Ultra-large-scale MIMO Visual Area Recognition Method for Limiting Beacon Spacing

Abstract

The present invention provides a method for identifying a visual area of a very large-scale MIMO by limiting beacon spacing, belonging to the technical field of multi-antenna transmission in wireless communication. The method includes the following steps: Step 1, establish a location-VR data set S; Step 2, set an upper limit D max and a lower limit D min for the position spacing of beacon users; Step 3, select a small amount of location-VR data from S as initial elements to form a beacon user set B; Step 4, each time select a data s from S that satisfies the condition that the distance from all beacon positions in B is within the range of [D min , D max ; Step 5, repeat Step 4, perform loop iteration, and expand the set B by one element each time; Step 6, obtain a position area-VR data set #imgabs0#; Step 7, obtain the corresponding VR information label. The present invention can optimize the method for identifying the visual area based on user position information in a very large-scale MIMO array.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication multi-antenna transmission, and particularly to a method for identifying the visual region of ultra-large-scale MIMO with a limited beacon spacing. Background Art

[0002] Ultra-large-scale multiple-input multiple-output (MIMO) is a hot technology for the sixth-generation mobile communication (6G). The visual region (VR) is a new channel characteristic that appears in its ultra-large-aperture array deployment mode, that is, users at different positions can see different parts of the overall antenna array respectively. By selecting VR orthogonal users for joint transmission design, the communication complexity can be greatly reduced. Therefore, the identification of the VR distribution of users in the ultra-large-scale MIMO system is crucial. Currently, VR identification usually randomly selects some users (beacon users) from a large number of users to send uplink pilots, measures their position coordinates and the corresponding user VRs on the antenna array, and constructs a position-VR data set for reference. However, the distribution of randomly selected beacon users is neither uniform nor controllable, which inevitably leads to a high identification accuracy in the area where beacon users are densely distributed, but may cause resource waste; while the identification accuracy in the area where beacon users are sparsely distributed is poor, resulting in transmission problems. Therefore, how to uniformly and reasonably select beacon users to achieve efficient VR identification is still an urgent problem to be solved. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for identifying the visual region of ultra-large-scale MIMO with a limited beacon spacing, which can optimize the method for identifying the visual region based on user position information in the ultra-large-scale MIMO array.

[0004] The inventive concept of the present invention is as follows: A method for identifying the visual region of ultra-large-scale MIMO with a limited beacon spacing provided by the present invention is a method for identifying the visual region of ultra-large-scale MIMO users based on user position information, mainly based on randomly selecting beacon users to send uplink pilot signals for detection; however, when the number of users is large, in order to ensure the orthogonality between uplink pilots, it is necessary to limit the number of sequences sending uplink pilots on the same time-frequency resource. Therefore, when beacon users send a limited number of uplink pilots, they can identify as many different user visual regions as possible. However, the method of randomly selecting beacon users to send uplink pilots is not efficient because the randomly selected beacon users are not evenly distributed in terms of position, which will lead to a high identification accuracy in the area where beacon users are densely distributed, while the identification accuracy in the area where beacon users are sparsely distributed decreases, and it is easy to cause misjudgment.

[0005] The object of the present invention is to solve the problem of uneven distribution of beacon user selection. By restricting a certain distance between the positions of each beacon user, the distribution of beacon users is made more uniform and reasonable. On the premise of a limited number of available beacon users, a better position-VR dataset is provided as a reference for a large number of ordinary users with unknown VR information, and the accuracy of VR recognition in a very large-scale MIMO system is improved to the greatest extent possible.

[0006] In order to achieve the above object of the invention, the technical solution adopted by the present invention is specifically as follows: A method for identifying a very large-scale MIMO visible area by limiting beacon spacing, comprising the following steps:

[0007] S1. First, establish a position-VR dataset S for candidate beacon user data. Each element s in the set S represents a candidate beacon user, and the position information of s is marked using two-dimensional coordinates (x, y), and the known VR information corresponding to s is marked using a vector label l.

[0008] S2. Set the number N of beacon users to be selected, and reasonably set the upper limit D of the position spacing of beacon users max and the lower limit D min .

[0009] The setting of the upper limit threshold D max is related to the distribution range of system users, that is, the application scenario. The larger the distribution range, the larger the value of D max should be, so as to achieve a larger detection range; the smaller the distribution range, the smaller the value of D max should be, so as to obtain more detection details. The setting of the lower limit threshold D min is related to the number N of beacons. When N is larger, D min should be set smaller to ensure that a sufficient number of beacon users can be selected from the dataset S; when N is smaller, D min should be set larger to ensure that the limited beacons selected can detect a larger VR range. All in all, the setting of the threshold interval [D min , D max should take the primary premise of ensuring the uniform distribution of all beacon users as much as possible.

[0010] S3. After reasonably setting the beacon spacing threshold, select a small amount of position-VR data from S as initial elements to form the initial beacon user set B.

[0011] S4. Then, each time select a data s from S that satisfies the distance from all beacon positions in B within the range of [D min , D max , and add it to the set B, so as to achieve the purpose of expanding the beacon user set.

[0012] S5. Iterate cyclically according to the above step S4, expanding set B by one element each time until the number of beacon users in B reaches N. The set B at this time is the optimized beacon user set for us.

[0013] As the number of selected beacon users increases, the threshold interval [D min , D max where the beacon spacing is located will also be adjusted dynamically to ensure that a sufficient number of beacon users can be selected from the dataset S.

[0014] S6. Based on this beacon user set B, divide all user distribution areas according to the location information of the beacon users, and merge the location areas with the same VR information, thereby obtaining the location area - VR dataset.

[0015] S7. A large number of ordinary users with unknown VR information will use the dataset as a reference, find the location area where they are located according to their own location information, and thus obtain the corresponding VR information, so as to achieve the goal of optimizing VR recognition.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] (1). The present invention proposes a method for identifying the visual area of ultra - large - scale MIMO by limiting the beacon spacing. The concept of uniform distribution is applied to the identification of the visual area of ultra - large - scale MIMO. By setting a threshold to limit the position spacing between beacon users, several beacon users are selected from the candidate user set that meets the position relationship. Then, the beacon users are used to send uplink pilots to detect the VR corresponding to their locations, and on this basis, a location - VR dataset is established. A large number of ordinary users with unknown VR information use this dataset as a reference, and can determine the corresponding VR according to their location information, so as to achieve the goal of optimizing VR recognition.

[0018] (2). Based on the improved idea of applying the limitation of the spacing between beacon users to the identification of the visual area of ultra - large - scale MIMO, the present invention gives a method for selecting the beacon spacing threshold. Referring to the user distribution range of the system and the required number of beacon users, through reasonable threshold design, the selection of beacon users will neither be unable to insert more beacon users due to too large a spacing, nor will the detection area range of beacon users be too small due to too small a position spacing, so that the distribution range of beacon users is more reasonable. The simulation results show that reasonable threshold design helps to improve the accuracy of VR recognition.

[0019] (3) Based on the improved idea of applying the limited beacon user spacing to the identification of the visual area of very large-scale MIMO, the present invention proposes a method for dynamically adjusting the beacon spacing threshold. As the number of screened beacon users increases, the threshold interval is adjusted in a timely manner, which can enable more beacon users to be selected in the later stage. This not only helps to depict the VR details but also helps to prevent the phenomenon that more beacon users cannot be inserted due to excessive spacing. The simulation results show that dynamically adjusting the beacon spacing threshold is beneficial to improving the accuracy of VR identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention.

[0021] Figure 1 It is a flowchart for identifying the VR of users with limited beacon spacing in the present invention.

[0022] Figure 2 It is a flowchart of the optimization algorithm for generating the beacon user set in the present invention.

[0023] Figure 3 It is a comparison chart of the VR identification accuracy achieved by the method of the present invention and the existing general method.

[0024] Figure 4 It is a comparison chart of the VR identification accuracy achieved by the method of dynamically adjusting the spacing and the method of fixed spacing in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. Of course, the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] Embodiment 1

[0027] Referring to Figure 1 and Figure 2 , the present invention provides a method for identifying the visual area of very large-scale MIMO with limited beacon spacing, including the following steps:

[0028] Step 1: Establish a position-VR data set S for candidate beacon user data. Each element s in the set S represents a candidate beacon user. The position information of s is marked with two-dimensional coordinates (x, y), and the known VR information corresponding to s is marked with a vector label l.

[0029] Step 2: Set the number N of beacon users to be selected and reasonably set the upper limit D max and the lower limit D min, specifically including the following steps:

[0030] 2a. The setting of the upper threshold D max is related to the distribution range of system users (i.e., the application scenario). The larger the distribution range, the larger the value of D max should be, so as to achieve a larger detection range; the smaller the distribution range, the smaller the value of D max should be, so as to obtain more detection details;

[0031] 2b. The setting of the lower threshold D min is related to the number of beacons N. When N is large, D min should be set smaller to ensure that a sufficient number of beacon users can be selected from the dataset S; when N is small, D min should be set larger to ensure that the limited beacons selected can detect a larger VR range;

[0032] 2c. The setting of the threshold interval [D min , D max should take the primary premise of ensuring the uniform distribution of all beacon users as much as possible. Here, let [D min , D max = [10, 500];

[0033] Step 3. Select the first n position-VR data from S as the initial elements to form the initial beacon user set B. Here, the value of n should not be too large. Let's take n = 10;

[0034] Step 4. Each time, select a data s from S that satisfies the distance from all beacon positions in B is within the range of [D min , D max , and add it to the set B to achieve the purpose of expanding the beacon user set. Specifically, it includes the following steps:

[0035] 4a. Calculate the distances between the position where s is located and all beacon positions in B respectively;

[0036] 4b. Screen out the smallest value among all the distances and denote it as d min ;

[0037] 4c. If d min satisfies the judgment relationship D min ≤ d min ≤ D max , then add s to the set B and increase the value of n by 1; otherwise, discard the data s and re-execute step 4;

[0038] Step 5: Repeat Step 4 in a loop. Each time, expand set B by one element until the number of beacon users in B reaches N, that is, n = N. At this time, set B is the optimized beacon user set.

[0039] Step 6: Based on this beacon user set B, divide all user distribution areas according to the location information of the beacon users, and merge the location areas with the same VR information to obtain a location area - VR data set.

[0040] Step 7: A large number of ordinary users with unknown VR information use the data set as a reference, find the location area where they are located according to their own location information, and obtain the corresponding VR information therefrom, so as to achieve the VR recognition optimization goal.

[0041] Simulate this embodiment according to the above steps. Set the initial training sampling number (beacon users) to 500, and then add 500 sampling points in each subsequent round, for a total of 7 additional rounds. Under the same conditions, test the user VR recognition accuracy rates under the original beacon selection scheme (random selection) and the optimized beacon selection scheme (limiting the beacon spacing) respectively, and simulate 100 times in a loop and take the average value to reduce the influence of random errors. The simulation results are as Figure 3 shown. It can be seen from the figure that the optimized scheme of limiting the beacon spacing proposed by the present invention is significantly better than the existing method of randomly selecting beacon users, and the VR recognition efficiency is significantly improved.

[0042] Embodiment 2

[0043] Referring to Figure 4 and Embodiment 1, the present invention provides a method for identifying the visible area of a very large-scale MIMO with limited beacon spacing. The setting of the threshold interval [D min , D max mentioned in Step 2 can be dynamically adjusted as the number of input beacons increases, so as to further improve the VR recognition efficiency:

[0044] For example, when the upper limit D max remains unchanged, every time 500 beacon users are added, the lower limit D min is dynamically adjusted once. The specific steps are as follows:

[0045] Step 1: Set the initial threshold interval [D min , D max = [10, 500]. Select 500 beacon users that meet the interval requirements from set S to form a beacon user set B, and use this as a reference to achieve VR recognition.

[0046] Step 2: Reset the lower bound threshold D min= 7, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0047] Step 3. Reset the lower bound threshold D min = 5, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0048] Step 4. Reset the lower bound threshold D min = 4, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0049] Step 5. Reset the lower bound threshold D min = 3.5, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0050] Step 6. Reset the lower bound threshold D min = 3, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0051] Step 7. Reset the lower bound threshold D min = 2.5, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference;

[0052] Step 8. Reset the lower bound threshold D min = 2, screen out 500 beacon users that meet the interval requirements from the set S again and add them to the beacon user set B, and implement VR recognition with this as a reference.

[0053] Similarly, set the initial training sampling number (beacon users) to 500, and add 500 sampling points in each subsequent round, with a total of 7 additional rounds. Simulate this embodiment according to the above steps. The optimization method of dynamically adjusting the lower bound threshold to implement beacon user screening, compared with the fixed lower bound threshold D min = 2 of the basic method, test the user VR recognition accuracy under the fixed beacon spacing scheme (basic scheme) and the adjusted beacon spacing scheme (optimization scheme) respectively under the same conditions. Similarly, simulate 100 times in a loop and take the average value to reduce the influence of random errors. The simulation results are as Figure 4 shown. It can be seen from the figure that the optimization scheme of dynamically adjusting the spacing threshold proposed by the present invention is significantly better than the original fixed threshold method, and the VR recognition efficiency is improved when the number of available beacons is limited.

[0054] The foregoing are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying the visible area of a very large-scale MIMO by limiting the beacon spacing, characterized in that Including the following steps: Step 1: In a given very large-scale MIMO environment, the mobile station randomly selects some beacon users from a large number of users, measures their position coordinates and sends uplink pilots, then measures the intensity of the received pilot signals on the base station side antenna array, determines the visible region (VR) corresponding to the user position, thereby establishing a position-VR data set S for candidate beacon user data; Step 2, set the number N of beacon users to be selected, and reasonably set the upper limit D and lower limit D of the position spacing of beacon users max ; min ; Step 3: Randomly select a small amount of position-VR data from S as initial elements to form an initial beacon user set B, and realize the initial division of the user VR based on the position according to the beacon user set B; Step 4: Each time, select a piece of data s from S that satisfies the condition that the distances to all beacon positions in the beacon user set B are within the range of [D min , D max , and add it to the beacon user set B, so as to achieve the purpose of expanding the beacon user set; Step 5: Repeat Step 4 in a loop. Each time, expand the beacon user set B by one element until the number of beacon users in the beacon user set B reaches N. At this time, the beacon user set B is the optimized beacon user set, and this beacon user set B contains beacon positions and corresponding VR label information; Step 6: Based on this beacon user set B, divide all user distribution areas according to the location information of beacon users, and merge the location areas with the same VR tag information together, so as to obtain the location area - VR data set Step 7. Ordinary users with a large amount of unknown VR information use the data set as a reference, and based on their own location information, find the beacon user s closest to them from . The location area where s is located is determined as the location area where the VR unknown user is located, and the corresponding VR information is obtained therefrom, so as to achieve the VR recognition optimization goal.

2. The method for identifying the visual area of a very large scale MIMO by defining beacon spacing according to claim 1, characterized in that The establishment of the data set S in the said Step 1 includes: Each element s in the set S represents a candidate beacon user. The position information of s is marked with two-dimensional coordinates (x, y), and the known VR information corresponding to s, the visible region of the user on the base station antenna array side, is marked with a vector label l.

3. The method for identifying the visual area of a very large scale MIMO by defining the beacon spacing according to claim 1, characterized in that, Set the upper limit D of the beacon spacing in step 2 max including: Upper limit threshold D max is set according to the distribution range of system users, i.e., the application scenario. The larger the distribution range, the larger the value of D max should be, so as to achieve a larger detection range; the smaller the distribution range, the smaller the value of D max should be, so as to obtain more detection details.

4. The method for identifying the visual area of a very large scale MIMO by defining the beacon spacing according to claim 1, characterized in that, Setting the lower limit D of the beacon spacing in step 2 min including: Lower threshold D min is set related to the number of beacons N. When N is large, D min should be set smaller to ensure that a sufficient number of beacon users can be selected from the dataset S; while when N is small, D min should be set larger to ensure that the selected limited beacons can detect a larger VR range.

5. The method for identifying the visual area of a very large scale MIMO by defining the beacon spacing according to claim 1, characterized in that, The setting of the beacon spacing threshold in the above step 2 further includes: the setting of the threshold interval [D min , D max , and the primary premise should be to ensure the uniform distribution of all beacon users.

6. The method for identifying the visual area of a very large scale MIMO by defining beacon spacing according to claim 1, characterized in that The setting of the beacon spacing threshold in the said Step 2 further includes: As the number N of beacons selected increases, the range of the threshold interval [D min , D max can be dynamically adjusted, so as to ensure that the VR detected by the selected finite beacons is more accurate.

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