VR-angle joint channel map construction method for super-large scale MIMO

Abstract

The invention discloses a VR-angle joint channel map construction method for super-large-scale MIMO, and the method comprises the steps: firstly, obtaining VR parameters after judging a VR identifier through measuring the received signal strength and arrival angle of a small number of sampling positions, and predicting the VR parameters of other positions through the spatial continuity interpolation of VR channel features; dividing VR areas according to VR parameters, and predicting angle parameters of other positions in each VR area by using known angle parameter interpolation; and finally, jointly constructing a VR-angle joint channel map for super-large-scale MIMO antenna array coverage in combination with two-stage parameters of the user VR and the angle. According to the method, the problem that the system overhead is too high when a VR-angle joint channel map is constructed in a full-measurement mode due to the fact that the coverage area of a super-large-scale MIMO antenna array is large is effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and more specifically, relates to a VR-angle joint channel map construction method for ultra-large-scale MIMO. Background Technology

[0002] In the development of future-oriented sixth-generation (6G) mobile communication systems, the development and utilization of spatial resources have encountered higher-level demands. As a core supporting technology of the physical layer, ultra-large-scale multiple-input multiple-output (XL-MIMO) technology plays an irreplaceable role in enhancing the spatial multiplexing performance of 6G systems. However, it is important to note that due to the non-stationary nature of electromagnetic wave propagation in space, XL-MIMO systems exhibit unique near-field visible area (VR) channel characteristics. Specifically, some elements in the antenna array can only form effective communication links with specific users; these antenna elements with dedicated communication directionality are defined as the user's VR. This channel characteristic provides a new approach to user grouping within the coverage area: users can be divided into different groups, ensuring that users within the same group have consistent VR parameters, while users in different groups have significantly different VR parameters. It is noteworthy that when the VR ranges of different user groups do not overlap, the transmission channels corresponding to each group will naturally possess orthogonality. Grouping users based on this characteristic enables ideal spatial separation between different user groups, thereby effectively reducing the dimensionality of the channel matrix and providing strong support for simplifying transmission schemes in ultra-large-scale MIMO systems.

[0003] For multiple users within the same VR (Vibration Array) group, although they "see" the same part of the antenna array from the base station side, their positions are different, resulting in different signal transmission angles between the users and the VR array. Based on this, angle parameters can be used to achieve fine separation of users within a VR group. Specifically, given the user VR and angle information, in the spatial domain, UML can first use VR information to achieve coarse user grouping, then use angle information to achieve fine separation of users within the group, ultimately achieving spatial separation of all users and providing technical support for orthogonal transmission. Therefore, user VR and angle information are crucial for orthogonal transmission in UML systems. However, due to the large coverage area of ​​UML antenna arrays, existing channel parameter acquisition methods based on full-position pilot measurement and feedback generate huge pilot overhead, making the system unsustainable. How to accurately acquire user VR and angle information with low overhead is a crucial problem to be solved in the design of orthogonal transmission for UML systems.

[0004] Considering that users in similar spatial locations within the coverage area of ​​a very large-scale MIMO antenna array have similar VR and angle characteristics, when performing uplink pilot measurements, only a small number of beacon users can be selected to send uplink pilot signals, and the VR and angle parameters of the remaining locations can be predicted by interpolation based on the measurement results, instead of having every user send an uplink pilot signal. This reduces the pilot overhead of the very large-scale MIMO system. Furthermore, although the electromagnetic propagation environment of a very large-scale MIMO system changes slowly over time, the channel environment can be considered quasi-static over a short period. Therefore, the VR and angle information obtained from current sampling measurements or interpolation predictions of users at different locations within the antenna coverage area can be used as a spatial grid as the basic storage unit to construct a joint VR-angle channel map. This provides a way to quickly retrieve and obtain VR and angle information, thereby avoiding repeated measurements of VR and angle parameters and further reducing system pilot overhead.

[0005] As can be seen from the above, the VR-angle joint channel map has many advantages in acquiring VR and angle information with low overhead. In order to use the VR-angle joint channel map to guide the orthogonal transmission design of a very large-scale MIMO system, it is first necessary to study the method of effectively constructing this map. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a VR-angle joint channel map construction method for ultra-large-scale MIMO (UML). This invention involves sampling a small number of locations within the coverage area of ​​the UML antenna array and transmitting uplink pilot signals. After the base station measures the received signal strength and angle of arrival, the VR parameters for each sampling location are determined based on the received signal strength, and the VR parameters for the remaining locations are predicted accordingly. Then, VR regions are divided based on the VR parameters of each location, and within each VR region, the angle parameters of the sampling locations are accurately predicted for the remaining locations, thereby constructing a user VR-angle joint channel map for UML systems. Using this map, rapid querying and inference of user VR and angle information within the coverage area of ​​the UML antenna array can be achieved, thus guiding the orthogonal transmission design of UML systems.

[0007] To address at least one of the aforementioned technical problems, according to one aspect of the present invention, a VR-angle joint channel map construction method for ultra-large-scale MIMO is provided, comprising the following steps: First, by measuring the received signal strength and angle of arrival at a small number of sampling locations, VR identifiers are determined to obtain VR parameters, and the spatial continuity of VR channel characteristics is used to interpolate and predict the VR parameters of the remaining locations; then, VR regions are divided according to the VR parameters, and the angle parameters of the remaining locations are predicted by interpolation within each VR region using known angle parameters; finally, the user VR and angle parameters are combined to jointly construct a VR-angle joint channel map for ultra-large-scale MIMO antenna array coverage.

[0008] Furthermore, the specific steps include the following:

[0009] S101. Map the target area covered by the ultra-large-scale MIMO antenna array as a map, with each sampling location corresponding to a pixel in the map;

[0010] S102. Sample a small number of locations within the target area and transmit uplink pilot signals, and measure the received strength and angle of arrival of each pilot signal at the base station.

[0011] S103. Determine the valid communication link based on the measurement results of the received signal strength, and take the corresponding antenna element as the visible area of ​​the sampling position, i.e., VR;

[0012] S104. Interpolate the VR parameters at other locations based on the VR decision results at the sampling locations;

[0013] S105. Divide the VR region according to the VR parameters of all locations on the user side, and interpolate the angles of the other locations based on the angles of the sampling locations within each VR region;

[0014] S106. Combine the user VR and angle parameters obtained from integrated sampling measurements and interpolation predictions to jointly construct a joint VR-angle channel map for users within the coverage area.

[0015] Furthermore, S101 specifically involves dividing the target area covered by the ultra-large-scale MIMO antenna into equally spaced sections. Using a grid of points, a planar channel map with the same number of pixels as the target area is constructed, and the number of rows and columns of the map pixels is the same as the number of rows and columns of the grid points. Therefore, each pixel corresponds to a grid point within the coverage area, and the attribute values ​​of the pixel are the user VR and angle channel parameters at the corresponding grid point; let the set of locations of all discrete grid points be denoted as . Then the target area With channel map The mapping relationship between them is expressed as:

[0016] ,

[0017] in, and These represent the number of rows and columns of grid points, respectively.

[0018] Furthermore, S102 specifically involves: selecting a subset of users within the target coverage area to send uplink probe pilot signals to the base station; to reduce pilot measurement overhead, only a small number of locations are sampled. Send pilot signal.

[0019] Furthermore, S103 specifically refers to: the base station's ultra-large-scale MIMO antenna receiving and parsing the pilot signals sent by the sampling users, in order to sample the users' signals. Taking signal reception strength and angle of arrival as an example, the user's angle of arrival The received signal strength measured by each antenna element With a pre-set threshold value Based on this comparison, it is determined whether each antenna element can establish a valid communication link with the user's location; the VR identifier of the antenna element that can establish a valid communication link is set to one, and the others are set to zero; if the antenna element is known... measured user The received signal strength of the uplink pilot is The VR recognition decision expression is as follows:

[0020] , , ,

[0021] in, This indicates the number of antenna elements; based on this, the user's... VR parameters corresponding to the location:

[0022]

[0023] The VR parameters of the sampling locations obtained by the base station provide data support for the subsequent VR spatial interpolation of other locations; they are used to assist the base station in measuring or inferring the VR parameters of all user locations within the coverage area.

[0024] Location of each sampling user Combined with VR parameters and angle parameters Construct the sampled dataset:

[0025] ,

[0026] in, Indicates sampling user Location, This represents the user's angle parameters; based on the sampled dataset. Two component datasets were obtained, namely the VR parameter sampling dataset. and angle parameter sampling dataset .

[0027] Furthermore, S104 specifically involves: after obtaining the VR parameters of the sampling beacon users, and based on the VR parameter dataset obtained from the sampling measurements... The VR parameters of other users were predicted using spatial interpolation techniques.

[0028] ,

[0029] in, and Representing users respectively Location and its VR parameters This represents the set of locations of the remaining users. The spatial interpolation function representing the VR parameters;

[0030] When VR sampling dataset When fixed, the interpolation prediction effect and the interpolation function Unique correlation; by integrating the VR parameter sampling measurement or interpolation prediction results of all grid points, a complete VR channel map of the target coverage area is obtained, i.e.

[0031] ,

[0032] in, Indicates the first [item] in the constructed VR map Line number The column pixels store the VR parameters, which are the VR information for the corresponding user position; user index. and and The correspondence is ;

[0033] Since VR parameters reflect whether a user can establish an effective communication link with all antenna elements in the entire antenna array, and the scale of a very large MIMO antenna array is large, each complete VR parameter will occupy a large amount of storage space; considering that all users in each VR partition share the same VR parameter, and the number of VR partitions is limited, there is a limit to the number of partitions. The number of valid values ​​of the VR parameter Satisfy the following relationship

[0034] ,

[0035] Therefore, when storing VR data, use VR tags with smaller numerical values. For VR parameters with larger exponent values, only one true value of each VR parameter is retained within each VR partition; during use, each user location is... "Addressing" retrieves the corresponding VR parameters; at the same time, different VR partitions correspond to different VR tags; generally speaking... The valid range of values ​​is [0, K].

[0036] Furthermore, S105 specifically involves: first, dividing the grid points according to the VR parameter values:

[0037] ,

[0038] Then, based on the grid point division results, the coverage area is divided into multiple sub-regions:

[0039] .

[0040] Each sub-region Each VR group corresponds to a unique VR parameter. ;

[0041] After obtaining the VR parameters of the sampling beacon users, the angle parameter dataset is based on the sampling measurements. Spatial interpolation techniques are used to predict the angle parameters of other users within the VR area:

[0042]

[0043] in, Indicates user Angular parameters, Represents the angle interpolation function;

[0044] Based on this prediction of angle parameters within all VR areas, and by combining the angle parameter sampling measurements or interpolation prediction results of all grid points, a complete angle channel map of the target coverage area can be obtained.

[0045] ,

[0046] in, Indicates the first element in the constructed map. Line number The column of pixels stores the angle parameters, which are the angle information of the corresponding user position;

[0047] Furthermore, S106 specifically refers to: jointly constructing VR maps. and angle map The VR and angle parameters of each grid point location. As attribute values ​​for corresponding pixels in the map, a user VR-angle joint channel map is constructed by storing the VR and angle parameters of all pixels.

[0048] ,

[0049] in, This indicates the first VR-angle joint map constructed. Line number The column of pixels stores the VR and angle parameters, which are the VR and angle information of the corresponding user position;

[0050] Within the coverage area, uplink pilot signals are first periodically sampled from a small number of locations and transmitted, thereby obtaining a new sampled dataset:

[0051] ,

[0052] in, This represents the latest set of user locations; then it is combined with the original sampling measurement data. Spatial interpolation is performed to obtain the updated VR and angle parameters:

[0053] ,

[0054] Next, the grid points and sub-regions are redefined based on the updated VR parameters:

[0055] ,

[0056] And in each sub-region Within, the angle parameters of the remaining positions are predicted and updated based on the known angle parameters:

[0057] ,

[0058] Finally, by combining the updated user VR and angle parameters, the VR-angle joint channel map is dynamically updated:

[0059] ,

[0060] in, and These represent the updated user VR and angle parameters, respectively.

[0061] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the VR-angle joint channel map construction method for ultra-large-scale MIMO of the present invention.

[0062] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the VR-angle joint channel map construction method for ultra-large-scale MIMO of the present invention.

[0063] Compared with existing technologies, the beneficial effects of the above-described method of the present invention are as follows:

[0064] This invention proposes a comprehensive approach and implementation method for constructing a VR-angle joint channel map within the coverage area of ​​a very large-scale MIMO antenna array with low overhead and high accuracy. Key algorithms include independent prediction of user VR and angle parameters, and angle interpolation based on VR partitioning. First, received signal strength and angle of arrival information are extracted from measurement data at a small number of sampling locations. Second, VR identification is determined for each sampling location based on the received signal strength of the antenna array, thus obtaining VR parameters. Then, user VR parameters at other locations are spatially interpolated using the known user VR parameters at the sampling locations, resulting in a complete user VR channel map. Next, VR regions are divided based on the VR parameters at each location, and the angle parameters at other locations are predicted within each VR region using the known angle parameters at the sampling locations. Finally, the angle parameters and VR parameters are combined to construct the VR-angle joint channel map. The map construction method proposed in this invention effectively alleviates the problem of excessive system overhead in constructing a VR-angle joint channel map using a full measurement approach due to the large coverage area of ​​a very large-scale MIMO antenna array. By utilizing the constructed VR-angle joint channel map, the effective communication links and propagation angle information between each antenna element and the target user can be quickly retrieved, greatly reducing information detection overhead and improving the overall transmission efficiency of the MIMO system. This alleviates the problem of low-overhead acquisition of VR and angle information in large-scale MIMO technology application scenarios and strongly supports the application of orthogonal transmission of user data in multi-antenna systems for future 6G. Attached Figure Description

[0065] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments will be briefly described below. Obviously, the drawings described below only relate to some embodiments of the present invention and are not intended to limit the present invention.

[0066] Figure 1 This is a schematic diagram illustrating the principle of a preferred embodiment of the present invention;

[0067] Figure 2 This is a flowchart illustrating the key technologies for constructing a VR-angle joint channel map for ultra-large-scale MIMO antenna coverage, according to a preferred embodiment of the present invention.

[0068] Figure 3This is a schematic diagram illustrating a preferred embodiment of the present invention, showing the coverage scenario of a base station-side ultra-large-scale MIMO antenna array and VR grouping and angle separation.

[0069] Figure 4 This is a schematic diagram illustrating angle parameter interpolation prediction based on VR partitioning, a preferred embodiment of the present invention.

[0070] Figure 5 This is a schematic diagram showing the MSE of the angle parameter prediction results achieved by different spatial interpolation methods as a function of the number of samples in a preferred embodiment of the present invention.

[0071] Figure 6 This is a schematic diagram of a VR-angle joint channel map for a preferred embodiment of the present invention for a very large-scale MIMO antenna array. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention.

[0073] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0074] like Figure 1-2 As shown, this invention provides a VR-angle joint channel map construction method for ultra-large-scale MIMO. This method utilizes a base station's ultra-large-scale MIMO antenna array to cover a target area. First, by measuring the received signal strength and angle of arrival at a small number of sampling locations, VR identifiers are determined to obtain VR parameters. Then, the spatial continuity of VR channel characteristics is used to interpolate and predict the VR parameters for the remaining locations. Subsequently, VR regions are divided based on the VR parameters, and within each VR region, known angle parameters are used to interpolate and predict the angle parameters for the remaining locations. Finally, the user VR and angle parameters are combined to construct a VR-angle joint channel map for ultra-large-scale MIMO antenna array coverage. Using this map, the VR and angle parameters of users within the target coverage area can be quickly queried, guiding the selection of effective transmission antenna elements and beamcodebook design, thereby providing technical support for orthogonal transmission design of ultra-large-scale MIMO systems.

[0075] Because the channel propagation characteristics within the coverage area of ​​a very large-scale MIMO antenna array exhibit spatial continuity, pilot signals can be first sampled from a small number of locations within the antenna array's coverage area. After the base station receives and measures the signal strength and angle of arrival, the VR (Vibration Response) identifier for each antenna element is determined based on the signal strength, and spatial interpolation algorithms are used to predict the VR parameters for the remaining locations. Subsequently, VR regions are divided, and spatial interpolation algorithms are used to predict the angle parameters for the remaining locations within each VR region. Finally, the VR and angle parameters for each user location obtained from sampling and prediction are combined to construct a complete user VR-angle joint channel map. This method requires the premise that the VR and angle parameters at different locations within the coverage area have spatial continuity; that is, users with similar spatial locations have similar VR and angle channel characteristics. Therefore, the construction method of the VR-angle joint channel map can be designed based on the spatial continuity of VR and angle channel characteristics within the coverage area.

[0076] The overall framework for constructing a user VR-angle joint channel map for ultra-large-scale MIMO antenna coverage is as follows: Figure 1 As shown, the key steps include target area map mapping, pilot transmission at sampling locations, angle measurement and user VR decision, VR interpolation at other locations, and VR-angle joint channel map construction.

[0077] Key technology implementation methods such as Figure 2 As shown, firstly, the target area covered by the ultra-large-scale MIMO antenna array is mapped as a channel map, with each possible sampling location corresponding to a pixel in the channel map. Secondly, uplink pilot signals are transmitted from a small number of locations within the target area, and the received strength and direction of arrival of each pilot signal are measured at the base station. Subsequently, based on the measured received signal strength, antenna array elements that can establish effective communication links with each sampling location are identified, and the antenna elements corresponding to the effective communication links are determined as the user VR of the sampling location. Next, the VR parameters of the remaining locations are interpolated based on the VR determination results of the sampling locations. Then, VR regions are divided according to the VR parameters of all locations on the user side, and the angle parameters of the remaining locations are interpolated based on the angle parameters of the sampling locations within each VR region. Finally, the VR and angle parameters obtained from the sampling measurements and interpolation predictions are combined to construct a joint VR-angle channel map for users within the ultra-large-scale MIMO coverage area.

[0078] 1. Target area map mapping

[0079] The target area covered by the ultra-large-scale MIMO antenna is divided into equal intervals. For each grid point, a planar channel map with the same number of pixels is constructed, and the number of rows and columns of the map pixels is the same as the number of rows and columns of the grid points. Therefore, each pixel corresponds to a grid point within the coverage area, and the attribute values ​​of the pixel are the user VR and angular channel parameters at the corresponding grid point. Let's denote the set of locations of all discrete grid points as... Then the target area With channel map The mapping relationship between them can be expressed as:

[0080] ,

[0081] in, and These represent the number of rows and columns of grid points, respectively.

[0082] 2. Pilot transmission at sampling location

[0083] Within the target coverage area, a subset of users transmit uplink probe pilot signals to the base station. Since signals transmitted by users in similar locations are highly likely to experience similar channel propagation environments, there will be significant spatial correlation between the received signals. To reduce pilot measurement overhead, only a small number of locations can be sampled. Send pilot signal.

[0084] It is important to note that the accuracy of the user VR-angle joint channel map construction is related to the density of sampled user locations. Generally, the higher the sampling density, the higher the accuracy of the constructed VR-angle joint map; however, excessively high sampling density also incurs significant pilot measurement overhead. To balance map construction accuracy and pilot measurement overhead, it is necessary to optimize the sampling strategy to achieve the highest possible map accuracy with a limited sampling density. Therefore, this invention employs a sampling strategy combining detection and refinement, which can improve map construction accuracy without increasing pilot measurement overhead. On the one hand, to quickly uncover the distribution characteristics of the received signal strength of the antenna array, uniform spatial sampling can be used in the early stages to obtain the channel feature contours; on the other hand, to accurately characterize the edge details of the received signal strength of the antenna array, non-uniform spatial sampling can be used subsequently to refine the channel feature boundaries.

[0085] 3. Angle measurement and user VR decision-making

[0086] The base station's ultra-large-scale MIMO antenna receives and analyzes the pilot signals sent by sampled users. Let's consider the sampled users... Taking signal reception strength and angle of arrival as an example, on the one hand, the angle of arrival of the user is measured. On the other hand, the received signal strength measured by each antenna element is... With a pre-set threshold value By comparison, it is determined whether each antenna element can establish a valid communication link with the user's location. The VR identifier of the antenna element that can establish a valid communication link is set to one, and the others are set to zero. If the antenna elements are known... measured user The received signal strength of the uplink pilot is The VR recognition decision expression is as follows:

[0087] , , ,

[0088] in, This indicates the number of antenna elements. Based on this, the user's... VR parameters corresponding to the location:

[0089] .

[0090] The VR parameters of the sampled locations obtained from the base station's decision can provide data support for subsequent VR spatial interpolation of other locations. It can be used to assist the base station in measuring or inferring the VR parameters of all user locations within its coverage area.

[0091] Location of each sampling user Combined with its VR parameters and angle parameters A sampled dataset can be constructed:

[0092] ,

[0093] in, Indicates sampling user Location, This represents the user's angle parameters. Based on the sampled dataset. Two component datasets can also be obtained, namely the VR parameter sampling dataset. and angle parameter sampling dataset .

[0094] 4. VR interpolation at other locations

[0095] Because the received signal strength of users within the coverage area of ​​a very large-scale MIMO antenna varies slowly in space, users in similar locations have similar VR channel characteristics. By utilizing the known spatial relationship between the sampled beacon user and other users, and combining this with the VR parameters obtained from the beacon user decision, spatial interpolation can be performed to predict the VR parameters of the other users. Specifically, after obtaining the VR parameters of the sampled beacon user, the VR parameter dataset obtained from the sampling measurements is used... Spatial interpolation techniques can be used to predict the VR parameters of other users:

[0096] ,

[0097] in, and Representing users respectively Location and its VR parameters This represents the set of locations of the remaining users. The spatial interpolation function representing the VR parameters.

[0098] When VR sampling dataset When fixed, the interpolation prediction effect and the interpolation function Unique correlation. Although the VR of users within the coverage area is numerically discrete, adjacent users have a high probability of having the same VR parameters, and the closer two users are, the higher the probability of their VR parameters being the same. Therefore, the classic nearest neighbor (NN) interpolation method can be used to predict the VR information of the remaining users' locations.

[0099] By combining the VR parameter sampling measurements or interpolation prediction results of all grid points, a complete VR channel map of the target coverage area can be obtained, i.e.

[0100] ,

[0101] in, Indicates the first [item] in the constructed VR map Line number The column pixels store VR parameters, which are the VR information for the corresponding user position. User Index and and The correspondence is .

[0102] Since VR parameters reflect whether a user can establish a valid communication link with all antenna elements in the entire antenna array, and very large-scale MIMO antenna arrays are quite large, each complete VR parameter will occupy a significant amount of storage space. Considering that all users within each VR partition share the same VR parameter, and that the number of VR partitions is finite, and there is a limit to the number of partitions... The number of valid values ​​of the VR parameter Satisfy the following relationship

[0103] .

[0104] Therefore, when storing VR data, smaller VR tags can be used. For VR parameters with large exponent values, only one true value of each VR parameter is retained within each VR partition, thus significantly saving VR data storage space. During use, each user location can be accessed via... "Addressing" retrieves the corresponding VR parameters. At any given time, different VR zones correspond to different VR tags. Generally speaking, The valid range of values ​​is [0, K].

[0105] 5. Angle interpolation within the VR region

[0106] Since the VR parameter is numerically discrete, the coverage area can be divided into multiple non-overlapping sub-regions based on the differences in the VR parameter of each grid point. Specifically, the grid points are first divided according to the VR parameter values:

[0107] ,

[0108] Then, based on the grid point division results, the coverage area is divided into multiple sub-regions:

[0109] .

[0110] Each sub-region Each VR group corresponds to a unique VR parameter. Different VR groups can contain users, enabling coarse separation of different users in the spatial domain.

[0111] Although the angle parameters vary considerably across the entire target area, within each VR area, the user's angle exhibits spatial continuity and a relatively small overall range of variation because the visible area of ​​the user on the antenna array side is the same set of antenna elements. Based on these characteristics, the angles of other users can be predicted within the VR area using interpolation based on the known angles of sampled users. Specifically, after obtaining the VR parameters of the sampled beacon users, the angle parameter dataset obtained from the sampling measurements is used... Spatial interpolation techniques can be used to predict the angle parameters of other users within the VR area:

[0112]

[0113] in, Indicates user Angular parameters, This represents the angle interpolation function.

[0114] Based on this prediction of angle parameters within all VR areas, and by combining the angle parameter sampling measurements or interpolation prediction results of all grid points, a complete angle channel map of the target coverage area can be obtained.

[0115] ,

[0116] in, Indicates the first element in the constructed map. Line number The column of pixels stores the angle parameters, which are the angle information of the corresponding user position.

[0117] It should be noted that since the range of user angle variation within the same VR area is relatively small, the beamcode design can be optimized by taking advantage of the similarity of angle parameter values, so that the beam direction is focused on the angle range where users are more concentrated within the VR area.

[0118] 6. Construction of User VR-Angle Joint Channel Map

[0119] Combine the above to build VR maps and angle map The VR and angle parameters of each grid point location. By storing the VR and angle parameters of all pixels as attribute values ​​for corresponding pixels in the map, a user VR-angle joint channel map can be constructed.

[0120] ,

[0121] in, This indicates the first VR-angle joint map constructed. Line number The column pixels store the VR and angle parameters, which are the VR and angle information of the corresponding user location. The VR-angle joint channel map can be used to quickly query and obtain the VR and angle information of users within the target coverage area, and thereby design an orthogonal transmission scheme.

[0122] Because the channel environment for signal propagation is quasi-static, i.e., slowly changing, it is necessary to periodically update the VR-angle joint channel map in practical applications. Specifically, within the coverage area, a small number of locations are periodically sampled and uplink pilot signals are transmitted, thereby obtaining a newly added sampling dataset:

[0123] ,

[0124] in, This represents the latest set of user locations; then it is combined with the original sampling measurement data. Spatial interpolation yields the updated VR and angle parameters:

[0125] ,

[0126] Next, the grid points and sub-regions are redefined based on the updated VR parameters:

[0127] ,

[0128] And in each sub-region Within, the angle parameters of the remaining positions are predicted and updated based on the known angle parameters:

[0129] ,

[0130] Finally, by combining the updated user VR and angle parameters, the VR-angle joint channel map is dynamically updated:

[0131] ,

[0132] in, and These represent the updated user VR and angle parameters, respectively.

[0133] Example 1:

[0134] To further illustrate the user VR grouping and angle separation process in a large-scale MIMO antenna array coverage scenario, this case study establishes a large-scale MIMO near-field communication coverage scenario, such as... Figure 3 As shown in the figure, the ultra-large-scale MIMO antenna array is deployed on the outer surface of a tall building, providing broadband communication services to nearby users. Because different users are located in different areas, the portion of the antenna array they can see may also differ. For example, by comparing the user circled by the red dashed line and the user terminal circled by the yellow dashed line, it can be seen that the sub-regions of the antenna array they can see are significantly different, thus they belong to two different VR (Vibration Zone) groups. Since the antenna elements seen by these two groups of users belong to non-overlapping parts of the antenna array, their channel environments are naturally orthogonal, and orthogonal separation of users between groups can be achieved based on VR grouping. Furthermore, for the same user group, because the users within it are located in different positions, the angles at which they establish communication links with the base station antenna are also different. Therefore, spatial separation of users within the group can be further achieved based on user angle parameters.

[0135] Example 2:

[0136] To further illustrate the VR-based angle space interpolation method, this case study presents the approach of dividing the user's VR parameters into sub-regions and interpolating and predicting the angle parameters of other positions within each sub-region. A schematic diagram is shown below. Figure 4As shown in the figure, different colors of the sampled users represent different VR parameter values. Based on the different VR parameters, the entire coverage area is divided into multiple non-overlapping sub-regions, thus realizing VR partitioning. Since the angular channel characteristics of users within each VR partition are relatively similar, while the angular channel characteristics of users in different VR partitions differ significantly, when using inverse distance weighted interpolation algorithms for prediction, sampling users within the VR partition to which the target user's location belongs can be prioritized.

[0137] Example 3:

[0138] To verify the performance of the proposed VR-partition-based angle parameter interpolation method, this case study simulates two different interpolation methods: classical inverse range weighting and VR-partition-based inverse range weighting. During the simulation, uplink pilot signals were transmitted to 400, 600, 800, 1000, and 2000 sampling points within the coverage area of ​​the ultra-large-scale MIMO antenna array, respectively. The VR and angle parameters at these locations were measured, and based on this, the VR and angle parameters at other locations were predicted. The size of the coverage area was set to... m2, the number of rows and columns of a uniformly divided grid is The distance between the centers of every two grid points is 2m, and the search radius for inverse distance-weighted interpolation is 25m. The performance curves of the MSE of the predicted angle parameters for the two spatial interpolation methods as a function of the number of samples are shown below. Figure 5 As shown in the figure, the proposed VR-partition-based inverse distance-weighted interpolation method exhibits lower MSE and better performance for different sampling numbers, achieving more accurate angle parameter prediction. The above analysis demonstrates that the simulation results validate the effectiveness of the proposed VR-partition-based angle parameter interpolation method.

[0139] Example 4:

[0140] To visually demonstrate the effect of VR-angle joint channel map construction, this case study presents a possible result of constructing a user VR-angle joint channel map matching the coverage area of ​​a very large-scale MIMO antenna array, such as... Figure 6 As shown in the figure, this is a VR-angle joint channel map. Each colored block corresponds to a VR zone, and the hexadecimal value marked in the center of the colored block is the VR parameter shared by all locations within that VR zone. This parameter records the location of the antenna element that can establish a valid communication link with users within that VR zone. Since the VR parameter is essentially a location label for the VR antenna element, its value is discrete. Furthermore, from... Figure 6It can also be seen that different VR zones do not overlap, and their VR parameters are different from each other. Each VR zone contains multiple grid points (the smallest squares in the figure), and each grid point corresponds to a pixel in the VR-angle joint channel map, where the color of the grid point represents the magnitude of the angle parameter of that pixel. Different VR zones have different main colors, which reflects the high degree of angle differentiation between VR zones; while the colors within the same VR zone are relatively uniform and gradually change, which reflects the spatial continuity of the angle channel characteristics within the VR zone, so the values ​​are relatively concentrated and gradually change in the map space. Using this map, it is possible to quickly query and infer the antenna array VR and angle channel parameters, thereby guiding the orthogonal transmission design of ultra-large-scale MIMO systems and providing users with better communication service quality.

[0141] In summary, the solution described in this embodiment has at least the following advantages:

[0142] (1) Low channel estimation overhead: Only a few key locations within the coverage area are sampled to measure user VR and angle parameters, and the VR and VR parameters of the remaining locations are predicted by spatial interpolation technology. Therefore, the overall pilot measurement overhead is small.

[0143] (2) High angle prediction accuracy: Based on the VR partitioning results, the user's angle can be coarsely grouped to eliminate the influence of irrelevant sampling points; within each VR partition, the inverse distance weighted spatial interpolation method can make full use of the angle information of the surrounding sampling points to improve the angle prediction accuracy.

[0144] (3) Good user coverage performance: First, the VR information in the map is used to select the antenna subarray that can establish an effective communication link with the target user. Then, the angle information is used to optimize the beamforming design of the selected antenna subarray, thereby significantly improving the user coverage quality of the ultra-large-scale MIMO system.

[0145] (4) Map supports dynamic updates: It can periodically sample the latest pilot signals as needed and supports dynamic updates of VR-angle joint channel maps, thereby adapting to the slow changes in the channel environment.

[0146] Example 5:

[0147] The computer-readable storage medium of this embodiment stores a computer program that, when executed by a processor, implements the steps in the preparation method of the VR-angle joint channel map construction method for ultra-large-scale MIMO in Embodiment 1.

[0148] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0149] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0150] Example 6:

[0151] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the preparation method of the VR-angle joint channel map construction method for ultra-large-scale MIMO in Embodiment 1.

[0152] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The memory can include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0153] Those skilled in the art will understand that the content disclosed in the embodiments can be provided as a method, system, or computer program product. Therefore, this solution can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this solution can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.

[0154] This solution is described with reference to flowchart illustrations and / or block diagrams of methods and computer program products according to embodiments of this solution. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0158] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

[0159] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the specific embodiments described above. The specific embodiments and descriptions in the specification are merely for further illustrating the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A VR-angle joint channel map construction method for ultra-large-scale MIMO, characterized in that, First, by measuring the received signal strength and angle of arrival at a small number of sampling locations, VR identifiers are determined and VR parameters are obtained. Then, the spatial continuity of VR channel characteristics is used to interpolate and predict the VR parameters of the remaining locations. Subsequently, VR regions are divided according to the VR parameters, and the angle parameters of the remaining locations are predicted by interpolation using known angle parameters within each VR region. Finally, the user VR and angle parameters are combined to jointly construct a VR-angle joint channel map for the coverage of ultra-large-scale MIMO antenna arrays.

2. The method as described in claim 1, characterized in that, Specifically, the steps include the following: S101. Map the target area covered by the ultra-large-scale MIMO antenna array as a map, with each sampling location corresponding to a pixel in the map; S102. Sample a small number of locations within the target area and transmit uplink pilot signals, and measure the received strength and angle of arrival of each pilot signal at the base station. S103. Determine the valid communication link based on the measurement results of the received signal strength, and take the corresponding antenna element as the visible area of ​​the sampling position, i.e., VR; S104. Interpolate the VR parameters at other locations based on the VR decision results at the sampling locations; S105. Divide the VR region according to the VR parameters of all locations on the user side, and interpolate the angles of the other locations based on the angles of the sampling locations within each VR region; S106. Combine the user VR and angle parameters obtained from integrated sampling measurements and interpolation predictions to jointly construct a joint VR-angle channel map for users within the coverage area.

3. The method as described in claim 2, characterized in that, S101 specifically involves dividing the target area covered by the ultra-large-scale MIMO antenna into equally spaced sections. Using a grid of points, a planar channel map with the same number of pixels as the target area is constructed, and the number of rows and columns of the map pixels is the same as the number of rows and columns of the grid points. Therefore, each pixel corresponds to a grid point within the coverage area, and the attribute values ​​of the pixel are the user VR and angle channel parameters at the corresponding grid point; let the set of locations of all discrete grid points be denoted as . Then the target area With channel map The mapping relationship between them is expressed as: ， in, and These represent the number of rows and columns of grid points, respectively.

4. The method as described in claim 3, characterized in that, S102 specifically involves: selecting a subset of users within the target coverage area to send uplink probe pilot signals to the base station; to reduce pilot measurement overhead, only a small number of locations are sampled. Send pilot signal.

5. The method as described in claim 4, characterized in that: S103 specifically refers to: the base station's ultra-large-scale MIMO antenna receiving and parsing the pilot signals sent by the sampling users, in order to sample the users' signals. Taking signal reception strength and angle of arrival as an example, the user's angle of arrival The received signal strength measured by each antenna element With a pre-set threshold value Based on this comparison, it is determined whether each antenna element can establish a valid communication link with the user's location; the VR identifier of the antenna element that can establish a valid communication link is set to one, and the others are set to zero; if the antenna element is known... measured user The received signal strength of the uplink pilot is The VR recognition decision expression is as follows: ， ， ， in, This indicates the number of antenna elements; based on this, the user's... VR parameters corresponding to the location: ， The VR parameters of the sampling locations obtained by the base station provide data support for the subsequent VR spatial interpolation of other locations; they are used to assist the base station in measuring or inferring the VR parameters of all user locations within the coverage area. Location of each sampling user Combined with VR parameters and angle parameters Construct the sampled dataset: ， in, Indicates sampling user Location, This represents the user's angle parameters; based on the sampled dataset. Two component datasets were obtained, namely the VR parameter sampling dataset. and angle parameter sampling dataset .

6. The method as described in claim 5, characterized in that: S104 specifically refers to: after obtaining the VR parameters of the sampling beacon users, based on the VR parameter dataset obtained from the sampling measurements. The VR parameters of other users were predicted using spatial interpolation techniques. ， in, and Representing users respectively Location and its VR parameters This represents the set of locations of the remaining users. The spatial interpolation function representing the VR parameters; When VR sampling dataset When fixed, the interpolation prediction effect and the interpolation function Unique correlation; by integrating the VR parameter sampling measurement or interpolation prediction results of all grid points, a complete VR channel map of the target coverage area is obtained, i.e. ， in, Indicates the first [item] in the constructed VR map Line number The column pixels store the VR parameters, which are the VR information for the corresponding user position; user index. and and The correspondence is ; Since VR parameters reflect whether a user can establish an effective communication link with all antenna elements in the entire antenna array, and the scale of a very large MIMO antenna array is large, each complete VR parameter will occupy a large amount of storage space; considering that all users in each VR partition share the same VR parameter, and the number of VR partitions is limited, there is a limit to the number of partitions. The number of valid values ​​of the VR parameter Satisfy the following relationship ， Therefore, when storing VR data, use VR tags with smaller numerical values. For VR parameters with larger exponent values, only one true value of each VR parameter is retained within each VR partition; during use, each user location is... "Addressing" retrieves the corresponding VR parameters; at the same time, different VR partitions correspond to different VR tags; generally speaking... The valid range of values ​​is [0, K].

7. The method as described in claim 6, characterized in that: S105 specifically involves: First, dividing the grid points according to the VR parameter values: ， Then, based on the grid point division results, the coverage area is divided into multiple sub-regions: ， Each sub-region Each VR group corresponds to a unique VR parameter. ; After obtaining the VR parameters of the sampling beacon users, the angle parameter dataset is based on the sampling measurements. Spatial interpolation techniques are used to predict the angle parameters of other users within the VR area: ， in, Indicates user Angular parameters, Represents the angle interpolation function; Based on this prediction of angle parameters within all VR areas, and by combining the angle parameter sampling measurements or interpolation prediction results of all grid points, a complete angle channel map of the target coverage area can be obtained. ， in, Indicates the first element in the constructed map. Line number The column of pixels stores the angle parameters, which are the angle information of the corresponding user position.

8. The method as described in claim 7, characterized in that: S106 specifically refers to: jointly constructing VR maps. and angle map The VR and angle parameters of each grid point location. As attribute values ​​for corresponding pixels in the map, a user VR-angle joint channel map is constructed by storing the VR and angle parameters of all pixels. ， in, This indicates the first VR-angle joint map constructed. Line number The VR and parameters stored in the column pixels are also the VR and information of the corresponding user location; Within the coverage area, uplink pilot signals are first periodically sampled from a small number of locations and transmitted, thereby obtaining a new sampled dataset: ， in, This represents the latest set of user locations; then it is combined with the original sampling measurement data. Spatial interpolation is performed to obtain the updated VR and angle parameters: ， Next, the grid points and sub-regions are redefined based on the updated VR parameters: ， And in each sub-region Within, the angle parameters of the remaining positions are predicted and updated based on the known angle parameters: ， Finally, by combining the updated user VR and angle parameters, the VR-angle joint channel map is dynamically updated: ， in, and These represent the updated user VR and angle parameters, respectively.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps in the VR-angle joint channel map construction method for ultra-large-scale MIMO as described in any one of claims 1 to 8.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the VR-angle joint channel map construction method for ultra-large-scale MIMO as described in any one of claims 1 to 8.

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