CN113259078B - Multi-cell large-scale MIMO pilot frequency distribution method based on arrival angle and distance - Google Patents

Multi-cell large-scale MIMO pilot frequency distribution method based on arrival angle and distance Download PDF

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CN113259078B
CN113259078B CN202110130435.8A CN202110130435A CN113259078B CN 113259078 B CN113259078 B CN 113259078B CN 202110130435 A CN202110130435 A CN 202110130435A CN 113259078 B CN113259078 B CN 113259078B
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cell
user
base station
pilot
pilot frequency
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CN113259078A (en
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周围
唐俊
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • 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
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H04L5/0069—Allocation based on distance or geographical location

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a multi-cell large-scale MIMO pilot frequency distribution method based on arrival angles and distances. Simulation shows that the scheme has good channel estimation and improves the spectral efficiency of the system.

Description

Multi-cell large-scale MIMO pilot frequency distribution method based on arrival angle and distance
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a multi-cell large-scale MIMO pilot frequency distribution method based on an arrival angle and a distance.
Background
Massive MIMO technology, which is one of the core technologies of 5G wireless systems in which a base station requires a large number of antennas to operate, involves a plurality of single-antenna user terminals, which are served by a large number of antennas deployed on the base station, and has been shown to provide a more significant improvement in spectral and energy efficiency than conventional MIMO systems due to the use of a large number of antennas and the application of multi-user detection and beamforming techniques.
But due to the limited range of the pilot length, the same pilot can be reused for different cell users, thereby causing the problem of pilot pollution. The pilot pollution may make the Channel State Information (CSI) estimation inaccurate, and the inaccurate CSI may affect the system performance.
Researchers have proposed many pilot allocation schemes to mitigate the effects of pilot pollution, and there are two common schemes: pilot and subspace based, wherein the pilot based scheme includes techniques to transmit pilots and exploit user covariance. For example, YIN H et al, published in IEEE Journal on Selected Areas in Communications,2012,31(2):264-273 entitled "A coded application to Channel Estimation in Large-scale Multiple-antenna Systems", published a highly accurate pilot pollution mitigation scheme for covariance matrix information and angle of arrival (AoA) of joint channels, and demonstrated that pilot pollution can be reduced using Minimum Mean Square Error (MMSE) detection when users without overlapping AOAs multiplex the same pilot. However, in this scheme, the complexity of the covariance matrix increases with the number of users, which is a non-negligible problem.
There is also a relationship between pilot allocation and user allocation by a Deep Learning algorithm to mitigate pilot pollution, which is achieved by Deep Learning (DL) for pilot allocation. Although the performance of this algorithm is almost the same as the exhaustive search algorithm, the DL algorithm requires a large amount of data and therefore requires a longer data processing time.
For example, Marzetta T L published in IEEE Transactions on Wireless Communications,2010, 9(11): 3590-.
Echigo H et al published an article entitled "Fair Pilot Assignment Based on AOA and path with Location Information in Massive MIMO" on 2017IEEE Global Communications conference, 2017:1-6, and proposed a scheme of assigning pilots by weighting coefficients, but this method assigns the same pilots to the target user and the interfering user closest thereto (as shown in FIG. 3) when the user weighting coefficients are equal, so that the problem of Pilot pollution is still serious.
Disclosure of Invention
In view of this, the present invention aims to provide a multi-cell massive MIMO pilot allocation method based on arrival angle and distance, which solves the problem in the prior art that when user measurement coefficients are equal, the same pilot is allocated to a target user and an interfering user closest to the target user, so that the pilot pollution problem is still relatively serious.
The invention solves the technical problems by the following technical means:
a multi-cell massive MIMO pilot frequency distribution method based on arrival angle and distance comprises the following steps:
s1, setting a pilot cell to be allocated as j (j is 1,2, … …, L), randomly allocating the pilot frequency of each user in the j cell when j is 1, and sequencing the users in an ascending order according to the distance between each user in the j cell and a base station of the j cell; setting the j cell which is allocated to be an i (i is 1,2, …, j-1) cell;
s2, when j is 2: L, the arrival angle interval from the user n to the base station of the j cell in the j cell is obtained by calculating the minimum arrival angle and the maximum arrival angle
Figure RE-GDA0003144334950000021
And the arrival angle interval from the user m to the base station of the cell j in the cell i
Figure RE-GDA0003144334950000022
Selecting the user nearest to the base station j in the j cell according to the position information
Figure RE-GDA0003144334950000023
S3, calculating the user according to the position information and the arrival angle information
Figure RE-GDA0003144334950000024
And each user u in the allocated cell i (i is 1,2, …, j-1)ikA measurement coefficient M of (A);
s4, comparing the minimum value of each group of weighing coefficients M, and defining the maximum minimum value as Q;
s5, verifying whether the conditions that the measurement coefficients M are Q exist: if not, the user
Figure RE-GDA0003144334950000025
Multiplexing the same pilot frequency with the interference user corresponding to the Q; otherwise, defining the user set with the measurement coefficients M and Q in the cell i as
Ii＝{uiv},i∈{1,2,...j-1},v∈{1,2,...,K}；
S6, calculating a user set IiThe distance between each user and the base station j and the sum is defined as
Figure RE-GDA0003144334950000026
Selecting a set
Figure RE-GDA0003144334950000027
Maximum value of, enable user
Figure RE-GDA0003144334950000028
And
Figure RE-GDA0003144334950000029
the interference user with the maximum value in the pilot frequency multiplexing mode multiplexes the same pilot frequency;
and S7, selecting the user closest to the base station j from the remaining users in the cell j, repeating the steps from S2 to S6 until the last user in the cell j is allocated, and reallocating the next cell.
Further, the calculation method of step S3 is: defining M (j, n, i, M) as a measurement coefficient of the mth user in the ith interference cell and the nth user in the jth target cell,
Figure RE-GDA0003144334950000031
wherein the content of the first and second substances,
Figure RE-GDA0003144334950000032
is the location of the mth user in the ith cell,
Figure RE-GDA0003144334950000033
is the location of the jth cell base station, η is the path loss exponent, and when the interfering cell user is to the left of the target user,
Figure RE-GDA0003144334950000034
otherwise
Figure RE-GDA0003144334950000035
4. Further, the multi-cell massive MIMO pilot allocation method based on the angle of arrival and the distance further includes a channel vector correlation coefficient algorithm between a target cell user and an interfering cell user, including the following steps:
B1. in a TDD mode, a model that L cells are established, each cell is provided with K single-antenna users, the number of base station antennas at the center of each cell is M, all the users in the same cell use orthogonal pilot frequency, wherein the length of a pilot frequency sequence is omega;
B2. assuming that each cell multiplexes the same set of pilots, the number of pilots is K, and considering uplink transmission, the channel vector from K users in cell to the base station in cell j is defined as
Figure RE-GDA0003144334950000036
Wherein, P is the number of paths,
Figure RE-GDA0003144334950000037
is the loss factor of the large scale and is,
Figure RE-GDA0003144334950000038
is the location of the kth user in the ith cell,
Figure RE-GDA0003144334950000039
is the position of the jth cell base station, η is the path loss exponent, α is a constant;
Figure RE-GDA00031443349500000310
defining the AOA of the p path from k users in the l cell to the base station channel of the j cell;
B3. when the base station antenna is a uniform linear array, the corresponding antenna steering vector is (1, e) with alpha (theta)-j2πDcos(θ)/λ,...,exp-j2π(M-1)Dcos(θ)/λ)TWherein, the antennas are distributed at equal intervals, D is the antenna interval, and lambda is the wavelength of the carrier signal;
B4. set of pilot sequences as
Figure RE-GDA00031443349500000311
K orthogonal pilot sequences of length omega are represented, satisfying the following condition SHS＝IK，τ≤CHI-NUL-NDLWhere CHI is represented as the channel coherence interval, NULAnd NDLRespectively representing the length of uplink and downlink transmission data in a frame to obtain the Mxomega pilot received by the base station end of j cellThe frequency signal is
Figure RE-GDA00031443349500000312
Where ρ ispilotWhich represents the transmission power of the pilot and,
Figure RE-GDA00031443349500000313
is additive white Gaussian noise in space and time, with a mean of 0 and a variance of
Figure RE-GDA00031443349500000314
B5. The channel estimate for the kth user in the jth cell is
Figure RE-GDA00031443349500000315
Wherein the content of the first and second substances,
Figure RE-GDA00031443349500000316
is an equivalent noise vector;
B6. deriving the kth cell in the jth cell1Channel vector between individual user and jth base station, and kth cell2The correlation coefficient of the channel vector between each user and the jth base station is expressed as
Figure RE-GDA0003144334950000041
The invention has the beneficial effects that: the invention relates to a multi-cell large-scale MIMO pilot frequency distribution method based on arrival angle and distance, which is based on AOA and distance, and carries out a pilot frequency distribution selection scheme by comparing the sizes of measurement coefficients, and aiming at the condition that the measurement coefficients are the same and the channel estimation precision between users is different, interference users with the same measurement coefficients are summarized into the same user set, and the distance between each user in the interference user set and a base station is calculated, so that the user and the interference user with the largest distance multiplex the same pilot frequency. Simulation shows that the scheme has good channel estimation and improves the spectral efficiency of the system.
Drawings
FIG. 1 is a system model;
FIG. 2 is a diagram showing the situation where no overlap occurs in the target cell and overlap occurs in other cells due to user AOAs;
FIG. 3 is a diagram of the case where the pilot measurement coefficients allocated to users are equal in size;
FIG. 4 is a uniform user profile during simulation according to the present invention;
FIG. 5 is a curve of the variation of the system uplink and rate with the number of antennas in the simulation result of the present invention;
FIG. 6 is a cumulative distribution function curve of user uplink SINR in the simulation result of the present invention;
FIG. 7 is a graph of average NMSE variation with antenna number in simulation results of the present invention;
table 1 is a pilot allocation scheme table;
table 2 is a simulation parameter table.
Detailed Description
The invention will be described in detail below with reference to the following figures and specific examples:
as shown in fig. 1-7, in order to facilitate understanding of the specific implementation method of the multi-cell massive MIMO pilot allocation method based on arrival angle and distance, a system model of the present invention is shown in fig. 1, in a TDD mode, there are L cells, each cell has K single-antenna users, the number of base station antennas M in the center of each cell, all users in the same cell use orthogonal pilots, where the pilot sequence length is ω.
Assuming that each cell multiplexes the same set of pilots, the number of pilots is K, and considering uplink transmission, the channel vector from K users in cell to the base station in cell j is defined as
Figure RE-GDA0003144334950000051
Where P is the number of paths, since at the base stationThe different antenna spacings are much smaller than the distance between the user and the base station, so the influence on the antenna spacings is generally ignored,
Figure RE-GDA0003144334950000052
is the loss factor of the large scale and is,
Figure RE-GDA0003144334950000053
is the location of the kth user in the ith cell,
Figure RE-GDA0003144334950000054
is the location of the jth cell base station, η is the path loss exponent, and α is a constant. The position is affected by various uncertainty sources, so that the real position of the user cannot be accurately obtained. The accuracy of the positioning depends on the positioning technique used and also on the environment (indoor or outdoor), in this embodiment we quantify the user position, and in practical applications, the actual position information is obtained by accurate positioning.
Figure RE-GDA0003144334950000055
Defined as the AOA of the p-th path of the k users in the l cell to the j cell base station channel.
When the base station antenna is a Uniform Linear Array (ULA), the corresponding antenna steering vector is expressed as
α(θ)＝(1,e-j2πDcos(θ)/λ,...,exp-j2π(M-1)Dcos(θ)/λ)T
Wherein, the antennas are distributed at equal intervals, D is the antenna interval, and lambda is the wavelength of the carrier signal.
The set of pilot sequences available for the present invention is
Figure RE-GDA0003144334950000056
K orthogonal pilot sequences of length omega are represented, satisfying the following condition SHS＝IK，τ≤CHI-NUL-NDL. Where CHI is represented as the channel coherence interval, NULAnd NDLRespectively indicating the length of uplink and downlink transmission data in a frame
Therefore, the pilot signal of M x omega received by the base station end of j cell is
Figure RE-GDA0003144334950000057
Where ρ ispilotWhich represents the transmission power of the pilot and,
Figure RE-GDA0003144334950000058
is additive white Gaussian noise in space and time, with a mean of 0 and a variance of
Figure RE-GDA0003144334950000059
It is found that the channel estimate for the kth user in the jth cell is
Figure RE-GDA00031443349500000510
Wherein the content of the first and second substances,
Figure RE-GDA00031443349500000511
is an equivalent noise vector.
Kth in jth cell1Channel vector between individual user and jth base station, and kth cell2The correlation coefficient of the channel vector between each user and the jth base station is expressed as
Figure RE-GDA00031443349500000512
Due to 1-xM＝(1-x)(1+x+x2+...+x(M-1)) Is provided with
Figure RE-GDA00031443349500000513
When in use
Figure RE-GDA0003144334950000061
And
Figure RE-GDA0003144334950000062
when the overlap is not complete
Figure RE-GDA0003144334950000063
Wherein the content of the first and second substances,
Figure RE-GDA0003144334950000064
respectively represent the kth cell2The minimum arrival angle and the maximum arrival angle of the user from the user to the jth base station.
Figure RE-GDA0003144334950000065
Indicating the estimated channel at the time of pilot pollution cancellation.
In the uplink transmission phase, since the length of the coherence interval cannot be extended indefinitely, there is a limit to the length of the pilot sequence when the system has access to a large number of users. Orthogonal pilots are used inside a cell in order to mitigate pilot pollution caused by the reuse of the same pilots by neighboring cells. The following pilot allocation scheme is proposed:
for the prior art, in this embodiment, when considering that a user n in a target cell j and a user m in an interfering cell i multiplex the same pilot frequency, an arrival angle interval from the user m in the cell n in the cell j to a base station in the cell j is obtained by calculating a minimum arrival angle and a maximum arrival angle:
Figure RE-GDA0003144334950000066
if it is
Figure RE-GDA0003144334950000067
(i.e., the angle intervals do not overlap), when the same pilot is multiplexed, the interference of j cells does not exist.
But at this time, i-cell, as shown in figure 2,
Figure RE-GDA0003144334950000068
there is an overlap of the angle intervals, thereby creating interference. As shown in fig. 2. The solution of this embodiment is as follows:
the method comprises the following steps: setting a pilot cell to be allocated as j (j is 1,2, … …, L), randomly allocating the pilot frequency of each user in the j cell when j is 1, and sorting the users according to the distance between each user in the j cell and the j cell base station in ascending order; setting the j cell which is allocated to be an i (i is 1,2, …, j-1) cell;
step two: when j is 2,3, … …, L, the arrival angle interval from the user n to the base station of j cell is obtained by calculating the minimum arrival angle and the maximum arrival angle
Figure RE-GDA0003144334950000069
And the arrival angle interval from the user m to the base station of the cell j in the cell i
Figure RE-GDA00031443349500000610
Selecting the user nearest to the base station j in the j cell according to the position information
Figure RE-GDA00031443349500000611
Step three: calculating the user according to the position information and the arrival angle information
Figure RE-GDA00031443349500000612
And each user u in the allocated cell i (i is 1,2, …, j-1)ikA measurement coefficient M of (A);
step four: comparing the minimum value of each group of weighing coefficients M, and defining the maximum minimum value as Q;
step five: verifying whether the situation that the measurement coefficients M are Q exists: if not, the user
Figure RE-GDA00031443349500000613
Multiplexing the same pilot frequency with the interference user corresponding to the Q; otherwise, defining the user set with the measurement coefficients M and Q in the cell i as
Ii＝{uiv},i∈{1,2,...j-1},v∈{1,2,...,K}；
Step six: computing a set of users IiThe distance between each user and the base station j and the sum is defined as
Figure RE-GDA00031443349500000614
Selecting a set
Figure RE-GDA00031443349500000615
Maximum value of, enable user
Figure RE-GDA00031443349500000616
And
Figure RE-GDA00031443349500000617
the interference user with the maximum value in the pilot frequency multiplexing mode multiplexes the same pilot frequency;
step seven: selecting the user closest to the base station j from the remaining users in the cell j, repeating the steps from S2 to S6 until the last user in the cell j is allocated, and re-allocating the next cell;
in the third step, when calculating the measurement coefficient M, defining M (j, n, i, M) as the measurement coefficient of the mth user in the ith interference cell and the nth user in the jth target cell,
Figure RE-GDA0003144334950000071
wherein the content of the first and second substances,
Figure RE-GDA0003144334950000072
is the location of the mth user in the ith cell,
Figure RE-GDA0003144334950000073
is the location of the jth cell base station, η is the path loss exponent, and when the interfering cell user is to the left of the target user,
Figure RE-GDA0003144334950000074
otherwise
Figure RE-GDA0003144334950000075
The following table is a table of pilot allocation schemes: gamma-shapedjFor unallocated pilot set, U, in cell jiIs the set of users waiting for pilot allocation in i cells,
Figure RE-GDA0003144334950000076
is multiplexing of gamma in i celligA user of a pilot.
Table 1 pilot allocation scheme
Figure RE-GDA0003144334950000077
Figure RE-GDA0003144334950000081
The invention relates to a multi-cell large-scale MIMO pilot frequency distribution method based on arrival angle and distance, which is based on AOA and distance, and carries out a pilot frequency distribution selection scheme by comparing the sizes of measurement coefficients, and aiming at the condition that the measurement coefficients are the same and the channel estimation precision between users is different, interference users with the same measurement coefficients are summarized into the same user set, and the distance between each user in the interference user set and a base station is calculated, so that the user and the interference user with the largest distance multiplex the same pilot frequency.
In order to visually represent the effect of the embodiment, the embodiment sets a 7-cell scene with a cell radius R. The users are uniformly distributed in all cells, as shown in fig. 4, and the simulation is performed according to the simulation parameters of the following table:
TABLE 2 simulation parameters
Number of cells 7
Number of users K 16
Cell radius R 1000m
User scattering radius r 100m
Distance between user and base station 100-1000m
Path loss exponent η 3.5
Antenna spacing D λ/2
Number of paths P 20
Channel coherence time T 100
Pilot sequence length omega 16
Assuming that all the users in all the cells have finished sending the pilot frequency and the users start sending data signals to their respective base stations in the second phase, the uplink data signals received by the base station in the jth cell can be expressed as
Figure RE-GDA0003144334950000082
Wherein x islkDefined as the transmitted data for k users in cell/in uplink and assuming E { | xlk|2}＝1，
Figure RE-GDA0003144334950000083
Defined as the complex gaussian received noise vector that obeys independent equal distribution. Known from the literature, rootsAccording to the channel estimation, a corresponding detection matrix can be obtained, a Matched Filter (MF) is used for receiving signals, and a base station in a jth cell can detect data x sent by a kth user in the celljkIs expressed as
Figure RE-GDA0003144334950000084
Wherein the signal to interference plus noise ratio (SINR) of user k in j cell in uplink can be expressed as M → ∞
Figure RE-GDA0003144334950000085
Where the numerator is expressed as the desired signal power and the denominator is expressed as the interference plus noise power. Therefore, M → ∞ cannot cancel an interference signal from another user multiplexing the same pilot.
At this time, the average uplink spectrum efficiency of the user is
Figure RE-GDA0003144334950000086
Where μ is a loss coefficient of spectral efficiency, defined as a ratio of a channel coherence time to a pilot sequence length, and μ τ/T.
The performance index of Normalized Mean Square Error (NMSE) for channel estimation is expressed as
Figure RE-GDA0003144334950000091
The invention refers to an article published by Echigo H et al in 2017IEEE Global Communications conference, IEEE,2017:1-6, entitled "Fair Pilot Assignment Based on AOA and route with Location Information in Massive MIMO", and takes a scheme of Pilot Assignment in the article as a comparative example, the contents are as follows:
but for the i-cell or cells,
Figure RE-GDA0003144334950000092
there is an overlap of the angle intervals, thereby creating interference, as shown in fig. 2. Ordering pilots in a target cell using a greedy algorithmThe columns are randomly distributed to users, then the same pilot frequency is multiplexed by the user closest to the target cell j base station and the edge cell i user through comparison of a measurement coefficient M, and the condition that the j cell has no interference and the i cell has interference is avoided. However, in this scheme, when the weighting coefficients of the user n and the user q are equal, the scheme may randomly select the user n, so that the user q with better performance is not selected.
The simulation results are shown in fig. 5-7:
fig. 5 shows that as the number of antennas M increases, the sum of the different schemes also increases. The proposed solution varies faster and at a higher rate than other solutions.
Compared with the three schemes in fig. 6, the probability that the uplink SINR is less than 10dB is about 55%, 40% and 34%, respectively, and thus, the scheme is superior to the conventional scheme and the comparative embodiment scheme. This is because in the proposed scheme, the same pilot is allocated to more users when the scaling factor is the same, reducing the impact of pilot pollution.
Fig. 7 is an important comparison of the embodiment and the improved scheme presented herein, and the algorithm herein reduces the average NMSE of all users in the target cell. This is because the algorithm herein avoids the inaccuracy of the estimated channel due to the selection precedence when the weighting coefficients are the same.
The simulation results of fig. 5-7 show that the invention has good channel estimation and improves the spectral efficiency of the system.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.

Claims (1)

1. A multi-cell massive MIMO pilot frequency distribution method based on arrival angle and distance is characterized by comprising the following steps:
s1, setting a pilot cell to be allocated as j (j is 1,2, … …, L), randomly allocating the pilot frequency of each user in the j cell when j is 1, and sequencing the users in an ascending order according to the distance between each user in the j cell and a base station of the j cell; setting the j cell which is allocated to be an i (i is 1,2, …, j-1) cell;
s2, when j is 2: L, the arrival angle interval from the user n to the base station of the j cell in the j cell is obtained by calculating the minimum arrival angle and the maximum arrival angle
Figure FDA0003532549580000011
And the arrival angle interval from the user m to the base station of the cell j in the cell i
Figure FDA0003532549580000012
Selecting the user nearest to the base station j in the j cell according to the position information
Figure FDA0003532549580000013
S3, calculating the user according to the position information and the arrival angle information
Figure FDA0003532549580000014
And each user u in the allocated cell i (i is 1,2, …, j-1)ikA measurement coefficient M of (A);
s4, comparing the minimum value of each group of weighing coefficients M, and defining the maximum minimum value as Q;
s5, verifying whether the conditions that the measurement coefficients M are Q exist: if not, the user
Figure FDA0003532549580000015
Multiplexing the same pilot frequency with the interference user corresponding to the Q; otherwise, defining the user set with the measurement coefficients M and Q in the cell i as
Ii＝{uiv},i∈{1,2,...j-1},v∈{1,2,...,K}；
S6, calculating a user set IiDistance between each user and base station jFrom, and together are defined as
Figure FDA0003532549580000016
Selecting a set
Figure FDA0003532549580000017
Maximum value of, enable user
Figure FDA0003532549580000018
And
Figure FDA0003532549580000019
the interference user with the maximum value in the pilot frequency multiplexing mode multiplexes the same pilot frequency;
s7, selecting the user closest to the base station j from the remaining users in the cell j, repeating the steps from S2 to S6 until the last user in the cell j is allocated, and re-allocating the next cell;
the calculation method of step S3 is: defining M (j, n, i, M) as a measurement coefficient of the mth user in the ith interference cell and the nth user in the jth target cell,
Figure FDA00035325495800000110
wherein the content of the first and second substances,
Figure FDA00035325495800000111
is the location of the mth user in the ith cell,
Figure FDA00035325495800000112
is the location of the jth cell base station, η is the path loss exponent, and when the interfering cell user is to the left of the target user,
Figure FDA00035325495800000113
otherwise
Figure FDA00035325495800000114
The multi-cell massive MIMO pilot frequency distribution method based on the arrival angle and the distance also comprises a channel vector correlation coefficient algorithm between a target cell user and an interference cell user, and comprises the following steps:
B1. in a TDD mode, a model that L cells are established, each cell is provided with K single-antenna users, the number of base station antennas at the center of each cell is M, all the users in the same cell use orthogonal pilot frequency, wherein the length of a pilot frequency sequence is omega;
B2. assuming that each cell multiplexes the same set of pilots, the number of pilots is K, and considering uplink transmission, the channel vector from K users in cell to the base station in cell j is defined as
Figure FDA0003532549580000021
Wherein, P is the number of paths,
Figure FDA0003532549580000022
is the loss factor of the large scale and is,
Figure FDA0003532549580000023
is the location of the kth user in the ith cell,
Figure FDA0003532549580000024
is the position of the jth cell base station, η is the path loss exponent, α is a constant;
Figure FDA0003532549580000025
defining the AOA of the p path from k users in the l cell to the base station channel of the j cell;
B3. when the base station antenna is a uniform linear array, the corresponding antenna steering vector is (1, e) with alpha (theta)-j2πDcos(θ)/λ,...,exp-j2π(M-1)Dcos(θ)/λ)TWherein, the antennas are distributed at equal intervals, D is the antenna interval, and lambda is the wavelength of the carrier signal;
B4. set of pilot sequences as
Figure FDA0003532549580000026
K orthogonal pilot sequences of length omega are represented, satisfying the following condition SHS＝IK，τ≤CHI-NUL-NDLWhere CHI is represented as the channel coherence interval, NULAnd NDLRespectively representing the length of uplink and downlink transmission data in a frame to obtain the Mxomega pilot signal received by the base station end of j cells as
Figure FDA0003532549580000027
Where ρ ispilotWhich represents the transmission power of the pilot and,
Figure FDA0003532549580000028
is additive white Gaussian noise in space and time, with a mean of 0 and a variance of
Figure FDA0003532549580000029
B5. The channel estimate for the kth user in the jth cell is
Figure FDA00035325495800000210
Wherein the content of the first and second substances,
Figure FDA00035325495800000211
is an equivalent noise vector;
B6. deriving the kth cell in the jth cell1Channel vector between individual user and jth base station, and kth cell2The correlation coefficient of the channel vector between each user and the jth base station is expressed as
Figure FDA00035325495800000212
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