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WO2025175475A1 - Structure de modèle - Google Patents

Structure de modèle

Info

Publication number
WO2025175475A1
WO2025175475A1 PCT/CN2024/077776 CN2024077776W WO2025175475A1 WO 2025175475 A1 WO2025175475 A1 WO 2025175475A1 CN 2024077776 W CN2024077776 W CN 2024077776W WO 2025175475 A1 WO2025175475 A1 WO 2025175475A1
Authority
WO
WIPO (PCT)
Prior art keywords
sub
module
modules
main module
main
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/077776
Other languages
English (en)
Inventor
Yijia Feng
Chen Hui YE
Yihang HUANG
Zhuoran XIAO
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nokia Shanghai Bell Co Ltd
Nokia Solutions and Networks Oy
Nokia Technologies Oy
Original Assignee
Nokia Shanghai Bell Co Ltd
Nokia Solutions and Networks Oy
Nokia Technologies Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nokia Shanghai Bell Co Ltd, Nokia Solutions and Networks Oy, Nokia Technologies Oy filed Critical Nokia Shanghai Bell Co Ltd
Priority to PCT/CN2024/077776 priority Critical patent/WO2025175475A1/fr
Publication of WO2025175475A1 publication Critical patent/WO2025175475A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • Example embodiments of the present disclosure generally relate to the field of communications, and in particular, to a terminal device, a network device, methods, apparatuses, and a computer-readable medium for a model framework.
  • a communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network.
  • a mobile or wireless communication network is one example of a communication network.
  • Such communication networks operate in according with standards such as those provided by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute) .
  • standards such as those provided by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute) .
  • standards are the so-called 5G (5th Generation) standards provided by 3GPP.
  • example embodiments of the present disclosure provide a solution for a model framework, especially a model adaptation framework for channel state information (CSI) feedback enhancement.
  • CSI channel state information
  • an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: train a main module and a plurality of sub-modules parallel connectable to the main module for channel state information (CSI) feedback, wherein the plurality of sub-modules are associated with a plurality of sites of different wireless environments; and perform, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • CSI channel state information
  • a method comprising: training a main module and a plurality of sub-modules parallel connectable to the main module for channel state information (CSI) feedback, wherein the plurality of sub-modules are associated with a plurality of sites of different wireless environments; and performing, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • CSI channel state information
  • an apparatus comprising: means training a main module and a plurality of sub-modules parallel connectable to the main module for channel state information (CSI) feedback, wherein the plurality of sub-modules are associated with a plurality of sites of different wireless environments; and means for performing, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • CSI channel state information
  • a non-transitory computer-readable storage medium having instructions stored thereon.
  • the instructions when executed on at least one processor, cause the at least one processor to perform the method of the second aspect.
  • a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: train a main module and a plurality of sub-modules parallel connectable to the main module for channel state information (CSI) feedback, wherein the plurality of sub-modules are associated with a plurality of sites of different wireless environments; and perform, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • CSI channel state information
  • a terminal device comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the terminal device at least to: receive, from a network device, an index of a first sub-module among first one or more sub-modules at the network device, wherein the first sub-module is to be parallel connected to a main decoder at the network device; and connect, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • a network device comprises at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the network device at least to: select, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device to be parallel connected to a main decoder at the network device; and transmit, to the terminal device, an index of the selected sub-module.
  • ID site identifier
  • a method comprises: receiving, from a network device, an index of a first sub-module among first one or more sub-modules at the network device, wherein the first sub-module is to be parallel connected to a main decoder at the network device; and connecting, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • a method comprises: selecting, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device to be parallel connected to a main decoder at the network device; and transmitting, to the terminal device, an index of the selected sub-module.
  • ID site identifier
  • an apparatus comprising: means for receiving, from a network device, an index of a first sub-module among first one or more sub-modules at the network device, wherein the first sub-module is to be parallel connected to a main decoder at the network device; and based on the index, means for connecting a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • an apparatus comprising: means for selecting, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device to be parallel connected to a main decoder at the network device; and means for transmitting, to the terminal device, an index of the selected sub-module.
  • ID site identifier
  • a non-transitory computer-readable storage medium having instructions stored thereon.
  • the instructions when executed on at least one processor, cause the at least one processor to perform the method of the ninth or tenth aspect.
  • a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: receive, from a network device, an index of a first sub-module among first one or more sub-modules at the network device, wherein the first sub-module is to be parallel connected to a main decoder at the network device; and connect, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • a computer program comprising instructions, which, when executed by an apparatus, cause the apparatus at least to: select, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device to be parallel connected to a main decoder at the network device; and transmit, to the terminal device, an index of the selected sub-module.
  • ID site identifier
  • a terminal device comprising: receiving circuitry configured to receive, from a network device, an index of a first sub-module among first one or more sub-modules at the network device, wherein the first sub-module is to be parallel connected to a main decoder at the network device; and connecting circuitry configured to connect, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • a network device comprising: selecting circuitry configured to select, based on a site identifier (ID) , a sub-module among one or more sub-modules at a network device to be parallel connected to a main decoder at the network device; and transmitting circuitry configured to transmit, to a terminal device, an index of the selected sub-module.
  • ID site identifier
  • FIG. 1 illustrates an example network environment in which some example embodiments of the present disclosure may be implemented
  • FIG. 2 illustrates a flowchart of an example method in accordance with some embodiments of the present disclosure
  • FIG. 3 illustrates a signaling chart illustrating an example communication process in accordance with some example embodiments of the present disclosure
  • FIG. 4A illustrates a schematic diagram of an example model adaptation framework in accordance with some embodiments of the present disclosure
  • FIG. 4B illustrates another schematic diagram of an example model adaptation framework in accordance with some embodiments of the present disclosure
  • FIG. 5A illustrates a block diagram of an example of sub modules parallel connectable to the main module in accordance with some embodiments of the present disclosure
  • FIG. 5B illustrates an example of input/output data of a main module and a sub module parallel connected to the main module in accordance with some embodiments of the present disclosure
  • FIG. 6A illustrates a block diagram of an example alternative hybrid training process in accordance with some embodiments of the present disclosure
  • FIG. 6B illustrates another block diagram of an example alternative hybrid training process in accordance with some embodiments of the present disclosure
  • FIG. 7A illustrates a block diagram of an example growable sub modular training process in accordance with some embodiments of the present disclosure
  • FIG. 7B illustrates another block diagram of an example growable sub modular training process in accordance with some embodiments of the present disclosure
  • FIG. 7C illustrates further another block diagram of an example growable sub modular training process in accordance with some embodiments of the present disclosure
  • FIG. 8 illustrates a block diagram for model inference in accordance with some embodiments of the present disclosure
  • FIG. 9 illustrates a signaling chart illustrating another example communication process in accordance with some embodiments of the present disclosure.
  • FIG. 10 illustrates a flowchart of another example method implemented at a terminal device in accordance with some embodiments of the present disclosure
  • FIG. 11 illustrates a flowchart of further another example method implemented at a network device in accordance with some embodiments of the present disclosure
  • FIG. 12A illustrates a block diagram of an example of a transformer-enabled neural network (NN) for CSI feedback in accordance with some example embodiments of the present disclosure
  • FIG. 12B illustrates a block diagram of a modified multi-head attention block in accordance with some example embodiments of the present disclosure
  • FIG. 13 illustrates a simplified block diagram of a device that is suitable for implementing some example embodiments of the present disclosure.
  • FIG. 14 illustrates a block diagram of an example of a computer-readable medium in accordance with some example embodiments of the present disclosure.
  • references in the present disclosure to “one embodiment, ” “an embodiment, ” “an example embodiment, ” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • first and second etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments.
  • the term “and/or” includes any and all combinations of one or more of the listed terms.
  • circuitry may refer to one or more or all of the following:
  • circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware.
  • circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.
  • the term “communication network” refers to a network following any suitable communication standards, such as Long Term Evolution (LTE) , LTE-Advanced (LTE-A) , Wideband Code Division Multiple Access (WCDMA) , High-Speed Packet Access (HSPA) , Narrow Band Internet of Things (NB-IoT) , Wireless Fidelity (WiFi) and so on.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • WCDMA Wideband Code Division Multiple Access
  • HSPA High-Speed Packet Access
  • NB-IoT Narrow Band Internet of Things
  • WiFi Wireless Fidelity
  • the communications between a terminal device and a network device in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the fourth generation (4G) , 4.5G, the future fifth generation (5G) , IEEE 802.11 communication protocols, and/or any other protocols either currently known or to be developed in the future.
  • 4G fourth generation
  • Embodiments of the present disclosure may be applied in various communication systems. Given the rapid development in communications, there will of course also be future type communication technologies and systems with which the present disclosure may be embodied. It should not be seen as limiting the scope of the present disclosure to only the aforementioned system.
  • the term “network device” refers to a node in a communication network via which a terminal device accesses the network and receives services therefrom.
  • the network device may refer to a base station (BS) or an access point (AP) , for example, a node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a NR NB (also referred to as a gNB) , a Remote Radio Unit (RRU) , a radio header (RH) , a remote radio head (RRH) , a WiFi device, a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology.
  • BS base station
  • AP access point
  • terminal device refers to any end device that may be capable of wireless communication.
  • a terminal device may also be referred to as a communication device, user equipment (UE) , a Subscriber Station (SS) , a Portable Subscriber Station, a Mobile Station (MS) , a station (STA) or station device, or an Access Terminal (AT) .
  • UE user equipment
  • SS Subscriber Station
  • MS Mobile Station
  • STA station
  • AT Access Terminal
  • the terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, voice over IP (VoIP) phones, wireless local loop phones, a tablet, a wearable terminal device, a personal digital assistant (PDA) , portable computers, desktop computer, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , USB dongles, smart devices, wireless customer-premises equipment (CPE) , an Internet of Things (loT) device, a watch or other wearable, a VR (virtual reality) device, an XR (eXtended reality) device, a head-mounted display (HMD) , a vehicle, a drone, a medical device and applications (for example, remote surgery) , an industrial device and applications (for example, a robot and/or other wireless devices operating in an industrial and/or an automated processing chain
  • a specific model trained for specific cells/sites or a general model trained with mixing datasets, or a model fine-tuned to fit the deployed environment.
  • a specific model is trained for specific cells/sites, training models for specific cell/site allows for tailored feature extraction, capturing the most relevant information and discarding unnecessary details for improved efficiency.
  • the device may switch from a model specific to the cell/site before the location change to a model specific to the cell/site after the location change.
  • the device should store multiple models to cover different cells/sites, which is storage consuming and resource intensive. If a general model is trained with mixing datasets from multiple cells/sites, the model can achieve consistent performance across different cells/sites. However, there may be a performance loss compared to the specific model in some cells/sites. If a model is fine-tuned to fit the deployed environment, the adaptation of a single model ensures the model to remain effective in the deployed environment. However, the model update needs real-time adjustment to the neural network (NN) parameters, which is difficult to implement in real-world deployment.
  • NN neural network
  • FIG. 1 illustrates an example communication system 100 in which some embodiments of the present disclosure can be implemented.
  • the communication system 100 which is a part of a communication network, includes a terminal device (UE) 110 and a network device 120.
  • the terminal device 110 may be, for example, an Internet of Things (IoT) device.
  • the network device 120 may be for example a random access network (RAN) device (like an NG-RAN device, also called as gNB) , or a communication module thereof.
  • RAN random access network
  • gNB NG-RAN device
  • the network device 120 is associated with a cell 121, and provides communication service to terminal devices (like UE 110) in the cell 121.
  • the terminal device 110 is in connection with the network device 120.
  • a link from the network device 120 to terminal device 110 is referred to as a downlink (DL)
  • a link from terminal device 110 to the network device 120 is referred to as an uplink (UL)
  • the network device 120 is a transmitting (TX) device (or a transmitter)
  • terminal device 110 is a receiving (RX) device (or a receiver)
  • terminal device 110 is a transmitting TX device (or a transmitter)
  • the network device 120 is a RX device (or a receiver) .
  • the communications in the communication system 100 may conform to any suitable standards including, but not limited to, Long Term Evolution (LTE) , LTE-Advanced (LTE-A) , Wideband Code Division Multiple Access (WCDMA) , Code Division Multiple Access (CDMA) and Global System for Mobile Communications (GSM) and the like.
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • WCDMA Wideband Code Division Multiple Access
  • CDMA Code Division Multiple Access
  • GSM Global System for Mobile Communications
  • the communications may be performed according to any generation communication protocols either currently known or to be developed in the future. Examples of the communication protocols include, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , the fourth generation (4G) , 4.5G, the fifth generation (5G) , 5.5G, 5G-Advanced networks, or the sixth generation (6G) communication protocols.
  • the communication system 100 may include any suitable number of devices adapted for implementing embodiments of the present disclosure.
  • FIG. 2 illustrates a flowchart of an example method 200 implemented at an apparatus (for example, the terminal device 110 or network device 120 as illustrated in FIG. 1) in accordance with some embodiments of the present disclosure.
  • an apparatus for example, the terminal device 110 or network device 120 as illustrated in FIG. 1.
  • the method 200 will be described from the perspective of the network device 120 with reference to FIG. 2.
  • the method 200 can also be described from the perspective of the terminal device 110.
  • the network device 120 trains a main module and a plurality of sub-modules parallel connectable to the main module for CSI feedback.
  • the plurality of sub-modules are associated with a plurality of sites of different wireless environments (for example, multi-path conditions) .
  • the image of “a main module and a plurality of sub-modules parallel connectable to the main module” may refer to FIGS. 4A, 4B or 5A, which will be described in more detail later.
  • the main module may be or comprise an encoder (herein also referred to as “a main encoder” ) in case that the method 200 is described from the perspective of the terminal device 110.
  • the main module may be or comprise a decoder (herein also referred to as “a main decoder” ) in case that the method 200 is described from the perspective of the network device 120.
  • the network device 120 performs, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • the main module and the plurality of sub-modules may be within at least one linear layer in an AI/ML model, as illustrated in FIGS. 4B and 5A.
  • the linear layer may be a neural network (NN) layer.
  • number of the plurality of sub-modules may be predetermined corresponding to number of the plurality of sites.
  • the plurality of sub-modules may be parallel connectable to the main module via respective switches, as illustrated, for example, in FIGS. 4B and 5A.
  • a sub-module is parallel connected to the main module, a same input may be provided to the main module and the sub-module to obtain a first output from the main module and a second output from the sub-module, then, for example, the first output and the second output may be added to obtain the overall output, as illustrated in FIG. 5B.
  • a sub-module is parallel connected to the main module, a sum of a first output of the main module and a second output of the sub-module may be provided to a next layer of the AI/ML model.
  • the network device 120 may perform a training process based on a plurality of datasets associated with the plurality of sites, and the training process may comprise a plurality of training steps. More specifically, among the plurality of datasets, a dataset may be specific to a site among the plurality of sites.
  • a site may be a location where the network device 120 is located, like a cell (for example, cell 121 as illustrated in FIG. 1) .
  • the network device 120 may train a sub-module jointly with the main module independently (separate) from other sub modules. Alternatively or in addition, the network device 120 may train the main module based on a general dataset. Alternatively or in addition, the network device 120 may train the plurality of sub-modules based on the plurality of datasets respectively.
  • the main module may be parallel connected with a sub-module among the plurality of sub-modules.
  • the main module and the sub-module may be trained based on a dataset among the plurality of datasets.
  • the dataset may correspond to a site among the plurality of sites and the cite may be associated with the sub-module, as mentioned above.
  • parameters of the main module may be updated based on an overall loss which is a sum of a plurality of sub-losses determined based on the plurality of datasets in the plurality of training steps.
  • parameters of the sub-module may be updated based on a sub-loss among the plurality of sub-losses.
  • the sub-loss may be determined based on the dataset in a specific training step among the plurality of training steps.
  • sub-loss-i 1, 2, ..., n
  • sub-loss-i 1, 2, ..., n
  • the network device 120 may train the main module based on a general dataset.
  • the general dataset may be, for example, a mixture of the plurality of datasets associated with the plurality of sites.
  • the main module may be parallel connected with a sub-module among the plurality of sub-modules, and the main module may be maintained unchanged and the sub-module may be trained based on a dataset among the plurality of datasets.
  • the dataset may correspond to a site among the plurality of sites, and the site may be associated with the sub-module.
  • data from the dataset may be fed through (input to) both the main module and the sub module to obtain a loss.
  • gradients of the loss with respect to trainable parameters of the sub-module may be computed, while the parameters of the main module may remain unaffected by the gradients calculated for the sub-module.
  • the network device 120 may calculate a plurality of statistical discrepancies between in-field data and a plurality of datasets associated with the plurality of sites. Then, the network device 120 may determine a target site among the plurality of sites based on the plurality of statistical discrepancies, and determine, among the plurality of sub-modules, a target sub-module associated with the target site as the selected sub-module to be parallel connected to the main module for the inference. For example, the target site may correspond to a dataset with a lowest statistical discrepancy among the plurality of statistical discrepancies.
  • a model architecture (also referred to as model generalization/adaptation framework) can be obtained.
  • model generalization/adaptation framework also referred to as model generalization/adaptation framework
  • FIG. 3 illustrates a signaling chart illustrating an example communication process 300 in accordance with some example embodiments of the present disclosure.
  • the communication process 300 will be described with reference to FIG. 1.
  • the communication process 300 may involve a terminal device (for example, the terminal device 110 as illustrated in FIG. 1) and a network device (for example, the network device 120 as illustrated in FIG. 1) .
  • the communication process 300 will be described with reference to the terminal device 110 and the network device 120 as illustrated in FIG. 1.
  • the network device 120 selects, based on a site identifier (ID) , a sub-module (hereafter, also referred to as “first sub-module” ) among one or more sub-modules (hereafter, also referred to as “first one or more sub-modules” ) at the network device 120 to be parallel connected to a main decoder at the network device 120.
  • ID site identifier
  • the network device 120 transmits (320) an index 301 of the selected (first) sub-module to the terminal device 110.
  • the terminal device receives (322) , from the network device 120, the index 301 of the first sub-module among the first one or more sub-modules at the network device 120.
  • the first sub-module is to be parallel connected to the main decoder at the network device 120, as mentioned above.
  • the terminal device 110 connects, based on the index 301, a second sub-module among second one or more sub-modules at the terminal device 110 parallel to a main encoder at the terminal device 110.
  • the second sub-module is associated with the first sub-module.
  • the terminal device 110 may transmit, to the network device 120, a message confirming that the second sub-module is to be parallel connected to the main encoder at the terminal device 110.
  • the network device 120 may receive, from the terminal device 110, the message confirming that the second sub-module at the terminal device 110 corresponding to the index 301 is to be parallel connected to the main encoder of the terminal device 110.
  • the terminal device 110 may transmit a request message to the network device 120 for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device 120 corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device 110.
  • encoder sub-module means a sub module among the second one or more sub-modules to be parallel connected with the (main) encoder (the main module) at the terminal device 110, and the request message may comprise a first ID list of the second one or more sub-modules at the terminal device 110.
  • the first ID list may comprise one or more IDs.
  • the network device 120 may receive, from the terminal device 110, the request message for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device 120 corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device 110, and determine, based on the first ID list of the second on or more sub-modules comprised in the request message, whether there is the at least one decoder sub-module.
  • “decoder sub-module” means a sub module among the first one or more sub-modules to be parallel connected with the (main) decoder (the main module) at the network device 120.
  • the network device 120 may transmit, to the terminal device 110, a response message corresponding to the request message comprising a second ID list of the at least one decoder sub-module. Similar to the first ID list, the second ID list may comprise one or more IDs. Then, at the terminal device 110, the terminal device 110 may receive, from the network device 120, the response message comprising the second ID list of the at least one decoder sub-module.
  • the network device 120 may transmit a request message to the terminal device 110 for determining whether there is at least one encoder sub-module among the second one or more sub-modules in the terminal device 110 corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device 120.
  • the request message may comprise a first ID list of the first one or more sub-modules at the network device 120.
  • the terminal device 110 may receive, from the network device 120, a request message for determining whether there is at least one encoder sub-module among the second one or more sub-modules at the terminal device 110 corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device 120.
  • the request message may comprise the first ID list of the first one or more sub-modules at the network device.
  • the terminal device 110 may then determine, based on the first ID list of the first one or more sub-modules, whether there is the at least one encoder sub-module. Based on determining that there is the at least one encoder sub-module, the terminal device 110 may transmit, to the network device, a response message corresponding to the request message comprising a second ID list of the at least one encoder sub-module. Then, the network device 120 may receive, from the terminal device 110, the response message corresponding to the request message comprising the second ID list of the at least one encoder sub-module.
  • the terminal device 110 may also transmit pre-defined information to the network device 120 for selecting the first sub-module.
  • the network device 120 may receive, from the terminal device 110, the pre-defined information for selecting the sub-module.
  • the pre-defined information may comprise channel state information (CSI) .
  • the channel state information (CSI) may comprise original CSI and/or compressed CSI codewords.
  • the network device 120 may classify the drifted site to determine the site ID.
  • the terminal device 110 may maintain a sub-module pool which comprises the second one or more sub-modules.
  • Sub-modules in the sub-module pool may be associated with different application conditions (for example, multi-path conditions) .
  • the network device 120 may maintain a sub-module pool which comprises the (first) one or more sub-modules.
  • Sub-modules in the sub-module pool may also be associated with different application conditions (for example, multi-path conditions) .
  • With the communication process 300 better performance and parameter efficiency can be obtained. Besides, no additional inference latency is introduced.
  • FIG. 2 a flow chart
  • FIG. 3 a high level signaling chart
  • FIGS. 4A-12 some further examples of the present disclosure are described with reference to FIGS. 4A-12.
  • FIG. 4A illustrates a schematic diagram 400A of an example model adaptation framework (also referred to as “model architecture” ) in accordance with some embodiments of the present disclosure.
  • a main module is connected in parallel with small-scale sub modules specified for different cells/sites of different wireless environments (for example, multi-path conditions) for CSI feedback enhancement.
  • UE for example, terminal device 110 as illustrated in FIGS. 1 and 3
  • gNB for example, network device 120 as illustrated in FIGS. 1 and 3
  • new signalings are introduced for sub module alignment in real-world deployment, which will be described in more detail with reference to FIG. 9.
  • UE possesses a main encoder parallel connected with one of the sub modules from the sub module pool at UE side corresponding to a specific cell/site
  • gNB possesses a main decoder parallel connected with one of the sub modules from the sub module pool at gNB side corresponding to the specific cell/site.
  • the main encoder also referred to as “main module”
  • main module together with the sub modules specific for different cells/sites are pre-trained in advance to ensure consistency between training stage and inference stage.
  • the sub module-1 at UE and the sub module-1 at gNB may be a sub-module pair.
  • the gNB uses the sub module-1 in parallel with main decoder to successfully decode UL transmission from UE to gNB.
  • the UL transmission may be, for example, channel state information (CSI) .
  • CSI channel state information
  • FIG. 4B illustrates another schematic diagram 400B of an example model adaptation framework for encoder/decoder in CSI feedback in accordance with some embodiments of the present disclosure.
  • a main module is connected in parallel with small-scale sub modules specified for different cells/sites for CSI feedback.
  • the main module at UE may be a (main) encoder
  • the main module at gNB may be a (main) decoder, as illustrated in FIG. 4A.
  • the example model adaptation framework may be utilized to enable the proposed model generalization framework in CSI feedback enhancement.
  • UE possesses a main encoder parallel connected with one of the sub modules from the sub module pool at UE side corresponding to a specific cell/site
  • gNB possesses a main decoder parallel connected with one of the sub modules from the sub module pool at gNB side corresponding to the specific cell/site.
  • the main module and the sub modules specific for different cells/sites of different wireless environments may be pre-trained together in advance to ensure consistency between training stage and inference stage.
  • n represents the number of the sub modules
  • i represents the index of a specific sub module among the n sub modules.
  • Each cell/site-specific sub module is parallel connectable to the main module with a switch. When a specific cell/site is detected (/selected) , the corresponding switch can be turned on to activate that specific sub module branch.
  • Such sub modules for specific cell/sites can be deployed at any dense layers in the main module, while the effectiveness may be different across different layers.
  • the main module and the parallel connectable cell/site-specific sub modules may be deployed at a terminal device (for example, the terminal device 110 as illustrated in FIGS. 1 and 3) and a network device (for example, the network device 120 as illustrated in FIGS. 1 and 3) for model adaptiveness enhancement in CSI feedback.
  • a terminal device for example, the terminal device 110 as illustrated in FIGS. 1 and 3
  • a network device for example, the network device 120 as illustrated in FIGS. 1 and 3
  • FIG. 5A illustrates an example block diagram 500A of an example of sub modules parallel connectable to the main module in accordance with some embodiments of the present disclosure.
  • the model adaptation framework (for example, as illustrated in FIG. 4A) may be obtained by joint pre-training the main module and the parallel connectable sub modules within the linear layers in a CSI feedback model.
  • a parallel sub module may be connected to the original NN structure (i.e., the main module shown in FIG. 5A) , the sub module and main module can be fed with same input, and output (s) from the sub module and output (s) from the main module can be numerically added and merged. Then the merged (/summarized) output (s) may be fed to the next layer.
  • Each sub module may correspond to a specific cell/site.
  • the sub module number may be pre-determined corresponding to the number of the given cells/sites.
  • Each sub module may be trained independently (/separately) from other sub modules, but each sub module may be trained jointly with the main module.
  • training it means all the NN model parameters (including weights and biases) are trainable.
  • parameters in main module and sub module-1 (which corresponds to the 1 st cell/site) are all updated according to the loss function calculated with dataset-1.
  • dataset-1 consists of input data and output labels of the 1 st cell/site, which are both CSI matrices (subband *port *I/Q) in CSI feedback use case.
  • I/Q is short for “In-phase/Quadrature” .
  • alternative hybrid training is designed for training the main module and the n (n is the number of the parallel-connectable sub modules) parallel-connectable sub modules from n different cells/sites, here, n is the number of sub modules which are parallel connectable to the main module. Since each sub module is specific to a cell/site, the number of the different cells/sites is also n.
  • alternative training (among sub modules) , it means for each epoch (or, stage) of loss calculation, it consists of n sub-loss from n scenarios calculated on n datasets corresponding to the n cell/sites of different wireless environments (for example, multi-path conditions) , where each dataset is for a cell/site among the n different cells/sites.
  • dataset-k corresponds to site/cell-k is selected, and sub-loss-k is calculated via supervised learning on dataset-k.
  • the overall loss is the summation of k sub-losses (i.e., sub-loss-1 for sub module-1, sub-loss-2 for sub module-2, ..., sub-loss-n for sub module-n) .
  • hybrid training between the main module and a sub module
  • the parameters of the main module are updated based on the overall loss (the sum of sub-loss-1, sub-loss-2, ..., sub-loss-n)
  • the parameters of sub module-k are updated only based on the sub-loss-k on the dataset-k.
  • FIG. 5B illustrates example input/output data 500B of a main module and a sub module parallel connected to the main module in accordance with some embodiments of the present disclosure.
  • the i-th i may be 1, 2, ...n, as shown in FIG. 5A, where n is the number of the sub modules which are parallel connectable to the main module
  • branch of the n sub modules is activated, which means that the i-th cell/site-specific sub module is parallel connected to the main module.
  • the same input x is input into (fed to) the main module and sub module-i (the i-th sub module for the i-th cell/site) .
  • the output of the main module is denoted as z 0
  • the output of the sub module-i is denoted as z i .
  • the main module and the parallel-connectable sub modules are trained using different datasets.
  • the main module may be trained with the general dataset, e.g. the mixture of different datasets of different cells/sites, while the i-th sub module may be trained with the dataset specific for the i-th cell/site.
  • the main module and the parallel connectable sub modules can be trained using “alternative hybrid training” (as roughly described above) or another training approach named “growable sub modular training” .
  • alterative hybrid training as roughly described above
  • Growable sub modular training another training approach named “growable sub modular training” .
  • FIGS. 6A-7C illustrates block diagrams for different steps of an example alternative hybrid training process in accordance with some embodiments of the present disclosure
  • FIGS. 7A-7C illustrates block diagrams for different steps of an example growable sub modular training process in accordance with some embodiments of the present disclosure.
  • FIG. 6A illustrates a block diagram 600A of an example alternative hybrid training process in accordance with some embodiments of the present disclosure.
  • the main module and the sub module-1 (the first sub module) are trained using the cell/site-specific dataset-1 with loss function L (e.g., cosine similarity (SGCS) loss, mean squared error (MSE) loss) .
  • L loss function
  • Other sub modules do not participate in both forward pass and backward propagation in this training step; in other words, in this step, among the n sub modules, only sub module-1 participates in forward pass and backward propagation.
  • FIG. 6B illustrates another block diagram 600B of an example alternative hybrid training process in accordance with some embodiments of the present disclosure.
  • the training step illustrated in FIG. 6B may follow the training step illustrated in FIG. 6A.
  • the main module and the sub module-2 (the 2 nd sub module) are trained using the cell/site-specific dataset-2 with the same loss function L.
  • Other sub modules do not participate in both forward pass and backward propagation in this training step; in other words, in this step, among the n sub modules, only sub module-2 participates in forward pass and backward propagation.
  • the alternative hybrid training process is conducted iteratively for all the sub modules (i.e., the n sub modules, for example, as illustrated in FIGS. 4B and 5A) . Therefore, the main module will be trained over all different cells/sites and the sub modules are trained for each specific cells/site.
  • FIG. 7A illustrates a block diagram 700A of an example growable sub modular training process in accordance with some embodiments of the present disclosure.
  • the main module is trained with the general dataset using the loss function L (e.g., SGCS loss, MSE loss) .
  • L the loss function
  • FIG. 7B illustrates another block diagram 700B of an example growable sub modular training process in accordance with some embodiments of the present disclosure.
  • the training step illustrated in FIG. 7B may follow the training step illustrated in FIG. 7A.
  • sub module-1 is trained using the cell/site-specific dataset-1 with the parameters in the main module frozen.
  • the forward pass involves feeding the input data from the specialized dataset-1 through both main module and the parallel connected cell/site-specific sub module-1 to obtain the loss.
  • the gradients of the loss with respect to the trainable parameters of the sub module-1 are computed.
  • the parameters of the main module are frozen during this training step, they remain unaffected by the gradients calculated for the sub-module-1.
  • This separation between the forward pass and backward propagation allows the sub module-1 to adapt to the specific cell/site (here, the first cell/site corresponding to sub module-1) while preserving the knowledge encoded in the main module.
  • Other sub modules do not participate in both forward pass and backward propagation; in other words, in this step, among the n sub modules, only sub module-1 participates in forward pass and backward propagation.
  • FIG. 7C illustrates further another block diagram 700C of an example growable sub modular training process in accordance with some embodiments of the present disclosure.
  • the training step illustrated in FIG. 7C may follow the training step illustrated in FIG. 7B.
  • sub module-2 is trained using the cell/site-specific dataset-2 with the parameters in the main module frozen.
  • the forward pass involves feeding the input data from the specialized dataset-2 through both main module and the parallel connected cell/site-specific sub module-2 to obtain the loss.
  • the gradients of the loss with respect to the trainable parameters of the sub module-2 are computed.
  • the parameters of the main module are frozen during this training step, they remain unaffected by the gradients calculated for the sub-module-2.
  • This separation between the forward pass and backward propagation allows the sub module-2 to adapt to the specific cell/site (here, the second cell/site corresponding to sub module-2) while preserving the knowledge encoded in the main module.
  • Other sub modules do not participate in both forward pass and backward propagation; in other words, in this step, among the n sub modules, only sub module-2 participates in forward pass and backward propagation.
  • the growable sub modular training process is conducted iteratively for all the sub modules (i.e., the n sub modules, for example, as illustrated in FIGS. 4B and 5A) .
  • the main module is trained with the general dataset, and the sub modules are then trained for each specific cells/site with the main module frozen.
  • the main module as well as the sub modules can be trained offline across vendors via Type I training or trained online via Type III training.
  • vendors should also align the sub-module ID corresponding to each cell/site offline.
  • each cell/site may correspond to a specific wireless environment (for example, a multi-path condition) .
  • model (which comprises the main module and the sub modules) has been prepared, it can be utilized for inference, as will be described in more detail with reference to FIG. 8.
  • FIG. 8 illustrates a block diagram 800 for model inference in accordance with some embodiments of the present disclosure.
  • the cell/site index may be determined by calculating the statistical discrepancy (for example, maximum mean discrepancy (MMD) ) between the in-field CSI data and the pre-stored datasets of each cell/site.
  • MMD maximum mean discrepancy
  • MMD maximum mean discrepancy
  • the cell/site corresponding to the dataset with the lowest statistical discrepancy may be considered as the current deployment cell/site. Therefore, it is determined that the parallel-connectable sub module indicated by that cell/site index is to be selected to work together with the main module for inference. Then, the determined sub module is activated to work parallel connect to the main module to be used to perform inference.
  • the most similar dataset is designated as current cell/site category-indicator to share between the terminal device and network device.
  • the i-th dataset i.e., dataset-i
  • the sub module-i branch in the terminal device and the network device may be activated to make it work in parallel with the main module.
  • the index i may be shared from gNB to UE, which is described in more detail with reference to FIG. 9.
  • FIG. 9 illustrates a signaling chart illustrating another example communication process 900 in accordance with some embodiments of the present disclosure.
  • the communication process 900 may be used for cell/site-specific sub-module selection in CSI feedback.
  • the communication process 900 may involve UE 906 and gNB 908.
  • UE 906 may be an example of the terminal device 110 as illustrated in FIGS. 1 and 3
  • gNB 908 may be an example of the network device 120 as illustrated in FIGS. 1 and 3.
  • the main encoder/decoder parallel connectable with the respective sub module pool are prepared.
  • the respective sub module pool for the UE 906 or gNB 908 comprises several cell/site-specific sub modules, as illustrated in FIGS. 4A, 4B and 5A.
  • UE 906 and gNB 908 pre-train the main encoder/decoder and the cell/site-specific sub modules and associate each sub module with a specific cell/site. Based on the determined cell/site category-indicator, UE 906 and gNB 908 use encoder/decoder parallel connectable with the sub module of current cell/site for model inference.
  • UE 906 and gNB 908 check if their NNs are from the same encoder-decoder pair.
  • gNB switches to one appropriate sub module parallel connected with the decoder based on the detected cell/site type.
  • UE 906 switches to one sub module parallel-connectable with the encoder based on the sub module ID indicated by gNB 908.
  • both UE 906 and gNB 908 maintain a pre-trained main encoder/decoder parallel connectable to cell/site-specific sub modules from sub module pool and have pre-defined the application condition for each sub-module.
  • the cell/site-specific sub modules parallel connectable to the main encoder/decoder may be referred to as a sub module pool. This may be a pre-condition for the communication process 900.
  • the main encoder may be the main module (which may also be referred to as “general model” , “main model” , “general module” ) at the UE 906, and the main decoder may be the main module at the gNB 908.
  • main encoder/decoder and the parallel-connected sub NN modules are pre-trained offline amongst different vendors and the vendors have aligned the application condition for each sub module. Then in deployment, when UE 906 and gNB 908 work, they use a pair of sub modules for a specific cell/site to parallel connect with the encoder/decoder.
  • a cell/site drift occurs and is detected, for example, by the UE 906 or the gNB 908.
  • the sub modules which are parallel connectable with the encoder/decoder may not fit the drifted cell/site. Therefore, UE 906 and gNB 908 may need to communicate with each other to switch to another sub-module pair from the sub module pool for CSI feedback enhancement, as described below.
  • UE 906 requests the gNB 908 to verify if there are corresponding sub modules in the sub module pools of UE 906 and gNB 908, i.e., UE 906 requests the gNB 908 to verify if there are sub module pair (s) between the sub module pools of UE 906 and gNB 908. If so (i.e., if there is sub module pair (s) between the sub module pools of UE 906 and gNB 908) , the gNB 908 sends the confirmation message to the UE 906. If not (if there is no sub module pair between the sub module pools of UE 906 and gNB 908) , the communication process 900 terminates.
  • gNB 908 requests the UE 906 to verify if there are corresponding sub modules in the sub module pools of UE 906 and gNB 908, i.e., gNB 908 requests UE 906 to verify if there are sub module pair (s) between the sub module pools of UE 906 and gNB 908.
  • the UE 906 sends the confirmation message and indicates the indices of corresponding sub modules to the UE 906. If not (if there is no sub module pair between the sub module pools of UE 906 and gNB 908) , the communication process 900 terminates.
  • the UE 906 sends the pre-defined requisite information for drifted cell/site classification.
  • the pre-defined requisite information may be one or more CSI codewords and/or one or more original CSI matrices.
  • the gNB 908 classifies the drifted cell/site with the pre-defined mechanism. For example, based on the received pre-defined requisite information, gNB 908 may determine the current cell/site ID of UE 906 using a pre-defined mechanism. Statistical discrepancy calculation may be an example of pre-defined mechanism to assess (/evaluate) the similarity between the in-field data and the pre-stored cell/site-specific dataset associated with the sub module pair (s) between the sub module pools of UE 906 and gNB 908 (such sub module pair (s) is determined (/verified) at 920 or 925, as described above) , then the cell/site with the highest similarity may be selected.
  • the gNB 908 selects the sub modules from the corresponding sub modules to be parallel connected with the main decoder for inference. More specifically, depending on the cell/site type determined by gNB 908, gNB 908 may select the sub module corresponding to the determined cell/site type from the corresponding sub modules to be parallel connected to the main decoder at the gNB 908 for inference.
  • the gNB 908 sends the index of the selected sub module to UE 906. In other words, gNB 908 informs UE 906 about the index of its newly selected sub NN module. At 950, UE 906 switches to the indicated sub module to parallel connect with the main encoder. At 955, UE 906 sends the model switch confirmation message to gNB 408. With the communication process 900, better performance and parameter efficiency can be obtained. Besides, no additional inference latency is introduced.
  • FIG. 10 illustrates a flowchart of an example method 1000 implemented at a terminal device (for example, the terminal device 110 as illustrated in FIGS. 1 and 3) in accordance with some other embodiments of the present disclosure.
  • a terminal device for example, the terminal device 110 as illustrated in FIGS. 1 and 3
  • the method 1000 will be described from the perspective of the terminal device 110 with reference to FIG. 3.
  • the terminal device 110 receives, from a network device (for example, the network device 120 as illustrated in FIGS. 1-2) , an index (for example, index 301 as illustrated in FIG. 3) of a first sub-module among first one or more sub-modules at the network device.
  • the first sub-module is to be parallel connected to a main decoder at the network device.
  • the terminal device 110 connects, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device, wherein the second sub-module is associated with the first sub-module.
  • the terminal device 110 may further transmit, to the network device, a message confirming that the second sub-module is to be parallel connected to the main encoder at the terminal device.
  • the terminal device 110 may transmit a request message to the network device for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device (here, the request message may comprise a first ID list of the second one or more sub-modules at the terminal device) , and receive, from the network device, a response message comprising a second ID list of the at least one decoder sub-module.
  • the terminal device 110 may receive, from the network device, a request message for determining whether there is at least one encoder sub-module among the second one or more sub-modules at the terminal device corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device (here, the request message comprises a third ID list of the first one or more sub-modules at the network device) , and determine, based on the third ID list of the first one or more sub-modules, whether there is the at least one encoder sub-module. Further, based on determining that there is the at least one encoder sub-module, the terminal device 110 may transmit, to the network device, a response message comprising a fourth ID list of the at least one encoder sub-module.
  • the terminal device 110 may transmit pre-defined information to the network device for selecting the first sub-module.
  • the pre-defined information may comprise channel state information (CSI) .
  • the channel state information (CSI) may comprise original CSI and/or compressed CSI codewords.
  • the terminal device 110 may maintain a sub-module pool which comprises the second one or more sub-modules, and the sub-module pool may be associated with different application conditions.
  • FIG. 11 illustrates a flowchart of an example method 1100 implemented at a network device (for example, the network device 120 as illustrated in FIGS. 1 and 3) in accordance with some other embodiments of the present disclosure.
  • a network device for example, the network device 120 as illustrated in FIGS. 1 and 3
  • the method 1100 will be described from the perspective of the network device 120 with reference to FIG. 3.
  • the network device 120 selects, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device 120 to be parallel connected to a main decoder at the network device 120.
  • the network device 120 transmits, to the terminal device, an index (for example, the index 301 as illustrated in FIG. 3) of the selected sub-module.
  • the one or more sub-modules are first one or more sub-modules corresponding to second one or more sub-modules at the terminal device to be parallel connected to a main encoder at the terminal device.
  • the sub-module may be a first sub-module
  • the network device 120 may further receive, from the terminal device, a message confirming that a second sub-module at the terminal device corresponding to the index is to be parallel connected to the main encoder of the terminal device.
  • the network device 120 may transmit a request message to the terminal device for determining whether there is at least one encoder sub-module among the second one or more sub-modules in the terminal device corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device (here, the request message comprises a first ID list of the first one or more sub-modules at the network device) , and receive, from the terminal device, a response message comprising a second ID list of the at least one encoder sub-module.
  • the network device 120 may receive, from the terminal device, a request message for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device (here, the request message comprises a third ID list of the second one or more sub-modules at the terminal device) , and determine, based on the third ID list of the second on or more sub-modules, whether there is the at least one decoder sub-module. Based on determining that there is at least one decoder sub-module, the network device 120 may transmit, to the terminal device, a response message comprising a fourth ID list of the at least one decoder sub-module.
  • the network device 120 may receive, from the terminal device, pre-defined information for selecting the sub-module.
  • the channel state information (CSI) may comprise at least one of original CSI or compressed CSI codewords. Based on the pre-defined information, the network device 120 may classify the drifted site to determine the site ID.
  • the network device 120 may maintain a sub-module pool which comprises the one or more sub-modules, and sub-modules in the sub-module pool are associated with different application conditions.
  • an apparatus capable of performing the method 200 may comprise means for performing the respective steps of the method 200.
  • the means may be implemented in any suitable form.
  • the means may be implemented in a circuitry or software module.
  • the apparatus comprises: means for training a main module and a plurality of sub-modules parallel connectable to the main module for CSI feedback, wherein the plurality of sub-modules are associated with a plurality of sites of different wireless environments; and means for performing, based on the main module parallel connected with a selected sub-module among the plurality of sub-modules, inference for CSI feedback.
  • the main module and the plurality of sub-modules may be within at least one linear layer in an artificial intelligence (AI) /machine learning (ML) model.
  • sub-module numbers of the plurality of sub-modules may be predetermined corresponding to site numbers of the plurality of sites.
  • the plurality of sub-modules may be parallel connectable to the main module via respective switches.
  • a same input may be provided to the main module and the sub-module.
  • a sum of a first output of the main module and a second output of the sub-module may be provided to a next layer of an AI/ML model.
  • the means for training may comprise means for performing a training process comprises a plurality of training steps based on a plurality of datasets associated with the plurality of sites.
  • a sub-module may be trained independently from other sub modules and is trained jointly with the main module.
  • the main module may be trained based on a general dataset.
  • the plurality of sub-modules may be trained based on the plurality of datasets respectively.
  • the main module in a training step among the plurality of training steps, may be parallel connected with a sub-module among the plurality of sub-modules, and in the training step, the main module and the sub-module may be trained based on a dataset among the plurality of datasets, wherein the dataset corresponds to a site, among the plurality of sites, associated with the sub-module.
  • parameters of the main module may be updated based on an overall loss which is a summation of a plurality of sub-losses determined based on the plurality of datasets in the plurality of training steps, and in the training process, parameters of the sub-module may be updated based on a sub-loss among the plurality of sub-losses.
  • the sub-loss is determined based on the dataset in the training step.
  • the main module may be trained based on a general dataset.
  • the main module may be parallel connected with a sub-module among the plurality of sub-modules, and in the training step, the main module may be maintained unchanged and the sub-module is trained based on a dataset among the plurality of datasets.
  • the dataset may correspond to a site, among the plurality of sites, associated with the sub-module.
  • data from the dataset may be fed through both the main module and the sub module to obtain a loss.
  • gradients of the loss with respect to trainable parameters of the sub-module may be computed, and the parameters of the main module may remain unaffected by the gradients calculated for the sub-module.
  • the means for performing may comprise means for calculating a plurality of statistical discrepancies between in-field data and a plurality of datasets associated with the plurality of sites; means for determining a target site among the plurality of sites based on the plurality of statistical discrepancies; and means for determining, among the plurality of sub-modules, a target sub-module associated with the target site as the selected sub-module to be parallel connected to the main module for the inference.
  • the target site may correspond to a dataset with a lowest statistical discrepancy among the plurality of statistical discrepancies.
  • the apparatus may be a terminal device (for example, the terminal device 110 as illustrated in FIGS. 1 and 3) and the main module may comprise an encoder for CSI feedback.
  • the apparatus may be a network device (for example, the network device 120 as illustrated in FIGS. 1 and 3) and the main module may comprise a decoder for CSI feedback.
  • the apparatus further comprises means for performing other steps in some embodiments of the method 200.
  • the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.
  • an apparatus capable of performing the method 1000 may comprise means for performing the respective steps of the method 1000.
  • the means may be implemented in any suitable form.
  • the means may be implemented in a circuitry or software module.
  • the apparatus comprises: means for receiving, from a network device, an index of a first sub-module among first one or more sub-modules at the network device (here, the first sub-module is to be parallel connected to a main decoder at the network device) ; and means for connecting, based on the index, a second sub-module among second one or more sub-modules at the terminal device parallel to a main encoder at the terminal device (here, the second sub-module is associated with the first sub-module) .
  • the apparatus may further comprise means for transmitting, to the network device, a message confirming that the second sub-module is to be parallel connected to the main encoder at the terminal device.
  • the apparatus may further comprise means for transmitting a request message to the network device for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device (here, the request message may comprise a first ID list of the second one or more sub-modules at the terminal device) , and means for receiving, from the network device, a response message comprising a second ID list of the at least one decoder sub-module.
  • the apparatus may further comprise means for receiving, from the network device, a request message for determining whether there is at least one encoder sub-module among the second one or more sub-modules at the terminal device corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device (here, the request message comprises a third ID list of the first one or more sub-modules at the network device) , and means for determining, based on the third ID list of the first one or more sub-modules, whether there is the at least one encoder sub-module. Further, based on determining that there is the at least one encoder sub-module, the apparatus may further comprise means for transmitting, to the network device, a response message comprising a fourth ID list of the at least one encoder sub-module.
  • the apparatus may further comprise means for transmitting pre-defined information to the network device for selecting the first sub-module.
  • the pre-defined information may comprise channel state information (CSI) .
  • the channel state information (CSI) may comprise original CSI and/or compressed CSI codewords.
  • the apparatus may further comprise means for maintaining a sub-module pool which comprises the second one or more sub-modules.
  • the sub-module pool may be associated with different application conditions.
  • the apparatus further comprises means for performing other steps in some embodiments of the method 1000.
  • the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.
  • an apparatus capable of performing the method 1100 may comprise means for performing the respective steps of the method 1100.
  • the means may be implemented in any suitable form.
  • the means may be implemented in a circuitry or software module.
  • the apparatus comprises: means for selecting, based on a site identifier (ID) , a sub-module among one or more sub-modules at the network device to be parallel connected to a main decoder at the network device; and means for transmitting, to the terminal device, an index of the selected sub-module.
  • ID site identifier
  • the one or more sub-modules may be first one or more sub-modules corresponding to second one or more sub-modules at the terminal device to be parallel connected to a main encoder at the terminal device.
  • the sub-module may be a first sub-module
  • the apparatus may further comprise means for receiving, from the terminal device, a message confirming that a second sub-module at the terminal device corresponding to the index is to be parallel connected to the main encoder of the terminal device.
  • the apparatus may further comprise means for transmitting a request message to the terminal device for determining whether there is at least one encoder sub-module among the second one or more sub-modules in the terminal device corresponding to at least one decoder sub-module among the first one or more sub-modules at the network device (here, the request message comprises a first ID list of the first one or more sub-modules at the network device) , and means for receiving, from the terminal device, a response message comprising a second ID list of the at least one encoder sub-module.
  • the apparatus may further comprise means for receiving, from the terminal device, a request message for determining whether there is at least one decoder sub-module among the first one or more sub-modules at the network device corresponding to at least one encoder sub-module among the second one or more sub-modules at the terminal device (here, the request message comprises a third ID list of the second one or more sub-modules at the terminal device) , and means for determine, based on the third ID list of the second on or more sub-modules, whether there is the at least one decoder sub-module. Based on determining that there is at least one decoder sub-module, the apparatus may further comprise means for transmitting, to the terminal device, a response message comprising a fourth ID list of the at least one decoder sub-module.
  • the apparatus may further comprise means for receiving, from the terminal device, pre-defined information for selecting the sub-module.
  • the CSI may comprise at least one of original CSI or compressed CSI codewords.
  • the network device 120 may classify the drifted site to determine the site ID.
  • the apparatus may comprise means for maintaining a sub-module pool which comprises the one or more sub-modules.
  • sub-modules in the sub-module pool are associated with different application conditions.
  • the apparatus further comprises means for performing other steps in some embodiments of the method 1100.
  • the means comprises at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the performance of the apparatus.
  • FIG. 12A provides a basic transformer architecture for CSI feedback.
  • FIG. 12A illustrates a block diagram of an example of a transformer-enabled neural network (NN) for CSI feedback in accordance with some example embodiments of the present disclosure.
  • the transformer-enabled neural network (NN) for CSI feedback enhancement is used in the simulation.
  • the left side of FIG. 12A represents blocks at UE encoder, ant the right side of FIG. 12A represents blocks at gNB decoder.
  • FIG. 12B illustrates a block diagram of a modified multi-head attention block in accordance with some example embodiments of the present disclosure.
  • the i-th sub module (sub module-i) is represented by new pairs of rank decomposition matrices (Q Ai , Q Bi , V Ai , V Bi ) .
  • Q Ai and V new V 0 +V Bi . V Ai .
  • both encoder and decoder use the transformer NN with 6 attention layers, where the embedding dimension equals 128.
  • the multi-head attention block is adopted in transformer. More specifically, in the specific schemes, the unmodified transformer architecture is applied. The model is trained with specific 442/282 dataset comprising 160K data, respectively. In the mixed scheme, the unmodified transformer architecture is applied. This model is trained with the mixed 160K 442 dataset and 160K 282 dataset.
  • the multi-head attention block is modified by parallel connecting to cell/site-specific sub modules from the sub module pool.
  • the transformer in which the modified multi-head attention block parallel connects with the sub module pool is applied.
  • the model is trained with the mixed 160K 442 dataset and 160K 282 dataset.
  • Table 2 illustrates the cosine similarity (SGCS) performances when the 442/282 dataset is tested with the models trained by different schemes.
  • FIG. 13 illustrates a simplified block diagram of a device 1300 that is suitable for implementing some example embodiments of the present disclosure.
  • the device 1300 may be provided to implement a communication device, for example, the terminal device 110 and the network device 120 as shown in FIGS. 1 and 3.
  • the device 1300 includes one or more processors 1310, one or more memories 1320 coupled to the processor 1310, and one or more communication modules 1340 coupled to the processor 1310.
  • the communication module 1340 is for bidirectional communications.
  • the communication module 1340 has at least one antenna to facilitate communication.
  • the communication interface may represent any interface that is necessary for communication with other network elements.
  • the processor 1310 may be of any type suitable for the local network and may include one or more of the following: general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples.
  • the device 1300 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.
  • the memory 1320 may include one or more non-volatile memories and one or more volatile memories.
  • the non-volatile memories include, but are not limited to, a Read Only Memory (ROM) 1324, an electrically programmable read only memory (EPROM) , a flash memory, a hard disk, a compact disc (CD) , a digital video disk (DVD) , and other magnetic storage and/or optical storage.
  • the volatile memories include, but are not limited to, a random access memory (RAM) 1322 and other volatile memories that will not last in the power-down duration.
  • a computer program 1330 includes computer executable instructions that are executed by the associated processor 1310.
  • the program 1330 may be stored in the ROM 1324.
  • the processor 1310 may perform any suitable actions and processing by loading the program 1330 into the RAM 1322.
  • the embodiments of the present disclosure may be implemented by means of the program 1330 so that the device 1300 may perform any process of the disclosure as discussed with reference to FIGS. 2-3 and 9-11.
  • the embodiments of the present disclosure may also be implemented by hardware or by a combination of software and hardware.
  • the program 1330 may be tangibly contained in a computer-readable medium which may be included in the device 1300 (such as in the memory 1320) or other storage devices that are accessible by the device 1300.
  • the device 1300 may load the program 1330 from the computer-readable medium to the RAM 1322 for execution.
  • the computer-readable medium may include any types of tangible non-volatile storage, such as ROM, EPROM, a flash memory, a hard disk, CD, DVD, and the like.
  • FIG. 14 illustrates a block diagram of an example of a computer-readable medium 1400 in accordance with some example embodiments of the present disclosure.
  • the computer-readable medium 1400 has the program 1330 stored thereon. It is noted that although the computer-readable medium 1400 is depicted in form of CD or DVD in FIG. 8, the computer-readable medium 1400 may be in any other form suitable for carry or hold the program 1330.
  • various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representations, it is to be understood that the block, apparatus, system, technique or method described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • the present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium.
  • the computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out any of the method 200-300, 900-1100 as described above with reference to FIGS. 2-3 and 9-11.
  • program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or split between program modules as desired in various embodiments.
  • Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.
  • Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • the computer program codes or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above.
  • Examples of the carrier include a signal, computer-readable medium, and the like.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • a computer-readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • non-transitory is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM) .

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Abstract

Des exemples de modes de réalisation de la présente divulgation concernent une structure de modèle, en particulier une structure d'adaptation de modèle pour l'amélioration d'un retour d'informations d'état de canal (CSI). Dans un procédé donné à titre d'exemple, un appareil entraîne un module principal et une pluralité de sous-modules pouvant être connectés en parallèle au module principal à des fins de retour d'informations d'état de canal (CSI), la pluralité de sous-modules étant associés à une pluralité de sites de différents environnements sans fil. Ensuite, l'appareil effectue, sur la base du module principal connecté en parallèle à un sous-module sélectionné au sein de la pluralité de sous-modules, une inférence pour un retour de CSI. Ainsi, une structure d'adaptation de modèle pour l'amélioration d'un retour de CSI peut être obtenue. Avec la structure d'adaptation de modèle, de meilleures performances et une meilleure efficacité des paramètres peuvent être obtenues. En outre, aucune latence d'inférence supplémentaire n'est introduite.
PCT/CN2024/077776 2024-02-20 2024-02-20 Structure de modèle Pending WO2025175475A1 (fr)

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