Multi-probe darkroom probe selection method, device, medium and computer program product

Abstract

The invention relates to the technical field of communication, and particularly provides a multi-probe darkroom probe selection method and device, a medium and a computer program product. The multi-probe darkroom probe selection method comprises the following steps: acquiring channel parameters and darkroom configuration parameters of a to-be-reconstructed channel model, calculating target spatial correlation based on the channel parameters, and constructing a transmission matrix of candidate probes based on the darkroom configuration parameters; based on the target spatial correlation and the transmission matrix, performing preliminary probe screening to obtain an initial probe set; adjusting the initial probe set to obtain an adjusted probe set; and determining the adjusted probe set as a target probe set under the condition that an iteration ending condition is met. By acquiring the channel parameters and the darkroom configuration parameters, it is ensured that the selected probe can accurately simulate the specific channel environment. In addition, probe configuration is optimized through multiple iterations in the processes of preliminary probe screening and selected probe adjustment, and the accuracy of channel simulation is further improved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method, device, medium and computer program product for selecting a probe in a multi-probe darkroom. Background Art

[0002] With the rapid growth of mobile data demand, wireless network technology has been widely used. Especially in the 5G ecosystem, the development and testing of massive multiple-input multiple-output (MIMO) technology is particularly critical. In order to conduct comprehensive multi-dimensional performance evaluation and verification of the MIMO system, dynamic channel simulation of the MIMO system is required.

[0003] In the related art, channel simulation is usually performed in a three-dimensional multi-probe darkroom. By selecting a suitable probe combination and determining the weight of each probe, the channel emulator (CE) physically reconstructs any propagation environment in the test area. When selecting probes, the related art usually selects the column that maximizes the inner product of the transmission matrix and the target space correlation. However, due to the characteristics of wireless channels such as large channel data, complex and diverse scenarios, and dynamic changes in channel characteristics, selecting the probe with the largest inner product cannot flexibly fit different channels, resulting in the characteristics of the channel itself not being fully considered during the probe selection process, thereby reducing the accuracy of channel reconstruction. Summary of the invention

[0004] In view of this, exemplary embodiments of the present disclosure provide a multi-probe darkroom probe selection method, device, medium and computer program product to solve the problems existing in the related art.

[0005] One aspect of the exemplary embodiments of the present disclosure provides a method for selecting a probe in a multi-probe darkroom, the method comprising:

[0006] Acquire channel parameters and darkroom configuration parameters of the channel model to be reconstructed, calculate target spatial correlation based on the channel parameters, and construct a transmission matrix of candidate probes based on the darkroom configuration parameters; the target spatial correlation is an objective function for constructing the channel model to be reconstructed, and the transmission matrix represents transmission characteristics corresponding to a plurality of candidate probes;

[0007] Based on the target spatial correlation and the transmission matrix, preliminary probe screening is performed to obtain an initial probe set;

[0008] Adjusting the initial probe set to obtain an adjusted probe set;

[0009] When the iteration end condition is met, the adjusted probe set is determined as the target probe set.

[0010] Another aspect of the exemplary embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in the exemplary embodiments of the present disclosure.

[0011] According to another aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0012] According to another aspect of the exemplary embodiments of the present disclosure, a computer program product is provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0013] As will be described in detail below, according to a multi-probe darkroom probe selection method of an embodiment of the present disclosure, by obtaining the channel parameters and darkroom configuration parameters of the channel model to be reconstructed, and calculating the target spatial correlation based on the channel parameters, the transmission matrix of the candidate probes is constructed based on the darkroom configuration parameters; the target spatial correlation is the objective function for constructing the channel model to be reconstructed, and the transmission matrix represents the transmission characteristics corresponding to multiple candidate probes; based on the target spatial correlation and the transmission matrix, preliminary probe screening is performed to obtain an initial probe set; the initial probe set is adjusted to obtain an adjusted probe set; when the iteration end condition is met, the adjusted probe set is determined as the target probe set. Therefore, the multi-probe darkroom probe selection method provided by the present disclosure ensures that the selected probe can accurately simulate a specific channel environment by obtaining channel parameters and darkroom configuration parameters. Furthermore, in the process of preliminary probe screening and adjustment of the selected probe, the probe configuration is optimized through multiple iterations to find the globally optimal target probe set, thereby further improving the accuracy of channel simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 A schematic diagram of a three-dimensional multi-probe darkroom for channel reconstruction provided as an example in the present disclosure;

[0016] Figure 2 A schematic diagram of a process for selecting a multi-probe darkroom probe provided as an example in the present disclosure;

[0017] Figure 3 A schematic diagram of a flow chart of a multi-probe darkroom probe selection method provided as an example in the present disclosure;

[0018] Figure 4 A schematic diagram of a process for preliminary screening of probes by a variable step-size constrained orthogonal matching pursuit provided as an example in the present disclosure;

[0019] Figure 5 A schematic diagram of a flow chart of a step of adjusting a selected probe by orthogonal matching pursuit with variable step-size constraint provided by an exemplary embodiment of the present disclosure;

[0020] Figure 6 A schematic diagram of spatial correlation errors of different probe selection methods provided as an example in the present disclosure;

[0021] Figure 7 A schematic block diagram of functional modules of a multi-probe darkroom probe selection device provided as an example in the present disclosure;

[0022] Figure 8 A structural block diagram of an electronic device provided as an example in the present disclosure;

[0023] Fig. 9 A schematic diagram of a computer program product provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0026] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] With the rapid growth of mobile data demand, wireless network technology has been widely used. Especially in the 5G ecosystem, the development and testing of massive multiple-input multiple-output (MIMO) technology is particularly critical. In order to conduct comprehensive multi-dimensional performance evaluation and verification of the MIMO system, dynamic channel simulation of the MIMO system is required.

[0033] In the related art, channel simulation is usually performed in a three-dimensional multi-probe darkroom. By selecting a suitable probe combination and determining the weight of each probe, the channel emulator (CE) physically reconstructs any propagation environment in the test area. When selecting probes, the related art usually selects the column that maximizes the inner product of the transmission matrix and the target space correlation. However, due to the characteristics of wireless channels such as large channel data, complex and diverse scenarios, and dynamic changes in channel characteristics, selecting the probe with the largest inner product cannot flexibly fit different channels, resulting in the characteristics of the channel itself not being fully considered during the probe selection process, thereby reducing the accuracy of channel reconstruction.

[0034] Therefore, in order to solve the above problems, an exemplary embodiment of the present disclosure provides a multi-probe darkroom probe selection method, which relies on two-step variable step size to select probes and handle constraints through a variable step size constrained orthogonal matching pursuit method, thereby reducing calculation time and improving the accuracy and efficiency of channel reconstruction. The variable step size constrained orthogonal matching pursuit method includes three modules: an initialization module, a preliminary screening module, and an adjustment module. In the initialization module, the corresponding dictionary matrix and target space correlation can be calculated according to the number of clusters, the power of each cluster, the azimuth angle, the pitch angle, the azimuth angle extension, and the pitch angle extension. The dictionary matrix is ​​a specific data structure used to describe the performance of a signal under specific parameter settings. In addition, the initialization module also performs data processing on the target space correlation and the dictionary matrix, separates the real and imaginary parts of the data, and recombine them into a new matrix to ensure that the calculated probe weight is a real number.

[0035] In the initial screening module, the inner product sorting can be used to select the first q1 probes in each round to form the initial probe set. The inner product sorting is based on the inner product from large to small, and the inner product is recalculated in each round to select the first q1 probes.

[0036] The adjustment module is further optimized based on the initial screening. By combining the inner product sorting and the closed solution sorting, q2 probes are selected in each round to adjust the initial probe set until the maximum number of cycles is met. After the maximum number of cycles is reached, the first q2 probes in the initial probe set form the target probe set.

[0037] In the calculation of the initial screening module and the adjustment module, if the calculated weight is negative, the probes selected in this round and the probes in the previous round are removed from the dictionary matrix, and then the cycle is repeated until the weights of all probes are greater than zero.

[0038] Based on this, the above-mentioned multi-probe darkroom probe selection method can not only meet the constraint that the weights are non-negative and the sum is one, but also flexibly fit different channel models, thereby improving the computational efficiency and accuracy of probe selection.

[0039] For example, Figure 1 A schematic diagram of a three-dimensional multi-probe darkroom for channel reconstruction provided as an example in the present disclosure, as shown in FIG. Figure 1 As shown, the three-dimensional multi-probe darkroom for channel reconstruction may specifically include: a base station simulator, a channel simulator, a switch matrix and a three-dimensional multi-probe darkroom.

[0040] Among them, the base station simulator is used to simulate the base station signals in the mobile phone network. It can generate and receive radio waves and simulate the behavior of the base station in the real environment.

[0041] Channel simulators are used to simulate various conditions in the process of wireless signal propagation, such as multipath effects, signal attenuation or interference, and are used to evaluate and test the performance of wireless communication equipment under various environmental conditions.

[0042] The switch matrix is ​​used to connect the channel simulator and different probes in the multi-probe darkroom to achieve flexible signal distribution and switching. The channel simulator and the switch matrix are connected through K transmission lines to ensure data transmission.

[0043] The three-dimensional multi-probe chamber includes multiple probe antennas, which can be arranged at different spatial positions around the device under test (DUT) to simulate different directions of receiving and sending signals. The switch matrix and the three-dimensional multi-probe chamber are connected through P lines to control P switches respectively.

[0044] Specifically, the working principle of the three-dimensional multi-probe darkroom for channel reconstruction is:

[0045] First, the base station simulator generates simulated base station signals, and the channel simulator receives these signals and processes them according to the preset environmental model to simulate the signal propagation effect in the real world.

[0046] The processed signals are then routed to specific probe antennas through a switch matrix based on test requirements. The switch matrix is ​​connected to the channel simulator via K transmission lines and controls the connections to different probe antennas via P lines.

[0047] The probe antenna receives signals from the device under test and captures the signals based on the relative position (distance R) and angle between the probe and the DUT. These signals are fed back to the channel simulator or directly to the test system for further analysis and evaluation.

[0048] During the test process, the settings of the channel simulator can be adjusted to simulate different environmental conditions to test the performance of the DUT under different conditions. The test results can help researchers and engineers evaluate the performance of the device and the effectiveness of the signal processing algorithms.

[0049] For example, Figure 2 This is a schematic diagram of a multi-probe darkroom probe selection method provided by the present disclosure as an example. Figure 3 A schematic flow chart of a multi-probe darkroom probe selection method provided as an example in the present disclosure.

[0050] like Figure 2 As shown, the multi-probe darkroom probe selection method may specifically include the following steps:

[0051] Step S210: Acquire channel parameters and darkroom configuration parameters of the channel model to be reconstructed, calculate target spatial correlation based on the channel parameters, and construct a transmission matrix of the candidate probe based on the darkroom configuration parameters.

[0052] First, the channel parameters of the channel model to be reconstructed can be obtained, and the target spatial correlation can be calculated based on the channel parameters, where the channel parameters may include the number of clusters, the power of each cluster, the azimuth, the elevation, ASA (Angle of Arrival Spread), AOA (Angle of Arrival), ESA (Elevation Spread of Arrival) and EoA (Elevation of Arrival).

[0053] The target spatial correlation is the objective function for constructing the channel model to be reconstructed, which is used to describe the correlation between the signal vector or the vectors within the signal set. The calculated spatial correlation is usually expressed as a complex vector N is the number of sampling logarithms, which contains the real and imaginary parts of the signal correlation. For ease of processing, the imaginary part can be spliced ​​under the real part to form an extended target vector

[0054] Exemplarily, the calculation formula of the target spatial correlation ρ can be expressed as the following formula (1):

[0055]

[0056] in, and Respectively represent the position vectors of two sampling antennas in the test area; represents the unit space vector; P(Ω) represents the spherical power spectrum of the channel; j represents the imaginary unit in Euler's formula; λ represents the wavelength.

[0057] Then, the darkroom configuration parameters can be obtained and the transmission matrices of all available probes can be calculated based on the darkroom configuration parameters. The darkroom configuration parameters may include: q1, q2, the position and number of probes in the three-dimensional multi-probe darkroom, and the distance R between the device under test and the probe. Among them, the Kth probe transmission matrix column T K The calculation formula can be expressed as the following formula (2): Due to T K For a complex matrix, we can separate the real and imaginary parts and concatenate the imaginary part under the real part to get

[0058]

[0059] in, and They represent the position vectors of the two sampling antennas in the test area respectively; j represents the imaginary unit in the Euler formula; Represents the unit position vector of the Kth probe.

[0060] After completing the calculation of the target vector and the transfer matrix, the residual res = ρ is initialized and the candidate matrix The dictionary matrix T'=T.

[0061] Among them, the residual is initially set to the target spatial correlation, the candidate matrix is ​​set to the empty set, and the dictionary matrix is ​​set to the transfer matrix, which is used to update and optimize the probe selection during the iteration process.

[0062] Step S220: Perform preliminary probe screening based on the target spatial correlation and the transmission matrix to obtain an initial probe set.

[0063] Specifically, step S220 may include:

[0064] Based on the target spatial correlation and the transmission matrix, the inner products corresponding to multiple candidate probes in the transmission matrix are calculated;

[0065] Add q1 candidate probes with the largest inner product to the candidate matrix; q1 is a positive integer;

[0066] Calculate the closed-form solution of the candidate matrix weights to obtain the weights corresponding to each probe in the candidate matrix;

[0067] When the weights corresponding to the probes in the candidate matrix are all greater than 0, the candidate matrix is ​​determined as the initial probe set, and the probes selected in this round are removed from the transmission matrix.

[0068] When the weights corresponding to the probes in the candidate matrix are less than 0, the probes selected in this round and the probes selected in the previous round are removed from the transmission matrix.

[0069] In the embodiment, after the initialization is completed, the probe screening process starts. First, the inner product is calculated based on the dictionary matrix and the target space correlation vector.<T’,ρ> And sort them from large to small. In order to maintain the non-negative property of the subsequent probe weight calculation, the calculation of the inner product does not consider taking the absolute value.

[0070] The inner product can be viewed as evaluating the degree of match between each probe (or combination thereof) in the transmission matrix and the target spatial correlation. Each column of the transmission matrix can represent the transmission characteristics of a probe, while the target spatial correlation matrix can represent the desired signal or spatial characteristics.

[0071] The purpose of calculating the inner product is to find the probes that best match the target spatial correlation. These probes can more effectively capture the target signal or characteristics. Therefore, the size of the inner product (regardless of the absolute value) directly affects the priority of probe selection.

[0072] In the process of probe screening, firstly, sort by inner product (sort by inner product from large to small) and select q1 probes in each round to form an initial probe set. Determine whether the number of selected probes in the initial probe set is greater than the target number of probes. If not, perform the first step of preliminary screening of probes. If greater, perform the second step of adjusting the selected probes. In the process of preliminary screening of probes and adjustment of selected probes, a closed-form solution that takes into account a constraint is used for calculation. At the same time, in each round of loops, if there is a weight less than zero, the probes selected in this round and the probes selected in the previous round are removed from the dictionary matrix, and the cycle is repeated again until the weights are all greater than zero.

[0073] Exemplarily, assuming that the number of target probes = 4, q1 = 1, q2 = 1, then in the preliminary probe screening step, the inner product is recalculated in each cycle, and sorted according to the inner product, the q1 probes corresponding to the maximum inner product are selected as the selected probes, and the selected probes are placed in the initial probe set until 4 selected probes are selected. In the step of adjusting the selected probes, the inner product is calculated again, and sorted according to the inner product, the q2 probes corresponding to the maximum inner product are selected as the selected probes, and the selected probes are placed in the initial probe set. At this time, the number of selected probes in the initial probe set is 5. The closed-form solutions of the 5 selected probes are calculated respectively, and sorted by weight, and the 4 probes with the largest weights are determined as the target probe combination.

[0074] Figure 4The following is a flow chart of the steps of preliminary screening of probes for variable step-size constrained orthogonal matching pursuit provided by the present disclosure as an example. Figure 4 As shown:

[0075] First, add the dictionary matrix columns corresponding to the q1 largest inner products to the candidate matrix Then calculate the closed-form solution of the following formula (3):

[0076]

[0077] in, represents the candidate matrix; ρ represents the target spatial correlation; w represents the probe weight.

[0078] In the process of solving formula (3), only the weights in the constraints and the constraint that the sum of all values ​​must be equal to one are considered, so it is necessary to further determine whether there is a situation where the weight is less than 0. If there is a situation where the solved weight is less than zero, the probes selected in this round and the probes selected in the previous round are removed from the dictionary matrix, and the cycle is repeated until the weights are all greater than zero.

[0079] Specifically, if there is a weight less than 0, it indicates that the probe configuration selected in the current round is unreasonable and needs to be adjusted. At this time, the residual remains unchanged, the dictionary matrix T' is updated, and the corresponding selected probe columns and newly added columns in the transmission matrix T are set to 0 to eliminate the interference of these probes on subsequent calculations.

[0080] If there is no case where the weight is less than 0, it means that the probe selected in this round is valid. At this time, update the dictionary matrix T' and set the columns corresponding to the selected probes to 0, thereby eliminating the influence of these probes in subsequent iterations. At the same time, update the residual according to the following formula (4). This screening method ensures that the weight obtained by calculating the closed-form solution is greater than 0.

[0081]

[0082] Among them, res represents the residual; represents the candidate matrix; w represents the probe weight.

[0083] Among them, the closed-form solution calculation formula for the probe weight and wavelength is as follows:

[0084]

[0085] in, represents the candidate matrix; w represents the probe weight; ρ represents the target spatial correlation; λ represents the wavelength; and e represents the unit vector.

[0086] Based on this, the optimal probe combination can be gradually screened out through the above-mentioned preliminary screening probe method, while ensuring that the weight vector calculated in each step satisfies the condition that all weights are non-negative.

[0087] Step S230: adjusting the initial probe set to obtain an adjusted probe set.

[0088] When the number of selected probes is greater than the target number of probes, the selected probes in the initial probe set begin to be adjusted.

[0089] Specifically, step S230 may include:

[0090] Based on the target spatial correlation and the transmission matrix, the inner products corresponding to multiple candidate probes in the transmission matrix are calculated;

[0091] Add q2 candidate probes with the largest inner product to the initial probe set; q2 is a positive integer;

[0092] Calculate the closed-form solution of the weight of the initial probe set to obtain the weight corresponding to each probe in the initial probe set;

[0093] The initial probe set is adjusted based on the weights corresponding to the respective probes in the initial probe set to obtain an adjusted probe set.

[0094] The initial probe set is adjusted based on the weights corresponding to the probes in the initial probe set to obtain an adjusted probe set, including:

[0095] The M probes with the largest weights in the initial probe set are determined as candidate matrices; M is the number of probes in the target probe set, and M is a positive integer;

[0096] Calculate the closed-form solution of the candidate matrix weights to obtain the weights corresponding to each probe in the candidate matrix;

[0097] When the weights corresponding to the respective probes in the candidate matrix are all greater than 0, the candidate matrix is ​​determined as the adjusted probe set, and the probes selected in this round are removed from the transmission matrix.

[0098] When the weights corresponding to the probes in the candidate matrix are less than 0, the probes selected in this round and the probes selected in the previous round are removed from the transmission matrix.

[0099] Figure 5 The following is a flow chart of the step of adjusting the selected probe by orthogonal matching pursuit with variable step size constraint provided by the present disclosure as an example. Figure 5 As shown:

[0100] First, the dictionary matrices corresponding to the q2 largest inner products are added to the initial probe set.

[0101] Then, the closed-form solution of the above formula (3) is calculated to obtain the weights corresponding to each probe in the initial probe set. The calculated closed-form solution weights are sorted according to the weight size, and the M probes with the largest weights in the initial probe set are determined as the candidate matrix The goal is to optimize the efficiency of the probe combination and ensure that all selected probes contribute actively to signal reconstruction.

[0102] In the next step, the closed-form solution is calculated and it is determined whether there is a weight less than 0. If so, the residual is not updated, and the dictionary matrix T' is updated to set the selected columns and newly added columns corresponding to the transmission matrix T in the previous round to 0 to avoid interference with subsequent calculations and help eliminate inappropriate probes that may be selected due to the local optimality of the algorithm.

[0103] If all weights are greater than 0, the residual res is updated according to the above formula (4), the dictionary matrix T' is updated, and the columns corresponding to the selected probes are set to 0, thereby eliminating the influence of these probes in subsequent iterations, and the probe set in the candidate matrix is ​​determined as the adjusted probe set.

[0104] In this step, the probe is optimized by selecting the required probe from q2 probes. At the same time, because the probe with the largest inner product is selected and if there is a negative value in the previous round, the corresponding related column will be set to 0, it can be further guaranteed that the weight obtained by the final calculation of the closed-form solution is greater than 0.

[0105] Based on this, the above-mentioned method of adjusting the selected probe calculates the inner product of the correlation between the candidate probe and the target space and selects the probe with the largest inner product value to ensure that the selected probe can capture the characteristics of the target signal to the maximum extent. This inner product-based selection not only improves the accuracy of signal reconstruction, but also enhances the system's adaptability to signal changes. Furthermore, the selected probes in the initial probe set are dynamically adjusted through the probe weights, and the non-negativity of the weights is checked to ensure that each probe has a positive contribution to signal reconstruction. This inner product-based probe selection and weight adjustment strategy not only ensures the optimality of the probe configuration, but also greatly enhances the system's adaptability to complex signal environments and reconstruction quality.

[0106] Step S240: When the iteration end condition is met, the adjusted probe set is determined as the target probe set.

[0107] Exemplarily, the iteration end condition may be: determining whether the number of cycles is greater than a threshold number. When the number of cycles is greater than the threshold number, the probe sequence number and weight corresponding to each probe in the adjusted probe set are combined into a target probe set, and the target probe set is output. When the number of cycles is not greater than the threshold number, step S220 is re-executed.

[0108] Based on the above embodiments, this embodiment also provides a comparison between the MultiShot algorithm, the OMP (Orthogonal Matching Pursuit) algorithm and the probe selection method (VCOMP) proposed in the present disclosure.

[0109] Figure 6 Schematic diagram of spatial correlation error of different probe selection methods provided as an example in the present disclosure. The compared algorithms include MultiShot, OMP and VCOMP. The ordinate represents the root mean square error (RMSE), and the abscissa represents the number of probes in the target probe combination. Figure 5 As shown, under different probe numbers, the root mean square error of the probe selection method (VCOMP) proposed in the present disclosure is lower than that of the other two methods.

[0110] Table 1 shows the average time consumption of different probe selection methods provided by the present disclosure, and the compared algorithms include MultiShot, OMP and VCOMP. As shown in Table 1, since the probe selection method (VCOMP) proposed in the present disclosure considers the non-negative sum-to-one constraint in the process of screening probes and does not use convex optimization to solve the problem, the average time consumption of the probe selection method (VCOMP) proposed in the present disclosure is lower than that of the other two methods.

[0111] Table 1 Average time consumption of different probe selection methods under different probe numbers

[0112] method Average time (seconds) MultiShot 341.8741 OMP 0.46714 VCOMP 0.0325

[0113] One or more technical solutions provided in the exemplary embodiments of the present disclosure select q1 probes in each cycle to form an initial probe set according to inner product sorting, and then select q2 probes in each cycle to adjust the initial probe set by combining inner product sorting and closed-form sorting. In the calculation of the preliminary screening of probes and the adjustment of the selected probes, a closed-form solution that takes into account the constraint of sum as one is used for calculation. At the same time, in each cycle, if there is a weight less than zero, the probe selected in this round and the probe selected in the previous round are removed from the dictionary matrix, and the cycle is repeated until the weights are all greater than zero.

[0114] Among them, in the process of preliminary screening of probes, selecting q1 probes can flexibly select the appropriate number of probes, making the channel model reconstruction more accurate and avoiding excessive redundancy in the probe search space. In the process of adjusting the selected probes, selecting q2 probes to add to the selected probes and selecting again can avoid falling into the local optimal solution.

[0115] In addition, a closed-form solution is used to solve the constraint that the sum of weight values ​​is one, and a loop and corresponding judgment conditions are used to solve the situation where the weights are negative. The combination of the two can handle all constraints on the weights without the need to use additional convex optimization algorithms, thereby greatly improving the calculation speed.

[0116] Therefore, the multi-probe darkroom probe selection method provided in the exemplary embodiment of the present invention can solve the problems of search redundancy and low computational efficiency caused by traversing the probes in the process of determining the target probe calculation, and can also solve technical problems such as low computational efficiency and long time consumption caused by the need to use additional convex optimization algorithms in the process of calculating probe weights.

[0117] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It is understandable that in order to implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0118] The exemplary embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0119] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a multi-probe darkroom probe selection device, which can be a server or a chip applied to a server. Figure 7 This is a schematic block diagram of the functional modules of a multi-probe darkroom probe selection device provided by the present disclosure. Figure 7 As shown, the multi-probe darkroom probe selection device 700 includes:

[0120] The data acquisition module 710 is used to obtain the channel parameters and darkroom configuration parameters of the channel model to be reconstructed, calculate the target space correlation based on the channel parameters, and construct the transmission matrix of the candidate probes based on the darkroom configuration parameters; the target space correlation is the objective function for constructing the channel model to be reconstructed, and the transmission matrix represents the transmission characteristics corresponding to multiple candidate probes respectively;

[0121] A data processing module 720 is used to perform preliminary probe screening based on the target spatial correlation and the transmission matrix to obtain an initial probe set;

[0122] The data processing module 720 is further used to adjust the initial probe set to obtain an adjusted probe set;

[0123] The data processing module 720 is further configured to determine the adjusted probe set as the target probe set when an iteration end condition is met.

[0124] In another embodiment provided by the present disclosure, the data processing module 720 is also used to calculate the inner products corresponding to multiple candidate probes in the transmission matrix based on the target spatial correlation and the transmission matrix; add q1 candidate probes with the largest inner products to the candidate matrix; q1 is a positive integer; calculate the closed-form solution of the weight of the candidate matrix to obtain the weights corresponding to each probe in the candidate matrix; when the weights corresponding to each probe in the candidate matrix are all greater than 0, determine the candidate matrix as the initial probe set, and remove the probes selected in this round from the transmission matrix.

[0125] In another embodiment provided by the present disclosure, the data processing module 720 is further used to remove the probes selected in this round and the probes selected in the previous round from the transmission matrix when the weights corresponding to the probes in the candidate matrix are less than 0.

[0126] In another embodiment provided by the present disclosure, the data processing module 720 is also used to calculate the inner products corresponding to multiple candidate probes in the transmission matrix based on the target spatial correlation and the transmission matrix; add q2 candidate probes with the largest inner products to the initial probe set; q2 is a positive integer; calculate the closed-form solution of the weights of the initial probe set to obtain the weights corresponding to each probe in the initial probe set; adjust the initial probe set based on the weights corresponding to each probe in the initial probe set to obtain an adjusted probe set.

[0127] In another embodiment provided by the present disclosure, the data processing module 720 is also used to determine the M probes with the largest weights in the initial probe set as a candidate matrix; M is the number of probes in the target probe set, and M is a positive integer; calculate the closed-form solution of the weight of the candidate matrix to obtain the weights corresponding to each probe in the candidate matrix; when the weights corresponding to each probe in the candidate matrix are all greater than 0, determine the candidate matrix as an adjusted probe set, and remove the probes selected in this round from the transmission matrix.

[0128] In another embodiment provided by the present disclosure, the data processing module 720 is further used to remove the probes selected in this round and the probes selected in the previous round from the transmission matrix when the weights corresponding to the probes in the candidate matrix are less than 0.

[0129] In another embodiment provided by the present disclosure, the data processing module 720 further includes: the weight closed-form solution is:

[0130]

[0131] in, represents the candidate matrix; ρ represents the target spatial correlation; w represents the probe weight.

[0132] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0133] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0134] Figure 8The block diagram of the electronic device provided as an example of the present disclosure, the block diagram of the electronic device 800 that can be used as the server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0135] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0136] A plurality of components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the electronic device 800, and the input unit 806 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 807 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 may include, but is not limited to, a disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0137] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. Each of the methods described above may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 800 via the ROM 802 and / or the communication unit 809.

[0138] Fig. 9 This is a schematic diagram of an exemplary computer program product provided by the present disclosure. The exemplary embodiment of the present disclosure also provides a computer program product 900, including a computer program 901, wherein the computer program 901 is used to enable the computer to perform a method according to an embodiment of the present disclosure when executed by a processor of the computer.

[0139] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0140] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0141] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0143] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0144] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0145] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0146] Although the present disclosure has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A method for selecting a probe in a multi-probe darkroom, characterized in that: The method comprises: Acquire channel parameters and darkroom configuration parameters of the channel model to be reconstructed, calculate target spatial correlation based on the channel parameters, and construct a transmission matrix of candidate probes based on the darkroom configuration parameters; the target spatial correlation is an objective function for constructing the channel model to be reconstructed, and the transmission matrix represents transmission characteristics corresponding to a plurality of candidate probes; Based on the target spatial correlation and the transmission matrix, preliminary probe screening is performed to obtain an initial probe set; Adjusting the initial probe set to obtain an adjusted probe set; When the iteration end condition is met, the adjusted probe set is determined as the target probe set.

2. The method according to claim 1, characterized in that The performing preliminary probe screening based on the target spatial correlation and the transmission matrix to obtain an initial probe set includes: Based on the target spatial correlation and the transmission matrix, calculating inner products corresponding to a plurality of candidate probes in the transmission matrix respectively; Add q1 candidate probes with the largest inner product to the candidate matrix; q1 is a positive integer; Calculate the closed-form solution of the weight of the candidate matrix to obtain the weight corresponding to each probe in the candidate matrix; When the weights corresponding to the probes in the candidate matrix are all greater than 0, the candidate matrix is ​​determined as the initial probe set, and the probes selected in this round are removed from the transmission matrix.

3. The method according to claim 2, characterized in that The method further comprises: When the weights corresponding to the probes in the candidate matrix are less than 0, the probes selected in this round and the probes selected in the previous round are removed from the transmission matrix.

4. The method according to claim 1, characterized in that: The step of adjusting the initial probe set to obtain an adjusted probe set includes: Based on the target spatial correlation and the transmission matrix, calculating inner products corresponding to a plurality of candidate probes in the transmission matrix respectively; Add q2 candidate probes with the largest inner product to the initial probe set; q2 is a positive integer; Calculating a closed-form solution of the weight of the initial probe set to obtain the weight corresponding to each probe in the initial probe set; The initial probe set is adjusted based on the weights corresponding to the respective probes in the initial probe set to obtain an adjusted probe set.

5. The method according to claim 4, characterized in that The adjusting the initial probe set based on the weights corresponding to the respective probes in the initial probe set to obtain an adjusted probe set includes: Determine the M probes with the largest weights in the initial probe set as candidate matrices; M is the number of probes in the target probe set, and M is a positive integer; Calculate the closed-form solution of the weight of the candidate matrix to obtain the weight corresponding to each probe in the candidate matrix; When the weights corresponding to the probes in the candidate matrix are all greater than 0, the candidate matrix is ​​determined as an adjusted probe set, and the probes selected in this round are removed from the transmission matrix.

6. The method according to claim 5, characterized in that The method further comprises: When the weights corresponding to the probes in the candidate matrix are less than 0, the probes selected in this round and the probes selected in the previous round are removed from the transmission matrix.

7. The method according to any one of claims 2 or 4, characterized in that: The closed-form solution of the weight is: in, represents the candidate matrix; ρ represents the target spatial correlation; w represents the probe weight.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method of claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

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