CN113365334A - Pilot frequency distribution and power control method and system in CF-MIMO - Google Patents

Pilot frequency distribution and power control method and system in CF-MIMO Download PDF

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CN113365334A
CN113365334A CN202110666519.3A CN202110666519A CN113365334A CN 113365334 A CN113365334 A CN 113365334A CN 202110666519 A CN202110666519 A CN 202110666519A CN 113365334 A CN113365334 A CN 113365334A
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pilot
users
downlink
power control
user
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罗志勇
王思宇
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Sun Yat Sen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • 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
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/32—TPC of broadcast or control channels
    • H04W52/325—Power control of control or pilot channels
    • 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

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The invention discloses a method and a system for pilot frequency distribution and power control in CF-MIMO, which uses a large number of access points to serve a small number of users under the same time-frequency resource through a reciprocal channel in a time division duplex mode; estimating a channel by using a pilot frequency sequence in an uplink, acquiring a channel coefficient, and reducing pilot frequency pollution by a greedy pilot frequency algorithm; before downlink transmission, zero-forcing precoding is adopted to process a sending signal in advance, and channel interference is eliminated; in the sending process, the downlink rate can be effectively optimized through a max-min power control algorithm, and the service quality balance enjoyed by the user is ensured. The specific implementation process of the greedy pilot algorithm is as follows: first, pilot sequences are allocated to users in a random allocation mode, and then the downlink rate of each user is calculated and the user with the minimum rate is found. The pilot sequence is reallocated for this user according to the principle of minimum pilot pollution.

Description

Pilot frequency distribution and power control method and system in CF-MIMO
Technical Field
The present invention relates to the field of signal transmission technologies, and in particular, to a method and a system for pilot allocation and power control in CF-MIMO.
Background
The cell-free massive multiple input multiple output system is proposed on the basis of massive MIMO. In an area, a large number of wireless Access Points (APs) are established to provide services for a small number of users, in a Time Division Duplex (TDD) mode, a base station uses the same Time-frequency resource to serve the users, and a Central Processing Unit (CPU) is connected to the APs through a backhaul link, wireless communication and signal processing between the users are performed under the model, the non-cell massive MIMO cancels Division of cells of a conventional cellular network, and the deployment of the access Points is changed from a collocated type to a distributed type. The CF-MIMO can solve the problem of edge users in the traditional massive MIMO technology in a breakthrough manner. The technology has the advantages of wide coverage range, balanced service quality, flexible antenna deployment and low-complexity signal processing capability, and can obviously improve the total spectral efficiency and energy efficiency of the system, so that the CF-MIMO is expected to be applied to 5G, the problem of scarce frequency band is solved, and better communication service is provided for users.
Disclosure of Invention
The invention provides a method and a system for pilot frequency distribution and power control in CF-MIMO, which can reduce the problems of pilot frequency pollution in the CF-MIMO system, signal interference among users and the like, and improve the service quality enjoyed by the users. In the subsequent downlink signal transmission process, zero-forcing pre-coding is carried out on the signals, and an expression of the received signals and an expression of the downlink reachable rate are obtained. Based on these two expressions, we propose a greedy pilot allocation algorithm and a maximum minimum power control algorithm.
The first aspect of the present invention provides a pilot allocation and power control method in CF-MIMO, including:
setting base station access points with the number not less than that of service users in a preset area, and carrying out transmission connection on the users and the base station access points to form a cell-free MIMO system;
in the time division duplex mode, all users simultaneously send pilot frequency sequences to all base station access points, and when all the base station access points calculate and obtain an estimated value of a channel coefficient, the estimated value of the channel coefficient is sent to all the users and a central processing unit;
and the central processing unit calculates a power coefficient according to the large-scale fading coefficient, forms a pre-coding vector through the estimated value of the channel coefficient and the power coefficient, and sends the pre-coding vector to a corresponding base station access point.
Further, after sending the precoding vector to the corresponding base station access point, the method further includes:
and screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
Further, the updating the pilot sequence of the user with the minimum downlink rate includes:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
Further, before the central processing unit calculates the power coefficient according to the large-scale fading coefficient, the method further includes:
the downlink rate is optimized by a max-min power control algorithm.
Further, before optimizing the downlink rate by the max-min power control algorithm, the method includes:
and processing the transmitted signals in advance through zero-forcing precoding to eliminate the channel interference among users.
The second aspect of the present invention provides a pilot allocation and power control system in CF-MIMO, comprising:
the system construction module is used for setting base station access points which are not less than the number of service users in a preset area and carrying out transmission connection on the users and the base station access points to form a cell-free MIMO system;
the uplink working module is used for simultaneously sending pilot frequency sequences to all base station access points by all users in a time division duplex mode, and sending the estimated values of the channel coefficients to all users and the central processing unit after the estimated values of the channel coefficients are obtained by calculation of all the base station access points;
and the downlink working module is used for calculating a power coefficient by the central processing unit according to the large-scale fading coefficient, forming a pre-coding vector through the estimated value of the channel coefficient and the power coefficient, and sending the pre-coding vector to the corresponding base station access point.
Further, the downlink operating module is further configured to:
and screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
Further, the downlink operating module is further configured to:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
Further, the pilot allocation and power control system further includes:
and the downlink preprocessing module is used for optimizing the downlink rate through a max-min power control algorithm.
Further, the downlink preprocessing module is further configured to:
and processing the transmitted signals in advance through zero-forcing precoding to eliminate the channel interference among users.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the invention discloses a method and a system for pilot frequency distribution and power control in CF-MIMO, which uses a large number of access points to serve a small number of users under the same time-frequency resource through a reciprocal channel in a time division duplex mode; a pilot frequency sequence is used for estimating a channel in an uplink to obtain a channel coefficient, a non-orthogonal pilot frequency is used due to the limited number of the pilot frequencies, and a useful signal of a user is seriously influenced by the problem of pilot frequency pollution caused by the non-orthogonal pilot frequency, so that a greedy pilot frequency algorithm is designed to reduce the pilot frequency pollution; before downlink transmission, in order to eliminate the interference between users, zero-forcing precoding is adopted to process a sending signal in advance, and the channel interference between users is eliminated; in the transmitting process, because the interference degrees of signals are different, the same power is used for transmitting, which is not beneficial to improving the frequency spectrum utilization rate and the energy utilization rate, and in order to enable different users to enjoy the same service quality, a max-min power control algorithm is provided, so that the downlink speed can be effectively optimized, and the service quality balance enjoyed by the users is ensured. The specific implementation process of the greedy pilot algorithm is as follows: first, pilot sequences are allocated to users in a random allocation mode, and then the downlink rate of each user is calculated and the user with the minimum rate is found. The pilot sequence is reallocated for this user according to the principle of minimum pilot pollution of the user. The allocation is performed iteratively, and the number of iterations is required to be specified each time. The maximum and minimum power algorithm of the application is mainly used for allocating large power to the user with the minimum downlink rate according to the downlink pilot rate and the power constraint condition. The max-min optimization problem is solved by setting its boundary values and by bisection.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for pilot allocation and power control in CF-MIMO according to an embodiment of the present invention;
fig. 2 is a flowchart of a pilot allocation and power control method in CF-MIMO according to another embodiment of the present invention;
FIG. 3 is a diagram of a system model provided by an embodiment of the invention;
FIG. 4 is pseudo code for implementing a pilot allocation algorithm according to an embodiment of the present invention;
fig. 5 is a comparison of the performance of a CF-MIMO system with or without pilot allocation according to an embodiment of the present invention;
FIG. 6 is pseudo code for implementing a power control algorithm provided by an embodiment of the present invention;
fig. 7 is a comparison of the performance of a CF-MIMO system with or without power control according to an embodiment of the present invention;
fig. 8 is a diagram of an apparatus of a pilot allocation and power control system in CF-MIMO according to an embodiment of the present invention;
fig. 9 is a diagram of an apparatus of a pilot allocation and power control system in CF-MIMO according to another embodiment of the present invention;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention 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.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
A first aspect.
Referring to fig. 1-2, the present application provides a method for pilot allocation and power control in CF-MIMO, comprising:
and S10, setting base station access points not less than the number of service users in a preset area, and carrying out transmission connection on the users and the base station access points to form a cell-free MIMO system.
And S20, in the time division duplex mode, simultaneously sending the pilot sequences to all base station access points by all users, and sending the estimated values of the channel coefficients to all users and a central processing unit after all the base station access points calculate the estimated values of the channel coefficients.
S30, the CPU calculates the power coefficient according to the large scale fading coefficient, forms the pre-coding vector through the estimated value of the channel coefficient and the power coefficient, and sends the pre-coding vector to the corresponding base station access point.
S40, screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
In a specific embodiment, the updating the pilot sequence of the user with the smallest downlink rate includes:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
In another specific embodiment, step S30 is preceded by:
s21, the transmission signal is processed in advance through zero-forcing precoding, and the channel interference among users is eliminated.
And S22, optimizing the downlink rate through a max-min power control algorithm.
In a specific embodiment, the present application provides a method for pilot allocation and power control in CF-MIMO, including:
(1) in a relatively large area without boundaries, M Access Points (APs) are randomly distributed and serve K users simultaneously, the locations of the users are also random, and the number of APs is much larger than the number of users. All APs access a Central Processing Unit (CPU) through a backhaul network. For simplicity of the model, it is assumed that both the user and the base station have a single antenna;
(2) we assume that M APs and K users share time-frequency resources and that this model is established in Time Division Duplex (TDD) mode, whether for uplink transmission procedures (user to AP) or downlink transmission procedures (AP to user). Therefore, a single-antenna user and a single-antenna base station can form a large-scale cell-free MIMO system;
we divide each coherence interval into three phases, respectively: uplink pilot frequency training, downlink signal transmission and uplink signal transmission;
(3) the channel coefficients include the effects of large-scale fading coefficients, which take into account path loss and shadowing, and small-scale fading coefficients. We assume that the small-scale fading coefficient of the channel is constant within one coherence interval, and the coefficient varies independently within different coherence intervals. Compared with the small-scale fading coefficient, the large-scale fading coefficient changes much more slowly and can be kept constant within a few coherence intervals, so that the large-scale fading coefficient can be assumed to be a known constant;
(4) the channel is assumed to be reciprocal, and the uplink and downlink channel states remain unchanged within a coherence interval. This reciprocity requires that communication be performed in a time division duplex mode, and also requires that hardware calibration be perfect;
(5) we use gmkTo represent the channel coefficients between the mth base station access point and the kth user. The channel can be represented as
Figure BDA0003117024990000081
Wherein h ismkRepresenting small scale fading, betamkRepresenting large scale fading. Beta is amkIs a constant independent of frequency. Let us assume hmkE.g. CN (0,1) m 1,2, …, M K1, 2, …, K. We can therefore assume this independent small-scale fading coefficient hmkThe reason for this is that the scatter set between each user and each AP is different because the users and APs are randomly distributed over a large area.
(6) We assume that all APs access the CPU through a perfect backhaul network, the transmission process is error free and the capacity is infinite. In practical situations, however, the backhaul network may have some limitations;
(7) the channel state is assumed to be constant over a coherence interval of time and a frequency coherence interval, and therefore pilot training must be performed within a coherence interval. In a general case, a scene that the moving speed of a user does not exceed 10km/h is considered;
(8) in Time Division Duplex (TDD) mode, all users synchronize pilot sequences simultaneously
Figure BDA0003117024990000082
To M APs, which then send them back to the user after obtaining the estimated values of the channel coefficients. We assume τ is the coherence interval length, whose value is equivalent to the product of the coherence time and the coherence bandwidth; t is the pilot duration in each coherence interval. In addition, τ needs to be satisfied<And T. Pilot sequence of kth and user
Figure BDA0003117024990000083
Satisfy the requirement of
Figure BDA0003117024990000084
(9)ymIs the pilot vector received by the mth AP
Figure BDA0003117024990000085
The mth AP will receive the pilot signal ymFor gmkAnd (6) estimating. Where ρ isrIs the uplink power, wmIs additive white gaussian noise with a mean of 0 and a variance of 1.
Figure BDA0003117024990000091
Is ymIn that
Figure BDA0003117024990000092
Mapping of
Figure BDA0003117024990000093
(10) Although for any pilot sequence
Figure BDA0003117024990000094
Is not gmkBut we can still use
Figure BDA0003117024990000095
A suboptimal estimate is obtained. And in the case where the pilot signals are perfectly orthogonal,
Figure BDA0003117024990000096
is gmkIs a sufficient estimator and is an optimal estimate thereof. At a given point
Figure BDA0003117024990000097
Under the condition of gmkIs that
Figure BDA0003117024990000098
(11) The channel estimation error is:
Figure BDA0003117024990000099
wherein
Figure BDA00031170249900000910
Figure BDA00031170249900000911
Figure BDA00031170249900000912
(12) AP m estimate betamkAnd sends them to the central processor; user-synchronized transmit pilot sequence
Figure BDA00031170249900000913
AP m obtains pilot estimates
Figure BDA00031170249900000914
And sends it to the central processing unit, which is based on the large-scale fading coefficient betamkCalculating power coefficients, the CPU using the channel estimation coefficients
Figure BDA00031170249900000915
And power control coefficient ηmkForming a ZF pre-coding vector and sending the ZF pre-coding vector to a corresponding AP;
(13) we assume that user k only focuses on the estimated channel coefficients
Figure BDA00031170249900000916
This is the effect of channel hardening in massive MIMO;
(14) the precoding matrix of ZF precoding is defined by its pseudo-inverse matrix
Figure BDA00031170249900000917
Wherein
Figure BDA00031170249900000918
Is a matrixAnd G is estimated. From matrix A we have
Figure BDA00031170249900000919
Wherein IkRepresenting an identity matrix of order k when the inter-user interference has been completely cancelled.
(15) We define the power control matrix in CF-MIMO as AZFWherein ═ A-
Figure BDA0003117024990000101
As indicates a hadamard product. Matrix AZFAn element of (1) can be represented as
Figure BDA0003117024990000102
To ensure
Figure BDA0003117024990000103
For diagonal matrices, η must be guaranteed1k＝η2k＝…＝ηMkIn this way the power control coefficient is a function of a single variable k, i.e. etamk＝ηk. Then we can get
Figure BDA0003117024990000104
Wherein P is a diagonal element of
Figure BDA0003117024990000105
A diagonal matrix of (a);
(16) the signal received by the kth user is
Figure BDA0003117024990000106
Wherein g isk＝[g1k,g2k…,gMk]T，s＝[s1,s2…,sK]T
ykIn the expression, the first part is the useful signal and the second part is the channel estimation error.
(17) According to the shannon formula, the expression of the reachable rate of the downlink can be expressed as
Figure BDA0003117024990000107
Wherein
Figure BDA0003117024990000108
γkiIs gammakThe ith element of
Figure BDA0003117024990000109
Figure BDA00031170249900001010
Is a diagonal element of betamk-mkA diagonal matrix of (a);
(18) in general, different users will use non-orthogonal pilot sequences since the length of the coherence interval is always limited. We can allocate K orthogonal pilots to K users when τ > K. However, when τ is less than or equal to K, the allocation method is the random pilot allocation mentioned earlier, and greedy pilot allocation is introduced on the basis of the random allocation to reduce pilot pollution.
(19) The greedy pilot algorithm optimizes pilot allocation in an iterative manner. First we randomly assign pilot sequences to K users. Then we find out the user with the minimum downlink rate (user K) from the K users*) Updating the pilot sequence to minimize pilot pollution;
(20) user K*Pilot pollution we can quantify with the following formula
Figure BDA0003117024990000111
(21) For the channel from the user to the mth AP, we constrain its new pilot sequence by the above equation to minimize its pilot pollution. Then we have for all of the APs,
Figure BDA0003117024990000112
here we still need to satisfy the pilot sequence
Figure BDA0003117024990000113
(22) We use
Figure BDA0003117024990000114
And
Figure BDA0003117024990000115
respectively represent matrix AzFAnd
Figure BDA0003117024990000116
then we define a vector deltamIs provided with
Figure BDA0003117024990000117
(23) For the transmit power of AP m, we have the following definitions:
Figure BDA0003117024990000118
Figure BDA0003117024990000121
(24) we will formulate a maximum minimum power allocation algorithm (max-min) under each antenna power constraint. We allocate the maximum transmit power to the user with the lowest downlink rate. We obtained in formula 3.8The expression of the downlink reachable rate is
Figure BDA0003117024990000122
Figure BDA0003117024990000123
Figure BDA0003117024990000124
Is dependent on the SINRk,ZFTherefore, we can have the following expressions and constraints
Figure BDA0003117024990000125
Figure BDA0003117024990000126
(25) When the power control coefficient is only related to k, zero-forcing precoding can completely cancel the interference of other users. Nevertheless, the transmission power from AP m to user k
Figure BDA0003117024990000127
Is a function of both variables m and k.
(26) At SINRk,ZFIn the expression (c), the numerator thereof is a linear function with respect to η, and thus the SINRk,ZFIs a quasi-linear function, we can use a bisection method to solve the optimization problem of formula (I). The feasibility problems of each step of the bisection method will translate into
findη
s.t.SINRk,ZF(η)≥t
Figure BDA0003117024990000128
(27) If we want to verify the SINRk,ZF(eta) is not less than t, only needs to verify SINRk,ZF(η) is equivalent to solving a linear equation of KThe convex optimization problem is avoided.
In another embodiment, referring to fig. 3, a schematic diagram of a large-scale MIMO without cell according to the present invention, a method for implementing the large-scale MIMO without cell, a base station, a user terminal and a channel are configured as follows:
(1) in a relatively large area without boundaries, M Access Points (APs) are randomly distributed and serve K users simultaneously, the locations of the users are also random, and the number of APs is much larger than the number of users. All APs access a Central Processing Unit (CPU) through a backhaul network. For simplicity of the model, it is assumed that both the user and the base station have a single antenna;
(2) we assume that M APs and K users share time-frequency resources and that this model is established in Time Division Duplex (TDD) mode, whether for uplink transmission procedures (user to AP) or downlink transmission procedures (AP to user). Therefore, a single-antenna user and a single-antenna base station can form a large-scale cell-free MIMO system;
(3) we divide each coherence interval into three phases, respectively: uplink pilot frequency training, downlink signal transmission and uplink signal transmission; the channel is assumed to be reciprocal, and the uplink and downlink channel states remain unchanged within a coherence interval.
(4) We use gmkTo represent the channel coefficients between the mth base station access point and the kth user. The channel can be represented as
Figure BDA0003117024990000131
Wherein h ismkRepresenting small scale fading, betamkRepresenting large scale fading. Beta is amkIs a constant independent of frequency. Let us assume hmkE.g. CN (0,1) M1, 2, …, M1, 2, …, K. We can therefore assume this independent small-scale fading coefficient hmkThe reason for this is that the scatter set between each user and each AP is different because the users and APs are randomly distributed over a large area.
(5) In thatAll users simultaneously synchronizing pilot frequency sequence in Time Division Duplex (TDD) mode
Figure BDA0003117024990000141
To M APs, which then send them back to the user after obtaining the estimated values of the channel coefficients. We assume τ is the coherence interval length, whose value is equivalent to the product of the coherence time and the coherence bandwidth; t is the pilot duration in each coherence interval. In addition, τ needs to be satisfied<And T. Pilot sequence of kth and user
Figure BDA0003117024990000142
Satisfy the requirement of
Figure BDA0003117024990000143
(6)ymIs the pilot vector received by the mth AP
Figure BDA0003117024990000144
The mth AP will receive the pilot signal ymFor gmkAnd (6) estimating. Where ρ isrIs the uplink power, wmIs additive white gaussian noise with a mean of 0 and a variance of 1.
Figure BDA0003117024990000145
Is ymIn that
Figure BDA0003117024990000146
Mapping of
Figure BDA0003117024990000147
(7) At a given point
Figure BDA0003117024990000148
Under the condition of gmkIs that
Figure BDA0003117024990000149
Figure BDA00031170249900001410
At a given point
Figure BDA00031170249900001411
Under the condition of gmkIs that
Figure BDA00031170249900001412
Figure BDA00031170249900001413
The channel estimation error is
Figure BDA00031170249900001414
Wherein
Figure BDA00031170249900001415
Figure BDA00031170249900001416
Figure BDA00031170249900001417
(8) AP m estimate betamkAnd sends them to the central processor; user-synchronized transmit pilot sequence
Figure BDA00031170249900001418
AP m obtains pilot estimates
Figure BDA00031170249900001419
And sends it to the central processing unit, which is based on the large-scale fading coefficient betamkCalculating power coefficients, the CPU using the channel estimation coefficients
Figure BDA00031170249900001420
And power control coefficient ηmkForm ZF precoding vectors and applyIt sends it to the corresponding AP;
(9) the precoding matrix of ZF precoding is defined by its pseudo-inverse matrix
Figure BDA0003117024990000151
Wherein
Figure BDA0003117024990000152
Is an estimate of the matrix G. From matrix A we have
Figure BDA0003117024990000153
Wherein IkRepresenting an identity matrix of order k when the inter-user interference has been completely cancelled.
(10) We define the power control matrix in CF-MIMO as AZFWherein ═ A-
Figure BDA0003117024990000154
As indicates a hadamard product. Matrix AZfAn element of (1) can be represented as
Figure BDA0003117024990000155
To ensure
Figure BDA0003117024990000156
For diagonal matrices, η must be guaranteed1k＝η2k＝…＝ηMkIn this way the power control coefficient is a function of a single variable k, i.e. etamk＝ηk. Then we can get
Figure BDA0003117024990000157
Wherein P is a diagonal element of
Figure BDA0003117024990000158
A diagonal matrix of (a);
(11) the signal received by the kth user is
Figure BDA0003117024990000159
Wherein g isk＝[g1k,g2k…,gMk]T，s＝[s1,s2…,sK]T
(12)ykIn the expression, the first part is the useful signal and the second part is the channel estimation error.
(13) According to the shannon formula, the expression of the reachable rate of the downlink can be expressed as
Figure BDA00031170249900001510
Wherein
Figure BDA00031170249900001511
γkiIs gammakThe ith element of
Figure BDA0003117024990000161
Figure BDA0003117024990000162
Is a diagonal element of betamk-mkA diagonal matrix of (a);
the greedy pilot algorithm pseudo-code is shown in FIG. 4;
(14) when tau is>K we can allocate K orthogonal pilots to K users. However, when τ is less than or equal to K, the allocation method is the random pilot allocation mentioned earlier, and greedy pilot allocation is introduced on the basis of the random allocation to reduce pilot pollution. The greedy pilot algorithm optimizes pilot allocation in an iterative manner. First we randomly assign pilot sequences to K users. Then we find out the user with the minimum downlink rate (user K) from the K users*) Update itPilot sequence to minimize pilot pollution;
comparison of system performance with and without pilot allocation algorithm see figure 5
(15) In fig. 5, we compare the random pilot allocation with the greedy pilot allocation, where the ordinate is the cumulative distribution function and the abscissa is the downlink rate. We initially set the number of APs M to 40, the number of users K to 10, and the coherence interval, i.e., the pilot sequence length, to 6. According to the illustrated result, the downlink rate of the user is generally lower than that of the greedy pilot frequency allocation during the random pilot frequency allocation, and the maximum rate of the user under the greedy pilot frequency allocation is greater than that of the random pilot frequency allocation, so that the performance of the greedy pilot frequency pairing system is optimized.
(16) We use
Figure BDA0003117024990000163
And
Figure BDA0003117024990000164
respectively represent matrix AzFAnd
Figure BDA0003117024990000165
then we define a vector deltamIs provided with
Figure BDA0003117024990000166
(17) For the transmit power of AP m, we have the following definitions:
Figure BDA0003117024990000167
Figure BDA0003117024990000171
the maximum and minimum power control algorithm implementation pseudo code is shown in fig. 6;
(18) we will be under per antenna power constraintsA maximum and minimum power allocation algorithm (max-min) is formulated. We allocate the maximum transmit power to the user with the lowest downlink rate. We obtain the expression for the downlink achievable rate in the above text as
Figure BDA0003117024990000172
Figure BDA0003117024990000173
Figure BDA0003117024990000174
Is dependent on the SINRk,ZFTherefore, we can have the following expressions and constraints
Figure BDA0003117024990000175
Figure BDA0003117024990000176
Comparison of System Performance Using maximum minimum Power control and No Power control see FIG. 7
(19) In fig. 7, we compare no power control with maximum and minimum power control, where the ordinate is the cumulative distribution function and the abscissa is the downlink rate. We initially set the number of APs M to 40, the number of users K to 10, and the coherence interval, i.e., the pilot sequence length τ to 6. The downlink rate of the user is lower than the maximum minimum power control without power control, and the maximum rate of the downlink reachable by the user is much higher than without power control under the maximum minimum power control. According to the results shown, the max/min power control algorithm improves the system performance.
In summary, the pilot allocation and power control algorithm in the CF-MIMO system proposed by the present invention is characterized in that a greedy pilot algorithm is applied during uplink pilot training, a pilot sequence is allocated to users in a random allocation manner, and then the downlink rate of each user is calculated and the user with the minimum rate is found. We reallocate the pilot sequence for this user according to the principle of minimum interference between users. The allocation is performed iteratively, and the number of iterations is required to be specified each time. The maximum and minimum power algorithm in the invention is mainly based on the downlink pilot frequency rate and the power constraint condition to allocate large power for the user with the minimum downlink rate. We set their boundary values and solve the max-min optimization problem by dichotomy.
The above embodiments are only examples of the present invention, and the embodiments of the present invention are not limited by the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be regarded as equivalent replacements within the protection scope of the present invention.
A second aspect.
Referring to fig. 8-9, an embodiment of the present invention provides a pilot allocation and power control system in CF-MIMO, including:
a system construction module 10, configured to set base station access points that are not less than the number of service users in a preset area, and perform transmission connection between the users and the base station access points to form a cell-free MIMO system;
the uplink working module 20 is configured to send pilot sequences to all base station access points simultaneously for all users in a time division duplex mode, and send the estimated value of the channel coefficient to all users and the central processing unit after the estimated value of the channel coefficient is calculated by all the base station access points;
and the downlink working module 30 is used for the central processing unit to calculate a power coefficient according to the large-scale fading coefficient, form a precoding vector through the estimation value of the channel coefficient and the power coefficient, and send the precoding vector to the corresponding base station access point.
In a specific embodiment, the downlink operating module 30 is further configured to:
and screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
In a specific embodiment, the downlink operating module 30 is further configured to:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
In a specific embodiment, the method further comprises:
a downlink pre-processing module 40 for optimizing the downlink rate by a max-min power control algorithm.
In a specific embodiment, the downlink preprocessing module 40 is further configured to:
and processing the transmitted signals in advance through zero-forcing precoding to eliminate the channel interference among users.
In a third aspect.
The present invention provides an electronic device, including:
a processor, a memory, and a bus;
the bus is used for connecting the processor and the memory;
the memory is used for storing operation instructions;
the processor is configured to invoke the operation instruction, and the executable instruction causes the processor to perform operations corresponding to the pilot allocation and power control method in CF-MIMO as shown in the first aspect of the present application.
In an alternative embodiment, an electronic device is provided, as shown in fig. 10, an electronic device 5000 shown in fig. 10 includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are coupled, such as via a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that the transceiver 5004 is not limited to one in practical application, and the structure of the electronic device 5000 is not limited to the embodiment of the present application.
The processor 5001 may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 5001 may also be a combination of processors implementing computing functionality, e.g., a combination comprising one or more microprocessors, a combination of DSPs and microprocessors, or the like.
Bus 5002 can include a path that conveys information between the aforementioned components. The bus 5002 may be a PCI bus or EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 10, but this is not intended to represent only one bus or type of bus.
The memory 5003 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 5003 is used for storing application program codes for executing the present solution, and the execution is controlled by the processor 5001. The processor 5001 is configured to execute application program code stored in the memory 5003 to implement the teachings of any of the foregoing method embodiments.
Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like.
A fourth aspect.
The present invention provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements a method for pilot allocation and power control in CF-MIMO as shown in the first aspect of the present application.
Yet another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when run on a computer, enables the computer to perform the corresponding content in the aforementioned method embodiments.

Claims (10)

1. A method for pilot allocation and power control in CF-MIMO, comprising:
setting base station access points with the number not less than that of service users in a preset area, and carrying out transmission connection on the users and the base station access points to form a cell-free MIMO system;
in the time division duplex mode, all users simultaneously send pilot frequency sequences to all base station access points, and when all the base station access points calculate and obtain an estimated value of a channel coefficient, the estimated value of the channel coefficient is sent to all the users and a central processing unit;
and the central processing unit calculates a power coefficient according to the large-scale fading coefficient, forms a pre-coding vector through the estimated value of the channel coefficient and the power coefficient, and sends the pre-coding vector to a corresponding base station access point.
2. The method of claim 1, wherein after sending the precoding vector to the corresponding bs ap, the method further comprises:
and screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
3. The method for pilot allocation and power control in CF-MIMO according to claim 2, wherein said updating the pilot sequence of the user with the smallest downlink rate comprises:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
4. The method as claimed in claim 1, wherein before said cpu calculates the power coefficient according to the large-scale fading coefficient, the method further comprises:
the downlink rate is optimized by a max-min power control algorithm.
5. The method of claim 1, wherein before optimizing the downlink rate by the max-min power control algorithm, the method comprises:
and processing the transmitted signals in advance through zero-forcing precoding to eliminate the channel interference among users.
6. A system for pilot allocation and power control in CF-MIMO, comprising:
the system construction module is used for setting base station access points which are not less than the number of service users in a preset area and carrying out transmission connection on the users and the base station access points to form a cell-free MIMO system;
the uplink working module is used for simultaneously sending pilot frequency sequences to all base station access points by all users in a time division duplex mode, and sending the estimated values of the channel coefficients to all users and the central processing unit after the estimated values of the channel coefficients are obtained by calculation of all the base station access points;
and the downlink working module is used for calculating a power coefficient by the central processing unit according to the large-scale fading coefficient, forming a pre-coding vector through the estimated value of the channel coefficient and the power coefficient, and sending the pre-coding vector to the corresponding base station access point.
7. The system for pilot allocation and power control in CF-MIMO according to claim 6, wherein said downlink operation module is further configured to:
and screening the user with the minimum downlink speed through a random pilot frequency distribution mode and greedy pilot frequency distribution, and updating the pilot frequency sequence of the user with the minimum downlink speed to ensure that the pilot frequency pollution is minimum.
8. The system for pilot allocation and power control in CF-MIMO according to claim 7, wherein said downlink operation module is further configured to:
and reallocating the pilot sequences for the users with the minimum downlink rate according to the principle of minimum pilot pollution.
9. The system for pilot allocation and power control in CF-MIMO according to claim 6, further comprising:
and the downlink preprocessing module is used for optimizing the downlink rate through a max-min power control algorithm.
10. The system of claim 9, wherein the downlink pre-processing module is further configured to:
and processing the transmitted signals in advance through zero-forcing precoding to eliminate the channel interference among users.
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