CN110048753A - The maximized distributed beamforming optimization method of efficiency is weighted based on mimo system - Google Patents

The maximized distributed beamforming optimization method of efficiency is weighted based on mimo system Download PDF

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CN110048753A
CN110048753A CN201811602773.1A CN201811602773A CN110048753A CN 110048753 A CN110048753 A CN 110048753A CN 201811602773 A CN201811602773 A CN 201811602773A CN 110048753 A CN110048753 A CN 110048753A
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base station
user
variable
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sca
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CN110048753B (en
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赵生捷
韩丰夏
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Tongji University
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    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radio Transmission System (AREA)

Abstract

The present invention relates to one kind to weight the maximized distributed beamforming optimization method of efficiency based on mimo system, comprising: step S1: all Base station initialization launching beam figuration vectors receive wave beam forming vector；Step S2: the user that each base station is respectively serviced to it sends the current launching beam figuration vector of the user；Step S3: each user updates the reception wave beam forming vector of itself according to the launching beam figuration vector of itself, and is fed back to all base stations；Step S4: the reception wave beam forming vector for all users that each base station receives calculates the efficient channel information for oneself arriving all users；Step S5: it obtains optimal launching beam figuration vector sum by ADMM process, SCA process and MMSE process three stackings generation according to above-mentioned calculated result and receives wave beam forming vector.Compared with prior art, the present invention has many advantages, such as to promote communication quality.

Description

The maximized distributed beamforming optimization method of efficiency is weighted based on mimo system
Technical field
The present invention relates to a kind of communication means, are maximumlly distributed more particularly, to one kind based on mimo system weighting efficiency Formula wave beam forming optimization method.
Background technique
The sustainable growth of mobile data communication makes a further demand to the capacity of wireless network, the performance master of mobile network Will be by the interference from neighboring community's same frequency radio wave, therefore the interference coordination technique of advanced design is to promotion wireless network Network performance is most important；Wherein COMP (cooperative multipoint transmission) technology suitable for multiple cell, multiuser mimo communication passes through more The collaborative design of a base station beam figuration can effectively realize the interference coordination of each minizone.
When optimizing each base station beam figuration vector, usually using minimize base station power consumption or maximize user throughput as Objective function, and be seldom involved in the problems, such as maximizing efficiency (power consumption/handling capacity)；Furthermore use weighting efficiency and as target into Row optimization can better meet heterogeneous network different community to the different demands of efficiency.
Cooperative beam figuration optimization problem mentioned above usually requires to summarize there are a central node (base station) all Channel state matrix is exchanged by backhaul link each other between base station and the information or base station of user, thus the calculating of centralization Optimal beam figuration vector；However real system backhaul link capacity is limited, and works as base station, user and receiving-transmitting sides antenna number When mesh is very big, existing hardware system is difficult to meet the high complexity requirement of centralized operation；Furthermore most of existing literatures The launching beam figuration optimization problem of MISO system is considered, and is often considered as circuit power consumption often to base station power consumption modeling Number, without also depending on system velocity in view of realistic model power consumption.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on mimo system Weight the maximized distributed beamforming optimization method of efficiency.
The purpose of the present invention can be achieved through the following technical solutions:
One kind weighting the maximized distributed beamforming optimization method of efficiency based on mimo system, comprising:
Step S1: all Base station initialization launching beam figuration vectors receive wave beam forming vector；
Step S2: the user that each base station is respectively serviced to it sends the current launching beam figuration vector of the user；
Step S3: each user updates the reception wave beam forming vector of itself according to the launching beam figuration vector of itself, and It is fed back to all base stations；
Step S4: the reception wave beam forming vector for all users that each base station receives calculates oneself to all users' Efficient channel information；
Step S5: it is obtained most according to above-mentioned calculated result by ADMM process, SCA process and MMSE process three stackings generation Excellent launching beam figuration vector sum receives wave beam forming vector.
Slack variable is also initialized in the step S1With introducing variableADMM global variable v(0), with And Lagrange multiplier；
The step S5 is specifically included:
Step S51: the iteration instruction initial value for setting ADMM process, SCA process and MMSE process is respectively l=0, n= 0, m=0；
Step S52: each base station executes innermost layer ADMM iterative process, updates local variable and isGlobal variable is v(l+1)And Lagrange multiplier isUntil the variable residual error before updated variable and update is less than the first setting Threshold value, the optimal solution after obtaining ADMM iteration optimization, and enter middle layer SCA iterative process；
Step S53: each base station carries out the update of SCA iteration according to the optimal solution of ADMM iteration, until after SCA iteration updates Variable and difference before updating less than the second given threshold, and obtain the optimal solution after SCA iteration optimization, wherein enabling:
{v(0), ξ(0)}={ v(l), ξ(l)}
Wherein:For user bkPass through the updated launching beam figuration vector of SCA iteration,For user bkPass through the updated introducing variable of SCA iteration,To pass through the updated slack variable of SCA iteration, v(0)And ξ(0)The initial value of global variable and Lagrange multiplier in the ADMM iterative process respectively updated through SCA iteration,v(l), ξ(l)WithFor the optimal solution of ADMM iteration optimization；
Step S54: the optimal solution that each base station is obtained according to SCA iteration updates and receives wave beam forming vector, and judges to update Whether the preceding difference for receiving wave beam forming vector of reception wave beam forming vector and update afterwards is less than third given threshold, if it is, Then optimization terminates, if it has not, then return step S2
It is current with itself after any one update in local variable, global variable and Lagrange multiplier in the step S52 Residual error between value enters middle layer SCA iterative process less than the first given threshold.
In the step S3, the reception wave beam forming vector of user's update are as follows:
Wherein:For user bkUpdated reception wave beam forming vector,For base station i to user bkLetter Road matrix,The user i serviced by base station ijUpdate before launching beam figuration vector, ()HTurn for the conjugation of matrix It sets, N0For noise power spectral density, I is unit matrix,For base station b to user bkChannel matrix,For user bk Current launching beam figuration vector, B be base station set, KiBy the set of the base station i user serviced.
In the step S4, base station b to user ijEfficient channel information specifically:
Wherein:For user ijUpdated reception wave beam forming vector,For base station b to user ijChannel Matrix.
The step S52 is specifically included:
Step S521: each base station calculates local variable
Wherein:For the local variable of updated base station b,For local variable sbThe constraint set of satisfaction, ωb For the efficiency weight of base station b, ηbFor the slack variable for representing each base station energy efficiency,For current Lagrange multiplier, θbFor The consistency variable that ADMM optimization algorithm introduces, ()TIndicating the transformation of ownership, ρ is penalty factor,For current global variable, Indicate square of Euclid norm.
Step S522: each base station updates global variable
Wherein: v(l+1)For updated global variable,
Step S523: Lagrange multiplier is updated:
Wherein:For update base station b Lagrange multiplier,For the Lagrange multiplier for updating preceding base station b；
Step S524: whether the difference of optimized variable is less than the first setting before judging updated any optimized variable and updating Threshold value, if it is, S53 is thened follow the steps, if it has not, then l=l+1, return step S521.
First given threshold is 10-5。
The step S53 is specifically included:
S531: current SCA optimized coefficients and ADMM initial parameter value are updated；
S532: whether the difference of launching beam figuration vector is less than before judging updated launching beam figuration vector and updating Second given threshold, if it is, S54 is thened follow the steps, if it has not, then n=n+1, return step S531.
The step S54 is specifically included:
Step S541: it updates and receives wave beam forming vector；
Step S542: judge that the updated difference for receiving the reception wave beam forming vector before wave beam forming vector and update is It is no to be less than third given threshold, if it has, then optimization terminates, if it has not, then m=m+1, return step S4.
The local variable sbThe constraint set of satisfaction specifically:
Wherein:It is updated through SCA nth iterationFirst order Taylor it is approximate,For It is updated through SCA nth iterationFirst order Taylor it is approximate, a is given constant, and ε is power amplifier efficiency, P0For base station Circuit power consumption, PbFor the specified transmission power of base station b, Γ0To receive user's snr threshold,It is base station b to user ij(i ≠ b) interference ICI variable,For user bkThe interference ICI variable from other base stations being subject to.
Compared with prior art, the invention has the following advantages: weighting energy based on cell each in practical MIMO system Imitate maximized distributed beamforming algorithm, in conjunction in approximate convex optimized algorithm SCA, machine learning ADMM algorithm and The least square error MSE criterion of receiving end, in the receiving-transmitting sides alternative optimization launching beam figuration and reception wave beam of mimo system Figuration vector, and when base station optimizes launching beam figuration vector, so that base station is independently calculated by separating local variable It does not need to exchange channel state information each other.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to Following embodiments.
The present invention consider include B cell multiuser MIMO downlink system: wherein each base station b ∈ B=1 ..., B } configuration NTTransmitting antenna, service KbUser, total K userEach user configuration NRReceiving antenna, and Each user bkIt is only serviced by single base station b, i.e., If base station b is to the channel matrix of user k ForMeet flat fading channel model；Base station uses uniform enconding mode,It is user bkTransmitting number According to symbol, user bkReception signal be Indicate additive white Gaussian noise；IfIt is user bkCombination Coding (receiving wave beam forming) vector,For user bkPrecoding (launching beam figuration) vector, then user bk Reception MSE (least squares error) are as follows:
Based on above-mentioned definition, the efficiency expression formula EE of base station b can be given by:
WhereinIndicate user bkReception SINR, ε~(0,1) indicate efficiency power amplifier, P0Indicate the electricity of base station Road power consumption is set as constant, and the present invention considers the actual power loss model of base station, therefore additionally includes power consumption item related with rateIf δ is convex function, and in simple power consumption model, can set FACTOR PRD=0.
In conclusion meeting user's QoS demand (SINR is greater than given threshold value), and Base Transmitter power consumption is less than limit value Under, keep the maximum wave beam forming optimization design problem of the efficiency weighted sum of all base stations of system as follows:
Wherein Γ0For the constraint to each user SINR minimum value, PbFor the maximum transmission power of each base station.
Since convexity matter is not satisfied in above-mentioned optimization problem objective function and constraint condition, Matlab cannot be directly applied certainly The convex optimization tool packet of band solves, approximate to former problem using MSE principle SCA algorithm (convex row optimization approximate algorithm) first.
It enables and receives end subscriber bkSelect classical MMSE receiver, though the smallest reception wave beam forming of receiving end MSE to Amount:
Then user bkSINR valueWith MSE valueMeet following relationships:
MSE circle is setAnd Taylor's formula is combined to convert one for the non-convex constraint in problem (3) Rank is approximate:
Problem (6) is given at this timeOrIn the case where be convex optimization problem, can be directly included using Matlab Convex optimization tool packet solve.
How be discussed below makes above-mentioned centralized optimization problem can be in a distributed manner by each base station complete independently, without that The complicated channel state information of this exchange.
Peep optimization problem (6),It is coupled to each other between each base station variable due to making comprising inter-cell interference ICI, Therefore we introduce local variableBase station b is respectively indicated to user ijICI and user bkWhat is be subject to comes from other The ICI and global variable of base station i (i ≠ b)Guarantee the consistency of bound variable:
At this moment all local variables of base station b can be summarised in a constraint setIn:
To sum up, formula (13) may be summarized as follows:
Wherein At this moment the approximation of optimization problem (7) is found out most using ADMM algorithm Excellent solution, the Augmented Lagrangian Functions of formula (7) are as follows:
Wherein ρ is penalty factor, ξbCorrespond to variableLagrange multiplier.
The thought of ADMM algorithm be using Gauss-seidel (Gauss-Seidel) iterative method alternately update local variable s, Global variable v and Lagrange multiplier ξ:
Update local variable:
Update global variable:
Since formula (10) are without constraint quadratic programming problem, it is as follows that there are closed solutions:
Final updating Lagrange multiplier is as follows:
The application proposes a kind of based on the mimo system weighting maximized distributed beamforming optimization method of efficiency, such as figure Shown in 1, comprising:
Step S1: all Base station initialization launching beam figuration vectors receive wave beam forming vector；
Step S2: the user that each base station is respectively serviced to it sends the current launching beam figuration vector of the user；
Step S3: each user updates the reception wave beam forming vector of itself according to the launching beam figuration vector of itself, and It is fed back to all base stations；
Step S4: the reception wave beam forming vector for all users that each base station receives calculates oneself to all users' Efficient channel information；
Step S5: it is obtained most according to above-mentioned calculated result by ADMM process, SCA process and MMSE process three stackings generation Excellent launching beam figuration vector sum receives wave beam forming vector.
Slack variable is also initialized in step S1With introducing variableADMM global variable v(0), Yi Jila Ge Lang multiplier；
Step S5 is specifically included:
Step S51: the iteration instruction initial value for setting ADMM process, SCA process and MMSE process is respectively l=0, n= 0, m=0；
Step S52: each base station executes innermost layer ADMM iterative process, updates local variable and isGlobal variable is v(l+1)And Lagrange multiplier isUntil the variable residual error before updated variable and update is less than the first setting Threshold value, the optimal solution after obtaining ADMM iteration optimization, and enter middle layer SCA iterative process；
Step S53: each base station carries out the update of SCA iteration according to the optimal solution of ADMM iteration, until after SCA iteration updates Variable and difference before updating less than the second given threshold, and obtain the optimal solution after SCA iteration optimization, wherein enabling:
{v(0), ξ(0)}={ v(l), ξ(l)}
Wherein:For user bkPass through the updated launching beam figuration vector of SCA iteration,For user bkPass through the updated introducing variable of SCA iteration,To pass through the updated slack variable of SCA iteration, v(0)And ξ(0)The initial value of global variable and Lagrange multiplier in the ADMM iterative process respectively updated through SCA iteration,v(l), ξ(l)WithFor the optimal solution of ADMM iteration optimization；
Step S54: the optimal solution that each base station is obtained according to SCA iteration updates and receives wave beam forming vector, and judges to update Whether the preceding difference for receiving wave beam forming vector of reception wave beam forming vector and update afterwards is less than third given threshold, if it is, Then optimization terminates, if it has not, then return step S2
In step S52, in local variable, global variable and Lagrange multiplier any one update after with itself current value it Between residual error less than the first given threshold, that is, enter middle layer SCA iterative process.
In step S3, the reception wave beam forming vector of user's update are as follows:
Wherein:For user bkUpdated reception wave beam forming vector,For base station i to user bkLetter Road matrix,The user i serviced by base station ijUpdate before launching beam figuration vector, ()HTurn for the conjugation of matrix It sets, N0For noise power spectral density, I is unit matrix,For base station b to user bkChannel matrix,For user bk Current launching beam figuration vector, B be base station set, KiBy the set of the base station i user serviced.
In step S4, base station b to user ijEfficient channel information specifically:
Wherein:For user ijUpdated reception wave beam forming vector,For base station b to user ijChannel Matrix.
Step S52 is specifically included:
Step S521: each base station calculates local variable
Wherein:For the local variable of updated base station b,For local variable sbThe constraint set of satisfaction, ωb For the efficiency weight of base station b, ηbFor the slack variable for representing each base station energy efficiency,For current Lagrange multiplier, θbFor The consistency variable that ADMM optimization algorithm introduces, ()TIndicating the transformation of ownership, ρ is penalty factor,For current global variable, Indicate square of Euclid norm.
Step S522: each base station updates global variable
Wherein: v(l+1)For updated global variable,
Step S523: Lagrange multiplier is updated:
Wherein:For update base station b Lagrange multiplier,For the Lagrange multiplier for updating preceding base station b；
Step S524: whether the difference of optimized variable is less than the first setting before judging updated any optimized variable and updating Threshold value, if it is, S53 is thened follow the steps, if it has not, then l=l+1, return step S521.
First given threshold is 10-5。
Step S53 is specifically included:
S531: current SCA optimized coefficients and ADMM initial parameter value are updated；
S532: whether the difference of launching beam figuration vector is less than before judging updated launching beam figuration vector and updating Second given threshold, if it is, S54 is thened follow the steps, if it has not, then n=n+1, return step S531.
Step S54 is specifically included:
Step S541: it updates and receives wave beam forming vector；
Step S542: judge that the updated difference for receiving the reception wave beam forming vector before wave beam forming vector and update is It is no to be less than third given threshold, if it has, then optimization terminates, if it has not, then m=m+1, return step S4.
Local variable sbThe constraint set of satisfaction specifically:
Wherein:It is updated through SCA nth iterationFirst order Taylor it is approximate,For It is updated through SCA nth iterationFirst order Taylor it is approximate, a is given constant, and ε is power amplifier efficiency, P0For base station Circuit power consumption, PbFor the specified transmission power of base station b, Γ0To receive user's snr threshold,It is base station b to user ij(i ≠ b) interference ICI variable,For user bkThe interference ICI variable from other base stations being subject to.

Claims (10)

1. one kind weights the maximized distributed beamforming optimization method of efficiency based on mimo system characterized by comprising
Step S1: all Base station initialization launching beam figuration vectors receive wave beam forming vector；
Step S2: the user that each base station is respectively serviced to it sends the current launching beam figuration vector of the user；
Step S3: each user updates the reception wave beam forming vector of itself according to the launching beam figuration vector of itself, and by its Feed back to all base stations；
Step S4: the reception wave beam forming vector for all users that each base station receives calculates oneself to the effective of all users Channel information；
Step S5: optimal hair is obtained by ADMM process, SCA process and MMSE process three stackings generation according to above-mentioned calculated result It penetrates wave beam forming vector sum and receives wave beam forming vector.
2. a kind of mimo system that is based on according to claim 1 weights the maximized distributed beamforming method of efficiency, It is characterized in that,
Slack variable is also initialized in the step S1With introducing variableADMM global variable v(0)And glug Bright day multiplier；
The step S5 is specifically included:
Step S51: the iteration instruction initial value for setting ADMM process, SCA process and MMSE process is respectively l=0, n=0, m= 0；
Step S52: each base station executes innermost layer ADMM iterative process, updates local variable and isGlobal variable is v(l +1), and Lagrange multiplier isUntil the variable residual error before updated variable and update is less than the first setting threshold Value, the optimal solution after obtaining ADMM iteration optimization, and enter middle layer SCA iterative process；
Step S53: each base station carries out the update of SCA iteration according to the optimal solution of ADMM iteration, until the updated change of SCA iteration Difference before amount and update obtains the optimal solution after SCA iteration optimization less than the second given threshold, wherein enabling:
{v(0),ξ(0)}={ v(l),ξ(l)}
Wherein:For user bkPass through the updated launching beam figuration vector of SCA iteration,For user bk's By the updated introducing variable of SCA iteration,To pass through the updated slack variable of SCA iteration, v(0)With ξ(0)The initial value of global variable and Lagrange multiplier in the ADMM iterative process respectively updated through SCA iteration,v(l),ξ(l)WithFor the optimal solution of ADMM iteration optimization；
Step S54: the optimal solution that each base station is obtained according to SCA iteration updates and receives wave beam forming vector, and judges updated Whether the difference for receiving wave beam forming vector and receiving wave beam forming vector before updating is less than third given threshold, if it has, then excellent Change terminates, if it has not, then return step S2.
3. a kind of mimo system that is based on according to claim 2 weights the maximized distributed beamforming method of efficiency, It is characterized in that, in the step S52, in local variable, global variable and Lagrange multiplier after any one update with itself Residual error between current value enters middle layer SCA iterative process less than the first given threshold.
4. a kind of mimo system that is based on according to claim 2 weights the maximized distributed beamforming method of efficiency, It is characterized in that, in the step S3, the reception wave beam forming vector of user's update are as follows:
Wherein:For user bkUpdated reception wave beam forming vector,For base station i to user bkChannel square Battle array,The user i serviced by base station ijUpdate before launching beam figuration vector, ()HFor the conjugate transposition of matrix, N0 For noise power spectral density, I is unit matrix,For base station b to user bkChannel matrix,For user bkWork as Preceding launching beam figuration vector, B are the set of base station, KiBy the set of the base station i user serviced.
5. a kind of mimo system that is based on according to claim 4 weights the maximized distributed beamforming method of efficiency, It is characterized in that, in the step S4, base station b to user ijEfficient channel information specifically:
Wherein:For user ijUpdated reception wave beam forming vector,For base station b to user ijChannel matrix.
6. a kind of mimo system that is based on according to claim 4 weights the maximized distributed beamforming method of efficiency, It is characterized in that,
The step S52 is specifically included:
Step S521: each base station calculates local variable
Wherein:For the local variable of updated base station b,For local variable sbThe constraint set of satisfaction, ωbFor base station The efficiency weight of b, ηbFor the slack variable for representing each base station energy efficiency,For current Lagrange multiplier, θbIt is excellent for ADMM Change the consistency variable that algorithm introduces, ()TIndicating the transformation of ownership, ρ is penalty factor,For current global variable,Indicate Europe Square of norm is obtained in several.
Step S522: each base station updates global variable
Wherein: v(l+1)For updated global variable,
Step S523: Lagrange multiplier is updated:
Wherein:For update base station b Lagrange multiplier,For the Lagrange multiplier for updating preceding base station b；
Step S524: whether the difference of optimized variable is less than the first setting threshold before judging updated any optimized variable and updating Value, if it is, S53 is thened follow the steps, if it has not, then l=l+1, return step S521.
7. being based on mimo system according to described one kind any in claim 2~6 weights the maximized distributed beams of efficiency Shaping method, which is characterized in that first given threshold is 10-5。
8. a kind of mimo system that is based on according to claim 2 weights the maximized distributed beamforming method of efficiency, It is characterized in that, the step S53 is specifically included:
S531: current SCA optimized coefficients and ADMM initial parameter value are updated；
S532: whether the difference of launching beam figuration vector is less than second before judging updated launching beam figuration vector and updating Given threshold, if it is, S54 is thened follow the steps, if it has not, then n=n+1, return step S531.
9. a kind of mimo system that is based on according to claim 2 weights the maximized distributed beamforming method of efficiency, It is characterized in that, the step S54 is specifically included:
Step S541: it updates and receives wave beam forming vector；
Step S542: whether the difference of the reception wave beam forming vector before judging updated reception wave beam forming vector and updating is small In third given threshold, if it has, then optimization terminates, if it has not, then m=m+1, return step S4.
10. a kind of mimo system that is based on according to claim 6 weights the maximized distributed beamforming method of efficiency, It is characterized in that, the local variable sbThe constraint set of satisfaction specifically:
Wherein:It is updated through SCA nth iterationFirst order Taylor it is approximate,For warp What SCA nth iteration updatedFirst order Taylor it is approximate, a is given constant, and ε is power amplifier efficiency, P0For the electricity of base station Road power consumption, PbFor the specified transmission power of base station b, Γ0To receive user's snr threshold,It is base station b to user ij(i≠b) Interference ICI variable,For user bkThe interference ICI variable from other base stations being subject to.
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CN110768704A (en) * 2019-10-22 2020-02-07 南京邮电大学 Mixed beam forming matrix optimization method based on residual error neural network
CN110988854A (en) * 2019-12-24 2020-04-10 西安电子科技大学 Robust self-adaptive beam forming algorithm based on alternative direction multiplier method

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