Systems and devices for initializing handover in a network and associated methods thereof

Abstract

Systems (100), devices (102), and methods (300) for initializing handoff in a network are disclosed. The method (300) includes an input step (302) comprising receiving at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value, and a processing step (304) comprising at least one of determining whether a current signal value is less than the first signal threshold, starting a timer based on the timer value if the current signal value is less than the first signal threshold, determining whether the current signal value is less than the second signal threshold upon expiration of the timer, starting an algorithm if the current signal value is less than the second signal threshold, and updating an inference dataset related to the current signal value for initializing handoff in the network.

Description

Technical Field

[0001] This disclosure generally relates to one or both of a system and a device for initiating a handover in a network associated with, for example, a user equipment (UE) that can be used for communication. This disclosure further relates to a method that can be associated with the system and / or the device. Background Technology

[0002] Generally speaking, energy efficiency and power savings will benefit communication networks, such as telecommunications networks based on the 3rd Generation Partnership Project (3GPP) 5G (fifth generation) New Radio (NR) standard.

[0003] Current technologies that rely on base stations to determine handovers from user equipment (UEs) in communication networks may face problems such as service interruptions, handover delays, and even handover failures. Therefore, current technologies may not optimally promote energy efficiency and power savings.

[0004] This disclosure envisions that it would be helpful to solve or at least alleviate one or more problems associated with conventional technologies used to promote energy efficiency and power savings. Summary of the Invention

[0005] According to a first aspect of the present invention, a method for initiating a handover in a network is provided, the method comprising: an input step, the input step comprising receiving at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value; and a processing step comprising at least one of: determining whether a current signal value is less than the first signal threshold; starting a timer based on the timer value if the current signal value is less than the first signal threshold; determining whether the current signal value is less than the second signal threshold when the timer expires; starting an algorithm if the current signal value is less than the second signal threshold; and updating an inference dataset associated with the current signal value for initiating a handover in the network.

[0006] Advantageously, methods with signal thresholds and timers, as described herein, can prevent service interruptions, delays, and handover failures. Therefore, UE power consumption and computational resource savings can be achieved.

[0007] In an embodiment, updating the inference dataset associated with the current signal value includes attaching the current signal to the inference dataset based on at least one of the following: after the algorithm is started, the current signal value is greater than a first signal threshold, the current signal value is greater than a second signal threshold, and / or the current signal value is less than the first signal threshold.

[0008] In an embodiment, the processing steps further include: determining whether the current signal value is less than a second signal threshold before the timer expires; and initiating a switching process without starting the algorithm.

[0009] In an embodiment, the processing steps further include: determining whether the current signal value is greater than a first signal threshold after the algorithm is started; and terminating the algorithm if the current signal value is greater than the first signal threshold.

[0010] In an embodiment, the processing steps further include: periodically importing previous signal values ​​from a previous inference dataset for initializing the switching.

[0011] In an embodiment, the processing steps further include: determining whether the current signal value is similar to a previous signal value in a previous handover process; and starting the algorithm if the current signal value is similar to a previous signal value in a previous handover process.

[0012] In an embodiment, the processing steps further include: monitoring multiple signal values ​​when the current signal value is greater than a first signal threshold.

[0013] In this embodiment, the algorithm is an artificial intelligence / machine learning (AIML) algorithm.

[0014] In an embodiment, the current signal value, the first signal threshold, and the second signal threshold correspond to at least one value that can be associated with the reference signal received power (RSRP) and the reference signal received quality (RSRQ).

[0015] In an embodiment, at least one base station is configured to: predetermine a first signal threshold, a second signal threshold, and a timer value; and communicate the first signal threshold, the second signal threshold, and the timer value.

[0016] In the embodiments, at least one base station corresponds to at least one next-generation node B (gNB).

[0017] In one embodiment, the user equipment (UE) is configured to perform input and processing steps, wherein a first signal threshold, a second signal threshold, and a timer value are transmittable from the gNB to the UE.

[0018] In the embodiment, the first signal threshold, the second signal threshold, and the timer value are received by the UE from the gNB via at least one of the following: System Information Block (SIB), Master Information Block (MIB), Radio Resource Control (RRC) reconfiguration message, and / or UE-specific message.

[0019] In one embodiment, a computer program is provided, the computer program including instructions that, when executed by a computer, cause the computer to perform at least one of the input step and processing step of the method according to the first aspect.

[0020] In one embodiment, a computer-readable storage medium is provided that stores data representing software executable by a computer, the software including instructions that, when executed by a computer, are used to perform at least one of an input step and a processing step according to the method of the first aspect.

[0021] In an embodiment, an apparatus for initiating a handover in a network is provided, the apparatus comprising: a first module (202) configured to receive at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value; a second module (204) configured to process and facilitate at least one of the processing steps (304) of the method (300) according to claims 1 to 13 to generate at least one output signal; and a third module (206) configured to convey at least one output signal, wherein the output signal corresponds to a control signal for initiating a handover in a network.

[0022] In one embodiment, the device corresponds to a user equipment (UE) capable of communicating with a device corresponding to a base station, and wherein the base station corresponds to a next-generation node B (gNB) configured to transmit at least one input signal to the UE.

[0023] In one embodiment, a system is provided, comprising: at least one device; and at least one means, wherein the device and the means are coupled via at least one of wired coupling and wireless coupling.

[0024] Advantageously, systems such as those disclosed herein can have energy savings at the UE and initiate UE handover in a timely manner to reduce handover latency. Attached Figure Description

[0025] Embodiments of this disclosure are described below with reference to the accompanying drawings, in which:

[0026] Figure 1A A schematic diagram illustrating a system for initiating a handover in a network according to an embodiment of the present invention is shown. The system may include at least one device.

[0027] Figures 1B to 1D An embodiment of the invention is shown with Figure 1A Example scenarios associated with the system.

[0028] Figure 2 Further detailed illustrations of embodiments according to the present invention are shown. Figure 1A A schematic diagram of the equipment.

[0029] Figure 3 An embodiment of the invention is shown with Figure 1AThe system-related methods.

[0030] Figure 4A and Figure 4B Examples of embodiments according to the present invention are shown. Figure 3 A schematic diagram of the information flow associated with the method. Detailed Implementation

[0031] Some parts of the following description are presented explicitly or implicitly based on the functional or symbolic representation of algorithms and data operations in computer memory. These algorithmic descriptions and functional or symbolic representations are means by which those skilled in the art of data processing most effectively communicate the substance of their work to others skilled in the art. Here and generally, an algorithm is considered as a self-consistent sequence of steps that produces a desired result. The steps require physical manipulation of physical quantities, such as electrical, magnetic, or optical signals that can be stored, transmitted, combined, compared, and otherwise manipulated.

[0032] This specification also discloses apparatus for performing the methods. Such apparatus may be specifically constructed for the desired purpose, or may include a computer or other means selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are inherently independent of any particular computer or other device. Various machines may be used with the program in accordance with the teachings herein. Alternatively, it may be appropriate to construct more specialized apparatus to perform the desired method steps. The architecture of a computer will become apparent from the following description.

[0033] Furthermore, this specification implicitly discloses a computer program, as it will be apparent to those skilled in the art that the various steps of the methods described herein can be implemented by computer code. The computer program is not intended to be limited to any particular programming language or implementation thereof. It should be understood that various programming languages ​​and their decoding can be used to implement the teachings contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. Many other variations of the computer program exist, which may use different control flows without departing from the spirit or scope of this disclosure.

[0034] Furthermore, one or more steps of a computer program can be executed in parallel rather than sequentially. Such a computer program can be stored on any computer-readable medium. The computer-readable medium may include storage devices such as disks or optical discs, memory chips, or other storage devices suitable for interfacing with a computer. The computer-readable medium may also include hardwired media, such as those exemplified in Internet systems, or wireless media, such as those exemplified in mobile phone systems. When the computer program is loaded onto such a computer and executed, it effectively produces a device for implementing the preferred method.

[0035] According to embodiments of the invention, this disclosure generally envisions promoting and optimizing network (e.g., associated with 3GPP-based standards / specifications, etc.) and / or user equipment (UE) efficiency and mobility (e.g., energy efficiency or power savings). Specifically, this disclosure envisions the possibility of initializing the UE's artificial intelligence and machine learning (AI / ML) in conjunction with 3GPP Release 19 (and later) standards for handover.

[0036] This disclosure envisions methods or mechanisms that can be used in a communication network to define how handover (HO) is performed. These may include network (NW) triggered HO, lower-layer triggered mobility (LTM) HO, and conditional HO (CHO) or event-triggered HO. Performing a successful handover is crucial because it allows the UE to maintain connectivity within the network and ensures that information is correctly transmitted from one base station to another. Handover decisions may be made based on historical measurements, rapid response measurements (RRM), and / or UE location. In the event of a handover failure, the UE may be unable to connect to the network; therefore, failure handling methods focus on the rapid recovery of the UE to the network.

[0037] This disclosure generally envisions that such handover mechanisms and failure handling methods can be designed to be reactive. For example, inaccurate rapid response measurements (RRM) and inaccurate UE location can affect good handover decisions. Furthermore, due to UE mobility, other issues such as radio link failures (RLF), beam failures, and handover failures (HOF) may exist. Complexity, measurement workload, and signaling overhead can also be high, and it may not be possible to eliminate interruptions or poor service caused by failure events.

[0038] This disclosure envisions that artificial intelligence / machine learning (AI / ML) can be a crucial technique in network optimization that can generate energy-efficient networks. Specifically, applying AI / ML in communication links (e.g., air interfaces) can generate handover optimizations by using AI / ML algorithms. If AI / ML is not executed at a specific time, the UE may experience handover latency. Therefore, problems such as service interruptions, latency, and handover failures may occur.

[0039] This disclosure envisions the possibilities of how AI / ML algorithms can be incorporated into standard network systems, signaling aspects, and triggering mechanisms. More specifically, the signaling aspects and triggering mechanisms used to execute AI / ML models may be important, considering the energy efficiency of the UE.

[0040] This disclosure also envisions enabling, for example, AI / ML-assisted mobility with handover optimization, on the network side or UE side. This could include, for example, candidate cell or target cell prediction in Layer 3 (L3) based mobility, and candidate beam or target beam and cell prediction in LTM (such as Radio Layer 2 and Radio Layer 3 Radio Resource Control (RAN2) and UTRAN / E-UTRAN / NG-RAN architectures and associated network interfaces (RAN3)).

[0041] This disclosure further envisions the possibility of methods that require minimal or zero assistance from a base station (e.g., a next-generation Node B (gNB)), or require no or minimal measurements from the UE, to perform operations such as handover using AI / ML algorithms implemented on the UE or gNB. Implementing UE-side AI / ML algorithms to predict the UE handover based on certain events triggered by the gNB can be helpful. The foregoing forms the core of several technical contributions that highlight the use of such AI / ML algorithms for HO prediction.

[0042] Therefore, this disclosure envisions the possibility of how a UE performing AI / ML can predict handover in order to avoid wasting computing and power resources.

[0043] According to embodiments of the present invention, in the manner described above, power savings and energy consumption efficiency can be promoted in the UE or network.

[0044] The foregoing will be discussed in further detail below with reference to Figures 1 to 4.

[0045] refer to Figure 1A According to an embodiment of the invention, a schematic diagram illustrating a system 100 for initiating a handover in a network is shown. According to an embodiment of the invention, system 100 may, for example, be adapted to promote energy / power efficiency.

[0046] As shown in the figure, according to an embodiment of the present invention, system 100 may include one or more devices 102, at least one apparatus 104, and optionally a communication network 106.

[0047] Device 102 may be coupled to device 104. Specifically, according to an embodiment of the invention, device 102 may be coupled to device 104, for example, via communication network 106.

[0048] In one embodiment, device 102 may be coupled to communication network 106, and device 104 may be coupled to communication network 106. Coupling may be performed via wired coupling and wireless coupling, or both. According to embodiments of the invention, device 102 may generally be configured to communicate with device 104 via communication network 106.

[0049] According to embodiments of the present invention, device 102 may be associated with or correspond to one or more user equipments (UEs), or may include one or more UEs that may carry one or more computers. For example, according to embodiments of the present invention, device 102 may correspond to a UE carrying at least one computer (e.g., according to embodiments of the present invention, an electronic device or module with computing capabilities, such as an electronic mobile device that can be carried in a vehicle or an electronic module that can be installed in a vehicle), which may be configured to perform one or more processing tasks associated with adaptive / dynamic / progressive control.

[0050] In an embodiment, device 102 may be configured, for example, to receive one or more input signals and, based on the input signals, to perform at least one processing task in a manner to generate one or more output signals. According to an embodiment of the invention, the input signals may be communicated from device 104 and received by device 102, for example. The input signals may be associated with a first signal threshold, a second signal threshold, and a timer value. As a possible option, according to an embodiment of the invention, the output signals may be communicated from device 102, for example. The output signals may correspond to control signals used to initiate a handover in the network. According to an embodiment of the invention, reference will be made later. Figure 2 Let’s discuss device 102 in further detail.

[0051] Device 104 may be associated with / correspond to at least one base station, which may be a next-generation Node B (gNB). Furthermore, device 104 may be configured, for example, to carry / associate with / include one or more computers (e.g., electronic devices / modules with computing capabilities), which may be configured, for example, to perform one or more processing tasks associated with the base station. According to embodiments of the invention, device 104 may be configured to generate one or more input signals that can be transmitted to device 102. This will be discussed in further detail later in the context of an example scenario, according to embodiments of the invention.

[0052] Communication network 106 may correspond, for example, to an Internet communication network, a cellular communication network, a wired communication network, a Global Navigation Satellite System (GNSS) communication network, a wireless communication network, or any combination thereof. Communication via communication network 106 (e.g., between devices 102 and / or between devices 102 and apparatus 104) may be conducted via one or both of wired and wireless communication.

[0053] As mentioned, device 102 may be configured, for example, to receive at least one input signal and perform at least one processing task associated with dynamic / adaptive / progressive control on the input signal in a manner that generates at least one output signal. Furthermore, according to embodiments of the invention, device 104 may be configured, for example, to generate (and communicate) the input signal to device 102. Therefore, device 104 may predetermine a first signal threshold, a second signal threshold, and a timer value, and also communicate the first signal threshold, the second signal threshold, and the timer value to device 102. According to embodiments of the invention, hereinafter referred to as... Figures 1B to 1D This will be discussed in the context of the example scenario.

[0054] Figures 1B to 1D An embodiment of the invention is shown with Figure 1A Example scenarios associated with the system. Specifically, Figures 1B to 1D It shows Figure 1A Different examples of switching optimization in the system. Figure 1B An example is illustrated by training only on one side of the system (either at device 104 / base station (or network side, e.g., gNB) or device 102 (or UE side)). As shown, the training entity can be provided to each of device 104 and device 102. The training entity for each of device 104 and device 102 may include a module (not shown) configured to implement a corresponding neural network for handover optimization such that the neural network determines the handover only by device 104 (network side) or only by device 102 (UE side).

[0055] In an alternative embodiment, Figure 1C An example is illustrated by joint training on both sides of the system (at device 104 / base station (or network side, e.g., gNB) and device 102 (or UE side)). In this embodiment, training entities may be provided to each of device 104 (network side) and device 102 (UE side). Each training entity may include a corresponding module (not shown) configured to implement a corresponding neural network for handover such that the corresponding neural network can determine the handover by both device 104 (network side) and device 102 (UE side). Specifically, the network-side neural network (or network-side neural network) is trained and configured to determine the handover by device 104, while the UE-side neural network (or UE-side neural network) is trained and configured to determine the handover by device 102. Furthermore, the network-side neural network may perform a backward gradient technique for device 102 (UE side), and the UE-side neural network may perform a forward activation technique for device 104 (network side) to determine and optimize the handover by both device 104 and device 102.

[0056] In another embodiment, Figure 1DAn example is illustrated by separate training at both sides of the system (at device 104 / base station (or network side, e.g., gNB) and device 102 (or UE side)). In this embodiment, training entities may be provided to each of device 104 (network side) and device 102 (UE side). Each training entity may include a corresponding neural network configured to implement a handover such that the corresponding neural network can be used by both device 104 (network side) and device 102 (UE side) to determine the handover (not shown). Specifically, the UE-side neural network (or UE-side neural network) may be trained using a first training dataset and configured to be used by device 102 to determine the handover. Subsequently, the UE-side neural network shares the training dataset with the network-side neural network (or network-side neural network), which is then trained and configured to be used by device 104 to determine the handover. It should be understood that the training order may be reversed, i.e., the network-side neural network may be trained first using the training dataset, and then the UE-side neural network may be trained after the network-side neural network shares the training dataset with the UE-side neural network.

[0057] The neural networks described in this disclosure can be artificial intelligence (AI) based neural network models, such as support vector machines, decision trees, ensemble models, k-nearest neighbor models, or Bayesian networks. It should be understood that neural networks can also be multimodal neural networks and can contain other types of models, including linear and / or nonlinear models, as well as feedforward neural networks, convolutional neural networks (CNNs), or recurrent neural networks (RNNs). It should also be understood that other types of neural network training are available and are not limited to the examples described herein.

[0058] The aspects of system 100 described above can also be similarly applied to all aspects of device 102 described below. Similarly, all aspects of device 102 described below can also be similarly applied to all aspects of system 100 described above.

[0059] The following will refer to Figure 2 The aforementioned device 102 or user equipment (UE) will be discussed in further detail.

[0060] refer to Figure 2 According to an embodiment of the present invention, a schematic diagram of the exemplary device 102 is shown in further detail in the context of example implementation 200.

[0061] In example implementation 200, device 102 may correspond to electronic module 200a. According to an embodiment of the invention, in one example, electronic module 200a may correspond to a mobile device that can, for example, be carried by a user into a vehicle. In another example, according to an embodiment of the invention, electronic module 200a may correspond to an electronic device that can be installed / assembled in a vehicle. In this regard, electronic module 200a can be considered as being carried by the vehicle (e.g., carried by a user into the vehicle or installed / assembled in the vehicle).

[0062] According to embodiments of the present invention, it is conceivable that the electronic module 200a may be able to perform one or more processing tasks associated with adaptive / dynamic / progressive control-related processing.

[0063] Electronic module 200a may include, for example, a housing 200b. Furthermore, electronic module 200a may, for example, carry any one or any combination of the first module 202, the second module 204, and the third module 206.

[0064] In one embodiment, electronic module 200a may carry first module 202, second module 204, and / or third module 206. In a particular example, according to an embodiment of the present invention, electronic module 200a may carry first module 202, second module 204, and third module 206.

[0065] In this regard, it should be understood that, in one embodiment, the shape and size of the housing 200b may be designed to carry any one or any combination of the first module 202, the second module 204 and the third module 206.

[0066] The first module 202 may be coupled to one or both of the second module 204 and the third module 206. The second module 204 may be coupled to one or both of the first module 202 and the third module 206. The third module 206 may be coupled to one or both of the first module 202 and the second module 204. In one example, according to an embodiment of the invention, 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 performed, for example, by one or both of wired coupling and wireless coupling. According to an embodiment of the invention, each of the first module 202, the second module 204, and the third module 206 may correspond to one or both of a hardware-based module and a software-based module.

[0067] In one example, the first module 202 may correspond to a hardware-based receiver that can be configured to receive one or more input signals. According to embodiments of the invention, the input signals may be transmitted, for example, from device 104 (or a base station, e.g., a gNB).

[0068] According to embodiments of the invention, the second module 204 may correspond, for example, to a hardware-based processor that can be configured to perform one or more processing tasks (e.g., to generate one or more output signals), as will be referred to later. Figure 3 To be discussed in further detail.

[0069] The third module 206 may correspond to a hardware-based transmitter configured to transmit one or more output signals from the electronic module 200a. According to embodiments of the invention, the output signals may, for example, include one or more instruction / command / control signals associated with the aforementioned dynamic / adaptive / progressive control configuration / deterministic strategy to promote efficiency (e.g., power / energy efficiency and / or communication efficiency). For example, the output signal may be a control signal used to initiate a handover in the network.

[0070] This disclosure envisions the possibility that the first module 202 and the second module 204 can be integrated hardware-software based modules (e.g., electronic components carrying software programs or algorithms associated with receiving and processing functions, or electronic modules programmed to perform receiving and processing functions). This disclosure further envisions the possibility that the first module 202 and the third module 206 can be integrated hardware-software based modules (e.g., electronic components carrying software programs / algorithms associated with receiving and transmitting functions, or electronic modules programmed to perform receiving and transmitting functions). This disclosure further envisions the possibility that the first module 202 and the third module 206 can be integrated hardware modules (e.g., hardware-based transceivers) capable of performing receiving and transmitting functions.

[0071] According to embodiments of the present invention, the UE may be further configured, for example, as will be referred to later. Figure 3 Further discussion focuses on processing input signals in a manner that generates one or more output signals to promote efficiency, such as power efficiency or energy efficiency. In a particular example, according to embodiments of the invention, the output signal may include one or more control signals to facilitate some form of dynamic / adaptive / incremental control configuration / deterministic strategy to promote efficiency, such as power efficiency or energy efficiency. For example, the output signal may be a control signal used to initiate switching in the network.

[0072] The aspects of the device 102 described above are also similarly applicable to all aspects of the processing / communication method described below. Similarly, all aspects of the method described below are also similarly applicable to all aspects of the device 102 described above. It should be understood that these statements also apply similarly to the system 100 discussed earlier in this disclosure.

[0073] refer to Figure 3 According to an embodiment of the present invention, a method 300 (or communication method) for initiating a handover in a network associated with system 100 is shown.

[0074] According to embodiments of the present invention, method 300 may be adapted, for example, to promote energy efficiency, network optimization and power saving.

[0075] According to an embodiment of the present invention, method 300 may include any one or any combination of input step 302, processing step 304 and output step 306.

[0076] In one embodiment, processing method 300 may include an input step 302. In another embodiment, processing method 300 may include an input step 302 and a processing step 304. In another embodiment, processing method 300 may include an input step 302, a processing step 304, and an output step 306. In yet another embodiment, processing method 300 may include a processing step 304 and one or both of input step 302 and output step 306. In still another embodiment, processing method 300 may include an input step 302, a processing step 304, and an output step 306. In yet another additional embodiment, processing method 300 may include a processing step 304. In yet another additional embodiment, processing method 300 may include any or any combination of input step 302, processing step 304, and output step 306 (i.e., input step 302, processing step 304, and / or output step 306).

[0077] Regarding input step 302, one or more input signals may be received. For example, according to an embodiment of the invention, the input signal may be transmitted from device 104 and received by device 102.

[0078] Input step 302 may include receiving at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value. The first and second signal thresholds may correspond to at least one value associated with a Reference Signal Received Power (RSRP) and a Reference Signal Received Quality (RSRQ). The first signal threshold, the second signal threshold, and the timer value may be predetermined by at least one base station (or device 104). The at least one base station may correspond to at least one Next Generation Node B (gNB), which may transmit the first signal threshold, the second signal threshold, and the timer value to device 102 (or UE). The first signal threshold, the second signal threshold, and the timer value may be received by the UE from the gNB via at least one of the following: System Information Block (SIB), Master Information Block (MIB), UE-specific messages, and / or Radio Resource Control (RRC) reconfiguration messages.

[0079] Regarding processing step 304, according to an embodiment of the present invention, at least one processing task associated with the received input signal can be performed in a manner that generates one or more output signals.

[0080] Processing step 304 may include at least one of the following: determining whether the current signal value is less than a first signal threshold; starting a timer based on a timer value if the current signal value is less than the first signal threshold; determining whether the current signal value is less than a second signal threshold when the timer expires; starting an algorithm if the current signal value is less than the second signal threshold; and updating the inference dataset associated with the current signal value for use in initiating a handover in the network. The current signal value may correspond to at least one value that may be associated with a Reference Signal Received Power (RSRP) and a Reference Signal Received Quality (RSRQ). The algorithm may be an artificial intelligence / machine learning (AI / ML) algorithm.

[0081] Processing step 304 may further include attaching the current signal to the inference dataset based on at least one of the following: after the algorithm is started, the current signal value is greater than a first signal threshold, the current signal value is greater than a second signal threshold, and / or the current signal value is less than the first signal threshold. Processing step 304 may also include: determining whether the current signal value is less than the second signal threshold before the timer expires; and initiating a switching process without starting the algorithm.

[0082] Processing step 304 may further include: determining whether the current signal value is greater than a first signal threshold after starting the algorithm; terminating the algorithm if the current signal value is greater than the first signal threshold; and periodically importing previous signal values ​​from the previous inference dataset for initial switching.

[0083] Processing step 304 may further include: determining whether the current signal value is similar to a previous signal value in a previous switching process; starting the algorithm if the current signal value is similar to a previous signal value in a previous switching process; and monitoring multiple signal values ​​if the current signal value is greater than a first signal threshold.

[0084] Processing step 304 may further include: pre-determining a first signal threshold, a second signal threshold, and a timer value; and communicating the first signal threshold, the second signal threshold, and the timer value.

[0085] In an embodiment, the gNB (or base station) may be configured with two RSRP or RSRQ thresholds (e.g., a first signal threshold T1 and a second signal threshold T2), and a short timer for the UE (or user equipment). Based on the pre-configured signal value thresholds and timer, the UE may run an AI / ML algorithm to predict handover, and then store the RSRP or RSRQ value (or current signal value) at the time of handover in a dataset (e.g., an inference dataset). The gNB (or base station) may use SIB, MIB, or RRC reconfiguration messages to signal the thresholds and timer. The UE may also monitor RSRP or RSRQ for each received signal and compare it with the configured thresholds (e.g., T1 and T2). If the RSRP or RSRQ threshold is less than the pre-configured threshold T1, the UE may start the configured timer. If the RSRP or RSRQ drops below T2 at any point during the timer's execution, the UE handover may be performed without executing the AI / ML algorithm. If the timer expires and RSRP or RSRQ is less than T1, the UE can then assume the possibility of a handover in the next few seconds and can proactively initiate the AI / ML algorithm to check if a handover is needed.

[0086] In one embodiment, the AI / ML model can periodically import the most recent RSRP or RSRQ values ​​from the dataset for inference and run the model to predict the switch. When RSRP or RSRQ is greater than T1, the dataset can be continuously updated with all received RSRP or RSRQ values ​​based on this condition. In this case, the dataset can be appended in real-time. In another embodiment, the dataset can be continuously updated when RSRP or RSRQ is less than T1. In this embodiment, the dataset is appended after the AI / ML model is executed, and the values ​​at the time of the switch can be stored in the dataset.

[0087] In another embodiment, the AI / ML algorithm running on the UE can be stopped when a handover is performed and the RSRP or RSRQ of the new gNB (or base station) is higher than T1. In this embodiment, the UE can then activate the AI / ML algorithm to predict the handover when the RSRP or RSRQ value at a later time is similar to the value at the time of the previous handover. This can result in further energy savings compared to running the algorithm every time T1 is met. In an alternative embodiment, the UE can execute the AI / ML algorithm only if the RSRP or RSRQ value differs from the previous value at the time of the handover. This can result in savings in UE power consumption and computing resources.

[0088] Regarding output step 306, according to embodiments of the invention, as an option, an output signal may be communicated, for example. For example, the output signal may optionally be communicated from device 102. In a more specific example, according to embodiments of the invention, the output signal may optionally be communicated from device 102 to one or both of at least one device 104 and another device 102. In embodiments, device 102 (or UE) may also perform input step 302, processing step 304, and output step 306.

[0089] This disclosure further envisions 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 in reference method 300. For example, according to an embodiment of the invention, 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.

[0090] This disclosure further envisions a computer-readable storage medium (not shown) storing data representing software executable by a computer (not shown), the software including instructions that, when executed by a computer, perform input step 302, processing step 304, and / or output step 306 as discussed in reference method 300. For example, according to an embodiment of the invention, the computer-readable storage medium may store data representing software executable by a computer, the software including instructions that, when executed by a computer, cause the computer to perform input step 302 and / or processing step 304.

[0091] In light of the foregoing, it should be understood that this disclosure generally envisions a device 102, which may include a first module 202, a second module 204, and / or a third module 206.

[0092] The first module 202 can be configured to receive one or more input signals. These input signals may, for example, be associated with a first signal threshold, a second signal threshold, and a timer value.

[0093] The second module 204 may be configured to process the input signal and / or facilitate the processing of the input signal according to the method 300 discussed earlier, to generate one or more output signals.

[0094] The third module 206 can be configured to convey one or more output signals. These output signals may, for example, correspond to one or more control signals used to initiate a switch in the network.

[0095] In one embodiment, device 102 may correspond to a user equipment (UE) capable of communicating with a device 104 corresponding to a base station. The base station may, for example, correspond to a next-generation node B (gNB) that can be configured to transmit one or more signals (e.g., input signals) to the UE.

[0096] Furthermore, in view of the foregoing, it should be understood that this disclosure generally contemplates a system 100 that may include one or more devices 102 and one or more apparatuses 104. Devices 102 and apparatuses 104 may be coupled, for example, via wired coupling and / or wireless coupling.

[0097] It should be understood that the embodiments described above can be combined in any way where 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).

[0098] Those skilled in the art will further understand that variations and combinations of the embodiments described above, rather than alternatives or substitutes, can be combined to form even further embodiments.

[0099] In one example, the possibility of an output signal being communicated from device 102 is discussed. It should be understood that the output signal does not necessarily need to be communicated from device 102. Specifically, according to embodiments of the invention, the possibility that the output signal does not necessarily need to be communicated outside device 102 is envisioned. More specifically, according to embodiments of the invention, the output signal may correspond, for example, to internal commands / instructions (e.g., communicated only within device 102) for adaptively controlling the operational configuration of device 102.

[0100] In another example, according to embodiments of the invention, the application of this disclosure may be possible in the context of low-power wake-up radios and / or ambient IoT (Internet of Things) type devices.

[0101] Figure 4A and Figure 4B Examples of embodiments according to the present invention are shown. Figure 3 A schematic diagram of the information flow associated with the method.

[0102] In such Figure 4AIn the example context shown, a user equipment (UE) may be configured to receive one or more input signals that can be transmitted from a gNB (or base station). The UE may, for example, be configured to process the input signals. These input signals may include a first signal threshold T1, a second signal threshold T2, and a timer value, or be associated with them. The UE may further monitor RSRP / RSRQ values ​​to compare them with the received pre-configured thresholds. Subsequently, the UE may, for example, be configured to determine or evaluate (e.g., detect or measure) the current RSRP / RSRQ signal value and determine whether the current RSRP / RSRQ signal value is less than the received first signal threshold T1. If the current RSRP / RSRQ signal value is less than the received first signal threshold T1, a timer is started or activated based on the received timer value. On the other hand, if the UE determines that the current RSRP / RSRQ signal value is not less than the received first signal threshold T1, the UE appends the RSRP / RSRQ value to the dataset and continues to evaluate or monitor the signal values.

[0103] After the short timer has started, the UE assesses whether the timer has expired and simultaneously monitors the RSRP / RSRQ signal values. If the UE determines that the timer has expired, it further determines whether the current RSRP / RSRQ signal value is less than the received second signal threshold T2. If the timer has not expired, the UE further determines whether the current RSRP / RSRQ signal value is less than the received second signal threshold T2. If the UE determines that the current RSRP / RSRQ signal value is less than the received second signal threshold T2, the UE initializes the AI / ML algorithm to predict handover. If the AI / ML algorithm predicts handover, the UE initializes the handover process and appends the RSRP / RSRQ signal values ​​to the dataset. On the other hand, if the AI / ML algorithm does not predict handover, the UE continues to monitor the RSRP / RSRQ signal value and determines whether the signal value is less than the received second signal threshold T2. If the UE determines that the current RSRP / RSRQ signal value is greater than the second signal threshold T2, the UE may also initialize the handover process and append the RSRP / RSRQ signal values ​​to the dataset.

[0104] In such Figure 4BIn the example context shown, according to embodiments of the invention, the gNB (or base station) may, for example, be configured to generate / define / (pre-)configure / set two RSRP / RSRQ signal thresholds and / or communicate one or more input signals that may correspond to, be associated with, or include the generated / defined / (pre-)configured / set RSRP / RSRQ signal thresholds. Furthermore, according to embodiments of the invention, the gNB (or base station) may, for example, be configured to perform or determine one or more processing tasks associated with at least one timer value (e.g., a short timer) for the UE. It should be understood that, according to embodiments of the invention, the input signals that may be communicated from the gNB (or base station) may, for example, further include or indicate the timer value.

[0105] Various embodiments of this disclosure for addressing at least one of the aforementioned disadvantages have been described in the foregoing manner. Such embodiments are intended to be covered by the appended claims and are not limited to the specific form or arrangement of the components so described, and it will be apparent to those skilled in the art, in view of this disclosure, that many changes and / or modifications can be made, which are also intended to be covered by the appended claims.

Claims

1. A method (300) for initiating handover in a network, the method comprising: Input step (302), the input step includes receiving at least one input signal associated with a first signal threshold, a second signal threshold and a timer value; as well as Processing step (304), the processing step includes at least one of the following: Determine whether the current signal value is less than the first signal threshold; If the current signal value is less than the first signal threshold, the timer is started based on the timer value; When the timer expires, determine whether the current signal value is less than the second signal threshold; The algorithm is initiated if the current signal value is less than the second signal threshold. as well as Update the inference dataset associated with the current signal value to initiate a switch in the network.

2. The method (300) according to claim 1, wherein, Updating the inference dataset associated with the current signal value includes attaching the current signal to the inference dataset based on at least one of the following: after the algorithm is started, the current signal value is greater than the first signal threshold, the current signal value is greater than the second signal threshold, and / or the current signal value is less than the first signal threshold.

3. The method (300) according to claim 1, wherein, The processing step (304) further includes: Before the timer expires, determine whether the current signal value is less than the second signal threshold; and Initiate the switching process without activating the algorithm.

4. The method (300) according to claim 1, wherein, The processing step (304) further includes: After initiating the algorithm, determine whether the current signal value is greater than the first signal threshold; and The algorithm is terminated if the current signal value is greater than the first signal threshold.

5. The method (300) according to claim 1, wherein, The processing step (304) further includes: periodically importing previous signal values ​​from the previous inference dataset for initializing the switching.

6. The method (300) according to claim 1, wherein, The processing step (304) further includes: Determine whether the current signal value is similar to a previous signal value during a previous handover process; and The algorithm is activated when the current signal value is similar to the previous signal value during the previous switching process.

7. The method (300) according to claim 1, wherein, The processing step (304) further includes: monitoring multiple signal values ​​when the current signal value is greater than the first signal threshold.

8. The method (300) according to claim 1, wherein, The algorithm is an artificial intelligence / machine learning (AI / ML) algorithm.

9. The method (300) according to claim 1, wherein, The current signal value, the first signal threshold, and the second signal threshold correspond to at least one value that can be associated with the reference signal received power (RSRP) and the reference signal received quality (RSRQ).

10. The method (300) according to claim 1, wherein, At least one base station is configured as follows: The first signal threshold, the second signal threshold, and the timer value are predetermined; as well as The first signal threshold, the second signal threshold, and the timer value are communicated.

11. The method (300) according to claim 10, wherein, The at least one base station corresponds to at least one next-generation node B, i.e., gNB.

12. The method (300) according to claim 11, wherein, The user equipment (UE) is configured to perform the input step (302) and the processing step (304), wherein the first signal threshold, the second signal threshold, and the timer value are capable of being transmitted from the gNB to the UE.

13. The method (300) according to claim 12, wherein, The first signal threshold, the second signal threshold, and the timer value are received by the UE from the gNB via at least one of the following: System Information Block (SIB), Master Information Block (MIB), Radio Resource Control (RRC) reconfiguration message, and / or UE-specific message.

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

15. A computer-readable storage medium storing data representing software executable by a computer, the software including instructions that, when executed by the computer, are used to perform at least one of the input step (302) and the processing step (304) of the method (300) according to any one of claims 1 to 13.

16. A device (102) for initiating a handover in a network, the device comprising: A first module (202) is configured to receive at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value; The second module (204) is configured to process and facilitate at least one of the processing steps (304) of the method (300) according to claims 1 to 13 to generate at least one output signal; as well as The third module (206) is configured to convey at least one output signal. The output signal corresponds to the control signal used to initiate switching in the network.

17. The device (102) according to claim 16, in, The device (102) corresponds to a user equipment (UE) capable of communicating with a device (104) corresponding to a base station, and The base station corresponds to a next-generation node B, i.e., a gNB, configured to transmit the at least one input signal to the UE.

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

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