Systems and devices for initializing algorithms and associated methods thereof

Abstract

A system (100), device (102), and method (300) for initializing an algorithm 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, the first signal threshold being greater than the second signal threshold; 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 greater than the second signal threshold when the timer expires; wherein if the current signal value is greater than the second signal threshold, the algorithm is initialized, and if the current signal value is less than the second signal threshold, a switching process is initialized.

Description

Technical Field

[0001] This disclosure generally relates to one or both of a system and apparatus for initializing an algorithm 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 apparatus. 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 determine handover from user equipment (UE) via base stations in communication networks may face problems such as service interruptions, handover delays, and even handover failures. Therefore, current technologies may not optimally promote efficiency and power savings.

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

[0005] According to a first aspect of the present invention, a method for initializing an algorithm 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, wherein the first signal threshold is greater than the second signal threshold; and a processing step comprising at least one of: determining whether a current signal value is less than the first signal threshold; if the current signal value is less than the first signal threshold, starting a timer based on the timer value; and when the timer expires, determining whether the current signal value is greater than the second signal threshold; wherein if the current signal value is greater than the second signal threshold, the algorithm is initialized, and if the current signal value is less than the second signal threshold, a switching process is initialized.

[0006] Advantageously, methods with signal thresholds and timers, as described herein, can prevent service interruptions, delays, and handover failures.

[0007] In this embodiment, the algorithm is an artificial intelligence / machine learning (AIML) algorithm.

[0008] In one embodiment, the processing steps further include determining whether the current signal value is less than a second signal threshold, wherein if the current signal value is less than the second signal threshold, the timer is stopped.

[0009] In this embodiment, if the current signal value is less than the second signal threshold, the switching process is initialized.

[0010] 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).

[0011] In an embodiment, the processing steps further include monitoring multiple signal values ​​if the current signal value is greater than a first signal threshold.

[0012] 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 transmit the first signal threshold, the second signal threshold, and the timer value.

[0013] In this embodiment, the at least one base station corresponds to at least one next-generation node B (gNB).

[0014] 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 transmitted from the gNB to the UE.

[0015] In the embodiment, the UE receives a first signal threshold, a second signal threshold, and a timer value from the gNB via at least one of a System Information Block (SIB) or a UE-specific message.

[0016] 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.

[0017] 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 the computer, are used to perform at least one of an input step and a processing step according to the method of the first aspect.

[0018] In one embodiment, an apparatus for initializing an algorithm is provided, the apparatus comprising: a first module configured to receive at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value, the first signal threshold being greater than the second signal threshold; a second module configured to process and facilitate at least one of the processing steps of the method according to a first aspect to generate at least one output signal; and a third module configured to transmit at least one output signal, wherein the output signal corresponds to a control signal configured to: initialize the algorithm if a current signal value is greater than the second signal threshold, and initialize a switching process if the current signal value is less than the second signal threshold.

[0019] In one embodiment, the device corresponds to a user equipment (UE) that can communicate with a device corresponding to a base station, and the base station corresponds to a next-generation node B (gNB) configured to transmit the at least one input signal to the UE.

[0020] 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.

[0021] Advantageously, systems as disclosed herein can save energy at the UE and initiate UE delivery in a timely manner to reduce handover latency. Attached Figure Description

[0022] Embodiments of this disclosure are described below with reference to the accompanying drawings, in which:

[0023] Figure 1A A schematic diagram of a system for initializing an algorithm according to an embodiment of the present invention is shown, the system including at least one device.

[0024] Figures 1B to 1D An embodiment of the invention is shown with Figure 1A Example scenarios associated with the system.

[0025] Figure 2 Further details illustrating embodiments according to the present invention are shown. Figure 1A A schematic diagram of the equipment.

[0026] Figure 3 An embodiment of the invention is shown with Figure 1A The system-related methods.

[0027] Figure 4A and Figure 4B The illustration shows an embodiment of the invention. Figure 3 A schematic diagram of the information flow associated with the method. Detailed Implementation

[0028] Some parts of the following description are presented explicitly or implicitly in the form of functional or symbolic representations of algorithms and operations on data within computer memory. These algorithmic descriptions and functional or symbolic representations are means used by those skilled in the art of data processing to most effectively communicate the substance of their work to others skilled in the art. An algorithm herein and generally is considered as a self-consistent sequence of steps that produce a desired result. These steps are steps that require physical manipulation of physical quantities, such as electrical, magnetic, or optical signals that can be stored, transmitted, combined, compared, and otherwise manipulated.

[0029] 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 device selectively activated or reconfigured by a computer program stored in a 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 required method steps. The structure of the computer will become apparent from the description below.

[0030] 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 encodings can be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. Many other variations of the computer program may be used, which may employ different control flows without departing from the spirit or scope of this disclosure.

[0031] Furthermore, one or more steps of a computer program may be executed in parallel, rather than sequentially. Such a computer program can be stored on any computer-readable medium. Computer-readable media may include storage devices such as disks or optical discs, memory chips, or other storage devices suitable for interfacing with a computer. Computer-readable media may also include hardwired media, as exemplified in an Internet system, or wireless media, as exemplified in a mobile phone system. When a computer program is loaded onto such a computer and executed, it effectively produces a device for implementing the steps of a preferred method.

[0032] 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 artificial intelligence and machine learning (AIML or AI / ML) for the UE in conjunction with the 3GPP Release 19 (and later) standards.

[0033] In communication networks, methods or mechanisms are used to define how handover (HO) is performed. These can include network (NW) triggered HO, lower-layer triggered mobility (LTM) HO, and conditional HO (CHO). Performing a successful handover is important because it allows the UE to maintain connectivity within the network and for information to be correctly transmitted from one base station to another. Handover decisions can be made based on historical measurements, rapid response measurements (RRM), and / or UE location. If a handover failure occurs, the UE may be unable to connect to the network, and therefore failure handling methods focus on enabling the UE to quickly recover to the network.

[0034] This disclosure generally envisions that such handover mechanisms and failure handling methods can be reactive in design. For example, inaccurate rapid response measurements (RRM) and inaccurate UE positioning can impair sound handover decisions. Furthermore, due to UE mobility, other issues such as radio link failure (RLF), beam failure, and handover failure (HOF) may exist. Complexity, measurement workload, and signaling overhead may also be high, and it may not be possible to eliminate interruptions or poor service caused by failure events.

[0035] This disclosure envisions artificial intelligence / machine learning (AI / ML or AIML) as a key technology in network optimization, enabling energy-efficient networks. Specifically, applying AIML (or AI / ML) to communication links (e.g., air interfaces) can optimize handover by using AIML (or AI / ML) algorithms. If AIML 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.

[0036] This disclosure envisions the possibility of incorporating AIML (or AI / ML) algorithms into standard network systems, signaling aspects, and triggering mechanisms. More specifically, the triggering mechanisms and signaling aspects used to execute AI / ML models may be important, considering the energy efficiency of the UE.

[0037] This disclosure also envisions AI / ML-assisted mobility and handover optimization that can be implemented on the network side or UE side. For example, this could include candidate cell or target cell prediction in Layer 3 (L3) based mobility, and candidate beam and cell 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)).

[0038] Base stations (e.g., next-generation Node B (gNB)) can determine the target location (HO) based on UE measurements and prepare for the HO. When the UE is mobile, there may be a delay between the UE measurements of the reference signal and the actual handover time. The UE can use AIML (or AI / ML) to predict the optimal target cell and other handover parameters related to the handover. This disclosure envisions the possibility of how the UE should optimize the AIML timer (i.e., when to execute AIML) to optimize UE energy consumption.

[0039] According to embodiments of the present invention, in the manner described above, it may be possible to promote power saving and energy consumption efficiency in the UE or network.

[0040] The foregoing will be discussed in further detail below with reference to Figures 1 to 4.

[0041] refer to Figure 1A According to an embodiment of the present invention, a schematic diagram illustrating a system 100 for initializing an algorithm is shown. According to an embodiment of the present invention, system 100 may be adapted, for example, to promote energy / power efficiency.

[0042] 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.

[0043] Device 102 can be coupled to device 104. Specifically, according to an embodiment of the invention, device 102 can be coupled to device 104, for example, via communication network 106.

[0044] 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 one or both wired and wireless coupling. According to embodiments of the invention, device 102 may generally be configured to communicate with device 104 via communication network 106.

[0045] 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 carrying 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 into 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.

[0046] 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 transmitted 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, wherein the first signal threshold is greater than the second signal threshold. As a possible option, according to an embodiment of the invention, output signals may be transmitted from device 102, for example. The output signals may correspond to control signals used to initialize an algorithm (e.g., an AI / ML algorithm). According to an embodiment of the invention, reference will be made later. Figure 2 Let’s discuss device 102 in further detail.

[0047] 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 / be associated with / comprise 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 an embodiment 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 an embodiment of the invention.

[0048] 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 through one or both of wired and wireless communication.

[0049] As mentioned, device 102 may, for example, be configured 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, for example, be configured to generate (and transmit) an input signal to device 102. Therefore, device 104 may predetermine a first signal threshold, a second signal threshold, and a timer value, and also transmit the first signal threshold, the second signal threshold, and the timer value to device 102. According to embodiments of the invention, reference will be made below. Figures 1B to 1D This will be discussed within the context of an example scenario.

[0050] Figures 1B to 1DAn embodiment of the invention is shown with Figure 1A Example scenarios associated with the system. Specifically, Figures 1B to 1D It shows in Figure 1A Different examples of switching optimizations initialized via AIML in the system. Figure 1B An example is shown where training is performed 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, a 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 through device 104 (network side) or only through device 102 (UE side).

[0051] In an alternative embodiment, Figure 1C An example of joint training of both sides of the system (at device 104 / base station (or network side, e.g., gNB) and device 102 (or UE side)) is shown. In this embodiment, training entities can be provided to each of device 104 (network side) and device 102 (UE side). Each of the training entities 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 through 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 through device 104, while the UE-side neural network (or UE-side neural network) is trained and configured to determine the handover through device 102. Furthermore, the network-side neural network can perform backward gradient techniques on device 102 (UE side), and the UE-side neural network can perform forward activation techniques on device 104 (network side) to determine and optimize the handover through device 104 and device 102.

[0052] In another embodiment, Figure 1DAn example of separate training on both sides of the system (at device 104 / base station (or network side, e.g., gNB) and device 102 (or UE side)) is illustrated. In this embodiment, training entities can be provided to each of device 104 (network side) and device 102 (UE side). Each of the training entities may include a corresponding module (not shown) configured to implement a corresponding neural network for handover, such that the corresponding neural network can be used to determine handover by both device 104 (network side) and device 102 (UE side). Specifically, the UE-side neural network (or UE-side neural network) can be trained using a first training dataset and configured to determine handover by device 102. 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 determine handover by device 104. It is understood that the training order can be reversed, i.e., the network-side neural network is trained first using the training dataset, and then the UE-side neural network is trained after the network-side neural network shares the training dataset with the UE-side neural network.

[0053] The neural network described in this disclosure can be an artificial intelligence (AI) based neural network model, such as a support vector machine, decision tree, ensemble model, k-nearest neighbor model, or Bayesian network. It is understood that the neural network can also be a multimodal neural network and can incorporate 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 is also understood that training other types of neural networks may be available and is not limited to the examples described herein.

[0054] 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.

[0055] The following will refer to Figure 2 The aforementioned device 102 or user equipment (UE) will be discussed in further detail.

[0056] refer to Figure 2 According to an embodiment of the present invention, a schematic diagram of the illustrated device 102 is shown in further detail in the context of example implementation 200.

[0057] 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 into a vehicle by a user. 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 respect, electronic module 200a can be considered as being carried by the vehicle (e.g., carried into the vehicle by a user or installed / assembled in the vehicle).

[0058] According to embodiments of the present invention, it is envisioned that electronic module 200a can be capable of performing one or more processing tasks associated with adaptive / dynamic / progressive control-related processing.

[0059] 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.

[0060] 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.

[0061] In this regard, it can 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.

[0062] 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 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.

[0063] 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).

[0064] According to an embodiment 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 Further detailed discussion is needed.

[0065] The third module 206 may correspond to a hardware-based transmitter that can be 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 for initializing an algorithm used for switching.

[0066] This disclosure envisions the possibility that the first module 202 and the second module 204 can be integrated software-hardware modules, such as 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 software-hardware modules, such as electronic components carrying software programs or 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.

[0067] According to embodiments of the present invention, the UE can also be configured, for example, to process input signals in a manner as will be referred to later. Figure 3 (To be discussed in further detail) so as to generate one or more output signals in a manner that promotes efficiency, such as power efficiency or energy efficiency. In a particular example, according to an embodiment of the invention, the output signal may include one or more control signals for promoting 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 for initializing an algorithm used for switching.

[0068] 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 are similarly applicable to the system 100 discussed earlier in this disclosure.

[0069] refer to Figure 3 According to an embodiment of the invention, a method 300 (or communication method) associated with system 100 for initializing an algorithm (e.g., an artificial intelligence / machine learning (AIML or AI / ML) algorithm) is shown.

[0070] According to embodiments of the present invention, method 300 may be adapted, for example, to promote energy efficiency, network optimization and power saving.

[0071] 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.

[0072] 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 yet another embodiment, processing method 300 may include an input step 302, a processing step 304, and an output step 306. In still another embodiment, processing method 300 may include a processing step 304 and one or both of input step 302 and output step 306. In yet 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 still 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).

[0073] For 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.

[0074] 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 signal threshold may be greater than the second signal threshold, and at least one of the first and second signal thresholds may be associated with a conditional switching threshold. The first and second signal thresholds 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 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 a system information block (SIB) or a UE-specific message (e.g., radio resource control (RRC) reconfiguration).

[0075] Regarding processing step 304, according to an embodiment of the present invention, at least one processing task may be performed in association with the received input signal in order to generate one or more output signals.

[0076] Processing step 304 may include at least one of the following: determining whether the current signal value is less than a first signal threshold; if the current signal value is less than the first signal threshold, starting a timer based on a timer value; and when the timer expires, determining whether the current signal value is greater than a second signal threshold, such that if the current signal value is greater than the second signal threshold, the algorithm is initialized, and if the current signal value is less than the second signal threshold, the handover process is initialized. 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).

[0077] Processing step 304 may further include determining whether the current signal value is less than a second signal threshold, and stopping the timer if the current signal value is less than the second signal threshold. Subsequently, if the current signal value is less than the second signal threshold, the handover process is initialized. In an example embodiment, a handover may be initiated to a candidate target cell (or another base station) without executing the AIML algorithm. Processing step 304 may also include monitoring multiple signal values ​​if the current signal value is greater than a first signal threshold.

[0078] Regarding output step 306, according to embodiments of the present invention, as an option, an output signal may be transmitted, for example. For example, an output signal may optionally be transmitted from device 102. In a more specific example, according to embodiments of the present invention, an output signal may optionally be transmitted 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.

[0079] 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.

[0080] 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.

[0081] In light of the foregoing, it is 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.

[0082] The first module 202 can be configured to receive one or more input signals. The input signals may be associated, for example, with a first signal threshold, a second signal threshold, and a timer value, wherein the first signal threshold is greater than the second signal threshold.

[0083] 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.

[0084] The third module 206 can be configured to transmit one or more output signals. For example, the output signals may correspond to one or more control signals used to: initialize the algorithm if the current signal value is greater than a second signal threshold, and initialize the switching process if the current signal value is less than the second signal threshold.

[0085] In one embodiment, device 102 may correspond to a user equipment (UE) that can communicate with device 104 corresponding to a base station. For example, the base station may 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.

[0086] In light of the foregoing, it is understood that this disclosure generally envisions a system 100, which 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.

[0087] 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).

[0088] 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.

[0089] In one example, the possibility of transmitting an output signal from device 102 is discussed. It is understood that the output signal does not necessarily need to be transmitted from device 102. Specifically, according to embodiments of the invention, the possibility that the output signal does not necessarily need to be transmitted outside of device 102 is envisioned. More specifically, according to embodiments of the invention, the output signal may, for example, correspond to internal commands / instructions for adaptively controlling the operational configuration of device 102 (e.g., transmitted only within device 102).

[0090] In another example, according to embodiments of the invention, the application of the invention in the context of low-power wake-up radios and / or ambient IoT (Internet of Things) type devices may be possible.

[0091] Figure 4A and Figure 4B The illustration shows an embodiment of the invention. Figure 3 A schematic diagram of the information flow associated with the method.

[0092] In such Figure 4AIn the illustrated example context, according to embodiments of the invention, a gNB (or base station) may, for example, be configured to generate / define / (pre)configure / set two or more RSRP / RSRQ signal thresholds defining an RSRP / RSRQ region and / or transmit one or more input signals that may correspond to or be associated with, or include, the generated / defined / (pre)configured / set RSRP / RSRQ threshold range. Furthermore, according to embodiments of the invention, a 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. Understandably, according to embodiments of the invention, input signals that may be transmitted from the gNB (or base station) may, for example, further include or indicate timer values.

[0093] In such Figure 4B In the illustrated example context, at step 1, the 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 be further configured, for example, to process the input signals. The input signals may include a first signal threshold, a second signal threshold, and a timer value, or be associated with the first signal threshold, the second signal threshold, and the timer value, wherein the first signal threshold is greater than the second signal threshold. At step 2, the UE may be configured, for example, to 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. If the current RSRP / RSRQ signal value is less than the received first signal threshold, then at step 3, 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, then at step 4, the UE continues to evaluate or monitor the signal value.

[0094] At step 5, the UE assesses whether the timer has expired. If the UE assessment timer has expired, at step 6, the UE further determines whether the current RSRP / RSRQ signal value is greater than the received second signal threshold. If the UE assessment timer has not expired, at step 7, the UE further determines whether the current RSRP / RSRQ signal value is less than the received second signal threshold.

[0095] At step 8, if the UE determines that the current RSRP / RSRQ signal value is greater than the received second signal threshold (step 6), the UE initializes the AI / ML algorithm to predict handover. Conversely, if the UE determines that the current RSRP / RSRQ signal value is not greater than the second signal threshold (step 6), the UE initializes the handover process at step 9. In an alternative embodiment, if the UE determines at step 7 that the current RSRP / RSRQ signal value is less than the received second signal threshold, the UE can stop the timer and initialize the handover process in step 9. If the UE determines at step 7 that the current RSRP / RSRQ signal value is not less than the received second signal threshold, the UE continues to evaluate whether the timer has expired at step 5.

[0096] 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 initializing an algorithm, the method comprising: Input step (302), the input step comprising receiving at least one input signal associated with a first signal threshold, a second signal threshold, and a timer value, wherein the first signal threshold is greater than the second signal threshold; and 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, then the timer is started based on the timer value; When the timer expires, determine whether the current signal value is greater than the second signal threshold; If the current signal value is greater than the second signal threshold, the algorithm is initialized; if the current signal value is less than the second signal threshold, the switching process is initialized.

2. The method (300) according to claim 1, wherein the algorithm is an artificial intelligence / machine learning AIML algorithm.

3. The method (300) according to claim 1, wherein at least one of the first signal threshold and the second signal threshold can be associated with a condition switching threshold.

4. The method (300) according to claim 1, wherein the processing step (304) further comprises: Determine whether the current signal value is less than the second signal threshold, wherein if the current signal value is less than the second signal threshold, then stop the timer.

5. The method (300) according to claim 4, wherein if the current signal value is less than the second signal threshold, the switching process is initialized.

6. 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.

7. The method (300) according to claim 1, wherein the processing step (304) further comprises: If the current signal value is greater than the first signal threshold, then multiple signal values ​​are monitored.

8. The method (300) according to claim 1, wherein at least one base station is configured to: The first signal threshold, the second signal threshold, and the timer value are predetermined; and Transmit the first signal threshold, the second signal threshold, and the timer value.

9. The method (300) according to claim 8, wherein the at least one base station corresponds to at least one next-generation node B (gNB).

10. The method (300) of claim 9, wherein the user equipment (UE) is configured to perform the input step (302) and the processing step (304), and wherein the first signal threshold, the second signal threshold, and the timer value are transmittable from the gNB to the UE.

11. The method (300) of claim 10, wherein the UE receives the first signal threshold, the second signal threshold, and the timer value from the gNB via at least one of a System Information Block (SIB) or a UE-specific message.

12. 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.

13. 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 claims 1 to 11.

14. A device (102) for initializing an algorithm, 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, wherein the first signal threshold is greater than the second signal threshold. 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 11 to generate at least one output signal; as well as The third module (206) is configured to transmit at least one output signal. The output signal corresponds to the control signal, which is used to: initialize the algorithm if the current signal value is greater than the second signal threshold, and initialize the switching process if the current signal value is less than the second signal threshold.

15. The device (102) according to claim 14, The device (102) therein 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 (gNB) configured to transmit the at least one input signal to the UE.

16. A system (100) comprising: At least one device (102) according to any one of claims 14 and 15; as well as At least one device (104) according to claim 15, The device (102) and the apparatus (104) are coupled via at least one of wired coupling and wireless coupling.

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