SYSTEM AND DEVICE SUITABLE FOR HANDOVER OPTIMISATION AND ASSOCIATED PROCESSING METHOD

Abstract

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

Description

Field of the invention

[0001] The present disclosure generally relates to a system and / or device suitable for enabling handover optimization, for example, in connection with a user equipment (UE) usable for communication. The present disclosure further relates to a processing / communication method that may be associated with the system and / or device. background

[0002] In general, handover (HO) may be necessary / required in communication networks. An example of a communication network would be a telecommunications network based on the 5th Generation New Radio (5G NR) standard of the 3rd Generation Partnership Project (3GPP).

[0003] For example, Release 16 of the 3GPP 5G NR standard introduced a Conditional Handover (CHO) feature. The CHO feature can be useful in enabling a user equipment (UE) to decide whether a handover should / should be performed when certain conditions are met.

[0004] In general, a CHO can be defined as an HO executed by the UE when one or more HO execution conditions are met.

[0005] However, as discussed above, conventional techniques related to CHO may not be optimized.

[0006] The present disclosure contemplates that to enable optimization of the handoff, it would be helpful to address (or at least mitigate) one or more problems associated with conventional techniques. Brief description of the invention

[0007] According to one aspect of the disclosure, a communication / processing method (which may be referred to, for example, as a processing method) is provided.

[0008] For example, according to one embodiment of the disclosure, the processing method may include an input step and a processing step.

[0009] The input step may include receiving one or more input signals. The input signal(s) may, for example, correspond to one or more configured events.

[0010] The processing step may include performing one or more processing tasks. The processing task(s) may include at least one of the following: • Perform one or more measurements in connection with the configured event(s) • Predict a trajectory based on the measurement(s) taken in connection with the configured event

[0011] In particular, the processing task(s) may include performing at least one measurement in connection with the configured event(s) and / or predicting a trajectory based on the performed measurement(s) in connection with the configured event.

[0012] In one embodiment, one or more output signals may be generated based on the processing task(s).

[0013] In one embodiment, the processing method may further include, for example, an output step. The generated output signal(s) may be communicated. According to one embodiment of the disclosure, the output signal may be processable (e.g., receivable and processable by a device), for example, to predict / determine an optimal handover radio cell.

[0014] In one embodiment, the generated output signal(s) that are communicable may include a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s) (i.e., a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s); a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s)). In one example, the generated output signal(s) that are communicable may include a predicted trajectory. In another example, the generated output signal(s) that are communicable may include a measurement result of the measurement(s) associated with the configured event(s).In yet another example, the generated output signal(s) that are communicable may include a predicted trajectory and a measurement result of the measurement(s) associated with the configured event(s).

[0015] In one embodiment, the predicted trajectory may include / be associated with / correspond to / indicate one or more parameters associated with a user device. The parameter(s) may include, for example, coordinates and / or parameters, according to one embodiment of the disclosure.

[0016] In one embodiment, a next-generation Node B (gNB) may be configured to generate and / or communicate the input signal(s). The input signal(s) may be received, for example, by a user equipment (UE) for processing to generate the output signal(s), according to one embodiment of the disclosure. In one example, a trajectory of the UE may be predicted.

[0017] In one embodiment, the gNB may be configured to select a target cell for handover based on a predicted trajectory of the UE. The selected target cell may, for example, correspond to an optimal handover cell. Furthermore, the gNB may, for example, be associated with an artificial intelligence (AI) / machine learning (ML)-based model, and the predicted trajectory may be used as an input for the AI / ML model to predict / determine an optimal handover cell.

[0018] In one embodiment, the gNB may be configured to select a target cell for handover based on a predicted trajectory of the UE and based on cross-radio access network (RAN) data. The selected target cell may, for example, correspond to an optimal handover cell. Furthermore, the gNB may, for example, be associated with an artificial intelligence (AI) / machine learning (ML) model, and the predicted trajectory, together with the cross-RAN data, may be used as input(s) for the AI / ML model to predict / determine an optimal handover cell.

[0019] The present disclosure further contemplates a computer program (not shown) that may include instructions that, when executed by a computer (not shown), cause the computer to perform the input step, the processing step, and / or the output step, as discussed with reference to the communication / processing method. For example, according to one embodiment of the disclosure, the computer program may include instructions that, when executed by a computer, cause the computer to perform the input step and / or the processing step.

[0020] The present disclosure further contemplates a computer-readable storage medium (not shown) having stored therein data representing software executable by a computer (not shown), the software including instructions that, when executed by the computer, cause the performance of the input step, the processing step, and / or the output step, as discussed with reference to the communication / processing method. For example, according to one embodiment of the disclosure, the computer-readable storage medium may have stored therein data representing computer-executable software, the software including instructions that, when executed by the computer, cause the computer to perform the input step and / or the processing step.

[0021] According to one aspect of the disclosure, a device is provided.

[0022] The device may, for example, include a first module, a second module, and / or a third module. The first module may, for example, be configured to receive one or more input signals. The second module may, for example, be configured to process the input signal(s) according to the processing method discussed above to generate one or more output signals. The third module may, for example, be configured to communicate one or more output signals.

[0023] In one embodiment, the device may correspond to a user equipment (UE) that can communicate with a device corresponding to a base station. The base station may, for example, correspond to a next-generation Node B (gNB), which may be configured to communicate one or more signals (e.g., an input signal(s)) to the UE.

[0024] According to one aspect of the disclosure, a system is provided.

[0025] The system may include one or more devices and one or more apparatuses. The device(s) and the apparatus(es) may be coupled, for example, via a wired coupling and / or a wireless coupling.

[0026] According to one aspect of the disclosure, an apparatus is provided.

[0027] For example, the device may be configured to configure a measurement event for a device in a manner that enables the device to provide information about a predicted trajectory associated with the device.

[0028] In one embodiment, the measurement event may, for example, correspond to a configured event, the device may, for example, correspond to a next-generation Node B (gNB), and the equipment may, for example, correspond to a user equipment (UE). Furthermore, the gNB may, for example, be configured to generate and / or communicate one or more input signals that may indicate the configured event. Furthermore, the input signal(s) may, for example, be communicated using system information block (SIB), master information block (MIB), and / or radio resource control (RRC) configuration messages.

[0029] In one embodiment, the gNB may be configured to receive one or more output signals from the UE and process the output signal(s) in a manner to determine / predict an optimal / best target radio cell based on a UE predicted trajectory and / or cross-RAN (Radio Access Network) data. Short description of the drawings

[0030] Embodiments of the disclosure are described below with reference to the following drawings, in which: Fig. 1a shows a system that may include at least one device, according to an embodiment of the disclosure; Fig. 1b to Fig. 1e show an exemplary scenario in connection with the system of Fig. 1a according to an embodiment of the disclosure; Fig. 2 shows the setup of Fig. 1a in detail according to an embodiment of the disclosure; and Fig. Figure 3 shows a processing / communication method in connection with the system of Fig. 1a according to an embodiment of the disclosure. Fig. 4a to Fig. 4c show an exemplary context in connection with the processing / communication method of Fig. 3 according to an embodiment of the disclosure. Fig. Figure 5 shows an illustrative example of possible training strategies in connection with Fig. 1 to Fig. 4 according to an embodiment of the disclosure. Detailed description

[0031] The present disclosure generally contemplates, according to one embodiment of the disclosure, the improvement of a handover (HO) in connection with, for example, a network (e.g., in connection with a 3GPP-based standard / specification, etc.) and / or at least one user equipment (UE).

[0032] In particular, the present disclosure contemplates HO optimization according to one embodiment of the disclosure.

[0033] More specifically, the present disclosure contemplates UE trajectory-based HO optimization according to one embodiment of the disclosure.

[0034] The present disclosure contemplates that a potential problem associated with conventional conditional handover (CHO) techniques occurs because an HO is performed based on inappropriate decision(s).

[0035] For example, an HO decision for mobility can be controlled by the network based on one or more measurement reports communicated by a UE.

[0036] However, the present disclosure contemplates that such an HO decision may lack the self-learning capability to make an HO decision, and this may not be optimal. In particular, it is contemplated that the lack of self-learning capability with respect to an HO decision may result in inappropriate decision(s) (e.g., inappropriate parameters, early / late / incorrect HO) being made in connection with an HO.

[0037] The present disclosure contemplates that a next-generation Node B (gNB) may be configured to configure a new measurement event (i.e., which may be referred to as a "configured event") for a UE to provide information about a predicted UE trajectory. The gNB may signal this, for example, by means of System Information Block (SIB) / Master Information Block (MIB), Radio Resource Control (RRC) configuration message(s), etc. The UE may be configured, upon receiving the configuration associated with the new measurement event, to perform one or more processing tasks related to one or more, or a combination of, the following: • Perform a measurement at the configured event. • Predicting a trajectory based on the measurement taken during the configured event. The prediction of a trajectory can, for example, be based on an inference from an artificial intelligence (AI) / machine learning (ML) model. • Providing measurement result(s) associated with the configured event (e.g., from the UE at the configured event) and / or a predicted UE trajectory to the gNB. The predicted UE trajectory may, for example, be related to / associated with / correspond to / include an inference of the UE's AI / ML model. In one example, according to an embodiment of the disclosure, the UE may provide parameters, such as coordinates and / or model parameters, etc., as part of the predicted UE trajectory.

[0038] Upon receiving information regarding a predicted UE trajectory, the gNB may potentially use this information together with cross-Radio Access Network (RAN) data as a basis for its AI / ML model to enable optimal HO (e.g., predict the best radio cell for an HO).

[0039] The present disclosure contemplates that an improved HO scenario may be enabled in that the probability of an HO failure (HOF) may be reduced. This may potentially enable an improvement in the quality of service.

[0040] The above is explained below with reference to Fig. 1 to Fig. 5 is explained in more detail.

[0041] Referring to Fig. 1a, a system 100 according to one embodiment of the disclosure is shown. According to one embodiment of the disclosure, the system 100 may, for example, be adapted to enable handover (HO) optimization.

[0042] As shown, according to an embodiment of the disclosure, the system 100 may include one or more devices 102, at least one apparatus 104, and optionally a communications network 106.

[0043] The device(s) 102 may be coupled to the device(s) 104. In particular, according to one embodiment of the disclosure, the device(s) 102 may be coupled to the device(s) 104, for example, via the communications network 106.

[0044] In one embodiment, the device(s) 102 may be coupled to the communication network 106, and the apparatus(es) 104 may be coupled to the communication network 106. The coupling may be via a wired coupling and / or a wireless coupling. The device(s) 102 may be generally configured to communicate with the device(s) 104 via the communication network 106, according to one embodiment of the disclosure.

[0045] For example, according to one embodiment of the disclosure, the device(s) 102 may be associated with / correspond to / include one or more user equipment (UE) devices that may carry one or more computers. For example, a device 102 may correspond to a UE carrying at least one computer (e.g., an electronic device / module with computing capabilities, such as a mobile electronic device that may be carried in a vehicle or an electronic module that may be installed in a vehicle, according to one embodiment of the disclosure) that may be configured to perform one or more processing tasks that may be associated with / include any of the following or any combination thereof, for example: • Receiving at least one input signal • Perform a measurement based on the input signal(s) • Predicting a trajectory (e.g., facility(ies) 102) • Communicating one or more output signals according to an embodiment of the disclosure.

[0046] For example, the input signal(s) may include / correspond to / be associated with / indicate at least one configured event, according to an embodiment of the disclosure.

[0047] According to one embodiment of the disclosure, a prediction of a trajectory may be based, for example, on a measurement taken at / during the configured event(s). Furthermore, according to one embodiment of the disclosure, a prediction of a trajectory may be based, for example, on an inference of an artificial intelligence (AI) / machine learning (ML) model.

[0048] For example, the output signal(s) may contain / correspond to / be related to / indicate measurement result(s) associated with the configured event (e.g., of the UE at the configured event). In particular, the output signal(s) may contain / correspond to / be related to / indicate measurement result(s) and / or a predicted trajectory. In a specific example, the predicted UE trajectory may, for example, be related to / be associated with / correspond to / contain an inference of the UE's AI / ML model. In an even more specific example, a UE may provide parameters, such as coordinates and / or model parameters, etc., as part of the predicted UE trajectory.

[0049] In general, in one embodiment, the device(s) 102 may, 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 that generates one or more output signals. The input signal(s) may, for example, be communicated by the device(s) 104 and received by the device(s) 102 according to one embodiment of the disclosure. As a possible option, the output signal(s) may, for example, be communicated by the device(s) 102 according to one embodiment of the disclosure. The device(s) 102 according to one embodiment of the disclosure will be described later with reference to Fig. 2. In one example, the output signal(s) may be communicated from device(s) 102 to apparatus(es) 104 according to an embodiment of the disclosure.

[0050] The device(s) 104 may, for example, be associated with / correspond to at least one base station (e.g., at least one gNB). Furthermore, the device(s) 104 may, for example, be configured to carry / be associated with / contain one or more computers (e.g., an electronic device / module with computing capabilities), which may, for example, be configured to perform one or more processing tasks in conjunction with the base station. The device(s) 104 may, according to an embodiment of the disclosure, be configured to generate one or more input signals that may be communicated to the device(s) 102. This will be discussed in more detail later in connection with an example scenario according to an embodiment of the disclosure.

[0051] The communication network 106 may, for example, correspond to an Internet communication network, a cellular-based communication network, a wired communication network, a Global Navigation Satellite System (GNSS)-based communication network, a wireless communication network, or any combination thereof. In a specific example, the communication network 106 may be a network associated with a 3GPP-based standard / specification, etc. Communication (e.g., between the devices 102 and / or between the device(s) 102 and the device(s) 104) via the communication network 106 may be via wired communication and / or wireless communication.

[0052] As previously mentioned, the present disclosure contemplates that a potential problem associated with conventional techniques regarding conditional handover (CHO) arises because an HO is performed based on inappropriate decision(s). For example, an HO decision for mobility may be controlled by the network based on one or more measurement reports communicated by a UE. However, the present disclosure contemplates that such an HO decision may lack self-learning capability for making an HO decision and may not be optimal. In particular, it is contemplated that the lack of self-learning capability regarding an HO decision may result in inappropriate decision(s) (e.g., inappropriate parameters, early / late / incorrect HO) being made in connection with an HO.This will be explained below according to one embodiment of the disclosure in the context of an exemplary scenario with reference to . Fig. 1b to Fig. 1e discussed.

[0053] In particular, Fig. 1b and Fig. Figure 1c illustrates an exemplary scenario where a UE may be configured to perform periodic Reference Signal Received Power (RSRP) / Reference Signal Received Quality (RSRQ) measurements and report these measurement results (e.g., to the network). Based on the measurement results, a source gNB may be configured to determine the HO criteria and transmit an HO request to at least one destination gNB (i.e., to a destination gNB and / or one or more other potential destination gNBs).

[0054] In such an exemplary scenario, the present disclosure contemplates that the delay between (a) temporal measurement(s) made by a UE at the time it receives reference signals (e.g., RSRP / RSRQ) from a gNB and 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 (a) study item(s) under Release-18 (Rel-18 SI - Release-18 Study Item(s)) regarding AI / ML for an air interface, which considers that one or more models may be used to improve 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 may include measurement(s) performed by a UE. In this regard, according to one embodiment of the disclosure, the present disclosure contemplates the possibility that UE-side models may be considered (e.g., examined and / or analyzed) for potential benefit.

[0055] Referring to Fig. 1d and Fig. 1e, the exemplary scenario considers that, based on the RAN (Radio Access Network) Study 3 on the implementation of AI / ML in a Next Generation Radio Access Network (NG-RAN) (regarding network energy savings, load balancing and mobility optimization), inappropriate decision(s) may be made, leading to a possibility of failure (e.g., failure may be possible due to degradation of the channel quality of the target radio cell).

[0056] In this regard, the present disclosure contemplates that the introduction / incorporation of a self-learning capability for making an HO decision may potentially enable optimization. In particular, it is contemplated that the introduction / incorporation of a self-learning capability with regard to an HO decision may potentially lead to at least a reduction (or ideally, elimination of) inappropriate decision(s) made in connection with an HO.

[0057] The present disclosure contemplates that such self-learning capability may be associated with / correspond to / relate to / include the previously discussed processing tasks, which may be associated with / include any of the following or any combination thereof, for example: • Receiving at least one input signal • Perform a measurement based on the input signal(s) • Predicting a trajectory (e.g., a UE) • Communicating one or more output signals according to an embodiment of the disclosure.

[0058] The present disclosure contemplates that an improved HO scenario may be enabled in that the probability of an HO failure (HOF) may be reduced. This may potentially enable an improvement in the quality of service.

[0059] The advantageous aspect(s) of the system 100 of the present disclosure described above may also apply analogously to the (all) aspect(s) of a device 102 of the present disclosure described further below. Likewise, the advantageous aspect(s) of the device 102 of the disclosure described below may also apply analogously to the (all) aspect(s) of the system 100 of the disclosure described above.

[0060] As previously mentioned, the device(s) 102 may, 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 that generates at least one output signal. Furthermore, according to an embodiment of the disclosure, the device(s) 104 may, for example, be configured to generate (and communicate) the input signal(s) to the device(s) 102.

[0061] The said facility(ies) 102 will be referred to below with reference to Fig. 2 is discussed in more detail.

[0062] Referring to Fig. 2, a device 102 is shown in more detail in the context of an exemplary implementation 200 according to an embodiment of the disclosure.

[0063] In the exemplary implementation 200, the device 102 may correspond to an electronic module 200a. According to an embodiment of the disclosure, the electronic module 200a may, in one example, correspond to a mobile device that may, for example, be brought into the vehicle by a user. In another example, according to an embodiment of the disclosure, the electronic module 200a may correspond to an electronic device that may be installed / mounted in the vehicle. In this context, the electronic module 200a may be considered to be carried by the vehicle (e.g., either brought into the vehicle by a user or installed / mounted in the vehicle).

[0064] It is contemplated that the electronic module 200a according to an embodiment of the disclosure may be capable of, as previously described in connection with the system 100 of Fig. 1a discusses performing one or more processing tasks.

[0065] The electronic module 200a may, for example, include a housing 200b. Furthermore, the electronic module 200a may, for example, support a first module 202, a second module 204, a third module 206, or any combination thereof.

[0066] In one embodiment, the electronic module 200a may support a first module 202, a second module 204, and / or a third module 206. In a specific example, the electronic module 200a may support a first module 202, a second module 204, and a third module 206 according to an embodiment of the disclosure.

[0067] In this regard, it will be appreciated that in one embodiment, the housing 200b may be shaped and sized to support the first module 202, the second module 204, the third module 206, or any combination thereof.

[0068] The first module 202 may be coupled to the second module 204 and / or the third module 206. The second module 204 may be coupled to the first module 202 and / or the third module 206. The third module 206 may be coupled to the first module 202 and / or the second module 204. In one example, according to an embodiment of the disclosure, the first module 202 may be coupled to the second module 204, and the second module 204 may be coupled to the third module 206. The coupling between the first module 202, the second module 204, and / or the third module 206 may be achieved, for example, by means of a wired coupling and / or a wireless coupling. The first module 202, the second module 204, and the third module 206 may correspond to a hardware-based module and / or a software-based module according to an embodiment of the disclosure.

[0069] In one example, the first module 202 may correspond to a hardware-based receiver that may be configured to receive one or more input signals. The input signal(s) may be communicated, for example, by the device(s) 104 (e.g., a gNB) according to an embodiment of the disclosure. The input signal(s) may, for example, contain / correspond to / be associated with / indicate at least one configured event according to an embodiment of the disclosure.

[0070] The second module 204 may, for example, correspond to a hardware-based processor that may be configured to perform one or more processing tasks (e.g., in a manner that generates one or more output signals), as described later with reference to Fig. 3, according to one embodiment of the disclosure. In general, the second module 204 according to one embodiment of the disclosure may, for example, be configured to perform the processing task(s) associated with / may include a measurement based on the input signal(s) and / or predicting a trajectory (e.g., of a UE).

[0071] The third module 206 may correspond to a hardware-based transmitter that may be configured to communicate one or more output signals from the electronic module 200a. The output signal(s) may, for example, contain / correspond to / be associated with / indicate measurement results associated with the configured event (e.g., the UE at the configured event).

[0072] The present disclosure contemplates the possibility that the first and second modules 202 / 204 may be an integrated software / hardware-based module (e.g., an electronic part capable of carrying a software program / algorithm associated with receiving and processing functions / an electronic module programmed to perform the receiving and processing functions). The present disclosure further contemplates the possibility that the first and third modules 202 / 206 may be an integrated software / hardware-based module (e.g., an electronic part capable of carrying a software program / algorithm associated with receiving and transmitting functions / an electronic module programmed to perform the receiving and transmitting functions). The present disclosure further contemplates the possibility that the first and third modules 202 / 206 may be an integrated hardware module (e.g.,a hardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0073] The present disclosure contemplates that an improved HO scenario may be enabled in that the probability of an HO failure (HOF) may be reduced. This may potentially enable an improvement in the quality of service.

[0074] The advantageous aspect(s) of the device 102 of the present disclosure described above may also apply analogously to the (all) aspect(s) of a processing / communication method of the present disclosure described below. Likewise, the advantageous aspect(s) of the processing / communication method of the disclosure described below may also apply analogously to the (all) aspect(s) of the device 102 of the disclosure described above. It should be appreciated that these statements apply analogously to the previously discussed system 100 of the present disclosure.

[0075] Referring to Fig. 3, a communication method (which may also be referred to as a processing method) in connection with the system 100 according to an embodiment of the disclosure is shown.

[0076] For example, the processing method 300 may be adapted / capable of enabling handover (HO) optimization according to an embodiment of the disclosure.

[0077] The processing method 300 may include an input step 302, a processing step 304, an output step 306, or any combination thereof, according to an embodiment of the disclosure.

[0078] In one embodiment, processing method 300 may include input step 302. In another embodiment, processing method 300 may include input step 302 and processing step 304. In another embodiment, processing method 300 may include input step 302, processing step 304, and output step 306. In yet another embodiment, processing method 300 may include processing step 304 and / or input step 302 and / or output step 306. In yet another embodiment, processing method 300 may include input step 302, processing step 304, and output step 306. In yet another additional embodiment, processing method 300 may include processing step 304.In yet another further additional embodiment, the processing method 300 may include the input step 302, the processing step 304, or the output step 306, or any combination thereof (ie, the input step 302, the processing step 304, and / or the output step 306).

[0079] With respect to input step 302, one or more input signals may be received. For example, according to one embodiment of the disclosure, the input signal(s) may be communicated from device(s) 104 and received by device 102.

[0080] With respect to processing step 304, according to an embodiment of the disclosure, at least one processing task may be performed in connection with the received input signal(s) in a manner that generates one or more output signals.

[0081] With respect to output step 306, the output signal(s) may, for example, be optionally communicated according to one embodiment of the disclosure. For example, the output signal(s) may optionally be communicated by device 102. In a more specific example, the output signal(s) may, according to one embodiment of the disclosure, be optionally communicated by device 102 to at least one device 104 and / or to another device 102.

[0082] In view of the foregoing, it can be seen that the present disclosure contemplates a processing method 300, which may include, for example, an input step 302 and a processing step 304, according to one embodiment of the disclosure.

[0083] Input step 302 may include receiving one or more input signals. The input signal(s) may, for example, correspond to one or more configured events.

[0084] Processing step 304 may include performing one or more processing tasks. The processing task(s) may include one or both of the following (ie, at least one): • Perform one or more measurements in connection with the configured event(s) • Predict a trajectory based on the measurement(s) taken in connection with the configured event.

[0085] In particular, the processing task(s) may include performing at least one measurement in connection with the configured event(s) and / or predicting a trajectory based on the performed measurement(s) in connection with the configured event.

[0086] In one embodiment, one or more output signals may be generated based on the processing task(s).

[0087] In one embodiment, the processing method may further include, for example, an output step 304. The generated output signal(s) may be communicated. According to one embodiment of the disclosure, the output signal may be processable (e.g., receivable and processable by a device 104), for example, to predict / determine an optimal handover radio cell.

[0088] In one embodiment, the generated output signal(s) that are communicable may include a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s) (i.e., a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s); a predicted trajectory and / or a measurement result of the measurement(s) associated with the configured event(s)). In one example, the generated output signal(s) that are communicable may include a predicted trajectory. In another example, the generated output signal(s) that are communicable may include a measurement result of the measurement(s) associated with the configured event(s).In yet another example, the generated output signal(s) that are communicable may include a predicted trajectory and a measurement result of the measurement(s) associated with the configured event(s).

[0089] In one embodiment, the predicted trajectory may include / be associated with / correspond to / indicate one or more parameters associated with a user device. The parameter(s) may include, for example, coordinates and / or parameters, according to one embodiment of the disclosure.

[0090] In one embodiment, a next-generation Node B (gNB) may be configured to generate and / or communicate the input signal(s). The input signal(s) may be received, for example, by a user equipment (UE) for processing to generate the output signal(s), according to one embodiment of the disclosure. In one example, a trajectory of the UE may be predicted.

[0091] In one embodiment, the gNB may be configured to select a target cell for handover based on a predicted trajectory of the UE. The selected target cell may, for example, correspond to an optimal handover cell. Furthermore, the gNB may, for example, be associated with an artificial intelligence (AI) / machine learning (ML)-based model, and the predicted trajectory may be used as an input for the AI / ML model to predict / determine an optimal handover cell.

[0092] In one embodiment, the gNB may be configured to select a target cell for handover based on a predicted trajectory of the UE and based on cross-radio access network (RAN) data. The selected target cell may, for example, correspond to an optimal handover cell. Furthermore, the gNB may, for example, be associated with an artificial intelligence (AI) / machine learning (ML) model, and the predicted trajectory, together with the cross-RAN data, may be used as input(s) for the AI / ML model to predict / determine an optimal handover cell.

[0093] The present disclosure further contemplates a computer program (not shown) that may include instructions that, when executed by a computer (not shown), cause the computer to perform input step 302, processing step 304, and / or output step 306, as discussed with reference to communication / processing method 300. For example, according to one embodiment of the disclosure, the computer program may include instructions that, when executed by a computer, cause the computer to perform input step 302 and / or processing step 304.

[0094] The present disclosure further contemplates a computer-readable storage medium (not shown) having stored therein data representing software executable by a computer (not shown), the software including instructions that, when executed by the computer, cause the performance of input step 302, processing step 304, and / or output step 306, as discussed with reference to communication / processing method 300. For example, according to one embodiment of the disclosure, the computer-readable storage medium may have stored therein data representing computer-executable software, the software including instructions that, when executed by the computer, cause the computer to perform input step 302 and / or processing step 304.

[0095] The present disclosure contemplates that an improved HO scenario may be enabled in that the probability of an HO failure (HOF) may be reduced. This may potentially enable an improvement in the quality of service.

[0096] Fig. 4a to Fig. 4c show an exemplary context associated with the processing method 300 according to an embodiment of the disclosure.

[0097] In particular, Fig. 4a provides an overview of the exemplary context associated with the processing method 300 according to an embodiment of the disclosure.

[0098] In addition, with reference to Fig. 4b, in the example context, a UE may be configured to receive a configuration to provide a predicted UE trajectory. The UE may be further configured to provide a predicted UE trajectory to a gNB. For example, one or more output signals that may correspond to / be associated with / include a predicted UE trajectory may be generated by the UE and communicated by the UE.

[0099] Furthermore, with reference to Fig. 4c, in the example context, the gNB may be configured to configure the UE to provide a predicted UE trajectory. For example, the gNB may be configured to generate / define at least one new measurement event (i.e., which may be referred to as a "configured event") and communicate one or more input signals that may correspond to / be associated with / include the configured event. The gNB may further be configured to determine the best / optimal target radio cell based on a predicted UE trajectory and / or cross-RAN data.

[0100] The present disclosure contemplates that an improved HO scenario may be enabled in that the probability of an HO failure (HOF) may be reduced. This may potentially enable an improvement in the quality of service. In particular, for example, according to one embodiment of the disclosure, a gNB may select the best target radio cell for a UE based on a future state of a UE, and this may potentially enable the risk of ping-pong and / or HOF to be reduced.

[0101] Fig. Figure 5 shows an illustrative example of possible training strategies in connection with the above discussion regarding Fig. 1 to Fig. 4 according to an embodiment of the disclosure.

[0102] Further, in view of the foregoing, it is understood that the present disclosure generally contemplates a device 102 that may include a first module 202, a second module 204, and / or a third module 206.

[0103] The first module 202 may be configured to receive one or more input signals.

[0104] The second module 204 may be configured to process the input signal(s) according to the processing method 300, as discussed above, to generate one or more output signals.

[0105] The third module 206 may be configured to communicate one or more output signals.

[0106] In one embodiment, device 102 may correspond to a user equipment (UE) that can communicate with a device 104 that corresponds to a base station. The base station may, for example, correspond to a next-generation Node B (gNB), which may be configured to communicate one or more signals (e.g., an input signal(s)) to the UE.

[0107] Furthermore, in light of the foregoing, it should be understood that the present disclosure generally contemplates a system 100 that may include one or more devices 102 and one or more apparatuses 104. The device(s) 102 and the apparatus(es) 104 may be coupled, for example, via a wired coupling and / or a wireless coupling.

[0108] Furthermore, in view of the foregoing, it will be understood that the present disclosure generally contemplates an apparatus 104 that may be configured, for example, to configure a measurement event for a device 102 to provide predicted trajectory information associated with the device 102.

[0109] In one embodiment, the measurement event may, for example, correspond to a configured event, the device 104 may, for example, correspond to a next-generation Node B (gNB), and the equipment 102 may, for example, correspond to a user equipment (UE). Furthermore, the gNB may, for example, be configured to generate and / or communicate one or more input signals that may indicate the configured event. Furthermore, the input signal(s) may, for example, be communicated using system information block (SIB), master information block (MIB), and / or radio resource control (RRC) configuration messages.

[0110] In one embodiment, the gNB may be configured to receive one or more output signals from the UE and process the output signal(s) in a manner to determine / predict an optimal / best target radio cell based on a UE predicted trajectory and / or cross-RAN (Radio Access Network) data.

[0111] It should be appreciated that the embodiments described above may be combined in any manner (e.g., one or more embodiments as discussed in the "Detailed Description" section may be combined with one or more embodiments as described in the "Summary of the Invention").

[0112] Furthermore, those skilled in the art should recognize that variations and combinations of embodiments described above that are not alternatives or replacements can be combined to form still further embodiments.

[0113] In a previously mentioned example, the gNB may, for example, be associated with an artificial intelligence (AI) / machine learning (ML)-based model, and the predicted trajectory may be used as an input to the AI / ML model to predict / determine an optimal handover cell. It is understood that a UE may, analogously, be associated with an artificial intelligence (AI) / machine learning (ML)-based model, which, for example, may improve a UE trajectory prediction according to an embodiment of the disclosure.

[0114] In another example, a device 104 may include a receiver (not shown), a processor (not shown), and / or a transmitter (not shown) in a manner analogous to a device 102. In this regard, the relevant portions of the above discussion concerning a first module 202, a second module 204, and / or a third module 206, although discussed in the context of a device 102 (e.g., a UE), may analogously apply to a device 104 (e.g., a gNB), as appropriate, according to an embodiment of the disclosure.

[0115] In the foregoing, various embodiments of the disclosure have been described to overcome at least one of the aforementioned disadvantages. These embodiments are intended to be encompassed by the following claims and are not limited to the specific forms or arrangements of parts so described, and it will be apparent to those skilled in the art, in light of this disclosure, that numerous changes and / or modifications may be made, which are also intended to be encompassed by the following claims.

Claims

[1] Processing methods (300) comprising: an input step (302) comprising receiving at least one input signal corresponding to at least one configured event; a processing step (304) comprising performing processing tasks and comprising at least one of the following: Performing at least one measurement in connection with the configured event; and Predicting a trajectory based on the measurement performed in connection with the configured event, wherein at least one output signal can be generated based on the processing tasks. [2] The processing method (300) of claim 1, further comprising an output step (304), wherein the at least one generated output signal is communicable to predict / determine an optimal handover radio cell. [3] The processing method (300) of claim 2, wherein the at least one generated output signal that is communicable comprises a predicted trajectory. [4] The processing method (300) of claim 2, wherein the at least one generated output signal that is communicable comprises a measurement result of the at least one measurement in connection with the configured event. [5] The processing method (300) of claim 2, wherein the at least one generated output signal that is communicable comprises: a predicted trajectory and a measurement result of at least one measurement in connection with the configured event. [6] The processing method (300) of claim 1, wherein the predicted trajectory comprises at least one parameter associated with a user device. [7] Processing method (300) according to claim 6, wherein the at least one parameter comprises coordinates and / or parameters. [8] The processing method (300) of 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 receiveable by a user equipment (UE) for processing to generate at least one output signal. [9] The processing method (300) of claim 8, wherein a trajectory of the UE is predicted. [10] The processing method (300) of claim 9, wherein the gNB is configurable to select a target cell for handover based on a predicted trajectory of the UE, wherein the selected target cell corresponds to an optimal handover cell. [11] Processing method (300) according to claim 9, where the gNB is assigned to an artificial intelligence (AI) / machine learning (ML) based model and where the predicted trajectory can be used as an input for the AI / ML model to predict / determine an optimal handover cell. [12] The processing method (300) of claim 9, wherein the gNB is configurable to select a target cell for handover based on a predicted trajectory of the UE and based on cross-radio access network (RAN) data, wherein the selected target cell corresponds to an optimal handover cell. [13] Processing method (300) according to claim 9, where the gNB is assigned to an artificial intelligence (AI) / machine learning (ML) based model and where the predicted trajectory together with cross-RAN data can be used as an input for the AI / ML model to predict / determine an optimal handover cell. [14] A computer program comprising instructions which, when executed by a computer, cause the computer to perform the input step (302) and / or the processing step (304) and / or the output signal (304) according to the processing method (300) of any one of the preceding claims. [15] A computer-readable storage medium having stored therein data representing computer-executable software, the software containing instructions which, when executed by the computer, cause the input step (302) and / or the processing step (304) and / or the output step (306) to be performed in accordance with the processing method (300) of any preceding claim. [16] Establishment (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 a predicted trajectory of the device (102). [17] Device (102) according to claim 16, wherein the device (102) corresponds to a user equipment (UE) capable of communicating with a device (104) corresponding to a base station, where the base station corresponds to a next generation Node B (gNB). [18] System (100), comprising: at least one device (102) according to one of claims 16 and 17; and at least one device (104) according to one of claims 16 and 17, wherein the device (102) and the apparatus (104) can be coupled via a wired coupling and / or a wireless coupling. [19] Apparatus (104) configurable to configure a measurement event for a device (102) to provide information about a predicted trajectory associated with the device (102). [20] Device (104) according to claim 19, wherein the measurement event corresponds to a configured event, the device (104) corresponds to a next generation Node B (gNB) and the device (102) corresponds to a user equipment (UE) and wherein the gNB is configurable to communicate at least one input signal indicative of the configured event, wherein the at least one input signal is communicable by means of system information block (SIB), master information block (MIB) and / or radio resource control (RRC) configuration messages.

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