CN113193945A - Pilot frequency and power distribution joint optimization method, system, medium and communication equipment - Google Patents

Pilot frequency and power distribution joint optimization method, system, medium and communication equipment Download PDF

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CN113193945A
CN113193945A CN202110497355.6A CN202110497355A CN113193945A CN 113193945 A CN113193945 A CN 113193945A CN 202110497355 A CN202110497355 A CN 202110497355A CN 113193945 A CN113193945 A CN 113193945A
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pilot frequency
optimization
power
pilot
cell
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CN113193945B (en
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林楷东
伍沛然
夏明华
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Sun Yat Sen University
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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
    • H04B7/0426—Power distribution
    • 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
    • H04L5/006—Quality of the received signal, e.g. BER, SNR, water filling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • 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

Abstract

Aiming at the limitations of the prior art, the invention provides a pilot frequency and power allocation combined optimization method, a system, a storage medium and communication equipment, which improve the throughput of the whole system by maximizing the total transmission rate of a Massive MIMO system, take user fairness into consideration while maximizing the transmission rate of the system, ensure the requirement of the minimum transmission rate of a user and are more suitable for practical application; the method can greatly improve the average transmission rate of the cell in the multi-cell Massive MIMO system and reduce the inhibition effect of pilot pollution on the total transmission rate of the system; in addition, the method has better convergence and lower calculation complexity.

Description

Pilot frequency and power distribution joint optimization method, system, medium and communication equipment
Technical Field
The invention relates to the technical field of wireless communication, in particular to a large-scale multiple-input multiple-output communication technology, and more particularly relates to a pilot frequency and power distribution joint optimization method, a system, a medium and communication equipment capable of reducing pilot frequency pollution of a multi-cell Massive MIMO system.
Background
A Massive MIMO (Massive Multiple-input Multiple-output) technology is one of the key technologies for 5G mobile communication. The core idea of the Massive MIMO technology is that a base station end is provided with a larger number of antennas, so that not only can a larger number of mobile terminal users be served simultaneously, but also noise and interference among the users can be reduced, and the communication quality is greatly improved. However, when the base station performs channel estimation, mutual interference occurs between users multiplexing the same pilot, and this phenomenon is called pilot pollution. The problem of pilot pollution can lead the quality of channel estimation carried out on a user by a base station to be poor, thereby further influencing the subsequent receiving judgment of an uplink signal and the sending pre-coding of a downlink signal and seriously restricting the performance of the whole Massive MIMO system; the pilot pollution problem thus becomes a performance bottleneck for Massive MIMO systems.
The publication date is 2020.11.13, and the publication number is CN 110958102B: a pilot frequency pollution suppression method based on pilot frequency distribution and power control combined optimization is disclosed, which tries to adopt an alternate iteration method, in the iteration process, a large-scale fading factor is firstly fixed to carry out uplink power control optimization, and then uplink transmitting power is fixed to carry out pilot frequency distribution optimization, so that the minimum frequency effect in a Massive MIMO system is improved under the condition of low calculation complexity, and the communication quality of cell edge users is improved.
However, the above patent adopts a weight map coloring algorithm to perform pilot allocation, and the core of the algorithm is to preferentially allocate the pilot with less interference to the user with poor channel quality; on the contrary, the users with higher channel quality are allocated with the pilots with larger interference, which undoubtedly sacrifices the performance of the users; secondly, when the number of cells and the number of users are large, namely when a multi-cell Massive MIMO system is faced, the interference graph constructed by the method is more complex and the processing time is longer. Thus, the prior art still has certain limitations.
Disclosure of Invention
Aiming at the limitation of the prior art, the invention provides a pilot frequency and power allocation joint optimization method, a system, a medium and communication equipment, and the technical scheme adopted by the invention is as follows:
a pilot frequency and power allocation joint optimization method comprises the following steps:
s1, initializing a pilot frequency distribution scheme, a pilot frequency power distribution scheme and a data power distribution scheme of each cell;
s2, according to the pilot frequency power distribution scheme and the data power distribution scheme, aiming at maximizing the total transmission rate of the cells, iteratively optimizing the pilot frequency distribution scheme by adopting a Hungarian algorithm in each cell one by one;
s3, solving the optimization problem which takes the preset maximum user sending power and the minimum user transmission rate as the limit and takes the maximum system total transmission rate as the objective function according to the pilot frequency distribution scheme, and carrying out iterative optimization on the pilot frequency power distribution scheme and the data power distribution scheme;
and S4, judging whether the preset joint optimization finishing condition is met, if so, outputting the optimized pilot frequency distribution scheme, pilot frequency power distribution scheme and data power distribution scheme, otherwise, returning to the step S2 to perform the next joint iteration.
Compared with the prior art, the throughput of the whole system is improved by maximizing the total transmission rate of the Massive MIMO system, the fairness of users is considered while the transmission rate of the system is maximized, the requirement of the minimum transmission rate of the users is ensured, and the method is more suitable for practical application; the method can greatly improve the average transmission rate of the cell in the multi-cell Massive MIMO system and reduce the inhibition effect of pilot pollution on the total transmission rate of the system; in addition, the method has better convergence and lower calculation complexity.
As a preferable scheme, in the initialization process of step S1, the pilot allocation schemes of the users are randomly set, the pilot power allocation schemes of the users are the same, and the data power allocation schemes of the users are the same.
As a preferable scheme, in the step S2, the method includes the following steps:
s21, for the optimizing cell, establishing a weight table between the cell user and the pilot frequency distribution according to the pilot frequency power distribution scheme and the data power distribution scheme by using the pilot frequency distribution scheme of other cells as a constant, and constructing a matching problem of solving the maximum weight sum of the weighted bipartite graph by taking the cell total transmission rate maximization as a target;
s22, solving the matching problem by adopting a Hungarian algorithm, and optimizing the pilot frequency allocation scheme of the cell;
s23, judging whether all cells in the pilot frequency optimization iteration of the current round are completely optimized, if so, executing a step S24, otherwise, returning to the step S21 to optimize the next cell;
and S24, judging whether the preset pilot frequency optimization finishing condition is met, if so, executing the step S3, and if not, returning to the step S21 to perform the next pilot frequency optimization iteration.
Further, the weight value of the weight table is the sum of the transmission rates of users multiplexing the same pilot frequency under the multi-cell Massive MIMO system
Figure BDA0003054945730000031
Figure BDA0003054945730000032
Wherein R isikIndicating the transmission rate of user k in cell i being optimized,
Figure BDA0003054945730000033
indicating the transmission rate of users in other cell j using pilot n;
Rik＝log2(1+SINRik)；
Figure BDA00030549457300000315
Figure BDA0003054945730000034
Figure BDA0003054945730000035
wherein, the SINRik、
Figure BDA0003054945730000036
Represents the signal-to-interference ratio, betaijkRepresenting the large-scale fading coefficient from the user in other cell j to the base station of cell i; m represents the number of base station antennas, L represents the number of cells, and K represents the number of users;
Figure BDA0003054945730000037
the pilot power representing the user is then determined,
Figure BDA0003054945730000038
a data power representing a user;
Figure BDA0003054945730000039
represents the variance of white gaussian noise during pilot transmission,
Figure BDA00030549457300000310
representing the variance of white gaussian noise during data transmission.
Further, the optimization problem in step S3 is expressed by the following formula:
Figure BDA00030549457300000311
subject to C1：
Figure BDA00030549457300000312
C2：
Figure BDA00030549457300000313
C3：
Figure BDA00030549457300000314
wherein, PmaxIndicating the maximum transmit power, R, of the userminIndicating the user's minimum transmission rate.
Further, in step S3, the optimization problem is solved by calculating the following convex optimization problem transformed by the optimization problem through a successive convex approximation method to obtain a lower bound that maximizes the total transmission rate of the system:
Figure BDA0003054945730000041
subject to C1：
Figure BDA0003054945730000042
C2：
Figure BDA0003054945730000043
C3：
Figure BDA0003054945730000044
wherein the content of the first and second substances,
Figure BDA0003054945730000045
Figure BDA0003054945730000046
coefficient of performance
Figure BDA0003054945730000047
Representing the signal-to-interference ratio SINR.
Further, the step S3 includes the following steps:
s31, calculating the convex optimization problem to obtain the pilot frequency power and the data power of each user as the optimization of the pilot frequency power distribution scheme and the data power distribution scheme;
s32, updating the signal-to-interference ratio and coefficient a of each user according to the pilot frequency power and data power obtained in the step S31 in the power optimization iteration of the current roundjk、bjk；
And S33, judging whether a preset power optimization finishing condition is met, if so, executing the step S4, and if not, returning to the step S31 to perform the next power optimization iteration.
The present invention also provides the following:
a pilot frequency and power distribution joint optimization system is used for carrying out pilot frequency and power distribution on users under a multi-cell Massive MIMO system and is characterized by comprising an initialization module, a pilot frequency distribution iteration optimization module, a power distribution iteration optimization module and a joint optimization judgment circulation module;
the pilot frequency distribution iteration optimization module is connected with the initialization module, the power distribution iteration optimization module and the joint optimization iteration judgment module, and the power distribution iteration optimization module is connected with the joint optimization judgment loop module; wherein:
the initialization module is used for initializing a pilot frequency distribution scheme, a pilot frequency power distribution scheme and a data power distribution scheme of each cell;
the pilot frequency allocation iterative optimization module is used for performing iterative optimization on the pilot frequency allocation scheme by adopting a Hungarian algorithm in each cell one by one according to the pilot frequency power allocation scheme and the data power allocation scheme by taking the maximization of the total transmission rate of the cells as a target;
the power allocation iterative optimization module is used for solving an optimization problem which takes preset maximum user sending power and minimum user transmission rate as limits and takes the maximum system total transmission rate as an objective function according to the pilot frequency allocation scheme, and performing iterative optimization on the pilot frequency power allocation scheme and the data power allocation scheme;
and the joint optimization iteration judgment module is used for judging whether a preset joint optimization ending condition is met, outputting the optimized pilot frequency distribution scheme, the optimized pilot frequency power distribution scheme and the optimized data power distribution scheme if the preset joint optimization ending condition is met, and otherwise calling the pilot frequency distribution iteration optimization module to perform the next round of joint optimization iteration.
A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the aforementioned method for joint optimization of pilot and power allocation.
A communication device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable by the processor, the computer program when executed by the processor implementing the steps of the aforementioned pilot and power allocation joint optimization method.
Drawings
Fig. 1 is a schematic flowchart of a pilot frequency and power allocation joint optimization method according to embodiment 1 of the present invention;
fig. 2 is a schematic flowchart of step S2 provided in embodiment 1 of the present invention;
fig. 3 is a schematic flowchart of step S3 provided in embodiment 1 of the present invention;
FIG. 4 is a schematic diagram of algorithm convergence in a simulation experiment according to embodiment 1 of the present invention;
fig. 5 is a schematic diagram illustrating comparison of the cumulative distribution of the average cell transmission rates of the algorithms in the simulation experiment of embodiment 1 of the present invention;
fig. 6 is a schematic diagram illustrating comparison between average transmission rates of each algorithm in a cell and changes of the number of antennas in a simulation experiment in embodiment 1 of the present invention;
fig. 7 is a schematic diagram of a pilot and power allocation joint optimization system provided in embodiment 2 of the present invention;
description of reference numerals: 1. initializing a module; 2. a pilot frequency distribution iteration optimization module; 3. a power distribution iterative optimization module; 4. and a joint optimization judgment circulation module.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the embodiments described are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the embodiments in the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The invention is further illustrated below with reference to the figures and examples.
In order to solve the limitation of the prior art, the present embodiment provides a technical solution, and the technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.
Example 1
Referring to fig. 1, a method for joint optimization of pilot frequency and power allocation includes the following steps:
s1, initializing a pilot frequency distribution scheme, a pilot frequency power distribution scheme and a data power distribution scheme of each cell;
s2, according to the pilot frequency power distribution scheme and the data power distribution scheme, aiming at maximizing the total transmission rate of the cells, iteratively optimizing the pilot frequency distribution scheme by adopting a Hungarian algorithm in each cell one by one;
s3, solving the optimization problem which takes the preset maximum user sending power and the minimum user transmission rate as the limit and takes the maximum system total transmission rate as the objective function according to the pilot frequency distribution scheme, and carrying out iterative optimization on the pilot frequency power distribution scheme and the data power distribution scheme;
and S4, judging whether the preset joint optimization finishing condition is met, if so, outputting the optimized pilot frequency distribution scheme, pilot frequency power distribution scheme and data power distribution scheme, otherwise, returning to the step S2 to perform the next joint iteration.
Compared with the prior art, the throughput of the whole system is improved by maximizing the total transmission rate of the Massive MIMO system, the fairness of users is considered while the transmission rate of the system is maximized, the requirement of the minimum transmission rate of the users is ensured, and the method is more suitable for practical application; the method can greatly improve the average transmission rate of the cell in the multi-cell Massive MIMO system and reduce the inhibition effect of pilot pollution on the total transmission rate of the system; in addition, the method has better convergence and lower calculation complexity.
Specifically, the invention decomposes the problem of joint optimization of pilot frequency and power allocation of users in a multi-cell Massive MIMO system: in each round of joint iteration, the pilot frequency power allocation scheme and the data power allocation scheme are used as constants, and the pilot frequency allocation scheme is iteratively optimized in each cell one by one, that is, the step S2 is executed once; after the pilot allocation scheme completes the iterative optimization, the pilot allocation scheme is used as a constant to perform the iterative optimization on the pilot power allocation scheme and the data power allocation scheme, that is, the step S3 is executed once. Therefore, to a certain extent, the operation process of the pilot frequency and power allocation joint optimization of the present invention can be understood as a large loop iteration process formed by two small loop iteration processes executed in sequence.
The pilot allocation scheme is a setting condition of a pilot signal used by a user, the pilot power allocation scheme is a setting condition of transmission power when the user sends the pilot signal, and the data power allocation scheme is a setting condition of the transmission power when the user sends a data signal.
The Hungarian algorithm is a combined optimization algorithm for solving task allocation problems in polynomial time, is one of algorithms for solving linear task allocation problems, is a classical algorithm for solving a bipartite graph maximum matching problem, can solve the problems in polynomial time, and is proposed by the American mathematician Harold Kuhn in 1955.
Judging whether the joint optimization ending condition is met, wherein the judgment can be to judge the number of joint iterations, namely whether the execution times of the steps S2 and S3 reach a preset value; or, it may be determined whether the joint optimization result of the current round has reached convergence, that is, whether the lifting amplitude of the total transmission rate calculated by the joint optimization result of the current round compared with the total transmission rate calculated by the joint optimization result of the previous round is smaller than a preset threshold.
As a preferred embodiment, in the initialization process of step S1, the pilot allocation schemes of the users are randomly set, the pilot power allocation schemes of the users are the same, and the data power allocation schemes of the users are the same.
More specifically, for the following multi-cell Massive MIMO system for performing signal transmission based on TDD (time division duplex), the system is composed of L regular hexagonal honeycomb cells, a base station of each cell is located at the center of the regular hexagonal honeycomb cell, and each base station is configured with M antennas respectively to provide service for K single-antenna users; in general, users are evenly distributedIn each cell. Because the base station is configured with a large-scale antenna array, M > K is satisfied. The number of the mutually orthogonal pilot frequencies is recorded as S, and the S is more than or equal to K, so that the serious interference in the cell is avoided. The transmission time of the pilot is recorded as taupTherefore, the set of orthogonal pilot sequences can be expressed as:
Figure BDA0003054945730000081
different users in the cell select different columns in the set as respective pilot sequences; after the user sends the pilot, the pilot signal received by the ith cell can be expressed as:
Figure BDA0003054945730000082
wherein the content of the first and second substances,
Figure BDA0003054945730000083
denotes the pilot transmission power, N, of the kth user of the jth celliRepresents a mean of 0 and a variance of
Figure BDA0003054945730000084
White gaussian noise. Next, the base station performs channel estimation according to the received pilot signal:
Figure BDA0003054945730000085
as can be seen from the above formula, the result of channel estimation receives interference from users reusing the same pilot in other cells.
In the data transmission stage, each cell user transmits a data signal to the base station, and the expression is as follows:
Figure BDA0003054945730000086
wherein the content of the first and second substances,
Figure BDA0003054945730000087
data transmission power, x, on behalf of the userjkRepresenting a data symbol, niRepresents a mean of 0 and a variance of
Figure BDA0003054945730000088
White gaussian noise. And judging the received signals by adopting a maximum ratio combining mode according to the result of channel estimation, wherein the result is as follows:
Figure BDA0003054945730000089
Figure BDA0003054945730000091
wherein epsilonikIs an uncorrelated noise term. Thus, the transmission rate of user k of cell i can be calculated as:
Rik＝log2(1+SINRik)
signal to interference ratio SINRikThe following were used:
Figure BDA0003054945730000092
wherein, betaijkRepresenting the large-scale fading coefficient of user k to base station j in cell i. Further, as can be seen from the user signal-to-interference ratio formula, the user transmission rate depends on a large-scale fading coefficient, rather than instantaneous fading associated with a short coherence time, so that pilot allocation and power allocation can be performed in a larger time interval, and the optimization result is kept unchanged. In addition, as the number of antennas increases, uncorrelated noise is averaged out leaving only pilot pollution that limits system performance.
In order to reduce the influence of pilot pollution and improve the total transmission rate of the Massive MIM0 system, the present embodiment proposes the following optimized objective function:
Figure BDA0003054945730000093
subject to C1：
Figure BDA0003054945730000094
C2：
Figure BDA0003054945730000095
C3：Dn∈{1，…K}，n＝1，…S，
C4：
Figure BDA0003054945730000096
wherein D isn＝[d1n，d2n，…dLn]TIndicates the situation of multiplexing pilot n by each cell user, dlnIndicating the 1 st cell uses the user, P, of pilot nmaxIndicating the maximum transmission power, RminRepresenting the minimum transmission rate requirement of the user. The optimization problem has four constraints including constraints on pilot power and data power, as well as pilot allocation and user minimum transmission rate requirements. In order to solve the optimization problem, the present embodiment divides the optimization problem into two sub-problems to be solved, namely, pilot allocation and power allocation.
Whereas in the case of considering only pilot allocation, the optimization problem can translate into
Figure BDA0003054945730000101
subject to C3：Dn∈{1，…K}，n＝1，…S
C4：
Figure BDA0003054945730000102
For the cell which is distributing the pilot frequency, the pilot frequency distribution scheme of other cells can influence the pilot frequency distribution realization of the cell, and in order to obtain the solution of the problem, the original distribution problem is decomposed into a plurality of sub-problems to be solved. In each sub-problem, only a single target cell is considered, and the pilot allocation problem in the cell is solved.
Therefore, as a preferred embodiment, referring to fig. 2, in the step S2, the following steps are included:
s21, for the optimizing cell, establishing a weight table between the cell user and the pilot frequency distribution according to the pilot frequency power distribution scheme and the data power distribution scheme by using the pilot frequency distribution scheme of other cells as a constant, and constructing a matching problem of solving the maximum weight sum of the weighted bipartite graph by taking the cell total transmission rate maximization as a target;
s22, solving the matching problem by adopting a Hungarian algorithm, and optimizing the pilot frequency allocation scheme of the cell;
s23, judging whether all cells in the pilot frequency optimization iteration of the current round are completely optimized, if so, executing a step S24, otherwise, returning to the step S21 to optimize the next cell;
and S24, judging whether the preset pilot frequency optimization finishing condition is met, if so, executing the step S3, and if not, returning to the step S21 to perform the next pilot frequency optimization iteration.
Specifically, when the pilot allocation scheme of the cell i is optimized, the pilot allocation schemes of other L-1 cells are used as constants, so as to construct a weight table between users of a single cell and pilots:
TABLE-1 weight representation of user and pilot assignments in cell i
Figure BDA0003054945730000103
Figure BDA0003054945730000111
Further, the weight value of the weight table is the sum of the transmission rates of users multiplexing the same pilot frequency under the multi-cell Massive MIMO system
Figure BDA0003054945730000112
Figure BDA0003054945730000113
Wherein R isikIndicating the transmission rate of user k in cell i being optimized,
Figure BDA0003054945730000114
indicating the transmission rate of users in other cell j using pilot n;
Rik＝log2(1+SINRik)；
Figure BDA0003054945730000115
Figure BDA0003054945730000116
Figure BDA0003054945730000117
wherein, the SINRik、
Figure BDA0003054945730000118
Represents the signal-to-interference ratio, betaijkRepresenting the large-scale fading coefficient from the user in other cell j to the base station of cell i; m represents the number of base station antennas, L represents the number of cells, and K represents the number of users;
Figure BDA0003054945730000119
the pilot power representing the user is then determined,
Figure BDA00030549457300001110
a data power representing a user;
Figure BDA00030549457300001111
represents the variance of white gaussian noise during pilot transmission,
Figure BDA00030549457300001112
representing the variance of white gaussian noise during data transmission.
Specifically, in order to consider the minimum transmission rate requirement of the user, if any user in the weight does not satisfy the condition, the weight is set to zero. At this time, the problem is a matching problem of obtaining the maximum weight sum of the weighted bipartite graph, and a pilot frequency allocation scheme of the maximum transmission rate of the cell can be found with low complexity by using the Hungarian algorithm. And by analogy, updating the pilot frequency allocation scheme of each cell in a continuous iteration mode.
Judging whether the pilot frequency optimization ending condition is met, wherein the judgment can be used for judging the number of pilot frequency optimization iteration, namely whether the optimization number of each cell reaches a preset value; or, it may be determined whether the pilot optimization result of the current round has reached convergence, that is, whether the lifting amplitude of the total transmission rate calculated by the pilot optimization result of the current round compared with the total transmission rate calculated by the pilot optimization result of the previous round is smaller than a preset threshold.
And after pilot frequency distribution optimization of each cell, solving the optimization problem of power distribution. In the case of considering only power optimization, the problem is an optimization problem with the pilot power and data power as variables under the constraint of maximum transmit power. Further, the optimization problem in step S3 is expressed by the following formula:
Figure BDA0003054945730000121
subject to C1：
Figure BDA0003054945730000122
C2：
Figure BDA0003054945730000123
C3：
Figure BDA0003054945730000124
wherein, PmaxIndicating the maximum transmit power, R, of the userminIndicating the user's minimum transmission rate.
Obviously, the above problem is not a convex optimization problem and is difficult to solve. In this respect, in this embodiment, the problem is converted into a convex optimization problem of a lower bound that maximizes the total transmission rate of the system by using a continuous convex approximation method, so that efficient solution can be realized.
First, the inequality is known:
log(1+ωjk)≥f(ωjk，ajk，bjk)＝ajklog(ωjk)+bjk；
the above equation gives the lower bound of the logarithmic function, which is in the form of a linear function. By calculating the coefficient ajkAnd bjkCan be obtained with respect to a certain point
Figure BDA0003054945730000125
The specific calculation method is as follows:
Figure BDA0003054945730000126
meanwhile, in order to ensure that the objective function is convex, variable substitution is required:
Figure BDA0003054945730000127
therefore, in step S3, the optimization problem is solved by computing the following convex optimization problem that is transformed by the optimization problem through a continuous convex approximation method to obtain a lower bound that maximizes the total transmission rate of the system:
Figure BDA0003054945730000128
subject to C1：
Figure BDA0003054945730000129
C2：
Figure BDA00030549457300001210
C3：
Figure BDA00030549457300001211
wherein the content of the first and second substances,
Figure BDA00030549457300001212
Figure BDA0003054945730000131
coefficient of performance
Figure BDA0003054945730000132
Representing the signal-to-interference ratio SINR.
Specifically, through the inequality transformation, the objective function of the optimization problem becomes the sum of a series of convex functions, which is still a convex function, and the rapid solution can be performed by using tool boxes such as CVX. In order to make the lower bound more approximate to the original function, the coefficient a is continuously updated in an iterative modejkAnd bjk。
Therefore, further referring to fig. 3, the step S3 includes the following steps:
s31, calculating the convex optimization problem to obtain the pilot frequency power and the data power of each user as the optimization of the pilot frequency power distribution scheme and the data power distribution scheme;
s32, updating the signal-to-interference ratio and coefficient a of each user according to the pilot frequency power and data power obtained in the step S31 in the power optimization iteration of the current roundjk、bjk；
And S33, judging whether a preset power optimization finishing condition is met, if so, executing the step S4, and if not, returning to the step S31 to perform the next power optimization iteration.
Specifically, the coefficient ajkAnd bjkCan be initialized to ajk1 and b jk0. Judging whether the power optimization ending condition is met, wherein the judgment can be to judge the number of power optimization iterations, namely whether the optimization times of the pilot frequency power distribution scheme and the data power distribution scheme reach preset values; or, it may be determined whether the power optimization result of the current round has reached convergence, that is, whether the lifting amplitude of the total transmission rate calculated by the power optimization result of the current round compared with the total transmission rate calculated by the power optimization result of the previous round is smaller than a preset threshold.
Next, the effect of this embodiment is further explained in a manner of simulation experiment, which specifically includes the following steps:
setting the cell number of a Massive MIMO system to be 3, the user number to be 4, the number of base station antennas to be 10-800, the cell radius to be 500m, the path loss coefficient to be 3.8 and the shadow fading standard deviation to be 8 dB. The comparative schemes are: a random pilot allocation scheme and an optimization scheme to add the proposed power.
As can be seen from fig. 4, the pilot allocation algorithm and the joint pilot allocation and power optimization algorithm based on the iterative hungarian algorithm have better convergence, and the transmission rate of the system can be increased by a lower iteration number. As can be seen from fig. 5, the effect of random pilot allocation is the worst, and the random pilot allocation is severely affected by pilot pollution. After optimization, compared with random pilot frequency distribution, the pilot frequency distribution based on the iterative Hungarian algorithm can improve the transmission rate of an average cell by 0.93 bps/Hz. On the other hand, power optimization plays an important role in the increase of system performance. The combination of pilot allocation and power optimization increases the transmission rate by 2.24bps/Hz compared with the random pilot allocation, and increases the transmission rate by 1.16bps/Hz compared with the random pilot allocation with power optimization. As can be seen from fig. 6, as the number of antennas increases, the average cell transmission rate gradually increases, since uncorrelated noise and small-scale fading are averaged out as the number of antennas increases. The average cell transmission rate is arranged from small to large as: random pilot allocation < pilot allocation based on the hungarian algorithm < random pilot allocation and power optimization < combined pilot allocation and power optimization, and the difference between them increases with the increase of the number of antennas, embodying the validity of the present invention.
In summary, in order to reduce adverse effects of pilot pollution in a Massive MIMO system, on one hand, this embodiment provides a pilot allocation algorithm based on an iterative hungarian algorithm by analyzing an SINR expression of a user, so as to find a pilot allocation scheme that improves a total transmission rate of the system and satisfies a minimum transmission rate of the user with a low-complexity method; on the other hand, the effective inhibition effect of power distribution on pilot frequency pollution is considered, the optimization problem of the maximized system transmission rate under the limitation of the maximum transmission power and the minimum user transmission rate is provided, the non-convex problem is converted into the convex optimization problem of the maximized lower bound through a continuous convex approximation method, and therefore efficient solution is achieved. In addition, in order to further improve the system performance, the pilot frequency allocation and power optimization results are continuously updated in an alternate iteration mode, convergence can be achieved under the condition of lower iteration times, too large calculation complexity is not needed, and the pilot frequency pollution of a Massive MIMO system is reduced, so that the pilot frequency allocation and power optimization method has good guiding significance for improving the throughput of the system and considering user fairness.
Example 2
A pilot frequency and power allocation joint optimization system, which is used for performing pilot frequency and power allocation on users in a multi-cell Massive MIMO system, please refer to fig. 7, and includes an initialization module 1, a pilot frequency allocation iterative optimization module 2, a power allocation iterative optimization module 3, and a joint optimization judgment loop module 4;
the pilot frequency distribution iteration optimization module 2 is connected with the initialization module 1, the power distribution iteration optimization module 3 and the joint optimization iteration judgment module 4, and the power distribution iteration optimization module 3 is connected with the joint optimization judgment loop module 4; wherein:
the initialization module 1 is configured to initialize a pilot allocation scheme, a pilot power allocation scheme, and a data power allocation scheme of each cell;
the pilot frequency allocation iterative optimization module 2 is used for performing iterative optimization on the pilot frequency allocation scheme by adopting a Hungarian algorithm in each cell one by one according to the pilot frequency power allocation scheme and the data power allocation scheme and with the aim of maximizing the total transmission rate of the cells;
the power allocation iterative optimization module 3 is configured to solve an optimization problem that a preset maximum user transmission power and a preset minimum user transmission rate are limited and a maximum system total transmission rate is an objective function according to the pilot frequency allocation scheme, and perform iterative optimization on the pilot frequency power allocation scheme and the data power allocation scheme;
the joint optimization iteration judgment module 4 is configured to judge whether a preset joint optimization ending condition is met, if the preset joint optimization ending condition is met, output the optimized pilot frequency allocation scheme, pilot frequency power allocation scheme and data power allocation scheme, and otherwise, call the pilot frequency allocation iteration optimization module 2 to perform the next round of joint optimization iteration.
Example 3
A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the joint optimization method for pilot and power allocation of embodiment 1.
Example 4
A communication device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable by the processor, the computer program when executed by the processor implementing the steps of the pilot and power allocation joint optimization method of embodiment 1.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A pilot frequency and power allocation joint optimization method is characterized by comprising the following steps:
s1, initializing a pilot frequency distribution scheme, a pilot frequency power distribution scheme and a data power distribution scheme of each cell;
s2, according to the pilot frequency power distribution scheme and the data power distribution scheme, aiming at maximizing the total transmission rate of the cells, iteratively optimizing the pilot frequency distribution scheme by adopting a Hungarian algorithm in each cell one by one;
s3, solving the optimization problem which takes the preset maximum user sending power and the minimum user transmission rate as the limit and takes the maximum system total transmission rate as the objective function according to the pilot frequency distribution scheme, and carrying out iterative optimization on the pilot frequency power distribution scheme and the data power distribution scheme;
and S4, judging whether the preset joint optimization finishing condition is met, if so, outputting the optimized pilot frequency distribution scheme, pilot frequency power distribution scheme and data power distribution scheme, otherwise, returning to the step S2 to perform the next joint iteration.
2. The method of claim 1, wherein in the initialization process of step S1, the pilot allocation scheme of each user is randomly set, the pilot power allocation scheme of each user is the same, and the data power allocation scheme of each user is the same.
3. The method for joint optimization of pilot frequency and power allocation according to claim 1, wherein the step S2 comprises the following steps:
s21, for the optimizing cell, establishing a weight table between the cell user and the pilot frequency distribution according to the pilot frequency power distribution scheme and the data power distribution scheme by using the pilot frequency distribution scheme of other cells as a constant, and constructing a matching problem of solving the maximum weight sum of the weighted bipartite graph by taking the cell total transmission rate maximization as a target;
s22, solving the matching problem by adopting a Hungarian algorithm, and optimizing the pilot frequency allocation scheme of the cell;
s23, judging whether all cells in the pilot frequency optimization iteration of the current round are completely optimized, if so, executing a step S24, otherwise, returning to the step S21 to optimize the next cell;
and S24, judging whether the preset pilot frequency optimization finishing condition is met, if so, executing the step S3, and if not, returning to the step S21 to perform the next pilot frequency optimization iteration.
4. The pilot and power allocation joint optimization method of claim 3, wherein the weight value of the weight table is the sum of user transmission rates of multiplexing the same pilot in the multi-cell Massive MIMO system
Figure FDA0003054945720000011
Figure FDA0003054945720000021
Wherein R isikIndicating the transmission rate of user k in cell i being optimized,
Figure FDA0003054945720000022
indicating the transmission rate of users in other cell j using pilot n;
Rik＝log2(1+SINRik)；
Figure FDA0003054945720000023
Figure FDA0003054945720000024
Figure FDA0003054945720000025
wherein, the SINRik、
Figure FDA0003054945720000026
Represents the signal-to-interference ratio, betaijkRepresenting the large-scale fading coefficient from the user in other cell j to the base station of cell i; m represents the number of base station antennas, L represents the number of cells, and K represents the number of users;
Figure FDA0003054945720000027
the pilot power representing the user is then determined,
Figure FDA0003054945720000028
a data power representing a user;
Figure FDA0003054945720000029
represents the variance of white gaussian noise during pilot transmission,
Figure FDA00030549457200000210
representing the variance of white gaussian noise during data transmission.
5. The method of claim 4, wherein the optimization problem in step S3 is expressed by the following formula:
Figure FDA00030549457200000211
Figure FDA00030549457200000212
Figure FDA00030549457200000213
Figure FDA00030549457200000214
wherein, PmaxIndicating the maximum transmit power, R, of the userminIndicating the user's minimum transmission rate.
6. The method of claim 5, wherein in the step S3, the optimization problem is solved by computing the following convex optimization problem transformed by the optimization problem through successive convex approximation to obtain a lower bound that maximizes the total transmission rate of the system:
Figure FDA00030549457200000215
Figure FDA00030549457200000216
Figure FDA0003054945720000031
Figure FDA0003054945720000032
wherein the content of the first and second substances,
Figure FDA0003054945720000033
Figure FDA0003054945720000034
coefficient of performance
Figure FDA0003054945720000035
Representing the signal-to-interference ratio SINR.
7. The method of claim 6, wherein the step S3 comprises the steps of:
s31, calculating the convex optimization problem to obtain the pilot frequency power and the data power of each user as the optimization of the pilot frequency power distribution scheme and the data power distribution scheme;
s32, updating the signal-to-interference ratio and coefficient a of each user according to the pilot frequency power and data power obtained in the step S31 in the power optimization iteration of the current roundjk、bjk；
And S33, judging whether a preset power optimization finishing condition is met, if so, executing the step S4, and if not, returning to the step S31 to perform the next power optimization iteration.
8. A pilot frequency and power distribution joint optimization system is used for carrying out pilot frequency and power distribution on users under a multi-cell Massive MIMO system and is characterized by comprising an initialization module (1), a pilot frequency distribution iterative optimization module (2), a power distribution iterative optimization module (3) and a joint optimization judgment circulation module (4);
the pilot frequency distribution iterative optimization module (2) is connected with the initialization module (1), the power distribution iterative optimization module (3) and the joint optimization iterative judgment module (4), and the power distribution iterative optimization module (3) is connected with the joint optimization judgment loop module (4); wherein:
the initialization module (1) is used for initializing a pilot frequency allocation scheme, a pilot frequency power allocation scheme and a data power allocation scheme of each cell;
the pilot frequency allocation iterative optimization module (2) is used for performing iterative optimization on the pilot frequency allocation scheme by adopting a Hungarian algorithm in each cell one by one according to the pilot frequency power allocation scheme and the data power allocation scheme and taking the maximization of the total transmission rate of the cells as a target;
the power allocation iterative optimization module (3) is used for solving an optimization problem which is limited by a preset user maximum sending power and a user minimum transmission rate and takes a maximum system total transmission rate as an objective function according to the pilot frequency allocation scheme, and performing iterative optimization on the pilot frequency power allocation scheme and the data power allocation scheme;
the joint optimization iteration judgment module (4) is used for judging whether a preset joint optimization ending condition is met, if so, outputting the optimized pilot frequency distribution scheme, pilot frequency power distribution scheme and data power distribution scheme, and otherwise, calling the pilot frequency distribution iteration optimization module (2) to perform the next round of joint optimization iteration.
9. A storage medium having a computer program stored thereon, the computer program comprising: the computer program when being executed by a processor realizes the steps of the pilot and power allocation joint optimization method according to any of the claims 1 to 7.
10. A communication device, characterized by: comprising a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, which computer program, when being executed by the processor, carries out the steps of the pilot and power allocation joint optimization method according to any of the claims 1 to 7.
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