Prediction of boot performance of communication device

Abstract

Embodiments of the present disclosure relate to prediction of boot performance of a communication device. In one aspect, an electronic device may obtain a set of configurations of a communication device and a set of test values for boot performance of the communication device for the set of configurations, and construct a model for boot performance prediction by using configurations in the set of configurations as inputs and using corresponding test results in the set of test values as outputs. On the other hand, the electronic equipment can obtain the configuration of the communication equipment to be tested; and determining a predicted value of startup performance of the communication device by using the configuration as an input to the model. In this way, prediction of boot performance for different configurations may be achieved based on limited test results.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of telecommunications, and more particularly, to methods, devices, apparatuses, and computer-readable storage media for predicting startup performance of communication devices. Background Art

[0002] System startup performance (e.g., startup time) depends on many factors, including different hardware (HW) combinations, software (SW) versions, SW configurations, and SW feature enablement / disablement. Customers often require nearly identical startup performance targets for communications equipment or products. This means that each configuration of a communications device should adhere to the customer's startup performance targets. However, communications equipment has thousands of configurations, and customers expect all of these configurations to meet the startup performance targets. Therefore, testing startup performance for such a large number of communications equipment configurations is a significant undertaking. Summary of the Invention

[0003] Generally speaking, example embodiments of the present disclosure provide a scheme for predicting startup performance of a communication device.

[0004] In a first aspect, an electronic device is provided. The electronic device includes at least one processor and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, together with the at least one processor, cause the electronic device to: obtain a configuration set of a communication device and a test value set of startup performance of the communication device for the configuration set; and construct a model for startup performance prediction by using configurations in the configuration set as input and corresponding test results in the test value set as output.

[0005] In a second aspect, an electronic device is provided. The electronic device includes at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code are configured to, together with the at least one processor, cause the electronic device to: obtain a configuration of a communication device to be tested; and determine a predicted value of startup performance of the communication device by using the configuration as input to a model for startup performance prediction.

[0006] In a third aspect, a method for communication is provided. The method includes: obtaining, at an electronic device, a configuration set of the communication device and a test value set of startup performance of the communication device for the configuration set; and constructing a model for startup performance prediction by using the configurations in the configuration set as input and the corresponding test results in the test value set as output.

[0007] In a fourth aspect, a method for communication is provided, comprising: obtaining, at an electronic device, a configuration of a communication device to be tested; and determining a predicted value of startup performance of the communication device by using the configuration as input to a model for startup performance prediction.

[0008] In a fifth aspect, a communication apparatus is provided. The apparatus includes: a component for acquiring, at an electronic device, a configuration set of the communication device and a test value set of startup performance of the communication device for the configuration set; and a component for constructing a model for startup performance prediction using configurations in the configuration set as input and corresponding test results in the test value set as output.

[0009] In a sixth aspect, a communication apparatus is provided, comprising: a component for acquiring, at an electronic device, a configuration of a communication device to be tested; and a component for determining a predicted value of startup performance of the communication device by using the configuration as an input of a model for startup performance prediction.

[0010] In a seventh aspect, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium comprising program instructions for causing an apparatus to execute the method according to the third or fourth aspect.

[0011] It should be understood that the summary is not intended to identify the key or essential features of the embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Some example embodiments will now be described with reference to the accompanying drawings, in which:

[0013] Figure 1 shows an example environment in which embodiments of the present disclosure may be implemented;

[0014] Figure 2 A flowchart illustrating an example method implemented at an electronic device according to some embodiments of the present disclosure is shown;

[0015] Figure 3 A flowchart illustrating another example method implemented at an electronic device according to some embodiments of the present disclosure; and

[0016] Figure 4 A simplified block diagram of an electronic device suitable for implementing embodiments of the present disclosure is shown.

[0017] Throughout the drawings, the same or similar reference numerals denote the same or similar elements. DETAILED DESCRIPTION

[0018] The principles of the present disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are described for illustrative purposes only and to help those skilled in the art understand and implement the present disclosure, without implying any limitation on the scope of the present disclosure. The disclosure described herein can be implemented in various ways different from the ways described below.

[0019] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0020] References in the specification to "one embodiment," "an embodiment," "an example embodiment," etc., indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes that particular feature, structure, or characteristic. Furthermore, these terms do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it should be understood that it is within the knowledge of those skilled in the art to affect that feature, structure, or characteristic in conjunction with other embodiments, whether or not explicitly described.

[0021] It should be understood that although the terms "first" and "second" etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. As used herein, the term "and / or" includes any and all combinations of one or more of the listed items.

[0022] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the example embodiments. As used herein, unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" are intended to include the plural forms as well. It will be further understood that the terms "comprise," "include," "have," "have," "contain," and / or "including" when used herein specify the presence of the features, elements, and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0023] As used in this application, the term "circuitry" may refer to one or more or all of the following:

[0024] (a) hardware circuit implementation only (e.g., implementation only in analog and / or digital circuitry) and

[0025] (b) a combination of hardware circuitry and software, such as (where applicable):

[0026] (i) a combination of analog and / or digital hardware circuitry and software / firmware, and

[0027] (ii) any portion of a hardware processor with software (including a digital signal processor, software, and memory that work together to enable a device such as a mobile phone or server to perform various functions), and

[0028] (c) Hardware circuits and / or processors, such as a microprocessor or portion of a microprocessor, whose operation requires software (such as firmware), but which may not be present when the operation does not require software.

[0029] This definition of circuitry applies to all uses of the term in this application, including in any claims. As another example, as used in this application, the term circuitry also covers merely a hardware circuit or processor (or multiple processors) or a portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware implementation. For example, the term circuitry also covers a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in a server, cellular network device, or other computing device or network device, if applicable to the particular claim element.

[0030] As used herein, the term "communication device" refers to a device used in a communication network. The term "communication network" refers to a network that follows any suitable communication standard such as long term evolution (LTE), advanced LTE (LTE-A), wideband code division multiple access (WCDMA), new radio (NR), etc. In addition, the communication between the terminal equipment and the network equipment in the communication network can be performed according to any suitable generation of communication protocol, including but not limited to first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), future sixth generation (6G) communication protocol and / or any other protocol currently known or to be developed in the future. The embodiments of the present disclosure can be applied to various communication systems. In view of the rapid development of communication, there are of course future types of communication technologies and systems that can be used to implement the present disclosure. It should not be regarded as limiting the scope of the present invention to the above-mentioned system.

[0031] In some embodiments, the term "communication device" may refer to a network device. The term "network device" may refer to a node in a communication network through which a terminal device accesses the network and receives services. The communication network may be a radio access network (RAN). Network devices in the RAN may refer to base stations (BSs) or access points (APs), such as Node Bs (Node Bs or NBs), evolved Node Bs (eNodeBs or eNBs), NR next-generation Node Bs (also known as gNBs), remote radio units (RRUs), radio heads (RHs), remote radio heads (RRHs), relays, low-power nodes such as femtos and pico nodes, etc., depending on the terminology and technology used. The radio access network (RAN) split architecture includes a gNB-CU (centralized unit, hosting the radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) layers) that controls multiple gNB-DUs (distributed units, hosting the radio link control (RLC), media access control (MAC), and physical (PHY) layers).

[0032] Alternatively, the communication network may be a core network (CN). Network devices in the CN may include a policy control function (PCF), an access management function (AMF), a session management function (SMF), a user plane function (UPF), a unified data management (UDM), a unified data repository (UDR), an authentication server function (AUSF), a ProSe key management function (PKMF), a direct discovery name management function (DDNMF), a network exposure function (NEF), and the like.

[0033] In some embodiments, the term "communication device" may refer to a terminal device. The term "terminal device" refers to any terminal device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices may include, but are not limited to, mobile phones, cellular phones, smart phones, voice over IP (VoIP) phones, wireless local loop phones, tablet computers, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices such as digital cameras, game terminal devices, music storage and playback devices, vehicle-mounted wireless terminal devices, wireless endpoints, mobile stations, laptop embedded devices (LEEs), laptop mounted devices (LMEs), USB dongles, smart devices, wireless customer premises equipment (CPEs), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated process chain environments), consumer electronic devices, devices operating on commercial and / or industrial wireless networks, etc.

[0034] While in various example embodiments, the functions described herein may be performed in fixed and / or wireless network nodes, in other example embodiments, the functions may be implemented in a user equipment device (such as a cellular phone or tablet computer or laptop computer or desktop computer or mobile IoT device or fixed IoT device). The user equipment device may, for example, be suitably equipped with the corresponding capabilities described in conjunction with the fixed and / or wireless network nodes. The user equipment device may be a user device and / or a control device, such as a chipset or processor, configured to control the user device when installed therein. Examples of such functions include a bootstrapping server function and / or a home subscriber server, which may be implemented in the user equipment device by providing the user equipment device with software configured to cause the user equipment device to perform from the perspective of these functions / nodes.

[0035] As mentioned above, communications devices have thousands of configurations, and customers expect all of these configurations to match startup performance targets. Each configuration can have a different hardware combination and / or software configuration. For each configuration, startup performance will fluctuate from version to version, and even within a single version, startup performance will fluctuate across different test iterations. Testing so many combinations for a single version would be a massive undertaking.

[0036] A conventional testing or verification method is to select several typical configurations and test the startup performance of the typical configurations. For each typical configuration, many test iterations can be run and the average of these test iterations can be used to compare with the startup performance target.

[0037] Since there are so many configuration combinations for communication devices, it is impossible to test each combination to determine whether the combination meets the startup performance goals, as this would take too much testing time and test environments. Therefore, only a few configurations are tested, and the test coverage of each version is very low.

[0038] For other untested configurations, it is assumed that they will have similar results. To avoid problems discovered by testing, there will be relaxed startup performance targets for untested configurations. This will make communication products with untested configurations non-competitive.

[0039] In view of this, embodiments of the present disclosure provide a method for predicting the startup performance of a communication device. In this method, a machine learning (ML) method is applied to construct a model for startup performance prediction based on a set of test results for a set of configurations, and the model is used to predict the startup performance of untested configurations of the communication device.

[0040] This allows predictions of startup performance for different configurations based on limited test results. This helps verify startup performance for each version combination. Furthermore, converging startup performance targets and providing performance profiles for all configurations are helpful. This allows for proactive action before providing customers with configurations with poor performance.

[0041] Furthermore, using ML methods, startup performance can be predicted for all configurations, eliminating the need to "guess" startup performance for untested configurations. This saves significant time spent testing or manually analyzing untested configurations. Based on historical test results, startup performance can be accurately predicted for configurations, even if they have not been tested before.

[0042] The principles and implementations of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0043] Example of application environment

[0044] Figure 1 1 shows a schematic diagram of an example environment 100 in which embodiments of the present disclosure may be implemented. Figure 1 As shown, environment 100 may include a communication device 110 , a test device 120 , a computing device 130 , and a prediction device 140 .

[0045] like Figure 1As shown, communication device 110 may have configurations 111, 112, and 113. Each configuration may include different hardware components, software configurations, and / or topologies. Test equipment 120 may test the startup performance of each of configurations 111, 112, and 113. In this way, test results corresponding to the configurations may be obtained.

[0046] Based on configurations 111, 112, and 113 and the corresponding test results, computing device 130 can construct model 131 based on an ML method. It should be understood that the ML method can be any suitable ML algorithm, whether currently available or developed in the future, and the present disclosure is not limited in this respect. It should also be understood that computing device 130 can be an electronic device that supports model construction, such as a computer, a computing cluster, etc. In some embodiments, the electronic device can be a terminal device. In some embodiments, the electronic device can be a network device.

[0047] like Figure 1 As shown, prediction device 140 can use model 131 to predict startup performance for untested configuration 114 of communication device 110. It should be understood that prediction device 140 can be an electronic device that supports model use, such as a computer, computing cluster, etc. In some embodiments, the electronic device can be a terminal device. In some embodiments, the electronic device can be a network device. It should also be understood that although computing device 130 and prediction device 140 are shown as separate devices, computing device 130 and prediction device 140 can be the same device.

[0048] In some embodiments, the communication device 110 may be an access network device, a core network device, or a terminal device.

[0049] In this example, communication device 110 is shown as a base station. Alternatively, communication device 110 may be a cloud base station. As another example, communication device 110 may be a base transceiver station (BTS). It should be noted that these are merely examples, and communication device 110 may be any other suitable type of network device or terminal device.

[0050] Furthermore, it should be understood that the number of devices and configurations is for illustrative purposes only and does not imply any limitation. Environment 100 may include any suitable number or type of devices and configurations suitable for implementing embodiments of the present disclosure.

[0051] To illustrate, the following will combine Figure 2 and Figure 3 Some example embodiments of model building and use for startup performance prediction are described.

[0052] Example implementation of model building

[0053] Figure 2 FIG2 shows a flow chart of an example method 200 implemented at an electronic device (eg, computing device 130) according to some embodiments of the present disclosure. For the purposes of discussion, reference will be made to FIG200. Figure 1 Method 200 is described.

[0054] In block 210, the computing device 130 obtains a configuration set of the communication device 110 and a test value set of startup performance of the communication device 110 for the configuration set. In other words, the computing device 130 may obtain a learning set for model building.

[0055] In some embodiments, the computing device 130 may obtain a configuration set and a set of test values ​​for startup performance from the testing device 120. In some embodiments, the configuration set and the corresponding set of test values ​​for startup performance are stored in a storage device (not shown), and the computing device 130 may obtain the configuration set and the corresponding set of test values ​​for startup performance from the storage device. In some embodiments, the storage device may be a local storage device. In some embodiments, the storage device may be a cloud storage device. It should be noted that the storage device may take any other suitable form, and the computing device 130 may obtain the configuration set and the corresponding set of test values ​​for startup performance in any other suitable manner.

[0056] In block 220 , computing device 130 constructs a model for startup performance prediction using the configurations in the configuration set as input and the corresponding test results in the test value set as output. Computing device 130 may construct the model using any suitable ML algorithm currently available or developed in the future.

[0057] In some embodiments, each configuration in the configuration set may include a set of factors associated with the startup performance of the communication device 110. In some embodiments, the set of factors in the configuration may include at least one of hardware, software, or topology for a component in the communication device 110. In some embodiments, the component may include a baseband unit (BBU) or a baseband processing unit. In some embodiments, the component may include a radio unit (RU) or a radio processing unit or an antenna unit. It should be understood that these are merely examples and that the set of factors may also relate to any other suitable component of the communication device 110.

[0058] In some embodiments, the set of factors may include the number or type of hardware in the BBU. For example, the set of factors may include the number or type of system boards, capacity boards, or common boards including system boards and capacity boards. It should be understood that this is merely an example, and any other suitable hardware is also feasible.

[0059] In some embodiments, the factor set may include the number or type of software in the BBU. For example, the factor set may include the number or type of cloud system board functions, cloud capacity board functions, or cloud common board functions including system board functions and capacity board functions. It should be understood that this is merely an example, and any other suitable software is also feasible.

[0060] In some embodiments, the factor set may include the number or type of hardware in the RU. For example, the factor set may include the number or type of antennas. It should be understood that this is merely an example, and any other suitable hardware is also feasible.

[0061] In some embodiments, the factor set may include the number or type of software in the RU. For example, the factor set may include the number or type of antenna technologies. As another example, the factor set may include the number or type of antenna protocols. It should be understood that these are merely examples, and any suitable software is also feasible.

[0062] In some embodiments, the factor set may include the number of radio access technologies (RATs). In some embodiments, the factor set may include the number of cells. It should be understood that these are merely examples, and any other suitable topology is also feasible. It should also be understood that the factor set may include any combination of the above or any other suitable information.

[0063] Using the ML model, computing device 130 can determine a set of parameters associated with the set of factors. In other words, computing device 130 can determine a weight for each factor.

[0064] For example, as shown in Table 1, configurations 1 to 9 can be obtained. Each row represents a configuration with different factors, and each row will have a test result (not shown). In this example, the factors considered include the number of BBU system boards of type A, the number of BBU system boards of type B, the number of BBU common boards of type A, the number of BBU common boards of type B, the number of BBU capacity boards of type A, the number of BBU capacity boards of type B, the number of cells of 5G RAT X, the number of cells of 5G RAT Y, the number of RUs of type A, and the number of RUs of type B.

[0065] Table 1

[0066]

[0067]

[0068] For the set of configurations shown in Table 1, computing device 130 may calculate the parameters or weights for each factor using the normal equation shown in equation (1) below.

[0069] θ=(X T X) -1 X T Y (1)

[0070] Where θ represents a parameter for a factor, X represents a matrix of factors in the configuration, and Y represents a matrix of test values ​​of startup performance. It should be understood that formula (1) is merely an example, and computing device 130 may calculate the parameter for each factor in any other suitable manner.

[0071] For example, a parameter set associated with a factor set may be obtained as shown in Table 2. It should be understood that Table 2 is for illustration only and is not intended to be limiting.

[0072] Table 2

[0073]

[0074] At this point, a model for startup performance prediction can be constructed.

[0075] In some embodiments, the computing device 130 may obtain another configuration set of the communication device 110 and another test value set of the startup performance of the communication device 110 for the another configuration set, and update the model by using the configurations in the another configuration set as input and the corresponding test results in the another test value set as output.

[0076] In this way, the parameter set can be updated based on the latest test results or learning set. By training the model based on more factors in the configuration and more test results, higher model accuracy can be achieved.

[0077] Example implementation of startup performance prediction

[0078] Figure 3 FIG. 3 is a flow chart illustrating an example method 300 implemented at an electronic device (eg, prediction device 140) according to some embodiments of the present disclosure. For discussion purposes, reference will be made to FIG. Figure 1 Method 300 is described.

[0079] At block 310 , the prediction device 140 obtains configurations of the communication device 110 that are to be tested or not tested.

[0080] In some embodiments, the configuration may include a set of factors associated with startup capabilities of the communication device 110. In some embodiments, the set of factors in the configuration may include at least one of hardware, software, or topology for components in the communication device 110.

[0081] In some embodiments, the component may include a BBU or baseband processing unit. In some embodiments, the component may include a RU or radio processing unit or antenna unit. It should be understood that these are merely examples, and the factor set may also involve any other suitable component of the communication device 110.

[0082] In some embodiments, the set of factors may include the number or type of hardware in the BBU. For example, the set of factors may include the number or type of system boards, capacity boards, or common boards including system boards and capacity boards. It should be understood that this is merely an example, and any other suitable hardware is also feasible.

[0083] In some embodiments, the factor set may include the number or type of software in the BBU. For example, the factor set may include the number or type of cloud system board functions, cloud capacity board functions, or cloud common board functions including system board functions and capacity board functions. It should be understood that this is merely an example, and any other suitable software is also feasible.

[0084] In some embodiments, the factor set may include the number or type of hardware in the RU. For example, the factor set may include the number or type of antennas. It should be understood that this is merely an example, and any other suitable hardware is also feasible.

[0085] In some embodiments, the factor set may include the number or type of software in the RU. For example, the factor set may include the number or type of antenna technologies. As another example, the factor set may include the number or type of antenna protocols. It should be understood that these are merely examples, and any suitable software is also feasible.

[0086] In some embodiments, the factor set may include the number of RATs. In some embodiments, the factor set may include the number of cells. It should be understood that these are merely examples, and any other suitable topology is also feasible. It should also be understood that the factor set may include any combination of the above or any other suitable information.

[0087] At block 320, prediction device 140 uses the configuration as input to a model for startup performance prediction to determine a predicted value for the startup performance of the communication device. Specifically, the trained parameter set is applied to the set of factors in the configuration, and a predicted value for startup performance for the configuration is obtained. In this way, startup performance for untested configurations of communication device 110 can be predicted.

[0088] In some embodiments, prediction device 140 may obtain a test value of the startup performance of communication device 110 for the configuration. Prediction device 140 may determine a deviation between the test value and the predicted value. In some embodiments, prediction device 140 may determine the usability of the configuration of communication device 110 based on a comparison between the deviation and a threshold deviation. In some embodiments, prediction device 140 may determine the usability of the model based on a comparison between the deviation and a threshold deviation. In other words, prediction device 140 may check the usability of the configuration of communication device 110 or the usability of the model.

[0089] For example, as shown in Table 3, untested configuration 10 is obtained.

[0090] Table 3

[0091]

[0092]

[0093] The prediction device 140 can predict the startup performance for the configuration and can also obtain the test value of the startup performance for the configuration from the test device 120. Table 4 shows the predicted value and the test value of the configuration and the deviation between the predicted value and the test value.

[0094] Table 4

[0095] Predicted value Test value deviation 816.47 845.5 3.43％

[0096] In this example, the deviation is acceptable for the design and target setting.It should be understood that Table 4 is for illustration only and is not intended to be limiting.

[0097] In some embodiments, prediction device 140 may send a configuration and corresponding startup performance test results to computing device 130. In some embodiments, prediction device 140 may also send deviations associated with the test results for that configuration to computing device 130. Computing device 130 may decide to add the configuration and corresponding test results to the learning set to update the model. This allows for more accurate predictions.

[0098] In some embodiments, the learning set can be updated as the configuration version of the communication device changes. For the new version, some typical configuration test results can be selected as high weights. This can also achieve more accurate predictions.

[0099] The model according to the embodiments of the present disclosure can be applied to any other suitable system startup performance prediction application, as long as the set of factors affecting startup performance is updated or changed according to the actual scenario. It should be understood that any other application of system startup performance prediction will also fall within the scope of protection of this disclosure.

[0100] Example implementations of apparatus and devices

[0101] Exemplary embodiments of the present disclosure also provide corresponding devices. In some embodiments, a device capable of performing method 200 (e.g., computing device 130) may include a component for performing each step of method 200. The component may be implemented in any suitable form. For example, the component may be implemented in a circuit system or a software module.

[0102] In some embodiments, the apparatus includes: a component for obtaining, at an electronic device, a configuration set of a communication device and a test value set of the startup performance of the communication device for the configuration set; and a component for constructing a model for startup performance prediction by using the configurations in the configuration set as input and using the corresponding test results in the test value set as output.

[0103] In some embodiments, a configuration in the set of configurations includes a set of factors associated with startup performance of the communication device. In some embodiments, the means for building the model includes means for determining a set of parameters associated with the set of factors for predicting startup performance for untested configurations.

[0104] In some embodiments, the set of factors in the configuration includes at least one of hardware, software, or topology for a component in the communication device. In some embodiments, the component includes at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of the following: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, the number of radio access technologies, or the number of cells.

[0105] In some embodiments, the apparatus may further include: a component for obtaining another configuration set of the communication device and another test value set of the startup performance of the communication device for the another configuration set; and a component for updating the model by using the configurations in the another configuration set as input and using the corresponding test results in the another test value set as output.

[0106] In some embodiments, the communication device is an access network device, a core network device or a terminal device.

[0107] In some embodiments, an apparatus capable of executing method 300 (e.g., prediction device 140) may include components for executing the corresponding steps of method 300. The components may be implemented in any suitable form. For example, the components may be implemented in a circuit system or a software module.

[0108] In some embodiments, the apparatus includes: means for acquiring, at an electronic device, a configuration of a communication device to be tested; and means for determining a predicted value of startup performance of the communication device by using the configuration as an input of a model for startup performance prediction.

[0109] In some embodiments, the configuration includes a set of factors associated with startup performance of the communication device. In some embodiments, the set of factors in the configuration includes at least one of hardware, software, or topology for components in the communication device.

[0110] In some embodiments, the component includes at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, the number of radio access technologies, or the number of cells.

[0111] In some embodiments, the apparatus may further include: a component for obtaining a test value of the startup performance of the communication device for the configuration; a component for determining a deviation between the test value and the predicted value; and a component for determining the usability of the configuration of the communication device or the usability of the model based on a comparison between the deviation and a threshold deviation.

[0112] In some embodiments, the communication device is an access network device, a core network device or a terminal device.

[0113] Figure 4 FIG. 4 is a simplified block diagram of an electronic device 400 suitable for implementing an embodiment of the present disclosure. The device 400 may be used to implement Figure 1 The computing device 130 or the prediction device 140. Figure 4 As shown, device 400 may include a central processing unit (CPU) 401, which may perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 402 or loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of device 400 may also be stored in RAM 403. CPU 401, ROM 402, and RAM 403 may be connected to each other via a bus 404. An input / output (I / O) interface 405 may also be connected to bus 404.

[0114] Various components in device 400 may be connected to I / O interface 405, including, for example: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows device 400 to exchange information or data with other devices via a computer network, such as the Internet and / or various telecommunication networks.

[0115] CPU 401 executes the various methods and processes described above, such as methods 200 and / or 300. For example, in some embodiments, methods 200 and / or 300 may be implemented as a computer software program tangibly embodied in a machine-readable medium such as storage unit 408. In some embodiments, a portion or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by CPU 401, one or more steps of methods 200 and / or 300 described above may be performed. Alternatively, in other embodiments, CPU 401 may be configured to execute methods 200 and / or 300 by any other suitable means (e.g., by firmware).

[0116] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module running in a device on a target real or virtual processor, to perform the above-referenced Figures 2 to 3 Methods 200 and / or 300 are described. Generally speaking, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The functionality of the program modules can be combined or separated between program modules as desired in various embodiments. The machine-executable instructions of the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in both local storage media and remote storage media.

[0117] The program code for executing the disclosed method can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code is implemented when the processor or controller is running. The program code can be run entirely on the machine, partially on the machine, as an independent software package, partially on the machine and partially on a remote machine, or completely on a remote machine or server.

[0118] In the context of the present disclosure, computer program codes or related data may be carried by any suitable carrier to enable a device, apparatus or processor to perform various processes and operations as described above. Examples of carriers include signals, computer-readable media, etc.

[0119] The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of computer readable storage media would include an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0120] In addition, although described operation in a particular order, this should not be understood as necessarily requiring to perform these operations or perform all shown operations in the particular order shown in order to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although included in the above discussion are some specific implementation details, these details should not be interpreted as limiting the scope of the present disclosure, but should be interpreted as descriptions of features peculiar to a particular embodiment. Some features described in the context of the separate embodiments may also be combined in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be realized individually or in any suitable subcombination in a plurality of embodiments.

[0121] Although the present disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Instead, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. An electronic device, comprising: at least one processor; as well as at least one memory including computer program code, The at least one memory and the computer program code are configured to, together with the at least one processor, cause the electronic device to: Acquire a configuration set of a communication device and a test value set of startup performance of the communication device for the configuration set; as well as A model for startup performance prediction is constructed by using configurations in the configuration set as inputs and using corresponding test results in the test value set as outputs.

2. The electronic device according to claim 1, wherein the configurations in the configuration set include: a set of factors associated with the startup performance of the communication device, and wherein the electronic device is caused to construct the model by: A set of parameters associated with the set of factors is determined for the prediction of the startup performance for an untested configuration.

3. The electronic device according to claim 2, wherein the set of factors in the configuration comprises: At least one of the following for components in the communication device: hardware, software, or topology.

4. The electronic device according to claim 3, wherein the component comprises: at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, The number of radio access technologies, or The number of cells.

5. The electronic device according to claim 1, wherein the electronic device is further configured to: acquiring another configuration set of the communication device and another test value set of startup performance of the communication device for the another configuration set; and The model is updated by using configurations in the another set of configurations as inputs and using corresponding test results in the another set of test values ​​as outputs. 6 . The electronic device according to claim 1 , wherein the communication device is an access network device, a core network device or a terminal device.

7. An electronic device comprising: at least one processor; as well as at least one memory including computer program code, The at least one memory and the computer program code are configured to, together with the at least one processor, cause the electronic device to: Obtaining the configuration of the communication device to be tested; as well as A predicted value of the startup performance of the communication device is determined by using the configuration as an input to a model for startup performance prediction.

8. The electronic device according to claim 7, wherein the configuration comprises: A set of factors associated with the activation capability of the communication device.

9. The electronic device of claim 8, wherein the set of factors in the configuration comprises: At least one of the following for components in the communication device: hardware, software, or topology.

10. The electronic device of claim 9, wherein the component comprises: at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, The number of radio access technologies, or The number of cells.

11. The electronic device according to claim 7, wherein the electronic device is further configured to: obtaining a test value of the startup performance of the communication device for the configuration; determining a deviation between the test value and the predicted value; and Based on a comparison between the deviation and a threshold deviation, a usability of the configuration of the communication device, or a usability of the model is determined. 12 . The electronic device according to claim 7 , wherein the communication device is an access network device, a core network device or a terminal device.

13. A method for predicting startup performance of a communication device, comprising: At the electronic device, obtaining a configuration set of the communication device and a test value set of the startup performance of the communication device for the configuration set; as well as A model for startup performance prediction is constructed by using configurations in the configuration set as inputs and using corresponding test results in the test value set as outputs.

14. The method according to claim 13, wherein the configurations in the configuration set include: a set of factors associated with the startup performance of the communication device, and wherein building the model comprises: A set of parameters associated with the set of factors is determined for the prediction of the startup performance for an untested configuration.

15. The method of claim 14, wherein the set of factors in the configuration comprises: At least one of the following for components in the communication device: hardware, software, or topology.

16. The method of claim 15, wherein the component comprises: at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, The number of radio access technologies, or The number of cells.

17. The method according to claim 13, further comprising: Acquire another configuration set of the communication device and another test value set of startup performance of the communication device for the another configuration set; as well as The model is updated by using configurations in the another set of configurations as inputs and using corresponding test results in the another set of test values ​​as outputs.

18. The method according to claim 13, wherein the communication device is an access network device, a core network device or a terminal device.

19. A method for predicting startup performance of a communication device, comprising: At the electronic device, obtaining a configuration of the communication device to be tested; as well as A predicted value of the startup performance of the communication device is determined by using the configuration as an input to a model for startup performance prediction.

20. The method of claim 19, wherein the configuring comprises: A set of factors associated with the activation capability of the communication device.

21. The method of claim 20, wherein the set of factors in the configuration comprises: At least one of the following for components in the communication device: hardware, software, or topology.

22. The method of claim 21, wherein the component comprises: at least one of a baseband unit or a radio unit, and wherein the set of factors includes at least one of: the number or type of hardware in the baseband unit, the number or type of software in the baseband unit, the number or type of hardware in the radio unit, the number or type of software in the radio unit, The number of radio access technologies, or The number of cells.

23. The method of claim 19, further comprising: obtaining a test value of the startup performance of the communication device for the configuration; determining a deviation between the test value and the predicted value; as well as Based on a comparison between the deviation and a threshold deviation, a usability of the configuration of the communication device, or a usability of the model is determined.

24. The method according to claim 19, wherein the communication device is an access network device, a core network device or a terminal device.

25. A communication device, comprising: A component for acquiring, at an electronic device, a configuration set of a communication device and a test value set of startup performance of the communication device for the configuration set; as well as Means for building a model for enabling performance prediction by using configurations in the set of configurations as inputs and using corresponding test results in the set of test values ​​as outputs.

26. A communication device, comprising: means for acquiring, at an electronic device, a configuration of a communication device to be tested; as well as Means for determining a predicted value of startup performance of the communication device by using the configuration as an input to a model for startup performance prediction.

27. A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform a method according to any one of claims 13 to 18 or any one of claims 19 to 24.

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