A method for constructing a user-oriented ultra-large-scale MIMO visible area map on the base station side

Abstract

This invention discloses a method for constructing a user-oriented ultra-large-scale MIMO visible area map on the base station side, comprising: mapping the base station-side ultra-large-scale MIMO antenna array into a planar map; using the target user to transmit pilot signals, and sampling several antenna elements in the base station-side antenna array and measuring the received pilot signal strength of the antenna elements; using spatial interpolation to predict the received pilot signal strength of the remaining antenna elements; determining whether the antenna elements can establish a communication link with the user, and filtering out feasible VR identifiers; and using the VR identifier of each antenna element as the value of the corresponding pixel in the map, thereby constructing a VR domain binary channel map of the base station-side antenna array. This invention features low channel estimation overhead, high VR prediction accuracy, good user coverage performance, and map dynamic updates: it can periodically sample the latest pilot signals and supports dynamic updates of the VR domain channel map, thus adapting to the slow changes in the channel environment.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, and in particular relates to a method for constructing a user-oriented ultra-large-scale MIMO visible area map on the base station side. Background Technology

[0002] The development of future-oriented sixth-generation (6G) mobile communication systems further enhances the utilization of spatial resources, and ultra-large-scale multiple-input multiple-output (XL-MIMO) is a key physical layer technology for improving the spatial multiplexing capability of 6G. However, due to the non-stationary propagation characteristics of electromagnetic waves in space, XL-MIMO systems exhibit near-field visible area (VR) channel characteristics, meaning that some antenna arrays are only "visible" to a portion of users; this portion of the antennas is referred to as the user VR. Notably, utilizing VR characteristics is beneficial for achieving orthogonal transmission between multiple users, thereby reducing the dimensionality of the channel matrix and simplifying transmission design. Therefore, VR is crucial for XL-MIMO transmission. Acquiring VR is a necessary prerequisite for utilizing VR, but with the continuous increase in antenna size, existing VR acquisition methods based on pilot measurement and feedback introduce huge pilot overhead, making them unsustainable for the system.

[0003] Considering that spatially close antenna elements in an XL-MIMO antenna array have similar channel transmission characteristics, when measuring pilot signals, only a subset of antenna elements can be measured, and the pilot signal reception data of the remaining antennas can be interpolated accordingly, rather than measuring the pilot reception data of all antenna elements. This reduces pilot signal estimation overhead. Furthermore, although the electromagnetic propagation environment changes slowly over time, the channel environment can be considered quasi-static over a short period. Therefore, the current VR information of target users within the antenna coverage area can be used to construct a VR domain channel map using antenna elements as the basic unit. This provides a pathway for subsequent VR information retrieval, supports rapid acquisition of VR information, avoids repeated measurements of VR channel characteristics, and further reduces pilot overhead.

[0004] As can be seen from the above, VR domain channel maps have many advantages in acquiring VR information with low overhead. To utilize VR domain channel maps to guide XL-MIMO in achieving low-complexity transmission design, it is first necessary to study effective methods for constructing such maps. Summary of the Invention

[0005] Objective of the Invention: The objective of this invention is to provide a user-oriented method for constructing a visible area map of a base station-side ultra-large-scale MIMO (XL-MIMO) network. By sampling, measuring, and interpolating the strength of the pilot signal received by the antenna elements from the target user via the base station-side XL-MIMO antenna array, a VR domain channel map is constructed based on the determination of the received signal strength, thereby optimizing user coverage and reducing transmission complexity.

[0006] Technical solution: The present invention provides a method for constructing a user-oriented ultra-large-scale MIMO visible area map on the base station side, comprising the following steps:

[0007] Step 1: Map the base station-side ultra-large-scale MIMO antenna array as a planar map, where each antenna element corresponds to a pixel in the map;

[0008] Step 2: Use the target user to send pilot signals, and sample several antenna elements in the base station side antenna array and measure the strength of the received pilot signals of the antenna elements;

[0009] Step 3: Based on the received pilot signal strength of the antenna elements, predict the received pilot signal strength of the remaining antenna elements using spatial interpolation.

[0010] Step 4: Determine whether an antenna unit can establish a communication link with the user based on the strength of the pilot signal received by each antenna unit, and filter out the VR identifier;

[0011] Step 5: Use the VR identifier of each antenna element as the value of the corresponding pixel in the map to construct a binary channel map of the VR domain of the base station side antenna array.

[0012] Further, step 1 specifically involves: constructing a planar channel map with the same number of pixels as the number of antenna elements in the XL-MIMO antenna array on the base station side, where the number of rows and columns of the pixels is the same as the number of rows and columns of the antenna elements; each antenna element corresponds to a pixel in the channel map, and the value of the pixel is the VR channel feature of the corresponding antenna element; the mapping relationship between the antenna array A and the channel map M is as follows:

[0013] f:

[0014] Where, N r and N c These represent the number of rows and columns of the antenna element, respectively.

[0015] Furthermore, step 2 specifically involves: the target user periodically sending uplink probe pilot signals to the base station-side antenna array; after being received and analyzed, the signals are used to assist the base station in real-time measurement of the received signal strength of the antenna elements; and the XL-MIMO antenna array samples several antenna elements. Measure the received signal strength g and construct a sampling dataset based on the antenna position c:

[0016] S={(c n ,g n )|c n ∈A s}

[0017] Among them, c n and g nThese represent the position of the sampling antenna element n and the received signal strength, respectively.

[0018] Furthermore, step 3 specifically involves: after obtaining the received signal strength of the sampling antenna element, using spatial interpolation techniques to predict the received signal strength of the remaining antenna elements based on the received signal strength dataset S obtained from the sampling measurement.

[0019] g u =F(S,c u ),u∈A\A s

[0020] Among them, c u and g u Let A and A represent the position of the antenna element u to be interpolated and the received signal strength, respectively. s Let F(·) represent the set of antenna elements to be interpolated, and let F(·) represent the interpolation function.

[0021] When the sampled dataset S is fixed, the interpolation prediction effect is uniquely related to the interpolation function F(·). Since the antenna elements are discretely distributed and the influence of the sampled antenna elements on the antenna elements to be interpolated is inversely proportional to the distance between them, the classic inverse range weighted IDW interpolation method is improved into an inverse range weighted SR-IDW interpolation algorithm based on the search radius. First, the radius of the search circle is set; second, with the target interpolated antenna element as the center, several sampled antenna elements close to the target antenna element are selected using the search circle; then, based on the selected sampled antenna elements, the inverse range weighted interpolation algorithm is used to predict the received signal strength of the target interpolated antenna element. According to the received signal strength corresponding to each antenna element in the antenna array on the base station side, the antenna array is divided into multiple sub-arrays, and then spatial interpolation is performed in each sub-array to improve the prediction accuracy.

[0022] Further, step 4 specifically involves: comparing the received signal strength measured or predicted for each antenna element with a pre-set threshold value ε to determine whether each antenna element can establish a valid communication link with the target user. The VR flag of the antenna elements that can establish a valid communication link is set to one, and the rest are set to zero. If the received signal strength of antenna element n is known to be g... n The VR recognition decision expression is as follows:

[0023]

[0024] Further, step 5 specifically involves: using the VR identifier v obtained from the decision of each antenna element as the value of the corresponding pixel in the map, i.e., the VR channel feature; and constructing a binary channel map of the base station-side antenna array VR domain by storing the VR identifier information of all pixels.

[0025]

[0026] in, Indicates the Nth element in the constructed VR domain map. r row N c The VR identifiers stored in each column of pixels represent the VR information of the corresponding antenna array. The VR domain channel map is dynamically updated. The base station-side antenna array first periodically samples several antenna elements to receive and measure the latest pilot signal data transmitted from the user side, thus obtaining the newly added sampled dataset.

[0027]

[0028] in, c represents the latest sampled antenna element. n and g n The positions of sampling antenna element n and the received signal strength are represented respectively; spatial interpolation is performed by combining the original sampling measurement data S:

[0029]

[0030] Where G(·) represents the interpolation function that combines the latest and historical sampled data, c u This indicates the position of the antenna element u to be interpolated; finally, the VR decision is updated, and the VR domain channel map is dynamically updated based on the decision result.

[0031]

[0032] in, This indicates the updated VR judgment result.

[0033] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0034] The present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method of the present invention.

[0035] The present invention also discloses a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method of the present invention.

[0036] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0037] (1) Antenna Element Sampling Measurement: To construct a VR domain channel map with low overhead, this invention first explores the correspondence between the VR channel characteristics of antenna elements and their positions. Through data analysis, it is found that the received signal strength of each antenna element in the antenna array exhibits relatively stable spatial variation characteristics due to its different positions. Since the received signal strength can be used to determine whether an effective communication link can be established between the antenna element and the target user, indirectly reflecting the VR channel characteristics, the VR channel characteristics of the antenna elements also exhibit a spatially stable variation law. Based on this, the VR channel characteristics of the entire antenna array can be predicted by sampling the measurement results of a small number of antenna elements, thereby constructing a VR domain channel map with low overhead. Under the constraint of limited sampling antenna elements, this invention adopts a non-uniform spatial sampling method combining detection and refinement to optimize the sampling strategy. On the one hand, it determines the outline of the VR channel characteristics, and on the other hand, it enriches the details of the VR channel characteristics, thereby mining as much VR channel information as possible and providing more prior knowledge for subsequent spatial interpolation.

[0038] (2) Antenna Array Interpolation Prediction: To fully utilize the received signal strength information of a small number of known sampled antenna elements to predict the received signal strength of other antenna elements, this invention performs spatial interpolation based on the data measured by the sampled antenna elements, thereby constructing a complete correspondence between the position of the antenna elements in the antenna array and their received signal strength. Since received signal strength has strong spatial continuity—that is, the closer two antenna elements are, the more similar their received signal strengths—this invention designs an IDW interpolation algorithm based on the search radius. This algorithm combines the distribution characteristics of received signal strength and utilizes the flexible focusing capability of the search radius to select a set of sampled antenna elements closest to the position of the antenna element to be interpolated, and then uses spatial interpolation to predict the received signal strength of that interpolated antenna element.

[0039] (3) This invention proposes a general approach and implementation method for constructing a VR domain channel map of a base station-side antenna array with low overhead, and designs key algorithms for antenna element sampling measurement and antenna array interpolation prediction. First, the received signal strength information is extracted from a small amount of antenna element sampling measurement data; second, the received signal strength information of the interpolation points is spatially interpolated using the known received signal strength of the sampling points; then, VR identification decision is performed on each antenna element, thereby obtaining a complete VR domain channel map. The map construction method proposed in this invention effectively alleviates the problem of excessive system overhead caused by the large scale of XL-MIMO antenna arrays when constructing VR domain channel maps using a full measurement method. Using the constructed VR domain channel map, the effective communication links between each antenna element and the target user can be quickly retrieved, greatly reducing VR information detection overhead and improving overall transmission efficiency. This alleviates the bandwidth pressure introduced by the massive pilot signal transmission in VR identification under large-scale MIMO technology application scenarios, and strongly supports low-complexity transmission applications of multi-antenna systems for future 6G.

[0040] (4) Low channel estimation overhead: The present invention measures pilot signals by sampling only a portion of the antenna elements in the base station-side antenna array, and predicts the channel characteristics of the remaining antenna elements through spatial interpolation. Therefore, the overall pilot measurement overhead is low. High VR prediction accuracy: On the one hand, the antenna element sampling method combining detection and refinement is used to improve the overall prior information; on the other hand, the spatial interpolation method based on the search radius is used to fully utilize prior information to improve VR prediction accuracy. Good user coverage performance: The transmission beam is designed by selecting antenna elements that can establish an effective communication link with the target user using the VR domain channel map, which significantly improves the user coverage quality of the XL-MIMO system. Map supports dynamic updates: The latest pilot signals can be sampled periodically, supporting dynamic updates of the VR domain channel map, thereby adapting to the slow changes in the channel environment. Attached Figure Description

[0041] Figure 1 A schematic diagram illustrating the overall concept of constructing a VR domain channel map for a base station-side XL-MIMO antenna array according to the present invention;

[0042] Figure 2 A flowchart illustrating the key technologies for constructing the VR domain channel map of the base station-side XL-MIMO antenna array of the present invention;

[0043] Figure 3 This is a schematic diagram of the sampling section of the base station-side antenna array of the present invention measuring the user's uplink pilot signal;

[0044] Figure 4 This is a schematic diagram illustrating the division of the antenna array into sub-regions based on the measurement results of the sampling antenna elements according to the present invention;

[0045] Figure 5 The curves showing the prediction performance of the present invention based on different spatial sampling results as a function of sampling interval are shown.

[0046] Figure 6 The curves showing the prediction performance of different spatial interpolation algorithms of the present invention as a function of sampling interval are shown.

[0047] Figure 7 This invention provides a user-coverage-oriented base station-side array antenna VR domain binary channel map. Detailed Implementation

[0048] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0049] This invention proposes a method for constructing a VR domain channel map for a base station-side XL-MIMO antenna array oriented towards user coverage. This method utilizes a base station-side XL-MIMO antenna array to cover the target user, predicts the received signal strength of each antenna element through sampling measurement or interpolation, determines the VR identifier, and constructs a VR domain channel map oriented towards user coverage based on the antenna element locations. Using this map, the base station can query the VR channel characteristics of target users within its coverage area in real time, guiding the selection of effective transmission antenna elements and thus designing a low-complexity XL-MIMO transmission scheme.

[0050] Because the received signal strength within a local area of ​​the antenna array exhibits spatial continuity, a method can be developed that first samples a small number of antenna elements based on their spatial locations to measure their received signal strength. Then, spatial interpolation is used to predict the received signal strength of the remaining antenna elements. Finally, the VR (Virtual Receiving) identifier for each antenna element is determined by combining the sampled and predicted received signal strengths, thereby constructing a complete VR domain channel map. This method relies on the spatial continuity of received signal strength between antenna elements at different locations; that is, antenna elements with similar spatial locations have similar received signal strengths. Therefore, the construction method for the VR domain channel map needs to be designed based on the spatial continuity of received signal strength on the array side.

[0051] The overall framework for constructing a VR domain channel map for an XL-MIMO antenna array on the base station side is shown in the diagram below. Figure 1 As shown, the key steps include antenna array map mapping, pilot transmission and sampling measurement, antenna array spatial interpolation, antenna element VR identifier determination, and base station-side VR domain map construction.

[0052] The specific process of this invention is as follows: Figure 2 As shown. First, the XL-MIMO antenna array on the base station side is mapped as a planar channel map, with each antenna element corresponding to a pixel in the map. Second, an uplink pilot signal is transmitted from the target user's location, and a small number of antenna elements (i.e., sampling antenna elements, or sampling units for short) in the base station side antenna array are selected to measure the strength of their received pilot signals. Next, based on the received signal strength measured by the sampling units, a spatial interpolation algorithm is used to predict the received signal strength of the remaining antenna elements. Then, the received signal strength measured or predicted by each antenna element is compared with a pre-set threshold value to determine whether each antenna element can establish a valid communication link with the target user. The VR identifier of the antenna elements that can establish a valid communication link is set to one, and the rest are set to zero. Finally, the VR identifier of each antenna element is used as the value of the corresponding pixel in the map, thereby constructing a binary channel map of the VR domain of the base station side antenna array. The specific process is as follows:

[0053] 1. Antenna Array Mapping

[0054] Based on the number of antenna elements in the XL-MIMO antenna array on the base station side, a planar channel map with the same number of pixels is constructed, and the number of rows and columns of the pixels is the same as the number of rows and columns of the antenna elements. Each antenna element corresponds to a pixel in the channel map, and the value of the pixel is the VR channel feature of the corresponding antenna element. The mapping relationship between antenna array A and channel map M is as follows:

[0055] f:

[0056] Where, N r and N c These represent the number of rows and columns of the antenna element, respectively.

[0057] 2. User-side pilot transmission and antenna element sampling measurement

[0058] The target user periodically sends uplink probe pilot signals to the base station's antenna array. After being received and analyzed, these signals can be used to assist the base station in real-time measurement of the received signal strength of the antenna elements. Analysis of the received signal strength data measured by the antenna elements reveals a significant spatial correlation between the received signal strengths of antenna elements located close to each other. Therefore, to reduce the measurement overhead of the base station's pilot signals, the XL-MIMO antenna array samples only a small number of antenna elements. Measure the received signal strength g and construct a sampling dataset based on the antenna position c:

[0059] S={(c n ,g n )|c n ∈A s}

[0060] Among them, c n and g n These represent the position of the sampling antenna element n and the received signal strength, respectively. The received signal strength measured by the sampling antenna element provides data support for subsequent spatial interpolation of the antenna array.

[0061] The accuracy of the VR domain channel map on the base station side is related to the density of the sampling antenna elements. Generally, the higher the sampling density, the higher the accuracy of the constructed VR domain map. However, excessively high sampling density also leads to greater 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 at a given sampling density. A sampling strategy combining detection and refinement can improve map construction accuracy without increasing pilot measurement overhead. On the one hand, to fully exploit the distribution characteristics of the received signal strength of the antenna array, uniform spatial sampling can be used to obtain the channel feature contours. On the other hand, to accurately depict the edge details of the received signal strength of the antenna array, non-uniform spatial sampling can be used to refine the channel feature boundaries.

[0062] 3. Antenna array spatial interpolation

[0063] Because the channel spatial propagation characteristics of the base station-side antenna array change slowly, the received signal strength of antenna elements located close to each other on the antenna array exhibits a stable trend. By utilizing the known spatial relationship between the sampled antenna element and the antenna element to be interpolated, and combining this with the measured received signal strength of the sampled antenna element, spatial interpolation can be performed to obtain the received signal strength of the antenna element to be interpolated. Specifically, after obtaining the received signal strength of the sampled antenna element, based on the received signal strength dataset S obtained from the sampling measurements, the received signal strength of the remaining antenna elements can be predicted using spatial interpolation techniques.

[0064] g u =F(S,c u ),u∈A\A s

[0065] Among them, c u and g u Let A and A represent the position of the antenna element u to be interpolated and the received signal strength, respectively. s Let F(·) represent the set of antenna elements to be interpolated, and let F(·) represent the interpolation function.

[0066] When the sampled dataset S is fixed, the interpolation prediction effect is uniquely related to the interpolation function F(·). Since the antenna elements are discretely distributed, and the influence of the sampled antenna elements on the antenna elements to be interpolated is inversely proportional to the distance between them, the classic inverse distance weighted (IDW) interpolation method can be used to predict the channel information of the antenna elements to be interpolated.

[0067] To further improve the accuracy of spatial interpolation, we propose an improved inverse range weighted interpolation (SR-IDW) algorithm based on the search radius. First, the radius of the search circle is set. Second, using the target interpolation antenna element as the center, a small number of sampling antenna elements close to the target element are selected using this search circle. Then, based on the selected sampling antenna elements, the SR-IDW algorithm is used to predict the received signal strength of the target interpolation antenna element.

[0068] Furthermore, by dividing the antenna array into multiple subarrays based on the received signal strength of each antenna element in the base station antenna array, and then performing spatial interpolation in each subarray, the prediction accuracy can be further improved.

[0069] 4. Antenna Unit VR Identification Decision

[0070] The received signal strength measured or predicted for each antenna element is compared with a pre-set threshold ε to determine whether each antenna element can establish a valid communication link with the target user. The VR flag of the antenna elements that can establish a valid communication link is set to one, and the rest are set to zero. If the received signal strength of antenna element n is known to be g... n The VR recognition decision expression is as follows:

[0071]

[0072] 5. Base station-side VR domain map construction

[0073] The VR identifier v obtained from the decisions of each antenna element is used as the value of the corresponding pixel in the map (i.e., VR channel feature). By storing the VR identifier information of all pixels, a binary channel map of the VR domain of the base station-side antenna array can be constructed.

[0074]

[0075] in, Indicates the Nth element in the constructed VR domain map. r row N c The VR identifiers stored in each column of pixels represent the VR information of the corresponding antenna array. The VR domain channel map reflects the ability of each antenna element to establish an effective communication link with the target user. This map allows for quick querying and acquisition of the target user's VR information, upon which a low-complexity transmission scheme can be designed.

[0076] Because the channel environment for signal propagation is quasi-static, i.e., slowly changing, it is necessary to periodically update the VR domain channel map dynamically in practical applications. Specifically, the base station-side antenna array first periodically samples a small number of antenna elements to receive and measure the latest pilot signal data transmitted by the user side, thereby obtaining a newly added sampling dataset:

[0077]

[0078] in, This represents the latest sampled antenna element; then spatial interpolation is performed by combining it with the original sampled measurement data S.

[0079]

[0080] Wherein, G(·) represents the interpolation function that combines the latest and historical sampling data, with the latest sampling data typically having a greater influence on the interpolation result; finally, the VR decision is updated, and the VR domain channel map is dynamically updated based on the decision result:

[0081]

[0082] in, This indicates the updated VR judgment result.

[0083] Example 1

[0084] To further illustrate key processes such as user-side pilot transmission, antenna element sampling measurement, and antenna array spatial interpolation, this invention constructs an XL-MIMO near-field communication coverage scenario, such as... Figure 3 As shown in the figure, the left side of the diagram is the XL-MIMO antenna array, which contains 32×32 antenna elements, and the right side represents the target user within the coverage area of ​​the antenna array. The target user periodically transmits uplink pilot signals to the base station. To reduce pilot estimation overhead, the base station only samples a small number of antenna elements from the antenna array (shown as blue squares in the figure) to receive and measure the pilot signals. The remaining large number of antenna elements (shown as gray squares in the figure) perform spatial interpolation based on the sampling measurement results and their own positions to obtain the pilot reception information of the entire antenna array.

[0085] Example 2

[0086] To further expand the spatial interpolation method for antenna arrays, this invention presents a method for dividing the antenna array into sub-regions based on sampling measurement results, as illustrated in the diagram below. Figure 4 As shown in the figure, the different colors of the antenna elements represent different levels of sampled measurement values. Based on the different levels of measurement values, spatial clustering can be used to divide the entire antenna array into multiple non-overlapping subarrays. Since the channel characteristics of the antenna elements within each subarray are relatively similar, while the channel characteristics of the antenna elements within different subarrays differ significantly, the sampling antenna elements within the subarray to which the antenna element to be interpolated belongs can be preferentially selected during interpolation prediction.

[0087] Example 3

[0088] To verify the performance of the proposed antenna element sampling optimization method, simulations were performed on different sampling methods. During the simulation, the row and column sampling intervals of the XL-MIMO planar antenna array were set to 4, 6, 8, 10, and 12 antenna elements, respectively, and the number of sampling points for inverse range-weighted interpolation was set to 10. Using uniform spatial sampling as the benchmark, the performance curves of the normalized mean square error (NMSE) of the prediction results of the selected sampling antenna elements for the remaining antenna elements, as a function of the sampling interval, are shown below. Figure 5 As shown in the figure, the proposed non-uniform spatial sampling scheme combining detection and refinement performs better under different sampling intervals. Its sampling results better support the inverse distance weighted interpolation algorithm for accurate prediction of channel information, and this performance advantage increases with decreasing sampling intervals. The above analysis demonstrates that the simulation results validate the effectiveness of the proposed non-uniform spatial sampling method.

[0089] Example 4

[0090] To verify the performance of the proposed antenna element spatial interpolation method, simulations were performed on different interpolation methods. During the simulations, the row and column sampling intervals of the XL-MIMO planar antenna array were set to 4, 6, 8, 10, and 12 antenna elements, respectively, to obtain the sampled antenna elements in a uniform spatial manner. Using the traditional inverse range-weighted interpolation method as a benchmark comparison, the performance curves of the NMSE prediction results of the two spatial interpolation methods as a function of the sampling interval are shown below. Figure 6 As shown in the figure, the proposed inverse distance weighted interpolation method based on the search radius outperforms other methods under different sampling intervals, achieving more accurate channel information prediction. Furthermore, this performance advantage becomes more significant as the sampling interval decreases. The above analysis demonstrates that the simulation results validate the effectiveness of the proposed inverse distance weighted interpolation method based on the search radius.

[0091] Example 5

[0092] To further demonstrate the effectiveness of VR domain channel map construction, this invention presents a possible result for constructing a binary VR domain channel map for a base station-side XL-MIMO antenna array oriented towards user coverage, such as... Figure 7 As shown in the figure, this is a VR domain channel map. Each small square in the figure represents a pixel in the VR domain channel map. Blue squares indicate that the VR identifier of the pixel is 1, meaning that its corresponding antenna element can establish a valid communication link with the target user; gray squares indicate that the VR identifier of the pixel is 0, meaning that its corresponding antenna element cannot establish a valid communication link with the target user. This map allows for rapid querying and inference of the VR channel characteristics of the antenna array, thereby guiding the low-complexity transmission design of XL-MIMO and providing users with better communication service quality.

Claims

1. A method for constructing a user-oriented ultra-large-scale MIMO visible area map on the base station side, characterized in that, Includes the following steps: Step 1: Map the base station-side ultra-large-scale MIMO antenna array as a planar map, where each antenna element corresponds to a pixel in the map; Step 2: Use the target user to send pilot signals, and sample several antenna elements in the base station side antenna array and measure the strength of the received pilot signals of the antenna elements; Step 3: Based on the received pilot signal strength of the antenna elements, predict the received pilot signal strength of the remaining antenna elements using spatial interpolation. Specifically, after obtaining the received signal strength of the sampled antenna elements, predict the received pilot signal strength dataset obtained from the sampling measurements. The received signal strength of the remaining antenna elements is predicted using spatial interpolation techniques. ； in, and These represent the antenna elements to be interpolated. Location and received signal strength, This represents the set of antenna elements to be interpolated. Represents the interpolation function; When sampling dataset When fixed, the interpolation prediction effect and the interpolation function The only relevant factor is that, due to the discrete distribution of antenna elements and the inverse relationship between the influence of the sampled antenna elements on the interpolated antenna elements and the distance between them, the classic inverse range weighted IDW interpolation method is improved into an inverse range weighted SR-IDW interpolation algorithm based on the search radius. First, the radius of the search circle is set; second, with the target interpolated antenna element as the center, several sampled antenna elements close to the target antenna element are selected using the search circle; then, based on the selected sampled antenna elements, the inverse range weighted interpolation algorithm is used to predict the received signal strength of the target interpolated antenna element. According to the received signal strength corresponding to each antenna element in the base station antenna array, the antenna array is divided into multiple sub-arrays, and then spatial interpolation is performed in each sub-array to improve the prediction accuracy. Step 4: Determine whether an antenna unit can establish a communication link with the user based on the strength of the pilot signal received by each antenna unit, and filter out the VR identifier; Step 5: Use the VR identifier of each antenna element as the value of the corresponding pixel in the map to construct a binary channel map of the VR domain of the base station side antenna array.

2. The method for constructing a user-oriented base station-side ultra-large-scale MIMO visible area map according to claim 1, characterized in that, Step 1 specifically involves: constructing a planar channel map with the same number of pixels as the number of antenna elements in the XL-MIMO antenna array on the base station side, where the number of rows and columns of the pixels is the same as the number of rows and columns of the antenna elements; each antenna element corresponds to a pixel in the channel map, and the value of the pixel is the VR channel feature of the corresponding antenna element; the antenna array... With channel map The mapping relationship between them is as follows: ； in, and These represent the number of rows and columns of the antenna element, respectively.

3. The method for constructing a user-oriented base station-side ultra-large-scale MIMO visible area map according to claim 1, characterized in that, Step 2 specifically involves: the target user periodically sending uplink probe pilot signals to the base station's antenna array. After being received and analyzed, these signals are used to assist the base station in real-time measurement of the received signal strength of the antenna elements. The XL-MIMO antenna array samples several antenna elements. Measure its received signal strength And in combination with antenna position Constructing the sampling dataset: ； in, and These represent the sampling antenna elements. Location and received signal strength.

4. The method for constructing a user-oriented base station-side ultra-large-scale MIMO visible area map according to claim 1, characterized in that, Step 4 specifically involves comparing the received signal strength measured or predicted for 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 target user. 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... The received signal strength is The VR recognition decision expression is as follows: 。 5. The method for constructing a user-oriented base station-side ultra-large-scale MIMO visible area map according to claim 1, characterized in that, Step 5 specifically involves: obtaining the VR identifier from the decisions of each antenna element. As the value of the corresponding pixel in the map, i.e., the VR channel feature, a binary channel map of the VR domain of the base station-side antenna array is constructed by storing the VR identifier information of all pixels. ； in, Indicates the first [item] in the constructed VR domain map Line number The VR identifiers stored in each column of pixels represent the VR information of the corresponding antenna array. The VR domain channel map is dynamically updated. The base station-side antenna array first periodically samples several antenna elements to receive and measure the latest pilot signal data transmitted from the user side, thus obtaining the newly added sampled dataset. ； in, This indicates the antenna element that was sampled most recently. and These represent the sampling antenna elements. Location and received signal strength; combined with existing sampling measurement data Perform spatial interpolation: ； in, This represents the interpolation function that combines the latest sampled data with historical sampled data. Indicates the antenna element to be interpolated The location; the VR decision is updated last, and the VR domain channel map is dynamically updated based on the decision result: ； in, This indicates the updated VR judgment result.

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

7. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

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