CN116232496A - Ultra-large-scale MIMO visible area identification method for limiting beacon interval - Google Patents

Ultra-large-scale MIMO visible area identification method for limiting beacon interval Download PDF

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CN116232496A
CN116232496A CN202310313194.XA CN202310313194A CN116232496A CN 116232496 A CN116232496 A CN 116232496A CN 202310313194 A CN202310313194 A CN 202310313194A CN 116232496 A CN116232496 A CN 116232496A
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beacon
user
users
max
interval
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厉凯
高锐锋
李业
胡英东
王珏
杨永杰
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Nantong University
Nantong Research Institute for Advanced Communication Technologies Co Ltd
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Nantong Research Institute for Advanced Communication Technologies Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a super-large-scale MIMO visible area identification method for limiting beacon intervals, and belongs to the technical field of wireless communication multi-antenna transmission. The method solves the problem of how to optimally select the beacon position to improve the identification accuracy of the user visible area when the number of beacons is small in the ultra-large-scale MIMO system. The technical proposal is as follows: the method comprises the following steps: step 1, establishing a position-VR data set S; step 2, setting the upper limit D of the position interval of the beacon user max And lower limit D min The method comprises the steps of carrying out a first treatment on the surface of the Step 3, selecting a small amount of position-VR data from the S as an initial element to form a beacon user set B; step 4, selecting one from S each time to satisfy the condition that the distance between the S and all the beacon positions in B is in [ D ] min ,D max ]Data s in range; step 5, repeatedly executing the step 4, and circularly iterating, wherein an element is expanded for the set B each time; step 6, obtaining a position area-VR data set
Figure DDA0004149253580000011
And 7, obtaining a corresponding VR information label. The beneficial effects of the invention are as follows: the method of identifying the visual area based on the user location information may be optimized in a very large-scale MIMO array.

Description

Ultra-large-scale MIMO visible area identification method for limiting beacon interval
Technical Field
The invention relates to the technical field of wireless communication multi-antenna transmission, in particular to a super-large-scale MIMO visible area identification method for limiting beacon intervals.
Background
Ultra-large-scale multiple-input multiple-output (MIMO) is a sixth generation mobile communication (6G) hotspot technology, while the Visible Region (VR) is a new channel characteristic that occurs in the ultra-large aperture array deployment manner, i.e., users at different locations each see a different portion of the overall antenna array. By selecting VR orthogonal user combinations for transmission design, communication complexity can be greatly reduced, so that identification of user VR distribution in a super-large-scale MIMO system is important. Current VR identification typically randomly selects some users (beacon users) from a large number of users to transmit uplink pilots, measures their location coordinates and the corresponding user VR on the antenna array, and constructs a location-VR dataset for reference. However, the distribution of the beacon users is neither uniform nor controllable, which necessarily results in higher accuracy of identifying the areas with dense beacon users, but may cause resource waste; and the beacon users have poor accuracy in identifying the sparse areas, and thus cause transmission trouble. Therefore, how to uniformly and reasonably choose beacon users for efficient VR identification remains a challenge to be addressed.
Disclosure of Invention
The invention aims to provide a method for identifying a visible area of ultra-large-scale MIMO with limited beacon intervals, which can optimize the method for identifying the visible area based on user position information in an ultra-large-scale MIMO array.
The invention is characterized in that: the invention provides a super-large-scale MIMO visible area identification method for limiting beacon intervals, which is a super-large-scale MIMO user visible area identification method based on user position information, and mainly comprises the steps of randomly selecting a beacon user to send an uplink pilot signal for detection; however, when the number of users is large, in order to ensure orthogonality between uplink pilots, the number of columns of uplink pilots transmitted on the same time-frequency resource must be limited, so that when beacon users transmit limited uplink pilots, as many different user visible areas as possible can be identified. However, the method for randomly selecting the beacon users to transmit the uplink pilot frequency is not efficient, because the randomly selected beacon users are not uniformly distributed in position, which results in higher accuracy of identifying the region with dense beacon users, and reduced accuracy of identifying the region with sparse beacon users, and is easy to cause erroneous judgment.
The invention aims to solve the problem of uneven selection distribution of the beacon users, ensures that the distribution of the beacon users is more uniform and reasonable by limiting the mode of keeping a certain distance among the positions of each beacon user, provides better position-VR data sets for a large number of common users with unknown VR information as a reference on the premise of limited number of available beacon users, and improves the accuracy of VR identification in a super-large-scale MIMO system to the greatest extent.
In order to achieve the aim of the invention, the invention adopts the technical scheme that: a super-large-scale MIMO visible area identification method for limiting beacon intervals comprises the following steps:
s1, firstly, a position-VR data set S for candidate beacon user data is established, each element S in the set S represents a candidate beacon user, the position information of the S is marked by using two-dimensional coordinates (x, y), and the known VR information corresponding to the S is marked by using a vector label l.
S2, setting the number N of the beacon users to be selected, and reasonably setting the upper limit D of the position spacing of the beacon users max And lower limit D min 。
Upper threshold D max Is related to the distribution range of the system user, namely the application scene, and D is the greater the distribution range is max The larger the value of (2) is, the larger the detection range is; the smaller the distribution range, the D max The smaller the value of (c) should be, so that more detection details are obtained. Lower threshold D min Is set in relation to the number of beacons N, D when N is large min Should be set smaller to ensure that a sufficient number of beacon users can be taken out of the data set S; and when N is smaller, D min Should be set larger to ensure that the selected limited beacon can detect a larger VR range. In summary, the threshold interval [ D min ,D max ]It is a primary premise to ensure as even a distribution of all beacon users as possible.
S3, after the beacon interval threshold value is reasonably set, a small amount of position-VR data is selected from the S as an initial element to form an initial beacon user set B.
S4, selecting one from S each time to satisfy the distance between the two beacon positions in the B and the distance between the two beacon positions in the B min ,D max ]And adding the data s in the range into the set B, thereby achieving the purpose of expanding the beacon user set.
S5, according to the step S4, iterating circularly, expanding an element for the set B each time until the number of the beacon users in the set B reaches N. The set B at this time is the beacon user set after optimization.
As the number of selected beaconing users increases, the beacon interval is within the threshold interval [ D ] min ,D max ]And also dynamically adjusted to ensure that a sufficient number of beacon users can be taken out of the data set S.
S6, based on the beacon user set B, dividing all user distribution areas according to the position information of the beacon users, and combining the position areas with the same VR information to obtain a position area-VR data set
Figure BDA0004149253560000021
S7, a large amount of common users with unknown VR information will use a data set
Figure BDA0004149253560000022
For reference, a location area is found according to the location information of the user, and corresponding VR information is obtained, so that VR recognition optimization targets are achieved.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention provides a super-large-scale MIMO visible area identification method for limiting beacon intervals. The uniform distribution concept is applied to ultra-large-scale MIMO visible area identification, the position distance between beacon users is limited by setting a threshold value, a plurality of beacon users are screened from candidate user sets meeting the position relation, then the beacon users are used for transmitting VR corresponding to the position where the uplink pilot frequency is detected, and a position-VR data set is established on the basis. A large number of common users with unknown VR information take the data set as a reference, and the corresponding VR can be judged according to the position information of the common users, so that the purpose of VR identification optimization is achieved.
(2) The invention provides a method for selecting the beacon interval threshold based on the improved thought of applying the limited beacon user interval to the ultra-large-scale MIMO visible area identification, and the method can ensure that the beacon users cannot be inserted into more beacon users due to overlarge interval and the area range detected by the beacon users is not too small due to overlarge position interval by referring to the distribution range of the system users and the number of the required beacon users through reasonable threshold design, thereby ensuring that the distribution range of the beacon users is more reasonable. Simulation results show that reasonable threshold design is helpful for improving accuracy of VR recognition.
(3) Based on the improved thought that the limited beacon user spacing is applied to the ultra-large-scale MIMO visible area identification, the invention provides a method for dynamically adjusting the beacon spacing threshold value, and the threshold value interval is timely adjusted along with the increase of the number of the screened beacon users, so that more beacon users can be selected in the later stage, which is beneficial to describing VR details and preventing the phenomenon that more beacon users cannot be inserted due to overlarge spacing. Simulation results show that the dynamic adjustment of the beacon interval threshold value is beneficial to improving the accuracy of VR recognition.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a flowchart of subscriber VR identification defining a beacon interval in the present invention.
Fig. 2 is a flowchart of an optimization algorithm for generating a beacon user set in the present invention.
Fig. 3 is a chart showing the VR recognition accuracy achieved by the method of the present invention and the conventional general method.
Fig. 4 is a chart showing the VR recognition accuracy achieved by the dynamic pitch adjustment method and the fixed pitch method according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. Of course, the specific embodiments described herein are for purposes of illustration only and are not intended to limit the invention.
Example 1
Referring to fig. 1 and 2, the present invention provides a method for identifying a super-large-scale MIMO visible area for defining a beacon interval, comprising the steps of:
step 1, establishing a position-VR data set S for candidate beacon user data, wherein each element S in the set S represents a candidate beacon user, marking the position information of S by using two-dimensional coordinates (x, y), and marking the known VR information corresponding to S by using a vector label l;
step 2, setting the number N of the beacon users to be selected, and reasonably setting the upper limit D of the position spacing of the beacon users max And lower limit D min The method specifically comprises the following steps:
2a, upper threshold D max Is set by the system userRegarding the distribution range (i.e., application scene), the larger the distribution range is, the D max The larger the value of (2) is, the larger the detection range is; the smaller the distribution range, the D max The smaller the value of (2) should be, so as to obtain more detection details;
2b, lower threshold D min Is set in relation to the number of beacons N, D when N is large min Should be set smaller to ensure that a sufficient number of beacon users can be taken out of the data set S; and when N is smaller, D min The larger should be set to ensure that the selected limited beacon can detect a larger VR range;
2c, threshold interval [ D ] min ,D max ]On the premise of ensuring as much as possible an even distribution of all beacon users, it is not necessary here to let [ D ] min ,D max ]＝[10,500]；
Step 3, selecting the first n position-VR data from S as an initial element to form an initial beacon user set B, wherein the value of n is not excessively large, and n=10 can be selected;
step 4, selecting one from S each time to satisfy the condition that the distance between the S and all the beacon positions in B is in [ D ] min ,D max ]The data s in the range is added into the set B, so that the purpose of expanding the beacon user set is achieved, and the method specifically comprises the following steps of:
4a, respectively calculating the distance between the position where s is located and all beacon positions in the B;
4b, screening out the smallest value in all distances, and marking as d min ；
4c, if d min Satisfy the decision relation D min ≤d min ≤D max Adding s into the set B, and increasing the value of n by 1; otherwise, discarding the data s and re-executing the step 4;
step 5, repeatedly executing the step 4, and circularly iterating, wherein an element is expanded for the set B each time until the number of the beacon users in the set B reaches N, namely n=N, and the set B at the moment is the optimized beacon user set;
step 6, based on the beacon user set B, according to the position information of the beacon user pairDividing all user distribution areas, merging the position areas with the same VR information to obtain a position area-VR data set
Figure BDA0004149253560000041
Step 7, a large number of common users with unknown VR information use a data set
Figure BDA0004149253560000042
For reference, a location area is found according to the location information of the user, and corresponding VR information is obtained, so that VR recognition optimization targets are achieved.
According to the above steps, the simulation is performed on the embodiment, the initial training sampling number (beacon users) is set to 500, 500 sampling points are added in each round, 7 rounds are added in total, the user VR recognition accuracy under the original beacon selection scheme (random selection) and the optimized beacon selection scheme (limited beacon interval) is tested under the same condition, the simulation is performed for 100 times in a circulating way, the average value is taken to reduce the influence of random errors, the simulation result is shown in figure 3, the optimized scheme limited beacon interval provided by the invention is obviously superior to the existing method for randomly selecting the beacon users, and the VR recognition efficiency is obviously improved.
Example 2
Referring to fig. 4 and embodiment 1, the present invention provides a method for identifying a visible region of a super-large MIMO for defining a beacon interval, wherein step 2 refers to a threshold interval [ D ] min ,D max ]The setting of (2) can be dynamically adjusted along with the increase of the number of input beacons, so that the VR recognition efficiency is further improved:
for example, at a fixed upper limit D max The lower bound D is dynamically adjusted once every 500 beacon users are added, without change min The method comprises the following specific steps:
step 1, setting an initial threshold interval [ D ] min ,D max ]＝[10，500]Screening 500 beacon users meeting interval requirements from the set S to form a beacon user set B, and taking the beacon user set B as a reference to realize VR identification;
step 2, reset lower threshold D min =7, screening out 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 3, reset lower threshold D min =5, screening out 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 4, resetting the lower threshold D min =4, screening out 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 5, reset lower threshold D min =3.5, screening out 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 6, resetting the lower threshold D min =3, screening out 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 7, resetting the lower threshold D min =2.5, again screening out 500 beacon users meeting the interval requirement from the set S, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification;
step 8, reset lower threshold D min And (2) screening 500 beacon users meeting the interval requirement from the set S again, adding the beacon users into the beacon user set B, and taking the beacon users as a reference to realize VR identification.
The initial training sampling number (beacon user) is set to be 500, and 500 sampling points are added for each round, 7 rounds are added in total, the simulation is carried out according to the steps, the optimization method for realizing the screening of the beacon user by dynamically adjusting the lower threshold value and the fixed lower threshold value D are carried out min The basic method of=2 is compared, the user VR recognition accuracy under the fixed beacon interval scheme (basic scheme) and the adjusted beacon interval scheme (optimization scheme) are respectively tested under the same condition, the simulation is also circularly performed 100 times to average to reduce the influence of random errors, the simulation result is shown in fig. 4, and the graph can be used for knowing the dynamic adjustment interval threshold value provided by the inventionThe optimization scheme of the method is obviously superior to the original fixed threshold method, and VR recognition efficiency is improved under the condition that the number of available beacons is limited.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims (6)

1. A method for identifying a super-large-scale MIMO visible area for limiting beacon intervals is characterized by comprising the following steps:
step 1, in a given ultra-large-scale MIMO environment, a mobile station side randomly selects some beacon users from a large number of users to measure the position coordinates of the beacon users and send uplink pilot frequency, then measures the intensity of received pilot frequency signals on an antenna array of a base station side, and determines a Visible Region (VR) corresponding to the position of the user, thereby establishing a position-VR data set S for candidate beacon user data;
step 2, setting the number N of the beacon users to be selected, and reasonably setting the upper limit D of the position spacing of the beacon users max And lower limit D min ；
Step 3, randomly selecting a small amount of position-VR data from the S as an initial element to form an initial beacon user set B, and realizing initial division of the position-based user VR according to the set;
step 4, selecting one from S each time to satisfy the condition that the distance between the S and all the beacon positions in B is in [ D ] min ，D max ]The data s in the range is added into the set B, so that the purpose of expanding the beacon user set is achieved;
step 5, repeatedly executing the step 4, and circularly iterating, wherein an element is expanded for the set B each time until the number of the beacon users in the set B reaches N, and the set B at the moment is an optimized beacon user set, wherein the set comprises a beacon position and corresponding VR tag information;
step 6, based on the beacon user set B, dividing all user distribution areas according to the position information of the beacon users, and dividing the position areas with the same VR label informationThe domains are merged together to obtain a location area-VR dataset
Figure FDA0004149253550000011
Step 7, a large number of common users with unknown VR information use a data set
Figure FDA0004149253550000012
For reference, from +.>
Figure FDA0004149253550000013
And finding out the position area of the beacon user s, s closest to the beacon user s, judging the position area as the position area of the VR unknown user, and obtaining corresponding VR information, thereby realizing the VR recognition optimization target.
2. The method for identifying a region of a super-MIMO visual field for defining a beacon interval according to claim 1, wherein establishing the data set S in step 1 comprises:
each element S in the set S represents a candidate beacon user, the location information of S is marked by using two-dimensional coordinates (x, y), the known VR information corresponding to S is marked by using a vector label l, and the user is in a visible area of the base station antenna array side.
3. The method for identifying a region of a super-MIMO visual field for defining a beacon interval as claimed in claim 1, wherein said step 2 sets an upper limit D of the beacon interval max Comprising the following steps:
upper threshold D max Is related to the distribution range of the system user, namely the application scene, and D is the greater the distribution range is max The larger the value of (2) is, the larger the detection range is; the smaller the distribution range, the D max The smaller the value of (c) should be, so that more detection details are obtained.
4. The method for identifying a very large scale MIMO visible area defining a beacon interval as claimed in claim 1,wherein the beacon interval lower limit D is set in the step 2 min Comprising the following steps:
lower threshold D min Is set in relation to the number of beacons N, D when N is large min Should be set smaller to ensure that a sufficient number of beacon users can be taken out of the data set S; and when N is smaller, D min Should be set larger to ensure that the selected limited beacon can detect a larger VR range.
5. The method for identifying a region of a super-MIMO visual area for defining a beacon interval according to claim 1, wherein said setting a beacon interval threshold in step 2 further comprises: threshold interval [ D min ，D max ]Is set, it is a primary premise to ensure that all beacon users are evenly distributed.
6. The method for identifying a region of a super-MIMO visual area for defining a beacon interval according to claim 1, wherein said setting a beacon interval threshold in step 2 further comprises:
as the number of screened beacons N increases, the threshold interval [ D min ，D max ]Can be dynamically adjusted to ensure that the VR detected by the selected limited beacon is more accurate.
CN202310313194.XA 2023-03-28 2023-03-28 Ultra-large-scale MIMO visible area identification method for limiting beacon interval Pending CN116232496A (en)

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