Model transfer in a network

Abstract

System (100), apparatus (102) and a method (300) for model transfer in a network are disclosed. The method (300) includes an input step (302) which comprises receiving at least one input signal associated with a model type data; and a processing step (304) which comprises at least one of: determining whether there is radio link failure (RLF); configuring a report if there is RLF; and communicating the report to a first base station, wherein the report comprises a mapping table indicative of mapping between a model identification and a segment identification.

Description

SYSTEM AND APPARATUS FOR MODEL TRANSFER IN A NETWORK AND A METHOD IN ASSOCIATION THERETOField Of Invention

[0001] The present disclosure generally relates to one or both of a system and an apparatus for model transfer in a network in association with, for example, a User Equipment (UE) usable for communication. The present disclosure further relates a method which can be associated with the system and / or the apparatus.Background

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

[0003] Typically, models (e.g. Artificial Intelligence Machine Learning AIML models) are stored in a base station and are transferred to the user equipment (UE) based on requirements or application they support. If there is a transfer failure (e.g. radio link failure) during the model transfer, the network may be unable to resolve such a situation. The present disclosure thus contemplates that conventional techniques for model transfer during radio link failure may not facilitate efficiency and energy savings in an optimal manner.

[0004] The present disclosure contemplates that it would be helpful to address (or at least mitigate) one or more issues in relation to conventional techniques for facilitating energy efficiency and energy savings.Summary of the Invention

[0005] In accordance with a first aspect of the present invention, there is provided a method comprising: for model transfer in a network comprising: an input step which comprises receiving at least one input signal associated with a model type data; anda processing step which comprises at least one of: determining whether there is radio link failure (RLF); configuring a report if there is RLF; and communicating the report to a first base station, wherein the report comprises a mapping table indicative of mapping between a model identification and a segment identification..

[0006] Advantageously, the method as described herein can allow users to experience faster functionality initiation, enhancing user satisfaction. It may also allow network resources to be used efficiently, thereby ensuring an optimized user experience while minimizing resource consumption. The method may also provide the AIML model to adapt to varying network conditions and prioritizing essential functionalities for immediate use.

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

[0008] In an embodiment, the processing step further comprises determining model segments of the model type data that are transferred; and incorporating the transferred model segments of the model type data into the report.

[0009] In an embodiment, the processing step further comprises wherein communicating the report comprises communicating via at least one of: Physical Layer (PHY), Medium Access Control (MAC), Packet Data Convergence Protocol (PDCP) and / or Radio Resource Control (RRC) configuration.

[0010] In an embodiment, the first base station is configured to: communicate the report to a second base station; receive the remaining model segments from the second base station; and communicate the remaining model segments.

[0011] In an embodiment, the second base station is configured to: receive the report from the first base station; determine remaining model segments that are not transferred based on the report; and communicate the remaining model segments to the first base station.

[0012] In an embodiment, wherein each of the first base station and the second base station corresponds to a Next Generation Node B (gNB).

[0013] In an embodiment, a User Equipment (UE) is configured to perform the input step and the processing step, and wherein the model type data is communicable from the gNB to the UE.

[0014] In an embodiment, there is provided a computer program (not shown) which can include instructions which, when the program is executed by a computer (not shown), cause the computer to carry out the input step, the processing step and / or the output step as discussed with reference to the method. For example, the computer program can include instructions which, when the program is executed by a computer, cause the computer to carry out the input step and / or the processing step, in accordance with an embodiment of the disclosure.

[0015] In an embodiment, there is provided a computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out at least one of the input step and the processing step according to the method of the first aspect.

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

[0017] In an embodiment, the apparatus can correspond to a User Equipment (UE) which can communicate with a device corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0018] In an embodiment, there is provided a system comprising one or more apparatuses and one or more devices. The apparatus(es) and the device(s) can, for example, be capable of being coupled via wired coupling and / or wireless coupling.

[0019] Advantageously, the system can allow the model (e.g. AIML model) to be transferred in case of RLF without re-transferring the complete model. This may help in optimizing New Radio (NR) resources.Brief Description of the Drawings

[0020] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which:

[0021] Fig. 1A shows a schematic diagram illustrating a system for model transfer in a network which can include at least one apparatus, according to an embodiment of the disclosure.

[0022] Fig. 1 B to Fig. 1 C show example scenarios in association with the system of Fig. 1 A, according to an embodiment of the disclosure.

[0023] Fig. 2 shows a schematic diagram illustrating the apparatus of Fig. 1A in further detail, according to an embodiment of the disclosure.

[0024] Fig. 3 shows a method in association with the system of Fig. 1A, according to an embodiment of the disclosure.

[0025] Fig. 4A and Fig. 4D show schematic diagrams illustrating example scenarios in association with the method of Fig. 3, according to an embodiment of the disclosure.Detailed Description

[0026] The detailed description set forth below, with reference to annexed drawings, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing 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. In particular, although terminology from 3GPP 5G NR may be used in this disclosure to exemplify embodiments herein, this should not be seen 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. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only 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, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0029] In some embodiments, the non-limiting term User Equipment (UE) or wireless device or user device may be used and 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 UE are target device, device-to-device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

[0030] In some embodiments, a more general term “network node” may be used and may correspond to any type of radio network node or any network node, which communicates with a User Equipment (directly or via another node) and / or with another network node. Examples of network nodes are NodeB, MeNB, ENB, a network node belonging to MCG or SCG, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc), Operations & Maintenance (O&M), Operations Support System (OSS), Self Optimized Network (SON), positioning node (e.g. Evolved- Serving Mobile Location Centre (E-SMLC)), Minimization of Drive Tests (MDT), test equipment (physical node or software), etc.

[0031] Additionally, terminologies such as base station / gNodeB and UE should be considered non-limiting and do in particular not imply a certain hierarchical relation between the two; in general, “gNodeB” could be considered as device 1 and “UE” could be considered as device 2 and these two devices communicate with each other over some radio channel. And in the following the transmitter or receiver could be either gNodeB (gNB), or UE.

[0032] Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description,numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise.

[0033] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model selection using association between models and index values. AIML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AIML lifecycle can be split into several stages such as data collection / pre-processing, model training, model testing / validation, model deployment / update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s). In applying AIML model for any use case or application, one of the challenging issues is to manage the lifecycle of AIML model. It is mainly because the data / model drift occurs during model deployment / inference and it results in performance degradation of AIML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similaraspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model selection is one of key issues for model performance maintenance as model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AIML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re- train / update the model or select alternative model. When AIML model enabled wireless communication network is deployed, it is then important to consider how to handle AIML model in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc. Therefore, there is a need for specification for signaling methods and gNB-UE behaviors when a set of multiple specific AIML models are supported for RAN-based model operation and a new mechanism about gNB-UE behaviors and procedures is necessary to avoid any performance impact on model operation using multiple specific AIML models.

[0034] The present disclosure generally contemplates the facilitation of, for example, network (e.g., in association with 3GPP based standard / specification etc.) and / or user equipment (UE) efficiency (e.g., energy / power efficiency), in accordance with an embodiment of the disclosure.

[0035] Specifically, the present disclosure contemplates the possibility of radio link failure at a main node (MN) or a main base station. In such a scenario, the UE may trigger a model status (e.g. AIML model status) report to a secondary node (SN) or secondary base station. The model (e.g. AIML model) status report may contain the segment identification (ID) which has been successfully received from the MN. The SN upon receiving the AIML status report may forward the AIML status report to the MN over the X2 interface. The MN upon receiving the AIML status report from the SN may then forward the remaining AIML segments to the SN and the SN may subsequently transmit the remaining segments to the UE.

[0036] The present disclosure also contemplates that AIML models may be stored in the base station (or gNB) and are transferred to the UE based on requirements or application they support. If the transfer fails during the model transfer (e.g. radio link failure), the network may not be able to resolve such a problem. The present disclosure thus contemplates the possibility of a method to handle model transfer during radio link failure (RLF).

[0037] The present disclosure further contemplates that current methods for dual connectivity do not address the problem of radio link failure in the case of AIML model transfer. The present disclosure thus contemplates the possibility of a method which can provide reliability over Radio Link Failure (RLF). Two example methods to mitigate radio link failure may be firstly, the UE downloads the AIML model via RRC messages and secondly, the UE downloads the AIML model via Data Radio Bearers (DRB). The present disclosure contemplates that such example methods may not provide an optimal solution having robust link connectivity.

[0038] The present disclosure contemplates that it may be helpful to consider some form of dynamic / adaptive / gradual configuration / determination strategy which will aid in power / energy consumption efficiency and energy savings at the UE (or user device), in accordance with an embodiment of the disclosure.

[0039] The present disclosure thus contemplates the possibility of a method for enhanced energy savings at UE for model transfer during radio link failure (RLF). Specifically, the present disclosure contemplates that a UE can trigger an AIML status report when there is RLF at a main base station (or main node MN). The status report may be sent to a secondary base station (or secondary node SN) which may then forward the report to the main base station (or MN). The main base station (or MN) may forward the remaining AIML segments to the secondary base station which will then be transmitted to the UE.

[0040] In the above manner, users may experience faster functionality initiation, leading to enhanced user satisfaction. It may also allow network resources to be used efficiently, thereby ensuring an optimized user experience while minimizingresource consumption. The method may also provide the AIML model to adapt to varying network conditions and prioritizing essential functionalities for immediate use, in accordance with an embodiment of the disclosure.

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

[0042] Referring to Fig. 1A, a system 100 is shown, according to an embodiment of the disclosure. The system 100 can, for example, be suitable for energy savings and facilitating energy / power efficiency in a network, in accordance with an embodiment of the disclosure.

[0043] As shown, the system 100 can include one or more apparatuses 102, at least one device 104 and, optionally, a communication network 106, in accordance with an embodiment of the disclosure.

[0044] The apparatus(es) 102 can be coupled to the device(s) 104. Specifically, the apparatus(es) 102 can, for example, be coupled to the device(s) 104 via the communication network 106, in accordance with an embodiment of the disclosure.

[0045] In one embodiment, the apparatus(es) 102 can be coupled to the communication network 106 and the device(s) 104 can be coupled to the communication network 106. Coupling can be by manner of one or both of wired coupling and wireless coupling. The apparatus(es) 102 can, in general, be configured to communicate with the device(s) 104 via the communication network 106, according to an embodiment of the disclosure.

[0046] The apparatus(es) 102 can, for example, be associated with / correspond to / include one or more user equipment (UE) which can carry one or more computers, in accordance with an embodiment of the disclosure. For example, an apparatus 102 can correspond to a UE carrying at least one computer (e.g., an electronic device / module having computing capabilities such as an electronic mobile device which can be carried into a vehicle or an electronic module which can be installed in a vehicle, in accordance with an embodiment of the disclosure) which can beconfigured to perform one or more processing tasks in association with adaptive / dynamic / gradual control, in accordance with an embodiment of the disclosure. In a more specific example, the apparatus(es) 102 can, in one embodiment, include one or more processors (not shown) which can be configured to perform one or more processing tasks in association with dynamic / adaptive / gradual control, in accordance with an embodiment of the disclosure. In one embodiment, the apparatus(es) 102 can, for example, be configured to receive one or more input signals and perform at least one processing task based on the input signal(s) in a manner to generate one or more output signals. The input signal(s) can, for example, be communicated from the device(s) 104 and received by the apparatus(es) 102, in accordance with an embodiment of the disclosure. As a possible option, the output signal(s) can, for example, be communicated from the apparatus(es) 102, in accordance with an embodiment of the disclosure. The apparatus(es) 102 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the disclosure.

[0047] The device(s) 104 can, for example, be associated with / correspond to at least one base station (e.g., at least one gNB). Moreover, the device(s) 104 can, for example, be configured to carry / be associated with / include one or more computers (e.g., an electronic device / module having computing capabilities) which can, for example, be configured to perform one or more processing tasks in association with the base station. The device(s) 104 can be configured to generate one or more input signals which can be communicated to the apparatus(es) 102, in accordance with an embodiment of the disclosure. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the disclosure.

[0048] The communication network 106 can, for example, correspond to an Internet communication network, a cellular-based communication network, a wired-based communication network, a Global Navigation Satellite System (GNSS) based communication network, a wireless-based communication network, or any combination thereof. Communication (e.g., between the apparatuses 102 and / or between the apparatus(es) 102 and the device(s) 104) via the communication network 106 can be by manner of one or both of wired communication and wireless communication.

[0049] Earlier mentioned, the apparatus(es) 102 can, for example, be configured to receive at least one input signal and perform at least one processing task in association with dynamic / adaptive / gradual control on the input signal(s) in a manner so as to generate at least one output signal. Moreover, the device(s) 104 can, for example, be configured to generate (and communicate) the input signal(s) to the apparatus(es) 102, in accordance with an embodiment of the disclosure. This will be discussed, in accordance with an embodiment of the disclosure, in the context of an example scenario with reference to Fig. 1 B to Fig. 1 C, hereinafter.

[0050] Fig. 1 B to 1 C show example scenarios in association with the system of Fig. 1A, according to an embodiment of the invention. Specifically, Fig. 1 B show examples of different embodiments for training of an Artificial Intelligence Machine Learning (AIML) model. Referring to the Figure, Type 1 shows an example of joint training of a two-sided model at a single side / entity, e.g., at the UE-sided or the Network-side. In this example, model transfer is required from one side to the other Type 2 shows an example of joint training of the two-sided model at the network side and the UE side, respectively. In this example, training iterations can exchange forward / backward propagation results needed and simultaneous interactions between network and UEs are required during the training process. Type 3 shows 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, training dataset needs to be shared with other sided model and delivery of dataset and reference model are required.

[0051] Specifically, Fig. 1 C show examples of Multi Radio Dual Connectivity (MR-DC) configuration. As shown in the Figure, four configurations of NR-DC 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 signalling, the UE has a single RRC state based on the MN RRC state whereby the UE connects to the CN via single control plane connection. Initial access (SRB0) and RRC configuration (SRB1 ) can be via the MNbut later reconfigurations can be from either MN or SN. EN-DC may start with EUTRA PDCP but this can be reconfigured to use NR PDCP. In the event there is a link failure in SCG but the MCG link is fine, no re-establishment procedure is triggered. An example implementation may include fast MCG link recovery. In other words, even when the MCG link fails but SCG link is fine, no re-establishment procedure will be triggered but instead, SCG link is used to recover MCG link.

[0052] The present disclosure further contemplates that it may be helpful to consider some form of dynamic / adaptive / gradual configuration / determination strategy which will aid in power / energy consumption efficiency, in accordance with an embodiment of the disclosure. Specifically, the present disclosure contemplates the possibility of how to perform model transfer during radio link failure (RLF). The dynamic / adaptive / gradual control configuration / determination strategy can, for example, be in relation to dynamic / adaptive / gradual control based on RLF and model segments, in accordance with an embodiment of the disclosure.

[0053] The above-described advantageous aspect(s) of the system 100 of the present disclosure can also apply analogously (all) the aspect(s) of a below described apparatus 102 of the present disclosure. Likewise, all below described advantageous aspect(s) of the apparatus 102 of the disclosure can also apply analogously (all) the aspect(s) of above described system 100 of the disclosure.

[0054] The aforementioned apparatus(es) 102 will be discussed in further detail with reference to Fig. 2 hereinafter.

[0055] Referring to Fig. 2, an apparatus 102 is shown in further detail in the context of an example implementation 200, according to an embodiment of the disclosure.

[0056] In the example implementation 200, the apparatus 102 can correspond to an electronic module 200a. The electronic module 200a can, in one example, correspond to a mobile device which can, for example, be carried into the vehicle by a user, in accordance with an embodiment of the disclosure. In another example, the electronic module 200a can correspond to an electronic device which can beinstalled / mounted in the vehicle, in accordance with an embodiment of the disclosure. In this regard, the electronic module 200a can be considered to be carried by the vehicle (e.g., either carried into the vehicle by a user or installed / mounted in the vehicle).

[0057] It is contemplated that the electronic module 200a can be capable of performing one or more processing tasks in association with adaptive / dynamic / gradual control related processing, in accordance with an embodiment of the disclosure.

[0058] The electronic module 200a can, for example, include a casing 200b. Moreover, the electronic module 200a can, for example, carry any one of a first module 202, a second module 204, a third module 206, or any combination thereof.

[0059] In one embodiment, the electronic module 200a can carry a first module 202, a second module 204 and / or a third module 206. In a specific example, the electronic module 200a can carry a first module 202, a second module 204 and a third module 206, in accordance with an embodiment of the disclosure.

[0060] In this regard, it is appreciable that, in one embodiment, the casing 200b can be shaped and dimensioned to carry any one of the first module 202, the second module 204 and the third module 206, or any combination thereof.

[0061] The first module 202 can be coupled to one or both of the second module 204 and the third module 206. The second module 204 can be coupled to one or both of the first module 202 and the third module 206. The third module 206 can be coupled to one or both of the first module 202 and the second module 204. In one example, the first module 202 can be coupled to the second module 204 and the second module 204 can be coupled to the third module 206, in accordance with an embodiment of the disclosure. Coupling between the first module 202, the second module 204 and / or the third module 206 can, for example, be by manner of one or both of wired coupling and wireless coupling. Each of the first module 202, the second module 204 and the third module 206 can correspond to one or both of ahardware-based module and a software-based module, according to an embodiment of the disclosure.

[0062] In one example, the first module 202 can correspond to a hardware-based receiver which can be configured to receive one or more input signals. The input signal(s) can, for example, be communicated from the device(s) 104 (e.g., a gNB), in accordance with an embodiment of the disclosure.

[0063] The second module 204 can, for example, correspond to a hardware-based processor which can be configured to perform one or more processing tasks (e.g., in a manner so as to generate one or more output signals) as will be discussed later in further detail with reference to Fig. 3, in accordance with an embodiment of the disclosure.

[0064] The third module 206 can correspond to a hardware-based transmitter which can be configured to communicate one or more output signals from the electronic module 200a. The output signal(s) can, for example, include / correspond to one or more instructions / commands / control signals in association with the aforementioned dynamic / adaptive / gradual control configuration / determination strategy so as to facilitate efficiency (e.g., power / energy efficiency and / or communication efficiency), in accordance with an embodiment of the disclosure.

[0065] The present disclosure contemplates the possibility that the first and second modules 202 / 204 can be an integrated software-hardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and processing functions / an electronic module programmed to perform the functions of receiving and processing). The present disclosure further contemplates the possibility that the first and third modules 202 / 206 can be an integrated softwarehardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and transmitting functions / an electronic module programmed to perform the functions of receiving and transmitting). The present disclosure yet further contemplates the possibility that the first and third modules 202 / 206 can be an integrated hardware module (e.g., ahardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0066] The above-described advantageous aspect(s) of the apparatus 102 of the present disclosure can also apply analogously (all) the aspect(s) of a below described processing / communication method of the present disclosure. Likewise, all below described advantageous aspect(s) of the processing / communication method of the disclosure can also apply analogously (all) the aspect(s) of above described apparatus 102 of the disclosure. It is to be appreciated that these remarks apply analogously to the earlier discussed system 100 of the present disclosure.

[0067] Referring to Fig. 3, a method (also referable to as a processing method) in association with the system 100 is shown, according to an embodiment of the disclosure.

[0068] The method 300 can, for example, be suitable for / capable of facilitating energy efficiency, in accordance with an embodiment of the disclosure.

[0069] The processing method 300 can include any one of an input step 302, a processing step 304 and an output step 306, or any combination thereof, in accordance with an embodiment of the disclosure.

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

[0071] With regard to the input step 302, one or more input signal(s) can be received. For example, the input signal(s) can be communicated from the device(s) 104 and can be received by an apparatus 102, in accordance with an embodiment of the disclosure.

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

[0073] With regard to the processing step 304, at least processing task can be performed in association with the received input signal(s) in a manner so as to generate one or more output signals, in accordance with an embodiment of the disclosure.

[0074] The processing step 304 may include at least one of: determining whether there is radio link failure (RLF); configuring a report if there is RLF; and communicating the report to a first base station, wherein the report comprises a mapping table indicative of mapping between a model identification and a segment identification. The model type data may include an Artificial Intelligence Machine Learning (AIML) model data.

[0075] The processing step 304 may further include determining model segments of the model type data that are transferred; and incorporating the transferred model segments of the model type data into the report, where communicating the report includes communicating via at least one of: Physical Layer (PHY), Medium Access Control (MAC), Packet Data Convergence Protocol (PDCP) and / or Radio Resource Control (RRC) configuration.

[0076] The processing step 304 may also include communicate the report to a second base station; receive the remaining model segments from the second base station; communicate the remaining model segments; receive the report from the first base station; determine remaining model segments that are not transferred based on the report; and communicate 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 a User Equipment (UE) may be configured to perform the input step 302 and the processing step 304, and the model type data is communicable from the gNB to the UE.

[0078] In an embodiment, the UE can trigger an AIML status report and send it to the Secondary Node (SN) when there is Radio Link Failure (RLF). The AIML Status Report may contain the AIML Segment ID, the AIML Model ID and indicate the successfully received AIML model segments. The AIML Status report can be sent through PHY, MAC, PDCP and / or RRC. The SN, upon receiving the AIML Status Report, may forward the AIML Status Report to the Main Node (MN). Thereafter, the MN upon receiving the AIML Status Report from the SN, may forward the remaining AIML segments to the SN. The SN may then transmit the remaining AIML segments to the UE. If there is RLF at the SN, the AIML Status Report may be sent directly to MN where it may forward the remaining AIML segments directly to the UE. Beneficially, the AIML Model can be transferred in the event of RLF without retransferring the complete model. This can help in optimizing the NR resources.

[0079] In an example embodiment as shown in Table 1 below, the status report may include a mapping table indicative of mapping between a segment identification and a model identification. Mapping of the segment ID and the model ID may help the gNB (or base station) and the UE to exactly determine which segment of what model failed to transfer during RLF.Table 1

[0080] With regard to the output step 306, the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the disclosure. For example, the output signal(s) can optionally be communicated from the apparatus 102. In a more specific example, the output signal(s) can optionally be communicated from the apparatus 102 to one or both of at least one device 104 and another apparatus 102, in accordance with an embodiment of the disclosure.

[0081] The present disclosure further contemplates a computer program (not shown) which can include instructions which, when the program is executed by a computer (not shown), cause the computer to carry out the input step 302, the processing step 304 and / or the output step 306 as discussed with reference to the method 300. For example, the computer program can include instructions which, when the program is executed by a computer, cause the computer to carry out the input step 302 and / or the processing step 304, in accordance with an embodiment of the invention.

[0082] The present disclosure yet further contemplates a computer readable storage medium (not shown) having data stored therein representing software executable by a computer (not shown), the software including instructions, when executed by the computer, to carry out the input step 302, the processing step 304 and / or the output step 306 as discussed with reference to the method 300. For example, the computer readable storage medium can have data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, cause the computer to carry out the input step 302 and / or the processing step 304, in accordance with an embodiment of the invention.

[0083] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates an apparatus 102 suitable for energy saving in a networkwhich can include a first module 202, a second module 204 and / or a third module 206.

[0084] The first module 202 can be configured to receive one or more input signals. The input signal(s) can, for example, be associated with a model type data.

[0085] The second module 204 can be configured to process and / or facilitate processing of the input signal(s) according to the method 300 as discussed earlier to generate one or more output signals.

[0086] The third module 206 can be configured to communicate one or more output signals. The output signal(s) can, for example, correspond to one or more control signals for model transfer in a network during radio link failure (RLF).

[0087] In one embodiment, the apparatus 102 can correspond to a User Equipment (UE) which can communicate with a device 104 corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0088] Yet further in view of the foregoing, it is appreciable that the present disclosure generally contemplates a system 100 which can include one or more apparatuses 102 and one or more devices 104. The apparatus(es) 102 and the device(s) 104 can, for example, be capable of being coupled via wired coupling and / or wireless coupling.

[0089] It should be appreciated that the embodiments described above can be combined in any manner as appropriate (e.g., 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).

[0090] It should be further appreciated by the person skilled in the art that variations and combinations of embodiments described above, not being alternatives or substitutes, may be combined to form yet further embodiments.

[0091] In one example, the possibility of the output signal(s) being communicated from the apparatus(es) 102 was discussed. It is appreciable that the output signal(s) need not necessarily be communicated from the apparatus(es) 102. Specifically, the possibility that the output signal(s) need not necessarily be communicated outside of the apparatus(es) 102 is contemplated, in accordance with an embodiment of the invention. More specifically, the output signal(s) can, for example, correspond to internal command(s) / instruction(s) (e.g., communicated only within an apparatus 102) for adaptively controlling operational configuration of an apparatus 102, in accordance with an embodiment of the invention.

[0092] Fig. 4A to Fig. 4D show schematic diagrams illustrating example scenarios in association with the method 300, in accordance with an embodiment of the disclosure.

[0093] In the example context as shown in Fig. 4A, there may be radio link failure (RLF) between the UE and the main node (MN). This may trigger the UE to send an AIML status report with segment ID (ML_n) and 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.

[0094] In the example context as shown in Fig. 4B, the UE (or user device) is configured to download AIML model from the MN or the SN. The UE then determines whether there is RLF. If RLF is present, the UE reports the segment ID and model ID to the SN or the MN. If RLF is not present, then the UE continues with the model transfer.

[0095] In the example context as shown in Fig. 4C, the SN (or gNB or base station) is configured to receive the AIML status report from the UE and forwards it to the MN.The SN is also configured to receive the remaining segments from the MN and forwards it to the UE.

[0096] In the example context as shown in Fig. 4D, the MN (or gNB or base station) is configured to forward the remaining segments to the SN based on the received AIML status report.

[0097] In the foregoing manner, various embodiments of the disclosure are described for addressing at least one of the foregoing disadvantages. Such embodiments are intended to be encompassed by the following claims, and are not to be limited to specific forms or arrangements of parts so described and it will be apparent to one skilled in the art in view of this disclosure that numerous changes and / or modification can be made, which are also intended to be encompassed by the following claims.Abbreviations:AIML Artificial Intelligence Machine LearningBWP Bandwidth partCPU CSI processing unitCRC Cyclic redundancy checkCRI CSI-RS Resource IndicatorCSI Channel state informationCSI-RS Channel state information reference signalCSI-RSRP CSI reference signal received powerCSI-RSRQ CSI reference signal received qualityCSI-SINR CSI signal-to-noise and interference ratioCW CodewordDCI Downlink control informationDL DownlinkDM-RS Demodulation reference signalsDRB Data Radio BearersDRX Discontinuous ReceptionEPRE Energy per resource elementIAB-MT Integrated Access and Backhaul - Mobile TerminalL1-RSRP Layer 1 reference signal received powerLI Layer IndicatorLP-WUR Low power wake up receiverLP-WUS Low power wake up signalMAC Medium Access ControlMCS Modulation and coding schemeMN Main NodeMR Main receiverMR-DC Multi Radio Dual ConnectivityNR New RadioPDCP Packet Data Convergence ProtocolPDCCH Physical Downlink Control ChannelPDSCH Physical downlink shared channelPHY Physical LayerPMI Precoding Matrix Indicator PRB Physical resource block PRG Precoding resource block group PRS Positioning reference signal PSS Primary Synchronisation signal PT-RS Phase-tracking reference signal PUCCH Physical uplink control channel QCL Quasi co-location RB Resource block RBG Resource block group Rl Rank Indicator RIV Resource indicator value RLF Radio Link Failure RRC Radio Resource Control RS Reference signal RSRP Reference Signal Received Power RSRQ Reference Signal Received Quality SCI Sidelink control information SN Secondary Node SLIV Start and length indicator value SR Scheduling Request SRS Sounding reference signal SS Synchronisation signal SS-RSRP SS reference signal received power SS-RSRQ SS reference signal received quality SSS Secondary Synchronisation signal SS-SINR SS signal-to-noise and interference ratio TB Transport Block TCI Transmission Configuration Indicator TDM Time division multiplexing UE User equipment UL Uplink

Claims

Claim(s)1 . A method (300) for model transfer in a network comprising: an input step (302) which comprises receiving at least one input signal associated with a model type data; and a processing step (304) which comprises at least one of: determining whether there is radio link failure (RLF); configuring a report if there is RLF; and communicating the report to a first base station, wherein the report comprises a mapping table indicative of mapping between a model identification and a segment identification.

2. The method (300) according to claim 1 , wherein the model type data comprises an Artificial Intelligence Machine Learning (AIML) model data.

3. The method (300) according to claim 1 , wherein the processing step (304) further comprises: determining model segments of the model type data that are transferred; and incorporating the transferred model segments of the model type data into the report.

4. The method (300) according to claim 1 , wherein communicating the report comprises communicating via at least one of: Physical Layer (PHY), Medium 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: communicate the report to a second base station; receive the remaining model segments from the second base station; and communicate 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; determine remaining model segments that are not transferred based on the report; and communicate the remaining model segments 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) according to claim 7, wherein a User Equipment (UE) is configured to perform the input step (302) and the processing step (304), and wherein the model type data is communicable from the gNB to the UE.

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

10. A computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out at least one of the input step (302) and the processing step (304) according to the method (300) of claims 1-8.

11. An apparatus (102) comprising: a first module (202) configured to receive at least one input signal associated with a model type data; a second module (204) configured to at least one of process and facilitate the processing step (304) according to the method (300) of claim 1 to claim 8 to generate at least one output signal; and a third module (206) configured to communicate at least one output signal, wherein the output signal corresponds to a control signal for model transfer in a network.

12. The apparatus (102) according to claim 11 ,wherein the apparatus (102) corresponds to a User Equipment (UE) communicable with a device (104) corresponding to a base station, and wherein the base station corresponds to a Next generation Node B (gNB) configured to communicate the at least one input signal to the UE.

13. A system (100) comprising: at least one apparatus (102) according to any of claims 11 and 12; and at least one device (104) according to claim 12, wherein the apparatus (102) and the device (104) are capable of being coupled via at least one of wired coupling and wireless coupling.

Images