System and method for searching optimal test position in OTA camera obscura
By real-time monitoring of MCS parameters and optimization of test locations using adaptive scheduling algorithms, the problems of low efficiency and insufficient accuracy in OTA testing methods are solved, enabling efficient and accurate performance testing of wireless devices and adapting to the testing needs of different types of devices.
Patent Information
- Application Number
- CN202511282995.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-09
- Publication Date
- 2025-11-18
AI Technical Summary
Existing OTA testing methods are inefficient and inaccurate, making it difficult to adapt to the personalized characteristics of different wireless devices, resulting in serious resource waste, and they are also difficult to meet the testing needs of multi-antenna devices using 5G and above technologies.
An optimal test location search system is adopted in an OTA dark box. By monitoring the MCS parameters of the equipment in real time, the test location is dynamically adjusted using an adaptive scheduling algorithm. Combined with the multi-probe method or reverberation chamber method to monitor the MCS parameters in real time, the position, orientation and transmission power of the test probe or antenna array are dynamically adjusted to achieve intelligent optimization of the optimal test location.
It improves testing efficiency, enhances testing accuracy, saves testing resources, improves system adaptability and the intelligence level of the testing process, and supports device performance testing in multiple scenarios.
Smart Images
Figure CN120979569A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of optimal test position search, and particularly relates to an OTA dark box optimal test position search system and method. BACKGROUND
[0002] With the rapid development of wireless communication technology, the functions of various wireless devices are becoming increasingly complex, and the requirements for their performance testing are also becoming higher and higher. As an important means of evaluating the radiation performance of wireless devices, OTA testing has been widely used in laboratory environments. Traditional OTA testing methods usually use fixed positions or preset paths for testing, which has obvious shortcomings:
[0003] Low test efficiency: fixed position testing needs to cover a large number of discrete points, and the test time is long; preset path testing may not cover all key areas, resulting in inaccurate test results.
[0004] Inadequate accuracy: the antenna radiation patterns of different wireless devices are different, and fixed positions or preset paths cannot adapt to the individual characteristics of the devices, resulting in test results that cannot truly reflect the performance of the devices in actual use.
[0005] Waste of resources: the selection of fixed test positions is often based on experience or theoretical models, which may not find the truly optimal test positions, resulting in waste of test resources.
[0006] Poor adaptability: traditional methods are difficult to adapt to the testing needs of different types of wireless devices, especially with the development of new technologies such as 5G, 6G, etc., complex devices such as multi-antenna, MIMO, etc. have higher requirements for testing methods.
[0007] In recent years, with the continuous evolution of wireless communication technology, MCS (Modulation and Coding Strategy) as a key parameter affecting wireless transmission performance, its optimization and adaptive adjustment has become an important means to improve the performance of wireless systems. MCS combines different modulation orders (such as QPSK, 16QAM, 64QAM, etc.) with specific forward error correction coding rates (Coding Rate) to achieve flexible control of data rate and transmission reliability. In practical applications, the selection of MCS directly affects the throughput and error rate performance of the wireless link.
[0008] However, existing OTA testing methods fail to fully utilize the real-time monitoring and adaptive adjustment capabilities of MCS parameters, resulting in test efficiency and accuracy that cannot meet the testing needs of current complex wireless devices. Therefore, developing an OTA testing system and method that can adaptively schedule based on MCS parameters to realize intelligent optimization of test positions is of great significance to improve the test efficiency and accuracy of wireless devices. SUMMARY
[0009] In view of the above prior art deficiencies, the purpose of the application is to provide an OTA dark box optimal test position search system and method, which can dynamically adjust the test position according to the real-time MCS parameters of the device under test, complete efficient and accurate wireless device performance testing, and realize intelligent optimization of the test position.
[0010] In a first aspect of the application, an OTA dark box optimal test position search system is provided, comprising: an OTA dark box, a real-time monitoring module, an adaptive scheduling algorithm module, a test control module, a data analysis and evaluation module, and a result output module,
[0011] The OTA dark box is internally provided with a plurality of test probes or antenna arrays for providing an interference-free test environment;
[0012] The real-time monitoring module is used to obtain the MCS parameters of the device under test;
[0013] The adaptive scheduling algorithm module is used to dynamically adjust the test position according to the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluate the performance of the current test position, and determine whether the optimal test position has been reached;
[0014] The test control module is used to control the position, direction and transmission power of the plurality of test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, dynamically adjust the test position to the optimal test position for testing to obtain test data;
[0015] The data analysis and evaluation module is used to analyze and evaluate the test data to determine whether the optimal test position has been found, and if the determination result is that the optimal test position has not been found, continue searching;
[0016] The result output module is used to output the optimal test position and the test result if the determination result is that the optimal test position has been found.
[0017] Further, in the above OTA dark box optimal test position search system, the real-time monitoring module at least comprises: a signal source, a power divider, a spectrum analyzer and a vector signal analyzer;
[0018] The signal source is used to generate test signals, which are distributed to the plurality of test probes through the power divider;
[0019] The device under test processes the test signals and returns feedback signals after receiving the test signals;
[0020] The spectrum analyzer and the vector signal analyzer are used to analyze the transmitted signals and the received signals to calculate the MCS parameters.
[0021] Further, in the OTA dark chamber optimal test position search system, the adaptive scheduling algorithm module comprises a parameter evaluation unit, a position prediction unit and a path planning unit.
[0022] The parameter evaluation unit is configured to evaluate the performance of the current test position according to the MCS parameters.
[0023] The position prediction unit is configured to predict the next possible optimal position according to historical data and the performance of the current test position.
[0024] The path planning unit is configured to plan the optimal path from the current position to the predicted position.
[0025] Further, in the OTA dark chamber optimal test position search system, the test control module comprises a motion control card, a driver and a controller, which are used to control the position, direction and transmission power of the multiple test probes or antenna arrays.
[0026] Further, in the OTA dark chamber optimal test position search system, the data analysis and evaluation module comprises a data processing unit, a feature extraction unit and an evaluation decision unit.
[0027] The data processing unit is configured to preprocess the original data.
[0028] The feature extraction unit is configured to extract key features from the preprocessed data.
[0029] The evaluation decision unit is configured to determine whether the optimal test position has been found or whether further search is needed according to the key features.
[0030] Further, in the OTA dark chamber optimal test position search system, the MCS parameters include bit error rate, modulation mode and coding rate.
[0031] Further, in the OTA dark chamber optimal test position search system, the real-time monitoring module is configured to obtain the MCS parameters of the device to be tested by using the multi-probe method or the reverberation chamber method.
[0032] Further, in the OTA dark chamber optimal test position search system, the OTA dark chamber is a shielded dark chamber with wave-absorbing material on all six sides.
[0033] Further, in the OTA dark chamber optimal test position search system, the result output module outputs the optimal test position and test results in the form of charts and reports, and the reports include test process data, optimized path and final results.
[0034] The second aspect of the application also provides an OTA dark box optimal test position search method, which is applied to the system and comprises the following steps:
[0035] Placing the device to be tested in the OTA dark box, setting an initial test position and parameters;
[0036] The real-time monitoring module monitors the MCS parameters of the device to be tested in real time through the multi-probe method or the reverberation chamber method;
[0037] The adaptive scheduling algorithm module dynamically adjusts the test position according to the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluates the performance of the current test position, and determines whether the optimal test position has been reached;
[0038] The test control module controls the position, direction and transmission power of the multiple test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, dynamically adjusts the test position to the optimal test position, and performs testing to obtain test data;
[0039] The data analysis and evaluation module analyzes and evaluates the test data, judges whether the optimal test position has been found, and if the judgment result is that the optimal test position has not been found, the steps of the real-time monitoring module, the test control module and the data analysis and evaluation module are repeated to continue searching;
[0040] The result output module is used to output the optimal test position and test results if the judgment result is that the optimal test position has been found.
[0041] The application has the following beneficial effects:
[0042] The test efficiency is improved: by dynamically adjusting the test position, the blindness and redundancy of fixed position or preset path testing are avoided, and the test time is greatly shortened.
[0043] The test accuracy is enhanced: based on real-time MCS parameters, the test position is optimized, the test position that can best reflect the real performance of the device is found, and the accuracy and reliability of the test results are improved.
[0044] The test resources are saved: accurate test position positioning reduces unnecessary test points and test times, reduces test cost, and improves resource utilization efficiency.
[0045] The adaptability is improved: the system can adapt to the test requirements of wireless devices of different types and different specifications, and has wide applicability.
[0046] The test process is optimized: the automatic test position search and optimization process simplifies the test operation and improves the intelligent level of the test process.
[0047] Support multi-scenario testing: can simulate a variety of wireless environments, support device performance testing in different scenarios, and provide comprehensive data support for device optimization. BRIEF DESCRIPTION OF DRAWINGS
[0048] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
[0049] Figure 1 A schematic diagram of an OTA dark box optimal test position search system provided by an embodiment of the application;
[0050] Figure 2 A schematic diagram of a real-time monitoring module provided by an embodiment of the application;
[0051] Figure 3 A schematic diagram of an adaptive scheduling algorithm module provided by an embodiment of the application;
[0052] Figure 4 A schematic diagram of a test control module provided by an embodiment of the application;
[0053] Figure 5 A schematic diagram of a data analysis and evaluation module provided by an embodiment of the application;
[0054] Figure 6 A schematic diagram of an OTA dark box optimal test position search method provided by an embodiment of the application. DETAILED DESCRIPTION
[0055] In order to enable persons skilled in the art to better understand the technical solutions in the embodiments of the application, the technical solutions of the application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments of the application. It should be understood that these descriptions are only exemplary, and are not used to limit the scope of the application. Based on the embodiments of the application, all other embodiments obtained by persons skilled in the art without creative work should fall within the scope of the protection of the application.
[0056] In addition, in the following description, the description of well-known structures and techniques is omitted to avoid unnecessary confusion of the concepts disclosed in the application.
[0057] In the description of the present application, the terms "first", "second", "third" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance. The terms "mounting", "connecting", "connecting" should be broadly understood, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.
[0058] The exemplary embodiments will be described in detail herein below, with examples shown in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of methods and systems consistent with some aspects of the present application as detailed in the appended claims.
[0059] The present application provides an OTA dark box optimal test position search system and method, which can dynamically adjust the test position according to the real-time MCS parameters of the device under test, complete efficient and accurate wireless device performance test, and realize intelligent optimization of test position.
[0060] System embodiment
[0061] Figure 1 An OTA dark box optimal test position search system provided by the embodiment of the present application is shown in the figure.
[0062] In a first aspect of the present application, an OTA dark box optimal test position search system is provided, which combines Figure 1 , comprising: an OTA dark box 11, a real-time monitoring module 12, an adaptive scheduling algorithm module 13, a test control module 14, a data analysis and evaluation module 15, and a result output module 16,
[0063] The OTA dark box 11 is internally provided with a plurality of test probes or antenna arrays for providing an interference-free test environment.
[0064] Specifically, in the embodiment of the present application, the OTA dark box is used to provide an interference-free test environment, and the OTA dark box is internally provided with a plurality of test probes or antenna arrays, which can form a uniform or specific distribution of electromagnetic field environment in the OTA dark box.
[0065] The real-time monitoring module 12 is used to obtain the MCS parameters of the device to be tested.
[0066] Specifically, in the embodiment of the present application, the real-time monitoring module monitors the MCS parameters of the device under test in real time through the multi-probe method or the reverberation chamber method, records the coordinates (x, y, z) of the current test position and the corresponding MCS parameters, and transmits the coordinates (x, y, z) of the current test position and the corresponding MCS parameters to the adaptive scheduling algorithm module and the data analysis and evaluation module.
[0067] The adaptive scheduling algorithm module 13 is used to dynamically adjust the test position according to the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluate the performance of the current test position, and determine whether the optimal test position has been reached.
[0068] The test control module 14 is used to control the position, direction and transmission power of the multiple test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, dynamically adjust the test position to the optimal test position for testing to obtain test data.
[0069] The data analysis and evaluation module 15 is used to analyze and evaluate the test data, determine whether the optimal test position has been found, and if the determination result is that the optimal test position has not been found, continue searching.
[0070] Specifically, in the embodiment of the present application, the evaluation stage is that the data analysis and evaluation module 15 evaluates the performance of the current test position based on the preset evaluation standard. The evaluation standard can be whether the bit error rate BLER is lower than a threshold (such as 10%), whether the modulation mode reaches the highest order (such as 256QAM), whether the coding rate reaches the maximum value (such as 0.9375), whether the current test position is the optimal test position, or whether it is necessary to continue searching. If the current test position is not optimal, the adaptive scheduling algorithm module calculates the next test position using the reinforcement learning algorithm according to the real-time monitored MCS parameters, and the test control module controls the test probe to move to the new test position according to the instructions, and updates the coordinates and parameters of the current test position.
[0071] The steps of repeating the real-time monitoring module, the test control module and the data analysis and evaluation module continue searching are to repeat the evaluation stage and the position adjustment stage until the optimal test position is found. The historical data and the algorithm state are updated each time, the subsequent search path is optimized, the maximum number of iterations or time limit can be set to prevent infinite loop.
[0072] The result output module 16 is used to output the optimal test position and the test result if the determination result is that the optimal test position has been found.
[0073] Specifically, in the embodiment of the present application, when the optimal test position is found, the result output module 16 outputs the coordinates of the optimal test position and the corresponding MCS parameters, generates a test report including test process data, an optimized path and final results, and in some embodiments, test data and results can also be saved to provide a reference for subsequent testing or device optimization.
[0074] Figure 2 A real-time monitoring module schematic diagram is provided for the embodiment of the present application.
[0075] Further, in the above-mentioned OTA dark box optimal test position search system, in combination with Figure 2 The real-time monitoring module at least includes a signal source 21, a power divider 22, a spectrum analyzer 23 and a vector signal analyzer 24.
[0076] The signal source 21 is used to generate a test signal, which is distributed to multiple test probes through the power divider 22.
[0077] After the test signal is received by the device to be tested, it is processed and a feedback signal is returned.
[0078] The spectrum analyzer 23 and the vector signal analyzer 24 are used to analyze the transmitted signal and the received signal to calculate the MCS parameters.
[0079] Specifically, in the embodiment of the present application, the real-time monitoring module 12 is composed of a signal source 21, a power divider 22, a spectrum analyzer 23 and a vector signal analyzer 24, and can monitor the MCS parameters of the device to be tested in real time through the multi-probe method. The test signal is generated by the signal source and distributed to each test probe through the power divider. After the signal is received by the device under test, it is processed and a feedback signal is returned. The real-time monitoring module analyzes the transmitted signal and the received signal to calculate the MCS parameters such as the bit error rate (BLER), the modulation method and the coding rate.
[0080] Figure 3 A self-adaptive scheduling algorithm module schematic diagram is provided for the embodiment of the present application.
[0081] Further, in the above-mentioned OTA dark box optimal test position search system, in combination with Figure 3 The self-adaptive scheduling algorithm module includes a parameter evaluation unit 31, a position prediction unit 32 and a path planning unit 33,
[0082] The parameter evaluation unit 31 is used to evaluate the performance of the current test position according to the MCS parameters.
[0083] The position prediction unit 32 is used to predict the next possible optimal position according to the historical data and the performance of the current test position.
[0084] The path planning unit 33 is configured to plan an optimal path from the current position to the predicted position.
[0085] Specifically, in the embodiment of the present application, the adaptive scheduling algorithm module 13 is based on a dynamic adaptive scheduling algorithm of reinforcement learning, and dynamically adjusts the positions of the multiple test probe arrays or antenna arrays according to the real-time monitored MCS parameters.
[0086] Figure 4 A test control module schematic diagram is provided for the embodiment of the present application.
[0087] Further, in the OTA dark box optimal test position search system, the adaptive scheduling algorithm module 13 is based on a dynamic adaptive scheduling algorithm of reinforcement learning, and dynamically adjusts the positions of the multiple test probe arrays or antenna arrays according to the real-time monitored MCS parameters. Figure 4 The test control module includes a motion control card 41, a driver 42 and a controller 43, and the positions, directions and transmission powers of the multiple test probes or antenna arrays are controlled through the motion control card 41, the driver 42 and the controller 43.
[0088] Specifically, in the embodiment of the present application, the test control module 14 is composed of a motion control card, a driver and a controller, and the test control module 14 controls the movement and parameter adjustment of the multiple test probe arrays or antenna arrays according to the instructions of the adaptive scheduling algorithm module.
[0089] Figure 5 A data analysis and evaluation module schematic diagram is provided for the embodiment of the present application.
[0090] Further, in the OTA dark box optimal test position search system, the adaptive scheduling algorithm module 13 is based on a dynamic adaptive scheduling algorithm of reinforcement learning, and dynamically adjusts the positions of the multiple test probe arrays or antenna arrays according to the real-time monitored MCS parameters. Figure 5 The data analysis and evaluation module includes a data processing unit 51, a feature extraction unit 52 and an evaluation decision unit 53,
[0091] The data processing unit 51 is configured to pre-process the original data.
[0092] The feature extraction unit 52 is configured to extract key features from the pre-processed data.
[0093] The evaluation decision unit 53 is configured to judge whether the optimal test position has been found or whether it is necessary to continue searching according to the key features.
[0094] Specifically, in the embodiment of the present application, the data analysis and evaluation module 15 analyzes and evaluates the test data to determine whether the optimal test position has been found, and the data analysis and evaluation module 15 mainly includes a data processing unit 51, a feature extraction unit 52 and an evaluation decision unit 53, the data processing unit 51 pre-processes the original data; the feature extraction unit 52 extracts key features from the processed data; and the evaluation decision unit 53 determines whether the optimal test position has been found or whether it needs to continue searching according to the features.
[0095] Further, in the above-mentioned OTA dark box optimal test position search system, the MCS parameters include: bit error rate, modulation mode and coding rate.
[0096] Further, in the above-mentioned OTA dark box optimal test position search system, the real-time monitoring module is used to obtain the MCS parameters of the device to be tested by using a multi-probe method or a reverberation chamber method to monitor the MCS parameters of the device to be tested in real time.
[0097] Further, in the above-mentioned OTA dark box optimal test position search system, the OTA dark box is a shielded dark room with wave-absorbing material attached to all six sides.
[0098] Specifically, in the embodiment of the present application, the OTA dark box is a shielded dark room with wave-absorbing material attached to all six sides, and the internal space size of the OTA dark box is determined according to the test requirements, and is usually 3m x 3m x 3m to 5m x 5m x 5m. A movable test probe array is arranged inside the dark box, the number of probes is 12-64, which can be configured according to the test requirements, and the movable test probe array can move in three-dimensional space with a position accuracy of ±0.5mm.
[0099] Further, in the above-mentioned OTA dark box optimal test position search system, the result output module outputs the optimal test position and the test results in the form of charts and reports, and the report includes: test process data, optimized path and final results.
[0100] Specifically, in the embodiment of the present application, when the optimal test position is found, the coordinates of the optimal test position and the corresponding MCS parameters are output, and a test report is generated, including test process data, optimized path and final results. In some embodiments, test data and results can also be saved to provide a reference for subsequent testing or device optimization.
[0101] Method embodiment
[0102] Figure 6 An OTA dark box optimal test position search method provided by the embodiment of the present application is shown in the figure.
[0103] The second aspect of the application also provides an OTA dark box optimal test position searching method, which is applied to the system and combined with Figure 6 , and comprises six steps S61-S66.
[0104] S61: placing a device to be tested in an OTA dark box, setting an initial test position and parameters.
[0105] Specifically, in the embodiment of the application, the device to be tested is placed in the OTA dark box, connected to the test system, and the initial test position and parameters such as the probe position, the transmission power, the modulation mode, etc. are set, and the parameters and states of the adaptive scheduling algorithm module are initialized.
[0106] S62: the real-time monitoring module monitors the MCS parameters of the device to be tested in real time by the multi-probe method or the reverberation chamber method.
[0107] Specifically, in the embodiment of the application, the MCS parameters of the device to be tested are monitored in real time by the multi-probe method or the reverberation chamber method, the coordinates (x, y, z) of the current test position and the corresponding MCS parameters are recorded, and the coordinates (x, y, z) of the current test position and the corresponding MCS parameters are transmitted to the adaptive scheduling algorithm module and the data analysis and evaluation module.
[0108] S63: the adaptive scheduling algorithm module dynamically adjusts the test position according to the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluates the performance of the current test position, and determines whether the optimal test position has been reached.
[0109] S64: the test control module controls the positions, directions and transmission powers of the multiple test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, dynamically adjusts the test position to the optimal test position for testing to obtain test data.
[0110] S65: the data analysis and evaluation module analyzes and evaluates the test data, judges whether the optimal test position has been found, and if the judgment result is that the optimal test position has not been found, the steps of the real-time monitoring module, the test control module and the data analysis and evaluation module are repeated to continue searching.
[0111] Specifically, in the embodiment of the present application, the above S63 to S65 stages are the evaluation stage and the position adjustment stage. In the evaluation stage, the data analysis and evaluation module evaluates the performance of the current test position based on the preset evaluation standard. The evaluation standard can be whether the bit error rate BLER is lower than a threshold (such as 10%), whether the modulation mode reaches the highest order (such as 256QAM), whether the coding rate reaches the maximum value (such as 0.9375), to determine whether the current test position is the optimal test position or whether it needs to continue searching. If the current test position is not optimal, the adaptive scheduling algorithm module calculates the next test position using the reinforcement learning algorithm based on the real-time monitored MCS parameters, and the test control module controls the test probe to move to the new test position according to the instruction, and updates the coordinates and parameters of the current test position.
[0112] The steps of repeating the real-time monitoring module, the test control module, and the data analysis and evaluation module continue searching are to repeat the evaluation stage and the position adjustment stage until the optimal test position is found. The historical data and the algorithm state are updated each time the iteration is performed, the subsequent search path is optimized, and the maximum number of iterations or time limit can be set to prevent infinite loop.
[0113] S66: The result output module is used to output the optimal test position and the test result if the judgment result is that the optimal test position has been found.
[0114] Specifically, in the embodiment of the present application, when the optimal test position is found, the coordinates of the optimal test position and the corresponding MCS parameters are output, a test report is generated, including test process data, optimized path and final result. In some embodiments, test data and results can also be saved to provide reference for subsequent testing or device optimization.
[0115] The OTA optimal test position search system and method based on MCS adaptive scheduling in the dark box of the present application can be applied to various scenarios. The following will be described in detail in combination with specific application scenarios.
[0116] Application scenario 1: 5G smart phone MIMO performance test
[0117] In the MIMO performance test of a 5G smart phone, the traditional fixed position test method often cannot accurately evaluate the performance of the device in different use scenarios. The system and method of the present application can dynamically adjust the test position according to the real-time monitored MCS parameters, find the test position that best reflects the real performance of the device, and improve the test efficiency and accuracy.
[0118] The specific application steps are as follows:
[0119] Step one: Place the 5G smart phone in the OTA dark box and connect it to the test system.
[0120] Step two: The system monitors the MCS parameters of the mobile phone in real time through the multi-probe method, including BLER, modulation mode and coding rate.
[0121] Step three: The adaptive scheduling algorithm module dynamically adjusts the position of the test probe based on the MCS parameters using the reinforcement learning algorithm.
[0122] Step four: The system continuously searches for the optimal test position until it finds a position where the BLER is below the threshold (such as 10%) and the modulation mode and coding rate reach the maximum value.
[0123] Step five: Output the optimal test position and test results to provide a reference for mobile phone antenna design and performance optimization.
[0124] Application scenario 2: IoT device OTA testing
[0125] In the OTA testing of IoT devices, due to the diversity of device types and the large performance difference, traditional testing methods often cannot adapt. The system and method of the present application can adaptively adjust the test position according to the characteristics of different devices to achieve efficient and accurate testing.
[0126] The specific application steps are as follows:
[0127] Step one: Place the IoT device in the OTA dark box and connect it to the test system.
[0128] Step two: The system monitors the MCS parameters of the device in real time through the multi-probe method.
[0129] Step three: The adaptive scheduling algorithm module dynamically adjusts the position of the test probe based on the MCS parameters using the genetic algorithm.
[0130] Step four: The system searches for the optimal test position based on the communication protocol and performance characteristics of the device.
[0131] Step five: Output the optimal test position and test results to ensure that the performance of the IoT device meets the requirements in actual application.
[0132] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, the combination of features of different embodiments means that it is within the scope of the present application and forms different embodiments.
[0133] Those skilled in the art will understand that the description of each embodiment is focused on different aspects, and the parts not detailed in a certain embodiment can be referred to the relevant description of other embodiments.
[0134] Although the embodiments of the present application have been described with reference to the accompanying drawings, it is to be understood that various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the present application as defined by the appended claims. The present application is not limited to the specific embodiments described above, but is intended to cover any equivalent modifications or alternatives that fall within the scope of the present application as defined by the appended claims.
[0135] The present application is not limited to the specific embodiments described above, but is intended to cover any equivalent modifications or alternatives that fall within the scope of the present application as defined by the appended claims.
Claims
1. An optimal test location search system within an OTA (Over-The-Air) darkroom, characterized in that, include: OTA black box, real-time monitoring module, adaptive scheduling algorithm module, test control module, data analysis and evaluation module, and result output module. The OTA dark box is equipped with multiple test probes or antenna arrays to provide an interference-free testing environment. The real-time monitoring module is used to acquire the MCS parameters of the device under test. The adaptive scheduling algorithm module is used to dynamically adjust the test position based on the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluate the performance of the current test position, and determine whether the optimal test position has been reached. The test control module is used to control the position, orientation and transmission power of multiple test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, and dynamically adjust the test position to the optimal test position to obtain test data. The data analysis and evaluation module is used to analyze and evaluate the test data, and determine whether the optimal test position has been found. If the result is that the optimal test position has not been found, the search continues. The result output module is used to output the optimal test position and test result if the judgment result is that the optimal test position has been found.
2. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The real-time monitoring module includes at least: a signal source, a power divider, a spectrum analyzer, and a vector signal analyzer; The signal source is used to generate a test signal, which is then distributed to multiple test probes by the power divider. The device under test receives the test signal, processes it, and returns a feedback signal. The spectrum analyzer and the vector signal analyzer are used to analyze the transmitted and received signals to calculate the MCS parameters.
3. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The adaptive scheduling algorithm module includes: a parameter evaluation unit, a location prediction unit, and a path planning unit. The parameter evaluation unit is used to evaluate the performance of the current test location based on the MCS parameters; The location prediction unit is used to predict the next possible optimal location based on historical data and the performance of the current test location. The path planning unit is used to plan the optimal path from the current location to the predicted location.
4. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The test control module includes a motion control card, a driver, and a controller, which control the position, orientation, and transmission power of multiple test probes or antenna arrays.
5. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The data analysis and evaluation module includes: a data processing unit, a feature extraction unit, and an evaluation decision unit. The data processing unit is used to preprocess the raw data; The feature extraction unit is used to extract key features from the preprocessed data; The evaluation and decision-making unit determines whether the optimal test location has been found or whether the search needs to continue based on key features.
6. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The MCS parameters include: bit error rate, modulation scheme, and coding rate.
7. The OTA (Over-The-Air) optimal test location search system according to claim 1, characterized in that, The real-time monitoring module is used to obtain the MCS parameters of the device under test by using a multi-probe method or a reverberation chamber method to monitor the MCS parameters of the device under test in real time.
8. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The OTA darkroom is a shielded darkroom with absorbing material on all six sides.
9. The OTA (Over-The-Air) optimal test location search system in a darkroom according to claim 1, characterized in that, The result output module outputs the optimal test location and test results in the form of charts and reports. The reports include: test process data, optimized path and final results.
10. A method for searching the optimal test location within an OTA darkroom, applied to the system described in claims 1-9, comprising: Place the device under test in the OTA dark box and set the initial test position and parameters; The real-time monitoring module monitors the MCS parameters of the device under test in real time using a multi-probe method or a reverberation chamber method. The adaptive scheduling algorithm module dynamically adjusts the test position based on the real-time monitored MCS parameters and the adaptive scheduling algorithm, evaluates the performance of the current test position, and determines whether the optimal test position has been reached. The test control module controls the position, orientation, and transmission power of multiple test probes or antenna arrays according to the instructions of the adaptive scheduling algorithm module, and dynamically adjusts the test position to the optimal test position to obtain test data. The data analysis and evaluation module analyzes and evaluates the test data to determine whether the optimal test position has been found. If the result is that the optimal test position has not been found, the steps of the real-time monitoring module, the test control module, and the data analysis and evaluation module are repeated to continue the search. The result output module is used to output the optimal test position and test result if the judgment result is that the optimal test position has been found.