CN115865148A - De-cellular MIMO robust beamforming method under non-ideal channel - Google Patents

De-cellular MIMO robust beamforming method under non-ideal channel Download PDF

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CN115865148A
CN115865148A CN202310134418.0A CN202310134418A CN115865148A CN 115865148 A CN115865148 A CN 115865148A CN 202310134418 A CN202310134418 A CN 202310134418A CN 115865148 A CN115865148 A CN 115865148A
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error
user
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CN115865148B (en
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高文奂
张余
张治中
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Nanjing University of Information Science and Technology
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Abstract

The invention discloses a de-cellular MIMO robust beamforming method under a non-ideal channel, which comprises the following steps: constructing a frame of the de-cellular distributed massive MIMO; acquiring parameters required by a frame; adding error constraint to the parameter error, and constructing an error channel model; constructing a power minimization model under the channel error constraint of an achievable rate and error channel model, and improving the robustness of the model; an objective function of a power minimization model from
Figure ZY_1
The norm minimization problem is approximated as a convex weighted
Figure ZY_2
A norm problem, and sufficient sparsity is obtained through iteration; converting the reachable rate constraint condition into a linear matrix inequality model; converting the channel error constraint into a linear matrix inequality model; and converting the obtained model into an SDP problem, and solving through a convex optimization tool box to obtain an optimal beamforming vector. The invention ensures the user reachable rate under the condition of channel error, and minimizes the system transmission power while meeting the service quality.

Description

De-cellular MIMO robust beamforming method under non-ideal channel
Technical Field
The invention relates to a beam forming design method with robustness, in particular to a de-cellular MIMO robust beam forming method under a non-ideal channel.
Background
The cellular network architecture greatly improves the spectrum efficiency through frequency reuse and cell splitting technologies, provides powerful support for the rapid development of mobile communication, but the continuous reduction of the cell area gradually increases the inter-cell interference and the complexity of handover, so that the improvement of the performance of the mobile communication system is subjected to bottleneck.
The concept of the traditional cell is removed from the cellular large-scale distributed MIMO (Multiple-Input Multiple-Output), the idea of 'taking users as the center' is introduced, the distance between the users and the Access points is shortened by deploying a large number of distributed Access Points (APs), and the spatial macro diversity gain is obtained, so that the whole area is uniformly covered, and the interference between the users is reduced by utilizing the Favorable Propagation (robust Propagation) brought by the large number of Access points. However, in the prior art, the research on the massive distributed MIMO is mainly based on the conventional theory and is based on ideal channel state information, and the information theory analysis of the decellularized massive MIMO system based on the ideal channel state information is not very accurate. In the de-cellular large-scale distributed MIMO system, due to the limited computation capability of the AP, the complexity of the AP is still high although the conventional linear minimum mean square error can obtain better estimation performance. Whether a low-complexity deep learning algorithm can be used for improving the channel accuracy of the large-scale cellular MIMO system still needs to be solved. With the gradual application and development of mobile communication networks, the rapid increase of the number of mobile users and mobile devices inevitably brings about a large number of deployments of the AP, a radio frequency circuit of high-precision hardware adopted by the AP consumes huge energy, and the problem of energy consumption is always and increasingly severe.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a de-cellular MIMO robust beamforming method under a non-ideal channel for realizing the minimization of system transmission power under the environment of the non-ideal channel.
The technical scheme is as follows: in order to achieve the above object, the robust beamforming method for de-cellular MIMO under non-ideal channel according to the present invention comprises the following steps:
s1: constructing a frame of the de-cellular distributed massive MIMO;
s2: acquiring parameters required by a frame;
s3: adding error constraint to the obtained parameter error, and constructing an error channel model;
s4: constructing a power minimization model under the channel error constraint of the user reachable rate and error channel model, and improving the robustness of the model;
s5: an objective function of a power minimization model from
Figure SMS_1
The norm minimization problem being approximated as a convex weighting
Figure SMS_2
A norm problem, and sufficient sparsity is obtained through iteration;
s6: converting the reachable rate constraint condition into a linear matrix inequality model;
s7: converting the channel error constraint into a linear matrix inequality model;
s8: and (5) converting the models obtained in the steps S5 to S7 into an SDP problem, and solving through a convex optimization tool box to obtain an optimal beamforming vector.
The step S1 of constructing a frame for de-cellular distributed massive MIMO specifically includes: the method comprises the steps that a large-scale cellular-removing distributed MIMO system which is provided with M transmitting antennas and L access points and used for serving K single-antenna users is deployed, the access points are responsible for data transmission, and a central processing unit is responsible for data processing, wherein N is the number of the transmitting antennas, L is the number of the access points, and K is the number of the single-antenna users.
Step S3, adding error constraint to the obtained parameter error, and constructing an error channel model specifically includes:
the channel error model is:
Figure SMS_4
wherein is present>
Figure SMS_7
Indicates the user is present>
Figure SMS_9
Indicating all access points to user->
Figure SMS_5
Is selected based on the true channel of (4)/', "is selected to be in>
Figure SMS_6
To estimate a channel, is>
Figure SMS_8
For error vectors, the constraint relationship is:
Figure SMS_10
，
Figure SMS_3
Is an error constraint;
estimating a channel
Figure SMS_11
And large scale fading is known on the estimated channel, so the total power is calculated based on the estimated channel, and the user->
Figure SMS_12
Receive signal of>
Figure SMS_13
Comprises the following steps:
Figure SMS_14
；
In the formula (I), the compound is shown in the specification,
Figure SMS_16
subscriber for all APs>
Figure SMS_19
Based on the transmit beamforming vector, < > v>
Figure SMS_21
Represents a conjugate transpose of the matrix h>
Figure SMS_17
Indicates that the user is removed>
Figure SMS_20
Any outside user, then>
Figure SMS_22
，
Figure SMS_23
Respectively indicate the user->
Figure SMS_15
Is expected to be 0, a gaussian distribution with a standard deviation of 1 and a reception noise is expected to be 0, and a standard deviation of->
Figure SMS_18
Gaussian distribution of (a).
S4, constructing a power minimization model under the channel error constraint of the user reachable rate and the error channel model, and improving the robustness of the model, specifically comprising the following steps:
user' s
Figure SMS_24
The signal to interference ratio SINR of (b) is:
Figure SMS_25
Introducing shannon formula and user->
Figure SMS_26
The achievable rates are:
Figure SMS_27
In which>
Figure SMS_28
Is an auxiliary variable;
the power consumed by the AP is:
Figure SMS_29
；
Figure SMS_30
for a power transfer efficiency factor, is>
Figure SMS_31
For the antenna to transmit power, is>
Figure SMS_32
Is the minimum power when AP is active>
Figure SMS_33
Power consumed when the AP is sleeping;
user
Figure SMS_34
The model for minimizing the total power of the system under the constraint of the achievable rate and the channel error is expressed as follows:
Figure SMS_35
；/>
in the formula (I), the compound is shown in the specification,
Figure SMS_41
subscriber for all APs>
Figure SMS_38
Transmit beamforming vector of (a)>
Figure SMS_43
For one of the access points, is>
Figure SMS_39
Is slave access point->
Figure SMS_42
Is transmitted to the user>
Figure SMS_46
The beamforming vector of (a) is calculated, if a fifth or fifth letter>
Figure SMS_50
AP not subscriber->
Figure SMS_44
Service, then>
Figure SMS_48
，
Figure SMS_36
Indicates a non-zero condition>
Figure SMS_40
Is greater than or equal to>
Figure SMS_45
Is the power difference between active and dormant AP->
Figure SMS_49
，
Figure SMS_47
For minimum signal-to-interference ratio constraints for all users, a value is based on the sum of the values of the sum>
Figure SMS_51
In order for the user to be able to reach the rate constraint,
Figure SMS_37
is a non-ideal channel constraint.
Step S5 of minimizing the objective function of the model from
Figure SMS_52
The norm minimization problem is approximated as being a convex weighted->
Figure SMS_53
Norm problem and obtaining sufficient sparsity through iteration, comprising the following sub-steps:
s501: because the AP dormancy power is constant, the optimization result is not influenced, the optimization result is removed, the objective function is deformed, and if the optimization result is used, the objective function is deformed
Figure SMS_54
The square of the norm is substituted>
Figure SMS_55
Norm>
Figure SMS_56
The total number of norms is kept unchanged, and the power difference between the AP active mode and the AP sleep mode is expressed as follows:
Figure SMS_57
；
S502: according to the theory of compressed sensing,
Figure SMS_58
the norm minimization problem can be approximated as a convex weighted->
Figure SMS_59
If the norm problem is present, the user->
Figure SMS_60
The optimization problem for minimizing the total power of the system under the constraint of the achievable rate and the channel error can be approximately expressed as follows:
Figure SMS_61
，
Figure SMS_62
is an access point>
Figure SMS_63
To the user>
Figure SMS_64
The weight occupied;
s503: will be provided with
Figure SMS_65
After combining the same terms, the following formula is obtained:
Figure SMS_66
；
S504: constant iteration weight
Figure SMS_67
And continuously solving the formula in the step S503 by using the iterated weights to finally obtain an optimal solution for the length of the time interval corresponding to the length of the time interval>
Figure SMS_68
The iterative reweighting formula is:
Figure SMS_69
Wherein is present>
Figure SMS_70
Is a positive index, is selected>
Figure SMS_71
，
Figure SMS_72
The prevention denominator is a very small positive number of 0.
The step S6 of converting the reachable rate constraint condition into a linear matrix inequality includes the following substeps:
s601: processing the constraint condition, and the user
Figure SMS_73
The achievable rate constraint and the channel error constraint are respectively:
Figure SMS_74
(1) ，/>
Figure SMS_75
(2)；
s602: handling users
Figure SMS_76
The rate constraint can be reached, and the left side of the inequality is approximated by the first order Taylor equation to a lower bound as follows:
is provided with
Figure SMS_77
Is iterated>
Figure SMS_78
A sub-optimal solution, then >>
Figure SMS_79
The lower linear bound of (c) is:
Figure SMS_80
In which
Figure SMS_81
Processing the user->
Figure SMS_82
The achievable rate constraints are:
Figure SMS_83
；
s603: introducing S-theorem, and converting the constraint into a linear matrix inequality model as follows:
Figure SMS_84
；(3)
wherein I is an identity matrix, I M Is an identity matrix of M multiplied by M,
Figure SMS_85
represents->
Figure SMS_86
Is evaluated by the evaluation unit>
Figure SMS_87
Are sparse variables.
The converting of the channel error constraint into a linear matrix inequality in step S7 includes the following substeps:
s701: the channel error constraint equation is written as follows using schur's complement:
Figure SMS_88
in which>
Figure SMS_89
；
S702: according to the Nemmarvenski theorem, and introducing sparse variables
Figure SMS_90
Converting the formula in the step S701 into a linear matrix inequality model:
Figure SMS_91
(4)。
step S8, converting the models obtained in the steps S5 to S7 into an SDP problem, and solving the SDP problem through a convex optimization tool box to obtain an optimal beamforming vector, wherein the method comprises the following substeps:
s801: converting the models obtained in the steps S5 to S7 into SDP problems:
Figure SMS_92
；
s802: according to
Figure SMS_93
Get->
Figure SMS_94
The optimal beamforming vector for this SDP problem.
Has the advantages that: the invention has the following advantages: 1. the AP transmitting power in the de-cellular large-scale distributed MIMO can be minimized in the non-ideal channel environment by the robust beam forming design, and can be applied to the fields of 5G,6G mobile communication and the like;
2. the method has the characteristics of low operation complexity and less convergence times while ensuring the robustness of the model, can reduce the operation amount of the system, improve the speed of the system for solving the model, achieve the effect of solving the optimal solution of the problem in the shortest time, and achieve the aim of minimizing the transmitting power.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of the de-cellular distributed massive MIMO of the present invention;
FIG. 3 is a pseudo-code diagram of the iterative algorithm of the present invention.
Detailed description of the preferred embodiments
The technical solution of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.
As shown in fig. 1, the method for robust beamforming in de-cellular MIMO under non-ideal channel according to the present invention includes the following steps:
s1: constructing a frame of the de-cellular distributed massive MIMO;
s2: acquiring parameters required by a frame;
s3: adding error constraint to the obtained parameter error, and constructing an error channel model;
s4: constructing a power minimization model under the channel error constraint of the user reachable rate and error channel model, and improving the robustness of the model;
s5: an objective function of a power minimization model from
Figure SMS_95
The norm minimization problem being approximated as a convex weighting
Figure SMS_96
A norm problem, and sufficient sparsity is obtained through iteration;
s6: converting the reachable rate constraint condition into a linear matrix inequality model;
s7: converting the channel error constraint into a linear matrix inequality model;
s8: and (5) converting the models obtained in the steps S5 to S7 into an SDP problem, and solving through a convex optimization tool box to obtain an optimal beamforming vector.
As shown in fig. 2, the step S1 of constructing a frame for de-cellular distributed massive MIMO specifically includes: the method comprises the steps that a large-scale cellular-removing distributed MIMO system which is provided with M transmitting antennas and L access points and used for serving K single-antenna users is deployed, the access points are responsible for data transmission, and a central processing unit is responsible for data processing, wherein N is the number of the transmitting antennas, L is the number of the access points, and K is the number of the single-antenna users.
Step S3, adding error constraint to the obtained parameter error, and constructing an error channel model specifically includes:
the channel error model is:
Figure SMS_98
in which>
Figure SMS_101
Indicates the user is present>
Figure SMS_103
Indicating all access points to user->
Figure SMS_99
Is selected based on the true channel of (4)/', "is selected to be in>
Figure SMS_100
For estimating a channel, <' > based on a time period>
Figure SMS_102
For error vectors, the constraint relationship is:
Figure SMS_104
，
Figure SMS_97
Is an error constraint;
estimating a channel
Figure SMS_105
And large scale fading is known on the estimated channel, so the total power is calculated based on the estimated channel, and the user->
Figure SMS_106
Receive signal of>
Figure SMS_107
Comprises the following steps:
Figure SMS_108
；/>
In the formula (I), the compound is shown in the specification,
Figure SMS_111
subscriber for all APs>
Figure SMS_114
Transmit beamforming vector of (a)>
Figure SMS_116
Represents a conjugate transpose of the matrix h>
Figure SMS_109
Indicates that the user is removed>
Figure SMS_113
Any outside user, then>
Figure SMS_115
，
Figure SMS_117
Respectively indicate the user->
Figure SMS_110
Is expected to be 0, a gaussian distribution with a standard deviation of 1 and a reception noise is expected to be 0, and a standard deviation of->
Figure SMS_112
A gaussian distribution of (a).
S4, constructing a power minimization model under the channel error constraint of the user reachable rate and the error channel model, and improving the robustness of the model, specifically comprising the following steps:
user' s
Figure SMS_118
The signal to interference ratio SINR of (b) is:
Figure SMS_119
Introducing shannon formula and user->
Figure SMS_120
The achievable rates are:
Figure SMS_121
Wherein->
Figure SMS_122
Is an auxiliary variable;
the power consumed by the AP is:
Figure SMS_123
；
Figure SMS_124
for a power transfer efficiency factor, is>
Figure SMS_125
For the antenna to transmit power, is>
Figure SMS_126
Is the minimum power when the AP is active>
Figure SMS_127
Power consumed when the AP is sleeping;
user' s
Figure SMS_128
The model for minimizing the total power of the system under the constraint of the achievable rate and the channel error is expressed as follows:
Figure SMS_129
；
in the formula (I), the compound is shown in the specification,
Figure SMS_141
subscriber for all APs>
Figure SMS_132
Based on the transmit beamforming vector, < > v>
Figure SMS_137
For one of the access points, is>
Figure SMS_138
Is slave access point->
Figure SMS_142
Is transmitted to the user>
Figure SMS_143
If the beamforming vector is ^ h>
Figure SMS_145
AP not subscriber->
Figure SMS_135
Service, then>
Figure SMS_139
，
Figure SMS_130
Representing non-zero>
Figure SMS_134
In a number of>
Figure SMS_133
Is the power difference between the AP is active and the AP is dormant>
Figure SMS_136
，
Figure SMS_140
For minimum signal-to-interference ratio constraints for all users, a value is based on the sum of the values of the sum>
Figure SMS_144
In order for the user to be able to reach the rate constraint,
Figure SMS_131
is a non-ideal channel constraint.
Step S5 of minimizing the target function of the modelNumber from
Figure SMS_146
The norm minimization problem is approximated as being a convex weighted->
Figure SMS_147
Norm problem and obtaining sufficient sparsity through iteration, comprising the following sub-steps:
s501: because the AP dormancy power is constant, the optimization result is not influenced, the optimization result is removed, the objective function is deformed, and if the optimization result is used, the objective function is deformed
Figure SMS_148
Square of norm for replacement>
Figure SMS_149
Norm>
Figure SMS_150
The total number of norms is kept unchanged, and the power difference between the AP active mode and the AP sleep mode is expressed as follows:
Figure SMS_151
；
S502: according to the theory of compressed sensing,
Figure SMS_152
the norm minimization problem can be approximated as a convex weighted->
Figure SMS_153
If the norm problem is present, the user->
Figure SMS_154
The optimization problem of the total power minimization of the system under the constraint of the achievable rate and the channel error can be approximately expressed as:
Figure SMS_155
，
Figure SMS_156
is an access point->
Figure SMS_157
To the user>
Figure SMS_158
The weight occupied;
s503: will be provided with
Figure SMS_159
After combining the same terms, the following formula is obtained:
Figure SMS_160
；
S504: as shown in fig. 3, the weights are iterated continuously
Figure SMS_161
And continuously solving the formula in the step S503 by using the iterated weights to finally obtain an optimal solution for the->
Figure SMS_162
The iterative reweighting formula is:
Figure SMS_163
Wherein, in the step (A),
Figure SMS_164
is a positive index, is selected>
Figure SMS_165
，
Figure SMS_166
The prevention denominator is a very small positive number of 0.
The step S6 of converting the reachable rate constraint condition into a linear matrix inequality includes the following substeps:
s601: processing the constraint condition, and the user
Figure SMS_167
The achievable rate constraint and the channel error constraint are respectively:
Figure SMS_168
(1) ，
Figure SMS_169
(2)；
s602: handling users
Figure SMS_170
The rate constraint can be reached, and the left side of the inequality is approximated by the first order Taylor equation to a lower bound as follows:
is provided with
Figure SMS_171
Is iterated>
Figure SMS_172
A next best solution, then>
Figure SMS_173
The lower linear bound of (c) is:
Figure SMS_174
Wherein
Figure SMS_175
Processing the user->
Figure SMS_176
The achievable rate constraints are:
Figure SMS_177
；
s603: introducing S-theorem, and converting the constraint into a linear matrix inequality model as follows:
Figure SMS_178
；(3)/>
wherein I is an identity matrix, I M Is an identity matrix of M multiplied by M,
Figure SMS_179
represents->
Figure SMS_180
Is evaluated by the evaluation unit>
Figure SMS_181
Are sparse variables.
The converting of the channel error constraint into a linear matrix inequality in step S7 includes the following substeps:
s701: the channel error constraint equation is written as follows using schur's complement:
Figure SMS_182
wherein->
Figure SMS_183
；
S702: according to the Nemmarvenski theorem, and introducing sparse variables
Figure SMS_184
Converting the formula in S701 into a linear matrix inequality model:
Figure SMS_185
(4)。
step S8, converting the models obtained in the steps S5 to S7 into an SDP problem, and solving the SDP problem through a convex optimization tool box to obtain an optimal beamforming vector, wherein the method comprises the following substeps:
s801: converting the models obtained in the steps S5 to S7 into SDP problems:
Figure SMS_186
；
s802: according to
Figure SMS_187
Get->
Figure SMS_188
Is the optimal beamforming vector of the present SDP problem.
The invention designs a beam forming with robustness, and the beam forming variable of the target function is quadratic and can reach in the constraint conditionThe problem of uncertainty in rate limiting and channel error is non-convex, and to solve this problem, a successive convex approximation is performed on the objective function from which it is derived
Figure SMS_189
The norm minimization problem is approximated as being a convex weighted->
Figure SMS_190
And solving the norm problem through an iterative algorithm. In order to solve the uncertainty of the reachable rate limit and the channel error, firstly, the constraint condition is linearized and approximated through continuous convex approximation, then the constraint condition is converted into a linear matrix inequality by utilizing an S-theorem, the whole problem is converted into a convex semi-definite programming (SDP) problem, and finally, the convex optimization tool box is used for solving the problem to obtain the optimal beamforming vector. />

Claims (8)

1. A de-cellular MIMO robust beamforming method under non-ideal channels is characterized in that: the method comprises the following steps:
s1: constructing a frame of the de-cellular distributed massive MIMO;
s2: acquiring parameters required by a frame;
s3: adding error constraint to the obtained parameter error, and constructing an error channel model;
s4: constructing a power minimization model under the channel error constraint of the user reachable rate and error channel model, and improving the robustness of the model;
s5: an objective function of a power minimization model from
Figure QLYQS_1
The norm minimization problem is approximated as being a convex weighted->
Figure QLYQS_2
A norm problem, and sufficient sparsity is obtained through iteration;
s6: converting the reachable rate constraint condition into a linear matrix inequality model;
s7: converting the channel error constraint into a linear matrix inequality model;
s8: and (5) converting the models obtained in the steps S5 to S7 into an SDP problem, and solving through a convex optimization tool box to obtain an optimal beamforming vector.
2. The method of claim 1, wherein the method comprises: the step S1 of constructing a frame for de-cellular distributed massive MIMO specifically includes: the method comprises the steps that a large-scale cellular-removing distributed MIMO system which is provided with M transmitting antennas and L access points and used for serving K single-antenna users is deployed, the access points are responsible for data transmission, and a central processing unit is responsible for data processing, wherein N is the number of the transmitting antennas, L is the number of the access points, and K is the number of the single-antenna users.
3. The method of claim 1, wherein the method comprises: step S3, adding error constraint to the obtained parameter error, and constructing an error channel model specifically includes:
the channel error model is:
Figure QLYQS_5
wherein is present>
Figure QLYQS_6
Represents a user, <' > or>
Figure QLYQS_8
Indicating all access points to user->
Figure QLYQS_4
Is selected based on the true channel of (4)/', "is selected to be in>
Figure QLYQS_7
To estimate a channel, is>
Figure QLYQS_9
Is a mistakeThe difference vector and the constraint relation are as follows:
Figure QLYQS_10
，
Figure QLYQS_3
Is an error constraint;
estimating a channel
Figure QLYQS_11
And large scale fading is known on the estimated channel, so the total power is calculated based on the estimated channel, and the user->
Figure QLYQS_12
Receiving signal of>
Figure QLYQS_13
Comprises the following steps:
Figure QLYQS_14
；
In the formula (I), the compound is shown in the specification,
Figure QLYQS_15
subscriber for all APs>
Figure QLYQS_19
Based on the transmit beamforming vector, < > v>
Figure QLYQS_21
Represents a conjugate transpose of the matrix h>
Figure QLYQS_17
Indicates that the user is removed>
Figure QLYQS_20
Any outside user, on or in the device>
Figure QLYQS_22
，
Figure QLYQS_23
Respectively indicate the user->
Figure QLYQS_16
Is subject to the expectation of 0, A Gaussian distribution with a standard deviation of 1 and a reception noise obedience are expected to be 0 with a standard deviation of ^ 0>
Figure QLYQS_18
A gaussian distribution of (a).
4. The method of robust beamforming for de-cellular MIMO in non-ideal channels according to claim 1, wherein: step 4, constructing a power minimization model under the constraint of channel errors of the user reachable rate and the error channel model, and improving the robustness of the model, specifically comprising the following steps:
user' s
Figure QLYQS_24
The signal to interference ratio SINR of (b) is:
Figure QLYQS_25
Introducing shannon formula and user->
Figure QLYQS_26
The achievable rates are:
Figure QLYQS_27
Wherein->
Figure QLYQS_28
Is an auxiliary variable;
the power consumed by the AP is:
Figure QLYQS_29
；
Figure QLYQS_30
for a power transfer efficiency factor, is>
Figure QLYQS_31
For the antenna to transmit power, is>
Figure QLYQS_32
Is the minimum power when the AP is active>
Figure QLYQS_33
Power consumed when the AP is sleeping;
user' s
Figure QLYQS_34
The model for minimizing the total power of the system under the constraint of the achievable rate and the channel error is expressed as follows:
Figure QLYQS_35
；
in the formula (I), the compound is shown in the specification,
Figure QLYQS_40
subscriber for all APs>
Figure QLYQS_37
Transmit beamforming vector of (a)>
Figure QLYQS_41
For one of the access points, is>
Figure QLYQS_36
Is slave access point->
Figure QLYQS_43
Is transmitted to the user>
Figure QLYQS_46
If the beamforming vector is ^ h>
Figure QLYQS_50
AP not subscriber->
Figure QLYQS_45
Service, then
Figure QLYQS_49
，
Figure QLYQS_39
Indicates a non-zero condition>
Figure QLYQS_42
Is greater than or equal to>
Figure QLYQS_44
The power difference between when the AP is active and when it is dormant,
Figure QLYQS_48
，
Figure QLYQS_47
for minimum signal-to-interference ratio constraints for all users, a value is based on the sum of the values of the sum>
Figure QLYQS_51
In order for the user to be able to reach the rate constraint,
Figure QLYQS_38
is a non-ideal channel constraint.
5. The method of claim 1, wherein the method comprises: step S5 of minimizing the objective function of the model from
Figure QLYQS_52
The norm minimization problem is approximated as being a convex weighted->
Figure QLYQS_53
Norm problem and is obtained by iterationTaking sufficient sparsity, comprising the sub-steps of:
s501: because the AP dormancy power is constant, the optimization result is not influenced, the optimization result is removed, the objective function is deformed, and if the optimization result is used, the objective function is deformed
Figure QLYQS_54
The square of the norm is substituted>
Figure QLYQS_55
Norm>
Figure QLYQS_56
The total number of norms is kept unchanged, and the power difference between the AP active mode and the AP sleep mode is expressed as follows:
Figure QLYQS_57
；
S502: according to the theory of compressed sensing,
Figure QLYQS_58
the norm minimization problem can be approximated as a convex weighted->
Figure QLYQS_59
If the norm problem is present, the user->
Figure QLYQS_60
The optimization problem of the total power minimization of the system under the constraint of the achievable rate and the channel error can be approximately expressed as:
Figure QLYQS_61
，
Figure QLYQS_62
is an access point->
Figure QLYQS_63
To the user>
Figure QLYQS_64
The weight occupied; />
S503: will be provided with
Figure QLYQS_65
After combining the same terms, the following formula is obtained:
Figure QLYQS_66
；
s504: constant iteration weight
Figure QLYQS_67
And continuously solving the formula in the step S503 by using the iterated weights to finally obtain an optimal solution for the length of the time interval corresponding to the length of the time interval>
Figure QLYQS_68
The iterative reweighting formula is:
Figure QLYQS_69
Wherein is present>
Figure QLYQS_70
Is a positive index of the total number of the cells,
Figure QLYQS_71
，
Figure QLYQS_72
the smallest preventing denominator is a positive number of 0.
6. The method of claim 1, wherein the method comprises: the step S6 of converting the reachable rate constraint condition into a linear matrix inequality includes the following substeps:
s601: processing the constraint condition, and the user
Figure QLYQS_73
The achievable rate constraint and the channel error constraint are respectively:
Figure QLYQS_74
(1)
Figure QLYQS_75
(2)；
s602: handling users
Figure QLYQS_76
The rate constraint can be reached, and the left side of the inequality is approximated by the first order Taylor equation to a lower bound as follows:
is provided with
Figure QLYQS_77
Is iterated>
Figure QLYQS_78
A sub-optimal solution, then >>
Figure QLYQS_79
The lower linear bound of (c) is:
Figure QLYQS_80
Wherein
Figure QLYQS_81
Processing the user->
Figure QLYQS_82
The achievable rate constraints are:
Figure QLYQS_83
；
s603: introducing S-theorem, and converting the constraint into a linear matrix inequality model as follows:
Figure QLYQS_84
；(3)
wherein I is an identity matrix, I M Is an identity matrix of M multiplied by M,
Figure QLYQS_85
represents->
Figure QLYQS_86
Is evaluated by the evaluation unit>
Figure QLYQS_87
Are sparse variables.
7. The method of claim 1, wherein the method comprises: the converting of the channel error constraint into a linear matrix inequality in step S7 includes the following substeps:
s701: the channel error constraint equation is written as follows using schur's complement:
Figure QLYQS_88
wherein->
Figure QLYQS_89
；
S702: according to the Nemmarvenski theorem, and introducing sparse variables
Figure QLYQS_90
Converting the formula in the step S701 into a linear matrix inequality model: />
Figure QLYQS_91
(4)。
8. The method of robust beamforming for de-cellular MIMO in non-ideal channels according to claim 1, wherein: step S8 converts the models obtained in steps S5 to S7 into an SDP problem, and solves the SDP problem through a convex optimization toolbox to obtain an optimal beamforming vector, including the following substeps:
s801: converting the models obtained in the steps S5 to S7 into SDP problems:
Figure QLYQS_92
；
s802: according to
Figure QLYQS_93
Get->
Figure QLYQS_94
The optimal beamforming vector for this SDP problem. />
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116865798A (en) * 2023-07-06 2023-10-10 河北大学 Intelligent super-surface phase shift method for high-speed railway honeycomb removing large-scale MIMO system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112564746A (en) * 2020-12-08 2021-03-26 重庆邮电大学 Optimal GEE-based power distribution method in CF mmWave mMIMO system
CN113162663A (en) * 2021-04-26 2021-07-23 中山大学 Design method of robust multi-carrier MIMO wireless energy carrying system under non-ideal channel information
CN113938173A (en) * 2021-10-20 2022-01-14 重庆邮电大学 Beam forming method for combining broadcast and unicast in satellite-ground converged network
CN115021846A (en) * 2022-05-23 2022-09-06 浙江师范大学 Balanced optimization method for spectrum efficiency and energy efficiency of large-scale cellular MIMO downlink

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112564746A (en) * 2020-12-08 2021-03-26 重庆邮电大学 Optimal GEE-based power distribution method in CF mmWave mMIMO system
CN113162663A (en) * 2021-04-26 2021-07-23 中山大学 Design method of robust multi-carrier MIMO wireless energy carrying system under non-ideal channel information
CN113938173A (en) * 2021-10-20 2022-01-14 重庆邮电大学 Beam forming method for combining broadcast and unicast in satellite-ground converged network
CN115021846A (en) * 2022-05-23 2022-09-06 浙江师范大学 Balanced optimization method for spectrum efficiency and energy efficiency of large-scale cellular MIMO downlink

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116865798A (en) * 2023-07-06 2023-10-10 河北大学 Intelligent super-surface phase shift method for high-speed railway honeycomb removing large-scale MIMO system
CN116865798B (en) * 2023-07-06 2024-01-05 河北大学 Intelligent super-surface phase shift method for high-speed railway honeycomb removing large-scale MIMO system

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