Model delivery in a network

Abstract

Systems (100), apparatuses (102), and methods (300) for model transfer in a network are disclosed. The method (300) includes an input step (302) comprising receiving at least one input signal associated with model type data, and a processing step (304) comprising at least one of: determining whether a radio link failure (RLF) exists; configuring a report if the RLF exists; and transmitting the report to a first base station, wherein the report includes a mapping table indicating a mapping between a model identification and a segment identification.

Description

Technical Field

[0001] This disclosure generally relates to one or both of a system and apparatus for model transmission in a network associated with, for example, a user equipment (UE) that can be used for communication. This disclosure further relates to a method that can be associated with the system and / or the apparatus. Background Technology

[0002] Generally, energy efficiency and energy savings are helpful or desirable in communication networks. An example of a communication network would be a telecommunications network based on the 3rd Generation Partnership Project (3GPP) 5G (Fifth Generation) New Radio (NR) standard.

[0003] Typically, models (such as AIML models) are stored in base stations and delivered to user equipment (UEs) based on the requirements or applications they support. If a delivery failure (such as a radio link failure) occurs during model delivery, the network may be unable to resolve such situations. Therefore, this disclosure contemplates that conventional techniques for model delivery may not optimally promote efficiency and energy savings during radio link failures.

[0004] This disclosure envisions that it would be helpful to address (or at least alleviate) one or more problems associated with conventional technologies used to promote energy efficiency and energy savings. Summary of the Invention

[0005] According to a first aspect of the invention, a method is provided, comprising: a model transfer in a network, including: an input step comprising receiving at least one input signal associated with model type data; and a processing step comprising: determining whether a radio link failure (RLF) exists; configuring a report if an RLF exists; and transmitting the report to a first base station, wherein the report includes a mapping table indicating a mapping between model identifiers and segment identifiers.

[0006] Advantageously, the method described in this paper allows users to experience faster feature initiation, thereby improving user satisfaction. It also allows for efficient use of network resources, ensuring an optimized user experience while minimizing resource consumption. Furthermore, the method enables AIML models to adapt to changing network conditions and prioritizes essential functions that are immediately available.

[0007] In this embodiment, the model type data includes Artificial Intelligence Machine Learning (AIML) model data.

[0008] In an embodiment, the processing step further includes: determining the model segment of the model type data to be transmitted; and incorporating the transmitted model segment of the model type data into the report.

[0009] In an embodiment, the processing step further includes transmitting the report via at least one of the following: physical layer (PHY), media access control (MAC), packet data convergence protocol (PDCP), and / or radio resource control (RRC) configuration.

[0010] In this embodiment, the first base station is configured to: transmit the report to the second base station; receive the remaining model segment from the second base station; and transmit the remaining model segment.

[0011] In this embodiment, the second base station is configured to: receive the report from the first base station; determine the remaining untransmitted model segments based on the report; and transmit the remaining model segments to the first base station.

[0012] In the embodiment, each of the first base station and the second base station corresponds to a next-generation node B (gNB).

[0013] In an embodiment, the user equipment (UE) is configured to perform the input step and the processing step, and the model type data can be transmitted from the gNB to the UE.

[0014] In an embodiment, a computer program (not shown) is provided, which may include instructions that, when executed by a computer (not shown), cause the computer to perform input steps, processing steps, and / or output steps as discussed with reference to the method. For example, according to an embodiment of this disclosure, the computer program may include instructions that, when executed by a computer, cause the computer to perform the input step and / or the processing step.

[0015] In one embodiment, a computer-readable storage medium is provided that stores data representing software executable by a computer, the software including instructions that, when executed by the computer, are used to perform at least one of the input step and the processing step of the method according to the first aspect.

[0016] According to a second aspect of this disclosure, an apparatus is provided comprising: a first module configured to receive at least one input signal associated with model type data; a second module configured to process and / or facilitate the processing step of the method according to the first aspect to generate at least one output signal; and a third module configured to transmit at least one output signal, wherein the output signal corresponds to a control signal for model transmission in a network.

[0017] In an embodiment, the equipment may correspond to a user equipment (UE) that can communicate with a device corresponding to a base station. The base station may, for example, correspond to a next-generation node B (gNB) that can be configured to transmit one or more signals (e.g., input signals) to the UE.

[0018] In one embodiment, a system is provided that includes one or more devices and one or more apparatuses. The devices and apparatuses may be coupled, for example, via wired and / or wireless coupling.

[0019] Advantageously, in the case of RLF, this system allows for the transfer of models (e.g., AIML models) without retransmitting the entire model. This can help optimize new radio (NR) resources. Attached Figure Description

[0020] Embodiments of this disclosure are described below with reference to the accompanying drawings, in which:

[0021] Figure 1A A schematic diagram of a system for model transfer in a network, according to an embodiment of the present disclosure, is shown. The system may include at least one device.

[0022] Figures 1B to 1C The embodiments of the present disclosure are shown with Figure 1A Example scenarios associated with the system.

[0023] Figure 2 Further detailed illustrations of embodiments according to this disclosure are shown. Figure 1A A schematic diagram of the equipment.

[0024] Figure 3 The embodiments of the present disclosure are shown with Figure 1A The system-related methods.

[0025] Figure 4A and Figure 4D An illustration of an embodiment according to this disclosure is shown. Figure 3 A diagram illustrating example scenarios associated with the method. Detailed Implementation

[0026] The detailed description set forth below with reference to the accompanying drawings is intended as a description of various configurations and is not intended to represent only configurations in which the concepts described herein may be practiced. The detailed description includes specific details and is intended to provide a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. Specifically, although the embodiments described herein may be illustrated using terminology from 3GPP 5G NR, this should not be construed as limiting the scope of the invention.

[0027] In addition, some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. However, other embodiments are also included within the scope of the subject matter disclosed herein, and the disclosed subject matter should not be construed as being limited to the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0028] Generally, unless a different meaning is clearly given and / or implied from the context of the use of the term, all terms used herein shall be interpreted according to their common meaning in the relevant art. Unless otherwise expressly stated, all references to elements, apparatus, components, means, steps, etc., shall be openly interpreted as referring to at least one instance of that element, apparatus, component, means, step, etc. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless a step is explicitly described as occurring after or before another step and / or where an implicit step must occur after or before another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, as long as applicable. Similarly, any advantage of any of the embodiments may be applied to any other embodiment, and vice versa. Other objects, features, and advantages of the appended embodiments will become apparent from the following description.

[0029] In some embodiments, the non-limiting terms User Equipment (UE) or Wireless Device or User Equipment may be used, and the term may refer to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UEs are target devices, device-to-device (D2D) UEs, machine-type UEs or UEs capable of machine-to-machine (M2M) communication, PDAs, PADs, tablet computers, mobile terminals, smartphones, laptop embedded devices (LEE), laptop mounted devices (LME), USB dongles, UE class M1, UE class M2, ProSe UE, V2V UE, V2X UE, etc.

[0030] In some embodiments, the more general term "network node" may be used, and this term may correspond to any type of radio network node or any network node that communicates with user equipment (directly or via another node) and / or with another network node. Examples of network nodes are NodeB, MeNB, ENB, network nodes belonging to MCG or SCG, base stations (BS), multi-standard radio (MSR) radio nodes (such as MSR BS), eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), repeater, donor node controlling repeater, base transceiver station (BTS), access point (AP), transport point, transport node, RRU, RRH, nodes in distributed antenna system (DAS), core network nodes (e.g., mobile switching center (MSC), mobility management entity (MME), etc.), operation and maintenance (O&M), operation support system (OSS), self-optimizing network (SON), location node (e.g., evolved servicing mobile location center (E-SMLC)), minimized drive test (MDT), test equipment (physical node or software), etc.

[0031] Furthermore, terms such as base station / gNodeB and UE should be considered non-restrictive and, in particular, do not imply any hierarchical relationship between the two; generally, "gNodeB" can be considered device 1 and "UE" can be considered device 2, and the two devices communicate with each other via a radio channel. And in the following text, a transmitter or receiver can be either a gNodeB (gNB) or a UE.

[0032] Furthermore, the features, structures, or characteristics described in the embodiments can be combined in any suitable manner. Numerous specific details, such as examples of programming, software modules, user selection, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., are provided in the following description to provide a thorough understanding of the embodiments. However, those skilled in the art will recognize that the embodiments can be practiced without one or more of these specific details or using other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the embodiments. References to “an embodiment,” “embodiment,” or similar language throughout the specification mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Therefore, unless expressly specified otherwise, the phrases “in one embodiment,” “in an embodiment,” and similar language appearing throughout the specification may, but not necessarily all, refer to the same embodiment, but rather mean “one or more, but not all, embodiments.” Unless expressly specified otherwise, the terms “comprising,” “including,” “having,” and variations thereof mean “including, but not limited to,” “including.” Unless expressly specified otherwise, the enumeration list of items does not imply that any or all of the items are mutually exclusive. Unless otherwise expressly specified, the terms “a,” “an,” and “the” also refer to “one or more.”

[0033] The following explanation provides a detailed description of the mechanism for pre-configuring and signaling specific information about model selection using the association between models and index values. AIML-based technologies are currently applied in many different applications, and 3GPP has also begun its research into the technology based on observed potential benefits for application in multiple use cases. The AIML lifecycle can be divided into several phases, such as data collection / preprocessing, model training, model testing / validation, model deployment / update, and model monitoring, each of which is equally important for achieving the target performance of any particular model. One of the challenging issues when applying AIML models to any use case or application is managing the AIML model lifecycle. This is primarily because data / model drift occurs during model deployment / inference, leading to performance degradation of the AIML model. Fundamentally, dataset statistics change after the model is deployed, and the model's inference capabilities are also affected by the unseen data as input. Similarly, the statistical properties of the dataset and the relationship between the inputs and outputs of the trained model can change with drift. In this context, model selection is one of the key issues in maintaining model performance, as model performance (such as inference and / or training) depends on different model execution environments with different configuration parameters. To address this issue, collaboration between the UE and gNB is crucial for tracking model performance and reconfiguring models to suit different environments. Because model performance cannot be consistently maintained due to drift, AIML models require post-deployment monitoring to provide update feedback for retraining / updating the model or selecting an alternative model. When deploying wireless communication networks supporting AIML models, it is important to consider how to handle AIML models during radio device activation and reconfiguration operations such as model training, inference, and updates. Therefore, when RAN-based model operations support a set of multiple specific AIML models, specifications regarding signaling methods and gNB-UE behavior are needed, along with new mechanisms for gNB-UE behavior and procedures to avoid any performance impact on model operations caused by using multiple specific AIML models.

[0034] According to embodiments of this disclosure, this disclosure generally envisions promoting, for example, network (e.g., associated with 3GPP-based standards / specifications, etc.) and / or user equipment (UE) efficiency (e.g., energy / power efficiency).

[0035] Specifically, this disclosure envisions the possibility of a radio link failure at the primary node (MN) or primary base station. In such a scenario, the UE can trigger a model status report (e.g., AIML model status) to the secondary node (SN) or secondary base station. This model (e.g., AIML model) status report may contain segment identifiers (IDs) successfully received from the MN. Upon receiving the AIML status report, the SN can forward it to the MN via the X2 interface. After receiving the AIML status report from the SN, the MN can then forward the remaining AIML segments to the SN, and the SN can subsequently transmit the remaining segments to the UE.

[0036] This disclosure also envisions that AIML models can be stored in a base station (or gNB) and delivered to the UE based on the requirements or applications it supports. If delivery fails during model delivery (e.g., a radio link failure), the network may be unable to resolve such issues. Therefore, this disclosure envisions the possibility of a method for handling model delivery during radio link failures (RLF).

[0037] This invention further envisions that current methods for dual connectivity do not address the problem of radio link failures in the case of AIML model delivery. Therefore, this disclosure envisions the possibility of a method capable of providing reliability over radio link failures (RLF). Two example methods for mitigating radio link failures could be: first, the UE downloads the AIML model via an RRC message; and second, the UE downloads the AIML model via a data radio bearer (DRB). This disclosure envisions that such example methods may not provide an optimal solution with robust link connectivity.

[0038] This disclosure envisions that, according to embodiments of this disclosure, it may be helpful to consider some form of dynamic / adaptive / progressive configuration / deterministic strategy for power / energy consumption efficiency and energy saving at the UE (or user equipment).

[0039] Therefore, this disclosure envisions the possibility of methods to enhance energy savings at the UE during a radio link failure (RLF). Specifically, this disclosure envisions that when an RLF exists at the primary base station (or primary node MN), the UE can trigger an AIML status report. This status report can be sent to a secondary base station (or secondary node SN), which can then forward the report to the primary base station (or MN). The primary base station (or MN) can then forward the remaining AIML segments to the secondary base station, which in turn transmits the remaining AIML segments to the UE.

[0040] In this way, users can experience faster feature initiation, leading to increased user satisfaction. It also allows for efficient use of network resources, ensuring an optimized user experience while minimizing resource consumption. According to embodiments of this disclosure, the method can also provide an AIML model to adapt to changing network conditions and prioritize immediately usable basic functions.

[0041] The foregoing will be discussed in further detail below with reference to Figures 1 to 4.

[0042] refer to Figure 1A The image illustrates a system 100 according to an embodiment of the present disclosure. According to embodiments of the present disclosure, system 100 may be suitable, for example, for energy saving and promoting energy / power efficiency in a network.

[0043] As shown in the figure, according to an embodiment of the present disclosure, system 100 may include one or more devices 102, at least one apparatus 104, and optionally a communication network 106.

[0044] Device 102 may be coupled to device 104. Specifically, according to embodiments of this disclosure, device 102 may be coupled to device 104, for example, via communication network 106.

[0045] In one embodiment, device 102 may be coupled to communication network 106, and device 104 may be coupled to communication network 106. Coupling may be achieved through one or both wired and wireless coupling. According to embodiments of this disclosure, device 102 may generally be configured to communicate with device 104 via communication network 106.

[0046] According to embodiments of this disclosure, equipment 102 may, for example, be associated with / correspond to / include one or more user equipments (UEs) that may carry one or more computers. For example, according to embodiments of this disclosure, equipment 102 may correspond to a UE carrying at least one computer (e.g., according to embodiments of this disclosure, an electronic device / module with computing capabilities, such as an electronic mobile device that can be carried in a vehicle or an electronic module that can be installed in a vehicle), which may be configured to perform one or more processing tasks associated with adaptive / dynamic / progressive control. In a more specific example, according to embodiments of this disclosure, in one embodiment, equipment 102 may include one or more processors (not shown) that may be configured to perform one or more processing tasks associated with dynamic / adaptive / progressive control. In one embodiment, equipment 102 may, for example, be configured to receive one or more input signals and perform at least one processing task based on the input signals in a manner that generates one or more output signals. According to embodiments of this disclosure, the input signals may, for example, be transmitted from device 104 and received by equipment 102. As a possible option, according to embodiments of this disclosure, the output signal may, for example, be transmitted from equipment 102. Embodiments of this disclosure will be referenced later. Figure 2 Let's discuss Equipment 102 in further detail.

[0047] Device 104 may be associated with / correspond to at least one base station (e.g., at least one gNB). Furthermore, device 104 may be configured, for example, to carry / associate with / include one or more computers (e.g., electronic devices / modules with computing capabilities), which may be configured, for example, to perform one or more processing tasks associated with the base station. According to embodiments of this disclosure, device 104 may be configured to generate one or more input signals that can be transmitted to equipment 102. This will be discussed in further detail later in the context of example scenarios according to embodiments of this disclosure.

[0048] Communication network 106 may correspond, for example, to an Internet communication network, a cellular communication network, a wired communication network, a Global Navigation Satellite System (GNSS) communication network, a wireless communication network, or any combination thereof. Communication via communication network 106 (e.g., between equipment 102 and / or between equipment 102 and device 104) may be conducted via one or both of wired and wireless communication.

[0049] As mentioned earlier, device 102 may be configured, for example, to receive at least one input signal and perform at least one processing task associated with dynamic / adaptive / progressive control on the input signal in a manner that generates at least one output signal. Furthermore, according to embodiments of this disclosure, device 104 may be configured, for example, to generate (and transmit) input signals to device 102. Embodiments of this disclosure will be described below with reference to... Figures 1B to 1C This will be discussed in the context of example scenarios.

[0050] Figures 1B to 1C An embodiment of the invention is shown with Figure 1A Example scenarios associated with the system. Specifically, Figure 1B Examples of different embodiments for training Artificial Intelligence Machine Learning (AIML) models are shown. Referring to the accompanying drawings, Type 1 illustrates an example of joint training of a two-sided model on a single side / entity (e.g., the UE side or the network side). In this example, the model needs to be passed from one side to the other. Type 2 illustrates an example of joint training of a two-sided model on the network side and the UE side, respectively. In this example, training iterations can exchange the required forward / backward propagation results, and simultaneous interaction between the network and the UE is required during the training process. Type 3 illustrates an example of separate training at the network side and the UE side, where the UE-side Channel State Information (CSI) generation part and the network-side CSI reconstruction part are trained by the UE side and the network side, respectively. In this example, the training dataset needs to be shared with other side models, and the dataset and reference model need to be delivered.

[0051] Specifically, Figure 1C An example of a Multi-Radio Dual Connectivity (MR-DC) configuration is shown. As illustrated in the figures, four NR-DC configurations can include E-UTRA-NR Dual Connectivity (EN-DC), NG-RAN E-UTRA-NR Dual Connectivity (NGEN-DC), NR-E-UTRA Dual Connectivity (NE-DC), and NR-DC. In an example implementation of MR-DC signaling, the UE has a single RRC state based on the MN RRC state, thereby connecting the UE to the CN via a single control plane connection. Initial access (SRB0) and RRC configuration (SRB1) can be via the MN, but subsequent reconfiguration can originate from either the MN or the SN. EN-DC can begin with EUTRA PDCP, but this can be reconfigured to use NR PDCP. In the event of a link failure in the SCG but a normal MCG link, a re-establishment process is not triggered. An example implementation can include fast MCG link recovery. In other words, even if the MCG link fails but the SCG link is normal, a re-establishment process is not triggered; instead, the SCG link is used to restore the MCG link.

[0052] This disclosure further envisions that, according to embodiments of this disclosure, considering some form of dynamic / adaptive / incremental configuration / determination strategy for auxiliary power / energy consumption efficiency may be helpful. Specifically, this disclosure envisions the possibility of how model transfer is performed during radio link failures (RLF). According to embodiments of this disclosure, the dynamic / adaptive / incremental control configuration / determination strategy may, for example, be associated with dynamic / adaptive / incremental control based on RLF and model segments.

[0053] The aforementioned advantageous aspects of system 100 of this disclosure can also be similarly applied to all aspects of the following apparatus 102 of this disclosure. Similarly, all the following advantageous aspects of apparatus 102 of this disclosure can also be similarly applied to all aspects of the aforementioned system 100 of this disclosure.

[0054] The following will refer to Figure 2 The aforementioned equipment 102 will be discussed in further detail.

[0055] refer to Figure 2 According to embodiments of this disclosure, the equipment 102 is shown in further detail in the context of example implementation 200.

[0056] In example implementation 200, equipment 102 may correspond to electronic module 200a. According to embodiments of this disclosure, in one example, electronic module 200a may correspond to and be, for example, a mobile device carried by a user into a vehicle. According to embodiments of this disclosure, in another example, electronic module 200a may correspond to an electronic device that can be installed / replaced in a vehicle. In this regard, electronic module 200a can be considered as being carried by the vehicle (e.g., carried by a user into the vehicle or installed / replaced in the vehicle).

[0057] According to embodiments of this disclosure, it is envisioned that electronic module 200a may be able to perform one or more processing tasks associated with adaptive / dynamic / progressive control-related processing.

[0058] Electronic module 200a may include, for example, a housing 200b. Furthermore, electronic module 200a may, for example, carry any one or any combination of the first module 202, the second module 204, and the third module 206.

[0059] In one embodiment, electronic module 200a may carry first module 202, second module 204, and / or third module 206. In a specific example, according to an embodiment of this disclosure, electronic module 200a may carry first module 202, second module 204, and third module 206.

[0060] In this regard, it should be understood that, in one embodiment, the shape and size of the housing 200b may be designed to carry any one or any combination of the first module 202, the second module 204 and the third module 206.

[0061] The first module 202 may be coupled to one or both of the second module 204 and the third module 206. The second module 204 may be coupled to one or both of the first module 202 and the third module 206. The third module 206 may be coupled to one or both of the first module 202 and the second module 204. In one example, according to an embodiment of the present disclosure, the first module 202 may be coupled to the second module 204, and the second module 204 may be coupled to the third module 206. The coupling between the first module 202, the second module 204, and / or the third module 206 may be performed, for example, by one or both of wired and wireless coupling. According to an embodiment of the present disclosure, each of the first module 202, the second module 204, and the third module 206 may correspond to one or both of hardware-based and software-based modules.

[0062] In one example, the first module 202 may correspond to a hardware-based receiver that can be configured to receive one or more input signals. According to embodiments of this disclosure, the input signals may be transmitted, for example, from device 104 (e.g., gNB).

[0063] According to embodiments of this disclosure, the second module 204 may correspond, for example, to a hardware-based processor that can be configured to perform one or more processing tasks (e.g., by generating one or more output signals), as will be referred to later. Figure 3 To be discussed in further detail.

[0064] The third module 206 may correspond to a hardware-based transmitter that can be configured to transmit one or more output signals from the electronic module 200a. According to embodiments of this disclosure, the output signals may, for example, include / correspond to one or more instruction / command / control signals associated with the aforementioned dynamic / adaptive / progressive control configuration / determination strategy in order to promote efficiency (e.g., power / energy efficiency and / or communication efficiency).

[0065] This disclosure envisions the possibility that the first and second modules 202 / 204 may be software-hardware integrated modules (e.g., electronic components carrying software programs / algorithms associated with receiving and processing functions / electronic modules programmed to perform receiving and processing functions). This disclosure further envisions the possibility that the first module 202 and the third module 206 may be software-hardware integrated modules (e.g., electronic components carrying software programs / algorithms associated with receiving and transmitting functions / electronic modules programmed to perform receiving and transmitting functions). This disclosure further envisions the possibility that the first and third modules 202 / 206 may be integrated hardware modules capable of performing receiving and transmitting functions (e.g., hardware-based transceivers).

[0066] The aforementioned advantageous aspects of the apparatus 102 of this disclosure are also similarly applicable to all aspects of the processing / communication methods described below. Similarly, all the aforementioned advantageous aspects of the processing / communication methods of this disclosure are also similarly applicable to all aspects of the apparatus 102 described above. It should be understood that these statements also apply similarly to the system 100 discussed earlier in this disclosure.

[0067] refer to Figure 3 This illustrates a method (also referred to as a processing method) associated with system 100 according to an embodiment of the present disclosure.

[0068] According to embodiments of this disclosure, method 300 may be, for example, adapted to / able to promote energy efficiency.

[0069] According to embodiments of the present disclosure, the processing method 300 may include any one or any combination of the input step 302, the processing step 304, and the output step 306.

[0070] In one embodiment, processing method 300 may include an input step 302. In another embodiment, processing method 300 may include an input step 302 and a processing step 304. In yet another embodiment, processing method 300 may include an input step 302, a processing step 304, and an output step 306. In still another embodiment, processing method 300 may include a processing step 304 and one or both of input step 302 and output step 306. In yet another further embodiment, processing method 300 may include an input step 302, a processing step 304, and an output step 306. In yet another further additional embodiment, processing method 300 may include a processing step 304. In yet another further additional embodiment, processing method 300 may include any one or any combination of input step 302, processing step 304, and output step 306 (i.e., input step 302, processing step 304, and / or output step 306).

[0071] Regarding input step 302, one or more input signals may be received. For example, according to embodiments of this disclosure, the input signals may be transmitted from device 104 and received by equipment 102.

[0072] Input step 302 may include receiving at least one input signal associated with model type data. In an embodiment, the input signal may be generated by and transmitted from device 104 to equipment 102. Alternatively, the input signal may be generated and received by equipment 102 to proceed to processing step 304. For example, the input signal may be generated by a transmitting UE (or user equipment) and received by a receiving UE (or user equipment).

[0073] Regarding processing step 304, according to embodiments of this disclosure, at least one processing task associated with the received input signal can be performed by generating one or more output signals.

[0074] Processing step 304 may include at least one of the following: determining whether a radio link failure (RLF) exists; configuring a report if an RLF exists; and transmitting the report to a first base station, wherein the report includes a mapping table indicating the mapping between model identifiers and segment identifiers. Model type data may include artificial intelligence machine learning (AIML) model data.

[0075] Processing step 304 may further include determining the transmitted model segment of model type data; and incorporating the transmitted model segment of model type data into a report, wherein transmitting the report includes transmission via at least one of the following: physical layer (PHY), media access control (MAC), packet data convergence protocol (PDCP), and / or radio resource control (RRC) configuration.

[0076] Processing step 304 may further include: transmitting the report to a second base station; receiving the remaining model segments from the second base station; transmitting the remaining model segments; receiving the report from a first base station; determining the untransmitted remaining model segments based on the report; and transmitting the remaining model segments to the first base station.

[0077] Each of the first base station and the second base station may correspond to at least one next-generation node B (gNB), and the user equipment (UE) may be configured to perform input step 302 and processing step 304, and model type data may be transmitted from the gNB to the UE.

[0078] In this embodiment, when a radio link failure (RLF) occurs, the UE can trigger an AIML status report and send it to the secondary node (SN). The AIML status report may include an AIML segment ID, an AIML model ID, and an indication of successfully received AIML model segments. The AIML status report can be sent via PHY, MAC, PDCP, and / or RRC. After receiving the AIML status report, the SN can forward it to the primary node (MN). Subsequently, after receiving the AIML status report from the SN, the MN can forward the remaining AIML segments to the SN. The SN can then transmit the remaining AIML segments to the UE. If an RLF exists at the SN, the AIML status report can be sent directly to the MN, where the MN can directly forward the remaining AIML segments to the UE. Advantageously, in the case of an RLF, the AIML model can be transmitted without retransmitting the entire model. This can help optimize NR resources.

[0079] In the example embodiment shown in Table 1 below, the status report may include a mapping table indicating the mapping between segment IDs and model IDs. The mapping between segment IDs and model IDs can help the gNB (or base station) and UE accurately determine which segment of which model failed to be delivered during RLF.

[0080]

[0081] Table 1

[0082] Regarding output step 306, according to embodiments of the present disclosure, as an option, an output signal may be transmitted, for example. For instance, the output signal may optionally be transmitted from device 102. In a more specific example, according to embodiments of the present disclosure, the output signal may optionally be transmitted from device 102 to one or both of at least one device 104 and another device 102.

[0083] This disclosure further envisions a computer program (not shown) that may include instructions that, when executed by a computer (not shown), cause the computer to perform input step 302, processing step 304, and / or output step 306 as discussed in reference method 300. For example, according to an embodiment of the invention, the computer program may include instructions that, when executed by a computer, cause the computer to perform input step 302 and / or processing step 304.

[0084] This disclosure further envisions a computer-readable storage medium (not shown) storing data representing software executable by a computer (not shown), the software including instructions that, when executed by the computer, perform input step 302, processing step 304, and / or output step 306 as discussed in reference method 300. For example, according to embodiments of the invention, the computer-readable storage medium may store data representing computer-executable software including instructions that, when executed by the computer, cause the computer to perform input step 302 and / or processing step 304.

[0085] In light of the foregoing, it is understood that this disclosure generally envisions an energy-saving device 102 suitable for use in a network, which may include a first module 202, a second module 204, and / or a third module 206.

[0086] The first module 202 can be configured to receive one or more input signals. For example, the input signals can be associated with model type data.

[0087] The second module 204 can be configured to process the input signal and / or facilitate the processing of the input signal according to the method 300 discussed earlier, to generate one or more output signals.

[0088] The third module 206 can be configured to transmit one or more output signals. These output signals may, for example, correspond to one or more control signals used for model transmission in the network during radio link failure (RLF).

[0089] In one embodiment, equipment 102 may correspond to a user equipment (UE) that can communicate with equipment 104 corresponding to a base station. The base station may, for example, correspond to a next-generation node B (gNB) that can be configured to transmit one or more signals (e.g., input signals) to the UE.

[0090] Furthermore, in view of the foregoing, it is understood that this disclosure generally envisions a system 100, which may include one or more devices 102 and one or more apparatuses 104. The devices 102 and 104 may be coupled, for example, via wired and / or wireless coupling.

[0091] It should be understood that the embodiments described above can be combined in any way where appropriate (for example, one or more embodiments as discussed in the “Detailed Description” section can be combined with one or more embodiments as described in the “Summary of the Invention” section).

[0092] Those skilled in the art will further understand that variations and combinations of the embodiments described above, rather than alternatives or substitutes, can be combined to form even further embodiments.

[0093] In one example, the possibility of transmitting an output signal from equipment 102 is discussed. It is understood that transmitting an output signal from equipment 102 is not necessarily required. Specifically, according to embodiments of the invention, the possibility that an output signal may not necessarily need to be transmitted outside of equipment 102 is envisioned. More specifically, according to embodiments of the invention, the output signal may, for example, correspond to internal commands / instructions for adaptively controlling the operational configuration of equipment 102 (e.g., transmitted only within equipment 102).

[0094] Figures 4A to 4D A schematic diagram illustrating an example scenario associated with method 300 according to an embodiment of this disclosure is shown.

[0095] In such Figure 4A In the example context shown, a radio link failure (RLF) may exist between the UE and the primary node (MN). This could trigger the UE to send an AIML status report with a segment ID (ML_n) and a model ID to the secondary node (SN). The SN forwards the status report to the MN and obtains the remaining segments from the MN. The SN then forwards the remaining segments to the UE.

[0096] In such Figure 4B In the example context shown, the UE (or user equipment) is configured to download an AIML model from the MN or SN. The UE then determines whether an RLF exists. If an RLF exists, the UE reports the segment ID and model ID to the SN or MN. If an RLF does not exist, the UE proceeds with model delivery.

[0097] In such Figure 4C In the example context shown, the SN (or gNB or base station) is configured to receive AIML status reports from the UE and forward them to the MN. The SN is also configured to receive remaining segments from the MN and forward them to the UE.

[0098] In such Figure 4D In the example context shown, the MN (or gNB or base station) is configured to forward the remaining segments to the SN based on the received AIML status report.

[0099] Various embodiments of this disclosure for addressing at least one of the aforementioned disadvantages have been described in the foregoing manner. Such embodiments are intended to be covered by the appended claims and are not limited to the specific form or arrangement of the parts so described, and it will be apparent to those skilled in the art, in light of this disclosure, that many changes and / or modifications may be made, which are also intended to be covered by the appended claims.

[0100] Abbreviations:

[0101] AIML Artificial Intelligence Machine Learning

[0102] BWP bandwidth portion

[0103] CPUCSI processing unit

[0104] CRC Cyclic Redundancy Check

[0105] CRICSI-RS resource indicator

[0106] CSI Channel Status Information

[0107] CSI-RS Channel State Information Reference Signal

[0108] CSI-RSRP CSI Reference Signal Received Power

[0109] CSI-RSRQ CSI reference signal reception quality

[0110] CSI-SINR (CSI Signal-to-Noise and Interference Ratio)

[0111] CW code

[0112] DCI downlink control information

[0113] DL downlink

[0114] DM-RS demodulation reference signal

[0115] DRB Data Radio Bearer

[0116] DRX discontinuous reception

[0117] EPRE Energy per Resource Element

[0118] IAB-MT Integrated Access and Backhaul - Mobile Terminal

[0119] L1-RSRP Layer 1 Reference Signal Received Power

[0120] LI layer indicator

[0121] LP-WUR Low Power Wake-up Receiver

[0122] LP-WUS Low Power Wake-up Signal

[0123] MAC Media Access Control

[0124] MCS modulation and coding scheme

[0125] MN master node

[0126] MR main receiver

[0127] MR-DC Multi-Radio Dual-Connection

[0128] NR New Radio

[0129] PDCP Packet Data Convergence Protocol

[0130] PDCCH Physical Downlink Control Channel

[0131] PDSCH Physical Downlink Shared Channel

[0132] PHY physical layer

[0133] PMI Precoding Matrix Indicator

[0134] PRB Physical Resource Block

[0135] PRG precoded resource block group

[0136] PRS positioning reference signal

[0137] PSS master synchronization signal

[0138] PT-RS phase tracking reference signal

[0139] PUCCH (Physical Uplink Control Channel)

[0140] QCL Quasi-Co-location

[0141] RB resource blocks

[0142] RBG resource block group

[0143] RI ranking indicator

[0144] RIV resource indicator value

[0145] RLF radio link failure

[0146] RRC Radio Resource Control

[0147] RS reference signal

[0148] RSRP reference signal received power

[0149] RSRQ reference signal reception quality

[0150] SCI sidelink control information

[0151] SN auxiliary node

[0152] SLIV start and length indicator values

[0153] SR scheduling request

[0154] SRS detection reference signal

[0155] SS Synchronization Signal

[0156] SS-RSRP SS reference signal received power

[0157] SS-RSRQ SS reference signal reception quality

[0158] SSS auxiliary synchronization signal

[0159] SS-SINR (Signal-to-Noise and Interference Ratio)

[0160] TB transport block

[0161] TCI Transport Configuration Indicator

[0162] TDM Time Division Multiplexing

[0163] UE User Equipment

[0164] UL uplink

Claims

1. A method (300) for model transfer in a network, comprising: Input step (302), the input step includes receiving at least one input signal associated with model type data; as well as Processing step (304), the processing step includes at least one of the following: Determine if a radio link failure (RLF) exists; If an RLF exists, then configure a report; as well as The report is transmitted to the first base station. The report includes a mapping table that indicates the mapping between model identifiers and segment identifiers.

2. The method (300) according to claim 1, wherein the model type data includes artificial intelligence machine learning (AIML) model data.

3. The method (300) according to claim 1, wherein the processing step (304) further comprises: Determine the model segment to be transmitted for the model type data; as well as The transmitted model segment of the model type data is incorporated into the report.

4. The method (300) of claim 1, wherein transmitting the report comprises transmitting via at least one of: physical layer (PHY), media access control (MAC), packet data convergence protocol (PDCP), and / or radio resource control (RRC) configuration.

5. The method (300) according to claim 1, wherein the first base station is configured to: The report is transmitted to the second base station; Receive the remaining model segments from the second base station; and Transmit the remaining model segments.

6. The method (300) according to claim 1, wherein the second base station is configured to: Receive the report from the first base station; Based on the report, determine the remaining untransmitted model segments; and The remaining model segments are transmitted to the first base station.

7. The method (300) according to claim 5 or 6, wherein each of the first base station and the second base station corresponds to a next-generation node B (gNB).

8. The method (300) of claim 7, wherein the user equipment (UE) is configured to perform the input step (302) and the processing step (304), and wherein the model type data can be transmitted from the gNB to the UE.

9. A computer program comprising instructions that, when executed by a computer, cause the computer to perform at least one of the input step (302) and the processing step (304) of the method (300) according to any one of the preceding claims.

10. A computer-readable storage medium storing data representing software executable by a computer, the software including instructions that, when executed by the computer, are used to perform at least one of the input step (302) and the processing step (304) of the method (300) according to any one of claims 1 to 8.

11. An apparatus (102) comprising: A first module (202) is configured to receive at least one input signal associated with model type data; The second module (204) is configured to process and / or facilitate the processing steps (304) of the method (300) according to claims 1 to 8 to generate at least one output signal; as well as The third module (206) is configured to transmit at least one output signal. The output signal corresponds to the control signal used for model transmission in the network.

12. The equipment (102) according to claim 11, The aforementioned equipment (102) corresponds to a user equipment (UE) capable of communicating with a device (104) corresponding to a base station, and The base station corresponds to a next-generation node B (gNB) configured to transmit the at least one input signal to the UE.

13. A system (100), the system comprising: At least one piece of equipment (102) according to any one of claims 11 and 12; as well as At least one device (104) according to claim 12, The equipment (102) and the device (104) can be coupled via at least one of wired coupling and wireless coupling.

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