WO2024251148A1 - 终端的配置方法、终端、网络设备、系统和存储介质 - Google Patents

终端的配置方法、终端、网络设备、系统和存储介质 Download PDF

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Publication number
WO2024251148A1
WO2024251148A1 PCT/CN2024/097506 CN2024097506W WO2024251148A1 WO 2024251148 A1 WO2024251148 A1 WO 2024251148A1 CN 2024097506 W CN2024097506 W CN 2024097506W WO 2024251148 A1 WO2024251148 A1 WO 2024251148A1
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WIPO (PCT)
Prior art keywords
group
configuration information
training
terminal
distributed
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PCT/CN2024/097506
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English (en)
French (fr)
Inventor
杨蓓
魏凤生
张子扬
佘小明
冯钢
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China Telecom Corp Ltd Beijing Research Institute
China Telecom Corp Ltd
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China Telecom Corp Ltd Beijing Research Institute
China Telecom Corp Ltd
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management
    • H04W76/20—Manipulation of established connections
    • H04W76/27—Transitions between radio resource control [RRC] states

Definitions

  • the present disclosure relates to the field of wireless communication technology, and in particular to a terminal configuration method, a terminal, a network device, a system and a storage medium.
  • One purpose of the present disclosure is to provide a configuration scheme for supporting distributed model training on the RAN (Radio Access Network) side, so as to improve the control capability of distributed model training in wireless communication networks.
  • a configuration method of a terminal comprising: when the RRC (Radio Resource Control) state of the terminal is inactive, the terminal receives first configuration information or second configuration information; and when the RRC state of the terminal is connected, the terminal receives first configuration information, second configuration information or third configuration information, wherein: the first configuration information is used to instruct the terminal to deactivate model training as a distributed node in a training group of a distributed model; the second configuration information is used to instruct the terminal to delete model training as a distributed node in a training group of a distributed model; and the third configuration information is used to instruct the terminal to activate model training as a distributed node in the training group.
  • the first configuration information is used to instruct the terminal to deactivate model training as a distributed node in a training group of a distributed model
  • the second configuration information is used to instruct the terminal to delete model training as a distributed node in a training group of a distributed model
  • the third configuration information is used to instruct the terminal to activate model training as a distributed node in the training group.
  • the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group, and the configuration method also includes: the terminal performs at least one of establishment, update, addition, deletion, activation or deactivation of the distributed node according to the group configuration information.
  • the group configuration information meets at least one of the following conditions: the group configuration information is carried through at least one of DRB (Data Radio Bearer), SRB (Signaling Radio Bearer) or IRB (Intelligent Radio Bearer); the group configuration information is received through at least one of the user plane, control plane, data plane and intelligent plane; or the group configuration information is carried through at least one of system messages, RRC signaling, MAC (Medium Access Control) CE (Control Element) signaling, DCI (Downlink Control Information) signaling and NAS (Non-access stratum) messages.
  • DRB Data Radio Bearer
  • SRB Signaling Radio Bearer
  • IRB Intelligent Radio Bearer
  • the group configuration information is received through at least one of the user plane, control plane, data plane and intelligent plane
  • the group configuration information is carried through at least one of system messages, RRC signaling, MAC (Medium Access Control) CE (Control Element) signaling, DCI (Downlink Control Information) signaling and NAS (Non-access stratum) messages.
  • the group configuration information includes at least one of a group identifier, a model identifier, or a central node identifier.
  • the group configuration information also includes an identification of at least one of the nodes, models, or functions whose status has been changed.
  • the group configuration information conforms to at least one of the following: the identification of the node whose state is changed includes at least one of the identification of a deleted distributed node, a list of deleted distributed nodes, the identification of an added distributed node, a list of added distributed nodes, the identification of an activated distributed node, a list of activated distributed nodes, the identification of a deactivated distributed node, or a list of deactivated distributed nodes; the identification of the model whose state is changed includes at least one of the identification of an activated model, a list of activated models, the identification of a deactivated model, or a list of deactivated models; or the identification of the function whose state is changed includes at least one of the identification of an activated function, a list of activated functions, the identification of a deactivated function, or a list of deactivated functions.
  • the group identifier includes a first bit and a second bit, the first bit is a unique identifier of the distributed model training type, and the second bit is a unique identifier of the training group in the same distributed model training type; or the group identifier is a unique identifier of the training group within the same TA (Tracking Area), PLMN (Public Land Mobile Network), 5G base station gNB or CU (Centralized Unit).
  • TA Track Area
  • PLMN Public Land Mobile Network
  • 5G base station gNB or CU Centralized Unit
  • the configuration method further includes: the terminal receives transmission indication information, and the transmission indication information includes node and path information for uploading, converging or sending information.
  • the central node of the training group is another user terminal, and the transmission indication information includes an identifier of the central node or interface information between terminals.
  • the central node of the training group is a base station
  • the transmission indication information includes a base station identifier
  • the central node of the training group is located in a TA or a base station group
  • the distributed nodes include service users of the central node and service users of at least one first base station in the TA or the base station group.
  • the transmission indication information also includes at least one of an identifier of the first base station, an identifier of a link between the central node and the first base station, or routing information between the central node and the first base station.
  • the configuration method also includes at least one of the following: the terminal performs model reasoning based on the local model; the terminal receives model parameter update information from the central node of the training group; or the terminal sends model training result information to the central node of the training group.
  • the configuration method further includes: the terminal configuring the current state according to any one of the first configuration information, the second configuration information or the third configuration information.
  • the terminal suspends training of the model belonging to the training group according to the first configuration information; the terminal exits the training group according to the second configuration information; or the terminal starts model training of the training group according to the third configuration information.
  • the group configuration information carries at least one of the first configuration information, the second configuration information, or the third configuration information through the identifier of at least one of the nodes, models, or functions whose states have been changed; the configuration method also includes: when the identifier of the terminal matches the identifier of the deleted distributed node, or is in the list of the deleted distributed nodes, exiting the training group corresponding to the group identifier in the group configuration information; when the identifier of the terminal matches the identifier of the added distributed node or is in the list of the added distributed nodes, joining the training group corresponding to the group identifier in the group configuration information; when the identifier of the terminal matches the identifier of the activated distributed node or is in the list of the activated distributed nodes Under the following conditions, start model training of the training group corresponding to the group identifier in the group configuration information; when the terminal identifier matches the identifier of the deactivated distributed node or is in the list of deactivated distributed nodes, suspend model training of
  • the configuration method also includes at least one of the following: the terminal receives group activation indication information, wherein the group activation indication information is used to indicate the start of model training belonging to the training group corresponding to the group identifier, and the group activation indication information includes the group identifier; or the terminal receives group deactivation indication information, wherein the group deactivation indication information is used to indicate the suspension of model training belonging to the training group corresponding to the group identifier, and the group deactivation indication information includes the group identifier.
  • the distributed model includes a federated learning model.
  • a configuration method of a terminal comprising: when the RRC state of the terminal is inactive, the central node of the training group of the distributed model sends first configuration information or second configuration information to the terminal; when the RRC state of the terminal is active, the training group of the distributed model
  • the central node sends first configuration information, second configuration information or third configuration information to the terminal, wherein: the first configuration information is used to instruct the terminal to deactivate the model training as a distributed node in the training group; the second configuration information is used to instruct the terminal to delete the model training as a distributed node in the training group; and the third configuration information is used to instruct the terminal to activate the model training as a distributed node in the training group.
  • the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group; the configuration method also includes: the central node generates the group configuration information according to at least one of the establishment, update, addition, deletion, activation or deactivation of the distributed node.
  • the configuration method further includes: the central node sends transmission indication information to the distributed nodes, and the transmission indication information includes node and path information for information uploading, aggregation or sending.
  • the configuration method further includes at least one of the following: the central node sends model parameter update information to the distributed nodes of the training group; or the central node receives model training result information from the distributed nodes of the training group.
  • the configuration method complies with at least one of the following: the distributed model includes a federated learning model; or the central node is at least one of a 5G base station gNB, an OAM (Operation Administration and Maintenance) entity, a LMF (Location Management Function) entity, or a UE (User Equipment).
  • the distributed model includes a federated learning model
  • the central node is at least one of a 5G base station gNB, an OAM (Operation Administration and Maintenance) entity, a LMF (Location Management Function) entity, or a UE (User Equipment).
  • a terminal comprising: an information receiving unit, configured to receive first configuration information or second configuration information when the wireless resource control state of the terminal is inactive; and, when the wireless resource control state of the terminal is in a connected state, receive first configuration information, second configuration information or third configuration information, wherein: the first configuration information is used to instruct the terminal to perform a deactivation or deletion operation of model training as a distributed node in a training group of a distributed model; the second configuration information is used to instruct the terminal to perform a deactivation or deletion operation of model training as a distributed node in a training group of a distributed model; and the third configuration information is used to instruct the terminal to perform activation of model training as a distributed node in the training group.
  • the terminal also includes at least one of the following: a configuration unit, configured to perform at least one of establishment, update, addition, deletion, activation or deactivation of distributed nodes according to the group configuration information, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group; or a training unit, configured to perform at least one of the following: perform model inference based on the local model; update the local model according to the model parameter update information from the central node of the training group; or send model training result information to the central node of the training group.
  • a configuration unit configured to perform at least one of establishment, update, addition, deletion, activation or deactivation of distributed nodes according to the group configuration information, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group
  • a training unit configured to perform at least one of the following: perform model inference based on the local model; update the local model according to the model parameter update information from the central node of the training group; or
  • a network device comprising: an information sending unit, It is configured that when the RRC state of the terminal is inactive, the central node of the training group of the distributed model sends the first configuration information or the second configuration information to the terminal; and when the RRC state of the terminal is active, the central node of the training group of the distributed model sends the first configuration information, the second configuration information or the third configuration information to the terminal, wherein: the first configuration information is used to instruct the terminal to deactivate the model training as a distributed node in the training group; the second configuration information is used to instruct the terminal to delete the model training as a distributed node in the training group; and the third configuration information is used to instruct the terminal to activate the model training as a distributed node in the training group.
  • the network device also includes at least one of the following: a generation unit, configured to generate group configuration information based on at least one of establishment, update, addition, deletion, activation or deactivation of distributed nodes, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group; or a training control unit, configured to perform at least one of the following: sending model parameter update information to the distributed nodes of the training group; or the central node receiving model training result information from the distributed nodes of the training group.
  • a generation unit configured to generate group configuration information based on at least one of establishment, update, addition, deletion, activation or deactivation of distributed nodes, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group
  • a training control unit configured to perform at least one of the following: sending model parameter update information to the distributed nodes of the training group; or the central node receiving model training result information from the distributed nodes of the training group.
  • a network device comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the terminal configuration methods described above based on instructions stored in the memory.
  • a non-transitory computer-readable storage medium on which computer program instructions are stored, and when the instructions are executed by a processor, the steps of any one of the terminal configuration methods described above are implemented.
  • a network system comprising at least one central node of a distributed model training group and multiple distributed nodes of a distributed model training group, wherein the distributed nodes are terminals, and the distributed nodes are configured to execute any one of the terminal configuration methods executed by the terminals described above; and the central node is configured to execute any one of the terminal configuration methods executed by the central node described above.
  • the central node includes at least one of a 5G base station gNB, an operation and maintenance management entity, a location management function entity, or a user terminal.
  • a computer program for causing a processor to execute any one of the above methods.
  • FIG1 is a flow chart of some embodiments of the terminal configuration method of the present disclosure.
  • FIG. 2 is a flow chart of other embodiments of the terminal configuration method of the present disclosure.
  • FIG. 3 is a schematic diagram of some embodiments of a terminal of the present disclosure.
  • FIG. 4 is a schematic diagram of some embodiments of the network device of the present disclosure.
  • FIG. 5 is a schematic diagram of some other embodiments of the network device disclosed herein.
  • FIG. 6 is a schematic diagram of some further embodiments of the network device of the present disclosure.
  • FIG. 7 is a schematic diagram of some embodiments of the network system of the present disclosure.
  • the inventors have found that when implementing distributed model training in a wireless communication network, the current RAN side does not support the configuration of distributed model training.
  • RAN needs to support the maintenance of the training group of the distributed model, including the establishment, update, addition, and deletion of group members, so as to improve and ensure the performance of distributed model training.
  • the present disclosure proposes a terminal configuration method, terminal, network device, system and storage medium to provide a configuration solution for supporting distributed model training on the RAN side and improve the control capability of distributed model training in wireless communication networks.
  • the present disclosure proposes a distributed network architecture, including distributed nodes (or local clients for distributed model training) and central nodes (or servers for distributed model training).
  • the distributed nodes perform model training, and the training results generated by the distributed nodes are aggregated to the central node; the central node can configure the distributed nodes, update the models, and aggregate the training results of each distributed node.
  • the distributed nodes are terminals.
  • the central node includes at least one of gNB, OAM, LMF, or UE.
  • FIG1 A flowchart of some embodiments of the terminal configuration method disclosed in the present invention, which is executed by a terminal side capable of serving as a distributed node, is shown in FIG1 .
  • the terminal receives information for configuring the state of the terminal in the training group of the distributed model.
  • the information may come from the central node of the training group of the distributed model.
  • the information includes first configuration information, second configuration information, and third configuration information, wherein the first configuration information is used to instruct the terminal to deactivate the model training as a distributed node in the training group of the distributed model; the second configuration information is used to instruct the terminal to delete the model training as a distributed node in the training group of the distributed model. and the third configuration information is used to instruct the terminal to activate as a distributed node in the training group to perform model training.
  • the distributed model may include a federated learning model, thereby avoiding the transmission of training data between distributed nodes and between distributed nodes and central nodes, thereby reducing the pressure of data transmission and improving data security.
  • step 130 includes step 131 and step 132.
  • step 131 if the current RRC state of the terminal is in an inactive state, the terminal can receive the first configuration information and the second configuration information, that is, the information received by the terminal for configuring the state of the terminal in the training group of the distributed model is any one of the first and second configuration information.
  • the central node of the training group will not configure the terminal as a distributed node in an active state in the training group, and therefore, the terminal cannot receive the third configuration information.
  • the terminal can receive the first, second and third configuration information, that is, the information received by the terminal for configuring the state of the terminal in the training group of the distributed model is any one of the first, second or third configuration information.
  • the central node of the training group can use the terminal for distributed model training when the RRC state of the terminal is connected, so the terminal can be configured as a distributed node in the training group in an activated state, and the terminal can receive the third configuration information.
  • the terminal may perform a training operation based on a local model without receiving configuration information from the central node.
  • the RRC state of the terminal can be associated with the state of the terminal as a distributed node in the training group of the distributed model, thereby avoiding the untimely training and data feedback caused by requiring inactive terminals to perform model training, and improving the reliability and operational stability of the distributed model training.
  • the terminal configuration method further includes step 140: the terminal configures its current state as a distributed node in the training group according to the received first, second or third configuration information.
  • the terminal if the terminal receives the first configuration information, the terminal switches to the inactive state of the distributed node and suspends the training of the model belonging to the training group indicated in the first configuration information; if the terminal receives the second configuration information, the terminal exits the training group indicated in the second configuration information; if the terminal receives the second configuration information, the terminal switches to the active state of the distributed node and starts the model training of the training group indicated in the third configuration information.
  • the terminal can modify its own state according to the information from the central node, including starting model training, pausing model training, and exiting the training group (being offline in the training group), so that the control information from the central node can timely affect the state of the distributed nodes, improving the flexibility of the distributed node control. speed, accuracy and efficiency.
  • FIG2 A flowchart of some embodiments of the terminal configuration method disclosed in the present invention that can be executed by a central node that can serve as a distributed model training group is shown in FIG2 .
  • the central node sends information for configuring the state of the terminal in the training group of the distributed model to the terminal.
  • the configured terminal is a distributed node in the same training group as the central node.
  • the information includes first configuration information, second configuration information, and third configuration information, wherein the first configuration information is used to instruct the terminal to perform deactivation of model training as a distributed node in the training group of the distributed model; the second configuration information is used to instruct the terminal to perform a deletion operation of model training as a distributed node in the training group of the distributed model; and the third configuration information is used to instruct the terminal to perform activation of model training as a distributed node in the training group.
  • step 220 includes step 221 and step 222.
  • step 221 if the current RRC state of the terminal is in an inactive state, the central node can send the first configuration information and the second configuration information to the terminal, that is, the information sent by the central node for configuring the state of the terminal in the training group of the distributed model is any one of the first and second configuration information.
  • the central node of the training group will not configure the terminal as a distributed node in an active state in the training group, and therefore, the central node cannot send the third configuration information.
  • the terminal can receive the first, second and third configuration information, that is, the information sent by the central node for configuring the state of the terminal in the training group of the distributed model is any one of the first, second or third configuration information.
  • the central node of the training group can use the terminal for distributed model training when the RRC state of the terminal is connected, so the terminal can be configured as a distributed node in the training group to be in an activated state, and the central node can receive the third configuration information.
  • the RRC state of the terminal can be associated with the state of the terminal as a distributed node in the training group of the distributed model, thereby avoiding the untimely training and data feedback caused by requiring inactive terminals to perform model training, and improving the reliability and operational stability of the distributed model training.
  • the terminal configuration method executed by the central node may further include step 210 executed before step 220 .
  • step 210 the central node generates first configuration information, second configuration information or third configuration information according to the RRC state of the terminal and its own control requirements.
  • control requirements of the central node may include at least one of activating model training, deactivating model training, and deleting distributed nodes.
  • a terminal whose RRC state is connected is selected from the terminal to generate third configuration information.
  • the control center needs to deactivate model training or delete a distributed node, it can be selected from terminals whose RRC state is deactivated and connected.
  • the control message of the control center is difficult to reach, so the control information for the terminal whose RRC state is offline is not generated.
  • the central node can select a suitable terminal from the terminals to generate control information in combination with the RRC state of the terminal and its own control requirements, thereby ensuring that the control information can be executed by the terminal and improving the control success rate and control efficiency.
  • the first configuration information, the second configuration information, and the third configuration information are located in the group configuration information of the training group.
  • the terminal can also perform at least one of establishment, update, and addition of distributed nodes.
  • the terminal can obtain the necessary information to become a distributed node, such as model information, according to the group configuration information from the central node, to achieve the establishment of a distributed node; the terminal can update the stored model or model parameters according to the group configuration information from the central node to achieve the update of the distributed node; the terminal can join the training group specified by the central node to achieve the addition of a distributed node.
  • the flexibility of the central node in controlling the distributed nodes can be further improved, and the control capability of distributed model training in the wireless communication network can be improved.
  • the distributed model training group has a group identifier (group ID), and the group identifier is unique within a cell, a cell group, a TA range, or a PLMN.
  • group ID group identifier
  • the group identifier as a whole is a unique identifier within the same TA, PLMN, gNB, or CU range, so that it is easy to uniquely identify the training group, facilitate subsequent control in units of training groups, and improve the accuracy of control identification.
  • the same terminal can be a distributed node for multiple training groups. The terminal determines the training group for which the configuration operation is targeted based on the group identifier in the received group configuration information, so as to avoid affecting the model training in other training groups and improve the reliability of distributed model training.
  • the format of the group identifier can be: two bits connected in series, including: a first bit for the distributed model training type and a second bit for distinguishing training groups in the same distributed model training type.
  • the group identifier obtained by splicing the first and second bits can uniquely identify the training group.
  • Such a group identifier can uniquely identify the distributed model training group while also facilitating identification of the distributed model training type to which it belongs, thereby improving the convenience of management.
  • the group configuration information includes at least one of a group identifier, a model identifier, or a central node identifier. A sort of.
  • the terminal determines the training group corresponding to the group configuration information based on the group identifier, thereby improving the accuracy of subsequent configuration and avoiding affecting the model training of other training groups; in some embodiments, the same training group may include one or more distributed models, and the terminal determines the model targeted by the group configuration information based on the model identifier, thereby improving the flexibility of control; in some embodiments, the same training group may include one or more central nodes, and the terminal determines the central node that initiates the control based on the central node identifier, thereby facilitating subsequent tracing of the control.
  • the group configuration information also includes an identifier of at least one of the nodes, models or functions whose states have been changed.
  • the group configuration information includes an identifier of a changed node, such as an identifier of a deleted distributed node, a list of deleted distributed nodes, an identifier of an added distributed node, a list of added distributed nodes, an identifier of an activated distributed node, a list of activated distributed nodes, an identifier of a deactivated distributed node, or a list of deactivated distributed nodes;
  • the group configuration information includes an identifier of a model whose state has been changed, such as an identifier of an activated model, a list of activated models, an identifier of a deactivated model, or a list of deactivated models;
  • the group configuration information includes an identifier of a function whose state has been changed, such as an identifier of an activated function, a list of activated functions, an identifier of a
  • the central node can initiate control over one or more distributed nodes from the node, model, and function dimensions, thereby improving control flexibility and efficiency.
  • the group configuration information may carry at least one of the first configuration information, the second configuration information, or the third configuration information through an identifier of at least one of a node, a model, or a function whose state has been changed.
  • the group configuration information may carry the first configuration information by at least one of an identifier of a deactivated distributed node, a list of deactivated distributed nodes, an identifier of a deactivated model, a list of deactivated models, an identifier of a deactivated function, or a list of deactivated functions.
  • the terminal suspends model training of the training group corresponding to the group identifier in the group configuration information.
  • the terminal suspends training of the model corresponding to the identifier of the deactivated model or the list of deactivated models in the group configuration information.
  • the terminal suspends training of the model corresponding to the identifier of the deactivated function or the list of deactivated functions in the group configuration information.
  • the group configuration information may be provided by the identifier of the deleted distributed node or the deleted distributed node.
  • the list of nodes carries the second configuration information.
  • the terminal's identifier matches the identifier of the deleted distributed node or is in the list of the deleted distributed node, the terminal exits the training group corresponding to the group identifier in the group configuration information.
  • the group configuration information may carry the third configuration information by at least one of the identifier of an activated distributed node, a list of activated distributed nodes, an identifier of an activated model, a list of activated models, an identifier of an activated function, or a list of activated functions.
  • the terminal when the identifier of the terminal matches the identifier of the activated distributed node or is in the list of activated distributed nodes, the terminal starts model training of the training group corresponding to the group identifier in the group configuration information. In some embodiments, the terminal starts training the model corresponding to the identifier of the activated model or the list of activated models in the group configuration information. In some embodiments, the terminal starts training the model corresponding to the identifier of the activated function or the list of activated functions in the group configuration information.
  • the group configuration information can be activated and deactivated from multiple angles of distributed nodes, models, and functions, further improving the flexibility of control; by utilizing the unique identification of nodes, models, and functions, the accuracy of the controlled object can be ensured and the reliability of control can be improved.
  • the group configuration information may include an identifier match of an added distributed node or a list of added distributed nodes, and in this way carries information requiring one or more terminals to become distributed nodes of a specified training group.
  • the identifier of the terminal matches the identifier of the added distributed node or is in the list of added distributed nodes, the terminal joins the training group corresponding to the group identifier in the group configuration information.
  • the group configuration information can use the identifier of the distributed node to control the terminal as a distributed node in the training group, realize the update of the distributed node in the training group, and further improve the control capability.
  • the group configuration information includes at least one of the following:
  • Group ID The group ID is unique within a TA area, PLMN, gNB or CU;
  • Model ID or function ID One or more models can be trained in a group, and different models have different models/functions;
  • Central node ID for example, cell ID, etc.
  • Delete related configuration information group deletion center distributed node ID, group deletion distributed center node list;
  • Add relevant configuration information add distributed central node ID to the group, add distributed central node list to the group;
  • Model/function activation ID model/function activation list
  • model/function deactivation ID model/function deactivation list
  • the group configuration information is transmitted via a data radio bearer, a control radio bearer, or a smart radio bearer. At least one of the transmissions is performed, thereby improving the flexibility of group configuration information transmission.
  • the terminal may receive the group configuration information through at least one of a user plane, a control plane, a data plane, and an intelligent plane, thereby improving the flexibility of transmitting the group configuration information.
  • the group configuration information can be carried by at least one of system messages, RRC signaling, MAC CE signaling, DCI signaling, and NAS messages, thereby improving the flexibility and timeliness of the group configuration information transmission.
  • the distributed model training group can also activate or deactivate training in units of training groups.
  • the terminal receives group activation indication information, and the group activation indication information is used to indicate the start of model training belonging to the training group corresponding to the group identifier, and the group activation indication information includes the group identifier. According to the group activation indication information, the terminal starts the training of all models in the training group corresponding to the group identifier.
  • the terminal receives group deactivation indication information, and the group deactivation indication information is used to indicate the suspension of model training belonging to the training group corresponding to the group identifier, and the group deactivation indication information includes the group identifier. According to the group activation indication information, the terminal suspends the training of all models in the training group corresponding to the group identifier.
  • training can be activated or deactivated in units of training groups, which improves control flexibility while also improving the efficiency of model start and pause control.
  • the configuration method of the terminal further includes: the central node sends transmission indication information to the terminal, and the terminal receives the transmission indication information.
  • the transmission indication information includes node and path information for information uploading, aggregation or sending.
  • the transmission indication information when the central node of the training group is a user terminal other than the distributed node of the current training group, the transmission indication information includes the identifier of the central node or the interface information between the terminals. In some embodiments, when the central node of the training group is a base station, the transmission indication information includes the base station identifier.
  • the transmission indication information also includes at least one of the identifier of the other base station, the link identifier between the central node and the first base station, or the routing information between the central node and the other base station.
  • the distributed model in the configuration method of the terminal disclosed in the present invention, can be a model for calculating the positioning problem, and in some embodiments, it can also be used to reduce the bit error rate; reduce interference, improve channel capacity and signal Any distributed model that can increase information transmission rate, reduce energy consumption and latency, and improve bandwidth utilization.
  • the computing power and data collection capabilities of each terminal in the network can be utilized to solve problems in wireless communication technology based on distributed machine learning models, thereby improving the intelligence and communication quality of wireless communications.
  • the distributed model can be a model for multiple purposes such as distributed positioning, distributed beam management, distributed CSI enhancement, and distributed wireless resource management, thereby reducing the transmission of user-location-related data between the air interface and the network element interface, protecting the security and privacy of user positioning-related data, reducing the transmission overhead related to the air interface and model training, and increasing the generalization and robustness of the model.
  • the training group of the distributed model of the present disclosure has a group identifier, which is unique within a cell, a cell group, a TA range, or a PLMN, and the format of the group identifier is any one of the following two:
  • Two parts of bits are connected in series, including: a distributed model training type bit, and a bit used to distinguish the same type.
  • a distributed model training type bit is used to indicate the type of distributed model training, such as horizontal federation, vertical federation, federated transfer learning, single-task distributed learning, multi-task distributed learning, etc.
  • an N-bit binary bit string is used to distinguish different models in the same type of distributed model training, for example: multiple models trained using the same type of distributed model using different gradient descent methods, different optimization objectives or different data sets, where M and N are positive integers greater than or equal to 1 respectively;
  • the identifiers of different training groups may be globally unique or unique within a certain area.
  • the group configuration information may be sent from the central node to the terminal, and may also be sent from other devices on the network side except the central node to the terminal.
  • the signaling carrying the group configuration information may include at least one of a system message, an RRC signaling, a MAC CE signaling, a DCI signaling, and a NAS message.
  • the group configuration information is transmitted via at least one of DRB, SRB, and IRB, wherein DRB and SRB are two types of radio bearers defined in relevant standards.
  • IRB is used to carry the transmission of model-related information related to AI (Artificial Intelligence)/ML (Machine Learning), or for the transmission of model training/inference-related information.
  • the distributed node has at least one of the following states in the training group: active, inactive, and offline.
  • the active state indicates that the distributed node participates in the distributed model training, and the local model of the distributed node can be used for model reasoning.
  • the inactive state indicates that the distributed node does not participate in the distributed model training temporarily, and the local model of the distributed node can be used for model reasoning.
  • the offline state indicates that the distributed node is unreachable, cannot participate in the distributed model training, and cannot perform model reasoning.
  • the state of the distributed node in the training group has the same RRC state as the distributed node.
  • the RRC state of a distributed node with different model identifiers/functional identifiers may be the same as or different from the state of the distributed node in the training group to which it belongs.
  • the state of the distributed node in the training group to which it belongs may be one of activated, inactivated, and offline; when the distributed node is in RRC Inactive, the state of the distributed node in the training group to which it belongs may be one of inactivated and offline; when the distributed node is in the RRC idle state, the state of the distributed node in the training group to which it belongs may be offline.
  • the trained model has the function of cell access/cell reselection, when the distributed node is in the RRC idle state, the state of the distributed node in the group to which it belongs may not be activated.
  • the training group of the distributed model supports the uploading (aggregation) of relevant information from distributed nodes to the central node, as well as the node and path indication of information sent from the central node to the distributed nodes. In some embodiments, this can be carried by transmission indication information.
  • the transmission indication information includes at least one of a central node ID, link or interface information, which is used to indicate the nodes and paths for uploading, aggregating, and sending model-related information in distributed model training.
  • the central node when the central node is a UE, it includes UE ID or interface information between UEs, such as: PC5link identifier.
  • the central node when it is a base station, it includes the central node base station ID.
  • the central node gNB When the central node gNB is in a TA area or a gNB group, and the distributed node UE includes the service users of the central gNB and some or all of the users served by other second gNBs in the TA area or gNB group where the central gNB is located, it is necessary to include the second base station ID, or the Xn link ID, or the corresponding routing information between the central gNB and the second gNB, for example: RAN node ID, TNL address of the Xn-C plane SCTP connection.
  • the training group of the distributed model supports deleting one or more distributed nodes in the training group. In some embodiments, by deleting relevant configuration information (group deleted distributed node identifier, group deleted distributed node list), the distributed node with the distributed node identifier is deleted from the group to stop participating in the model training of the current training group.
  • the training group of the distributed model supports adding one or more distributed nodes in the distributed model training.
  • by adding relevant configuration information group adding distributed node identifier, group adding distributed node list
  • a distributed node with a distributed node identifier is added or newly added from the training group to participate in the group's model training.
  • the distributed model training group supports activation/deactivation of distributed model training.
  • the group activation identifier indicates to start all training work of the group with the corresponding group identifier;
  • the deactivation flag indicates that all training work of the group with the corresponding group flag is suspended.
  • the training group of the distributed model supports activation/deactivation of the training of a single or multiple models or functions of the distributed model training group.
  • the activation/deactivation of the training of a single or multiple models or functions in the group is indicated by a model or function activation identifier or a model or function activation list; the deactivation of the training of a single or multiple models or functions in the group is indicated by a model or function deactivation identifier or a model or function deactivation list.
  • FIG. 3 A schematic diagram of some embodiments of the terminal 31 of the present disclosure is shown in Figure 3.
  • the terminal 31 can serve as a distributed node in a training group of distributed model training.
  • the terminal 31 includes an information receiving unit 311, which can receive the first configuration information or the second configuration information when the radio resource control state of the terminal is inactive, and can receive the first configuration information, the second configuration information or the third configuration information when the radio resource control state of the terminal is connected.
  • the first configuration information is used to instruct the terminal to perform a deactivation or deletion operation of model training as a distributed node in a training group of a distributed model
  • the second configuration information is used to instruct the terminal to perform a deactivation or deletion operation of model training as a distributed node in a training group of a distributed model
  • the third configuration information is used to instruct the terminal to perform activation of model training as a distributed node in the training group.
  • Such a terminal can associate the RRC state of the terminal with the state of the terminal as a distributed node in the training group of the distributed model, thereby avoiding untimely training and data feedback caused by inactive terminals being required to perform model training, and improving the reliability and operational stability of distributed model training.
  • the terminal further includes a configuration unit 312, which can perform at least one of establishment, update, addition, deletion, activation or deactivation of a distributed node according to the group configuration information, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group.
  • the group configuration information can be any one of the above-mentioned.
  • Such a terminal can modify its own status according to the information from the central node, including starting model training, pausing model training, and exiting the training group (offline in the training group). It can also execute the establishment, update, and addition of distributed nodes, so that the control information from the central node can timely affect the status of the distributed nodes, thereby improving the flexibility, accuracy and efficiency of distributed node control.
  • the terminal also includes a training unit 313, which can perform at least one of the following: perform model reasoning based on the local model; update the local model based on model parameter update information from the central node of the training group; or send model training result information to the central node of the training group.
  • Such terminals can act as distributed nodes in the training group, based on the configuration of the central node, using their own Data and computing power are used to execute distributed model training, which improves the efficiency of model training in wireless communication networks and reduces the requirements for the capabilities of machine learning equipment.
  • the distributed model can be a federated learning model, which further avoids the transmission of original data between nodes and improves data security.
  • the network device 42 can be used as a central node in a training group of a distributed model training.
  • the network device can be at least one of a 5G base station gNB, an operation and maintenance management entity OAM, a location management function entity LMF, or a user terminal UE.
  • the network device includes an information sending unit 43, which can send the first configuration information or the second configuration information to the terminal when the radio resource control RRC state of the terminal is inactive, and send the first configuration information, the second configuration information or the third configuration information to the terminal when the radio resource control RRC state of the terminal is active.
  • the first configuration information is used to instruct the terminal to deactivate the model training as a distributed node in the training group;
  • the second configuration information is used to instruct the terminal to delete the model training as a distributed node in the training group;
  • the third configuration information is used to instruct the terminal to activate the model training as a distributed node in the training group.
  • Such network equipment can associate the RRC state of the terminal with the state of the terminal as a distributed node in the training group of the distributed model, thereby avoiding the untimely training and data feedback caused by requiring inactive terminals to perform model training, and improving the reliability and operational stability of the distributed model training.
  • the network device also includes a generation unit 422, which can generate group configuration information according to at least one of establishment, update, addition, deletion, activation or deactivation of the distributed node, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group.
  • a generation unit 422 which can generate group configuration information according to at least one of establishment, update, addition, deletion, activation or deactivation of the distributed node, wherein the first configuration information, the second configuration information and the third configuration information are located in the group configuration information of the training group.
  • Such network equipment can require the terminal to modify its own status according to demand, including starting model training, pausing model training, and exiting the training group (offline in the training group). It can also execute the establishment, update, and addition of distributed nodes, thereby improving the flexibility, accuracy, and efficiency of distributed node control.
  • the network device may further include a training control unit 41 capable of performing at least one of the following: sending model parameter update information to distributed nodes of the training group; or the central node receiving model training result information from distributed nodes of the training group.
  • a training control unit 41 capable of performing at least one of the following: sending model parameter update information to distributed nodes of the training group; or the central node receiving model training result information from distributed nodes of the training group.
  • Such a network device can serve as the central node in the training group, control the model parameters and status of the distributed nodes, and summarize the training results based on each distributed node, thereby improving the efficiency of model training in the wireless communication network; in addition, the distributed model can be a federated learning model, thereby further avoiding the original data between nodes. Transmission improves data security.
  • the network device includes a memory 501 and a processor 502.
  • the memory 501 can be a disk, a flash memory or any other non-volatile storage medium.
  • the memory is used to store the instructions in the corresponding embodiment of the terminal configuration method executed by the terminal or the central node above.
  • the processor 502 is coupled to the memory 501 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller.
  • the processor 502 is used to execute instructions stored in the memory, which can improve the reliability and operational stability of the training of the distributed model.
  • the network device 600 includes a memory 601 and a processor 602.
  • the processor 602 is coupled to the memory 601 via a BUS 603.
  • the network device 600 can also be connected to an external storage device 605 via a storage interface 604 to call external data, and can also be connected to a network or another computer system (not shown) via a network interface 606. No further detailed description will be given here.
  • the reliability and operational stability of the training of the distributed model can be improved.
  • a computer-readable storage medium stores thereon computer program instructions, which, when executed by a processor, implement the steps of the method in the corresponding embodiment of the configuration method of the terminal executed by the terminal or the central node.
  • the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • the network system includes at least one central node 72 of a distributed model training group and multiple distributed nodes 711-71n of distributed model training groups, wherein n is an integer greater than 1, the distributed nodes are terminals, and the distributed nodes can execute any of the terminal configuration methods executed by the terminals described above; and the central nodes can execute any of the terminal configuration methods executed by the central nodes described above.
  • the central node includes at least one of a 5G base station gNB, an operation and maintenance management entity OAM, a location management function entity LMF, or a user terminal UE.
  • Such a network system can associate the RRC state of the terminal with the state of the terminal as a distributed node in the training group of the distributed model, thereby avoiding the delay in training and data feedback caused by requiring inactive terminals to perform model training, and improving the reliability and operational stability of distributed model training.
  • the structure of the network system can be applicable to various solutions such as distributed deployment, centralized deployment or hybrid deployment of computing resources on the RAN side in the future, which is conducive to promotion and application.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • the method and apparatus of the present disclosure may be implemented in many ways.
  • the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware.
  • the above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated.
  • the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

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Abstract

本公开提出一种终端的配置方法、终端、网络设备、系统和存储介质，涉及无线通信技术领域。本公开的一种终端的配置方法包括：在终端的无线资源控制状态为非激活的情况下，终端接收第一配置信息或第二配置信息；和在终端的无线资源控制状态为连接态的情况下，终端接收第一配置信息、第二配置信息或第三配置信息，其中：第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。

Description

终端的配置方法、终端、网络设备、系统和存储介质
相关申请的交叉引用
本申请是以CN申请号为202310656930.1，申请日为2023年6月5日的申请为基础，并主张其优先权，该CN申请的公开内容在此作为整体引入本申请中。
技术领域
本公开涉及无线通信技术领域，特别是一种终端的配置方法、终端、网络设备、系统和存储介质。
背景技术
随着无线通信技术的发展，由于通信场景越来越复杂等原因，面临的困难也更多。将机器学习引入无线通信技术中，为更高效的解决问题提供了可能性。
通过分布式学习框架实现无线机器学习模型训练，能够减少传输开销，降低对于单个设备的运算压力，提高机器学习效率。
发明内容
本公开的一个目的在于提供一种RAN(Radio Access Network，无线接入网)侧支持分布式模型训练的配置方案，以提高在无线通信网络中分布式模型训练的控制能力。
根据本公开的一些实施例的一个方面，提出一种终端的配置方法，包括：在终端的RRC(Radio Resource Control，无线资源控制)状态为非激活的情况下，终端接收第一配置信息或第二配置信息；和在终端的RRC状态为连接态的情况下，终端接收第一配置信息、第二配置信息或第三配置信息，其中：第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
在一些实施例中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中，配置方法还包括：终端根据组配置信息执行分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项。
在一些实施例中，组配置信息符合以下至少一项：组配置信息为通过DRB(Data Radio Bearer，数据无线承载)、SRB(Signaling Radio Bearer，控制无线承载)或IRB(Intelligent Radio Bearer，智能无线承载)中的至少一种承载；组配置信息为通过用户面、控制面、数据面、智能面中的至少一种接收；或组配置信息为通过系统消息、RRC信令、MAC(Medium Access Control，媒体接入控制)CE(Control Element，控制单元)信令、DCI(Downlink Control Information，下行控制信息)信令、NAS(Non-access stratum，非接入层)消息中的至少一种承载。
在一些实施例中，组配置信息中包括组标识、模型标识或中心节点标识中的至少一种。
在一些实施例中，组配置信息中还包括变更状态的节点、模型或功能中至少一项的标识。
在一些实施例中，组配置信息符合以下至少一项：变更状态的节点的标识包括删除的分布式节点的标识、删除的分布式节点的列表、添加的分布式节点的标识、添加的分布式节点的列表、激活的分布式节点的标识、激活的分布式节点的列表、去激活的分布式节点的标识或去激活的分布式节点的列表中的至少一项；变更状态的模型的标识包括激活的模型的标识、激活的模型的列表、去激活的模型的标识或去激活的模型的列表中的至少一项；或变更状态的功能的标识包括激活的功能的标识、激活的功能的列表、去激活的功能的标识或去激活的功能的列表中的至少一项。
在一些实施例中，组标识包括第一比特位和第二比特位，第一比特位为分布式模型训练类型的唯一标识，第二比特位为相同分布式模型训练类型中的训练组的唯一标识；或组标识为相同的TA(Tracking Area，跟踪区)、PLMN(Public Land Mobile Network，公共陆地移动网络)、5G基站gNB或CU(Centralized Unit,集中单元)范围内，训练组的唯一标识。
在一些实施例中，配置方法还包括：终端接收传输指示信息，传输指示信息中包括信息上传、汇聚或下发的节点和路径信息。
在一些实施例中，训练组的中心节点为其他用户终端，传输指示信息中包括中心节点的标识或终端间的接口信息。
在一些实施例中，训练组的中心节点为基站，传输指示信息包括基站标识。
在一些实施例中，训练组的中心节点位于一个TA或基站组内，且分布式节点中包括中心节点的服务用户，以及为TA或基站组内的至少一个第一基站的服务用户， 传输指示信息还包括第一基站的标识、中心节点与第一基站间的链路标识，或中心节点与第一基站间的路由信息中的至少一项。
在一些实施例中，配置方法还包括以下至少一项：终端根据本地模型执行模型推理；终端接收来自训练组的中心节点的模型参数更新信息；或终端向训练组的中心节点发送模型训练结果信息。
在一些实施例中，配置方法还包括：终端根据第一配置信息、第二配置信息或第三配置信息中的任意一项配置当前状态。
在一些实施例中，终端根据第一配置信息暂停训练归属于训练组的模型；终端根据第二配置信息退出训练组；或终端根据第三配置信息启动训练组的模型训练。
在一些实施例中，组配置信息中通过变更状态的节点、模型或功能中至少一项的标识承载第一配置信息、第二配置信息或第三配置信息中的至少一项；配置方法还包括：在终端的标识与删除的分布式节点的标识匹配，或在删除的分布式节点的列表中的情况下，退出组配置信息中组标识对应的训练组；在终端的标识与添加的分布式节点的标识匹配或在添加的分布式节点的列表中的情况下，加入组配置信息中组标识对应的训练组；在终端的标识与激活的分布式节点的标识匹配或在激活的分布式节点的列表中的情况下，启动组配置信息中组标识对应的训练组的模型训练；在终端的标识与去激活的分布式节点的标识匹配或在去激活的分布式节点的列表中的情况下，暂停组配置信息中组标识对应的训练组的模型训练；启动训练组配置信息中激活的模型的标识或激活的模型的列表对应的模型；暂停训练组配置信息中去激活的模型的标识或去激活的模型的列表对应的模型；启动训练组配置信息中激活的功能的标识或激活的功能的列表对应的模型；或暂停训练组配置信息中去激活的功能的标识或去激活的功能的列表对应的模型。
在一些实施例中，配置方法还包括以下至少一项：终端接收组激活指示信息，其中，组激活指示信息用于指示启动归属于组标识对应的训练组的模型训练，组激活指示信息中包括组标识；或终端接收组去激活指示信息，其中，组去激活指示信息用于指示暂停归属于组标识对应的训练组的模型训练，组去激活指示信息中包括组标识。
在一些实施例中，分布式模型包括联邦学习模型。
根据本公开的一些实施例的一个方面，提出一种终端的配置方法，包括：在终端的RRC状态为非激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息或第二配置信息；在终端的RRC状态为激活的情况下，分布式模型的训练组 的中心节点向终端发送第一配置信息、第二配置信息或第三配置信息，其中：第一配置信息用于指示终端作为训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
在一些实施例中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中；配置方法还包括：中心节点根据分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项生成组配置信息。
在一些实施例中，配置方法还包括：中心节点向分布式节点发送传输指示信息，传输指示信息中包括信息上传、汇聚或下发的节点和路径信息。
在一些实施例中，配置方法还包括以下至少一项：中心节点向训练组的分布式节点发送模型参数更新信息；或中心节点接收来自训练组的分布式节点的模型训练结果信息。
在一些实施例中，配置方法符合以下至少一项：分布式模型包括联邦学习模型；或中心节点为5G基站gNB、OAM(Operation Administration and Maintenance，操作维护管理)实体、LMF(Location Management Function，位置管理功能)实体或UE(User Equipment，用户终端)中的至少一项。
根据本公开的一些实施例的一个方面，提出一种终端，包括：信息接收单元，被配置为在终端的无线资源控制状态为非激活的情况下，接收第一配置信息或第二配置信息；和，在终端的无线资源控制状态为连接态的情况下，接收第一配置信息、第二配置信息或第三配置信息，其中：第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
在一些实施例中，终端还包括以下至少一项：配置单元，被配置为根据组配置信息执行分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项，其中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中；或训练单元，被配置为执行以下至少一项：根据本地模型执行模型推理；根据来自训练组的中心节点的模型参数更新信息更新本地模型；或向训练组的中心节点发送模型训练结果信息。
根据本公开的一些实施例的一个方面，提出一种网络设备，包括：信息发送单元， 被配置为在终端的RRC状态为非激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息或第二配置信息；和在终端的RRC状态为激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息、第二配置信息或第三配置信息，其中：第一配置信息用于指示终端作为训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
在一些实施例中，网络设备还包括以下至少一项：生成单元，被配置为根据分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项生成组配置信息，其中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中位于训练组的组配置信息中；或训练控制单元，被配置为执行以下至少一项：向训练组的分布式节点发送模型参数更新信息；或中心节点接收来自训练组的分布式节点的模型训练结果信息。
根据本公开的一些实施例的一个方面，提出一种网络设备，包括：存储器；以及耦接至存储器的处理器，处理器被配置为基于存储在存储器的指令执行上文中任意一种终端的配置方法。
根据本公开的一些实施例的一个方面，提出一种非瞬时性计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现上文中任意一种终端的配置方法的步骤。
根据本公开的一些实施例的一个方面，提出一种网络系统，包括至少一个分布式模型训练组的中心节点和多个分布式模型训练组的分布式节点，其中，分布式节点为终端，分布式节点被配置为执行上文中任意一种由终端执行的终端的配置方法；和中心节点被配置为执行上文中任意一种由中心节点执行的终端的配置方法。
在一些实施例中，中心节点包括5G基站gNB、操作维护管理实体、位置管理功能实体或用户终端中的至少一项。
根据本公开的一些实施例的一个方面，提出一种计算机程序，用于使处理器执行上文中任意一种方法。
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图1为本公开的终端配置方法的一些实施例的流程图。
图2为本公开的终端配置方法的另一些实施例的流程图。
图3为本公开的终端的一些实施例的示意图。
图4为本公开的网络设备的一些实施例的示意图。
图5为本公开的网络设备的另一些实施例的示意图。
图6为本公开的网络设备的又一些实施例的示意图。
图7为本公开的网络系统的一些实施例的示意图。
具体实施方式
下面通过附图和实施例，对本公开的技术方案做进一步的详细描述。
发明人发现，在无线通讯网络中实现分布式模型训练时，当前RAN侧尚不支持对于分布式模型训练的配置。为实现分布式模型训练的性能监测，需要RAN支持对分布式模型的训练组的维护，包括组成员的建立、更新、添加、删除等，以用于提高和保障分布式模型训练的性能。
针对上述问题，本公开提出一种终端的配置方法、终端、网络设备、系统和存储介质，以提供RAN侧支持分布式模型训练的配置方案，提高在无线通信网络中分布式模型训练的控制能力。
在一些实施例中，本公开提出一种分布式网络架构，包括分布式节点(或称为分布式模型训练的本地客户端)和中心节点(或称为分布式模型训练的服务器端)。由分布式节点执行模型的训练，分布式节点产生的训练结果汇总至中心节点；中心节点能够对分布式节点进行配置、更新模型，以及汇总各个分布式节点的训练结果。在一些实施例中，分布式节点为终端。在一些实施例中，中心节点包括gNB、OAM、LMF或UE中的至少一项。
本公开由能够作为分布式节点的终端侧执行的终端配置方法的一些实施例的流程图如图1所示。
在步骤130中，终端接收用于配置终端在分布式模型的训练组中状态的信息。在一些实施例中，该信息可以来自分布式模型的训练组的中心节点。在一些实施例中，该信息包括第一配置信息、第二配置信息和第三配置信息，其中，第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的删除操 作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
在一些实施例中，分布式模型可以包括联邦学习模型，从而避免了在分布式节点间、分布式节点与中心节点间的训练数据的传输，在降低数据传输压力的同时，提高了数据的安全保障。
根据终端当前的RRC状态的不同，终端能够接收到的用于配置终端在分布式模型的训练组中状态的信息种类也有所不同。具体的，步骤130包括步骤131和步骤132。
在步骤131中，若终端的当前RRC状态为非激活态，则终端能够接收到第一配置信息和第二配置信息，即终端接收到的用于配置终端在分布式模型的训练组中状态的信息为第一、第二配置信息中的任意一种。训练组的中心节点在终端的RRC状态为非激活态的情况下，不会配置终端作为分布式节点在训练组中处于激活态，因此，终端不能够收到第三配置信息。
在步骤132中，若终端的当前RRC状态为连接态，则终端能够接收到第一、第二和第三配置信息，即终端接收到的用于配置终端在分布式模型的训练组中状态的信息为第一、第二或第三配置信息中的任意一种。训练组的中心节点在终端的RRC状态为连接的情况下，能够使用该终端进行分布式模型训练，因此能够配置终端作为分布式节点在训练组中处于激活态，终端能够收到第三配置信息。
在一些实施例中，若终端为离线状态，则终端可以基于本地模型执行训练操作，不接收来自中心节点的配置信息。
基于上述实施例中的方法，能够将终端的RRC状态，与终端在分布式模型的训练组中作为分布式节点的状态相关联，从而避免要求非激活态的终端执行模型训练所造成的训练和数据反馈不及时，提高分布式模型的训练的可靠性和运行稳定性。
在一些实施例中，如图1中所示，终端配置方法还包括步骤140：终端根据收到的第一、第二或第三配置信息，配置自身的作为分布式节点在训练组中的当前状态。在一些实施例中，若终端收到的为第一配置信息，则终端切换为分布式节点非激活态，暂停训练归属于第一配置信息中所指示的训练组的模型；若终端收到的为第二配置信息，则终端退出第二配置信息中所指示的训练组；若终端收到的为第二配置信息，则终端切换为分布式节点激活态，启动第三配置信息中所指示的训练组的模型训练。
基于该实施例中的方法，终端能够根据来自中心节点的信息修改自身的状态，包括启动模型训练、暂停模型训练、退出训练组(在训练组中呈离线状态)，从而使来自中心节点的控制信息能够及时影响分布式节点的状态，提高分布式节点控制的灵活 度、准确度和效率。
本公开由能够作为分布式模型训练组的中心节点执行的终端配置方法的一些实施例的流程图如图2所示。
在步骤220中，中心节点向终端发送用于配置终端在分布式模型的训练组中状态的信息。在一些实施例中，被配置的终端为与中心节点处于同一个训练组的分布式节点。在一些实施例中，该信息包括第一配置信息、第二配置信息和第三配置信息，其中，第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
根据终端当前的RRC状态的不同，中心节点能够生成和发送的用于配置终端在分布式模型的训练组中状态的信息种类也有所不同。具体的，步骤220包括步骤221和步骤222。
在步骤221中，若终端的当前RRC状态为非激活态，则中心节点能够向终端发送第一配置信息和第二配置信息，即中心节点发送的用于配置终端在分布式模型的训练组中状态的信息为第一、第二配置信息中的任意一种。训练组的中心节点在终端的RRC状态为非激活态的情况下，不会配置终端作为分布式节点在训练组中处于激活态，因此，中心节点不能够发送第三配置信息。
在步骤222中，若终端的当前RRC状态为连接态，则终端能够接收到第一、第二和第三配置信息，即中心节点发送的用于配置终端在分布式模型的训练组中状态的信息为第一、第二或第三配置信息中的任意一种。训练组的中心节点在终端的RRC状态为连接的情况下，能够使用该终端进行分布式模型训练，因此能够配置终端作为分布式节点在训练组中处于激活态，中心节点能够第三配置信息。
基于上述实施例中的方法，能够将终端的RRC状态，与终端在分布式模型的训练组中作为分布式节点的状态相关联，从而避免要求非激活态的终端执行模型训练所造成的训练和数据反馈不及时，提高分布式模型的训练的可靠性和运行稳定性。
在一些实施例中，如图2所示，由中心节点执行的终端的配置方法还可以包括在步骤220之前执行的步骤210。
在步骤210中，中心节点根据终端的RRC状态和自身的控制需求，生成第一配置信息、第二配置信息或第三配置信息。
在一些实施例中，中心节点的控制需求可以包括激活模型训练、去激活模型训练、删除分布式节点中的至少一种。
在一些实施例中，当控制中心需要激活模型训练时，从终端中选择RRC状态为连接态的终端，生成第三配置信息。当控制中心需要去激活模型训练或删除分布式节点时，可以在RRC状态为去激活和连接态的终端中选择。对于RRC状态为离线的终端，控制中心的控制消息难以到达，因此不生成对于RRC离线的终端的控制信息。
基于上述实施例中的方法，中心节点能够结合终端的RRC状态和自身的控制需求，在终端中选择合适的终端来生成控制信息，从而确保控制信息能够被终端执行，提高控制的成功率和控制效率。
在一些实施例中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中，终端除了能够根据组配置信息执行删除、激活或去激活外，还可以执行分布式节点的建立、更新、添加等中的至少一项。在一些实施例中，终端能够根据来自中心节点的组配置信息，获取成为分布式节点的必要信息，如模型信息，实现分布式节点的建立；终端能够根据来自中心节点的组配置信息更新存储的模型或模型参数，实现分布式节点的更新；终端能够加入中心节点指定的训练组，实现分布式节点的添加。
基于该实施例中的方法，能够进一步提高中心节点对分布式节点控制的灵活度，提高在无线通信网络中分布式模型训练的控制能力。
在一些实施例中，分布式模型训练组具有组标识(组ID)，组标识在小区、小区组、TA范围或PLMN内唯一。在一些实施例中，组标识作为一个整体，是相同的TA、PLMN、gNB或CU范围内的唯一标识，从而能够便于唯一的识别训练组，便于后续以训练组为单位进行控制，提高控制识别的准确度。例如，同一个终端可以为多个训练组的分布式节点，终端根据收到的组配置信息中的组标识确定配置操作针对的训练组，避免影响其他训练组中的模型训练，提高分布式模型训练的可靠性。
在一些实施例中，组标识的格式可以为：两部分比特位串联组成，包括：用于分布式模型训练类型的第一比特位和用于在相同分布式模型训练类型中区分训练组的第二比特位，将第一、第二比特位拼接后得到的组标识则能够唯一的标识训练组。这样的组标识能够在唯一的标识分布式模型的训练组的同时，也能够便于识别归属的分布式模型训练类型，提高管理的便捷度。
在一些实施例中，组配置信息中包括组标识、模型标识或中心节点标识中的至少 一种。
在一些实施例中，终端根据组标识确定组配置信息对应的训练组，从而提高后续配置的准确性，避免影响其他训练组的模型训练；在一些实施例中，同一个训练组可以包括一个或多个分布式模型，终端根据模型标识确定组配置信息针对的模型，提高控制的灵活度；在一些实施例中，同一个训练组中可以包括一个或多个中心节点，终端根据中心节点标识确定发起控制的中心节点，从而便于后续对控制的溯源。
在一些实施例中，组配置信息中还包括变更状态的节点、模型或功能中至少一项的标识。在一些实施例中，组配置信息中包括变更的节点的标识，如删除的分布式节点的标识、删除的分布式节点的列表、添加的分布式节点的标识、添加的分布式节点的列表、激活的分布式节点的标识、激活的分布式节点的列表、去激活的分布式节点的标识或去激活的分布式节点的列表中的至少一项；组配置信息中包括变更状态的模型的标识，如激活的模型的标识、激活的模型的列表、去激活的模型的标识或去激活的模型的列表中的至少一项；组配置信息中包括变更状态的功能的标识，如激活的功能的标识、激活的功能的列表、去激活的功能的标识或去激活的功能的列表中的至少一项。在一些实施例中，可以在组配置信息中可以设置不同的用于承载标识信息的字段，终端通过识别组配置信息中标识信息所位于的字段，确定标识的种类和后续的配置方式。
通过这样的方法，中心节点能够从节点、模型、功能维度，对一个或多个分布式节点发起控制，提高控制的灵活度和控制效率。
在一些实施例中，组配置信息中可以通过变更状态的节点、模型或功能中至少一项的标识承载第一配置信息、第二配置信息或第三配置信息中的至少一项。
在一些实施例中，组配置信息可以通过去激活的分布式节点的标识、去激活的分布式节点的列表、去激活的模型的标识、去激活的模型的列表、去激活的功能的标识或去激活的功能的列表中的至少一项，承载第一配置信息。在一些实施例中，在终端的标识与去激活的分布式节点的标识匹配或在去激活的分布式节点的列表中的情况下，终端暂停组配置信息中组标识对应的训练组的模型训练。在一些实施例中，终端暂停训练组配置信息中去激活的模型的标识或去激活的模型的列表对应的模型。在一些实施例中，终端暂停训练组配置信息中去激活的功能的标识或去激活的功能的列表对应的模型。
在一些实施例中，组配置信息可以通过删除的分布式节点的标识或删除的分布式 节点的列表，承载第二配置信息。在一些实施例中，在终端的标识与删除的分布式节点的标识匹配，或在删除的分布式节点的列表中的情况下，终端退出组配置信息中组标识对应的训练组。
在一些实施例中，组配置信息可以通过激活的分布式节点的标识、激活的分布式节点的列表、激活的模型的标识、激活的模型的列表、激活的功能的标识或激活的功能的列表中的至少一项，承载第三配置信息。在一些实施例中，在终端的标识与激活的分布式节点的标识匹配或在激活的分布式节点的列表中的情况下，终端启动组配置信息中组标识对应的训练组的模型训练。在一些实施例中，终端启动训练组配置信息中激活的模型的标识或激活的模型的列表对应的模型。在一些实施例中，终端启动训练组配置信息中激活的功能的标识或激活的功能的列表对应的模型。
通过上文所示实施例中的方法，组配置信息能够从分布式节点、模型、功能多个角度进行激活、去激活的控制，进一步提高了控制的灵活度；利用节点、模型、功能的唯一标识，能够确保控制对象的准确性，提高控制的可靠度。
在一些实施例中，组配置信息中可以包括添加的分布式节点的标识匹配或添加的分布式节点的列表，通过这种方式承载要求一个或多个终端成为指定训练组的分布式节点的信息。在终端的标识与添加的分布式节点的标识匹配或在添加的分布式节点的列表中的情况下，终端加入组配置信息中组标识对应的训练组。通过这样的配置方法，组配置信息能够利用分布式节点的标识控制训练组中作为分布式节点的终端，实现训练组中分布式节点的更新，进一步提高控制能力。
在一些实施例中，组配置信息中包括如下至少一项：
组ID：组ID在一个TA区域或PLMN或gNB或CU内唯一；
模型ID或功能ID：在一个组内可通过训练一个或多个模型，不同模型具有不同的模型/功能；
中心节点ID、链路或接口信息：例如，小区ID等；
删除相关配置信息：组删除中心分布式节点ID、组删除分布式中心节点列表；
添加相关配置信息：组添加分布式中心节点ID、组添加分布式中心节点列表；
组激活/去激活ID；
模型/功能激活ID、模型/功能激活列表、模型/功能去激活ID、模型/功能去激活列表。
在一些实施例中，组配置信息经过数据无线承载、控制无线承载或智能无线承载 中的至少一种传输，从而提高组配置信息传输的灵活度。
在一些实施例中，终端可以通过用户面、控制面、数据面、智能面中的至少一种接收组配置信息，从而提高组配置信息传递的灵活度。
在一些实施例中，组配置信息可以通过系统消息、RRC信令、MAC CE信令、DCI信令、NAS消息中的至少一种承载，从而提高组配置信息传递的灵活度和及时性。
在一些实施例中，分布式模型训练组还可以以训练组为单位进行训练的激活或去激活。在一些实施例中，终端接收组激活指示信息，组激活指示信息用于指示启动归属于组标识对应的训练组的模型训练，组激活指示信息中包括组标识。终端根据组激活指示信息，启动属于组标识对应的训练组中所有模型的训练。在一些实施例中，终端接收组去激活指示信息，组去激活指示信息用于指示暂停归属于组标识对应的训练组的模型训练，组去激活指示信息中包括组标识。终端根据组激活指示信息，暂停属于组标识对应的训练组中所有模型的训练。
通过这样的方法，能够以训练组为单位进行训练的激活或去激活，提高了控制灵活度的同时，也提高了模型启动、暂停控制的效率。
在一些实施例中，终端的配置方法还包括：中心节点向终端发送传输指示信息，终端接收该传输指示信息。传输指示信息中包括信息上传、汇聚或下发的节点和路径信息。通过这样的方法，能够配置分布式节点与中心节点间交互的路径，提高分布式模型训练的信息交互的可控性和可靠性。
在一些实施例中，在训练组的中心节点为除了当前训练组的分布式节点外的其他用户终端的情况下，传输指示信息中包括中心节点的标识或终端间的接口信息。在一些实施例中，在训练组的中心节点为基站的情况下，传输指示信息包括基站标识。通过这样的方法，能够明确分布式节点与中心节点的传输路径。
在一些实施例中，在训练组的中心节点为基站，且该基站位于一个TA或基站组内，且分布式节点中包括中心节点的服务用户，以及为TA或基站组内的至少一个其他基站的服务用户的情况下，传输指示信息还包括该其他基站的标识、中心节点与第一基站间的链路标识，或中心节点与该其他基站间的路由信息中的至少一项。通过这样的方法，能够提供分布式节点与中心节点能够利用区域或基站组内的其他基站进行通信的路径，提高分布式节点与中心节点间交互的灵活度。
在一些实施例中，本公开的终端的配置方法中，分布式模型可以为运算定位问题的模型，在一些实施例中，还可以为用于降低误码率；降低干扰、提升信道容量和信 息传输速率、降低能耗和时延、提升频带利用率的任意一种分布式模型。
通过这样的方法，能够利用网络中各个终端的运算能力和数据采集能力，基于分布式的机器学习模型解决无线通信技术中的问题，提高无线通信的智能性和通信质量。
在一些实施例中，分布式模型可以为分布式定位、分布式波束管理、分布式CSI增强、分布式无线资源管理等多种用途的模型，从而减少了用户与位置相关的数据在空口和网元接口间的传递，保护了用户定位相关数据的安全与隐私，减少空口与模型训练相关的传输开销，增加模型的泛化性和鲁棒性。
在一些实施例中，本公开的分布式模型的训练组具有组标识，组标识在小区、小区组、TA范围、PLMN内唯一，组标识的格式为如下两种中的任意一种：
1、两部分比特位串联组成，包括：分布式模型训练类型比特位，和同类型中用于区分的比特位，例如，M位二进制比特串用于表示分布式模型训练的类型，例如：横向联邦、纵向联邦、联邦迁移学习、单任务分布式学习、多任务分布式学习等类型；N位二进制比特串用于同一类分布式模型训练中用于区分不同模型，例如：采用相同类型的分布式模型使用不同梯度下降方式、不同优化目标或不同数据集等训练的多个模型，其中，M、N分别为大于等于1的正整数；
2、用于区分不同的分布式模型训练组的比特位，在一些实施例中，不同训练组的标识可以全局唯一，或在一定的区域范围内唯一。
在一些实施例中，组配置信息可以由中心节点发送给终端，还可以由网络侧除中心节点的其他设备发送给终端。在一些实施例中，承载组配置信息的信令可以包括系统消息、RRC信令、MAC CE信令、DCI信令、NAS消息中的至少一种。
在一些实施例中，组配置信息通过DRB、SRB、IRB中的至少一种传输，其中，DRB与SRB为相关标准中定义的两种无线承载。在一些实施例中，IRB用于承载AI(Artificial Intelligence，人工智能)/ML(Machine Learning，机器学习)相关的模型相关信息的传输，或用于模型训练/推理的相关信息的传输。
在一些实施例中，分布式节点在训练组中具有如下状态中的至少一项：激活active，非激活inactive和离线offline。激活状态表示该分布式节点参与分布式模型训练，可以使用该分布式节点的本地模型进行模型推理。非激活状态表示分布式节点暂时不参与分布式模型训练，可以使用该分布式节点的本地模型进行模型推理。离线状态表示分布式节点不可达，无法参与分布式模型训练，不能进行模型推理。
在一些实施例中，分布式节点在训练组中的状态与分布式节点的RRC状态具有 如下关系：
具有不同模型标识/功能标识的分布式节点的RRC状态与分布式节点在所属训练组的状态可以相同，也可以不同，例如：当分布式节点处于RRC连接态时，分布式节点在所属训练组的状态可以为激活，非激活，离线中的一种；当分布式节点处于RRC Inactive时，分布式节点在所属训练组的状态为非激活，离线中的一种；当分布式节点处于RRC空闲态时，分布式节点在所属训练组的状态为离线状态。举个例子，当训练的模型具有小区接入/小区重选的功能时，当分布式节点处于RRC空闲态，分布式节点在所属组的状态不可以为激活态。
在一些实施例中，分布式模型的训练组支持分布式节点向中心节点的相关信息上传(汇聚)，以及中心节点向分布式节点的信息下发的节点与路径指示，在一些实施例中，可以通过传输指示信息承载。
在一些实施例中，传输指示信息中包括中心节点ID、链路或接口信息中的至少一项，用于指示分布式模型训练中模型相关信息上传、汇聚、下发的节点与路径。
在一些实施例中，当中心节点为UE时，包含UE ID或UE间接口信息，例如：PC5link identifier。
在一些实施例中，当中心节点为基站时，包含中心节点基站ID，当中心节点gNB在一个TA区域或gNB组内，分布式节点UE包括该中心gNB的服务用户及该中心gNB所在TA区域或gNB组内的其他第二gNB服务的部分或全部用户时，需要包含第二基站ID，或Xn link ID，或中心gNB与第二gNB间的相应路由信息等，例如：RAN node ID，Xn-C面SCTP连接的TNL address。
在一些实施例中，分布式模型的训练组支持删除训练组中的一个或多个分布式节点。在一些实施例中，通过删除相关配置信息(组删除分布式节点标识、组删除分布式节点列表)，从组内删除具有分布式节点标识的分布式节点，停止其参与当前训练组的模型训练。
在一些实施例中，分布式模型的训练组支持添加分布式模型训练中的一个或多个分布式节点。在一些实施例中，通过添加相关配置信息(组添加分布式节点标识、组添加分布式节点列表)，从训练组内添加或新增具有分布式节点标识的分布式节点，使其参与组的模型训练。
在一些实施例中，分布式模型的训练组支持分布式模型训练的激活/去激活。在一些实施例中，通过组激活标识指示启动具有对应组标识的组的全部训练工作；通过组 去激活标识指示暂停具有对应组标识的组的全部训练工作。
在一些实施例中，分布式模型的训练组支持激活/去激活分布式模型训练组的单个或多个模型或功能的训练。在一些实施例中，通过模型或功能激活标识、模型或功能激活列表，指示在组内激活去激活单个或多个模型或功能的训练；通过模型或功能去激活标识、模型或功能去激活列表，指示在组内去激活去去激活单个或多个模型或功能的训练。
本公开的终端31的一些实施例的示意图如图3所示。终端31可以在分布式模型训练的训练组中作为分布式节点。
终端31包括信息接收单元311，信息接收单元311能够在终端的无线资源控制状态为非激活的情况下，接收第一配置信息或第二配置信息，以及能够在终端的无线资源控制状态为连接态的情况下，接收第一配置信息、第二配置信息或第三配置信息。在一些实施例中，第一配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；第二配置信息用于指示终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
这样的终端能够将终端的RRC状态，与终端在分布式模型的训练组中作为分布式节点的状态相关联，从而避免非激活态的终端被要求执行模型训练所造成的训练和数据反馈不及时，提高分布式模型的训练的可靠性和运行稳定性。
在一些实施例中，终端还包括配置单元312，能够根据组配置信息执行分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项，其中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中。在一些实施例中，组配置信息可以为上文中提到的任意一种。
这样的终端能够根据来自中心节点的信息修改自身的状态，包括启动模型训练、暂停模型训练、退出训练组(在训练组中呈离线状态)，另外还能够执行分布式节点的建立、更新、添加，从而使来自中心节点的控制信息能够及时影响分布式节点的状态，提高分布式节点控制的灵活度、准确度和效率。
在一些实施例中，终端还包括训练单元313，能够执行以下至少一项：根据本地模型执行模型推理；根据来自训练组的中心节点的模型参数更新信息更新本地模型；或向训练组的中心节点发送模型训练结果信息。
这样的终端能够作为训练组中的分布式节点，基于中心节点的配置，利用自身的 数据和运算能力执行分布式模型的训练，提高无线通信网络中模型训练的效率，降低对机器学习运行设备能力的要求；另外，分布式模型可以为联邦学习模型，从而进一步避免了原始数据在节点间的传输，提高了数据的安全性。
本公开的网络设备42的一些实施例的示意图如图4所示。网络设备42可以在分布式模型训练的训练组中作为中心节点。在一些实施例中，网络设备可以为5G基站gNB、操作维护管理实体OAM、位置管理功能实体LMF或用户终端UE中的至少一项。
网络设备包括信息发送单元43，能够在终端的无线资源控制RRC状态为非激活的情况下，向终端发送第一配置信息或第二配置信息，以及在终端的无线资源控制RRC状态为激活的情况下，向终端发送第一配置信息、第二配置信息或第三配置信息。在一些实施例中，第一配置信息用于指示终端作为训练组中的分布式节点执行模型训练的去激活；第二配置信息用于指示终端作为训练组中的分布式节点执行模型训练的删除操作；和第三配置信息用于指示终端作为训练组中的分布式节点执行模型训练的激活。
这样的网络设备能够将终端的RRC状态，与终端在分布式模型的训练组中作为分布式节点的状态相关联，从而避免要求非激活态的终端执行模型训练所造成的训练和数据反馈不及时，提高分布式模型的训练的可靠性和运行稳定性。
在一些实施例中，如图4所示，网络设备还包括生成单元422，能够根据分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项生成组配置信息，其中，第一配置信息、第二配置信息和第三配置信息位于训练组的组配置信息中位于训练组的组配置信息中。
这样的网络设备能够根据需求要求终端修改自身的状态，包括启动模型训练、暂停模型训练、退出训练组(在训练组中呈离线状态)，另外还能够执行分布式节点的建立、更新、添加，从而提高分布式节点控制的灵活度、准确度和效率。
在一些实施例中，如图4所示，网络设备还可以包括训练控制单元41，能够执行以下至少一项：向训练组的分布式节点发送模型参数更新信息；或中心节点接收来自训练组的分布式节点的模型训练结果信息。
这样的网络设备能够作为训练组中的中心节点，控制分布式节点的模型参数和状态，并基于各个分布式节点的训练结果进行汇总，提高无线通信网络中模型训练的效率；另外，分布式模型可以为联邦学习模型，从而进一步避免了原始数据在节点间的 传输，提高了数据的安全性。
本公开网络设备的一个实施例的结构示意图如图5所示。网络设备包括存储器501和处理器502。其中：存储器501可以是磁盘、闪存或其它任何非易失性存储介质。存储器用于存储上文中由终端或中心节点执行的终端的配置方法的对应实施例中的指令。处理器502耦接至存储器501，可以作为一个或多个集成电路来实施，例如微处理器或微控制器。该处理器502用于执行存储器中存储的指令，能够提高分布式模型的训练的可靠性和运行稳定性。
在一个实施例中，还可以如图6所示，网络设备600包括存储器601和处理器602。处理器602通过BUS总线603耦合至存储器601。该网络设备600还可以通过存储接口604连接至外部存储装置605以便调用外部数据，还可以通过网络接口606连接至网络或者另外一台计算机系统(未标出)。此处不再进行详细介绍。
在该实施例中，通过存储器存储数据指令，再通过处理器处理上述指令，能够提高分布式模型的训练的可靠性和运行稳定性。
在另一个实施例中，一种计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现由终端或中心节点执行的终端的配置方法对应实施例中的方法的步骤。本领域内的技术人员应明白，本公开的实施例可提供为方法、装置、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用非瞬时性存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开的网络系统的一些实施例的示意图如图7所示。网络系统中包括至少一个分布式模型训练组的中心节点72和多个分布式模型训练组的分布式节点711-71n，其中，n为大于1的整数，分布式节点为终端，分布式节点能够执行上文中任意一种由终端执行的终端的配置方法；和中心节点能够执行上文中任意一种由中心节点执行的终端的配置方法。
在一些实施例中，中心节点包括5G基站gNB、操作维护管理实体OAM、位置管理功能实体LMF或用户终端UE中的至少一项。
这样的网络系统能够将终端的RRC状态，与终端在分布式模型的训练组中作为分布式节点的状态相关联，从而避免要求非激活态的终端执行模型训练所造成的训练和数据反馈不及时，提高分布式模型的训练的可靠性和运行稳定性。另外，本公开的 网络系统的机构能够适用于未来RAN侧计算资源的分布式部署或集中式部署或混合部署等多种方案，有利于推广应用。
本公开是参照根据本公开实施例的方法、设备(系统)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
至此，已经详细描述了本公开。为了避免遮蔽本公开的构思，没有描述本领域所公知的一些细节。本领域技术人员根据上面的描述，完全可以明白如何实施这里公开的技术方案。
可能以许多方式来实现本公开的方法以及装置。例如，可通过软件、硬件、固件或者软件、硬件、固件的任何组合来实现本公开的方法以及装置。用于所述方法的步骤的上述顺序仅是为了进行说明，本公开的方法的步骤不限于以上具体描述的顺序，除非以其它方式特别说明。此外，在一些实施例中，还可将本公开实施为记录在记录介质中的程序，这些程序包括用于实现根据本公开的方法的机器可读指令。因而，本公开还覆盖存储用于执行根据本公开的方法的程序的记录介质。
需要说明的是，本公开的说明书和权利要求书及附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本公开的实施例能够以除了在这里 图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
最后应当说明的是：以上实施例仅用以说明本公开的技术方案而非对其限制；尽管参照较佳实施例对本公开进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本公开的具体实施方式进行修改或者对部分技术特征进行等同替换；而不脱离本公开技术方案的精神，其均应涵盖在本公开请求保护的技术方案范围当中。

Claims (31)

  1. 一种终端的配置方法，包括：
    在所述终端的无线资源控制状态为非激活的情况下，终端接收第一配置信息或第二配置信息；和
    在所述终端的无线资源控制状态为连接态的情况下，终端接收第一配置信息、第二配置信息或第三配置信息，
    其中，
    所述第一配置信息用于指示所述终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活；
    所述第二配置信息用于指示所述终端作为分布式模型的训练组中的分布式节点执行模型训练的删除操作；和
    所述第三配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的激活。
  2. 根据权利要求1所述的配置方法，其中，所述第一配置信息、第二配置信息和第三配置信息位于所述训练组的组配置信息中，
    所述配置方法还包括：所述终端根据所述组配置信息执行分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项。
  3. 根据权利要求2所述的配置方法，其中，所述组配置信息符合以下至少一项：
    所述组配置信息为通过数据无线承载、控制无线承载或智能无线承载中的至少一种承载；
    所述组配置信息为通过用户面、控制面、数据面、智能面中的至少一种接收；或
    所述组配置信息为通过系统消息、无线资源控制信令、媒体接入控制MAC控制单元CE信令、下行控制信息信令、非接入层消息中的至少一种承载。
  4. 根据权利要求2或3所述的配置方法，其中，所述组配置信息中包括组标识、模型标识或中心节点标识中的至少一种。
  5. 根据权利要求4所述的配置方法，其中，所述组配置信息中还包括变更状态的节点、模型或功能中至少一项的标识。
  6. 根据权利要求5所述的配置方法，其中，所述组配置信息符合以下至少一项：
    所述变更状态的节点的标识包括删除的分布式节点的标识、删除的分布式节点的列表、添加的分布式节点的标识、添加的分布式节点的列表、激活的分布式节点的标识、激活的分布式节点的列表、去激活的分布式节点的标识或去激活的分布式节点的列表中的至少一项；
    所述变更状态的模型的标识包括激活的模型的标识、激活的模型的列表、去激活的模型的标识或去激活的模型的列表中的至少一项；或
    所述变更状态的功能的标识包括激活的功能的标识、激活的功能的列表、去激活的功能的标识或去激活的功能的列表中的至少一项。
  7. 根据权利要求4所述的配置方法，其中：所述组标识包括第一比特位和第二比特位，所述第一比特位为分布式模型训练类型的唯一标识，所述第二比特位为相同分布式模型训练类型中的所述训练组的唯一标识；或所述组标识为相同的跟踪区、公共陆地移动网络、5G基站或控制面单元范围内，所述训练组的唯一标识。
  8. 根据权利要求1所述的配置方法，还包括：
    所述终端接收传输指示信息，所述传输指示信息中包括信息上传、汇聚或下发的节点和路径信息。
  9. 根据权利要求8所述的配置方法，其中，所述训练组的中心节点为其他用户终端，所述传输指示信息中包括中心节点的标识或终端间的接口信息。
  10. 根据权利要求8所述的配置方法，其中，所述训练组的中心节点为基站，所述传输指示信息包括基站标识。
  11. 根据权利要求10所述的配置方法，其中，所述训练组的中心节点位于一个跟踪区TA或基站组内，且所述分布式节点中包括所述中心节点的服务用户，以及为所述TA或基站组内的至少一个第一基站的服务用户，所述传输指示信息还包括所述第一基站的标识、所述中心节点与所述第一基站间的链路标识，或所述中心节点与所述第一基站间的路由信息中的至少一项。
  12. 根据权利要求1～3任意一项所述的配置方法，还包括以下至少一项：
    所述终端根据本地模型执行模型推理；
    所述终端接收来自所述训练组的中心节点的模型参数更新信息；或
    所述终端向所述训练组的中心节点发送模型训练结果信息。
  13. 根据权利要求1～3任意一项所述的方法，还包括：
    所述终端根据所述第一配置信息、所述第二配置信息或所述第三配置信息中的任意一项配置当前状态。
  14. 根据权利要求1所述的配置方法，其中，
    所述终端根据所述第一配置信息暂停训练归属于所述训练组的模型；
    所述终端根据所述第二配置信息退出所述训练组；或
    所述终端根据所述第三配置信息启动所述训练组的模型训练。
  15. 根据权利要求6所述的配置方法，其中，所述组配置信息中通过变更状态的节点、模型或功能中至少一项的标识承载所述第一配置信息、第二配置信息或第三配置信息中的至少一项；
    所述配置方法还包括：
    在所述终端的标识与删除的分布式节点的标识匹配，或在删除的分布式节点的列表中的情况下，退出所述组配置信息中组标识对应的所述训练组；
    在所述终端的标识与添加的分布式节点的标识匹配或在添加的分布式节点的列表中的情况下，加入所述组配置信息中组标识对应的所述训练组；
    在所述终端的标识与激活的分布式节点的标识匹配或在激活的分布式节点的列表中的情况下，启动所述组配置信息中组标识对应的所述训练组的模型训练；
    在所述终端的标识与去激活的分布式节点的标识匹配或在去激活的分布式节点的列表中的情况下，暂停所述组配置信息中组标识对应的所述训练组的模型训练；
    启动训练所述组配置信息中激活的模型的标识或激活的模型的列表对应的模型；
    暂停训练所述组配置信息中去激活的模型的标识或去激活的模型的列表对应的模型；
    启动训练所述组配置信息中激活的功能的标识或激活的功能的列表对应的模型；或
    暂停训练所述组配置信息中去激活的功能的标识或去激活的功能的列表对应的模型。
  16. 根据权利要求1所述的配置方法，还包括以下至少一项：
    所述终端接收组激活指示信息，其中，所述组激活指示信息用于指示启动归属于组标识对应的训练组的模型训练，所述组激活指示信息中包括所述组标识；或
    所述终端接收组去激活指示信息，其中，所述组去激活指示信息用于指示暂停归 属于组标识对应的训练组的模型训练，所述组去激活指示信息中包括所述组标识。
  17. 根据权利要求1～3任意一项所述的配置方法，其中，所述分布式模型包括联邦学习模型。
  18. 一种终端的配置方法，包括：
    在所述终端的无线资源控制状态为非激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息或第二配置信息；
    在所述终端的无线资源控制状态为激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息、第二配置信息或第三配置信息，
    其中：
    所述第一配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的去激活；
    所述第二配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的删除操作；和
    所述第三配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的激活。
  19. 根据权利要求18所述的配置方法，其中，所述第一配置信息、第二配置信息和第三配置信息位于所述训练组的组配置信息中；
    所述配置方法还包括：所述中心节点根据分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项生成所述组配置信息。
  20. 根据权利要求18所述的配置方法，还包括：
    所述中心节点向分布式节点发送传输指示信息，所述传输指示信息中包括信息上传、汇聚或下发的节点和路径信息。
  21. 根据权利要求19所述的配置方法，还包括以下至少一项：
    所述中心节点向所述训练组的分布式节点发送模型参数更新信息；或
    所述中心节点接收来自所述训练组的分布式节点的模型训练结果信息。
  22. 根据权利要求19所述的配置方法，其中，所述配置方法符合以下至少一项：
    所述分布式模型包括联邦学习模型；或
    所述中心节点为5G基站gNB、操作维护管理实体、位置管理功能实体或用户终端中的至少一项。
  23. 一种终端，包括：
    信息接收单元，被配置为在所述终端的无线资源控制状态为非激活的情况下，接收第一配置信息或第二配置信息；和，在所述终端的无线资源控制状态为连接态的情况下，接收第一配置信息、第二配置信息或第三配置信息，
    其中：
    所述第一配置信息用于指示所述终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；
    所述第二配置信息用于指示所述终端作为分布式模型的训练组中的分布式节点执行模型训练的去激活或删除操作；和
    所述第三配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的激活。
  24. 根据权利要求23所述的终端，还包括以下至少一项：
    配置单元，被配置为根据组配置信息执行分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项，其中，所述第一配置信息、第二配置信息和第三配置信息位于所述训练组的组配置信息中；或
    训练单元，被配置为执行以下至少一项：
    根据本地模型执行模型推理；
    根据来自所述训练组的中心节点的模型参数更新信息更新本地模型；或
    向所述训练组的中心节点发送模型训练结果信息。
  25. 一种网络设备，包括：
    信息发送单元，被配置为在所述终端的无线资源控制状态为非激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息或第二配置信息；和在所述终端的无线资源控制状态为激活的情况下，分布式模型的训练组的中心节点向终端发送第一配置信息、第二配置信息或第三配置信息，
    其中：
    所述第一配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的去激活；
    所述第二配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的删除操作；和
    所述第三配置信息用于指示所述终端作为所述训练组中的分布式节点执行模型训练的激活。
  26. 根据权利要求25所述的网络设备，还包括以下至少一项：
    生成单元，被配置为根据分布式节点的建立、更新、添加、删除、激活或去激活中的至少一项生成组配置信息，其中，所述第一配置信息、第二配置信息和第三配置信息位于所述训练组的组配置信息中位于所述训练组的组配置信息中；或
    训练控制单元，被配置为执行以下至少一项：
    向所述训练组的分布式节点发送模型参数更新信息；或
    所述中心节点接收来自所述训练组的分布式节点的模型训练结果信息。
  27. 一种网络设备，包括：
    存储器；以及
    耦接至所述存储器的处理器，所述处理器被配置为基于存储在所述存储器的指令执行如权利要求1至22任一项所述的方法。
  28. 一种非瞬时性计算机可读存储介质，其上存储有计算机程序指令，该指令被处理器执行时实现权利要求1至22任意一项所述的方法的步骤。
  29. 一种网络系统，包括至少一个分布式模型训练组的中心节点和多个分布式模型训练组的分布式节点，其中，所述分布式节点为终端，所述分布式节点被配置为执行权利要求1～17任意一项所述的方法；和
    所述中心节点被配置为执行权利要求18～22任意一项所述的方法。
  30. 根据权利要求29所述的系统，其中，所述中心节点包括5G基站gNB、操作维护管理实体、位置管理功能实体或用户终端中的至少一项。
  31. 一种计算机程序，用于使处理器执行权利要求1～22任意一项所述的方法。
PCT/CN2024/097506 2023-06-05 2024-06-05 终端的配置方法、终端、网络设备、系统和存储介质 Ceased WO2024251148A1 (zh)

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CN114095969A (zh) * 2020-08-24 2022-02-25 华为技术有限公司 一种智能的无线接入网络
WO2022245459A1 (en) * 2021-05-18 2022-11-24 Qualcomm Incorporated Ml model training procedure

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CN114095969A (zh) * 2020-08-24 2022-02-25 华为技术有限公司 一种智能的无线接入网络
WO2022245459A1 (en) * 2021-05-18 2022-11-24 Qualcomm Incorporated Ml model training procedure

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