Channel model establishing method and apparatus, device, storage medium, and program product

Abstract

The present application provides a channel model establishing method and apparatus, a device, a storage medium, and a program product. The method comprises: calibrating initial antenna working parameters of a communication node on the basis of first channel features, to obtain calibrated first antenna working parameters, wherein the first channel features are determined by the communication node on the basis of detection data of a wireless channel; and performing ray tracing by means of the first antenna working parameters, to establish a channel model of the wireless channel.

Description

Channel modeling methods, devices, equipment, storage media and program products

[0001] This disclosure claims priority to Chinese patent application No. 202411515179.4, filed on October 29, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of communication technology, and in particular to a channel modeling method, apparatus, device, storage medium, and program product. Background Technology

[0003] As wireless communication systems become increasingly complex and sophisticated, wireless channel modeling techniques have shifted from traditional modeling methods based on averaging large amounts of measurement data to scenario-based modeling methods based on specific outer diameters, in order to provide specific channel models that better match real-world scenarios.

[0004] For example, current wireless channel modeling techniques employ a ray tracing method to establish a channel model. The ray tracing method generally consists of two steps: scene construction and ray propagation. Scene construction first establishes a spatial scene model, including buildings and terrain. Ray propagation involves emitting rays according to certain rules and simulating the propagation and reflection paths of the rays within the constructed spatial scene model based on the antenna parameters of the base station. Once the ray reaches the target receiver, the channel characteristics can be calculated, and a channel model of the wireless channel can be established. Summary of the Invention

[0005] This disclosure provides a channel modeling method, apparatus, device, storage medium, and program product.

[0006] In a first aspect, this disclosure provides a channel modeling method, which includes:

[0007] Based on the first channel characteristics, the initial antenna parameters of the communication node are calibrated to obtain the calibrated first antenna parameters; the first channel characteristics are determined by the communication node based on the detection data of the wireless channel.

[0008] Ray tracing was performed using the parameters of the first antenna to establish a channel model for the wireless channel.

[0009] Secondly, this disclosure provides a channel modeling apparatus, which includes: a processing unit;

[0010] The processing unit is used to calibrate the initial antenna parameters of the communication node based on the first channel characteristics to obtain the calibrated first antenna parameters; the first channel characteristics are determined by the communication node based on the detection data of the wireless channel; and ray tracing is performed using the first antenna parameters to establish a channel model of the wireless channel.

[0011] Thirdly, this disclosure provides an electronic device, including: a processor and a memory;

[0012] The memory stores instructions that the processor can execute;

[0013] When the processor is configured to execute instructions, it causes the electronic device to perform the method described in the first aspect above.

[0014] Fourthly, this disclosure provides a readable storage medium, including: software instructions;

[0015] When software instructions are executed in an electronic device, the electronic device causes the electronic device to perform the method described in the first aspect above.

[0016] Fifthly, this disclosure provides a computer program product including computer instructions that, when executed in an electronic device, cause the electronic device to perform the method described in the first aspect above. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this disclosure to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0018] Figure 1 is a schematic diagram of the composition of the channel modeling system provided in the embodiments of this disclosure.

[0019] Figure 2 is a flowchart illustrating the channel modeling method provided in this embodiment of the present disclosure.

[0020] Figure 3 is a schematic diagram of the composition of a channel modeling device provided in an embodiment of this disclosure.

[0021] Figure 4 is a schematic diagram of another channel modeling device provided in an embodiment of this disclosure.

[0022] Figure 5 is a schematic diagram of the composition of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0023] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0024] Unless the context otherwise requires, throughout the specification and claims, the term "comprise" and other forms such as the third-person singular "comprises" and the present participle "comprising" are interpreted as open-ended and encompassing, meaning "including, but not limited to." In the description of the specification, terms such as "one embodiment," "some embodiments," "exemplary embodiments," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this disclosure. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics mentioned may be included in any suitable manner in any one or more embodiments or examples.

[0025] The terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0026] In this disclosure, the terms "exemplarily" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplarily" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplarily" or "for example" is intended to present the relevant concepts by way of example.

[0027] In addition, the use of “based on” implies openness and inclusivity, because processes, steps, calculations or other actions “based on” one or more of the stated conditions or values ​​may in practice be based on additional conditions or values ​​beyond those stated.

[0028] As wireless communication systems become increasingly complex and sophisticated, wireless channel modeling techniques have shifted from traditional modeling methods based on averaging large amounts of measurement data to scenario-based modeling methods based on specific outer diameters, in order to provide specific channel models that better match real-world scenarios.

[0029] For example, current wireless channel modeling techniques mainly provide the following three methods for establishing channel models:

[0030] 1. Ray tracing method.

[0031] Ray tracing primarily uses mathematical simulations of the propagation and reflection paths of rays in space to perform computer simulations and evaluate the performance and characteristics of wireless channels. This method is widely used in the design and optimization of wireless communication systems and generally involves the following two steps:

[0032] 1.1) Scene Construction: First, a spatial scene model is established, including buildings and terrain, which will affect the propagation and reflection of wireless signals. The ray tracing method simulates the transmission process of wireless signals in the actual scene by modeling these objects in three dimensions, determining their position, shape, and surface materials.

[0033] 1.2) Ray Propagation: Next, the ray tracing method emits rays according to certain rules and simulates the propagation and reflection path of the rays in the constructed spatial scene model based on the antenna parameters of the base station (i.e., the antenna's operating parameters, used to describe the antenna's working state; antenna parameters may include, for example, azimuth angle and downtilt angle). Then, it records all points traversed by the current ray propagation path and calculates the ray's reflection angle and direction when it encounters an object. Once the ray reaches the target receiver, the channel characteristics can be calculated, and a channel model of the wireless channel can be established.

[0034] The advantages of ray tracing are its simplicity and low cost. Once a digital map and image of the site are obtained, no additional measuring equipment needs to be deployed, and a channel model of the wireless channel can be established even without a network. However, the channel model established by ray tracing has errors and is not accurate enough.

[0035] 2. Wireless channel detection method.

[0036] Wireless channel sounding is a method that deploys specialized wireless channel detectors and utilizes multiple antennas and transmit / receive circuits to measure the propagation characteristics of signals in space (including channel response, channel loss, and transmission time delay). This method generally includes the following steps:

[0037] 2.1) Deploying the detectors: First, multiple Sounder detectors need to be deployed in the target area. These Sounder detectors need to be interconnected via a wireless network to collect data.

[0038] 2.2) Data Collection: Once deployed, each Sounder detector will automatically measure the wireless channel and upload the measurement data to the central server. This measurement data may include received signal strength indicator (RSSI) and round-trip time (RTT), etc.

[0039] 2.3) Data processing: After the measurement data is stored on the central server, it needs to be processed and analyzed, including noise reduction, channel parameter estimation, and propagation path analysis.

[0040] 2.4) Modeling: Finally, based on the analysis results, a channel model is established to predict future channel performance.

[0041] Wireless channel probing can be used to optimize wireless network design and improve network performance and efficiency. The advantages of this method are high accuracy, but its disadvantages include the need for specialized equipment, resulting in high costs, and the long measurement time required.

[0042] 3. Online channel recording for wireless networks.

[0043] This method utilizes already deployed and operational wireless network equipment. During communication, devices such as base stations or terminals estimate the wireless channel to receive data. Therefore, by recording the estimation results, processing and extracting the data, the detection data of the wireless channel can be obtained, from which information such as the wireless channel's latency, power, horizontal and vertical angle of arrival can be estimated.

[0044] The advantage of this method is that it does not require the deployment of expensive specialized equipment and can achieve relatively high accuracy. However, this method has high requirements for base station bandwidth, as well as the number and spacing of antennas, and the established channel model contains errors and is not accurate enough.

[0045] Based on this, embodiments of this disclosure provide a channel modeling method, apparatus, device, storage medium, and program product, which can calibrate antenna parameters based on the first channel characteristics determined by the actual detection data of the wireless channel, thereby improving the accuracy of the channel model established by the ray tracing method.

[0046] The following description is provided in conjunction with the accompanying drawings.

[0047] Figure 1 is a schematic diagram of the composition of the channel modeling system provided in this embodiment of the present disclosure. As shown in Figure 1, the system includes: at least one communication node 100 (shown as a base station in Figure 1) and a channel modeling device 200.

[0048] Communication node 100 can be a node with communication capabilities in a wireless network. For example, communication node 100 can be a base station in a wireless network.

[0049] Taking communication node 100 as an example, communication node 100 can be a base station (BS), a base transceiver station (BTS), a 3G base station (NodeB), a 4G base station (evolved NodeB, eNB), a 5G base station (next generation NodeB, gNB), etc. This disclosure does not limit the type of communication node 100.

[0050] The communication node 100 can be used to estimate the wireless channel and process and extract the estimation results to obtain the detection data of the wireless channel.

[0051] In some embodiments, the communication node 100 can also be used to record or store probe data of the wireless channel.

[0052] For example, the communication node 100 may have a built-in recording system or log function, which can record or store the probe data of the wireless channel.

[0053] In other embodiments, the communication node 100 may send probe data of the wireless channel to other devices, which may then record or store the probe data.

[0054] The channel modeling device 200 can be an electronic device with computing and processing capabilities, such as a server or a computer.

[0055] The server can be a single server or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. In some embodiments, the server can also be implemented on a cloud platform, such as a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, and multi-cloud, or any combination thereof. This disclosure does not limit this.

[0056] The channel modeling device 200 can be used to calibrate the antenna parameters of the communication node 100 based on the wireless channel detection data obtained by the communication node 100, and then use the calibrated antenna parameters to perform ray tracing to establish a channel model of the wireless channel. This process can be referred to the channel modeling method provided in the following method embodiment, and will not be repeated here.

[0057] In some embodiments, as described above, the probe data of the wireless channel can be stored in the communication node 100. In this case, the channel modeling apparatus 200 can obtain the probe data of the wireless channel from the communication node 100.

[0058] For example, the channel modeling device 200 may request probe data of the wireless channel from the communication node 100, or the communication node 100 may be configured to send probe data of the wireless channel to the channel modeling device 200 at a preset period. This disclosure does not impose any limitations on these aspects.

[0059] In other embodiments, as described above, the probe data for the wireless channel can be stored in other devices. In this case, the channel modeling apparatus 200 can acquire the probe data for the wireless channel from other devices.

[0060] It should be noted that the above description uses the communication node 100 and the channel model modeling device 200 as separate devices. In some embodiments, the communication node 100 and the channel model modeling device 200 can also be integrated into one device. That is, the communication node 100 or its corresponding functions, and the channel model modeling device 200 or its corresponding functions, can be integrated into one device. For example, a base station with channel model modeling function. This disclosure does not limit this aspect.

[0061] The execution entity of the channel modeling method provided in this disclosure is a channel modeling device (such as the channel modeling device 200 described above). As mentioned above, the channel modeling device can be an electronic device with computing processing capabilities, such as a server or a computer. In some embodiments, the channel modeling device can also be a processor (e.g., a central processing unit, CPU) in the aforementioned electronic device; or, the channel modeling device can also be an application program with channel modeling capabilities installed in the aforementioned electronic device; or, the channel modeling device can also be a software system or platform in the aforementioned electronic device; or, the channel modeling device can also be a functional module or functional unit in the aforementioned electronic device used to execute the channel modeling method, etc. This disclosure does not impose any limitations on these aspects.

[0062] For the sake of simplicity, the following description will take the channel modeling device as the execution subject of the channel modeling method provided in the embodiments of this disclosure as an example.

[0063] Figure 2 is a flowchart illustrating the channel modeling method provided in this embodiment of the present disclosure. As shown in Figure 2, the method includes the following steps S101 to S102.

[0064] S101. Based on the first channel characteristics, calibrate the initial antenna parameters of the communication node to obtain the calibrated first antenna parameters.

[0065] The first channel characteristic is determined by the communication node based on the probe data of the wireless channel. The process of the communication node acquiring the probe data of the wireless channel can be referred to the description in the channel model modeling device 200 in Figure 1 above, and will not be repeated here. The first channel characteristic can be a set of multipath components, including the path component characteristics corresponding to each signal transmission path in multiple signal transmission paths. The initial antenna parameters can include the azimuth angle and downtilt angle of the antenna.

[0066] For example, the first channel feature can be denoted as This includes the path component characteristics of J1 signal transmission paths. The path component characteristics corresponding to each signal transmission path (or the j-th signal transmission path) can be denoted as: in, This represents the power of the j-th signal transmission path in the first channel characteristic. This represents the delay of the j-th signal transmission path in the first channel characteristic. This represents the azimuth angle (or horizontal angle of arrival) of the j-th signal transmission path in the first channel characteristic. This represents the elevation angle (or vertical angle of arrival) of the j-th signal transmission path in the first channel characteristic.

[0067] As an example, the channel modeling apparatus can perform ray tracing using initial antenna parameters to obtain second channel features, and then calibrate the initial antenna parameters based on the comparison between the first and second channel features. This process can be referred to in the following embodiments and will not be repeated here.

[0068] The second channel feature can also be a set of multipath components, including the path component features corresponding to each signal transmission path in multiple signal transmission paths.

[0069] For example, the second channel feature can be denoted as This includes the path component characteristics of J2 signal transmission paths. The path component characteristics corresponding to each signal transmission path (or the j-th signal transmission path) can be denoted as: in, This represents the power of the j-th signal transmission path in the second channel characteristic. This represents the delay of the j-th signal transmission path in the second channel feature. This represents the azimuth angle (or horizontal angle of arrival) of the j-th signal transmission path in the second channel characteristic. This represents the elevation angle (or vertical angle of arrival) of the j-th signal transmission path in the second channel characteristic.

[0070] S102. Use the first antenna parameters to perform ray tracing and establish a channel model for the wireless channel.

[0071] For example, the channel modeling device can acquire a three-dimensional (3D) map of the corresponding network region, input the coordinates of the transmitter and receiver, use the first antenna parameters as the receiver's antenna parameters, input a ray tracing model, and generate a channel model. The process of generating the channel model can be referred to in related technologies, and will not be repeated here.

[0072] It should be understood that the process of building a channel model using ray tracing requires the antenna parameters of the base station. However, due to factors such as installation errors, it is difficult to obtain accurate antenna parameters (such as azimuth and downtilt) of the base station during ray tracing. Therefore, the channel model obtained by ray tracing contains errors and is not accurate enough.

[0073] In the channel modeling method provided in this disclosure, the channel modeling device can calibrate the initial antenna parameters of the communication node based on a first channel feature to obtain calibrated first antenna parameters. The first channel feature is determined by the communication node's detection data of the wireless channel. That is, this disclosure can use the first channel feature determined by the actual detection data of the wireless channel to calibrate the antenna parameters. Using the calibrated first antenna parameters for ray tracing can reduce errors and establish a more accurate channel model.

[0074] In some embodiments, prior to S101 described above, the channel modeling apparatus may also acquire first channel features.

[0075] For example, taking a 5G base station as a communication node, the channel measurement module in the 5G base station can receive a sounding reference signal (SRS) through a receiver, and then use the least squares algorithm to obtain the estimated value H(t) of the current channel. It also collects H(t) over 120 milliseconds. The channel modeling device can estimate the obtained H(t) using the estimation of signal parameters via rotational invariance techniques (ESPRIT) spectrum estimation algorithm to obtain the first channel feature.

[0076] For example, taking a 4G base station as the communication node, the channel measurement module in 4G can receive SRS through the receiver, and then use the minimum mean square error (MMSE) algorithm to obtain the estimated value H(t) of the current channel. It also collects H(t) within 240 milliseconds and estimates the obtained H(t) using the ESPRIT spectrum estimation algorithm to obtain the first channel feature.

[0077] The following describes an example of the process in S101 above.

[0078] In some possible embodiments, the channel modeling apparatus can incorporate a second channel feature to calibrate the initial antenna parameters. In this case, S101 described above may include steps 1a to 2a.

[0079] Step 1a: Determine the antenna operating parameter deviation value based on the first channel characteristics and the second channel characteristics.

[0080] The second channel characteristic was determined after ray tracing based on the initial antenna parameters.

[0081] As an example, antenna operating parameter deviations may include azimuth deviation and downtilt deviation.

[0082] As an example, as described above, both the first channel feature and the second channel feature can include the path component features corresponding to each of the multiple signal transmission paths, and the azimuth deviation value can be the average of the deviations between the azimuth angles of the multiple signal transmission paths in the first channel feature and the azimuth angles in the second channel feature.

[0083] For example, a channel modeling device can calculate the azimuth deviation value using the following formula:

[0084] In formula (1), This represents the azimuth deviation value. J can be understood as the minimum value between J1 and J2 mentioned above.

[0085] As an example, the downtilt deviation value can be the average of the deviations between the pitch angles of multiple signal transmission paths in the first channel characteristic and the pitch angles in the second channel characteristic.

[0086] For example, a channel modeling device can calculate the downtilt deviation value using the following formula:

[0087] In formula (2), Δθ represents the difference in downtilt angle.

[0088] Step 2a: Based on the antenna operating parameter deviation value, calibrate the initial antenna operating parameters to obtain the calibrated first antenna operating parameters.

[0089] For example, as described above, the antenna operating parameter deviation values ​​may include azimuth deviation values ​​and downtilt deviation values. In this case, step 2a above may include step 2.1a.

[0090] Step 2.1a: The channel modeling device can use the azimuth deviation value as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt deviation value as the adjustment value of the downtilt angle in the initial antenna parameters to calibrate the initial antenna parameters and obtain the calibrated first antenna parameters.

[0091] For example, the azimuth angle in the initial antenna parameters is taken as... downhill angle is For example, the channel modeling device can add the azimuth deviation value to the azimuth angle and the downtilt deviation value to the downtilt angle to obtain the calibrated first antenna operating parameters. That is... θ E ←θ E +Δθ.

[0092] In one possible implementation, the channel modeling device can directly adjust the initial antenna parameters using the aforementioned adjustment values.

[0093] In another possible implementation, the channel modeling apparatus can calculate the calibration confidence level and only use the aforementioned adjustment value for adjustment when the calibration confidence level is high. In this case, step 2.1a may include steps 2.1.1a to 2.1.2a.

[0094] Step 2.1.1a: Determine the calibration confidence level based on the deviation between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, as well as the deviation between the elevation angles of multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature.

[0095] For example, the channel modeling device can determine the calibration confidence level based on the variance of the deviation between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature (e.g., it can be called the first variance), and the variance of the deviation between the elevation angles of multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature (e.g., it can be called the second variance).

[0096] As an example, the calibration confidence level may include a first confidence level and a second confidence level. The channel modeling device can determine the first confidence level based on the first variance and the second confidence level based on the second variance.

[0097] There is a negative correlation between the first variance and the first confidence level. There is a negative correlation between the second variance and the second confidence level.

[0098] As another example, the channel modeling device can also perform a weighted summation of the first variance and the second variance to obtain a calibration confidence level.

[0099] It should be noted that the larger the first variance and the second variance are, the lower the calibration confidence level; conversely, the smaller the first variance and the second variance are, the higher the calibration confidence level.

[0100] Step 2.1.2a: When the calibration confidence level is greater than the confidence level threshold, the azimuth deviation value is used as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt deviation value is used as the adjustment value of the downtilt angle in the initial antenna parameters. The initial antenna parameters are calibrated to obtain the first calibrated antenna parameters.

[0101] It should be understood that when the deviation between the azimuth angle in the first channel feature and the azimuth angle in the second channel feature, as well as the deviation between the elevation angle in the first channel feature and the elevation angle in the second channel feature, is large, it indicates that the first channel feature and the second channel feature are significantly different. In this case, the determined antenna parameter deviation value may also have a large error.

[0102] In the channel modeling method provided in this embodiment, the channel modeling device can calculate the calibration confidence level based on the deviation between the azimuth angle in the first channel feature and the azimuth angle in the second channel feature, as well as the deviation between the elevation angle in the first channel feature and the elevation angle in the second channel feature. Only when the calibration confidence level is greater than the confidence level threshold is the initial antenna parameters calibrated using the antenna parameter deviation value. This avoids using the antenna parameter deviation value, which may have a large error, to calibrate the initial antenna parameters, avoids error propagation, and thus improves the accuracy of the calibrated first antenna parameters.

[0103] In other possible embodiments, the channel modeling apparatus may first calibrate the first channel characteristics before calibrating the initial antenna parameters using the first channel characteristics. In this case, S101 described above may include steps 1b to 3b.

[0104] Step 1b: Perform the first channel feature calibration step: Use the second channel feature to calibrate the first channel feature to obtain the calibrated first channel feature.

[0105] The second channel characteristic was determined after ray tracing based on the initial antenna parameters.

[0106] In one possible implementation, step 1b above may include steps 1.1b to 1.2b.

[0107] Step 1.1b: Based on the path component features corresponding to each signal transmission path in the first channel features, determine the angle candidate domain corresponding to each signal transmission path.

[0108] The angle candidate domain includes multiple candidate angle features. The candidate angle features are angle features within a preset angle range whose correlation parameter with the angle feature of the path component feature in the first channel feature is greater than the correlation threshold. For example, the preset angle range can be: azimuth angle range [-180°, 180°], pitch angle range [-90°, 90°].

[0109] As an example, taking any one of the multiple signal transmission paths as an example (that is, for any one of the multiple signal transmission paths), step 1.1b above may include steps 1.1.1b to 1.1.5b.

[0110] Step 1.1.1b: Determine the spatial feature vector of the first path based on the angular features of the first path in the first channel features.

[0111] For example, a spatial eigenvector can be expressed by the following formula:

[0112] In formula (3), M represents the spatial characteristic vector. h M represents the number of horizontal elements in the antenna array of a communication node. v This represents the number of vertical elements in the antenna array of the communication node. (d) h d represents the inter-element spacing in the horizontal direction. v The vertical spacing between the elements is represented by λ, and the wavelength is represented by λ. This spatial characteristic vector comprises a total of M elements. v ×M h Okay, first M v During the journey, The coefficient of the term is 0, d v The coefficient of the cosθ term increases by 1 from 0 to M. v -1. The Mth v From line +1 to line 2M v OK, The coefficient of the term increases from 0 to 1, and then d v The coefficient of the cosθ term increases by 1 from 0 to M. v -1. The 2M v From line +1 to line 3M v OK, The coefficient of the term increases from 1 to 2, then d v The coefficient of the cosθ term increases by 1 from 0 to M. v-1. And so on, until the last line. The coefficient increases to M h -1,d v The coefficient of cosθ increases to M v -1.

[0113] Step 1.1.2b: Scan within a preset angle range to obtain multiple initial angle features.

[0114] Step 1.1.3b: Based on multiple initial angle features, determine the spatial feature vector corresponding to each initial angle feature.

[0115] The spatial feature vector corresponding to each initial angular feature can be calculated by referring to the above formula (3). It will not be elaborated here.

[0116] Step 1.1.4b: Based on the spatial feature vector of the first path, the spatial feature vector corresponding to each initial angle feature, and the angle correlation function, determine the correlation parameter between each initial angle feature and the angle feature of the first path in the first channel features.

[0117] For example, the channel modeling apparatus can calculate the correlation parameters according to the angle correlation function shown in the following formula (4):

[0118] In formula (4), θ1 represents the azimuth and pitch angles in an angular feature. θ2 represents the azimuth and elevation angles, another angular feature. γ j This represents the angle correlation function, which can also be understood as the correlation parameter mentioned above. `dot(·)` represents the dot product operation. `‖·‖` represents the norm operation.

[0119] Step 1.1.5b: Use the initial angle features with correlation parameters greater than the correlation threshold as candidate angle features to obtain the angle candidate domain corresponding to the first path.

[0120] Step 1.2b: Select the target angle feature that is closest to the reference angle feature corresponding to each signal transmission path from the angle candidate domain corresponding to each signal transmission path, and calibrate the angle features in the path component features corresponding to each signal transmission path in the first channel features.

[0121] The reference angle feature is the angle feature of the path component feature of the corresponding signal transmission path in the second channel feature.

[0122] For example, the channel modeling apparatus can select target angular features to calibrate the first channel features according to the following formula:

[0123] In formula (5), the left side of the equal sign This can be understood as the target angle feature, or the first target feature after calibration.

[0124] It should be understood that the methods used in related technologies for online channel recording modeling of wireless networks typically involve scanning to determine an angle candidate domain, and then selecting angle features from the angle candidate domain to obtain the first channel feature. However, in current commercial networks, the horizontal and vertical antenna spacing used in the mainstream 5G frequency bands of 2.6GHz and 3.5GHz are greater than 0.5 half a wavelength. Therefore, "false sidelobes" may exist in the angle candidate domain. The angle features selected from the angle candidate domain to form the first channel feature may result in "false sidelobes" in the first channel feature.

[0125] In the channel modeling method provided in this embodiment, the channel modeling device can reconstruct the angle candidate domain corresponding to each signal transmission path, and then select the angle features corresponding to the path component features in the second channel features from the angle candidate domain to calibrate the first channel features. Since the second channel features are determined based on ray tracing simulation and do not have "false sidelobes", the target angle features selected from the second channel features also do not have "false sidelobes". By calibrating the first channel features using the target angle features, the "false sidelobes" in the first channel features can be eliminated.

[0126] Step 2b: Perform antenna parameter calibration: Based on the calibrated first and second channel characteristics, calibrate the initial antenna parameters to obtain the calibrated initial antenna parameters.

[0127] The process of step 2b can be referred to in steps 1a to 2a above, and will not be repeated here.

[0128] Step 3b: Based on the calibrated initial antenna parameters, obtain the first antenna parameters.

[0129] In one possible implementation, the channel modeling device can directly use the calibrated initial antenna parameters as the first antenna parameters.

[0130] In another possible implementation, the channel modeling apparatus can perform cyclic calibration on the first channel characteristics, initial antenna parameters, and second channel characteristics. In this case, after step 2b above, the method may further include step 1c, and step 3b above may include steps 3.1b to 3.2b.

[0131] Step 1c: Perform the second channel feature calibration step: Use the calibrated initial antenna parameters to perform ray tracing to obtain the calibrated second channel features.

[0132] Step 3.1b: Repeat the first channel feature calibration step (i.e., step 1b above), the antenna parameter calibration step (i.e., step 2b above), and the second channel feature calibration step (i.e., step 1c above) until the iteration stop condition is met.

[0133] As an example, the iteration stopping condition may include: the number of times the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step are performed reaches a threshold.

[0134] The number of times threshold can be preset in the channel modeling device. For example, the number of times threshold can be set to 5 times, 10 times, 20 times, 50 times, or 100 times, etc. The embodiments of this disclosure do not limit the value of the number of times threshold.

[0135] As another example, the iteration stopping condition may include: the adjustment value when calibrating antenna parameters is less than a preset threshold.

[0136] For example, as mentioned above, the antenna operating parameter deviation values ​​may include azimuth deviation values ​​and downtilt deviation values. When the azimuth deviation values ​​and downtilt deviation values ​​are relatively small, the current antenna operating parameters can be considered relatively accurate, and the adjustment value required is relatively small, so iteration can be stopped. The preset threshold can be preset in the channel modeling device. For example, the preset threshold can be set to 0.1° or 0.2°, etc. This disclosure embodiment does not limit the value of the preset threshold.

[0137] As another example, the iteration stopping condition may include: the number of times the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step are performed reaches a threshold, and the adjustment value when calibrating the antenna parameter is less than a preset threshold.

[0138] Step 3.2b: The antenna parameters that satisfy the iteration stopping condition are taken as the first antenna parameters.

[0139] In the channel modeling method provided in this embodiment, the channel modeling device can repeatedly execute the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step until the iteration stopping condition is met. Then, the antenna parameters at the time the iteration stopping condition is met are used as the first antenna parameters. In this way, the first channel features, the initial antenna parameters, and the second channel features can be calibrated iteratively, thereby improving the accuracy of the calibrated first antenna parameters and the accuracy of the channel model established based on the ray tracing of the first antenna parameters.

[0140] In some embodiments, the correlation threshold in step 1.1.5b above may be a preset value in the channel model modeling device.

[0141] In other embodiments, the correlation threshold in step 1.1.5b above can also be dynamically calculated based on the maximum correlation parameter of the current initial angle feature. In this case, the method may further include steps 1d to 2d before step 1.1.5b above.

[0142] Step 1d: Determine the maximum correlation parameter from the correlation parameters between each initial angular feature and the angular features of the first path in the first channel features.

[0143] For example, the channel modeling apparatus can determine the maximum correlation parameter according to the following formula:

[0144] In formula (6), γ j,max This represents the parameter of maximum correlation. θ represents the azimuth and pitch angles in the initial angular characteristics.

[0145] Step 2d: Determine the correlation threshold based on the difference between the maximum correlation parameter and the preset deviation threshold.

[0146] The preset deviation threshold can be preset in the channel modeling device. For example, the preset deviation threshold can be set to 0.1 or 0.2, etc. This disclosure embodiment does not limit the value of the preset deviation threshold.

[0147] For example, taking a preset deviation threshold of 0.1 as an example, assuming that the maximum correlation parameter among the correlation parameters between each initial angle feature and the angle feature of the first path in the first channel feature is 0.8, the channel model modeling device can obtain a correlation threshold of 0.7 based on 0.8-0.1=0.7.

[0148] For example, as described above, the correlation threshold can be determined based on the difference between the maximum correlation parameter and the preset deviation threshold. In this case, the angle candidate domain in step 1.1.5b above can be expressed as the following formula:

[0149] In formula (7), S j This represents the candidate angle domain corresponding to the j-th signal transmission path. T represents the preset deviation threshold.

[0150] In some possible embodiments, as described above, the path component features also include power and delay. The channel modeling apparatus can also perform pairwise matching of the path component features in the first channel features and the second channel features based on power and delay to determine the reference angle features corresponding to each signal transmission path. In this case, before step 1.2b above, the method may further include steps 1e to 2e.

[0151] Step 1e: Based on the similarity between power and the similarity between time delay, match the path component features in the first channel features and the path component features in the second channel features to obtain multiple matching pairs.

[0152] Each matching pair includes a path component feature in a first channel feature and a path component feature in a second channel feature.

[0153] For example, a channel modeling apparatus can determine the degree of similarity between powers based on the ratio between a first difference and a first power. The first difference is the difference between a second power and a first power. The first power is the power in a path component feature of a first channel feature. The second power is the power in a path component feature of a second channel feature.

[0154] For example, the channel modeling device can determine the similarity between delays based on the ratio between a first difference and a first delay. The first difference is the difference between a second delay and a first delay. The first delay is the delay in a path component feature of a first channel feature. The second delay is the delay in a path component feature of a second channel feature.

[0155] As an example, the channel modeling device can perform a weighted summation of the similarity between power and the similarity between delay to obtain the similarity between a path component feature in the first channel feature and a path component feature in the second channel feature, and select the two path component features with the highest similarity to form a matching pair.

[0156] Step 2e: Determine the reference angle characteristics corresponding to each signal transmission path based on multiple matching pairs.

[0157] For example, taking the path component feature 1 corresponding to the signal transmission path 1 in the first channel feature as an example, the channel modeling device can select a matching pair including the path component feature 1 from multiple matching pairs, and use the angle feature in the path component feature of the second channel feature in the matching pair as the reference angle feature corresponding to the signal transmission path 1.

[0158] It should be understood that the first channel feature and the second channel feature may include multiple path component features, but the correspondence between the path component features in the two channel features is not clear.

[0159] In the channel modeling method provided in this embodiment, before the channel modeling device selects the target angle feature based on the reference angle feature in the second channel feature to calibrate the first channel feature, it can also perform pairwise matching of the path component features in the first channel feature and the second channel feature to establish multiple matching pairs, which facilitates the selection of the target angle feature based on the reference angle feature.

[0160] The foregoing primarily describes the solutions provided by the embodiments of this disclosure from a methodological perspective. To achieve the aforementioned functions, each device, such as a cell handover device, includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the algorithmic steps of the examples described in the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0161] In an exemplary embodiment, this disclosure also provides a channel modeling apparatus. Figure 3 is a schematic diagram of the composition of a channel modeling apparatus provided in this disclosure. As shown in Figure 3, the channel modeling apparatus 300 can be connected to the channel measurement module 400. The channel modeling apparatus 300 includes: a channel measurement calibration module 301, an antenna parameter calibration module 302, and a ray tracing module 303.

[0162] The channel measurement module 400 is responsible for performing channel measurement and estimation based on the received wireless signals from the wireless network, and generating channel characteristic 1 (i.e., the aforementioned first channel characteristic). The wireless signal is a signal transmitted by the transmitter and received by the receiver in the wireless network, including pilot or reference signals, which can be used for wireless channel characteristic estimation.

[0163] The ray tracing module 303 is responsible for generating channel feature 2 (i.e., the second channel feature mentioned above) based on the input 3D map and antenna parameter data. The 3D map is a data file containing the terrain of the network area, as well as the height, location, and material of buildings. The antenna parameters include the horizontal azimuth angle and vertical downtilt angle of the base station antenna, as well as the number of elements and the horizontal and vertical spacing of the receiver antenna array. When this module is run for the first time, the input antenna parameters for the ray tracing module 303 come from the original antenna parameters of the network management system; when subsequent iterative optimizations are performed, the input antenna parameters for the ray tracing module 303 come from the "calibrated antenna parameters" generated by the antenna parameter calibration module 302. Channel feature 2 includes the power, delay, and horizontal and vertical angles of arrival of each path component of the channel.

[0164] The channel measurement and calibration module 301 is used to calibrate the channel data 1 based on the input channel feature 2, eliminate the "false sidelobes" caused by the non-ideal antenna array (the antenna element spacing is greater than half a wavelength, and there is interference and noise), and generate the "calibrated channel feature 1".

[0165] The antenna parameter calibration module 302 is used to compare the input calibrated channel feature 1 and channel feature 2, estimate the error caused by the inaccuracy of antenna parameters contained in channel feature 2, compensate for it, and generate calibrated antenna parameters.

[0166] In an exemplary embodiment, FIG4 is a schematic diagram of another channel modeling apparatus provided in this disclosure. As shown in FIG4, the channel modeling apparatus 500 includes: a processing unit 501.

[0167] The processing unit 501 is used to calibrate the initial antenna parameters of the communication node based on the first channel characteristics to obtain the calibrated first antenna parameters; the first channel characteristics are determined by the communication node based on the detection data of the wireless channel; and ray tracing is performed using the first antenna parameters to establish a channel model of the wireless channel.

[0168] In some possible embodiments, the processing unit 501 can be used to determine the antenna operating parameter deviation value based on the first channel feature and the second channel feature; the second channel feature is determined after ray tracing based on the initial antenna operating parameters; and the initial antenna operating parameters are calibrated based on the antenna operating parameter deviation value to obtain the calibrated first antenna operating parameters.

[0169] In other possible embodiments, the processing unit 501 can be used to use the azimuth deviation value as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt deviation value as the adjustment value of the downtilt angle in the initial antenna parameters to calibrate the initial antenna parameters and obtain the calibrated first antenna parameters.

[0170] In some other possible embodiments, the processing unit 501 can be used to determine the calibration confidence level based on the deviation between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, and the deviation between the elevation angles of multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature; if the calibration confidence level is greater than the confidence level threshold, the azimuth angle deviation value is used as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt angle deviation value is used as the adjustment value of the downtilt angle in the initial antenna parameters, and the initial antenna parameters are calibrated to obtain the calibrated first antenna parameters.

[0171] In some other possible embodiments, the processing unit 501 may be used to determine the calibration confidence level based on the variance of the deviation between the azimuth angles of the multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, and the variance of the deviation between the elevation angles of the multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature.

[0172] In some other possible embodiments, the processing unit 501 may be used to perform a first channel feature calibration step: using a second channel feature to calibrate the first channel feature to obtain a calibrated first channel feature; the second channel feature is determined based on ray tracing of the initial antenna parameters; and to perform an antenna parameter calibration step: based on the calibrated first and second channel features, calibrate the initial antenna parameters to obtain calibrated initial antenna parameters; and based on the calibrated initial antenna parameters, obtain the first antenna parameters.

[0173] In some other possible embodiments, processing unit 501 is further configured to perform a second channel feature calibration step after executing the antenna parameter calibration step: using the calibrated initial antenna parameters for ray tracing to obtain the calibrated second channel features. Processing unit 501 may be configured to repeatedly execute the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step until the iteration stop condition is met; the antenna parameters at the time the iteration stop condition is met are used as the first antenna parameters.

[0174] In some other possible embodiments, the processing unit 501 may be used to determine the angle candidate domain corresponding to each signal transmission path based on the path component features corresponding to each signal transmission path in the first channel features; the angle candidate domain includes multiple candidate angle features; the candidate angle features are angle features within a preset angle range whose correlation parameter with the angle features of the path component features of the signal transmission path in the first channel features is greater than a correlation threshold; from the angle candidate domain corresponding to each signal transmission path, the target angle feature that is closest to the reference angle feature corresponding to that signal transmission path is selected, and the angle features of the path component features corresponding to each signal transmission path in the first channel features are calibrated; the reference angle feature is the angle feature of the path component features of the corresponding signal transmission path in the second channel features.

[0175] In some other possible embodiments, the processing unit 501 is further configured to match the path component features in the first channel feature and the path component features in the second channel feature based on the similarity between power and the similarity between time delay, to obtain multiple matching pairs; each matching pair includes a path component feature in the first channel feature and a path component feature in the second channel feature; and determine the reference angle feature corresponding to each signal transmission path based on the multiple matching pairs.

[0176] In some other possible embodiments, the processing unit 501 can be used to determine the spatial feature vector of any first path among multiple signal transmission paths based on the angle features of the first path in the first channel features; scan within a preset angle range to obtain multiple initial angle features; determine the spatial feature vector corresponding to each initial angle feature based on the multiple initial angle features; determine the correlation parameter between each initial angle feature and the angle features of the first path in the first channel features based on the spatial feature vector of the first path, the spatial feature vector corresponding to each initial angle feature, and the angle correlation function; and take the initial angle features with correlation parameters greater than the correlation threshold as candidate angle features to obtain the angle candidate domain corresponding to the first path.

[0177] In some other possible embodiments, the processing unit 501 is further configured to determine the maximum correlation parameter from the correlation parameters between each initial angular feature and the angular feature of the first path in the first channel features; and to determine the correlation threshold based on the difference between the maximum correlation parameter and a preset deviation threshold.

[0178] In an exemplary embodiment, this disclosure also provides an electronic device in which the aforementioned channel modeling apparatus can be applied. Figure 5 is a schematic diagram of the composition of the electronic device provided in this disclosure. As shown in Figure 5, the electronic device 60 includes: a processor 602, a communication interface 603, and a bus 604. As an example, the electronic device 60 may also include a memory 601.

[0179] Processor 602 may implement or execute various exemplary logic blocks, modules, and circuits described in connection with embodiments of this disclosure. Processor 602 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. Processor 602 may implement or execute various exemplary logic blocks, modules, and circuits described in connection with embodiments of this disclosure. Processor 602 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0180] Communication interface 603 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0181] The memory 601 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0182] In one possible implementation, the memory 601 can exist independently of the processor 602. The memory 601 can be connected to the processor 602 via a bus 604 and is used to store instructions or program code. When the processor 602 calls and executes the instructions or program code stored in the memory 601, it can implement the channel modeling method provided in the embodiments of this disclosure.

[0183] In another possible implementation, the memory 601 can also be integrated with the processor 602.

[0184] Bus 604 can be an extended industry standard architecture (EISA) bus, etc. Bus 604 can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in Figure 5, but this does not mean that there is only one bus or one type of bus.

[0185] In an exemplary embodiment, this disclosure also provides a readable storage medium including software instructions that, when executed on an electronic device (e.g., the electronic device 60 described above), cause the electronic device to perform any of the methods provided in the above embodiments.

[0186] In an exemplary embodiment, this disclosure also provides a computer program product containing computer instructions that, when run on an electronic device (such as the electronic device 60 described above), causes the electronic device to perform any of the methods provided in the above embodiments.

[0187] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer-executable instructions. When these computer-executable instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer-executable instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape) or an optical medium (e.g., Digital Versatile Disc (DVD), etc.).

[0188] Although this disclosure has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed disclosure. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0189] Although this disclosure has been described in conjunction with exemplary features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely exemplary illustrations of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

[0190] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A channel modeling method, comprising: Based on the first channel characteristics, the initial antenna parameters of the communication node are calibrated to obtain the calibrated first antenna parameters; wherein, the first channel characteristics are determined by the communication node based on the detection data of the wireless channel; Ray tracing is performed using the parameters of the first antenna to establish a channel model for the wireless channel.

2. The method according to claim 1, wherein, The process of calibrating the initial antenna parameters of the communication node based on the first channel characteristics to obtain the calibrated first antenna parameters includes: The antenna operating parameter deviation value is determined based on the first channel characteristic and the second channel characteristic; wherein, the second channel characteristic is determined after ray tracing based on the initial antenna operating parameters. Based on the antenna operating parameter deviation value, the initial antenna operating parameters are calibrated to obtain the calibrated first antenna operating parameters.

3. The method according to claim 2, wherein, The antenna operating parameter deviation values ​​include azimuth deviation value and downtilt deviation value.

4. The method according to claim 3, wherein, The azimuth deviation value is the average of the deviations between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles in the second channel feature.

5. The method according to claim 3, wherein, The downtilt angle deviation is the average value of the deviations between the pitch angles of multiple signal transmission paths in the first channel feature and the pitch angles of the second channel feature.

6. The method according to claim 3, wherein, The step of calibrating the initial antenna parameters based on the antenna parameter deviation value to obtain the calibrated first antenna parameters includes: The azimuth deviation value is used as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt deviation value is used as the adjustment value of the downtilt angle in the initial antenna parameters. The initial antenna parameters are calibrated to obtain the calibrated first antenna parameters.

7. The method according to claim 6, wherein, The step of using the azimuth deviation value as the adjustment value of the azimuth angle in the initial antenna parameters and the downtilt angle deviation value as the adjustment value of the downtilt angle in the initial antenna parameters to calibrate the initial antenna parameters and obtain the calibrated first antenna parameters includes: The calibration confidence level is determined based on the deviation between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, as well as the deviation between the elevation angles of multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature. If the calibration confidence level is greater than the confidence level threshold, the azimuth deviation value is used as the adjustment value of the azimuth angle in the initial antenna parameters, and the downtilt deviation value is used as the adjustment value of the downtilt angle in the initial antenna parameters. The initial antenna parameters are then calibrated to obtain the calibrated first antenna parameters.

8. The method according to claim 7, wherein, The determination of calibration confidence based on the deviations between the azimuth angles of multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, and the deviations between the elevation angles of multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature, includes: The calibration confidence level is determined based on the variance of the deviation between the azimuth angles of the multiple signal transmission paths in the first channel feature and the azimuth angles of the second channel feature, and the variance of the deviation between the elevation angles of the multiple signal transmission paths in the first channel feature and the elevation angles of the second channel feature.

9. The method according to claim 1, wherein, The process of calibrating the initial antenna parameters of the communication node based on the first channel characteristics to obtain the calibrated first antenna parameters includes: Perform the first channel feature calibration step: use the second channel feature to calibrate the first channel feature to obtain the calibrated first channel feature; wherein, the second channel feature is determined based on ray tracing of the initial antenna parameters; Perform antenna parameter calibration steps: Based on the calibrated first channel characteristics and second channel characteristics, calibrate the initial antenna parameters to obtain the calibrated initial antenna parameters; Based on the calibrated initial antenna parameters, the first antenna parameters are obtained.

10. The method according to claim 9, wherein, After performing the antenna parameter calibration step, the method further includes: Perform the second channel feature calibration step: use the calibrated initial antenna parameters to perform ray tracing to obtain the calibrated second channel features; The process of obtaining the first antenna operating parameters based on the calibrated initial antenna operating parameters includes: Repeat the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step until the iteration stop condition is met; The antenna parameters that satisfy the iteration stopping condition are taken as the first antenna parameters.

11. The method according to claim 10, wherein, The iteration stopping conditions include: the number of times the first channel feature calibration step, the antenna parameter calibration step, and the second channel feature calibration step are executed reaches a threshold, and / or the adjustment value when calibrating the antenna parameters is less than a preset threshold.

12. The method according to claim 9, wherein, Both the first channel feature and the second channel feature include path component features corresponding to each signal transmission path in the multiple signal transmission paths, and the path component features include angle features; The angular features include azimuth and elevation angles; The step of calibrating the first channel feature using the second channel feature to obtain the calibrated first channel feature includes: Based on the path component features corresponding to each signal transmission path in the first channel features, the angle candidate domain corresponding to each signal transmission path is determined. The candidate angle domain includes multiple candidate angle features; the candidate angle features are angle features within a preset angle range whose correlation parameter with the angle features of the path component features of the signal transmission path in the first channel features is greater than a correlation threshold. From the angle candidate domain corresponding to each signal transmission path, select the target angle feature that is closest to the reference angle feature corresponding to that signal transmission path, and calibrate the angle features in the path component features corresponding to each signal transmission path in the first channel features; The reference angle feature is the angle feature of the path component feature of the corresponding signal transmission path in the second channel feature.

13. The method according to claim 12, wherein, The path component features also include power and time delay; the method further includes: Based on the similarity between power and the similarity between time delay, the path component features in the first channel feature and the path component features in the second channel feature are matched to obtain multiple matching pairs; each matching pair includes one path component feature in the first channel feature and one path component feature in the second channel feature. The reference angle features corresponding to each signal transmission path are determined based on the multiple matching pairs.

14. The method according to claim 12, wherein, For any first path among the multiple signal transmission paths, the determination of the angle candidate domain corresponding to each signal transmission path based on the path component features corresponding to each signal transmission path in the first channel features includes: Based on the angular features of the first path in the first channel features, determine the spatial feature vector of the first path; Scanning is performed within the preset angle range to obtain multiple initial angle features; Based on the multiple initial angle features, determine the spatial feature vector corresponding to each initial angle feature; Based on the spatial feature vector of the first path, the spatial feature vector corresponding to each initial angle feature, and the angle correlation function, determine the correlation parameter between each initial angle feature and the angle feature of the first path in the first channel features; Initial angle features with correlation parameters greater than the correlation threshold are used as candidate angle features to obtain the angle candidate domain corresponding to the first path.

15. The method of claim 14, further comprising: From the correlation parameters between each initial angular feature and the angular features of the first path in the first channel features, determine the maximum correlation parameter; The correlation threshold is determined based on the difference between the maximum correlation parameter and the preset deviation threshold.

16. A channel modeling apparatus, comprising: Processing unit; The processing unit is used to calibrate the initial antenna parameters of the communication node based on the first channel characteristics to obtain the calibrated first antenna parameters; wherein, the first channel characteristics are determined by the communication node based on the detection data of the wireless channel; and to perform ray tracing using the first antenna parameters to establish a channel model of the wireless channel.

17. An electronic device comprising: Processor and memory; The processor stores instructions executable by the memory; When the processor is configured to execute the instructions, the electronic device performs the method according to any one of claims 1-15.

18. A readable storage medium, comprising: Software instructions; When the software instructions are executed in an electronic device, the electronic device causes the electronic device to perform the method according to any one of claims 1-15.

19. A computer program product, wherein, The computer program product includes computer instructions that, when executed in an electronic device, cause the electronic device to perform the method according to any one of claims 1-15.

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