CN111953463B - Pilot frequency distribution method based on user clustering - Google Patents
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L5/00—Arrangements affording multiple use of the transmission path
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- H04W48/20—Selecting an access point
Abstract
The invention discloses a pilot frequency distribution method based on user clustering, which comprises the steps of constructing a relation matrix between a base station and users according to the signal intensity from the users to the base station, and selecting the base station with the maximum signal intensity of each user as a main base station for serving the users; calculating the number of connected users of each base station according to the connection relation between the users and the main base station, and sequencing; dividing users of a first base station into a cluster, wherein the maximum number of the containable users of the cluster is the number of the available pilot frequency, and increasing or reducing the users in the cluster according to the relation between the users and the base station and the number of the users served by the base station, so that the number of the users in the cluster reaches the maximum number of the containable users. Sequencing the base stations again according to the number of users, and repeating the clustering process until all the users finish the clustering process; and finally, distributing orthogonal pilot frequency in each cluster according to the clustering result. The method of the invention can effectively reduce the influence of pilot pollution in the cluster on the system performance and improve the data rate of the system.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a pilot frequency distribution method based on user clustering in a user-centered ultra-dense network.
Background
The intelligent terminal has gone into thousands of households unconsciously, and the total data traffic in the mobile communication network has been rapidly increased in recent years driven by the great increase in mobile bandwidth usage, which will continue in the future, and is expected to be thousands of times increased in the next decade. About 50 hundred million wireless mobile devices are now using wireless access communication, most of which are mobile broadband devices in handheld terminals, tablet computers or portable computers, and the number of these network access devices will increase from 50 hundred million to 500 hundred million or more in the future, which presents a serious challenge to future mobile communication systems. Under the popularization rate of the equipment and the increasing demand of data flow, a user-centered ultra-dense network is a research hotspot in the field of 5G wireless communication systems at present, and the system performance can be obviously improved.
Compared with the traditional cellular network system taking the base station as the center, the ultra-dense network system taking the user as the center reduces the distance between the user and the base station due to the dense base station deployment, improves the transmission rate of signals, and also brings the problem of interference among users. In the uplink communication link, the user sends a pilot signal to the base station, and the base station performs channel estimation through the received pilot sequence, so that the downlink channel information is obtained due to channel reciprocity in the TDD mode. But due to limited time-frequency resources, usable pilot frequency resources are also limited, so that pilot frequency multiplexing among users is inevitably caused. I.e. there are multiple users using the same pilot, resulting in mutual interference between users, i.e. pilot pollution. In large-scale MIMO, when the number of antennas tends to be infinite, the influence of uncorrelated noise and fast fading on the channel will almost disappear, so that pilot pollution becomes a bottleneck problem limiting the system performance.
Currently, in user-centric ultra-dense networks, the main aspects of pilot pollution mitigation can be elucidated from three aspects: firstly, through a precoding technique; second, the channel estimation technique; thirdly, a pilot frequency distribution method. Both the precoding technique and the channel estimation technique are to acquire accurate channel information from a mathematical level, and there may be operations of precoding or channel estimation under poor channel conditions or large interference between users. The problem of poor channel state can be fundamentally solved by pilot frequency allocation, and the pilot frequency allocation strategy can be said to have the greatest influence on the system performance. The pilot frequency distribution technology reasonably arranges different users to multiplex the same pilot frequency by utilizing the difference of channel quality among different users, reduces the mutual interference among the users, and lightens or even eliminates the influence of pilot frequency pollution on the system performance.
Therefore, it is necessary to provide a technical solution to solve the technical problems of the prior art.
Disclosure of Invention
In view of this, the present invention provides a user-centered pilot frequency allocation method based on user clustering in an ultra-dense network, which effectively improves the data transmission rate of the system, improves the overall performance of the system, and reduces pilot frequency pollution.
In order to solve the problems in the prior art, the technical scheme of the invention is as follows:
a pilot frequency distribution method based on user clustering comprises the following steps:
step S1: setting a main base station for each user according to the initial connection between the user and the base station;
step S2: clustering users according to the connection relation between the users and the main base station and the interference between the users;
step S3: performing pilot frequency distribution according to the clustering result, wherein each user in each cluster is distributed with orthogonal pilot frequency to realize that the user in the cluster has no pilot frequency interference;
wherein the step S2 includes the steps of:
step S21: determining the number of clusters according to the number K of users and the available orthogonal frequency guide number B, wherein the maximum capacity of each cluster is the available frequency guide number B;
step S22: clustering all users according to the number of clusters and the signal intensity between the users and each base station;
calculating the degree D value of the base station in each clustering process, namely calculating the number of connected users of each base station according to the connection between the users and the main base station; then, selecting the base station with the largest D value, and clustering according to the orthogonal pilot frequency B value, and specifically executing the following processes:
if the value of D is smaller than the value of the pilot frequency B, the system executes the following operations:
step 201 a: if the degree of the base station is less than B, the fact that the users in the cluster do not reach the full cluster after the D users are classified into the cluster is indicated, and at the moment, the clustering operation needs to be continuously carried out on the users, namely, other users are classified into the cluster;
step 202 a: the system takes all other users which are not clustered in the area as candidate users, and calculates the interference sum of the candidate users to each user in the cluster;
wherein, the sum of the interference from the non-clustered users to the clustered users is represented by the following formula:
wherein, in the above formula
Indicating pilot interference between user k and each user in cluster i using the same pilot,
represents the sum of the signal strengths of the users k to the serving base stations l in the cluster i; beta is aklFor large scale fading coefficient, is represented by formula
Is calculated to obtainklThe distance between the kth user and the l base station; r is the coverage reference distance of the base station; α is a path loss factor;
the sum of the interference from the user k to be clustered to each user in the cluster is expressed as
Wherein IiA set of serving base stations l representing users in the cluster i;
step 203 a: according to the sum of the interference from the candidate user to the user in the cluster
As interference to the cluster by the user; select to make
K is the maximum value and is taken as a new user to be classified into a cluster i;
step 204 a: judging the number of users in the cluster and the size of B at the moment, finishing the cluster when the number of the users is equal to B, otherwise turning to step 201a, and continuing to select users meeting the conditions from the users which are not clustered to add to the cluster;
if the value of D is equal to the value of B, then the following is performed:
after D users are classified into the cluster, the cluster is full, and other users are not selected to be classified into the cluster;
if the value of D is greater than the value of B, the following operations are performed:
step 201 c: if the degree D of the base station is greater than B, the fact that D users are classified into the cluster indicates that the users in the cluster exceed the maximum user B capable of being accommodated in the cluster, and the users are deleted from the cluster so as to ensure that the users in the cluster do not exceed a full cluster;
step 202 c: according to D users connected with the main base station, B users with the maximum signal intensity received by the base station are selected to be classified into a cluster, namely according to betakm 2B, sorting from big to small, selecting the first B users, and returning other users to the non-clustered user class;
and repeating the clustering process until all the users finish clustering.
As a further improvement, the number of clusters is selected to be the smallest integer not less than K/B.
As a further improvement, in step S1, a large-scale fading coefficient of a channel between each user and each base station within a certain area is obtained first, and a signal strength between each user and the base station is obtained; then according to the signal intensity between the user and each base station, selecting and setting the base station with the maximum signal intensity to the user as the main base station of the user
Compared with the prior art, the invention divides the users originally distributed in the whole area into a plurality of clusters, and then carries out the intra-cluster pilot frequency distribution method of each cluster, divides the pilot frequency distribution of the whole area into different clusters for distribution, reduces the complexity of the whole resource distribution of the macro base station, and the macro base station can carry out the distribution of the pilot frequency resources by using smaller resource exchange, thereby improving the whole efficiency.
Drawings
FIG. 1 is a user-centric ultra-dense network system model;
FIG. 2 is a flowchart illustrating steps of a proposed pilot allocation scheme;
FIG. 3 is a schematic diagram of clustering of the proposed scheme;
fig. 4 shows the system simulation clustering result.
FIG. 5 is a graph of a cumulative distribution probability simulation of a system and rate.
The following specific embodiments will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
The technical solution provided by the present invention will be further explained with reference to the accompanying drawings.
From the whole communication process, pilot frequency reuse is a source problem of pilot frequency pollution, so that solving the problem from the source is a key for reducing pilot frequency interference, and a pilot frequency allocation principle and method are of great importance. The invention adopts a pilot frequency distribution method based on clustering in a super-dense network system taking a user as a center. The specific system model is as follows:
considering a multi-user-centered ultra-dense network system, the system comprises L multi-antenna base stations and K single-antenna users, and the base stations are more densely deployed; the users and the base stations are randomly and uniformly distributed in the area range, and the users are connected with the two closest base stations; system channel estimation is based on Time Division Duplex (TDD) protocol in massive MIMO systems;
FIG. 1 is a schematic diagram of a system scenario of the present invention. A scenario includes densely distributed users and serving base stations or access points. In a super-dense network system, the signal coverage in an area is improved by densely distributed access points or base stations, and the system throughput is improved by reducing the path loss between the base stations and user terminals. This allows the system to amplify the interfering signal while increasing the effective received signal. The impact of thermal noise on the capacity of a wireless network system is reduced, making it an interference limited system.
Fig. 2 is a specific clustering flow chart of the present invention. In conjunction with the scenario set forth in fig. 1, a specific embodiment is given to the problem solving flowchart disclosed in fig. 2.
Compared with the traditional pilot frequency distribution scheme, the method considers that in a super-dense network system taking users as the center, the users in the cluster are distributed with orthogonal pilot frequencies by reducing the user clustering which possibly has the maximum interference, so that the pilot frequency pollution among the potential users with the maximum interference is reduced, and the system performance is improved.
As shown in the clustering diagram shown in fig. 3, users originally distributed in the whole area are divided into multiple clusters, and then the intra-cluster pilot allocation method of each cluster is performed, the pilot allocation of the whole area is divided into different clusters for allocation, so that the complexity of the whole resource allocation of the macro base station is reduced, the macro base station can perform the pilot resource allocation with smaller resource exchange, and the whole efficiency is improved.
In the example shown in fig. 4, there are a total of 16 users and 20 base stations or access points in a user-centric ultra-dense network system. In this example, all users and base stations or access points are randomly and uniformly distributed throughout the area, which is 4000 x 4000m2。
In a simulation test, users and a base station are uniformly distributed randomly, small-scale fading of the users and the base station obeys Rayleigh fading channels, and large-scale fading coefficients
rklThe distance between the kth user and the l base station; r is the coverage reference distance of the base station; α is the path loss factor.
The technical effect of the invention is verified by system simulation, and the specific simulation parameters are shown in table 1 below.
Table 1 parameter values for system simulation are given in the following table
| Number of available pilots | B＝4 |
| Number of base stations | L＝20 |
| Number of users | K＝16 |
| Selected area coverage | 4000m*4000m |
| Path loss factor | α＝3.5 |
The specific pilot allocation scheme is as follows:
based on a user-centric ultra-dense network; firstly, according to the distance between each user and each base station in a certain area range, obtaining a large-scale fading coefficient of a channel between each user and the base station according to a formula, and setting the square of the large-scale coefficient to express the signal intensity between the user and the base station;
after acquiring the path loss of the user and the base station, performing the relevant operation according to the following steps:
step 1: and determining the initial connection between the user and the base station, and setting a main base station.
And acquiring a large-scale fading coefficient of a channel between each user and each base station in a certain area range, and obtaining the signal strength between each user and each base station. And selecting and setting the base station with the maximum signal intensity to the user as a main base station serving the user according to the signal intensity between the user and each base station.
The number of clusters is related to the number of users K and the number of available pilot frequencies B, and in order to ensure that no pilot interference exists between users in the clusters, the maximum capacity of each cluster is the number of available pilot frequencies, so that the number of clusters is the minimum integer not less than K/B.
Step 2: and clustering the users according to the connection relation between the users and the main base station and the interference between the users.
Firstly, the number of connected users of each base station is calculated according to the connection between the users and the main base station, and all the users are clustered according to the obtained degree of each base station and the signal intensity between the users and each base station.
The reason is that the maximum degree of the base station means that the users connected with the same base station are the most, at the moment, the users of the base station interfere the most, and the mutual interference among the users is the maximum, so that the users with larger interference are preferentially allocated with the orthogonal pilot frequency, and the system performance can be effectively improved. After selecting the base station with the maximum degree, comparing the D value with the B value (namely the orthogonal frequency guide number) of the maximum number of people capable of accommodating the cluster, so as to determine whether to continuously add users to the cluster on the basis, and further adopting different clustering strategies. Therefore, this step may perform the following processes according to the degree of the base station:
in the embodiment shown in fig. 4, there are 16 total Users (UEs) in the ultra-dense network system, where four users UE1, UE3, UE5, and UE11 are connected to the same base station, UE9 and UE16 are connected to the same base station, UE2 and UE14 are connected to the same base station, UE4 and UE7 are connected to the same base station, and the remaining users are connected to different base stations in a distributed manner, so that the value of D of the base station with the largest time is equal to B, then step 2 is executed:
step 201 b: if the degree of the base station is equal to B, the number of users serving the base station as a main base station is B, and at the moment, after B users are classified into a cluster, the cluster is full; there is no need to select other users to be included in the cluster.
At this time, users which are not clustered exist, so that the step 2 is returned, and clustering of other users is continuously completed;
if there are three base stations with the same degree, randomly selecting a group, continuing the clustering step, and if the value of D of the base station is smaller than B, executing the first condition in the step 2:
if the value of D is less than the value of B, the system performs the following operations:
step 201 a: if the degree of the base station is less than B, the fact that the users in the cluster are full after the D users are classified into the cluster is indicated, and at the moment, the clustering operation needs to be continuously carried out on the users, namely, other users are classified into the cluster;
step 202 a: the system takes all other users which are not clustered in the area as candidate users, and calculates the sum of interference from the candidate users to each user in the cluster.
Step 203 a: and taking the sum of the interference from the candidate user to the user in the cluster as the interference of the user to the cluster. The user with the largest interference in the cluster is selected, wherein the interference refers to the interference when the user distributes the same pilot frequency, on the premise, the user with the largest interference is divided into one cluster, and then all the users in the cluster redistribute orthogonal pilot frequency sequences, so that the mutual interference in the cluster can be counteracted. The interference between users with large implicit interference is eliminated, and the overall performance can be better improved. I.e., UE13, puts it as a new user in the cluster.
Step 204 a: at this time, the number of users in the cluster and the size of B are judged, and at this time, the number of users is still smaller than B, the process goes to step 201a, users meeting the conditions are continuously selected from the candidate users to be added into the cluster, and the user with the largest interference in the cluster, namely the UE2, is selected and is included in the cluster as a new user.
At this time, the number of users in the cluster is equal to B, that is, the allocation is completed, and the allocation of other users is continuously completed.
Since the UE2 has completed clustering, the D value of the base station served by the UE4 and UE7 is maximum at this time, and the D value is smaller than the first case in step 2:
if the value of D is less than the value of B, the system performs the following operations:
step 201 a: if the degree of the base station is less than B, the fact that the user in the cluster does not reach the full cluster after the UE4 and the UE7 are classified into the cluster is indicated, and at the moment, the clustering operation needs to be continuously carried out on the user, namely, other users are classified into the cluster;
step 202 a: the system takes all other users which are not clustered in the area as candidate users, and calculates the interference sum from the candidate users to each user in the cluster.
Step 203 a: and according to the sum of the interference from the candidate users to the users in the cluster, the interference from the users to the cluster is taken as the interference of the users. The user with the greatest interference in the cluster, UE6, is selected and grouped as the new user in the cluster.
Step 204 a: at this time, the number of users in the cluster and the size of B are judged, and at this time, the number of users is still smaller than B, the process goes to step 201a, users meeting the conditions are continuously selected from the candidate users to be added into the cluster, and the user with the largest interference in the cluster, namely the UE12, is selected and is included in the cluster as a new user.
At this time, the number of users in the cluster is equal to B, that is, the allocation is completed, and the allocation of other users is continuously completed.
At this time, if the remaining other users are all connected to different base stations, and after randomly selecting one user to be classified into a cluster, executing the first condition in step 2 once, and if the value of D is smaller than the value of B, the system executes the following operations:
step 201 a: if the degree of the base station is less than B, the fact that the users in the cluster are full after the D users are classified into the cluster is indicated, and at the moment, the clustering operation needs to be continuously carried out on the users, namely, other users are classified into the cluster;
step 202 a: the system takes all other users which are not clustered in the area as candidate users, and calculates the interference sum from the candidate users to each user in the cluster.
Step 203 a: and taking the sum of the interference from the candidate user to the user in the cluster as the interference of the user to the cluster. And selecting the user with the largest interference in the cluster, and classifying the user as a new user into the cluster.
Step 204 a: and judging the number of the users in the cluster and the size of B at the moment, finishing the cluster when the number of the users is equal to B, otherwise turning to step 201a, and continuing to select the users meeting the conditions from the candidate users to add into the cluster.
And when all the users finish clustering, outputting a clustering result.
And step 3: and performing pilot frequency allocation according to the clustering result, wherein the user in each cluster is allocated with the orthogonal pilot frequency when the available orthogonal pilot frequency number is B because the user in each cluster at most accommodates B users, so that the user in the cluster can be free from pilot frequency interference.
Fig. 5 is a simulation diagram of cumulative distribution probability of sum rate, and according to the clustering method, the users in the system are clustered, and the clustering can eliminate the pilot interference between users with serious interference. And performing channel estimation on the signal by using minimum mean square error estimation, and performing pre-coding processing on the signal to obtain a system and a rate. As can be seen from the simulation results of FIG. 5, the system and rate obtained by the clustering method are significantly better than those of the random allocation method and the greedy algorithm.
Fig. 5 is a simulation diagram of cumulative distribution probability of a system and a rate, in which after clustering users in the system according to the clustering method, limited pilot sequences are used first, and each user in each cluster is allocated a completely orthogonal pilot sequence to ensure that there is no pilot interference for users in the cluster. And then, after channel estimation is carried out on the signal according to the minimum mean square error estimation, pre-coding processing is carried out on the signal, and finally, the system and the rate are obtained through calculation. Compared with random allocation, the problem of pilot interference generated by the same pilot frequency used by adjacent users is solved to the maximum extent, so that the problem is obviously improved. Compared with a greedy algorithm, the greedy algorithm can improve the condition of the user with the largest interference, a local optimal effect can be formed, and the overall performance is not effectively improved. As can be seen from the simulation results in fig. 5, the system performing pilot allocation according to the proposed clustering method is significantly better than the random allocation method and the greedy algorithm allocation.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, it is possible to make various improvements and modifications to the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A pilot frequency distribution method based on user clustering is characterized by comprising the following steps:
step S1: setting a main base station for each user according to the initial connection between the user and the base station;
step S2: clustering users according to the connection relation between the users and the main base station and the interference between the users;
step S3: performing pilot frequency distribution according to the clustering result, wherein each user in each cluster is distributed with orthogonal pilot frequency to realize that the user in the cluster has no pilot frequency interference;
wherein the step S2 includes the steps of:
step S21: determining the number of clusters according to the number K of users and the available orthogonal frequency guide number B, wherein the maximum capacity of each cluster is the available frequency guide number B;
step S22: clustering all users according to the number of clusters and the signal intensity between the users and each base station;
calculating the degree D value of the base station in each clustering process, namely calculating the number of connected users of each base station according to the connection between the users and the main base station; then, selecting the base station with the largest D value, and clustering according to the orthogonal pilot frequency B value, and specifically executing the following processes:
if the value of D is smaller than the value of the pilot frequency B, the system executes the following operations:
step 201 a: if the degree of the base station is less than B, the fact that the users in the cluster do not reach the full cluster after the D users are classified into the cluster is indicated, and at the moment, the clustering operation needs to be continuously carried out on the users, namely, other users are classified into the cluster;
step 202 a: the system takes all other users which are not clustered in the area as candidate users, and calculates the interference sum of the candidate users to each user in the cluster;
wherein, the sum of the interference from the non-clustered users to the clustered users is represented by the following formula:
wherein, in the above formula
Indicating pilot interference between user k and each user in cluster i using the same pilot,
represents the sum of the signal strengths of the users k to the serving base stations l in the cluster i; beta is aklFor large scale fading coefficient, the formula
Is calculated to obtainklThe distance between the kth user and the l base station; r is the coverage reference distance of the base station; α is a path loss factor;
the sum of the interference from the user k to be clustered to each user in the cluster is expressed as
Wherein IiA set of serving base stations l representing users in the cluster i;
step 203 a: according to the sum of the interference from the candidate user to the user in the cluster
As interference to the cluster by the user; select to make
K is the maximum value and is taken as a new user to be classified into a cluster i;
step 204 a: judging the number of users in the cluster and the size of B at the moment, finishing the cluster when the number of the users is equal to B, otherwise turning to step 201a, and continuing to select users meeting the conditions from the users which are not clustered to add to the cluster;
if the value of D is equal to the value of B, then the following is performed:
after D users are classified into the cluster, the cluster is full, and other users are not selected to be classified into the cluster;
if the value of D is greater than the value of B, the following operations are performed:
step 201 c: if the degree D of the base station is greater than B, the fact that D users are classified into the cluster indicates that the users in the cluster exceed the maximum user B capable of being accommodated in the cluster, and the users are deleted from the cluster so as to ensure that the users in the cluster do not exceed a full cluster;
step 202 c: according to the D users connected with the main base station, selecting the B users with the maximum signal intensity received by the base station to be classified into a cluster, namely according to betakm 2Sorting from big to small, selecting the first B users, and returning other users to the non-clustered user class;
and repeating the clustering process until all the users finish clustering.
2. The method of claim 1, wherein the number of clusters is selected to be a minimum integer no less than K/B.
3. The pilot frequency allocation method based on user clustering according to claim 1 or 2, characterized in that in step S1, the large-scale fading coefficients of the channels between each user and each base station within a certain area are obtained first, and the signal strength between each user and the base station is obtained; and then according to the signal intensity between the user and each base station, selecting and setting the base station with the maximum signal intensity to the user as the main base station of the user.
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