System and apparatus for predicting measurement in a network and a method in association thereto

Abstract

System (100), apparatus (102) and a method (300) for predicting measurement in a network are disclosed. The method (300) comprises an input step (302) which comprises receiving at least one input signal associated with a signal threshold value; and a processing step (304) which comprises at least one of: determining whether a current signal value is more or less than the signal threshold value; and initiating a measurement technique for predicting measurement based on the determination; wherein a cell level radio resource management (RRM) measurement technique is initiated if the current signal value is less than the signal threshold value; and wherein a beam level RRM measurement technique is initiated if the current signal value is more than the signal threshold value.

Description

SYSTEM AND APPARATUS FOR PREDICTING MEASUREMENT 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 predicting measurement 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 of Invention

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

[0003] Current techniques may not address the issue of enabling a User Equipment (UE) to select an Artificial Intelligence / Machine Learning (AI / ML) model for radio resource management (RRM) measurement prediction. Specifically, enabling both cell level RRM Measurement and Beam level RRM Measurement prediction models can be difficult for the UE to know when to use beam cell level RRM Measurement and when to use Beam level RRM Measurement. Thus, the current techniques may not facilitate energy efficiency and power saving 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 power saving when predicting measurement (for example RRM measurement) in a network.Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a method for predicting measurement in a network, the method comprising: an input step which comprises receiving at least one input signal associated with a signal threshold value; and a processing step which comprises at least one of: determining whether a current signal value is more or less than the signal threshold value; and initiating a measurement technique for predicting measurement based on the determination; wherein a cell level radio resource management (RRM) measurement technique is initiated if the current signal value is less than the signal threshold value; and wherein a beam level RRM measurement technique is initiated if the current signal value is more than the signal threshold value.

[0006] Advantageously, the method as described herein can allow a user equipment (UE) or a user device to know which radio resource management (RRM) measurement is to be performed for the best prediction of RRM Measurement result. In addition, fundamental mechanisms of interworking and data information flow in radio access network collaboration for AI / ML support can also be realized.

[0007] In an embodiment, the processing step further comprises configuring the signal threshold value; and communicating the signal threshold value to a user device.

[0008] In an embodiment, communicating the signal threshold value to the user device comprises communicating via system information message and / or User Equipment (UE) specific message.

[0009] In an embodiment, the signal threshold value and the current signal value correspond to at least one value associable with Reference Signal Received Power (RSRP).

[0010] In an embodiment, each of the cell level radio RRM measurement technique and the beam level RRM measurement technique comprises an Artificial Intelligence / Machine Learning (AI / ML) model.

[0011] In an embodiment, there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first aspect.

[0012] 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 the method of the first aspect.

[0013] In an embodiment, there is provided an apparatus for predicting measurement in a network comprising: a first module configured to receive at least one input signal associated with a signal threshold value; 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 predicting measurement in a network.

[0014] In an embodiment, the apparatus corresponds to a User Equipment (UE) communicable with a device 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.

[0015] In an embodiment, there is provided a system comprising: at least one apparatus(es); and at least one device(s), wherein the apparatus(es) and the device(s) are capable of being coupled via at least one of wired coupling and wireless coupling.

[0016] Advantageously, the system as described herein can provide the UE (or user device) to perform the appropriate RRM measurement so as to obtain a best prediction of the RRM measurement result. Accordingly, the cell (or base station orgNB) quality can be accurately determined. Thus, the proposed gNB-UE collaboration operation for AI / ML support can improve the AI / ML performance for wireless communication.Brief Description of the Drawings

[0017] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which:

[0018] Fig. 1A shows a schematic diagram illustrating a system for predicting measurement in a network which can include at least one apparatus, according to an embodiment of the invention.

[0019] Figs. 1 B shows an example scenario in association with the system of Fig.1 A, according to an embodiment of the invention.

[0020] Fig. 2 shows a schematic diagram illustrating the apparatus of Fig. 1A in further detail, according to an embodiment of the invention.

[0021] Fig. 3 shows a method in association with the system of Fig. 1A, according to an embodiment of the invention.

[0022] Fig. 4A to Fig. 4B show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.Detailed Description

[0023] 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 ofproviding 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.

[0024] The present specification discloses apparatus and / or device for performing the operations of the methods. Such apparatus and / or device may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a computer will appear from the description below.

[0025] In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the disclosure.

[0026] Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readablemedium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the mobile telephone system. The computer program when loaded and executed on such a computer effectively results in an apparatus and / or a device that implements the steps of the preferred method.

[0027] 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.

[0028] 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.

[0029] Additionally, terminologies such as base station / gNodeB and UE should be considered non-limiting and do in particular not imply a certain hierarchical relationbetween 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.

[0030] 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.

[0031] The present disclosure generally contemplates the facilitation and optimization of a network (for example in association with 3GPP based standard / specification etc.) and / or user equipment (UE) efficiency (for example energy efficiency or power saving), in accordance with an embodiment of the invention. Specifically, the present disclosure contemplates the possibility ofpredicting measurement by a user device (or UE) to determine a cell (or base station or gNB) quality in connection with 3GPP standard(s).

[0032] The present disclosure generally contemplates that Artificial Intelligence / Machine Learning (AI / ML) can be an important technology in 3GPP, specifically Rel-19 in RAN2, and AI / ML can facilitate network optimization and energy efficient networks.

[0033] The present disclosure further contemplates that a cell or a base station (e.g. a Next generation Node B gNB) can indicate to the UE (or user device) whether cell level radio resource management (RRM) Measurement or Beam level RRM Measurement are to be used for RRM Measurement prediction. The present disclosure thus contemplates that by enabling both cell level RRM Measurement and Beam level RRM Measurement prediction models, it can be difficult for the UE (or user device) to know when to use beam cell level RRM Measurement and when to use Beam level RRM Measurement.

[0034] The present disclosure also contemplates that the cell (or base station or gNB) can indicate to the UE (or user device) a signal threshold value so that the UE (or user device) may determine whether cell level RRM Measurement or Beam level RRM Measurement are to be used for RRM Measurement prediction.

[0035] In the above manner, the UE (or user device) can effectively select a model (e.g. AI / ML model) for RRM Measurement prediction as the UE (or user device) may know which RRM measurement is to be performed for the best prediction of the RRM Measurement result. Power saving and energy consumption efficiency can possibly be facilitated in the network, in accordance with an embodiment of the invention.

[0036] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig. 4 hereinafter.

[0037] Referring to Fig. 1A, a schematic diagram illustrating a system 100 for predicting measurement in a network is shown, according to an embodiment of the invention. The system 100 can, for example, be suitable for facilitating energy and improve power efficiency, in accordance with an embodiment of the invention.

[0038] 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 invention.

[0039] 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 invention.

[0040] 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 invention.

[0041] The apparatus(es) 102 can, for example, be associated with or correspond to or include one or more user equipment (UE) which can carry one or more computers, in accordance with an embodiment of the invention. For example, an apparatus 102 can correspond to a UE (or user device) carrying at least one computer (e.g. an electronic device or 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 invention) which can be configured to perform one or more processing tasks in association with the UE (or user device), in accordance with an embodiment of the invention.

[0042] In an 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 generated by the device(s) 104 and communicated from the device(s) 104 and received by the apparatus(es) 102, in accordance with an embodiment of the invention. In an alternate embodiment, the input signal may be generated from a separate apparatus(es) 102. The input signal can be a signal associated with a signal threshold value, for example Reference Signal Received Power (RSRP). 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 invention. The output signal may correspond to a control signal for predicting measurement by the user device (or UE) to determine the cell quality. The apparatus(es) 102 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the invention.

[0043] The device(s) 104 can, for example, be associated with / correspond to at least one base station, where the at least one base station can be a Next Generation Node B (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 invention. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the invention.

[0044] 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 communicationnetwork 106 can be by manner of one or both of wired communication and wireless communication.

[0045] As 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 invention. In an alternate embodiment, a separate apparatus(es) 102 may be configured to generate (and communicate) the input signal(s). Accordingly, the apparatus(es) 102 or device(s) 104 can configure or pre-determine a signal threshold value and communicate or transmit at least one input signal associated with the signal threshold value to the apparatus(es) 102. This will be discussed, in accordance with an embodiment of the invention, in the context of an example scenario with reference to Fig. 1 B, hereinafter.

[0046] Fig. 1 B shows an example scenario in association with the system of Fig. 1 A, according to an embodiment of the invention. Specifically, Fig. 1 B shows an example embodiment of a measurement model for determining a cell (or base station or gNB) quality. As shown in the Figure, a User Equipment UE (or a user device) can average the measurement results of one or more beams to derive the cell (or base station or gNB) quality. Specifically, beam level measurements may be obtained to determine or derive the cell quality of a cell (or base station or gNB).

[0047] In an embodiment, the UE (or user device) can predict beam level measurements in cell level measurements. Together with cell level measurements, the UE (or user device) can predict cell level measurements. In a further embodiment, cell level measurement for Frequency Range 1 (FR1 ) can be considered for prediction as the number of beams in FR1 are limited. By considering both the models to predict cell level and beam level measurements by the UE (or user device), it may involve a complex model such as an ArtificialIntelligence / Machine Learning (AI / ML) model. The present disclosure contemplates that this can lead to an increase in computational resources and may also require more power to process both the beam level and cell level inputs for prediction.

[0048] In an example embodiment, a cell level measurement prediction model can have the following methods, either alone or in combination. A first method can involve predicting beam level results and subsequently generating cell level results based on the predicted beam results. A second method may involve directly predicting cell level results based on cell level results. A third method can involve directly predicting cell level results based on beam level results. It may not be efficient for the UE (or user device), from an energy savings perspective, to have all methods work in parallel as the AI / ML model to predict beam level and cell level measurements can be different.

[0049] The above-described aspect(s) of the system 100 of the present invention can also apply analogously (all) the aspect(s) of a below described apparatus 102 of the present invention. Likewise, all below described aspect(s) of the apparatus 102 of the invention can also apply analogously (all) the aspect(s) of above-described system 100 of the invention.

[0050] The aforementioned apparatus(es) 102 (or UE or user device) will be discussed in further detail with reference to Fig. 2 hereinafter.

[0051] Referring to Fig. 2, a schematic diagram illustrating an apparatus 102 is shown in further detail in the context of an example implementation 200, according to an embodiment of the invention.

[0052] 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 invention. 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 invention. 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).

[0053] 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 invention.

[0054] 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.

[0055] 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 invention.

[0056] 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.

[0057] 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 invention. 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, thesecond 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 invention.

[0058] 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 (or base station e.g., a gNB), in accordance with an embodiment of the invention.

[0059] 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 invention.

[0060] 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 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 invention. For example, the output signal(s) can be a control signal(s) for predicting measurement by the user device (or UE) to determine a cell (or base station or gNB) quality.

[0061] The present disclosure contemplates the possibility that the first and second modules 202, 204 can be an integrated software-hardware based module, for example, an electronic part which can carry a software program or algorithm in association with receiving and processing functions or 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, for example anelectronic part which can carry a software program or algorithm in association with receiving and transmitting functions or 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, for example a hardware-based transceiver, capable of performing the functions of receiving and transmitting.

[0062] The UE (or user device) can, for example, be further configured to process the input signal(s), 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 efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention. In one specific example, the output signal(s) can include one or more control signals to facilitate some form of dynamic / adaptive / gradual control configuration / determination strategy so as to facilitate efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention. For example, the output signal(s) can be a control signal(s) to predict measurement in a network.

[0063] The above-described aspect(s) of the apparatus 102 of the present invention can also apply analogously (all) the aspect(s) of a below described processing / communication method of the present invention. Likewise, all below described aspect(s) of the method of the invention can also apply analogously (all) the aspect(s) of above described apparatus 102 of the invention. It is to be appreciated that these remarks apply analogously to the earlier discussed system 100 of the present disclosure.

[0064] Referring to Fig. 3, a method 300 (or a communication method) for predicting measurement in a network in association with the system 100 is shown, according to an embodiment of the invention.

[0065] The method 300 can, for example, be suitable for facilitating energy efficiency, network optimization and power saving in accordance with an embodiment of the invention.

[0066] The 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 invention.

[0067] In an 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).

[0068] 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 invention.

[0069] The input step 302 can include receiving at least one input signal associated with a signal threshold value.

[0070] With regard to the processing step 304, at least a processing task can be performed in association with the received input signal(s) in a manner so as togenerate one or more output signals, in accordance with an embodiment of the invention.

[0071] The processing step 304 may include at least one of: determining whether a current signal value is more or less than the signal threshold value; and initiating a measurement technique for predicting measurement based on the determination; wherein a cell level radio resource management (RRM) measurement technique is initiated if the current signal value is less than the signal threshold value; and wherein a beam level RRM measurement technique is initiated if the current signal value is more than the signal threshold value. The signal threshold value and the current signal value can correspond to at least one value associable with Reference Signal Received Power (RSRP).

[0072] The processing step 304 may further include configuring the signal threshold value; and communicating the signal threshold value to a user device. Communicating the signal threshold value to the user device may include communicating via system information message and / or User Equipment (UE) specific message. Each of the cell level radio RRM measurement technique and the beam level RRM measurement technique may include an Artificial Intelligence / Machine Learning (AI / ML) model.

[0073] In an embodiment, the UE (or user device) may determine whether to report cell level measurement or beam level measurement based on the signal threshold value, e.g. RSRP threshold, where the RSRP threshold may be configured by the network. The UE (or user device) may receive the signal threshold (e.g. RSRP threshold) value through system information message and / or UE specific message (e.g. L1 / L2 signaling). If the current UE measurement is below the configured signal threshold, the UE (or user device) may then perform cell level measurement taking cell measurement as input for the model (e.g. AI / ML model). On the other hand, if the current UE measurement is above the configured signal threshold, the UE (or user device) may then perform beam level measurement taking beam level measurement as input for the model (e.g. AI / ML model). In this way, the UE (or userdevice) may know which RRM measurement is to be performed for the best prediction of the RRM Measurement result.

[0074] With regards to the output step 306, the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the invention. 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 invention. In an embodiment, the apparatus 102 (or UE) may also perform the input step 302, the processing step 304 and the output step 306.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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 signal threshold value.

[0079] 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.

[0080] 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 predicting measurement by the user device (or UE) to determine a cell (or base station or gNB) quality.

[0081] 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., output signal(s)) to the UE.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] Fig. 4A to Fig. 4B show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.

[0087] In the example context as shown in Fig. 4A, a gNB (or cell or base station) can, for example, be configured to provide a configuration of RSRP threshold value to the User Equipment UE (or user device) whether to use cell level radio resource management (RRM) measurement or beam level RRM measurement, in accordance with an embodiment of the invention. The threshold value may be communicated to the UE (or user device) via system information message and / or UE specific message (e.g. L1 / L2 signaling).

[0088] In the example context as shown in Fig. 4B, the User Equipment UE (or user device) can, for example, be configured to determine if the current signal value (e.g. RSRP value) is above the received RSRP threshold value. If the current signal value is above the threshold value, the UE (or user device) performs beam levelmeasurement. On the other hand, if the current signal value is below the threshold value, the UE (or user device) performs cell level measurement.

[0089] 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

CLAIMS1. A method (300) for predicting measurement in a network, the method comprising: an input step (302) which comprises receiving at least one input signal associated with a signal threshold value; and a processing step (304) which comprises at least one of: determining whether a current signal value is more or less than the signal threshold value; and initiating a measurement technique for predicting measurement based on the determination; wherein a cell level radio resource management (RRM) measurement technique is initiated if the current signal value is less than the signal threshold value; and wherein a beam level RRM measurement technique is initiated if the current signal value is more than the signal threshold value.

2. The method (300) according to claim 1 , wherein the processing step (304) further comprises: configuring the signal threshold value; and communicating the signal threshold value to a user device.

3. The method (300) according to claim 2, wherein communicating the signal threshold value to the user device comprises communicating via system information message and / or User Equipment (UE) specific message.

4. The method (300) according to claim 1 , wherein the signal threshold value and the current signal value correspond to at least one value associable with Reference Signal Received Power (RSRP).

5. The method (300) according to claim 1 , wherein each of the cell level radio RRM measurement technique and the beam level RRM measurement technique comprises an Artificial Intelligence / Machine Learning (AI / ML) model.

6. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method (300) according to any of the preceding claims.

7. 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 method (300) according to any one of claims 1 -5.

8. An apparatus (102) for predicting measurement in a network comprising: a first module (202) configured to receive at least one input signal associated with a signal threshold value; 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 5 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 predicting measurement in a network.

9. The apparatus (102) according to claim 8, 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.

10. A system (100) comprising: at least one apparatus (102) according to any of claims 8 and 9; andat least one device (104) according to claim 9, 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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