CN106851833B - MIMO power distribution method and system based on maximum ratio transmission precoding - Google Patents

MIMO power distribution method and system based on maximum ratio transmission precoding Download PDF

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CN106851833B
CN106851833B CN201611238670.2A CN201611238670A CN106851833B CN 106851833 B CN106851833 B CN 106851833B CN 201611238670 A CN201611238670 A CN 201611238670A CN 106851833 B CN106851833 B CN 106851833B
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CN106851833A (en
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王莹
王心水
孙瑞锦
孟萨出拉
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/23—Control channels or signalling for resource management in the downlink direction of a wireless link, i.e. towards a terminal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The embodiment of the invention provides a MIMO power distribution method and a system based on maximum ratio transmission precoding, wherein the method comprises the following steps: the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station of the MIMO system with the maximum ratio transmission precoding are obtained, and an uplink channel model and a downlink channel model are determined. And determining the approximation sum rate according to the uplink channel model and the downlink channel model. From the approximation and rate, a non-convex optimization function is established with the goal of maximizing the approximation and rate. The non-convex optimization function is converted into a convex optimization function by a formula for converting the function from non-convex to convex. And when the approximation and the speed are maximum, determining the optimal solution of the convexity optimization function, and configuring the downlink power of the multi-input multi-output system according to the optimal solution. By the MIMO power distribution method based on the maximum ratio transmission precoding, the reasonability of resource allocation of a communication system is improved, and the communication resources of the system are saved.

Description

MIMO power distribution method and system based on maximum ratio transmission precoding
Technical Field
The invention relates to the technical field of wireless communication, in particular to a MIMO power distribution method and a system based on maximum ratio transmission precoding.
Background
MIMO (Multiple-Input Multiple-Output) systems are considered to be an important technology in cellular communication systems because they have superior performance such as diversity and multiplexing gain, higher speed and reliability, and the like, compared with single-antenna systems. In recent years, a massive MIMO system, that is, a base station installs a large number of antennas, and increases the number of antennas to several hundred for simultaneously serving several tens of users, in order to obtain higher rate and reliability. Massive MIMO systems are considered an important key technology in 5G communication systems.
The base station performs precoding by using downlink CSI (Channel State Information), which can better serve more users. For FDD (Frequency Division duplex) systems, the number of downlink pilots transmitted by the base station to the user should be at least equal to the number of antennas of the base station in order to estimate the downlink channel state information. Therefore, when the system becomes large in scale, that is, the number of antennas of the base station increases to several hundreds, the downlink pilot overhead and the feedback of the downlink CSI become unacceptable. For a TDD (Time Division duplex) system, because the uplink and the downlink are in the same frequency band, when the transmission Time interval is smaller than the channel coherence Time, the uplink and downlink channel models will experience the same physical attenuation, that is, the reciprocity of the channel is satisfied, that is, the downlink channel is the transpose of the uplink channel. Thus, an estimate of the downlink channel can be obtained by uplink channel estimation. The estimation of the uplink channel can be completed as long as the number of the uplink pilot frequency transmitted by the user is greater than or equal to the number of the users, and obviously, the magnitude of the user is much smaller than the number of the base station antennas. This may greatly reduce pilot overhead and CSI feedback.
In the prior art, a base station obtains each uplink signal in an uplink channel, determines the power of each downlink signal according to the reciprocity of the channels, and configures the determined power of the downlink signal to a corresponding antenna. However, a practical communication system includes not only a Radio propagation channel but also an RF (Radio Frequency) circuit portion of a link-end transceiver. In general, the RF circuit includes a mixer, a/D and D/a converters, a power amplifier, and the like, and is greatly affected by the temperature and humidity of the external environment, and the like. Therefore, random variations in the transceiver RF circuitry will cause the uplink and downlink signals to be in the same frequency band, making it difficult to maintain the reciprocity of the channel. Therefore, in the prior art, downlink power is directly configured according to the uplink signal model, which causes unreasonable resource configuration of the communication system and waste of system communication resources.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for distributing downlink power of a multi-input multi-output system based on maximum ratio transmission precoding, so as to improve the rationality of resource allocation of a communication system and save communication resources of the system. The specific technical scheme is as follows:
a MIMO power distribution method based on maximum ratio transmission precoding is applied to the radio frequency mismatching condition of a large-scale MIMO multi-input multi-output system of MRT maximum ratio transmission precoding, and comprises the following steps:
acquiring the amplitude and the phase of the gain of a radio frequency circuit of a terminal and a base station of a multi-input multi-output system with the maximum ratio transmission precoding, and determining an uplink channel model and a downlink channel model;
determining an approximation sum rate according to the uplink channel model and the downlink channel model, wherein the approximation sum rate is obtained through a preset approximation formula, and the total transmission rate corresponds to the downlink channel model;
establishing a non-convex optimization function with the aim of maximizing the approximation sum rate according to the approximation sum rate;
converting the non-convexity optimization function into a convexity optimization function by a formula for converting the function from non-convexity to convexity;
and when the approximation and the speed are maximum, determining the optimal solution of the convexity optimization function, and configuring the downlink power of the multi-input multi-output system according to the optimal solution.
A MIMO power distribution system based on maximum ratio transmission precoding is applied to the radio frequency mismatching condition of a large-scale MIMO multi-input multi-output system of MRT maximum ratio transmission precoding, and comprises the following steps:
the channel model building module is used for obtaining the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station of the MIMO system with the maximum ratio transmission precoding, and determining an uplink channel model and a downlink channel model;
a first calculation module, configured to determine an approximation sum rate according to the uplink channel model and the downlink channel model, where the approximation sum rate is obtained through a preset approximation formula, and the total transmission rate corresponds to the downlink channel model;
a second calculation module for establishing a non-convex optimization function with the goal of maximizing the approximation sum rate according to the approximation sum rate;
the third calculation module is used for converting the non-convexity optimization function into a convexity optimization function through a formula for converting the function from non-convexity to convexity;
and the power configuration module is used for determining the optimal solution of the convexity optimization function when the approximation sum rate is maximum, and configuring the downlink power of the multi-input multi-output system according to the optimal solution.
According to the MIMO power distribution method and system based on the maximum ratio transmission precoding, the uplink channel model and the downlink channel model are determined according to the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station, the approximate sum rate is determined according to the uplink channel model and the downlink channel model, and the optimal solution of the downlink power is determined when the approximate sum rate is maximum. And distributing the downlink power according to the optimal solution, so that the reasonability of resource allocation of the communication system can be improved, and the communication resources of the system can be saved. Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a MIMO power allocation method based on maximum ratio transmission precoding according to an embodiment of the present invention;
fig. 2 is another flowchart of a MIMO power allocation method based on maximum ratio transmission precoding according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of determining an optimal solution by an iterative method according to an embodiment of the present invention;
FIG. 4 is a graph of system and rate as a function of base station antenna number for an embodiment of the present invention;
FIG. 5 is a graph of system and rate as a function of base station transmit power for an embodiment of the present invention;
FIG. 6 is a graph of system and rate variance with RF circuit mismatch according to an embodiment of the present invention;
FIG. 7 is a graph of rate gain as a function of base station transmit power in accordance with an embodiment of the present invention;
FIG. 8 is a graph of rate gain as a function of variance for RF circuit mismatches in accordance with an embodiment of the present invention;
fig. 9 is a schematic diagram of a MIMO power allocation system based on maximum ratio transmission precoding 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.
In a practical MIMO communication system, not only the radio propagation channel but also the RF circuit part of the link-end transceiver are included. Generally, the transceiver circuitry of the RF circuit causes a change in the gain of the transceiver RF circuit due to external environmental influences, such as temperature and humidity. This makes it impossible for a TDD system in which an uplink channel and a downlink channel operate in the same frequency band to satisfy channel reciprocity. Such mismatch of RF circuits obviously degrades the performance of the system, adversely affecting the precoding and power allocation of the base station. Even if reciprocity calibration studies are conducted from both hardware circuitry and software algorithms, it is difficult to ideally eliminate the mismatch problem of RF circuitry.
Therefore, it is necessary to evaluate the performance of the system when there is non-matching channel, and to solve the problem of resource allocation such as base station power allocation when there is channel mismatch; it is necessary to research the performance obtained by the system when the base station adopts various precoding methods under the condition of channel mismatch, and further research the influence of each mismatch parameter on the system, and how to perform resource allocation, such as power allocation, according to the performance obtained by the system, so as to determine the optimal configuration of the system and reduce the influence of RF mismatch on the system.
In the embodiment of the present invention, taking a single-cell scenario as an example, the base station installs M (M is a positive integer) antennas to simultaneously serve K (K is a positive integer) users, so that M > K is satisfied, and the system operates in the TDD mode. Firstly, on the basis of an RF circuit gain model, a channel model when RF is not matched is established, MRT (maximum Ratio Transmission) precoding is used for a base station, and the approximation and the rate obtained when the channels of the massive MIMO system are not matched are analyzed. Then, an optimization problem with the goal of maximizing the approximation sum rate is established according to the constraint condition of the base station transmitting power. Because the objective function of the established optimization problem is non-convex, the objective function of the optimization problem is converted into the lower bound of the optimization problem through a preset logarithm lower bound inequality (the lower bound is close to a specific value and becomes very tight). Since the power allocated to each user by the transformed function is still non-convex, an exponential transformation is used to transform the problem into the difference between a linear function and a logarithmic function, making the problem convex. And finally, updating parameters in the lower bound inequality through iteration to gradually approach the optimal solution.
Referring to fig. 1, fig. 1 is a schematic flowchart of a MIMO power allocation method based on maximum ratio transmission precoding according to an embodiment of the present invention, which is applied to a radio frequency mismatch environment of a large-scale MIMO system, and includes:
s101, obtaining the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station of the MIMO system with the maximum ratio transmission precoding, and determining an uplink channel model and a downlink channel model.
And (3) researching amplitude and phase models of the terminal side (user side) and base station side RF circuit gains, and then establishing an uplink channel model and a downlink channel model when the RF circuits are not matched.
And S102, determining an approximate sum rate according to the uplink channel model and the downlink channel model, wherein the approximate sum rate is a total transmission rate corresponding to the downlink channel model and is obtained through a preset approximate formula.
And calculating the total transmission rate corresponding to the downlink channel model to be used as a system sum rate, and converting the system sum rate into an approximate sum rate through an approximate formula.
And S103, establishing a non-convex optimization function with the goal of maximizing the approximation sum rate according to the approximation sum rate.
After determining the approximation sum rate, in order to maximize the transmission rate of the MIMO system, a non-convex optimization function is established with the goal of maximizing the approximation sum rate.
And S104, converting the non-convexity optimization function into a convexity optimization function through a formula for converting the non-convexity of the function into the convexity.
The non-convex function cannot solve the extremum, and the non-convex optimization function needs to be converted into a convex optimization function.
And S105, when the approximation sum rate is maximum, determining the optimal solution of the convexity optimization function, and configuring the downlink power of the multi-input multi-output system according to the optimal solution.
And when the convexity optimization function takes the maximum value (namely when the approximation sum and the rate are maximum), determining the solution of the convexity function as the basis for distributing the downlink power of the MIMO system.
The MIMO power distribution method based on the maximum ratio transmission precoding provided by the embodiment of the invention determines an uplink channel model and a downlink channel model according to the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station, determines the approximate sum rate according to the uplink channel model and the downlink channel model, and determines the optimal solution of the downlink power when the approximate sum rate is maximum. And distributing the downlink power according to the optimal solution, so that the reasonability of resource allocation of the communication system can be improved, and the communication resources of the system can be saved.
Optionally, the establishing a radio frequency mismatch channel model includes:
establishing a downlink channel model when the radio frequencies are not matched:
Figure BDA0001195842950000051
wherein HDIs as followsLine channel model, UrGain matrix of RF circuit received for terminal, and Ur＝diag{ur,1,ur,2,…,ur,k,…,ur,K}，ur,kFor the gain of the RF circuit received by the kth terminal, K ∈ [1, K]K is a positive integer, and
Figure BDA0001195842950000052
Figure BDA0001195842950000053
amplitude obeying a lognormal distribution
Figure BDA0001195842950000054
u,rIs a preset parameter, and calculates according to the amplitude of the radio frequency circuit gain received by the kth terminal,
Figure BDA0001195842950000055
phase obeys uniform distribution
Figure BDA0001195842950000056
Figure BDA0001195842950000057
The maximum value of the gain phase distortion of the radio frequency circuit received by the kth terminal,
Figure BDA0001195842950000058
is a normal Rayleigh channel, and
Figure BDA0001195842950000059
subject to complex Gaussian variables with mean 0 and variance 1, BtGain matrix of radio frequency circuit for base station transmission, and Bt＝diag{bt,1,bt,2,…,bt,m,…,bt,M}，bt,mGain of the mth RF circuit transmitted by the base station, M ∈ [1, M]And is and
Figure BDA00011958429500000510
amplitude obeying a lognormal distribution
Figure BDA00011958429500000511
b,tIs a preset parameter, and is calculated according to the amplitude of the mth radio frequency circuit gain sent by the base station,
Figure BDA00011958429500000512
phase obeys uniform distribution
Figure BDA00011958429500000513
Figure BDA00011958429500000514
The maximum value of the gain phase distortion of the mth radio frequency circuit transmitted by the base station.
Establishing an uplink channel model when the radio frequencies are not matched:
Figure BDA0001195842950000061
wherein HUFor the uplink channel model, BrIs a gain matrix of the radio frequency circuit received by the base station, and Br＝diag{br,1,br,2,…,br,m,…,br,M}，br,mGain for the mth RF circuit received by the base station, and
Figure BDA0001195842950000062
amplitude obeying a lognormal distribution
Figure BDA0001195842950000063
b,tIs a preset parameter, and calculates according to the amplitude of the mth radio frequency circuit gain received by the base station,
Figure BDA0001195842950000064
phase obeys uniform distribution
Figure BDA0001195842950000065
Figure BDA0001195842950000066
The maximum value of the gain phase distortion of the mth radio frequency circuit received by the base station,
Figure BDA0001195842950000067
is composed of
Figure BDA0001195842950000068
Transpose of, UtGain matrix of radio frequency circuit for terminal transmission, and Ut＝diag{ut,1,ut,2,…,ut,k,…,ut,K}，ut,kGain of the RF circuit for the k terminal transmission, and
Figure BDA0001195842950000069
amplitude obeying a lognormal distribution
Figure BDA00011958429500000610
u,rIs a preset parameter, and calculates according to the amplitude of the gain of the radio frequency circuit sent by the kth terminal,
Figure BDA00011958429500000611
phase obeys uniform distribution
Figure BDA00011958429500000612
Figure BDA00011958429500000613
The maximum value of the gain phase distortion of the radio frequency circuit transmitted by the kth terminal.
Due to mismatch of RF circuit, there are
Figure BDA00011958429500000614
Figure BDA00011958429500000615
Is HUThe transposing of (1).
In the embodiment of the invention, an uplink channel model and a downlink channel model of the radio frequency unmatched channel are established, and technical support is provided for subsequently determining the approximation and the rate of the system.
Optionally, determining an approximation sum rate according to the uplink channel model and the downlink channel model includes:
step one, determining a precoding coefficient of maximum ratio transmission precoding according to an uplink channel model and a downlink channel model and through a power constraint condition.
And step two, determining the signal interference noise ratio of the terminal through an uplink channel model and a downlink channel model according to the pre-coding coefficient.
And step three, determining the approximation sum rate sequentially through a fragrance concentration formula and a preset approximation formula according to the signal interference noise ratio.
The approximation formula referred to herein is any approximation formula capable of converting the system sum rate into an approximation sum rate that is easy to solve, e.g.
Figure BDA0001195842950000071
In the embodiment of the invention, the approximation sum rate of the system is determined through the uplink channel model and the downlink channel model, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate is maximum.
In order to meet the requirement that the power of the transmitted signals is equal before and after MRT precoding, the precoding coefficient is calculated to be
Figure BDA0001195842950000072
Optionally, determining an approximation sum rate according to the uplink channel model and the downlink channel model includes:
acquiring an output signal pre-coded by a maximum ratio transmission method in a downlink channel model:
Figure BDA0001195842950000073
wherein X is the signal output by the maximum ratio transmission precoding, α is the precoding coefficient, W is the precoding matrix transmitted by the maximum ratio transmission precoding matrix adopted by the base station, and
Figure BDA0001195842950000074
d is a large scale fading matrix, and D is diag { β1,β2,…,βk,…,βK}，βkFor the large-scale fading coefficient of the kth terminal, P is the transmit power matrix of the base station, P ═ diag { P1,P2,…,Pk,…,PK}，PkThe transmission power allocated to the kth terminal for the base station, M ∈ [1, M]S is a random signal vector sent by the terminal, and S is ═ S1,s2,…,sk,…,sK]And satisfies E (ss)H)＝IK，E(ssH) Is ssHExpectation of (1)KIs an identity matrix of order K, skIs the random signal vector of the k terminal, sHIs the conjugate transpose of S.
Derived from the nature of the norm
Figure BDA0001195842950000081
Therefore, it is
Figure BDA0001195842950000082
Can obtain the product
Figure BDA0001195842950000083
Will be provided with
Figure BDA0001195842950000084
Substituting, determining a precoding coefficient:
Figure BDA0001195842950000085
wherein, the power constraint condition is E (| | x | | non-woven phosphor)2)＝pmax，pmaxIs the maximum transmission power of the base station, E (| | x | | non woven phosphor2) Is | | | x | | non-conducting phosphor2Is expected to be, | x | | | represents the norm of x, M is the number of base station-mounted antennas, Tr () is a track function, WHIs the conjugate transpose of W,
Figure BDA0001195842950000086
is HUThe conjugate matrix of (2).
Determining a signal received by the kth terminal according to the precoding coefficient, the uplink channel model and the downlink channel model:
Figure BDA0001195842950000087
wherein, ykFor signals received by the kth terminal, pkThe power allocated to the kth terminal by the base station,h kis composed of
Figure BDA0001195842950000088
The (c) th row of (a),
Figure BDA0001195842950000089
is BrConjugate matrix of, nkThe noise of the signal is received for the kth terminal.
Figure BDA00011958429500000810
Is composed ofh kThe conjugate transpose of (a) is performed,
Figure BDA00011958429500000811
is composed ofh jThe conjugate transpose of (a) is performed,h jis composed of
Figure BDA00011958429500000812
J ∈ [1, K ] th row of]。pkSatisfy the requirement of
Figure BDA00011958429500000813
Wherein p ismaxIs the maximum transmit power of the base station,h kis composed of
Figure BDA00011958429500000814
The k-th row of (1).
Determining the signal interference noise ratio of the kth terminal according to the signal received by the kth terminal:
Figure BDA00011958429500000815
wherein，γkIs the signal to interference plus noise ratio of the kth terminal.
Determining the system and rate according to the signal interference noise ratio by a fragrance concentration formula:
Figure BDA0001195842950000091
wherein R is the system sum rate, identifies the sum of the transmission rates of all terminals,
Figure BDA0001195842950000092
is composed of
Figure BDA0001195842950000093
The expected system sum rate is the total transmission rate corresponding to the downlink channel model.
By approximation of formula
Figure BDA0001195842950000094
Convert system sum rate to approximate sum rate:
Figure BDA0001195842950000095
when defining
Figure BDA0001195842950000096
Figure BDA0001195842950000097
Then the approximation and rate of the system can be abbreviated
Figure BDA0001195842950000098
Wherein the content of the first and second substances,
Figure BDA0001195842950000101
are approximations and rates.
In the embodiment of the invention, the approximation sum rate of the system is determined through the uplink channel model and the downlink channel model, and technical support is provided for determining the maximum value of the approximation sum rate.
Optionally, establishing a non-convex optimization function with the objective of maximizing the approximation sum rate according to the approximation sum rate, including:
and establishing a non-convex optimization function taking the maximum approximation sum rate as a target according to the approximation sum rate and taking the power of the base station as a constraint condition:
Figure BDA0001195842950000102
Figure BDA0001195842950000103
pk≥0,k＝1,2,…,K
wherein the content of the first and second substances,
Figure BDA0001195842950000104
the optimal power allocated to the kth terminal by the base station,
Figure BDA0001195842950000105
in order to be a set of optimal powers,
Figure BDA0001195842950000106
to approximate sum rate, pkPower allocated to the kth terminal by the base station, pmaxK is a positive integer representing the number of terminals for the maximum transmit power that the base station can provide, and s.t (subject to) means that a prescribed condition is satisfied.
In the embodiment of the invention, a non-convex optimization function with the aim of maximizing the approximation sum rate is established, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate is maximum.
Optionally, converting the non-convexity optimization function into the convexity optimization function by a formula of converting the function from non-convexity to convexity includes:
step one, according to the non-convex optimization function, the non-convex optimization function is converted into an optimized lower bound function through a preset logarithm lower bound inequality.
And step two, determining a convexity optimization function through preset exponential transformation according to the optimized lower bound function.
In the embodiment of the invention, the non-convex optimization function is converted into the convex optimization function, the maximum value of the approximation sum rate can be calculated, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate takes the maximum value.
Optionally, converting the non-convexity optimization function into the convexity optimization function by a formula of converting the function from non-convexity to convexity includes:
according to the non-convex optimization function, the non-convex optimization function is converted into an optimized lower bound function through a logarithm lower bound inequality log (1+ z) which is more than or equal to lambda logz + mu:
Figure BDA0001195842950000111
wherein the content of the first and second substances,
Figure BDA0001195842950000112
in order to optimize the lower bound function, λ, μ and z are all preset parameters,
Figure BDA0001195842950000113
Figure BDA0001195842950000114
in that
Figure BDA0001195842950000115
(z0A predetermined value), when z is equal to z0In the vicinity, the inequality log (1+ z) ≧ λ log z + μ will become very tight, i.e., the lower bound of the approximation sum rate is near a particular value, which will be very close to the value of the optimization lower bound function sum rate.
The optimization problem can thus be translated into a lower bound for optimizing the problem:
Figure BDA0001195842950000116
Figure BDA0001195842950000117
pk≥0,k＝1,2,...,K
at a specific value, the optimal solution of the optimized lower bound function is very close to the optimal solution of the original problem.
According to an optimized lower bound function, by
Figure BDA0001195842950000118
Can obtain
Figure BDA0001195842950000119
Because of the fact that
Figure BDA00011958429500001110
And mukIs a parameter that is independent of the optimization variables, so the convex optimization function of the optimization problem is equivalent to:
Figure BDA0001195842950000121
wherein the content of the first and second substances,
Figure BDA0001195842950000122
is composed of
Figure BDA0001195842950000123
The optimal solution set.
The function obtained through the above conversion is a convex function because
Figure BDA0001195842950000124
Is a linear one, and the linear one,
Figure BDA0001195842950000125
is about { pkJ, K is 1,2, …, K joint convex function.
In the embodiment of the invention, the non-convex optimization function is converted into the convex optimization function, the maximum value of the approximation sum rate can be calculated, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate takes the maximum value.
Optionally, when the approximation sum rate is maximum, determining an optimal solution of the convex optimization function includes:
and step A, obtaining and calculating the parameter values in the convexity optimization function according to the initial values of the power distribution results of the base stations.
And step B, substituting the calculated values of the parameters in the convexity optimization function into the convexity optimization function, and determining the latest solution of the convexity optimization function.
And step C, determining the latest solution after exponential transformation through exponential transformation according to the latest solution of the convexity optimization function.
And D, determining whether the latest solution after the index transformation meets a preset stopping condition, if the latest solution after the index transformation meets the stopping condition, taking the latest solution after the index transformation as an optimal solution, if the latest solution after the index transformation does not meet the stopping condition, updating the numerical value of the initial value of the power distribution result of the base station into the numerical value of the latest solution after the index transformation, and returning to the step A to continue to execute until the latest solution of the convexity optimization function meets the stopping condition.
Setting initial value of base station power distribution result
Figure BDA0001195842950000126
For example, the base station equally divides power for K users
Figure BDA0001195842950000127
According to
Figure BDA0001195842950000128
Wherein
Figure BDA0001195842950000129
Calculating the initial value
Figure BDA00011958429500001210
Solving the above formula (1) to obtain the latest solution
Figure BDA00011958429500001211
Then, according to the variation relation
Figure BDA00011958429500001212
Computing
Figure BDA00011958429500001213
According to what is obtained
Figure BDA00011958429500001214
Updating the parameter lambda1,λ2,...,λKUntil a stop condition is satisfied.
The stopping condition in the implementation of the present invention is any condition that meets the embodiments of the present invention, including but not limited to: reach the specified iteration step number or satisfy the convergence condition | | pn+1-pnAnd | l <, wherein, is the error limit,
Figure BDA0001195842950000131
in the embodiment of the invention, the optimal solution is determined by an iteration method, the optimal solution can enable the approximation sum rate to be maximum, the downlink power of the MIMO system is distributed by utilizing the optimal solution, the rationality of the resource allocation of the communication system can be improved, and the communication resources of the system are saved.
The mismatch of RF circuits deteriorates the performance of the system and also brings new challenges for precoding and power allocation of the base station. For a TDD system, even if reciprocity calibration research is carried out from two aspects of hardware circuits and software algorithms to ensure the symmetry of channels, the mismatching problem of an RF circuit is difficult to be ideally eliminated, and the reliability of reciprocity calibration is low. Therefore, it is necessary to evaluate the performance of the system when there is a channel mismatch and to solve the resource allocation problem, such as the base station power allocation problem, when there is a channel mismatch. Reciprocity calibration needs to be employed.
In the embodiment of the invention, firstly, on the basis of a channel model established by RF mismatch, the approximation and the rate obtained by the MRT precoding system are analyzed. Then, based on the constraints of the base station transmit power, an optimization problem is established that aims to maximize the sum of the overall system rates. Because of the non-convexity of the objective function, the embodiment of the invention adopts a logarithm lower bound inequality, and the lower bound of the logarithm lower bound inequality is close to a specific value and becomes very tight, so that the optimized objective is converted into the optimized lower bound. Because the power distributed to each user by the converted objective function is still non-convex, the problem is converted into the difference between a linear function and a logarithmic function by adopting an exponential transformation, so that the problem becomes a convex problem; and finally, gradually approaching the optimal solution through iterative updating.
Referring to fig. 2, fig. 2 is another schematic flow chart of a downlink power allocation method for a mimo system based on maximum ratio transmission precoding according to an embodiment of the present invention, where the method includes:
s201, according to the gain models of the RF circuits at the user side and the base station side, the whole communication channel model including the wireless channel and the RF circuit is established.
And establishing an uplink channel model with unmatched RF circuits and a downlink channel model with unmatched RF circuits.
S202, based on the communication channel model, a signal interference noise ratio of a downlink channel user receiving end is established according to an MRT pre-coding method, and then the approximation and the rate of the system are analyzed according to a fragrance concentration formula.
First, the precoding coefficient of MRT precoding is determined to meet the base station transmit power requirement. Secondly, writing a Signal received by the kth user according to the precoding coefficient, thereby determining the SINR (Signal to interference plus Noise Ratio) of the kth user. And then, according to the SINR of the kth user, the sum rate of system traversal is obtained by using a fragrance formula. Finally by means of approximation formula
Figure BDA0001195842950000141
The approximation and rate obtained by the system are determined.
S203, based on the obtained approximation and rate of the system, and with the base station power as a constraint condition, establishing an optimization problem as follows:
Figure BDA0001195842950000142
Figure BDA0001195842950000143
pk≥0,k＝1,2,…,K
and S204, converting the optimization problem from a non-convex problem to a convex problem through logarithm lower bound inequality and exponential transformation, and solving the problem.
By using the logarithm lower bound inequality log (1+ z) is more than or equal to lambda log z + mu, the method can be obtained
Figure BDA0001195842950000144
By exponential transformation
Figure BDA0001195842950000145
Can obtain the product
Figure BDA0001195842950000146
The convex optimization function of the optimization problem is equivalent to:
Figure BDA0001195842950000147
and S205, updating the parameters, and iterating until a convergence condition is reached.
Updating the iteration parameters according to the result of S204
Figure BDA0001195842950000148
Until a convergence condition is reached: such as the preset number of iteration steps or the satisfaction of the convergence condition | | | pn+1-pnAnd | l <, wherein, the error limit is.
Figure BDA0001195842950000151
Figure BDA0001195842950000152
And
Figure BDA0001195842950000153
n in the upper right corner each represents the parameter obtained at the nth iteration, e.g.
Figure BDA0001195842950000154
P is calculated when representing the nth iterationK。
The MIMO power distribution method based on the maximum ratio transmission precoding provided by the embodiment of the invention determines an uplink channel model and a downlink channel model according to the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station, determines the approximate sum rate according to the uplink channel model and the downlink channel model, and determines the optimal solution of the downlink power when the approximate sum rate is maximum. And distributing the downlink power according to the optimal solution, so that the reasonability of resource allocation of the communication system can be improved, and the communication resources of the system can be saved.
Referring to fig. 3, fig. 3 is a schematic flowchart of determining an optimal solution by an iterative method according to an embodiment of the present invention, including:
s301, initializing parameters.
Initializing power allocation results of base stations
Figure BDA0001195842950000155
According to
Figure BDA0001195842950000156
Calculating the initial value
Figure BDA0001195842950000157
Then according to
Figure BDA0001195842950000158
Recalculate the initial value
Figure BDA0001195842950000159
And setting an error limit and the iteration step number n ═ 1.
And S302, solving the transformed convexity optimization function.
According toS301, calling the CVX toolkit to solve the problem of the formula (1) to obtain the optimal solution
Figure BDA00011958429500001510
S303, solving the original problem.
According to the mapping relation
Figure BDA00011958429500001511
And the solution in S302, calculating the solution of the original problem
Figure BDA00011958429500001512
And S304, updating the iteration parameters.
According to
Figure BDA00011958429500001513
And in S303
Figure BDA00011958429500001514
As a result, the parameters are updated
Figure BDA00011958429500001515
Then according to
Figure BDA00011958429500001516
Updating parameters
Figure BDA00011958429500001517
S305, judging an iteration termination condition.
Judging whether the iteration results of the previous iteration and the next iteration are changed, if the iteration results do not meet the convergence condition: i pn+1-pnIf | <, return to S302 to continue iteration; if the convergence condition is not satisfied, the iterative execution is terminated S306.
And S306, outputting the result.
Outputting the optimal power distribution result, i.e. the optimal solution
Figure BDA0001195842950000161
ForAnd (5) precoding the base station.
In the embodiment of the invention, the optimal solution is determined by an iterative method, and the value of the optimal solution is more accurate.
Fig. 4 is a graph of the system and rate varying with the number of base station antennas according to the embodiment of the present invention, where when the total transmit power of the base station is p ═ 20dB, two cases of channel ideal matching and channel mismatching (i.e., both the user side and the base station side are mismatching) are given, and the base station performs power allocation by using equal power allocation and the MIMO power allocation method based on maximum ratio transmission precoding provided by the embodiment of the present invention, the system and rate varying with the number of base station antennas.
Wherein, curve 1 is the system and rate curve of the equal power distribution when the ideal matching is calculated, curve 2 is the system and rate curve of the method of the present invention when the ideal matching is calculated, curve 3 is the system and rate curve of the equal power distribution when the ideal matching is simulated, curve 4 is the system and rate curve of the method of the present invention when the ideal matching is simulated, curve 5 is the system and rate curve of the equal power distribution when the variance of the radio frequency circuit mismatch calculated is 0.3, curve 6 is the system and rate curve of the method of the present invention when the variance of the radio frequency circuit mismatch calculated is 0.3, curve 7 is the system and rate curve of the equal power distribution when the variance of the radio frequency circuit mismatch calculated is 0.3, and curve 8 is the system and rate curve of the method of the present invention when the variance of the radio frequency circuit mismatch calculated is 0.3.
As can be seen from fig. 4, the system rate is better when the channel is ideal than when the channel is not matched in the whole simulation system, i.e. the channel mismatch deteriorates the performance of the system. And whether perfect matching is in the channel
Figure BDA0001195842950000162
When the mismatching of the radio frequency circuit occurs in the channel, the mismatching variance of the radio frequency circuit is 0.3
Figure BDA0001195842950000163
When the embodiment of the invention is adoptedWhen the method allocates the power of the base station, the sum rate performance obtained by the system is better than the sum rate obtained by the base station performing equal power allocation. In addition, as the number of base station antennas increases, the system and rate increase value obtained by using the method provided by the invention are also continuously improved. When the number of the base station antennas is small and the number of the antennas is increased, the rate increase value obtained by using the method provided by the embodiment of the invention is obvious.
Fig. 5 is a graph of the system and rate varying with the transmission power of the base station according to the embodiment of the present invention, where the number of base station antennas M is 100, and two cases of channel ideal matching and channel mismatching (i.e., both the user side and the base station side are mismatching) are given, and the rate obtained by the system varies with the transmission power of the base station when the base station performs power allocation by using equal power allocation and the algorithm provided by the present invention.
Wherein, curve 11 is the system and rate curve of the equal power distribution when the calculated ideal matching is adopted, curve 12 is the system and rate curve of the method of the present invention when the calculated ideal matching is adopted, curve 13 is the system and rate curve of the equal power distribution when the simulated ideal matching is obtained, curve 14 is the system and rate curve of the method of the present invention when the simulated ideal matching is adopted, curve 15 is the system and rate curve of the equal power distribution when the calculated mismatch variance of the radio frequency circuit is 0.3, curve 16 is the system and rate curve of the method of the present invention when the calculated mismatch variance of the radio frequency circuit is 0.3, curve 17 is the system and rate curve of the equal power distribution when the mismatch variance of the radio frequency circuit is 0.3, and curve 18 is the system and rate curve of the method of the present invention when the mismatch variance of the radio frequency circuit is 0.3.
As can be seen from fig. 5, the system and rate at which the channel is ideal are still better than the system rate at which the channel is mismatched over the entire simulated base station transmit power range, i.e., channel mismatch can degrade system performance. And perfect match and channel mismatch at the channel
Figure BDA0001195842950000171
In both cases, use is made ofWhen the method provided by the invention is used for distributing the power of the base station, the performance of the sum rate obtained by the system is better than that obtained by the base station for distributing the equal power. In addition, as the transmission power of the base station increases, the rate increase value obtained by using the method provided by the invention also becomes larger and larger. However, when the base station transmission power itself is larger, the obtained rate performance of the system is almost not increased any more by increasing the transmission power. This is because when the transmission power of the base station is relatively large, the MRT precoding also starts to increase the interference between users, so that the rate increase of the system is not significant. Therefore, when the transmission power of the base station is larger, the interference among users becomes larger, and the rate increase value obtained by using the method of the invention does not become larger all the time, but is kept at a certain fixed value.
Fig. 6 is a graph of the system and the variance variation of the rate with the mismatch of the RF circuit according to the embodiment of the present invention, under the condition that the transmission power of the base station is p-20 dB, two cases of the number M of base station antennas being 100 and M being 200 are examined, and when the channels are not matched, the rate obtained by the system during power distribution according to the method of the present invention is matched with the variance variation of the RF circuit mismatch at the user side/base station side
Figure BDA0001195842950000172
A variation diagram of (2).
As can be seen from fig. 6, the system sum rate is decreasing as the RF circuit mismatch variance becomes larger. I.e., the more mismatched the channel, the worse the sum rate performance of the system. This is because the more mismatched the channel, the greater the uncertainty of the channel, the worse the MRT precoding effect, and thus the worse the performance of the system. In addition, when the method provided by the embodiment of the invention is adopted to allocate the power of the base station under the two conditions of M-100 and M-200, the rate performance obtained by the system is better than the performance obtained by equal power allocation. However, as the variance of the RF circuit mismatch increases, the performance degradation obtained by allocating and isodynamically allocating the power of the base station using the algorithm provided by the present invention decreases. Meanwhile, when the RF circuit mismatch variance is large, the rate obtained by the system is also closer and closer in both cases of M100 and M200, that is, when the RF circuit mismatch variance is large, the advantage of increasing the number of base station antennas tends to disappear. This is because the greater the uncertainty of the channel when the RF circuit mismatch variance is large, the more limited the contribution of allocating power to the base station and increasing the number of antennas when the channel becomes completely uncertain.
FIG. 7 is a graph of rate gain as a function of base station transmit power, when the number of base station antennas M is 100, and channel perfect matching and channel mismatching are examined
Figure BDA0001195842950000181
In four cases, the base station adopts equal power distribution and the rate gain obtained by the system when the method provided by the invention is adopted for power distribution is a graph along with the change of the transmitting power of the base station.
Wherein the rate gain is defined as:
Figure BDA0001195842950000182
as can be seen from fig. 7, when the base station power is relatively small, the gain variation is relatively obvious when the transmitting power is increased. When the transmitting power of the base station is larger, the gain obtained by increasing the transmitting power tends to be flat. The reason is that, as already mentioned in the analysis of fig. 5, when the base station power is larger, the interference of the user is also larger, and the sum rate increase of the system for increasing the transmission power becomes very limited, so that the gain increase starts to slow down. In addition, when the RF circuit mismatch variance is small, the rate gain of the system is greater than that of the ideally matched channel, but when the mismatch variance is large, the rate gain of the system is smaller than that of the ideally matched channel. This is because the system and rate performance start to deteriorate when severe mismatch of channels occurs, and since uncertainty of channels becomes large, the rate increase value obtained even by applying the method proposed by the present invention will become small, and thus the gain becomes small.
FIG. 8 is a graph of variance variation of rate gain with mismatch in RF circuits, in accordance with an embodiment of the present inventionIn the diagram, under the condition that the transmitting power of the base station is p-20 dB and under the three conditions that the antenna number of the base station is M-100, 200 and 300, the rate gain obtained by the system when the method is adopted for power distribution is matched with the variance of the mismatching of the RF circuit at the user side and the RF circuit at the base station side
Figure BDA0001195842950000183
A variation diagram of (2).
As can be seen from fig. 8, under three values of M100, 200, and 300, the rate gain increases first with the RF circuit mismatch variance and then decreases. That is, when the variance of the mismatch of the RF circuit is small, the rate gain obtained by using the method proposed by the present invention is significant. However, when the channel has severe mismatch, that is, the channel becomes completely uncertain, the equal power allocation method can also obtain good performance compared with the method proposed by the present invention. In addition, when the ratio of the antennas of the base station is larger, the mismatch variance value of the corresponding RF circuit is larger when the optimal rate gain is obtained.
Referring to fig. 9, fig. 9 is a schematic diagram of a MIMO power allocation system based on maximum ratio transmission precoding according to an embodiment of the present invention, including:
the channel model building module 901 is configured to obtain the amplitude and phase of the gain of the radio frequency circuit of the terminal and the base station of the mimo system with the maximum ratio transmission precoding, and determine an uplink channel model and a downlink channel model.
A first calculating module 902, configured to determine an approximate sum rate according to the uplink channel model and the downlink channel model, where the approximate sum rate is a total transmission rate corresponding to the downlink channel model and obtained through a preset approximate formula.
A second calculation module 903 for establishing a non-convex optimization function with the objective of maximizing the approximation sum rate according to the approximation sum rate.
A third calculating module 904 for converting the non-convexity optimization function into a convexity optimization function by a formula for converting the function from non-convexity to convexity.
And a power configuration module 905, configured to determine an optimal solution of the convex optimization function when the approximation sum rate is maximum, and configure the downlink power of the mimo system according to the optimal solution.
The MIMO power distribution system based on the maximum ratio transmission precoding provided by the embodiment of the invention determines an uplink channel model and a downlink channel model according to the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station, determines the approximate sum rate according to the uplink channel model and the downlink channel model, and determines the optimal solution of the downlink power when the approximate sum rate is maximum. And distributing the downlink power according to the optimal solution, so that the reasonability of resource allocation of the communication system can be improved, and the communication resources of the system can be saved.
It should be noted that the system according to the embodiment of the present invention is applied to the MIMO power allocation method based on maximum ratio transmission precoding, and all embodiments of the MIMO power allocation method based on maximum ratio transmission precoding are applicable to the apparatus and all can achieve the same or similar beneficial effects.
Optionally, the channel model building module 901 includes:
the downlink channel model building submodule is used for building a downlink channel model when the radio frequencies are not matched:
Figure BDA0001195842950000191
wherein HDFor the downlink channel model, UrGain matrix of RF circuit received for terminal, and Ur＝diag{ur,1,ur,2,…,ur,k,…,ur,KJ, ur, K is the RF circuit gain received by the kth terminal, K ∈ [1, K]K is a positive integer, and
Figure BDA0001195842950000201
Figure BDA0001195842950000202
amplitude obeying a lognormal distribution
Figure BDA0001195842950000203
Figure BDA0001195842950000204
Phase obeys uniform distribution
Figure BDA0001195842950000205
Figure BDA0001195842950000206
Is composed of
The k terminal receives the maximum value of the gain phase distortion of the radio frequency circuit,
Figure BDA0001195842950000207
is a normal Rayleigh channel, and
Figure BDA0001195842950000208
subject to complex Gaussian variables with mean 0 and variance 1, BtGain matrix of radio frequency circuit for base station transmission, Bt＝diag{bt,1,bt,2,…,bt,m,…,bt,M}，bt,mGain of the mth RF circuit transmitted by the base station, M ∈ [1, M]And is and
Figure BDA0001195842950000209
amplitude obeying a lognormal distribution
Figure BDA00011958429500002010
Phase obeys uniform distribution
Figure BDA00011958429500002011
Figure BDA00011958429500002012
The maximum value of the gain phase distortion of the mth radio frequency circuit transmitted by the base station.
The uplink channel model building submodule is used for building an uplink channel model when the radio frequencies are not matched:
Figure BDA00011958429500002013
wherein HUFor the uplink channel model, BrIs a gain matrix of the radio frequency circuit received by the base station, and Br＝diag{br,1,br,2,…,br,m,…,br,M}，br,mGain for the mth RF circuit received by the base station, and
Figure BDA00011958429500002014
amplitude obeying a lognormal distribution
Figure BDA00011958429500002015
Phase obeys uniform distribution
Figure BDA00011958429500002016
Figure BDA00011958429500002017
The maximum value of the gain phase distortion of the mth radio frequency circuit received by the base station,
Figure BDA00011958429500002018
is composed of
Figure BDA00011958429500002019
Transpose of, UtGain matrix of radio frequency circuit for terminal transmission, and Ut＝diag{ut,1,ut,2,…,ut,k,…,ut,K}，ut,kGain of the RF circuit for the k terminal transmission, and
Figure BDA0001195842950000211
amplitude obeying a lognormal distribution
Figure BDA0001195842950000212
Phase obeys uniform distribution
Figure BDA0001195842950000213
Figure BDA0001195842950000214
Shot sent for kth terminalThe frequency circuit gains the maximum value of the phase distortion.
In the embodiment of the invention, an uplink channel model and a downlink channel model of the radio frequency unmatched channel are established, and technical support is provided for subsequently determining the approximation and the rate of the system.
Optionally, the first calculating module 902 includes:
and the precoding coefficient determining submodule is used for determining the precoding coefficient of the maximum ratio transmission precoding according to the uplink channel model and the downlink channel model and through a power constraint condition.
And the signal-to-interference-and-noise ratio determining submodule is used for determining the signal-to-interference-and-noise ratio of the terminal through the uplink channel model and the downlink channel model according to the precoding coefficient.
And the approximation and rate determination submodule is used for determining the approximation and rate sequentially through the aroma formula and a preset approximation formula according to the signal interference noise ratio.
In the embodiment of the invention, the approximation sum rate of the system is determined through the uplink channel model and the downlink channel model, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate is maximum.
Optionally, the precoding coefficient determining sub-module is specifically configured to:
acquiring an output signal pre-coded by a maximum ratio transmission method in a downlink channel model:
Figure BDA0001195842950000215
wherein X is the signal output by the maximum ratio transmission precoding, α is the precoding coefficient, W is the precoding matrix transmitted by the maximum ratio transmission precoding matrix adopted by the base station, and
Figure BDA0001195842950000216
d is a large scale fading matrix, and D is diag { β1,β2,…,βk,…,βK}，βkFor the large-scale fading coefficient of the kth terminal, P is the transmit power matrix of the base station, P ═ diag { P1,P2,…,Pk,…,PK}，PkWork of transmission allocated to kth terminal for base stationRate, S is a random signal vector transmitted by the terminal, and S ═ S1,s2,…,sk,…,sK]And satisfies E (ss)H)＝IK，E(ssH) Is ssHExpectation of (1)KIs an identity matrix of order K, skIs the random signal vector of the kth terminal.
Determining a precoding coefficient through a power constraint condition and a downlink channel model according to the output signal:
Figure BDA0001195842950000221
wherein, the power constraint condition is E (| | x | | non-woven phosphor)2)＝pmax，E(||x||2) Is | | | x | | non-conducting phosphor2M is the number of base station installed antennas.
Optionally, the sir determining submodule is specifically configured to:
determining a signal received by the kth terminal according to the precoding coefficient, the uplink channel model and the downlink channel model:
Figure BDA0001195842950000222
wherein, ykFor signals received by the kth terminal, pkThe power allocated to the kth terminal by the base station,h kis composed of
Figure BDA0001195842950000223
The (c) th row of (a),
Figure BDA0001195842950000224
is BrConjugate matrix of, nkThe noise of the signal is received for the kth terminal.
Determining the signal interference noise ratio of the kth terminal according to the signal received by the kth terminal:
Figure BDA0001195842950000225
wherein, γkIs the signal to interference plus noise ratio of the kth terminal.
Optionally, the approximation and rate determination submodule is specifically configured to:
determining the system and rate according to the signal interference noise ratio by a fragrance concentration formula:
Figure BDA0001195842950000226
wherein R is the system sum rate, identifies the sum of the transmission rates of all terminals,
Figure BDA0001195842950000227
is composed of
Figure BDA0001195842950000231
(iii) a desire;
by approximation of formula
Figure BDA0001195842950000232
Convert system sum rate to approximate sum rate:
Figure BDA0001195842950000233
wherein the content of the first and second substances,
Figure BDA0001195842950000234
b and c are set coefficients for approximation and rate, and
Figure BDA0001195842950000235
in the embodiment of the invention, the approximation sum rate of the system is determined through the uplink channel model and the downlink channel model, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate is maximum.
Optionally, the second calculating module 903 includes:
and the non-convex optimization function establishing submodule is used for establishing a non-convex optimization function which takes the maximum approximation sum rate as a target according to the approximation sum rate and the power of the base station as a constraint condition:
Figure BDA0001195842950000236
Figure BDA0001195842950000237
pk≥0,k＝1,2,...,K
wherein the content of the first and second substances,
Figure BDA0001195842950000238
the optimal power allocated to the kth terminal by the base station,
Figure BDA0001195842950000239
in order to be a set of optimal powers,
Figure BDA00011958429500002310
to approximate sum rate, pkPower allocated to the kth terminal by the base station, pmaxK is a positive integer representing the number of terminals for the maximum transmit power that the base station can provide.
In the embodiment of the invention, a non-convex optimization function with the aim of maximizing the approximation sum rate is established, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate is maximum.
Optionally, the third calculating module 904 includes:
and the optimization lower bound function establishing submodule is used for converting the non-convex optimization function into the optimization lower bound function through a preset logarithm lower bound inequality according to the non-convex optimization function.
And the convexity optimization function establishing submodule is used for determining the convexity optimization function through preset exponential transformation according to the optimized lower bound function.
In the embodiment of the invention, the non-convex optimization function is converted into the convex optimization function, the maximum value of the approximation sum rate can be calculated, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate takes the maximum value.
Optionally, the optimized lower bound function establishing sub-module is specifically configured to:
according to the non-convex optimization function, the non-convex optimization function is converted into an optimized lower bound function through a logarithm lower bound inequality log (1+ z) which is more than or equal to lambda log z + mu:
Figure BDA0001195842950000241
wherein the content of the first and second substances,
Figure BDA0001195842950000242
in order to optimize the lower bound function, λ, μ and z are all preset parameters,
Figure BDA0001195842950000243
Figure BDA0001195842950000244
optionally, the convex optimization function establishing submodule is specifically configured to:
according to an optimized lower bound function, by
Figure BDA0001195842950000245
Determining a convexity optimization function:
Figure BDA0001195842950000246
Figure BDA0001195842950000247
wherein the content of the first and second substances,
Figure BDA0001195842950000248
is composed of
Figure BDA0001195842950000249
The optimal solution set.
In the embodiment of the invention, the non-convex optimization function is converted into the convex optimization function, the maximum value of the approximation sum rate can be calculated, and technical support is provided for determining the corresponding optimal solution when the approximation sum rate takes the maximum value.
Optionally, the power configuration module 905 includes:
and the parameter calculation submodule is used for acquiring and calculating the value of the parameter in the convexity optimization function according to the initial value of the power distribution result of the base station.
And the first latest solution calculation submodule is used for substituting the calculated values of the parameters in the convexity optimization function into the convexity optimization function and determining the latest solution of the convexity optimization function.
And the second latest solution calculation submodule is used for determining the latest solution after the exponential transformation through the exponential transformation according to the latest solution of the convexity optimization function.
And the optimal solution output submodule is used for determining whether the latest solution after the index transformation meets a preset stop condition, taking the latest solution after the index transformation as the optimal solution if the latest solution after the index transformation meets the stop condition, updating the numerical value of the initial value of the power distribution result of the base station into the numerical value of the latest solution after the index transformation if the latest solution after the index transformation does not meet the stop condition, and returning to the parameter calculation submodule to continue to execute until the latest solution of the convexity optimization function meets the stop condition.
In the embodiment of the invention, the optimal solution is determined by an iteration method, the optimal solution can enable the approximation sum rate to be maximum, the downlink power of the MIMO system is distributed by utilizing the optimal solution, the rationality of the resource allocation of the communication system can be improved, and the communication resources of the system are saved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (9)

1. A MIMO power distribution method based on maximum ratio transmission precoding is applied to the radio frequency mismatching condition of a large-scale MIMO multi-input multi-output system of MRT maximum ratio transmission precoding, and is characterized by comprising the following steps:
acquiring the amplitude and the phase of the gain of a radio frequency circuit of a terminal and a base station of a multi-input multi-output system with the maximum ratio transmission precoding, and determining an uplink channel model and a downlink channel model;
determining an approximation sum rate according to the uplink channel model and the downlink channel model, wherein the approximation sum rate is determined through a preset approximation formula
Figure FDA0002423169930000011
Converting the system sum rate into the approximate sum rate to obtain a total transmission rate corresponding to the downlink channel model;
establishing a non-convex optimization function with the aim of maximizing the approximation sum rate according to the approximation sum rate;
converting the non-convexity optimization function into a convexity optimization function by a formula for converting the function from non-convexity to convexity;
when the approximation and the speed are maximum, determining the optimal solution of the convexity optimization function, and configuring the downlink power of the multi-input multi-output system according to the optimal solution;
the determining the uplink channel model and the downlink channel model includes:
establishing a downlink channel model when the radio frequencies are not matched:
Figure FDA0002423169930000012
wherein, the HDFor the downlink channel model, the UrIs a gain matrix of the radio frequency circuit received by the terminal, and Ur＝diag{ur,1,ur,2,…,ur,k,…,ur,KU, the ur,kFor the gain of the RF circuit received by the kth terminal, K ∈ [1, K]K is the number of terminals, an
Figure FDA0002423169930000021
The above-mentioned
Figure FDA0002423169930000022
Amplitude obeying a lognormal distribution
Figure FDA0002423169930000023
Figure FDA0002423169930000024
A variance of the radio frequency gain mismatch for the terminal receive module, said
Figure FDA0002423169930000025
Phase obeys uniform distribution
Figure FDA0002423169930000026
The above-mentioned
Figure FDA0002423169930000027
Is the maximum value of gain phase distortion of the radio frequency circuit received by the kth terminal
Figure FDA0002423169930000028
Is a normal Rayleigh channel, and
Figure FDA0002423169930000029
subject to a complex Gaussian variable distribution with a mean of 0 and a variance of 1, BtGain matrix of radio frequency circuit transmitted for said base station, Bt＝diag{bt,1,bt,2,…,bt,m,…,bt,MB oft,mGain of the mth RF circuit transmitted for the base station, M ∈ [1, M]M is the number of antennas installed in the base station, and
Figure FDA00024231699300000210
the above-mentioned
Figure FDA00024231699300000211
Amplitude obeying a lognormal distribution
Figure FDA00024231699300000212
Figure FDA00024231699300000213
For the base station transmit module RF gain mismatch variance, the
Figure FDA00024231699300000214
Phase obeys uniform distribution
Figure FDA00024231699300000215
The above-mentioned
Figure FDA00024231699300000216
The maximum value of the gain phase distortion of the mth radio frequency circuit transmitted by the base station;
establishing an uplink channel model when the radio frequencies are not matched:
Figure FDA00024231699300000217
wherein, the HUFor the uplink channel model, BrIs a gain matrix of the radio frequency circuit received by the base station, and Br＝diag{br,1,br,2,…,br,m,…,br,MB ofr,mGain for the mth RF circuit received by the base station, and
Figure FDA00024231699300000218
the above-mentioned
Figure FDA00024231699300000219
Amplitude obeying a lognormal distribution
Figure FDA00024231699300000220
Figure FDA00024231699300000221
Receiving a module RF gain mismatch variance for a base station, said
Figure FDA0002423169930000031
Phase obeys uniform distribution
Figure FDA0002423169930000032
The above-mentioned
Figure FDA0002423169930000033
Is the maximum value of the gain phase distortion of the mth radio frequency circuit received by the base station, the
Figure FDA0002423169930000034
Is that it is
Figure FDA0002423169930000035
The U oftA gain matrix of a radio frequency circuit to be transmitted by the terminal, and Ut＝diag{ut,1,ut,2,…,ut,k,…,ut,KU, the ut,kGain of the RF circuit for the k terminal transmission, and
Figure FDA0002423169930000036
the above-mentioned
Figure FDA0002423169930000037
Amplitude obeying a lognormal distribution
Figure FDA0002423169930000038
Figure FDA0002423169930000039
A variance of the radio frequency gain mismatch for the terminal transmit module, said
Figure FDA00024231699300000310
Phase obeys uniform distribution
Figure FDA00024231699300000311
The above-mentioned
Figure FDA00024231699300000312
The maximum value of the gain phase distortion of the radio frequency circuit transmitted by the kth terminal.
2. The method of claim 1, wherein determining an approximation and a rate based on the uplink channel model and the downlink channel model comprises:
determining a precoding coefficient of the maximum ratio transmission precoding according to the uplink channel model and the downlink channel model and through a power constraint condition;
determining a signal interference noise ratio of the terminal through the uplink channel model and the downlink channel model according to the pre-coding coefficient;
and determining the approximation sum rate sequentially through a fragrance concentration formula and the approximation formula according to the signal interference noise ratio.
3. The method of claim 1, wherein determining an approximation and a rate based on the uplink channel model and the downlink channel model comprises:
obtaining the output signal pre-coded by the maximum ratio transmission method in the downlink channel model:
Figure FDA00024231699300000313
wherein x is the signal output by the maximal ratio transmission precoding, α is the precoding coefficient, W is the maximal ratio transmission precoding matrix adopted by the base station, and
Figure FDA00024231699300000314
Figure FDA00024231699300000315
is HUD is a large-scale fading matrix, and D ═ diag { β1,β2,…,βk,…,βKβ ofkIs the large-scale fading coefficient of the kth terminal, where P is the transmit power matrix of the base station, and P is diag { P ═ P1,p2,…,pk,…,pKIs said pkThe base station is allocated with the transmitting power of the k terminal, s is a random signal vector sent by the terminal, and s is ═ s1,s2,…,sk,…,sK]And satisfies E (ss)H)＝IK，E(ssH) Is ssHExpectation of (1), sHIs a conjugate transpose of s, ssHIs s and sHThe product of (a), the said IKIs an identity matrix of order K, said skFor the kth terminalA machine signal;
determining the precoding coefficient according to the output signal through a power constraint condition and the downlink channel model:
Figure FDA0002423169930000041
wherein the content of the first and second substances,
Figure FDA0002423169930000042
for the terminal transmit module rf gain mismatch variance,
Figure FDA0002423169930000043
the power constraint condition is E (| | x | | sweet calculation) for the base station receiving module radio frequency gain mismatch variance2)＝pmaxSaid p ismaxIs the maximum transmission power of the base station, the E (| | x | | sweet food)2) To the | | x | | non-conducting phosphor2M is the number of antennas installed in the base station;
determining a signal received by the kth terminal according to the precoding coefficient, the uplink channel model and the downlink channel model:
Figure FDA0002423169930000044
wherein, said ykFor signals received by the k terminal, said pkPower allocated to the kth terminal by the base station, theh kIs that it is
Figure FDA0002423169930000045
The k line of (2), the
Figure FDA0002423169930000046
Is the said BrThe conjugate matrix of (1), the nkThe noise of the signal received for the kth terminal,
Figure FDA0002423169930000047
is composed ofh kThe conjugate transpose of (a) is performed,
Figure FDA0002423169930000048
is composed ofh jThe conjugate transpose of (1);
determining the signal interference noise ratio of the kth terminal according to the signal received by the kth terminal:
Figure FDA0002423169930000051
wherein, said γ iskThe signal interference noise ratio of the kth terminal;
determining the system and rate according to the signal interference noise ratio by a fragrance concentration formula:
Figure FDA0002423169930000052
wherein, R is the system sum rate, the sum of the transmission rates of all terminals is marked, and
Figure FDA0002423169930000053
is composed of
Figure FDA0002423169930000054
(iii) a desire;
by said approximation formula
Figure FDA0002423169930000055
Converting the system sum rate to the approximate sum rate:
Figure FDA0002423169930000056
wherein, the
Figure FDA0002423169930000057
Is that it isApproximate sum rate, said b and said c are set coefficients, and
Figure FDA0002423169930000058
4. the method of claim 3, wherein establishing a non-convex optimization function based on the approximation and rate with the goal of maximizing the approximation and rate comprises:
according to the approximate sum rate and with the power of the base station as a constraint condition, establishing a non-convex optimization function taking maximization of the approximate sum rate as a target:
Figure FDA0002423169930000061
Figure FDA0002423169930000062
pk≥0,k＝1,2,…,K
wherein, the
Figure FDA0002423169930000063
Optimal power allocated to the kth terminal by the base station, the
Figure FDA0002423169930000064
For the set of optimum powers, the
Figure FDA0002423169930000065
For the approximation and rate, the pkPower allocated to the kth terminal by the base station, pmaxAnd K is a positive integer and represents the number of the terminals, wherein K is the maximum transmitting power which can be provided by the base station.
5. The method of claim 1, wherein converting the non-convex optimization function into a convex optimization function by a formula that converts the function from non-convex to convex comprises:
converting the non-convex optimization function into an optimized lower bound function through a preset lower logarithmic bound inequality according to the non-convex optimization function;
and determining the convexity optimization function through preset exponential transformation according to the optimized lower bound function.
6. The method of claim 4, wherein converting the non-convex optimization function into a convex optimization function by a formula that converts the function from non-convex to convex comprises:
according to the non-convex optimization function, converting the non-convex optimization function into an optimized lower bound function through a logarithm lower bound inequality log (1+ z) being more than or equal to lambda logz + mu:
Figure FDA0002423169930000066
wherein, the
Figure FDA0002423169930000067
For the optimized lower bound function, the λ, the μ and the z are all preset parameters,
Figure FDA0002423169930000071
Figure FDA0002423169930000072
according to the optimized lower bound function, by
Figure FDA0002423169930000073
Determining the convexity optimization function:
Figure FDA0002423169930000074
Figure FDA0002423169930000075
wherein, the
Figure FDA0002423169930000076
Is composed of
Figure FDA0002423169930000077
The set of optimal solutions of (a) is,
Figure FDA0002423169930000078
to pass through
Figure FDA0002423169930000079
The median value obtained.
7. The method of claim 1, wherein determining an optimal solution for the convex optimization function when the approximation and rate are maximum comprises:
step A, obtaining and calculating the value of a parameter in the convexity optimization function according to an initial value of a base station power distribution result;
step B, substituting the calculated values of the parameters in the convexity optimization function into the convexity optimization function to determine the latest solution of the convexity optimization function;
step C, determining the latest solution after exponential transformation through exponential transformation according to the latest solution of the convexity optimization function;
and step D, determining whether the latest solution after the exponential transformation meets a preset stopping condition, if so, taking the latest solution after the exponential transformation as the optimal solution, if not, updating the numerical value of the initial value of the power distribution result of the base station to the numerical value of the latest solution after the exponential transformation, and returning to the step A to continue executing until the latest solution of the convex optimization function meets the stopping condition.
8. A MIMO power distribution system based on maximum ratio transmission precoding is applied to the radio frequency mismatching condition of a large-scale MIMO multi-input multi-output system of MRT maximum ratio transmission precoding, and is characterized by comprising the following steps:
the channel model building module is used for obtaining the amplitude and the phase of the gain of the radio frequency circuit of the terminal and the base station of the MIMO system with the maximum ratio transmission precoding, and determining an uplink channel model and a downlink channel model;
a first calculating module, configured to determine an approximation sum rate according to the uplink channel model and the downlink channel model, where the approximation sum rate is determined according to a preset approximation formula
Figure FDA0002423169930000081
Converting the system sum rate into the approximate sum rate to obtain a total transmission rate corresponding to the downlink channel model;
a second calculation module for establishing a non-convex optimization function with the goal of maximizing the approximation sum rate according to the approximation sum rate;
the third calculation module is used for converting the non-convexity optimization function into a convexity optimization function through a formula for converting the function from non-convexity to convexity;
the power configuration module is used for determining the optimal solution of the convexity optimization function when the approximation sum rate is maximum, and configuring the downlink power of the multi-input multi-output system according to the optimal solution;
the determining the uplink channel model and the downlink channel model includes:
establishing a downlink channel model when the radio frequencies are not matched:
Figure FDA0002423169930000082
wherein, the HDFor the downlink channel modelSaid UrIs a gain matrix of the radio frequency circuit received by the terminal, and Ur＝diag{ur,1,ur,2,…,ur,k,…,ur,KU, the ur,kFor the gain of the RF circuit received by the kth terminal, K ∈ [1, K]K is the number of terminals, an
Figure FDA0002423169930000083
The above-mentioned
Figure FDA0002423169930000084
Amplitude obeying a lognormal distribution
Figure FDA0002423169930000091
Figure FDA0002423169930000092
A variance of the radio frequency gain mismatch for the terminal receive module, said
Figure FDA0002423169930000093
Phase obeys uniform distribution
Figure FDA0002423169930000094
The above-mentioned
Figure FDA0002423169930000095
Is the maximum value of gain phase distortion of the radio frequency circuit received by the kth terminal
Figure FDA0002423169930000096
Is a normal Rayleigh channel, and
Figure FDA0002423169930000097
subject to a complex Gaussian variable distribution with a mean of 0 and a variance of 1, BtGain matrix of radio frequency circuit transmitted for said base station, Bt＝diag{bt,1,bt,2,…,bt,m,…,bt,MB oft,mGain of the mth RF circuit transmitted for the base station, M ∈ [1, M]M is the number of antennas installed in the base station, and
Figure FDA0002423169930000098
the above-mentioned
Figure FDA0002423169930000099
Amplitude obeying a lognormal distribution
Figure FDA00024231699300000910
Figure FDA00024231699300000911
For the base station transmit module RF gain mismatch variance, the
Figure FDA00024231699300000912
Phase obeys uniform distribution
Figure FDA00024231699300000913
The above-mentioned
Figure FDA00024231699300000914
The maximum value of the gain phase distortion of the mth radio frequency circuit transmitted by the base station;
establishing an uplink channel model when the radio frequencies are not matched:
Figure FDA00024231699300000915
wherein, the HUFor the uplink channel model, BrIs a gain matrix of the radio frequency circuit received by the base station, and Br＝diag{br,1,br,2,…,br,m,…,br,MB ofr,mGain for the mth RF circuit received by the base station, and
Figure FDA00024231699300000916
the above-mentioned
Figure FDA00024231699300000917
Amplitude obeying a lognormal distribution
Figure FDA00024231699300000918
Figure FDA00024231699300000919
Receiving a module RF gain mismatch variance for a base station, said
Figure FDA0002423169930000101
Phase obeys uniform distribution
Figure FDA0002423169930000102
The above-mentioned
Figure FDA0002423169930000103
Is the maximum value of the gain phase distortion of the mth radio frequency circuit received by the base station, the
Figure FDA0002423169930000104
Is that it is
Figure FDA0002423169930000105
The U oftA gain matrix of a radio frequency circuit to be transmitted by the terminal, and Ut＝diag{ut,1,ut,2,…,ut,k,…,ut,KU, the ut,kGain of the RF circuit for the k terminal transmission, and
Figure FDA0002423169930000106
the above-mentioned
Figure FDA0002423169930000107
Amplitude obeying a lognormal distribution
Figure FDA0002423169930000108
Figure FDA0002423169930000109
A variance of the radio frequency gain mismatch for the terminal transmit module, said
Figure FDA00024231699300001010
Phase obeys uniform distribution
Figure FDA00024231699300001011
The above-mentioned
Figure FDA00024231699300001012
The maximum value of the gain phase distortion of the radio frequency circuit transmitted by the kth terminal.
9. The system of claim 8, wherein the first computing module comprises:
a precoding coefficient determining submodule, configured to determine a precoding coefficient of the maximum ratio transmission precoding according to the uplink channel model and the downlink channel model and through a power constraint condition;
a signal-to-interference-and-noise ratio determining submodule, configured to determine, according to the precoding coefficient, a signal-to-interference-and-noise ratio of the terminal through the uplink channel model and the downlink channel model;
and the approximation and rate determination submodule is used for determining the approximation and rate sequentially through a fragrance concentration formula and the approximation formula according to the signal interference noise ratio.
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