: determining, by the user equipment, a trained generic model for performing or assisting in performing a machine-learning enabled generic task; determining one or more generic model-based outputs based on the one or more signals or inputs based on the trained generic model; transmitting, by the user equipment, to a network node, one or more generic model-based outputs; receiving, by a user equipment, from a network node, a request for a subtask-specific model based at least in part on one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; Validating the requirements for the subtask-specific model; The user equipment modifies the subtask-specific model based on the configuration parameters of the trained generic model and the subtask or subtask-specific model; performing or executing, by the user equipment, the machine-learnable subtask based on or using the modified subtask-specific model; transmitting, by the user equipment to the network node, the subtask-specific model output based on or performing the machine-learnable subtask using the modified subtask-specific model; A method comprising:

24. To correct, modifying one or more weightings of the subtask-specific models; training subtask-specific models; retraining subtask-specific models; Configuring or updating one or more weights or parameters of the subtask-specific model; and / or 24. The method of claim 23, comprising at least one of upgrading or downgrading the subtask-specific model.

25. Verifying requirements against the subtask-specific model The requested subtask-specific model is on the list of allowed subtask-specific models, a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or A threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific models. At least one of 25. The method of claim 23 or 24, comprising verifying:

26. receiving a request for a subtask-specific model including configuration parameters for the subtask-specific model; 26. The method of claim 23, comprising receiving a trigger instruction for triggering or causing meta-learning or training of a subtask-specific model and one or more parameters of the subtask or subtask-specific model to be used to train the subtask-specific model based on the generic model, and comprising receiving a parameterization of the subtask including constraints of the subtask-specific model or a subtask-specific cost function of the subtask-specific model.

27. The configuration parameters of the subtask or subtask-specific model include one or more constraints of the subtask-specific model, and the user equipment modifies the subtask-specific model based on the generic model based on the one or more constraints of the generic model and the subtask-specific model. pruning or reducing the size of the generic model so that the subtask-specific models fall within a maximum allowable subtask-specific model depth or size; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from the one or more subtask-specific model collectors; Replacing the generic model activation function with a subtask-specific model-specific activation function, or Defining subtask-specific model-specific cost functions 27. The method of any of claims 23 to 26, comprising performing one or more of:

28. Determining one or more generic model-based outputs receiving, by a user equipment, from a network node, a request to train a generic model for a machine-learnable generic task; training, by the user equipment, a generic model based on configurations or inputs received from a network node; performing or executing a machine-learning enabled generic task using the trained generic model to obtain one or more generic model-based outputs; 28. The method of any of claims 23 to 27, comprising:

29. 30. The method of claim 28, wherein the request for the subtask-specific model is received by the user equipment in response to the user equipment transmitting to the network node one or more generic model-based outputs of the trained generic model.

30. at least one processor; at least one memory containing computer program code; An apparatus comprising: At least one memory and computer program code are configured by at least one processor to cause the device to at least: determining, by the user equipment, a trained generic model for performing or assisting in performing a generic machine-learnable task; determining one or more generic model-based outputs based on the one or more signals or inputs based on the trained generic model; transmitting, by the user equipment, to a network node, one or more generic model-based outputs; receiving, by a user equipment, from a network node, a request for a subtask-specific model based at least in part on one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; Validating the requirements for the subtask-specific model; The user equipment modifies the subtask-specific model based on the configuration parameters of the trained generic model and the subtask or subtask-specific model; performing or executing, by the user equipment, the machine-learnable subtask based on or using the modified subtask-specific model; transmitting, by the user equipment to the network node, a subtask-specific model output based on or using the trained subtask-specific model to perform or execute the machine-learnable subtask; An apparatus configured to cause

31. 1. An apparatus comprising: means for the user equipment to determine a trained generic model for performing or assisting in performing a generic machine-learnable task; means for determining one or more generic model-based outputs based on one or more signals or inputs based on the trained generic model; means for transmitting, by the user equipment, to a network node, one or more generic model-based outputs; means for the user equipment to receive from a network node a request for a subtask-specific model based at least in part on one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; and means for validating the request against the subtask-specific model; means for the user equipment to modify the subtask-specific model based on configuration parameters of the trained generic model and the subtask or subtask-specific model; means, at the user equipment, for implementing or executing the machine-learnable subtask based on or using the modified subtask-specific model; means for the user equipment to transmit to the network node a subtask-specific model output based on or using the trained subtask-specific model to perform or execute the machine-learnable subtask; An apparatus comprising:

32. A non-transitory computer-readable storage medium containing stored instructions, the instructions, when executed by at least one processor, causing a computing system to: determining, by the user equipment, a trained generic model for performing or assisting in performing a generic machine-learnable task; determining one or more generic model-based outputs based on the one or more signals or inputs based on the trained generic model; transmitting, by the user equipment, to a network node, one or more generic model-based outputs; receiving, by a user equipment, from a network node, a request for a subtask-specific model based at least in part on one or more generic model-based outputs, the subtask-specific model including configuration parameters for the subtask-specific model, the subtask-specific model being for performing or assisting in performing a machine-learnable subtask; Validating the requirements for the subtask-specific model; The user equipment modifies the subtask-specific model based on the configuration parameters of the trained generic model and the subtask or subtask-specific model; performing or executing, by the user equipment, the machine-learnable subtask based on or using the modified subtask-specific model; transmitting, by the user equipment to the network node, a subtask-specific model output based on or using the trained subtask-specific model to perform or execute the machine-learnable subtask; 1. A non-transitory computer-readable storage medium configured to cause

33. 1. A method comprising: a network node determining a trained generic model for performing or assisting in performing a machine-learnable generic task; a network node providing a trained generic model to a user equipment; receiving, by a network node, a request for a subtask-specific model from a user equipment; Validating the requirements of the subtask-specific model; transmitting, by the network node to the user equipment, a request for at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; receiving, by a network node, from a user equipment, at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configurations or constraints and / or the subtask-specific model training data; the network node transmitting the modified subtask-specific model to the user equipment; A method comprising:

34. To correct, modifying one or more weightings of the subtask-specific models; training subtask-specific models; retraining subtask-specific models; Setting or updating one or more weights or parameters of the subtask-specific model; and / or 34. The method of claim 33, comprising at least one of upgrading or downgrading the subtask-specific model.

35. Verifying requirements against the subtask-specific model The requested subtask-specific model is on the list of allowed subtask-specific models, a threshold amount of training data and / or input signals is available for training the subtask-specific model; and / or A threshold amount of processor and / or memory resources is available for training and / or using the subtask-specific models.

35. The method of claim 33 or 34, comprising verifying at least one of:

36. modifying, by the network node, the subtask-specific model based on the generic model based on one or more constraints of the generic model and the subtask-specific model; pruning or reducing the size of the generic model so that the subtask-specific models fall within a maximum allowable subtask-specific model depth or size; deactivating one or more inputs of the generic model so that the depth or size inputs of the subtask-specific model match the format, size, or depth of the training data received from the one or more subtask-specific model collectors; Replacing the generic model activation function with a subtask-specific model-specific activation function, or Defining subtask-specific model-specific cost functions 36. The method of any of claims 33 to 35, comprising performing one or more of:

37. at least one processor; at least one memory containing computer program code; An apparatus comprising: At least one memory and computer program code are configured by at least one processor to cause the device to at least: a network node determining a trained generic model for performing or assisting in performing a machine-learnable generic task; a network node providing a trained generic model to a user equipment; receiving, by a network node, a request for a subtask-specific model from a user equipment; Validating the requirements for the subtask-specific model; transmitting, by the network node to the user equipment, a request for at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; receiving, by a network node, from a user equipment, at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configurations or constraints and / or the subtask-specific model training data; the network node transmitting the modified subtask-specific model to the user equipment; An apparatus configured to cause

38. 1. An apparatus comprising: means for a network node to determine a trained generic model for performing or assisting in performing a machine-learnable generic task; means for the network node to provide the trained generic model to the user equipment; means for receiving, by a network node, from a user equipment, a request for a subtask-specific model; means for validating the requirements of the subtask-specific model; means for the network node to transmit to the user equipment a request for at least one of the subtask-specific model configurations or constraints and / or subtask-specific model training data; means for the network node to receive from the user equipment at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; means for the network node to modify the subtask-specific model based on the generic model and at least one of the subtask-specific model configurations or constraints and / or subtask-specific model training data; means for the network node to transmit the modified subtask-specific model to the user equipment; An apparatus comprising:

39. A non-transitory computer-readable storage medium containing stored instructions, the instructions, when executed by at least one processor, causing a computing system to: a network node determining a trained generic model for performing or assisting in performing a machine-learnable generic task; a network node providing a trained generic model to a user equipment; receiving, by a network node, a request for a subtask-specific model from a user equipment; Validating the requirements of the subtask-specific model; transmitting, by the network node to the user equipment, a request for at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; receiving, by a network node, from a user equipment, at least one of subtask-specific model configurations or constraints and / or subtask-specific model training data; the network node modifying the subtask-specific model based on the generic model and at least one of the subtask-specific model configurations or constraints and / or the subtask-specific model training data; the network node transmitting the modified subtask-specific model to the user equipment; 1. A non-transitory computer-readable storage medium configured to cause

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