A beacon user selection method for refining the VR boundary in an XL-MIMO system

Abstract

The application discloses a beacon user selection method for refining VR boundary in an XL-MIMO system, and comprises the following steps: selecting part of users from a candidate user set A to form an initial beacon user set B , obtaining the estimated value of the VR label of the rest of the users in B according to the distance relationship; dividing all the users in A into several non-overlapping subsets according to the VR label, so that each subset corresponds to an estimated VR region, calculating the coordinate center point of each VR region and the distance between the beacon point in the region and the center point; if the distance between a user in B and any VR region center point is less than A , and the estimated value of the VR label of the user is different from the VR label of the region, then the user can be selected as a beacon user for refining the VR boundary and added to B ; the above process is repeated until all the beacon users are selected. The application can effectively solve the problem of low VR recognition efficiency caused by too few boundary beacon users in the XL-MIMO system, and make the distribution structure of the beacon users more reasonable.

Description

Technical Field

[0001] This invention relates to the field of wireless communication multi-antenna transmission technology, and in particular to a beacon user selection method for refining the visible area (VR) boundary in an ultra-large-scale MIMO (XL-MIMO) system. Background Technology

[0002] Extra-Large MIMO (XL-MIMO) is a key technology for future 6G systems. Due to the explosive growth in the number of antenna array elements, the electromagnetic propagation environment in the near-field region exhibits non-stationary characteristics. This results in some antenna array elements being visible only to a subset of users, which we call the Visibility Region (VR). Properly utilizing the user's VR in transmission design can effectively suppress interference between users, thereby significantly reducing the transmission complexity of the XL-MIMO system. However, VR transmission presupposes accurate VR recognition; therefore, to utilize VR, we first need to identify the user's VR information.

[0003] Estimating user VR at known locations by directly utilizing existing location VR relationships can effectively reduce channel overhead and is a major scheme for VR identification in existing technologies. The identification error of this scheme mainly comes from misjudgment of VR tags at the VR region boundary. However, given the limited number of beacons, the current mainstream method of randomly selecting beacons for users cannot solve this problem.

[0004] One feasible approach is to increase the proportion of beacon users selected near the VR boundary, i.e., to refine the VR boundary, thereby effectively reducing the occurrence of VR tag misjudgments. However, given the unknown VR area and limited number of beacons, how to accurately and efficiently select beacon users at the VR boundary remains a problem worth exploring. Summary of the Invention

[0005] The problem to be solved by this invention is to provide a beacon user selection method for refining VR boundaries in an XL-MIMO system, which effectively refines VR boundaries and improves the accuracy of VR user recognition in the system.

[0006] The technical solution provided by this invention is: a beacon user selection method for refining VR boundaries in an XL-MIMO system, comprising the following steps:

[0007] Step 1: Establish a candidate user set It stores all user data and is used to generate candidate beacon user sets. The initial user set Let T be an empty set; set the total number of transmission batches under the same propagation environment characteristics to T.

[0008] Candidate User Set In the diagram, each element 'a' represents a user, and the user's location coordinates are... Represented using two-dimensional coordinates (x, y).

[0009] beacon user set Depend on and It consists of two subsets, the subsets The subset includes all randomly selected beacon users. Includes all beacon users selected when refining VR boundaries.

[0010] Step 2: During each batch of transmission, from the candidate user set Randomly select several user data points and add them to the beacon user set. and from user set Subtract from the current number of transmissions (t), the current selectable user set is... .

[0011] Specifically, it has a refined scaling factor. This is used to allocate the number of beacon users in each batch for random probing and boundary refinement, respectively. Each beacon user is used for detection, and the data is stored in a subset. ,back Individual beacon users are used to refine VR boundaries and stored in a subset. .

[0012] Step 3: Starting from the second transmission, during each batch of transmission, the beacon users obtained in the previous batch (i.e., batch t-1) send probe pilots and receive base station feedback signals to obtain the VR tags of the beacon users in batch t-1.

[0013] The methods for obtaining beacon user VR tags include:

[0014] By measuring the pilot signal strength received by each antenna element on the base station array side, if the received signal strength of a certain antenna element is greater than a set threshold, then the antenna element is visible to the beacon user transmitting the pilot signal; otherwise, it is not visible. The user's VR tag information is a combination of information of a group of visible antenna elements.

[0015] Step 4: From the user set Find the optional user set in Each user is located near the nearest beacon user, and the user set is... The VR tags of the beacon users are used as the current selectable user set. Estimated values ​​of VR tags for candidate users.

[0016] Specifically, for the optional user set Any user a in the list, The following formula (1) is used to obtain the data from the user set. Find the beacon user closest to its location. :

[0017] ;

[0018] In the formula, Let be the coordinates of user 'a'. For users The coordinates of user b in the China Beacon system.

[0019] User A's estimated VR tag value is The result can be obtained from the following formula:

[0020] ;

[0021] In the formula, To find the beacon user whose location is closest to user a based on the formula above. The VR tag.

[0022] Step 5: Based on the different VR tags, the user set All users are divided into several non-overlapping subsets, and each subset uniquely corresponds to an estimated user VR region.

[0023] User set All users are divided into several non-overlapping subsets, including: based on the user set The differences and similarities of VR tags among various users will be used to aggregate user data. Divided into Subset , Each subset corresponds to an estimated VR region and has a unique VR region label. .

[0024] Step 6: Calculate the coordinate center point of each estimated VR area, and the farthest distance between the beacon user within the area and the center point of that area. .

[0025] The calculation of the coordinate center point of each estimated VR region includes:

[0026] If the subset of users in the VR region contains only one beacon user, then the region does not have a center point; if the region contains the number of beacon users... Then the coordinates of the center point of the region The following formula can be used to obtain:

[0027] ;

[0028] In the formula, Let be the coordinates of the k-th beacon user in this region.

[0029] Calculate the farthest distance between beacon users and the center point of the area. ,include:

[0030] for There are VR regions with a center point, the m-th region. The user furthest from the center point for:

[0031] ;

[0032] Longest distance for:

[0033] ;

[0034] In the formula, For users coordinates To obtain the region according to the above formula The coordinates of the center point.

[0035] Step 7: If the optional user set The distance from a user to the center point of any VR area is less than If a user's estimated VR tag value differs from the VR tag value for that region, then that user can be added to the user set as a beacon user for refining VR boundaries. .

[0036] Specifically, for the optional user set For any user a in the dataset, the estimated VR label value for user a is... User A moves to the center point of any VR area. distance Calculate as follows:

[0037] ;

[0038] like ,at the same time Then user a can be added to the user set as a beacon user of the refined VR boundary. .

[0039] Step 8: Repeat step 7 above until all beacon users required for the current transmission batch t are selected.

[0040] Step 9: Repeat steps 2 through 8 until all beacon users are selected after T transmissions, thus obtaining the final beacon user set. .

[0041] Compared with the prior art, the beneficial effects of the above technical solution adopted in this invention are as follows:

[0042] 1. The beacon user selection method for refining VR boundaries in the XL-MIMO system proposed in this invention differs from random beacon user selection. The beacon users selected using the scheme of this invention are mainly distributed near the boundary of the VR region. This beacon user distribution method is more conducive to highlighting VR boundary details, providing more VR identification reference information for unknown VR users, and effectively suppressing VR label misjudgment of users near the boundary, thereby helping to improve the accuracy of VR identification of system users.

[0043] 2. This invention is based on an iterative optimization approach of selecting beacon users near the VR boundary one after another. Within a relatively stable propagation environment, beacon users are selected in multiple batches. After each batch of beacon users finishes probing the VR tag, the current beacon users are immediately grouped according to the VR tag information, so that each group uniquely corresponds to a VR region. By continuously refining the grouping, the VR boundary can be effectively refined, so that the VR boundary becomes clearer and clearer as the number of transmission batches increases.

[0044] 3. This invention is based on the idea of ​​jointly estimating the VR region range and user VR tags. On the one hand, it estimates the distribution range of each VR by calculating the region's center point and maximum radius, so as to predict whether a candidate user is within a certain VR region range based on location coordinate information. On the other hand, it determines whether the user is near the boundary of the VR region by comparing the estimated VR tag value of the current candidate user with the VR tag of the region to which the user belongs. By using both location distance and VR tag as indicators for judgment, it helps to improve the accuracy of selecting beacon users near the boundary when the VR region is unknown. Attached Figure Description

[0045] Figure 1 A schematic diagram of the temporal transmission structure of the beacon user selection method for refining VR boundaries according to the present invention;

[0046] Figure 2 This is a flowchart illustrating the beacon user selection method for VR boundaries as detailed in this invention.

[0047] Figure 3 In the figures, (a) and (b) represent the beacon user location distribution selected by the existing random method and the beacon user location distribution selected by the beacon user selection method of the present invention that refines the VR boundary, respectively.

[0048] Figure 4A comparison chart showing the VR recognition accuracy achieved by the beacon user selection method for refining VR boundaries in this invention and existing solutions;

[0049] Figure 5 This is a flowchart illustrating the steps of the beacon user selection method for refining the VR boundary in this invention. Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the examples involved in the present invention, and all non-innovative embodiments implemented by other researchers in the art based on these embodiments are within the protection scope of the present invention. Furthermore, the step numbers in the embodiments of the present invention are only set for ease of explanation, and no limitation is made on the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0051] Since current technologies lack methods for refining VR boundaries when the VR region's extent is unknown, this invention proposes a specific and feasible method for selecting beacon users to refine VR boundaries. This method groups known beacon users based on VR tag information, with each group corresponding to a specific VR region. By continuously refining these groups, the VR boundaries can be effectively refined, thereby suppressing misjudgments of VR tags and achieving better VR recognition.

[0052] like Figure 5 As shown, this invention provides a specific method for beacon user selection in an XL-MIMO system to refine the VR boundary, comprising the following steps:

[0053] Step 1: Establish a complete set of candidate users Used to select beacon users, set Each element 'a' represents a user, and the user's location coordinates are shown in the table. It can be represented using two-dimensional coordinates (x, y);

[0054] like Figure 1 As shown, within a time period when the characteristics of the propagation environment remain relatively stable, the number of transmission batches is set to T, that is, the beacon users are selected in T batches, and the number of beacon users selected in each batch is K;

[0055] Initialize beacon user set An empty set, a set Depend on and It consists of two subsets, where the subset Used to store all randomly selected beacon users, a subset Used to store all beacon users selected when refining VR boundaries;

[0056] Step 2: Before each transmission batch begins, a judgment is made:

[0057] If it is the initial batch, then from the set K users are randomly selected and added to a subset. and using user sets Subtract the selected beacon user set This allows us to obtain the current set of available users. ;

[0058] If not the initial batch, then from Random selection Individual user data, add it to a subset The original user set is also used. Subtract the selected beacon user set This allows us to obtain the current set of available users. ;

[0059] Step 3: Starting from the second transmission batch, each time a new beacon user acquired in the previous batch is used to send a probe pilot and receive a feedback signal from the base station, thereby obtaining the VR tag of the beacon user;

[0060] Step 4: From the set respectively Find the optional user set in The nearest beacon to each user in the set, and the collection China Telecom VR Tags as an Optional User Set Estimated values ​​of VR tags for Chinese users;

[0061] Set the refinement scale factor This is used to allocate the number of beacon users in this batch for random probing and boundary refinement, for example, the previous Each beacon user is used for detection, and the data is stored in a subset. ,back Individual beacon users are used to refine VR boundaries and stored in a subset. ;

[0062] Reusing the original user set Subtract the selected beacon user set This allows you to obtain the selectable user set for the current transmission batch. ;

[0063] Step 5: Based on the different VR tags, group the collections. All users are divided into Each subset is a unique subset that does not overlap with the others, and each subset uniquely corresponds to an estimated VR region;

[0064] Find all subsets with more than 1 element, where each subset corresponds to a VR region, and there are a total of One region;

[0065] Step 6: Calculate the coordinate center point of each VR region, and the distance of the beacon point in the region from the center point. ;

[0066] Step 7: If an optional user set is available The distance from a user to the center point of any VR area is less than If a user's estimated VR tag value differs from the VR tag value of the region, then that user can be considered a beacon user for refining VR boundaries and added to the subset. ;

[0067] Reference Figure 2 As shown in the flowchart, step 7 above is repeated iteratively until all beacon users in the current batch are selected; then steps 2 to 7 above are repeated iteratively until all beacon users are selected, thus obtaining the final beacon user set. .

[0068] This embodiment selects the simulation results of beacon user location distribution as follows: Figure 3 As shown in (b), with Figure 3 As shown in Figure (a), the results of randomly selecting beacon users can be compared, and it can be seen that the technical solution of selecting VR boundary beacon users in batches proposed in this invention can effectively select beacon users at the VR boundary.

[0069] In addition, the simulation results of selecting beacon users for VR recognition in this embodiment are as follows: Figure 4 As shown in the figure, the refined VR boundary beacon user selection technology proposed in this invention is significantly better than the existing random beacon user selection method, and can significantly improve the VR recognition accuracy.

[0070] In addition to the embodiments described above, the present invention may have other implementations. All technical solutions formed by equivalent substitution or equivalent transformation fall within the scope of protection claimed by the present invention.

[0071] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed herein can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the present invention.

Claims

1. A beacon user selection method for refining VR boundaries in an XL-MIMO system, characterized in that, Includes the following steps: Step 1: Establish a candidate user set It stores all user data and is used to generate candidate beacon user sets. The initial user set Let T be the total number of transmission batches under the same propagation environment characteristics, and K be the number of beacon users selected in each batch. The beacon user set ,Depend on and It consists of two subsets, subset Includes all randomly selected beacon users, a subset Includes all beacon users selected when refining VR boundaries; Step 2: Before each batch of transmission, a judgment is made. If it is the initial batch, it is selected from the candidate user set. Randomly select several user data points and add them to a subset. and from the candidate user set Subtract from the current number of transmissions (t), the current selectable user set is... ; If it is not the initial batch, set a refined scaling factor. From the optional user set Random selection before Beacon user data, added to subset ; Step 3: Starting from the second transmission, during each batch of transmission, use the beacon users obtained in the previous batch (i.e., batch t-1) to send probe pilots and receive base station feedback signals to obtain the VR tags of the beacon users in batch t-1. Step 4: From the user set Find the optional user set in Each user is located near the nearest beacon user, and the user set is... The VR tags of the beacon users are used as the current selectable user set. Estimated values ​​of VR tags for candidate users; Step 5: Based on the different VR tags, the user set All users are divided into several non-overlapping subsets, and each subset uniquely corresponds to an estimated user VR region; Step 6: Calculate the coordinate center point of each estimated VR area, and the farthest distance between the beacon user within the area and the center point of that area. ; Step 7: If the optional user set The distance from a user to the center point of any VR area is less than If the estimated VR tag value differs from the VR tag value of the region, then the user is considered a beacon user for refining the VR boundary and added to the subset. ; Step 8: Repeat step 7 above until all beacon users required for the current transmission batch t are selected; Step 9: Repeat steps 2 through 8 until all beacon users are selected after T transmissions, thus obtaining the final beacon user set. .

2. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 1, characterized in that, The candidate user set In the diagram, each element 'a' represents a user, and the user's location coordinates are... Represented using two-dimensional coordinates (x, y).

3. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 2, characterized in that, The refinement scaling factor This is used to allocate the number of beacon users in each batch for random probing and boundary refinement, respectively. Each beacon user is used for detection, and the data is stored in a subset. ,back Individual beacon users are used to refine VR boundaries and stored in a subset. .

4. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 3, characterized in that, The method for obtaining the beacon user VR tag in step 3 includes: By measuring the pilot signal strength received by each antenna element on the base station array side, if the received signal strength of a certain antenna element is greater than a set threshold, then the antenna element is visible to the beacon user transmitting the pilot signal; otherwise, it is not visible. The user's VR tag information is a combination of information of a group of visible antenna elements.

5. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 4, characterized in that, In step 4, for the optional user set For any user a in the set of users, use the following formula (1) to select from the user set. Find the beacon user closest to its location. : （1） In the formula, Let be the coordinates of user 'a'. For users The coordinates of user b in the China Beacon system.

6. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 5, characterized in that, The method for solving the estimated user VR tag value in step 4 includes: For the optional user set Any user a, Its VR tag estimate is The result can be obtained from the following formula: （2） In the formula, To find the beacon user whose location is closest to user a based on the formula (1) above. The VR tag.

7. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 6, characterized in that, In step 5, the user set All users are divided into several non-overlapping subsets, including: According to the user set The differences and similarities of VR tags among various users will be used to aggregate user data. Divided into Subset , Each subset corresponds to an estimated VR region and has a unique VR region label. .

8. The beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 7, characterized in that, In step 6, calculating the coordinate center point of each estimated VR region includes: If the subset of users in the VR region contains only one beacon user, then the region does not have a center point; if the region contains the number of beacon users... Then the coordinates of the center point of the region The following formula can be used to obtain: （3） In the formula, Let be the coordinates of the k-th beacon user in this region.

9. A beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 8, characterized in that, In step 6, the farthest distance between the beacon user and the center point of the calculation area is determined. ,include: for There are VR regions with a center point, the m-th region. The user furthest from the center point for: （4） Longest distance for: （5） In the formula, For users coordinates To obtain the region according to the above formula (3) The coordinates of the center point.

10. A beacon user selection method for refining VR boundaries in an XL-MIMO system according to claim 8 or 9, characterized in that, In step 7, for the optional user set For any user a in the dataset, the estimated VR label value for user a is... User A moves to the center point of any VR area. distance Calculate as follows: (6) like ,at the same time Then user a is added to the user set as a beacon user of the refined VR boundary. .

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