System and apparatus suitable for handover optimization and a processing method in association thereto

Abstract

There is provided a processing method (300) which can include an input step (302) and a processing step (304). The input step (302) can include receiving at least one input signal corresponding to at least one configured event. The processing step (304) can include performing one or more processing tasks which can include performing at least one measurement in association with the configured event and / or predicting trajectory based on the performed measurement in association with the configured event. At least one output signal can be generated based on the processing task(s).

Description

[0001] SYSTEM AND APPARATUS SUITABLE FOR HANDOVER OPTIMIZATION 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 Handover optimization in association with, for example, a User Equipment (UE) usable for 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, Handover (HO) may be necessary / required in communication networks. An example of a communication network would be a 3rd Generation Partnership Project (3GPP) 5G (fifth generation) New Radio (NR) standard-based telecommunications network.

[0006] For example, in 5G-NR Release-16, 3GPP a Conditional Handover (CHO) feature was introduced. The CHO feature can be helpful for allowing a User Equipment (UE) to decide whether HO should be / ought to be performed when certain conditions are met.

[0007] Generally, a CHO can be defined as a HO that is executed by the UE when one or more HO execution conditions are met.

[0008] However, conventional technique(s) in relation to CHO, as discussed above, may not be optimized.

[0009] The present disclosure contemplates that it would be helpful to address (or at least mitigate) one or more issues in relation to conventional technique(s) so as to facilitate optimization of Handover. 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 and a processing step, in accordance with an embodiment of the disclosure.

[0012] The input step can include receiving one or more input signals. The input signal(s) can, for example, correspond to one or more configured events.

[0013] The processing step can include performing one or more processing tasks. The processing task(s) can include at least one of:

[0014] • performing one or more measurements in association with the configured event(s)

[0015] • predicting trajectory based on the performed measurement(s) in association with the configured event

[0016] Specifically, the processing task(s) can include performing at least one measurement in association with the configured event(s) and / or predicting trajectory based on the performed measurement(s) in association with the configured event.

[0017] In one embodiment, one or more output signals can be generated based on the processing task(s).

[0018] In one embodiment, the processing method can, for example, further include an output step. The generated output signal(s) can be communicated. The output signal can, for example, be capable of being processed (e.g., received and processed by a device) for predicting / determining optimal handover cell, in accordance with an embodiment of the disclosure.

[0019] In one embodiment, the generated output signal(s) being communicable can include one or both of predicted trajectory and measurement result of the measurement(s) in association with the configured event(s) (i.e., at least one of predicted trajectory and measurement result of the measurement(s) in association with the configured event(s); predicted trajectory and / or measurement result of the measurement(s) in association with the configured event(s)). In one example, the generated output signal(s) being communicable can include predicted trajectory. In another example, the generated output signal(s) being communicable can include measurement result of the measurement(s) in association with the configured event(s). In yet another example, the generated output signal(s) being communicable can include predicted trajectory and measurement result of the measurement(s) in association with the configured event(s).

[0020] In one embodiment, predicted trajectory can include / be associated with / correspond to / be indicative of one or more parameters associated with a user equipment. The parameter(s) can, for example, include co-ordinates and / or parameters, in accordance with an embodiment of the disclosure.

[0021] In one embodiment, a Next generation Node B (gNB) can be configured to generate and / or communicate the input signal(s). The input signal(s) can, for example, be received by a user equipment (UE) for processing to generate the output signal(s), in accordance with an embodiment of the disclosure. In one example, trajectory of the UE can be predicted.

[0022] In one embodiment, the gNB can be configured to select a target cell for handover based on predicted trajectory of the UE. The selected target cell can, for example, correspond to an optimal handover cell. Moreover, the gNB can, for example, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and predicted trajectory can be used as an input for the AI / ML model for predicting / determining optimal handover cell.

[0023] In one embodiment, the gNB can be configured to select a target cell for handover based on predicted trajectory of the UE and based on inter Radio Access Network (RAN) data. The selected target cell can, for example, correspond to an optimal handover cell. Moreover, the gNB can, for example, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and predicted trajectory along with inter RAN data can be used as input(s) for the AI / ML model for predicting / determining optimal handover cell.

[0024] 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, 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.

[0025] 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, 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.

[0026] In accordance with an aspect of the disclosure, there is provided an apparatus.

[0027] The apparatus can, for example, include a first module, a second module and / or a third module. The first module can, for example, be configured to receive one or more input signals. The second module can, for example be configured to process the input signal(s) according to the processing method as discussed earlier to generate one or more output signals. The third module can, for example, be configured to communicate one or more output signals.

[0028] In one embodiment, the apparatus can correspond to a User Equipment (UE) which can communicate with a device corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0029] In accordance with an aspect of the disclosure, there is provided a system.

[0030] 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.

[0031] In accordance with an aspect of the disclosure, there is provided a device.

[0032] The device can, for example, be configured to configure a measurement event for an apparatus in a manner so as to facilitate the apparatus providing predicted trajectory information associated with the apparatus.

[0033] In one embodiment, the measurement event can, for example, correspond to a configured event, the device can, for example, correspond to a Next generation Node B (gNB) and the apparatus can, for example, correspond to a User Equipment (UE). Furthermore, the gNB can, for example, be configured to generate and / or communicate one or more input signals which can be indicative of the configured event. Moreover, the input signal(s) can, for example, be communicated by manner of System Information Block (SIB), Master Information Block (MIB) and / or Radio Resource Control (RRC) configuration messages.

[0034] In one embodiment, the gNB can be configured to receive one or more output signals from the UE and process the output signal(s) in a manner so as to determ ine / predict optimal / best target cell based on UE predicted trajectory and / or inter-RAN (Radio Access Network) data.

[0035] Brief Description of the Drawings

[0036] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which: Fig. 1a shows a system which can include at least one apparatus, according to an embodiment of the disclosure;

[0037] Fig. 1 b to Fig. 1e show an example scenario in association with the system of Fig. 1a, according to an embodiment of the disclosure;

[0038] Fig, 2 shows the apparatus of Fig, 1a in further detail, according to an embodiment of the disclosure; and

[0039] Fig. 3 shows a processing / communication method in association with the system of Fig. 1a, according to an embodiment of the disclosure.

[0040] Fig. 4a to Fig. 4c show an example context in association with the processing / communication method of Fig. 3, according to an embodiment of the disclosure.

[0041] Fig. 5 shows an illustrative example of possible training strategies in association with Fig. 1 to Fig. 4, according to an embodiment of the disclosure.

[0042] Detailed Description

[0043] The present disclosure generally contemplates the facilitation of Handover (HO) in association with, for example, a network (e.g., in association with 3GPP based standard / specification etc.) and / or at least one user equipment (UE), in accordance with an embodiment of the disclosure.

[0044] Specifically, the present disclosure contemplates HO optimization, in accordance with an embodiment of the disclosure.

[0045] More specifically, the present disclosure contemplates UE trajectory-based HO optimization, in accordance with an embodiment of the disclosure. The present disclosure contemplates that one possible issue associated with conventional technique(s) in relation to Conditional Handover (CHO) can be due to HO being performed based on inappropriate decision(s).

[0046] For example, HO decision for mobility may be controlled by the network based on one or more measurement reports communicated from a UE.

[0047] However, the present disclosure contemplates that such HO decision may possibly be lacking in self-learning ability for making a HO decision and this may not be optimal. Specifically, it is contemplated that the lack of self-learning ability in regard to HO decision may possibly lead to inappropriate decision(s) (e.g., inappropriate parameters, early / late / incorrect HO) being made in connection with HO.

[0048] The present disclosure contemplates that a Next generation Node B (gNB) can be configured to configure a new measurement event (i.e., referable to as a “configured event”) for a UE to provide predicted UE trajectory information. The gNB can, for example, signal this by manner of System Information Block (SIB) / Master Information Block (MIB), Radio Resource Control (RRC) configuration message(s) etc. The UE, upon receiving the configuration associated with the new measurement event, can be configured to perform one or more processing tasks in relation to any one or more of, or any combination of, the following:

[0049] • performing measurement at the configured event.

[0050] • predicting trajectory based on the measurement performed at the configured event. Prediction of trajectory can, for example, be based on Al (Artificial lntelligence) / ML (Machine Learning) model inference.

[0051] • Providing one or both of measurement result(s) associated with the configured event (e.g., of the UE at the configured event) and predicted UE trajectory, to the gNB. The predicted UE trajectory can, for example, be in relation to / associated with / correspond to / include inference of UE’s AI / ML model. In one example, the UE can provide parameters such as co-ordinates and / or model parameters, etc. as part of the predicted UE trajectory, in accordance with an embodiment of the disclosure. The gNB, upon receiving information concerning predicted UE trajectory, may possibly use such information along with inter Radio Access Network (RAN) data as basis for its AI / ML model to facilitate optimal HO (e.g., predict the best cell for HO).

[0052] The present disclosure contemplates that, in the above manner, an improved HO scenario can possibly with facilitated in that chances of HO Failure (HOF) can be reduced. This may possibly facilitate improvement in quality of service.

[0053] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig. 5 hereinafter.

[0054] 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 Handover (HO) optimization, in accordance with an embodiment of the disclosure.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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, any one of, or any combination of, for example:

[0059] • receiving at least one input signal

[0060] • performing measurement based on the input signal(s)

[0061] • predicting trajectory (e.g., of the apparatus(es) 102).

[0062] • communicating one or more output signals, in accordance with an embodiment of the disclosure.

[0063] The input signal(s) can, for example, include / correspond to / be associated with / be indicative of at least one configured event, in accordance with an embodiment of the disclosure.

[0064] Prediction of trajectory can, for example, be based on measurement performed at / during the configured event, in accordance with an embodiment of the disclosure. Moreover, prediction of trajectory can, for example, be based on Al (Artificial lntelligence) / ML (Machine Learning) model inference., in accordance with an embodiment of the disclosure.

[0065] The output signal(s) can, for example, include / correspond to / be associated with / be indicative of measurement result(s) associated with the configured event (e.g., of the UE at the configured event) and / or predicted trajectory (e.g., predicted UE trajectory). Specifically, for example, the output signal(s) can include one or both of measurement result(s) and / or predicted trajectory. In one specific example, the predicted UE trajectory can, for example, be in relation to / associated with / correspond to / include inference of UE’s AI / ML model. In yet a more specific example, a UE can provide parameters such as co-ordinates and / or model parameters, etc. as part of the predicted UE trajectory. Generally, 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. In one example, the output signal(s) can be communicated from the apparatus(es) 102 to the device(s) 104, in accordance with an embodiment of the disclosure.

[0066] The device(s) 104 can, for example, be associated with / correspond to at least one base station (e.g., at least one gNB). Moreover, the device(s) 104 can, for example, be configured to carry / be associated with / include one or more computers (e.g., an electronic device / module having computing capabilities) which can, for example, be configured to perform one or more processing tasks in association with the base station. The device(s) 104 can be configured to generate one or more input signals which can be communicated to the apparatus(es) 102, in accordance with an embodiment of the disclosure. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the disclosure.

[0067] 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. In one specific example, the communication network 106 be can a network associated with 3GPP based standard / specification etc. 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. Earlier mentioned, the present disclosure contemplates that one possible issue associated with conventional technique(s) in relation to Conditional Handover (CHO) can be due to HO being performed based on inappropriate decision(s). For example, HO decision for mobility may be controlled by the network based on one or more measurement reports communicated from a UE. However, the present disclosure contemplates that such HO decision may possibly be lacking in self-learning ability for making a HO decision and this may not be optimal. Specifically, it is contemplated that the lack of self-learning ability in regard to HO decision may possibly lead to inappropriate decision(s) (e.g., inappropriate parameters, early / late / incorrect HO) being made in connection with HO. This will be discussed, in accordance with an embodiment of the disclosure, in the context of an example scenario with reference to Fig. 1b to Fig. 1 e, hereinafter.

[0068] Specifically, Fig. 1 b and Fig. 1c show an example scenario where a UE can be configured to perform periodic Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) measurements and report such measurement results (e.g., to the network). Based on the measurement results a source gNB can be configured to determine the HO criteria and transmit a HO Request to at least one Target gNB (i.e. , to a Target gNB and / or one or more other potential Target gNBs).

[0069] In such an example scenario, the present disclosure contemplates that temporal measurement(s) taken by a UE at a time when it receives reference signals (e.g., RSRP / RSRQ) from a gNB and the delay between the actual HO decision made by the source gNB may need to be considered (e.g., in a mobile UE scenario). The present disclosure considers Rel-18 SI (Release-18 Study ltem(s)) in relation to AI / ML for Air Interface in which there is consideration that one or more models can possibly be utilized for improvement of network management. Examples of such models include Network side models, UE side models, UE and gNB side models etc. The present disclosure contemplates that L3 (Layer-3) mobility management can involve measurement(s) made by a UE. In this regard, the present disclosure contemplates the possibility that UE side models can possibly be considered (e.g., studied and / or analyzed) for potential benefits (e.g., in relation to L3 mobility enhancements), in accordance with an embodiment of the disclosure. Referring to Fig. 1d and Fig. 1e, in the example scenario, it is contemplated that, based on RAN (Radio Access Network) 3 study on implementation of AI / ML in Next Generation Radio Access Network, NG-RAN, (in relation to Network energy savings, Load balancing and Mobility optimization), inappropriate decision(s) may possibly be made leading to possibility in failure (e.g., failure may be possible due to worsening of channel quality of target cell).

[0070] In this regard, in the above example scenario, the present disclosure the present disclosure contemplates that introduction / incorporation of self-learning ability for making a HO decision may possibly facilitate optimization. Specifically, it is contemplated that incorporation / introduction of self-learning ability in regard to HO decision may possibly lead to at least reduction (or, ideally, elimination of) inappropriate decision(s) being made in connection with HO.

[0071] The present disclosure contemplates that such self-learning ability can be associated with / correspond to / be in relation to / include the earlier discussed processing tasks in association with / which can include, any one of, or any combination of, for example:

[0072] • receiving at least one input signal

[0073] • performing measurement based on the input signal(s)

[0074] • predicting trajectory (e.g., of a UE).

[0075] • communicating one or more output signals, in accordance with an embodiment of the disclosure.

[0076] The present disclosure contemplates that, in the above manner, an improved HO scenario can possibly with facilitated in that chances of HO Failure (HOF) can be reduced. This may possibly facilitate improvement in quality of service.

[0077] 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. 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.

[0078] The aforementioned apparatus(es) 102 will be discussed in further detail with reference to Fig. 2 hereinafter.

[0079] 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.

[0080] 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).

[0081] It is contemplated that the electronic module 200a can be capable of performing one or more processing tasks as discussed earlier in association with the system 100 of Fig. 1a, in accordance with an embodiment of the disclosure.

[0082] 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. 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.

[0083] 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.

[0084] 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.

[0085] 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. The input signal(s) can, for example, include / correspond to / be associated with / be indicative of at least one configured event, in accordance with an embodiment of the disclosure.

[0086] 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. Generally, the second module 204 can, for example, be configured to perform the processing task(s) associated with / which can include measurement based on the input signal(s) and / or predicting trajectory (e.g., of a UE), in accordance with an embodiment of the disclosure.

[0087] The third module 206 can correspond to a hardware-based transmitter which can be configured to communicate one or more output signals from the electronic module 200a. The output signal(s) can, for example, include / correspond to / be associated with / be indicative of measurement result(s) associated with the configured event (e.g., of the UE at the configured event) and / or predicted trajectory (e.g., predicted UE trajectory).

[0088] The present disclosure contemplates the possibility that the first and second modules 202 / 204 can be an integrated software-hardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and processing functions / an electronic module programmed to perform the functions of receiving and processing). The present disclosure further contemplates the possibility that the first and third modules 202 / 206 can be an integrated softwarehardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and transmitting functions / an electronic module programmed to perform the functions of receiving and transmitting). The present disclosure yet further contemplates the possibility that the first and third modules 202 / 206 can be an integrated hardware module (e.g., a hardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0089] The present disclosure contemplates that, in the above manner, an improved HO scenario can possibly with facilitated in that chances of HO Failure (HOF) can be reduced. This may possibly facilitate improvement in quality of service.

[0090] 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.

[0091] 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.

[0092] The processing method 300 can, for example, be suitable for / capable of facilitating Handover (HO) optimization, in accordance with an embodiment of the disclosure.

[0093] 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.

[0094] 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).

[0095] With regard to the input step 302, one or more input signal(s) can be received. For example, the input signal(s) can be communicated from the device(s) 104 and can be received by an apparatus 102, in accordance with an embodiment of the disclosure. With regard to the processing step 304, at least one 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.

[0096] With regard to the output step 306, the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the disclosure. For example, the output signal(s) can optionally be communicated from the apparatus 102. In a more specific example, the output signal(s) can optionally be communicated from the apparatus 102 to one or both of at least one device 104 and another apparatus 102, in accordance with an embodiment of the disclosure.

[0097] In view of the foregoing, it is appreciable that the present disclosure contemplates a processing method 300 which can, for example, include an input step 302 and a processing step 304, in accordance with an embodiment of the disclosure.

[0098] The input step 302 can include receiving one or more input signals. The input signal(s) can, for example, correspond to one or more configured events.

[0099] The processing step 304 can include performing one or more processing tasks. The processing task(s) can include one or both of (i.e. , at least one of):

[0100] • performing one or more measurements in association with the configured event(s)

[0101] • predicting trajectory based on the performed measurement(s) in association with the configured event

[0102] Specifically, the processing task(s) can include performing at least one measurement in association with the configured event(s) and / or predicting trajectory based on the performed measurement(s) in association with the configured event.

[0103] In one embodiment, one or more output signals can be generated based on the processing task(s), In one embodiment, the processing method can, for example, further include an output step 304. The generated output signal(s) can be communicated. The output signal can, for example, be capable of being processed (e.g., received and processed by a device 104) for predicting / determining optimal handover cell, in accordance with an embodiment of the disclosure.

[0104] In one embodiment, the generated output signal(s) being communicable can include one or both of predicted trajectory and measurement result of the measurement(s) in association with the configured event(s) (i.e., at least one of predicted trajectory and measurement result of the measurement(s) in association with the configured event(s); predicted trajectory and / or measurement result of the measurement(s) in association with the configured event(s)). In one example, the generated output signal(s) being communicable can include predicted trajectory. In another example, the generated output signal(s) being communicable can include measurement result of the measurement(s) in association with the configured event(s). In yet another example, the generated output signal(s) being communicable can include predicted trajectory and measurement result of the measurement(s) in association with the configured event(s).

[0105] In one embodiment, predicted trajectory can include / be associated with / correspond to / be indicative of one or more parameters associated with a user equipment. The parameter(s) can, for example, include co-ordinates and / or parameters, in accordance with an embodiment of the disclosure.

[0106] In one embodiment, a Next generation Node B (gNB) can be configured to generate and / or communicate the input signal(s). The input signal(s) can, for example, be received by a user equipment (UE) for processing to generate the output signal(s), in accordance with an embodiment of the disclosure. In one example, trajectory of the UE can be predicted.

[0107] In one embodiment, the gNB can be configured to select a target cell for handover based on predicted trajectory of the UE. The selected target cell can, for example, correspond to an optimal handover cell. Moreover, the gNB can, for example, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and predicted trajectory can be used as an input for the AI / ML model for predicting / determining optimal handover cell.

[0108] In one embodiment, the gNB can be configured to select a target cell for handover based on predicted trajectory of the UE and based on inter Radio Access Network (RAN) data. The selected target cell can, for example, correspond to an optimal handover cell. Moreover, the gNB can, for example, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and predicted trajectory along with inter RAN data can be used as input(s) for the AI / ML model for predicting / determining optimal handover cell.

[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. The present disclosure contemplates that, in the above manner, an improved HO scenario can possibly with facilitated in that chances of HO Failure (HOF) can be reduced. This may possibly facilitate improvement in quality of service.

[0111] Fig. 4a to Fig. 4c show an example context in association with the processing method 300, in accordance with an embodiment of the disclosure.

[0112] Specifically, Fig. 4a shows an overview of the example context in association with the processing method 300, in accordance with an embodiment of the disclosure.

[0113] Moreover, referring to Fig. 4b, in the example context, a UE can be configured to receive configuration to provide predicted UE trajectory. The UE can be further configured to provide predicted UE trajectory to a gNB. For example, one or more outputs signals which can correspond to / be associated with / include predicted UE trajectory can be generated by and communicated from the UE.

[0114] Furthermore, referring to Fig. 4c, in the example context, the gNB can be configured to configure the UE to provide predicted UE trajectory. For example, the gNB can be configured to generate / define at least one new measurement event (i.e., referable to as a “configured event”) and communicate one or more input signals which can correspond to / be associated with / include the configured event. The gNB can be further configured to determine best / optimal target call based on UE predicted trajectory and / or inter-RAN data.

[0115] The present disclosure contemplates that, in the above manner, an improved HO scenario can possibly with facilitated in that chances of HO Failure (HOF) can be reduced. This may possibly facilitate improvement in quality of service. Specifically, for example, a gNB may possibly select the best target cell for a UE based on UE future state and this may possible facilitate reducing risk of Ping-pong and / or HOF, in accordance with an embodiment of the disclosure. Fig. 5 shows an illustrative example of possible training strategies in association with the foregoing discussion in relation to Fig. 1 to Fig. 4, according to an embodiment of the disclosure.

[0116] 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.

[0117] The first module 202 can be configured to receive one or more input signals.

[0118] The second module 204 can be configured to process the input signal(s) according to the processing method 300 as discussed earlier to generate one or more output signals.

[0119] The third module 206 can be configured to communicate one or more output signals.

[0120] In one embodiment, the apparatus 102 can correspond to a User Equipment (UE) which can communicate with a device 104 corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0121] 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.

[0122] Yet further in view of the forgoing, it is appreciable that the present disclosure general contemplates a device 104 which can, for example, be configured to configure a measurement event for an apparatus 102 to provide predicted trajectory information associated with the apparatus 102. In one embodiment, the measurement event can, for example, correspond to a configured event, the device 104 can, for example, correspond to a Next generation Node B (gNB) and the apparatus 102 can, for example, correspond to a User Equipment (UE). Furthermore, the gNB can, for example, be configured to generate and / or communicate one or more input signals which can be indicative of the configured event. Moreover, the input signal(s) can, for example, be communicated by manner of System Information Block (SIB), Master Information Block (MIB) and / or Radio Resource Control (RRC) configuration messages.

[0123] In one embodiment, the gNB can be configured to receive one or more output signals from the UE and process the output signal(s) in a manner so as to determ ine / predict optimal / best target cell based on UE predicted trajectory and / or inter-RAN (Radio Access Network) data.

[0124] 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).

[0125] 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.

[0126] In one example, earlier mentioned, the gNB can, for example, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and predicted trajectory can be used as an input for the AI / ML model for predicting / determining optimal handover cell. It is appreciable that a UE can, analogously, be associated with an Al (Artificial lntelligence) / ML (Machine Learning) model which can, for example, facilitate prediction of UE trajectory, in accordance with an embodiment of the disclosure.

[0127] In another example, a device 104 can, in a manner analogous to an apparatus 102, include a receiver (not shown), a processor (not shown) and / or a transmitter (not shown). In this regard, the relevant portions of the earlier discussion concerning a first module 202, a second module 204 and / or a third module 206, though discussed in the context of an apparatus 102 (e.g., a UE), can analogously apply to a device 104 (e.g., a gNB) where appropriate, in accordance with an embodiment of the disclosure.

[0128] 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 corresponding to at least one configured event; a processing step (304) comprising performing processing tasks which comprises at least one of: performing at least one measurement in association with the configured event; and predicting trajectory based on the performed measurement in association with the configured event, wherein at least one output signal is generatable based on the processing tasks.

2. The processing method (300) as in claim 1 further comprising an output step (304), the at least one generated output signal being communicable for predicting / determining optimal handover cell.

3. The processing method (300) as in claim 2, wherein the at least one generated output signal being communicable comprises predicted trajectory4. The processing method (300) as in claim 2, wherein the at least one generated output signal being communicable comprises measurement result of the at least one measurement in association with the configured event.

5. The processing method (300) as in claim 2, wherein the at least one generated output signal being communicable comprises: predicted trajectory, and measurement result of the at least one measurement in association with the configured event.

6. The processing method (300) as in claim 1 , wherein predicted trajectory comprises at least one parameter associated with a user equipment.

7. The processing method (300) as in claim 6, where the at least one parameter comprises at least one of co-ordinates and parameters.

8. The processing method (300) as in claim 1 , wherein a Next generation Node B (gNB) is configurable to generate and communicate the at least one input signal, the input signal being receivable by a user equipment (UE) for processing to generate at least one output signal.

9. The processing method (300) as in claim 8, wherein trajectory of the UE is predicted.

10. The processing method (300) as in claim 9, wherein the gNB is configurable to select a target cell for handover based on predicted trajectory of the UE, the selected target cell corresponding to an optimal handover cell.11 . The processing method (300) as in claim 9, wherein the gNB is associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and wherein predicted trajectory is usable as an input for the AI / ML model for predicting / determining optimal handover cell.

12. The processing method (300) as in claim 9, wherein the gNB is configurable to select a target cell for handover based on predicted trajectory of the UE and based on inter Radio Access Network (RAN) data, the selected target cell corresponding to an optimal handover cell.

13. The processing method (300) as in claim 9, wherein the gNB is associated with an Al (Artificial lntelligence) / ML (Machine Learning) model, and wherein predicted trajectory along with inter RAN data being usable as input for the AI / ML model for predicting / determining optimal handover cell.

14. 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 at least one of the input step (302), the processing step (304) and the output signal (304) according to the processing method (300) of any of the preceding claims.

15. A computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out at least one of the input step (302), the processing step (304) and the output step (306) according to the processing method (300) of any of the preceding claims.

16. An apparatus (102) comprising: a first module (202) configurable to receive at least one input signal, the input signal corresponding to at least one configured event; a second module (204) configurable to process the input signal according to the processing method (300) of claim 1 to generate at least one output signal; and a third module (206) configurable to communicate at least one output signal, wherein the output signal comprises predicted trajectory of the apparatus (102).

17. The apparatus (102) as in claim 16, 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).

18. A system (100) comprising: at least one apparatus (102) according to any of claims 16 and 17; and at least one device (104) according to any of claims 16 and 17, wherein the apparatus (102) and the device (104) are capable of being coupled via at least one of wired coupling and wireless coupling.

19. A device (104) configurable to configure a measurement event for an apparatus (102) to provide predicted trajectory information associated with the apparatus (102).

20. The device (104) as in claim 19, wherein the measurement event corresponds to a configured event, the device (104) corresponds to a Next generation Node B (gNB) and the apparatus (102) corresponds to a User Equipment (UE), and wherein the gNB can configurable to communicate at least one input signal indicative of the configured event, the at least one input signal being communicable by manner of at least one of System Information Block (SIB), Master Information Block (MIB) and Radio Resource Control (RRC) configuration messages.

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