Method and device for analyzing antenna performance of mobile terminal

Abstract

The invention discloses an antenna performance analysis method and device for a mobile terminal, and relates to the technical field of communication, and the method comprises the steps: obtaining different antenna digital models and the working frequency corresponding to each antenna digital model; placing each antenna digital model at different positions of the mobile terminal shell model to obtain a plurality of mobile terminal models of each antenna digital model; according to various mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model, and different simulation scenes are constructed; determining information such as current density distribution of the mobile terminal model in a free space, OTA performance and electromagnetic radiation in different simulation scenes by using an electromagnetic simulation algorithm; training the neural network model by using the simulation result to obtain an antenna performance analysis model; and performing antenna performance analysis by using the antenna performance analysis model. The antenna performance analysis cost of the mobile terminal can be reduced, and the analysis efficiency and the accuracy of the analysis result can be improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and device for analyzing antenna performance of a mobile terminal. Background Art

[0002] This section is intended to provide a background or context for embodiments of the present invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] The antenna of a mobile terminal is an important part of wireless communication. The OTA performance of the antenna directly affects the signal reception and transmission quality of the mobile terminal, and thus affects the stability and efficiency of communication. At the same time, in order to ensure the electromagnetic radiation safety of users using mobile terminals, there are relevant regulations requiring that when users use near-field electromagnetic exposure equipment, the specific absorption rate (SAR) of electromagnetic waves is less than the prescribed basic limit. In order to evaluate the radiation performance and safety of mobile terminal antennas, a variety of scenarios for OTA performance analysis are also specified, including free space testing, handheld testing, chest testing, and head testing.

[0004] In the prior art, the antenna performance analysis of mobile terminals usually adopts the test method or the simulation calculation method. The test method can be carried out in a real communication environment, and its results are closer to the actual use scenario, which can more truly reflect the antenna performance of the mobile terminal. However, this method is relatively complicated and requires professional test equipment and testers, which is costly. The simulation calculation method requires the establishment of corresponding numerical simulation scenarios for the antenna performance of different scenarios, which has high requirements on the technical level of the testers and computer hardware. At the same time, it takes a lot of time to simulate complex antennas and scenarios, and the accuracy of the simulation results cannot be ensured, which affects the accuracy of the antenna performance analysis results of the mobile terminal.

[0005] Therefore, in the prior art, when analyzing the antenna performance of a mobile terminal, the cost is high, the efficiency is low, and the accuracy of the analysis result of the antenna performance of the mobile terminal cannot be ensured. Summary of the invention

[0006] An embodiment of the present invention provides a method for analyzing antenna performance of a mobile terminal, which is used to reduce the cost of analyzing antenna performance of the mobile terminal and improve the analysis efficiency and the accuracy of the analysis result. The method includes:

[0007] Acquire different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database;

[0008] For each antenna digital model, the antenna digital model is placed at different positions of a preset mobile terminal housing model to obtain multiple mobile terminal models of each antenna digital model;

[0009] For each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal;

[0010] The pre-built neural network model is trained using the current density distribution information of each mobile terminal model in free space of multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model;

[0011] The antenna performance analysis model is used to analyze the antenna performance of the mobile terminal to be analyzed.

[0012] The embodiment of the present invention further provides an antenna performance analysis device for a mobile terminal, which is used to reduce the cost of antenna performance analysis of the mobile terminal, improve analysis efficiency and accuracy of analysis results, and the device includes:

[0013] An acquisition module, used to acquire different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database;

[0014] An assembling module, for placing each antenna digital model at different positions of a preset mobile terminal housing model, to obtain multiple mobile terminal models of each antenna digital model;

[0015] A simulation module is used for each mobile terminal model of each antenna digital model: combining the mobile terminal model with the head model and the hand model according to the preset multiple mobile terminal position association information to construct different simulation scenarios; using the electromagnetic simulation algorithm to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal;

[0016] A model training module is used to train a pre-built neural network model using current density distribution information of each mobile terminal model in free space of multiple antenna digital models, OTA performance information in different simulation scenarios, electromagnetic radiation information, and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model;

[0017] The analysis module is used to analyze the antenna performance of the mobile terminal to be analyzed using the antenna performance analysis model.

[0018] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the antenna performance analysis method of the mobile terminal is implemented.

[0019] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the antenna performance analysis method of the mobile terminal is implemented.

[0020] In an embodiment of the present invention, different antenna digital models and the operating frequencies corresponding to each antenna digital model are obtained from a preset antenna database; for each antenna digital model, the antenna digital model is placed at different positions of a preset mobile terminal shell model to obtain multiple mobile terminal models of each antenna digital model; for each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information in different simulation scenarios, and the electromagnetic radiation information; the mobile terminal position association information refers to the positional relationship between the head, the hand, and the mobile terminal; the pre-constructed neural network model is trained using the current density distribution information of each mobile terminal model in free space of the multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information, and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model; the antenna performance of the mobile terminal to be analyzed is analyzed using the antenna performance analysis model. Compared with the existing antenna performance analysis scheme for mobile terminals, mobile terminal models with different antenna layouts are assembled using different antenna digital models and mobile terminal shell models. The mobile terminal model is combined with a head model and a hand model according to preset multiple mobile terminal position association information to construct different simulation scenarios. This can quickly simulate complex antennas and scenarios and reduce costs. Then, the mobile terminal model is numerically simulated using an electromagnetic simulation algorithm. The neural network model is trained using the numerical simulation results to obtain an antenna performance analysis model, which can improve the analysis efficiency and the accuracy of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 A flowchart of a method for analyzing antenna performance of a mobile terminal provided in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of an antenna performance analysis solution for a mobile terminal provided in an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of various digital models of patch antennas and corresponding antenna reflection coefficients provided in an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of a mobile terminal housing model provided in an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of different simulation scenarios provided in embodiments of the present invention;

[0027] Figure 6 A structural diagram of a neural network model provided in an embodiment of the present invention;

[0028] Figure 7 A schematic diagram showing a comparison between the antenna performance analysis model in an embodiment of the present invention and the SAR, TRP and TIS results calculated by SEMCAD;

[0029] Figure 8 Schematic diagram of EIRP analysis results obtained based on the antenna performance analysis model in an embodiment of the present invention;

[0030] Fig. 9 Schematic diagram of comparison of EIRP and FSV indicators obtained based on the antenna performance analysis model in an embodiment of the present invention;

[0031] Fig.10 A schematic diagram of an antenna performance analysis device for a mobile terminal provided in an embodiment of the present invention;

[0032] Fig.11 A schematic diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0034] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0035] After research, it is found that in the prior art, the antenna performance analysis of mobile terminals usually adopts the test method or the simulation calculation method. The test method can be carried out in a real communication environment, and its results are closer to the actual use scenario, which can more truly reflect the antenna performance of the mobile terminal. However, this method is relatively complicated and requires professional test equipment and testers, which is costly. The simulation calculation method requires the establishment of corresponding numerical simulation scenarios for the antenna performance of different scenarios, which has high requirements on the technical level of the testers and computer hardware. At the same time, it takes a lot of time to simulate complex antennas and scenarios, and the accuracy of the simulation results cannot be ensured, which affects the accuracy of the antenna performance analysis results of the mobile terminal.

[0036] With the development of science and technology, surrogate modeling is a powerful tool for quickly estimating antenna performance. Typical surrogate modeling methods use training examples obtained by sampling parameter space. However, the difficulty of modeling increases with the increase of dimension, and the requirements for training examples also increase exponentially. With the expansion of deep learning applications in different fields, its application in antenna design, optimization and performance evaluation has become a hot topic in current research.

[0037] Based on this, an embodiment of the present invention provides an antenna performance analysis solution for a mobile terminal, which uses a real-time composite antenna performance analysis model based on deep learning to perform antenna performance analysis of the mobile terminal, so as to reduce the antenna performance analysis of the mobile terminal and improve the analysis efficiency and the accuracy of the analysis results.

[0038] It should be noted that, in the embodiment of the present invention, the antenna performance analysis of the mobile terminal mainly analyzes the OTA performance and electromagnetic radiation of the mobile terminal antenna. Specifically, the OTA performance information may include: TRP (antenna total radiated power) value and TIS (isotropic sensitivity) value; the electromagnetic radiation information may include: full-head average WBSAR, peak-1g SAR and peak-10g SAR.

[0039] Figure 1 A flowchart of a method for analyzing antenna performance of a mobile terminal provided by an embodiment of the present invention, the method comprising the following steps:

[0040] Step 101, obtaining different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database;

[0041] Step 102, for each antenna digital model, placing the antenna digital model at different positions of a preset mobile terminal housing model to obtain multiple mobile terminal models of each antenna digital model;

[0042] Step 103, for each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios are determined by using an electromagnetic simulation algorithm; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal;

[0043] Step 104, using the current density distribution information of each mobile terminal model in free space of the multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information and the operating frequency corresponding to each antenna digital model, the pre-built neural network model is trained to obtain an antenna performance analysis model;

[0044] Step 105: Analyze the antenna performance of the mobile terminal to be analyzed using the antenna performance analysis model.

[0045] In an embodiment of the present invention, different antenna digital models and the operating frequencies corresponding to each antenna digital model are obtained from a preset antenna database; for each antenna digital model, the antenna digital model is placed at different positions of a preset mobile terminal shell model to obtain multiple mobile terminal models of each antenna digital model; for each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information in different simulation scenarios, and the electromagnetic radiation information; the mobile terminal position association information refers to the positional relationship between the head, the hand, and the mobile terminal; the pre-constructed neural network model is trained using the current density distribution information of each mobile terminal model in free space of the multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information, and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model; the antenna performance of the mobile terminal to be analyzed is analyzed using the antenna performance analysis model. Compared with the existing antenna performance analysis scheme for mobile terminals, mobile terminal models with different antenna layouts are assembled using different antenna digital models and mobile terminal shell models. The mobile terminal model is combined with a head model and a hand model according to preset multiple mobile terminal position association information to construct different simulation scenarios. This can quickly simulate complex antennas and scenarios and reduce costs. Then, the mobile terminal model is numerically simulated using an electromagnetic simulation algorithm. The neural network model is trained using the numerical simulation results to obtain an antenna performance analysis model, which can improve the analysis efficiency and the accuracy of the analysis results.

[0046] Figure 2 The schematic diagram of the antenna performance analysis solution for the mobile terminal provided by the embodiment of the present invention. Figure 2 right Figure 1 The antenna performance analysis method of the mobile terminal shown is described in detail.

[0047] like Figure 2 As shown, before the above step 101, an antenna database needs to be established in advance, and the antenna database contains multiple antenna digital models and the operating frequency corresponding to each antenna digital model.

[0048] In specific implementation, a variety of digital models of patch antennas covering typical frequency bands of 2G / 3G / 4G / 5G can be established, and the step files of these antenna digital models can be stored in the antenna database. For example, Figure 3 Schematic diagram of various digital models of patch antennas and corresponding antenna reflection coefficients provided in an embodiment of the present invention.

[0049] In the above step 101, a plurality of different antenna digital models and the operating frequency corresponding to each antenna digital model may be directly selected from the antenna database.

[0050] In the above step 102, for each antenna digital model, the antenna digital model can be placed at different positions of the preset mobile terminal shell model to obtain multiple mobile terminal models for each antenna digital model.

[0051] In specific implementation, each antenna digital model can be selected from the antenna database and randomly placed at different positions of the mobile terminal housing model, for example, Figure 2 In the example, the antenna selected from the antenna database is placed at the position (x, y, z) shown by the coordinate axes of the simplified mobile terminal housing. The preset mobile terminal housing model can be a simplified mobile terminal housing with a fixed size, for example, Figure 4 A schematic diagram of a mobile terminal housing model provided by an embodiment of the present invention, such as Figure 4 As shown, the mobile terminal housing model is 6.1 inches, with a width of 71.5 mm, a length of 147.5 mm, and a thickness of 7.85 mm, and the mobile terminal housing model only includes a housing component with a thickness of 1 mm. By randomly placing any antenna digital model at different positions inside the mobile terminal housing model (e.g., the mobile terminal housing model includes 200 different positions), different antenna layouts of actual mobile terminals can be simulated. In this way, for each antenna digital model, multiple mobile terminal models with different antenna layouts are obtained.

[0052] In the above step 103, based on the multiple mobile terminal models of the multiple antenna digital models obtained in step 102, for each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model can be combined with the head model and the hand model to construct different simulation scenarios; then, the electromagnetic simulation algorithm can be used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information in different simulation scenarios, and the electromagnetic radiation information.

[0053] The preset mobile terminal position association information may be the position relationship between the finger, the hand and the mobile terminal.

[0054] In specific implementation, in order to obtain the electromagnetic radiation performance of the mobile terminal, the mobile terminal can be positioned in advance according to the CTIA international standard test scheme and IEC / IEEE 62209-1528 requirements to determine the positional relationship between the head, hand and mobile terminal that meets different standard requirements. Then, according to the positional relationship between the head, hand and mobile terminal, the mobile terminal model is combined with the head model (SAM) and the hand model to construct different simulation scenarios. Among them, when the mobile terminal model is combined with the head model (SAM) and the hand model, the dielectric parameters of the SAM and the hand model are derived from the standard file, and the values ​​at a specific frequency are obtained by linear interpolation. For example, Figure 2 The OTA configuration and SAR configuration in are the simulation scenarios for OTA performance testing and SAR performance testing, respectively.

[0055] In one embodiment, according to the requirements of the CTIA international standard test solution, the test device should be able to stably fix the test equipment (EUT) in free space and with a head / hand model to better simulate actual usage and ensure the accuracy of the test results; at the same time, according to the IEC / IEEE 62209-1528 standard, there are two DUT test positions for the head model, namely the "cheek" position and the "tilted" position. These two test positions are intended to simulate different postures of users when actually using a mobile terminal, so as to better evaluate the performance of the antenna. Figure 5 Schematic diagram of different simulation scenarios provided by embodiments of the present invention. Figure 5 As shown, it includes the simulation scene of the mobile terminal model in free space, the simulation scene of only the position of the head model and the mobile terminal model, the simulation scene of only the position of the hand model and the mobile terminal model, the simulation scene of the position of the head and hand model and the mobile terminal model (the simulation scene is the call mode), the simulation scene of the "cheek" position, and the simulation scene of the "tilted" position.

[0056] In specific implementation, after constructing different simulation scenarios, an electromagnetic simulation algorithm can be used to perform simulation calculations to obtain the current density distribution information of the mobile terminal model in free space, OTA performance information in different simulation scenarios, and electromagnetic radiation information.

[0057] In one embodiment, the electromagnetic simulation algorithm may be a finite-difference time-domain algorithm (FDTD algorithm); the finite-difference time-domain algorithm uses a non-uniform grid to divide the calculation area.

[0058] In one embodiment, the above step 103 may specifically include:

[0059] The finite-difference time-domain algorithm is used for simulation calculation to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios.

[0060] In specific implementation, simulation calculations can be performed based on the FDTD algorithm. To ensure that the antenna details can be maintained in the discrete division, FDTD uses non-uniform grids to divide the calculation area. The maximum grid step is 2 mm, the ratio of adjacent grid sides in the non-uniform area does not exceed 1.2, and the maximum number of iterations in the calculation is 30 cycles. The calculation results derive the current density (complex form J) of the mobile terminal model in free space. x , J y , J z ), as well as SAR and OTA performance parameters in different simulation scenarios. When all calculation results are exported, the input power is normalized to 1W.

[0061] In the above step 104, the pre-constructed neural network model can be trained using the current density distribution information of each mobile terminal model of multiple antenna digital models in free space, the operating frequency corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios, to obtain an antenna performance analysis model.

[0062] In specific implementation, a neural network model can be pre-built, and the neural network model can be trained using simulation data (i.e., the electromagnetic simulation algorithm determines the current density distribution information of the mobile terminal model in free space, the OTA performance information and electromagnetic radiation information in different simulation scenarios) and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model. Figure 2 Real-time evaluation model shown - mobile terminal antenna.

[0063] In one embodiment, the above step 104 may specifically include:

[0064] The current density distribution information of each mobile terminal model in free space, the operating frequency information corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as sample data to construct training sets and test sets;

[0065] The neural network model is trained using the training set to obtain an antenna performance analysis model;

[0066] The antenna performance analysis model is tested using the test set.

[0067] In one embodiment, the current density distribution information of each mobile terminal model in free space and the operating frequency information corresponding to each antenna digital model are used as input information of the neural network model, and the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as output information of the neural network model; a training set and a test set are constructed based on the input information and output information.

[0068] In one embodiment, the antenna performance analysis model may include an OTA performance analysis module and an electromagnetic radiation module.

[0069] During specific implementation, the input of the neural network model may be the current density distribution information of the mobile terminal model in free space, and the operating frequency information corresponding to the antenna digital model of the mobile terminal model. Figure 6 A structural diagram of a neural network model provided by an embodiment of the present invention, such as Figure 6 As shown, during model training and prediction, the current density distribution in free space is input into the model in the form of a current density matrix, and the current density matrix is ​​resampled to a spatial interval of 0.5×0.5×0.5mm 3 The uniformly distributed array is subjected to feature extraction by the convolutional feature extraction network to obtain a 1×147456 feature vector, after which the operating frequency information corresponding to the antenna digital model of the mobile terminal model is embedded. At the end of the model is an OTA performance analysis module and an electromagnetic radiation analysis module (SAR analysis module) constructed using multiple fully connected layers. Among them, the SAR analysis module outputs the full-head average WBSAR, peak-1g SAR, and peak-10gSAR for four scenarios: the mobile terminal is placed on the left face, on the right face, at an angle of 15° to the left cheek, and at an angle of 15° to the right face. The OTA performance analysis module outputs the EIRP and TIS values ​​of the mobile terminal model placed in free space, held in the left or right hand alone, combined with the left and right ears of the SAM head model, and held in the left and right hands on the left and right sides of the SAM head model. The TRP value can be calculated based on the EIRP and the following formula:

[0070]

[0071] Among them, the convolutional feature extraction network of the neural network model adopts the downsampling part of VNET. Figure 6 As shown in Figure 1, the convolutional feature extraction network is divided into different stages that operate at different resolutions. Each stage includes one to three convolutional layers. The residual function learned in each stage is designed: the input of each stage is used for the convolutional layer and added to the output of the last convolutional layer of the stage through nonlinear processing to learn the residual function. In this way, the convolutional feature extraction network architecture ensures that the convergence time is much shorter than the time required for similar networks that do not learn residual functions.

[0072] In specific implementation, for example, the training set and the test set can be randomly allocated in a ratio of 8:2, and the optimization hyperparameters of the neural network model can be set as follows:

[0073] 1. Batch Size: 32;

[0074] 2. Basic learning rate: 0.02;

[0075] 3. Optimizer: Adam;

[0076] 4. Maximum number of iterations: 30.

[0077] In model training, the root mean square error (RMSE) constraint can be used to optimize network parameters:

[0078]

[0079] In order to evaluate the accuracy of the antenna performance analysis model obtained for antenna performance analysis, Figure 2 As shown, the inference results of the antenna performance analysis model can be compared with the results calculated using SEMCAD, and the Wilcoxon signed rank check is used to evaluate the accuracy of the antenna performance analysis model for regression of different SAR, TRP and TIS parameters.

[0080] In the embodiment of the present invention, Figure 2 As shown, a feature selection verification method (FSV) is used to evaluate the accuracy of the EIRP (equivalent isotropic radiated power) calculation results. The indicator of FSV can be an amplitude difference metric (ADM).

[0081] In specific implementation, after completing the training of the neural network model and obtaining the antenna performance analysis model, the antenna performance analysis model can be tested using a test set to evaluate the robustness of the antenna performance analysis model.

[0082] For example, Figure 7 The figure is a schematic diagram comparing the results of SAR, TRP and TIS calculated by the antenna performance analysis model in the embodiment of the present invention and SEMCAD. Table 1 shows the difference analysis of the results of SAR, TRP and TIS predicted by the antenna performance analysis model in the embodiment of the present invention and the results calculated by SEMCAD using the Wilcoxon signed rank test. It can be seen from Table 1 that the results obtained using the antenna performance analysis model in the embodiment of the present invention are not significantly different from the results calculated by SEMCAD. Figure 8 Schematic diagram of EIRP analysis results obtained based on the antenna performance analysis model in an embodiment of the present invention (θ=30°). Figure 8It can be seen that the analysis results of the antenna performance analysis model in the embodiment of the present invention have a consistent distribution trend with the SEMCAD calculation results, but for the area with extreme values, the antenna performance analysis model in the embodiment of the present invention is smoother and loses the details of the lobe changes. The FSV index is used to evaluate the accuracy of the EIRP obtained based on the antenna performance analysis model. Fig. 9 Schematic diagram of the comparison of EIRP and FSV indicators obtained based on the antenna performance analysis model in the embodiment of the present invention. Fig. 9 The analysis shows that, from the perspective of ADM indicators, the EIRP obtained based on the antenna performance analysis model and the SEMCAD calculation results show "excellent" results, proving that the antenna performance analysis model of the embodiment of the present invention and the SEMCAD calculation results have good consistency in amplitude.

[0083] In terms of the computational efficiency of the model, the embodiment of the present invention uses SEMCAD to evaluate all states of one frequency point of an antenna. Without considering the time it takes for the simulation engineer to build the simulation scenario, the FDTD calculation time is about 3 hours. The antenna performance analysis model of the embodiment of the present invention can complete the SAR and OTA performance evaluation of all the above scenarios within 5 seconds, greatly improving the evaluation efficiency.

[0084] Table 1 compares the antenna performance analysis model and SEMCAD calculation results in the embodiment of the present invention.

[0085]

[0086] Based on the above steps, the antenna performance analysis model can be obtained.

[0087] In the above step 105, the antenna performance analysis model may be used to analyze the antenna performance of the mobile terminal to be analyzed.

[0088] In one embodiment, the above step 105 may specifically include:

[0089] Obtaining current density distribution information and operating frequency of the mobile terminal to be analyzed in free space;

[0090] Inputting the current density distribution information and the operating frequency of the mobile terminal to be analyzed in free space into the antenna performance analysis model, obtaining the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states; the different states refer to the states in which the mobile terminal to be analyzed, the head and the hand to be analyzed are combined according to the preset multiple mobile terminal position association information when the mobile terminal to be analyzed is in free space;

[0091] The antenna performance of the mobile terminal to be analyzed is determined according to the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states.

[0092] During specific implementation, the current density distribution information and operating frequency of the mobile terminal to be analyzed in free space can be input into the antenna performance analysis model to obtain the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states; and based on the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states, the antenna performance of the mobile terminal to be analyzed can be determined.

[0093] In summary, the antenna performance analysis method of the mobile terminal in the embodiment of the present invention performs antenna performance analysis based on a real-time composite antenna performance analysis model based on deep learning, thereby improving the analysis efficiency of the radiation performance of the mobile terminal antenna; moreover, the antenna performance analysis model in the embodiment of the present invention does not need to model and analyze the geometric characteristics and electromagnetic characteristic parameters of the antenna, but takes the current density and operating frequency when the mobile terminal is in free space as the input of the model to analyze the OTA performance and SAR, thereby improving the practicality of the antenna performance analysis model for different antennas; moreover, during model training, by establishing an antenna database, selecting different antenna digital models from the antenna database and randomly placing them in different positions of the simplified mobile terminal shell model, and establishing OTA simulation and SAR simulation scenarios according to CTIA and IEC / IEEE standards, the cost and complexity of the antenna performance analysis of the mobile terminal are reduced; in addition, the embodiment of the present invention uses an electromagnetic simulation algorithm to evaluate the current density distribution and OTA performance of the mobile terminal antenna in free space, as well as the OTA performance of the mobile terminal with a standard head model (SAM), a hand model and in a "talk mode", and the SAR value when the mobile terminal is in a "cheek" and "tilted" state for model training, which can improve the accuracy of the model and thereby improve the accuracy of the antenna analysis results.

[0094] The present invention also provides a mobile terminal antenna performance analysis device, as described in the following embodiments. Since the principle of the device to solve the problem is similar to the mobile terminal antenna performance analysis method, the implementation of the device can refer to the implementation of the mobile terminal antenna performance analysis method, and the repeated parts will not be repeated.

[0095] like Fig.10 FIG. 1 is a schematic diagram of an antenna performance analysis device for a mobile terminal provided by an embodiment of the present invention. The device may include:

[0096] An acquisition module 1001 is used to acquire different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database;

[0097] An assembling module 1002 is used to place each antenna digital model at different positions of a preset mobile terminal housing model to obtain multiple mobile terminal models of each antenna digital model;

[0098] The simulation module 1003 is used for: for each mobile terminal model of each antenna digital model: combining the mobile terminal model with the head model and the hand model according to the preset multiple mobile terminal position association information to construct different simulation scenarios; using the electromagnetic simulation algorithm to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal;

[0099] The model training module 1004 is used to train the pre-built neural network model by using the current density distribution information of each mobile terminal model of the multiple antenna digital models in free space, the OTA performance information in different simulation scenarios, the electromagnetic radiation information and the operating frequency corresponding to each antenna digital model to obtain the antenna performance analysis model;

[0100] The analysis module 1005 is used to analyze the antenna performance of the mobile terminal to be analyzed by using the antenna performance analysis model.

[0101] In one embodiment, the electromagnetic simulation algorithm may be a finite-difference time-domain algorithm; the finite-difference time-domain algorithm may use a non-uniform grid to divide the calculation region.

[0102] In one embodiment, the simulation module 1003 may be specifically used for:

[0103] The finite-difference time-domain algorithm is used for simulation calculation to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios.

[0104] In one embodiment, the model training module 1004 may be specifically used to:

[0105] The current density distribution information of each mobile terminal model in free space, the operating frequency information corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as sample data to construct training sets and test sets;

[0106] The neural network model is trained using the training set to obtain an antenna performance analysis model;

[0107] The antenna performance analysis model is tested using the test set.

[0108] In one embodiment, the model training module 1004 may also be used to:

[0109] The current density distribution information of each mobile terminal model in free space and the operating frequency information corresponding to each antenna digital model are used as the input information of the neural network model, and the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as the output information of the neural network model;

[0110] Construct training sets and test sets based on input information and output information.

[0111] In one embodiment, the analysis module 1005 may be specifically used to:

[0112] Obtaining current density distribution information and operating frequency of the mobile terminal to be analyzed in free space;

[0113] Inputting the current density distribution information and the operating frequency of the mobile terminal to be analyzed in free space into the antenna performance analysis model, obtaining the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states; the different states refer to the states in which the mobile terminal to be analyzed, the head and the hand to be analyzed are combined according to the preset multiple mobile terminal position association information when the mobile terminal to be analyzed is in free space;

[0114] The antenna performance of the mobile terminal to be analyzed is determined according to the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states.

[0115] The embodiment of the present invention also provides a computer device, such as Fig.11 As shown, it is a schematic diagram of a computer device in an embodiment of the present invention, the computer device 1100 includes a memory 1110, a processor 1120 and a computer program 1130 stored in the memory 1110 and executable on the processor 1120, and the processor 1120 implements the antenna performance analysis method of the above-mentioned mobile terminal when executing the computer program 1130.

[0116] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the antenna performance analysis method of the mobile terminal is implemented.

[0117] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the antenna performance analysis method of the mobile terminal is implemented.

[0118] In an embodiment of the present invention, different antenna digital models and the operating frequencies corresponding to each antenna digital model are obtained from a preset antenna database; for each antenna digital model, the antenna digital model is placed at different positions of a preset mobile terminal shell model to obtain multiple mobile terminal models of each antenna digital model; for each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information in different simulation scenarios, and the electromagnetic radiation information; the mobile terminal position association information refers to the positional relationship between the head, the hand, and the mobile terminal; the pre-constructed neural network model is trained using the current density distribution information of each mobile terminal model in free space of the multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information, and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model; the antenna performance of the mobile terminal to be analyzed is analyzed using the antenna performance analysis model. Compared with the existing antenna performance analysis scheme for mobile terminals, mobile terminal models with different antenna layouts are assembled using different antenna digital models and mobile terminal shell models. The mobile terminal model is combined with a head model and a hand model according to preset multiple mobile terminal position association information to construct different simulation scenarios. This can quickly simulate complex antennas and scenarios and reduce costs. Then, the mobile terminal model is numerically simulated using an electromagnetic simulation algorithm. The neural network model is trained using the numerical simulation results to obtain an antenna performance analysis model, which can improve the analysis efficiency and the accuracy of the analysis results.

[0119] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing antenna performance of a mobile terminal, It is characterized in that include: Acquire different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database; For each antenna digital model, the antenna digital model is placed at different positions of a preset mobile terminal housing model to obtain multiple mobile terminal models of each antenna digital model; For each mobile terminal model of each antenna digital model: according to the preset multiple mobile terminal position association information, the mobile terminal model is combined with the head model and the hand model to construct different simulation scenarios; the electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal; The pre-built neural network model is trained using the current density distribution information of each mobile terminal model in free space of multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model; The antenna performance analysis model is used to analyze the antenna performance of the mobile terminal to be analyzed.

2. The method according to claim 1, It is characterized in that The electromagnetic simulation algorithm is a finite difference time domain algorithm; the finite difference time domain algorithm uses a non-uniform grid to divide the calculation area.

3. The method according to claim 2, It is characterized in that The electromagnetic simulation algorithm is used to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and electromagnetic radiation information in different simulation scenarios, including: The finite-difference time-domain algorithm is used for simulation calculation to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios.

4. The method according to claim 1, It is characterized in that The pre-built neural network model is trained using the current density distribution information of each mobile terminal model in free space of multiple antenna digital models, the OTA performance information in different simulation scenarios, the electromagnetic radiation information and the operating frequency corresponding to each antenna digital model to obtain the antenna performance analysis model, including: The current density distribution information of each mobile terminal model in free space, the operating frequency information corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as sample data to construct training sets and test sets; The neural network model is trained using the training set to obtain an antenna performance analysis model; The antenna performance analysis model is tested using the test set.

5. The method according to claim 4, It is characterized in that The current density distribution information of each mobile terminal model in free space, the operating frequency information corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as sample data to construct training sets and test sets, including: The current density distribution information of each mobile terminal model in free space and the operating frequency information corresponding to each antenna digital model are used as the input information of the neural network model, and the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as the output information of the neural network model; Construct training sets and test sets based on input information and output information.

6. The method according to claim 1, It is characterized in that The antenna performance analysis model is used to analyze the antenna performance of the mobile terminal to be analyzed, including: Obtaining current density distribution information and operating frequency of the mobile terminal to be analyzed in free space; Inputting the current density distribution information and the operating frequency of the mobile terminal to be analyzed in free space into the antenna performance analysis model, obtaining the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states; the different states refer to the states in which the mobile terminal to be analyzed, the head and the hand to be analyzed are combined according to the preset multiple mobile terminal position association information when the mobile terminal to be analyzed is in free space; The antenna performance of the mobile terminal to be analyzed is determined according to the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states.

7. An antenna performance analysis device for a mobile terminal, It is characterized in that include: An acquisition module, used to acquire different antenna digital models and the operating frequency corresponding to each antenna digital model from a preset antenna database; An assembling module, for placing each antenna digital model at different positions of a preset mobile terminal housing model, to obtain multiple mobile terminal models of each antenna digital model; A simulation module is used for each mobile terminal model of each antenna digital model: combining the mobile terminal model with the head model and the hand model according to the preset multiple mobile terminal position association information to construct different simulation scenarios; using the electromagnetic simulation algorithm to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios; the mobile terminal position association information refers to the positional relationship between the head, the hand and the mobile terminal; A model training module is used to train a pre-built neural network model using current density distribution information of each mobile terminal model in free space of multiple antenna digital models, OTA performance information in different simulation scenarios, electromagnetic radiation information, and the operating frequency corresponding to each antenna digital model to obtain an antenna performance analysis model; The analysis module is used to analyze the antenna performance of the mobile terminal to be analyzed using the antenna performance analysis model.

8. The device according to claim 7, It is characterized in that The electromagnetic simulation algorithm is a finite difference time domain algorithm; the finite difference time domain algorithm uses a non-uniform grid to divide the calculation area.

9. The device as claimed in claim 8, It is characterized in that Simulation module, specifically used for: The finite-difference time-domain algorithm is used for simulation calculation to determine the current density distribution information of the mobile terminal model in free space, the OTA performance information and the electromagnetic radiation information in different simulation scenarios.

10. The device according to claim 7, It is characterized in that Model training module, specifically used for: The current density distribution information of each mobile terminal model in free space, the operating frequency information corresponding to each antenna digital model, the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as sample data to construct training sets and test sets; The neural network model is trained using the training set to obtain an antenna performance analysis model; The antenna performance analysis model is tested using the test set.

11. The device according to claim 10, It is characterized in that Model training module, also used for: The current density distribution information of each mobile terminal model in free space and the operating frequency information corresponding to each antenna digital model are used as the input information of the neural network model, and the OTA performance information and electromagnetic radiation information in different simulation scenarios are used as the output information of the neural network model; Construct training sets and test sets based on input information and output information.

12. The device according to claim 7, It is characterized in that Analysis module, specifically used for: Obtaining current density distribution information and operating frequency of the mobile terminal to be analyzed in free space; Inputting the current density distribution information and the operating frequency of the mobile terminal to be analyzed in free space into the antenna performance analysis model, obtaining the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states; the different states refer to the states in which the mobile terminal to be analyzed, the head and the hand to be analyzed are combined according to the preset multiple mobile terminal position association information when the mobile terminal to be analyzed is in free space; The antenna performance of the mobile terminal to be analyzed is determined according to the OTA performance information and electromagnetic radiation information of the mobile terminal to be analyzed in different states.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

15. A computer program product, It is characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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