System and apparatus suitable for facilitating communication and a processing method in association thereto

Abstract

There is provided there is provided a processing method (300) which can include an input step (302), a processing step (304) and an output step (306). The input step (302) can include receiving at least one input signal. The processing step (304) can include providing at least one dataset based on the input signal, for network side dataset collection. The output step (306) can include communicating the dataset

Description

[0001] SYSTEM AND APPARATUS SUITABLE FOR FACILITATING COMMUNICATION AND A PROCESSING METHOD IN ASSOCIATION THERETO

[0002] Field Of Invention

[0003] The present disclosure generally relates to one or both of a system and an apparatus suitable for facilitating communication. The present disclosure further relates a processing / communication method which can be associated with the system and / or the apparatus.

[0004] Background

[0005] Generally, in association with communication / telecommunication networks, User Equipment (UE) measurement(s) can possibly be configured (e.g., by an operator) independently from network configuration during MDT (Minimization of Drive Tests) procedure(s).

[0006] An example of a communication network would be a 3rd Generation Partnership Project (3GPP) 5G (fifth generation) New Radio (NR) standard-based communication / telecommunications network.

[0007] Typically, a UE can report one or more measurement logs during one or more particular event such as radio link failure.

[0008] The present disclosure contemplates that conventional techniques (e.g., by manner of reporting measurement log(s) during particular event(s)) may not facilitate dataset collection required for AI / ML (Artificial Intelligence / Machine Learning) model training in an optimal manner and / or efficient manner.

[0009] 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 dataset collection required for AI / ML model training. Summary of the Invention

[0010] In accordance with an aspect of the disclosure, there is provided a communication / processing method (e.g., referable to as a processing method).

[0011] The processing method can, for example, include an input step, a processing step and an output step, in accordance with an embodiment of the disclosure.

[0012] The input step can include receiving one or more input signals.

[0013] The processing step can include providing / generating one or more datasets based the input signal(s) for network side dataset collection.

[0014] The output step can include communicating the dataset(s).

[0015] In one embodiment, the network side can configure one or more User Equipment (UE) to periodically communicate the dataset(s) for network side dataset collection. The dataset(s) can, for example, be periodically communicated based on of time, distance and / or location. In one example, the dataset(s) can be communicated to the network side periodically in time (e.g., time-based periodicity). In another example, the dataset(s) can be communicated to the network side periodically based on fixed distance intervals (e.g., distance-based periodicity) and / or location co-ordinates. In yet another example, the dataset(s) can be communicated to the network side periodically in time and based on fixed distance intervals (i.e., time-based periodicity and distance-based periodicity) and / or location co-ordinates.

[0016] In one embodiment, periodicity of communication of the dataset(s) can be varied based on network side requirement. Network side requirement can, for example, be associated with network side model configuration. For example, periodicity can be either reduced or increased depending on the number of UEs. Moreover, in one example, periodicity of each UE can be varied in a manner such that the periodicity associated with one UE (i.e., a first from the number of UEs) can be different from the periodicity associated with another UE (i.e., a second from the number of UEs). In another example, periodicity of each UE can be varied in a manner such that the periodicity associated with one UE (i.e., a first UE from the number of UEs) can be the same as the periodicity associated with another UE (i.e., a second UE from the number of UEs).

[0017] In one embodiment, the network side can be configured to provide one or more dedicated uplink resources for communication of the dataset. In one specific example, the network side can be configured to provide at least one dedicated uplink resource based on predetermined timing for communication of the dataset by at least one User Equipment (UE).

[0018] In one embodiment, the network side can be configured to collect one or more datasets on ad-hoc basis. For example, ad-hoc basis can be determined (e.g., by the network side and / or the UE(s)) based on network side addition conditions which can include model convergence, model bias and / or model accuracy etc.

[0019] In one embodiment, the dataset(s) can include location information.

[0020] In one embodiment, an input signal can, for example, correspond to a Radio Resource Control (RRC) configuration message. The RRC configuration message can, for example, be capable of being dynamically adjusted via Layer 1 (L1 ), Layer 2 (L2) and / or Layer 3 (L3) signaling.

[0021] In one embodiment, communication of the dataset(s) can be impeded when a condition of insufficient datapoints to form a dataset for communication is determinable (e.g., by the network side and / or the UE(s)).

[0022] In one embodiment, the network side can be associated with / correspond to / include one or more Next Generation Node B (gNB).

[0023] The present disclosure further contemplates a computer program which can include instructions which, when the program is executed by a computer, cause the computer to carry out the input step, the processing step and / or the output step as discussed with reference to the communication / processing 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.

[0024] The present disclosure yet further contemplates 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 the input step, the processing step and / or the output step as discussed with reference to the communication / processing method. 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 and / or the processing step, in accordance with an embodiment of the disclosure.

[0025] In accordance with an aspect of the disclosure, there is provided an apparatus.

[0026] The apparatus can include a first module, a second module and / or a third module.

[0027] The first module can be configured to receive one or more input signals.

[0028] The second module can be configured to process and / or facilitate processing of the input signal(s) according to the processing method, as discussed earlier, to generate one or more output signals.

[0029] The third module can be configured to communicate the output signal(s).

[0030] In one embodiment, the apparatus can, for example, correspond to a User Equipment (UE) communicable with a device corresponding to, for example, a base station. The base station can, for example, correspond to a Next generation Node B (gNB).

[0031] In accordance with an aspect of the disclosure, there is provided a system. The system can include 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.

[0032] Brief Description of the Drawings

[0033] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which:

[0034] Fig. 1 a shows a system which can include at least one apparatus, according to an embodiment of the disclosure;

[0035] Fig. 1 b to Fig. 1e show an example scenario in association with the system of Fig. 1 a, according to an embodiment of the disclosure;

[0036] Fig, 2 shows the apparatus of Fig, 1a in further detail, according to an embodiment of the disclosure;

[0037] Fig. 3 shows a processing / communication method in association with the system of Fig. 1 a, according to an embodiment of the disclosure; and

[0038] Fig. 4a and Fig. 4b show an example context in association with the processing / communication method of Fig. 3, according to an embodiment of the disclosure.

[0039] Detailed Description

[0040] The present disclosure, as mentioned earlier, generally contemplates that conventional techniques such as the reporting of measurement log(s) during particular event(s)) may not, for example, facilitate AI / ML (Artificial Intelligence / Machine Learning) model training in an optimal manner and / or efficient manner. The present disclosure contemplates the possibility of configuring a UE (User Equipment) to communicate at least one dataset based on, for example, MDT (Minimization of Drive-Tests) procedure(s) at a preconfigured / predetermined interval and / or based on newly configured conditions, in accordance with an embodiment of the disclosure.

[0041] It is contemplated that in the above manner, a UE can provide sufficient data for network sided model, and can possibly facilitate model training (e.g., AI / ML model training) in an optimal manner and / or efficient manner.

[0042] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig. 4 hereinafter.

[0043] 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 facilitating communication, in accordance with an embodiment of the disclosure. In a specific example, the system 100 can, for example, be suitable for facilitating communication of data, in accordance with an embodiment of the disclosure. In a more specific example, the system 100 can, for example, be suitable for facilitating communication of data (e.g., one or more datasets) based on MDT procedure, in accordance with an embodiment of the disclosure. For example, one or more datasets can be communicated based on MDT procedure at a preconfigured interval and / or based on configured condition(s) (e.g., newly configured conditions), in accordance with an embodiment of the disclosure.

[0044] 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. In one embodiment, the system 100 can include, for example, at least one apparatus 102, at least one device 104 and a communication network 106. 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 be configured to perform one or more processing tasks in association with / which can include, for example, providing / generating and / or communicating one or more output signals based on one or more input signals. 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 so as 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. The input signal(s) can, for example, correspond to / be associated with / be indicative of / include one or more control parameters, preconfigured intervals (e.g., periodicity) and / or configured conditions (e.g., network side conditions). 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 based 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 e, hereinafter.

[0050] Further earlier mentioned, UE measurement(s) can possibly be configured (e.g., by an operator) independently from network configuration and a UE can report measurement log(s) at a particular event such as radio link failure. It is contemplated that an operator can, for example, have the possibility of configuring logging in geographical area(s). The measurement(s) can, for example, be linked / associated with information which can facilitate derivation of location information. Moreover, the measurement can, for example, be linked to / be associated with a time stamp. A terminal for measurement(s) can, for example, provide device type information, to facilitate selection of appropriate terminal(s) for specific measurement(s), in accordance with an embodiment of the disclosure. Furthermore, it is contemplated that the MDT can, for example, be operational independently from SON (Self Organizing / Optimizing Network), in accordance with an embodiment of the disclosure.

[0051] Referring to Fig. 1 b to Fig. 1 e, the present disclosure contemplates, in regard to the example scenario, MDT functionalities which can include immediate MDT and logged MDT, in accordance with an embodiment of the disclosure.

[0052] Immediate MDT functionality can, for example, be associated with one or more measurements performed by a UE in connected stated. With regard to Immediate MDT functionality, the state and reporting of the measurement(s) can, for example, be made available at the time of reporting. It is contemplated that Immediate MDT can, for example, be the baseline framework for 0AM (Operation and Maintenance) -centric data collection for the training of a network-sided model.

[0053] Logged MDT can, for example, be associated with a UE in idle mode. With regard to Logged MDT, the measurement(s) can, for example, be logged and reported at a later point in time.

[0054] The present disclosure contemplates that Immediate MDT and logged MDT may be possible ways by which the network can obtain data about a UE. The present disclosure contemplates that, immediate MDT and logged MDT data may not be sufficient or may not facilitate model training (e.g., AI / ML model training) in an optimal and / or efficient manner, as data may only be provided to the network at certain preconfigured condition(s). The present disclosure contemplates the issue of enabling / facilitating data collection based on MDT procedure for the network-sided Model, in accordance with an embodiment of the disclosure.

[0055] Generally, the present disclosure contemplates the possibility that the network can configure one or more UEs to provide / communicate dataset based on MDT procedure at one or more preconfigured interval(s) and / or based on one or more configured conditions (e.g., newly configured conditions), in accordance with an embodiment of the disclosure. It is contemplated that in this manner, a UE can possibly provide sufficient data for network sided model, in accordance with an embodiment of the disclosure.

[0056] Generally, in the example scenario, the present disclosure contemplates the possibility that at least one Next generation Node B (gNB) can be configured to configure at least one UE to provide / generate and / or communicate dataset based on MDT procedure, in accordance with an embodiment of the disclosure.

[0057] In the example scenario, a gNB can be configured to communicate one or more input signals (e.g., the input signal(s) can correspond to / be associated with / include / be indicative of one or more configuration / control signals) to configure a UE to periodically provide one or more datasets (e.g., output signals can correspond to / be associated with / include / be indicative of dataset) with location information for network side dataset collection. The dataset(s) can be communicated by the UE to the gNB in one or both of a time-based manner (e.g., time-based periodicity) and a distance-based manner (e.g., based on a fixed distance periodicity) as, for example, configured / defined / predetermined by the gNB, in accordance with an embodiment of the disclosure. Specifically, the dataset(s) can be communicated by the UE to the gNB by manner of time-based periodicity (e.g., predetermined fixed time intervals) and / or distance-based periodicity (e.g., predetermined fixed distance intervals), in accordance with an embodiment of the disclosure.

[0058] In one example, the input signal(s) communicated by the gNB can correspond to a RRC (Radio Resource Control) message which can include one or more parameters indicative of the periodicity (e.g., time at which the dataset(s) collected should be communicated to the gNB and / or predetermined distance at which dataset(s) collected should be communicated to the gNB). Where a plurality of UEs may be available, each UE can, for example, be possibly configured with a different periodicity (one UE can be configured to communicated at least one dataset at a time-based and / or distance based period which can differ from a time-based and / or distance-based period associated with another UE) based on network requirement of data (i.e., dataset(s)) from UE.

[0059] In another example, a gNB can provide one or more dedicated uplink resources for at least one UE at a configured time to facilitate communication of dataset(s) by the UE(s) to the gNB.

[0060] In yet another example, a gNB can provide additional configuration parameters (e.g., the RRC message can include additional configuration parameters which can be associated with network side additional conditions) to at least one UE which may configure the UE(s) to communicate at least one dataset (e.g., on ad-hoc or required basis instead of periodically). Examples of network side additional conditions may include any one of model convergence, model bias, model accuracy, or any combination thereof.

[0061] In a further example, the present disclosure contemplates that a UE may skip / decline / refrain from communicating dataset if datapoints are determined (e.g., by the UE and / or a gNB) to be insufficient. For example, at a periodic interval (i.e., periodicity) where a UE is supposed to communicate a dataset, the UE may be configured to impede communication of dataset if it is determinable (e.g., by the UE and / or the gNB) that there are insufficient datapoints to communicate an appropriate / sensible / usable / useful dataset to a gNB.

[0062] In yet a further additional example, a gNB can configure at least one UE based network side model configuration. For example, periodicity can be varied based on number of UEs. In one specific example, a network may have a smaller periodicity for models which require data from smaller number of UEs. In another specific example, a network may have a larger periodicity for models which require data from a larger number of UEs. At least one gNB may possibly provide the configuration (e.g., network side model configuration) using RRC Reconfiguration message and / or may dynamically adjust the configuration via L1 / L2 / L3 (Layer 1 / Layer 2 / Layer 3) signaling, in accordance with an embodiment of the disclosure.

[0063] In the above example scenario, a network can appreciably, for example, be based on one or more gNBs, in accordance with an embodiment of the disclosure.

[0064] In the above manner, sufficient data can be provided for network sided model, and model training (e.g., AI / ML model training) can be possibly facilitated in an optimal manner and / or efficient manner.

[0065] In this regard, it is appreciable that a UE (e.g., an apparatus 102) can, for example, be further configured to process the input signal(s) (e.g., communicated from a device 104), as will be discussed later in further detail with reference to Fig. 3, in a manner so as to generate one or more output signals in a manner so as to facilitate model training (e.g., AI / ML model training) in an optimal manner and / or efficient manner, in accordance with an embodiment of the disclosure.

[0066] 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.

[0067] The aforementioned apparatus(es) 102 will be discussed in further detail with reference to Fig. 2 hereinafter.

[0068] 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. 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 be installed / 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).

[0069] It is contemplated that the electronic module 200a can be capable of performing one or more processing tasks in association with, for example, triggered processing, in accordance with an embodiment of the disclosure. Triggered processing can, for example, refer to a network (e.g., which can be based on / associated with / include / correspond to one or more gNBs) configuring at least one UE to provide at least one dataset based on MDT procedure at, for example, one or more preconfigured intervals and / or based on one or more configured condition(s), in accordance with an embodiment of the disclosure, as discussed earlier with reference to the earlier example scenario.

[0070] 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.

[0071] 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.

[0072] 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. 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 a hardware-based module and a software-based module, according to an embodiment of the disclosure.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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 software-hardware 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., a hardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0077] In this regard, it is appreciable that the electronic module 200a can, for example, be further configured to process the input signal(s) (e.g., communicated from a device 104), as will be discussed later in further detail with reference to Fig. 3, in a manner so as to generate one or more output signals in a manner so as to facilitate model training (e.g., AI / ML model training) in an optimal manner and / or efficient manner, in accordance with an embodiment of the disclosure, in accordance with an embodiment of the disclosure.

[0078] Specifically, in the above manner, sufficient data can be provided for network sided model, and model training (e.g., AI / ML model training) can be possibly facilitated in an optimal manner and / or efficient manner.

[0079] 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.

[0080] Referring to Fig. 3, a communication method (also referable to as a processing method) in association with the system 100 is shown, according to an embodiment of the disclosure.

[0081] The processing method 300 can, for example, be suitable for / capable of facilitating communication, in accordance with an embodiment of the disclosure. Specifically, the processing method 300 can, for example, be suitable for facilitating communication in one or both of an efficient manner and reliable manner (i.e., efficient manner and / or reliable manner), in accordance with an embodiment of the disclosure. In a more specific example, the processing method 300 can, for example, be suitable for facilitating communication of data (e.g., one or more datasets) based on MDT procedure, in accordance with an embodiment of the disclosure. For example, one or more datasets can be communicated based on MDT procedure at a preconfigured interval and / or based on configured condition(s) (e.g., newly configured conditions), in accordance with an embodiment of the disclosure.

[0082] 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.

[0083] 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).

[0084] With regard to the input step 302, one or more input signal(s) can, for example, be generated and / or received. For example, the input signal(s) can be generated by the device(s) 104. In a further example, the input signal(s) can be communicated by the device(s) 104 and can be received by an apparatus 102, in accordance with an embodiment of the disclosure. The input signal(s) can, for example, include / correspond to / be associated with / be indicative of at least one triggering event. A triggering event can be associated with the aforementioned triggered processing which can, for example, refer to a network (e.g., based on one or more gNBs) configuring at least one UE to provide dataset based on MDT procedure at, for example, one or more preconfigured intervals and / or based on one or more configured condition(s), in accordance with an embodiment of the disclosure, as discussed earlier with reference to the earlier example scenario. In one specific example, as discussed with reference to the example implementation 200, the input signal(s) can be received by the first module 202 (e.g., a receiver), in accordance with an embodiment of the disclosure.

[0085] 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. The processing task(s) can, for example, include identifying one or more parameters associated with the input signal(s) which can, for example, correspond to / be associated with / include / be indicative of one or more configuration / control signals, in accordance with an embodiment of the disclosure. The configuration / control signals can, for example, include / correspond to / be associated with / be associated with one or more parameters which can be indicative of one or more triggering events. The triggering event(s) can, for example, be time-based, distance-based, capacity-based and / or ad-hoc based. For example, as discussed earlier with reference to the example scenario:

[0086] • one or more datasets can be communicated periodically in time (i.e., timebased trigger),

[0087] • one or more datasets can be communicated periodically based on fixed / predetermined distance(s) (i.e., distance-based trigger)

[0088] • one or more datasets can be communicated at one or more specific instance(s) (e.g., which can be time-based and / or distance-based) based on network side additional condition(s) (i.e., ad-hoc based trigger)

[0089] • one or more datasets can be communicated based on network side model configuration where periodicity can be varied based on number of UEs (i.e., capacity-based trigger). For example, a network may have a smaller periodicity for models which require data from smaller number of UEs / a network may have a larger periodicity for models which require data from a larger number of UEs. With regard to the output step 306, the output signal(s) can, for example, be communicated, in accordance with an embodiment of the disclosure. For example, the output signal(s) can be communicated from the apparatus 102. In a more specific example, the output signal(s) can 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. In one specific example, as discussed with reference to the example implementation 200, the output signal(s) can be communicated by the third module 206 (e.g., a transmitter), in accordance with an embodiment of the disclosure.

[0090] In the above manner, the present disclosure contemplates that sufficient data can be provided for network sided model, and model training (e.g., AI / ML model training) can be possibly facilitated in an optimal manner and / or efficient manner.

[0091] Fig. 4a and Fig. 4b shows an example context in association with the processing method 300, in accordance with an embodiment of the disclosure.

[0092] In the example context as shown in Fig. 4a, a gNB can, for example, be configured to one or both of:

[0093] • configure the periodicity for dataset collection (e.g., from at least one UE).

[0094] • provide network side additional conditions (e.g., to at least on UE) for, for example, dataset collection.

[0095] In this regard, one or more input signals can be communicated from a gNB and the input signal(s) can, for example, include / be indicative of / correspond to / be associated with periodicity and / or network side additional conditions, in accordance with an embodiment of the disclosure.

[0096] In the example context as shown in Fig. 4b, a UE can be configured to receive the input signal(s) and communicate at least one dataset based on configured periodicity and / or based on network side additional conditions. In one embodiment, a UE can be configured to receive the input signal(s) and communicate at least one dataset based on configured periodicity. In another embodiment, a UE can be configured to receive the input signal(s) and communicate at least one dataset based on network side additional conditions. In yet another embodiment, a UE can be configured to receive the input signal(s) and communicate at least one dataset based on configured periodicity and network side additional conditions.

[0097] In view of the foregoing, the present disclosure generally contemplates a processing method 300 which can, for example, include an input step 302, a processing step 304 and an output step (306), in accordance with an embodiment of the disclosure.

[0098] The input step (302) can include receiving one or more input signals.

[0099] The processing step (304) can include providing / generating one or more datasets based the input signal(s) for network side dataset collection.

[0100] The output step (306) can include communicating the dataset(s).

[0101] In one embodiment, the network side can configure one or more User Equipment (UE) to periodically communicate the dataset(s) for network side dataset collection. The dataset(s) can, for example, be periodically communicated based on one or both of time and distance (i.e. , time and / or distance). In one example, the dataset(s) can be communicated to the network side periodically in time (e.g., time-based periodicity). In another example, the dataset(s) can be communicated to the network side periodically based on fixed distance intervals (e.g., distance-based periodicity). In yet another example, the dataset(s) can be communicated to the network side periodically in time and based on fixed distance intervals (i.e., time-based periodicity and distancebased periodicity).

[0102] In one embodiment, periodicity of communication of the dataset(s) can be varied based on network side requirement. Network side requirement can, for example, be associated with network side model configuration. For example, periodicity can be either reduced or increased depending on the number of UEs. In specific example, based on network side model configuration(s), a network may have a smaller periodicity for models which require data from smaller number of UEs / a network may have a larger periodicity for models which require data from a larger number of UEs.

[0103] Moreover, in one example, periodicity of each UE can be varied in a manner such that the periodicity associated with one UE (i.e., a first from the number of UEs) can be different from the periodicity associated with another UE (i.e., a second from the number of UEs). In another example, periodicity of each UE can be varied in a manner such that the periodicity associated with one UE (i.e., a first UE from the number of UEs) can be the same as the periodicity associated with another UE (i.e., a second UE from the number of UEs).

[0104] In one embodiment, the network side can be configured to provide one or more dedicated uplink resources for communication of the dataset. In one specific example, the network side can be configured to provide at least one dedicated uplink resource based on predetermined timing for communication of the dataset by at least one User Equipment (UE).

[0105] In one embodiment, the network side can be configured to collect one or more datasets on ad-hoc basis. For example, ad-hoc basis can be determined (e.g., by the network side and / or the UE(s)) based on network side addition conditions which can include model convergence, model bias and / or model accuracy etc.

[0106] In one embodiment, the dataset(s) can include location information.

[0107] In one embodiment, an input signal can, for example, correspond to a Radio Resource Control (RRC) configuration message. The RRC configuration message can, for example, be capable of being dynamically adjusted via Layer 1 (L1 ), Layer 2 (L2) and / or Layer 3 (L3) signaling.

[0108] In one embodiment, communication of the dataset(s) can be impeded when a condition of insufficient datapoints to form a dataset for communication is determinable (e.g., by the network side and / or the UE(s)). In one embodiment, the network side can be associated with / correspond to / include one or more Next Generation Node B (gNB).

[0109] 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 communication / processing 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 disclosure.

[0110] 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 communication / processing 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 disclosure.

[0111] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates an apparatus 102 which can include a first module 202, a second module 204 and / or a third module 206.

[0112] The first module 202 can be configured to receive one or more input signals.

[0113] The second module 204 can be configured to process and / or facilitate processing of the input signal(s) according to the processing method 300, as discussed earlier, to generate one or more output signals.

[0114] The third module 206 can be configured to communicate the output signal(s). In one embodiment, the apparatus 102 can, for example, correspond to a User Equipment (UE) communicable with a device 104 corresponding to, for example, a base station. The base station can, for example, correspond to a Next generation Node B (gNB).

[0115] 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.

[0116] 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).

[0117] 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.

[0118] In one example, as mentioned earlier, communication of dataset can be impeded in the event of insufficient datapoints for forming an appropriate / useful dataset. It is appreciable that where desired, communication of dataset can optionally be possible (if desired) even in the event of insufficient datapoints, in accordance with an embodiment of the disclosure.

[0119] In another example, as mentioned earlier, where a plurality of UEs may be available, each UE can, for example, be possibly configured with a different periodicity (one UE can be configured to communicated at least one dataset at a time and / or distance periodicity which can differ from a time and / or distance periodicity associated with another UE) based on network requirement of data (i.e., dataset(s)) from UE. It is contemplated that each UE can, for example, be possibly configured with the same / substantially the same periodicity (one UE can be configured to communicated at least one dataset at a time and / or distance periodicity which can be the same as a time and / or distance periodicity associated with another UE) based on network requirement of data.

[0120] In yet another example, as mentioned earlier, the dataset(s) can be periodically communicated based on time and / or distance. The present disclosure contemplates that other basis for periodic communication can also be possible. For example, the dataset(s) can be periodically communicated based on location, in accordance with an embodiment of the disclosure. In this regard, the dataset(s) can, for example, be periodically communicated based on time, distance and / or location, in accordance with an embodiment of the disclosure.

[0121] In yet a further example, ad-hoc based communication / collection of dataset(s) can, for example, be based on location, in accordance with an embodiment of the disclosure. In this regard, it is appreciable that the aforementioned ad-hoc based trigger can, for example, be time-based, distance-based and / or location-based, in accordance with an embodiment of the disclosure.

[0122] 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.

Claims

Claim(s)1 . A processing method (300) comprising: an input step (302) which comprises receiving at least one input signal; a processing step (304) which comprises providing at least one dataset based on the input signal, for network side dataset collection; and an output step (306) which comprises communicating the dataset.

2. The processing method (300) as in claim 1 , wherein the network side is capable of configuring at least one User Equipment (UE) to periodically communicate dataset for network side dataset collection.

3. The processing method (300) as in claim 2, dataset being periodically communicated based on at least one of time, distance and location.

4. The processing method (300) as in claim 3, the dataset being communicable to the network side periodically in time.

5. The processing method (300) as in claim 3, the dataset being communicable to the network side periodically based on fixed distance intervals.

6. The processing method (300) as in claim 3, the dataset being communicable to the network side periodically in time and based on fixed distance intervals and / or location.

7. The processing method (300) as in claim 2, periodicity of communication of dataset being variable based on network side requirement.

8. The processing method (300) as in claim 7, network side requirement being associated with network side model configuration.

9. The processing method (300) as in claim 7, wherein periodicity capable of being one of reduced and increased based on number of UEs.

10. The processing method (300) as in claim 9, periodicity of each UE capable of being varied in a manner such that the periodicity associated with one UE from the number of UEs is different from the periodicity associated with another UE from the number of UEs.11 . The processing method (300) as in claim 9, periodicity of each UE capable of being varied in a manner such that the periodicity associated with one UE from the number of UEs is the same as the periodicity associated with another UE from the number of UEs.

12. The processing method (300) as in claim 1 , the network side capable of being configured to provide at least one dedicated uplink resource for communication of the dataset.

13. The processing method (300) as in claim 12, the network side capable of being configured to provide at least one dedicated uplink resource based on predetermined timing for communication of the dataset by at least one User Equipment (UE).

14. The processing method (300) as in claim 1 , wherein the network side is capable of being configured to collect at least one dataset on ad-hoc basis.

15. The processing method (300) as in claim 14, wherein ad-hoc basis being determinable based on network side addition conditions comprising at least one of: model convergence, model bias and model accuracy.

16. The processing method (300) as in claim 1 , wherein the dataset comprises location information.

17. The processing method (300) as in claim 1 , the input signal corresponding to Radio Resource Control (RRC) configuration message.

18. The processing method (300) as in claim 17, wherein the RRC configuration message capable of being dynamically adjusted via at least one of Layer 1 (L1 ), Layer 2 (L2) and Layer 3 (L3) signaling.

19. The processing method (300) as in claim 1 , wherein communication of dataset being impeded when a condition of insufficient datapoints to form a dataset for communication is determinable.

20. The processing method (300) as in claim 1 , wherein the network side is associated with at least one Next Generation Node B (gNB).

21. 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, the processing step and the output step according to the processing method of any of the preceding claims.

22. 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, the processing step and the output step according to the processing method of any of the preceding claims.

23. An apparatus (102) comprising: a first module (202) configurable to receive at least one input signal; a second module (204) configurable to at least one of process and facilitate processing of the input signal according to the processing method (300) of claim 1 to 20 to generate at least one output signal; and a third module (206) configurable to communicate at least one output signal.

24. The apparatus (102) as in claim 23, wherein the apparatus (102) corresponds to a User Equipment (UE) communicable with a device (104) corresponding to a base station, wherein the base station corresponds to a Next generation Node B (gNB).

125. A system comprising: at least one apparatus (102) according to any of claims 23 and 24; and at least one device (104) according to any of claims 23 and 24, wherein the apparatus (102) and the device (104) are capable of being coupled via at least one of wired coupling and wireless coupling.

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