CN106209191A - A kind of MU mimo system true environment low complex degree user choosing method - Google Patents

Abstract

The present invention relates to a kind of MU mimo system true environment low complex degree user choosing method, it is analyzed based on real wireless transmission environments (rich scattering and non-lipid scattering all exist), obtain the dynamic capacity upper limit that user selects in non-lipid scattering environments, and made a concrete analysis of the change that user's equivalent channel matrix dimension and order select the increase of iteration to be occurred along with user, and by this change application to user's selection reference；Additionally, user's equivalent channel matrix be multiplied with its conjugate matrices nonzero eigenvalue of the matrix obtained of the present invention is long-pending, i.e. low complex degree token state is as user's selection reference, the admissible rate of user can be characterized to a certain extent, this product value can be obtained by last iterative value recurrence, reduce the computation complexity of algorithm, simultaneously on the basis of the differentiated service QoS that becomes more meticulous, improve the effective throughput of system.

Description

MU-MIMO system real environment low complexity user selection method

Technical Field

The invention relates to a low-complexity user selection method for a MU-MIMO system real environment, belonging to the technical field of communication.

Background

With the increasing requirements on the transmission rate and the service quality of wireless communication, the Multiple-Input Multiple-Output (MIMO) technology is receiving more and more attention. Among them, the multi-user MIMO (MU-MIMO) technology can significantly improve the throughput of a wireless communication system by space division without increasing frequency resources by effectively using a spatial Degree of Freedom (DoF).

In a multi-user MIMO system, when data is transmitted to a plurality of users only by dividing spatial resources, multi-user interference is inevitably caused because data of different users overlap spatially. In order to distinguish the signals transmitted on different transmit antennas from each other and to distinguish these parallel sub-streams at the receiver, it is necessary to provide enough spatial freedom to make the user sub-channels orthogonal to each other without interfering with each other. And spatial degrees of freedom are reflected in the number of base station transmit antennas that can be independent of each other in a MIMO system. The limited number of antennas limits the number of spatial channels. In practical applications, since IP services are bursty (e.g., the activation factor of voice services is 0.47), in order to improve the utilization efficiency of limited space resources, the number of users connected to the system is greater than the number of users that the system can simultaneously communicate. The bursty service users can statistically multiplex the spatial resources, and in this case, how to efficiently perform user selection to optimize the system performance becomes a research hotspot.

Most of the literatures need to determine the upper limit of the user capacity and the user selection benchmark when researching the user selection method, and the common problems are as follows: 1) and only the condition that the MIMO channel is in a rich scattering environment is considered, namely the channel matrixes of the user antennas are in a full rank state and the channels among the users are mutually independent. However, the actual radio transmission environment is not necessarily sufficiently dispersive, so it is necessary to consider the case where the user antenna channel matrix is at full rank or not at full rank at the same time. 2) When the existing research selects users based on throughput maximization, a large number of Singular Value Decomposition (SVD) steps are used in the process of solving a pre-coding matrix for interference elimination and obtaining the user throughput by calculating the singular value of an equivalent channel matrix of each user, so that the algorithm complexity is very high, and the method is difficult to apply to an actual system. 3) Most user selection schemes only consider the optimization problem of a series of user selection criteria represented by the actual throughput of the system, and do not distinguish the QoS requirements of users with different service types. For real-time services, it is meaningless to improve the quality of real-time services if the rate of the real-time services is greater than the corresponding rate requirement upper limit, and the rate of the real-time services is also invalid throughput, and at the same time, less resources are allocated to non-real-time services.

Disclosure of Invention

The invention aims to solve the technical problem of providing a user selection method with low complexity in a real environment of an MU-MIMO system, which can effectively improve the effective throughput of the system by adopting a brand-new design architecture method.

The invention adopts the following technical scheme for solving the technical problems: the invention designs a low-complexity user selection method for a real environment of an MU-MIMO system, which is used for selecting a user aiming at a base station with the MU-MINO system; the method for selecting the low-complexity user in the real environment of the MU-MIMO system comprises the following steps:

step 001, initializing a selected user set S as an empty set, initializing an alternative user set K comprising real-time users and non-real-time users in a downlink of a base station MU-MINO system, randomly generating channel matrixes respectively corresponding to the users in the alternative user set K, initializing a to-be-selected set Q as an empty set, setting a parameter m to be 1, and then entering the step 002;

step 002, calculating the determinant of the product matrix of the channel matrix and the conjugate matrix of the channel matrix of the user respectively for each user in the alternative user set K, and obtaining the determinant value corresponding to the determinant, and further obtaining the determinant value of each user in the alternative user set K, then moving the user corresponding to the maximum determinant value in the alternative user set K into the selected user set S, and calculating the null space of the user, and deleting the user in the alternative user set K, and then entering step 003;

step 003, judge whether there is a user in the user set S selected, the rank of the joint channel matrix of all users except that in the user set S selected is greater than the antenna number of the base transceiver station, if yes, the user chooses to finish; otherwise, go to step 004;

step 004, after the mth user in the alternative user set K is copied and moved into the selected user set S, performing block diagonalization on each user in the selected user set S, calculating a precoding matrix of the user newly added into the selected user set S, and calculating an equivalent channel matrix of the user by combining the channel matrix of the user according to the precoding matrix of the user; then, according to the product matrix determinant of the equivalent channel matrix and the conjugate matrix of the user, obtaining the low-speed and low-complexity characterization quantity of the user; then, go to step 005;

step 005, continuously calculating equivalent channel matrixes of other users except the user in the selected user set S on the basis of supposing that the mth user in the alternative user set K is copied and moved into the selected user set S, calculating low-complexity characteristic quantities of rate variation corresponding to the users respectively according to the equivalent channel matrixes of the users and the transition matrixes of the users, updating the low-complexity characteristic quantities of the rate variation corresponding to the users respectively, and entering step 006;

step 006, continuing to judge whether the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit on the basis of supposing that the mth user in the alternative user set K is copied into the selected user set S, copying the mth user in the alternative user set K into the to-be-selected set Q if the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit, and entering step 007; otherwise, abandoning the assumption that the mth user in the alternative user set K is copied and moved into the selected user set S, and entering step 007;

step 007, judging whether the traversal for all the users in the alternative user set K is completed, if so, entering the step 008; otherwise, traversing the user serial number m plus 1, giving the result to m, and then returning to the step 004;

step 008, selecting a user with the largest user selection reference from the to-be-selected set Q, moving the user into the selected user set S, deleting the user from the alternative user set K, emptying the to-be-selected set Q, and then entering step 009;

step 009, calculating a transition matrix of each original user in the selected user set S according to the channel matrix of the newly added user in the selected user set S and the precoding matrix of each original user in the selected user set S, updating the precoding matrix of each corresponding user in the selected user set S according to the transition matrix of each original user in the selected user set S, and returning to step 003.

As a preferred technical scheme of the invention: step 010 is as follows, in step 003, after the user finishes selecting, step 010 is entered;

and 010, calculating to obtain a precoding matrix of each user in the selected user set S according to the channel matrix of each user in the selected user set S, and eliminating the interference among the users in the selected user set S by using a block diagonalization method.

As a preferred technical scheme of the invention: in the step 010, for any user j in the alternative user set K, the step is carried out byFor eliminating interference from other users, precoding matrix V_jIf there is a non-zero solution, the number of equations that are required to be satisfied is less than the number of variables, i.e.:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<M,\&ForAll;j=1,2,...,\left|S\right|---\left(4\right)$

therefore, for any user accessing the base station, only if the rank of the joint channel matrix of other users is less than the sum M of the number of the transmitting antennas of the base station, the joint channel matrix can be storedIn a precoding matrix V_jEach user is guaranteed not to be interfered by other users, which is also the limit of the user capacity | S | of the selected user set S when the block diagonalization technique is applied in the real environment.

As a preferred technical scheme of the invention: in the steps 004 to 006, suppose that one user in the alternative user set K is copied and moved into the selected user set S, and in the step 008, one user which makes the user selection reference maximum is selected from the to-be-selected set Q and moved into the selected user set S, and when the user channel is respectively in the rich scattering environment and the non-rich scattering environment, the influence of the newly added user in the selected user set S on the equivalent channel matrix dimension and the rank of the served user is analyzed, wherein the precoding matrix V for any user j in the set S_jHas the dimension ofBy n_j ^(|S|)To representWhen the user capacity of the selected user set S is | S |, the number of spatial degrees of freedom which can not generate interference among users j is taken as n in a real environment with rich scattering and low scattering_j ^(|S|)The expression of (a) is:

${n_j}^{\left(\right|S\left|\right)}=M-{{\hat{L}}_j}^{\left(\right|S\left|\right)}\&GreaterEqual;M-\underset{k=1,k\&NotEqual;j}{\overset{\left|S\right|}{\&Sigma;}}{N}_k---\left(5\right)$

the equivalent channel matrix of user j isFrom the above to the precoding matrix V_jThe analysis of the dimension can know that,thenRank ofSuppose that the number of users that have been served by the system at a certain time is | S | in the process of performing the user selection algorithm, letA joint channel matrix representing all users in the set S of selected users, then Ne_SRepresenting a joint channel matrix He_SRank of (1), Ne_SAndthere is a relationship between: specifically, the value is taken according to the following formula;

thereby obtaining

As a preferred technical scheme of the invention: in the steps 004 to 006, the low-rate and low-complexity characterization quantity of the user is obtained by using an iterative recursion method, and the assumption is made that the number of users served at the same time is | S |, and the total power of the system is P₀In the base station MU-MIMO system, the power is equally distributed on each transmitting antenna, so that the data rate of the user j can be adjustedExpressed as:

$\begin{array}{c}{R}_j^{\left(\right|S\left|\right)}\&ap;{\log }_2{\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)}^{min(N_j,{n_j}^{\left(\right|S\left|\right)})}+{\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\\ =min(N_j,{n_j}^{\left(\right|S\left|\right)}){\log }_2\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right){\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\end{array}---\left(19\right)$

wherein sigma²Which represents the power of a gaussian white noise, the representation corresponding to a user j in the set S of selected usersThe product of all non-zero eigenvalues of; p₀Represents the total transmit power of the base station;a product matrix representing the equivalent channel matrix of the user j in the selected user set S and its conjugate matrix,equivalent channel matrix representing any user j in selected user set SRank ofFurther obtainThe values with the addition of new users are:

de-interference precoding matrix V for user j at the (| S | +1) th iteration_j ^(|S|+1)The value can be obtained by an iterative recursive method:

V_j ^(|S|+1)＝V_j ^(|S|)G_j ^(|S|+1)(21)

whereby the concept of product angle from subspace can be derivedAndthe relationship between them is:

in the formula, theta_lRepresentation spaceAndthe ith pair of basis vectors e_lAnd a_lThe base angle therebetween.Can be defined as spaceAnd spaceThe angle of the product between.

As a preferred technical scheme of the invention: in the step 008, a user with the largest user selection reference is selected from the candidate set Q and moved into the selected user set S, the user is deleted from the candidate user set K, and the candidate set Q is cleared, wherein values of the following formulas corresponding to the users are obtained for the users Q in the candidate set Q respectively:selecting the user corresponding to the maximum value, namely the user with the maximum reference, moving the user into the selected user set S, deleting the user from the alternative user set K, and emptying the to-be-selected set Q; wherein, P_avgRepresenting the average transmitted power per transmit antenna of the base station, i.e.

Compared with the prior art, the method for selecting the user with low complexity in the real environment of the MU-MIMO system has the following technical effects: the method for selecting the user with low complexity in the real environment of the MU-MIMO system is characterized in that the method is analyzed based on the real wireless transmission environment (both rich scattering and non-rich scattering exist), the upper limit of the dynamic capacity selected by the user in the non-rich scattering environment is obtained, the change of the equivalent channel matrix dimension and the rank of the user along with the increase of the user selection iteration is specifically analyzed, and the change is applied to the user selection reference; in addition, the invention takes the product of the nonzero eigenvalue of the matrix obtained by multiplying the equivalent channel matrix of the user by the conjugate matrix thereof, namely the low-complexity eigenvalue as the selection reference of the user, so that the low-complexity eigenvalue can represent the available rate of the user to a certain extent, the product value can be obtained by the recursion of the last iteration value, the calculation complexity of the algorithm is reduced, and meanwhile, the effective throughput of the system is improved on the basis of refining and distinguishing the QoS (quality of service).

Drawings

FIG. 1 is a flow chart of a method for selecting a low-complexity user in a real environment of a MU-MIMO system according to the present invention;

fig. 2 is a MU-MIMO system downlink channel model.

Detailed Description

The following description will explain embodiments of the present invention in further detail with reference to the accompanying drawings.

As shown in fig. 1, the invention designs a low-complexity user selection method for a MU-MIMO system in a real environment, which is used for selecting a user for a base station with a MU-mion system; in the practical application process, the method specifically comprises the following steps:

step 001, initializing the selected user set S as an empty set, initializing the alternative user set K including real-time users and non-real-time users in the downlink of the base station MU-MINO system, randomly generating channel matrices respectively corresponding to each user in the alternative user set K, initializing the selected set Q as an empty set, setting the parameter m to be 1, and then entering step 002.

Step 002, calculating the determinant of the product matrix of the channel matrix of the user and the conjugate matrix thereof respectively for each user in the alternative user set K, and obtaining the determinant value corresponding to the determinant, and further obtaining the determinant value of each user in the alternative user set K, then moving the user corresponding to the maximum determinant value in the alternative user set K into the selected user set S, and calculating the null space of the user, and deleting the user in the alternative user set K, and then entering step 003.

Step 003, judge whether there is a user in the user set S selected, the rank of the joint channel matrix of all users except that in the user set S selected is greater than the antenna number of the base transceiver station, if yes, the user chooses to finish, and enter step 010; otherwise step 004 is entered.

In the above step 003, the upper limit of the user capacity and the equivalent channel condition in the rich scattering environment and the non-rich scattering environment are analyzed to letA joint channel matrix representing all users except user j in the selected user set S, anThenIs oneA dimension matrix.

To pairPerforming singular value decomposition to obtain:

wherein,is composed ofOf a column vector of∑, ∑_jIs composed ofDiagonal matrix of dimension, elements on diagonal matrix beingThe singular value of (a);is an M × M-order unitary matrix;is corresponding to ∑_jInA feature vector of singular value is oneA matrix of dimensions; order toRepresenting a precoding matrix T_jThe part of the spectrum for eliminating the multi-user interference of the user j is the null space of other users, V_jHas the dimension of

For user j, the precoding matrix T_jCan be obtained by multiplying two matrices, i.e. T_j＝V_jB_j. We can make byFor eliminating interference from other users, e.g. for V_jIf there is a non-zero solution, the number of equations that are required to be satisfied is less than the number of variables, i.e.:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<M,\&ForAll;j=1,2,...,\left|S\right|---\left(4\right)$

therefore, for any user accessing the base station, the precoding matrix V can exist only if the rank of the joint channel matrix of other users is smaller than the sum M of the number of the transmitting antennas of the base station_jEach user is guaranteed not to be interfered by other users, and the capacity | S | of the user is limited when the diagonalization technology is applied to the block in the real environment.

In a rich scattering environment, due toThe upper limit of the system user capacity becomes:this is the user capacity limitation condition of the MIMO system using BD for precoding that is ubiquitous in the existing research; in a low scattering environment, due toSo the user capacity at this time: | S | O_{Low scattering}≥|S|_{Rich scattering}. Therefore, if the channel under the low scattering environment is processed under the rich scattering environment, the upper limit of the user capacity will be affected.

Therefore, when considered based on a rich scattering environment, | S | is subject to the number of base station antennas M and the number of user-efficient receive antennas (n)_r＝N₁＝N₂＝···＝N_|K|) Is a constant valueAnd the user capacity upper limit | S | in the non-rich scattering environment needs to be calculated specifically.

Step 004, after the mth user in the alternative user set K is copied and moved into the selected user set S, performing block diagonalization on each user in the selected user set S, calculating a precoding matrix of the user newly added into the selected user set S, and calculating an equivalent channel matrix of the user by combining the channel matrix of the user according to the precoding matrix of the user; then, according to the product matrix determinant of the equivalent channel matrix and the conjugate matrix of the user, obtaining the low-speed and low-complexity characterization quantity of the user; then, the process proceeds to step 005.

In step 004 and step 005, the equivalent channel matrix of the user j in set S needs to be calculated, and for the user j in set S, the equivalent channel matrix of the user j in S is added with the user m in set KWill vary, thereby affecting its data rate R_j. For the users m in the copied K to S in the set S, the equivalent channel matrix of each user in S is calculated (steps 004 and 005) by performing block diagonalization on each user in the set S and performing block diagonalization on each user in the set SThe matrix dimension is analyzed, the change condition of the rank is obtained, and the user rate R is given on the basis_j。

From the analysis in step 003, V_jHas the dimension ofBy n_j ^(|S|)To representCan make itThe number of spatial degrees of freedom is considered to be the number of users j that will not cause interference between users when the number of access users is | S |. Then n is in a real environment where rich scattering and low scattering coexist_j ^(|S|)The expression of (a) is:

${n_j}^{\left(\right|S\left|\right)}=M-{{\hat{L}}_j}^{\left(\right|S\left|\right)}\&GreaterEqual;M-\underset{k=1,k\&NotEqual;j}{\overset{\left|S\right|}{\&Sigma;}}{N}_k---\left(5\right)$

by looking for V_jSo thatSo that the received signal vector of user j in the downlink channelAnd transmitting the signal vectorThe relationship of (c) can be expressed as:

y_j＝H_jT_jx_j+n_j＝H_jV_jB_jx_j+n_j(6)

wherein, the equivalent channel matrix of the user j isFrom the above to V_jAs can be seen from the analysis of the matrix dimensions,thus, it is possible to provideRank ofFor a specific value thereof as N_jOr n_j ^(|S|)We need to discuss the situation.

Suppose that the number of users that have been served by the system at a certain time is | S | in the process of performing the user selection algorithm, letA joint channel matrix representing all users in S, then Ne_SRepresenting a joint channel matrix He_SRank of (1), Ne_SAndthere is a relationship between:

① whenDue toTherefore, the following inequality can be satisfied:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<{\mathrm{Ne}}_S\&le;\underset{k=1}{\overset{\left|S\right|}{\&Sigma;}}{N}_k,\&ForAll;j\&Element;S---\left(7\right)$

therefore must haveAccording to formula (4), thenThere must be a non-zero solution. In addition, for n_j ^(|S|)Comprises the following steps:

${n_j}^{\left(\right|S\left|\right)}\&GreaterEqual;M-\underset{k=1,k\&NotEqual;j}{\overset{\left|S\right|}{\&Sigma;}}{N}_k\&GreaterEqual;N_j---\left(8\right)$

therefore, the method can obtain:

${\stackrel{\&OverBar;}{L}}_j^{\left(\right|S\left|\right)}=N_j---\left(9\right)$

at this time, the equivalent channel matrixThe singular value decomposition comprises the following steps:

${{\stackrel{\&OverBar;}{H}}_j}^{\left(\right|S\left|\right)}={\stackrel{\&OverBar;}{U}}_j\left[\begin{array}{cc}{\stackrel{\&OverBar;}{\&Sigma;}}_j& 0\end{array}\right]{\left[\begin{array}{cc}{\stackrel{\&OverBar;}{V}}_j^{\left(1\right)}& {\stackrel{\&OverBar;}{V}}_j^{\left(0\right)}\end{array}\right]}^H---\left(10\right)$

(10) in the formula,is formed byN of non-zero singular values_j×N_jA diagonal matrix is maintained;is composed ofThe left singular vector of (a) is,to correspond toN in (1)_jRight singular vectors of singular values.

② whenWhen, as shown in case ①, becauseTherefore, the method comprises the following steps:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<{\mathrm{Ne}}_S,\&ForAll;j\&Element;S---\left(11\right)$

thus, it is possible to provideThere must be a non-zero solution, in case ②, andis not judged as n_j ^(|S|)Or N_jOnly by specifically calculating user jValue to solve forAnd n is_j ^(|S|)And N_jA comparison is made.

③ when M < Ne_SAnd is andat this time, the pressure must satisfyThere is a non-zero solution. Due to the channel matrix H of the user j in the real environment_jVector of (2) andthere is a possibility of correlation, and the following equation is satisfied:

$N_j+{{\hat{L}}_j}^{\left(\right|S\left|\right)}>{\mathrm{Ne}}_S>M,\&ForAll;j\&Element;S---\left(12\right)$

thus, n can be obtained from the formula (12)_j ^(|S|)And N_jThe relation of (A) is as follows:

n_j ^(|S|)＜N_j(13)

thus:

${\stackrel{\&OverBar;}{L}}_j^{\left(\right|S\left|\right)}={n_j}^{\left(\right|S\left|\right)}---\left(14\right)$

at this time, the equivalent channel matrixThe singular value decomposition comprises the following steps:

${{\stackrel{\&OverBar;}{H}}_j}^{\left(\right|S\left|\right)}=\left[\begin{array}{cc}{\stackrel{\&OverBar;}{U}}_j^{\left(1\right)}& {{\stackrel{\&OverBar;}{U}}_j}^{\left(0\right)}\end{array}\right]\left[\begin{array}{c}{\stackrel{\&OverBar;}{\&Sigma;}}_j\\ 0\end{array}\right]{\stackrel{\&OverBar;}{V}}_j^{\left(1\right)H}---\left(15\right)$

in the above formula, the first and second carbon atoms are,is dimension n_j ^(|S|)×n_j ^(|S|)The diagonal matrix of (a) is,is composed ofThe left singular vector of (a) is,is the right singular vector.

From the above analysis, it can be seen that,is N_jOr n_j ^(|S|)Depends on Ne_S，The size comparison with M is specifically summarized as follows:

and 005, continuously calculating equivalent channel matrixes of other users except the user in the selected user set S on the basis of supposing that the mth user in the alternative user set K is copied and moved into the selected user set S, calculating low-complexity characteristic quantities of rate variation corresponding to the users respectively according to the equivalent channel matrixes of the users and the transition matrixes of the users, updating the low-complexity characteristic quantities of the rate variation corresponding to the users respectively, and entering the step 006.

Step 006, continuing to judge whether the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit on the basis of supposing that the mth user in the alternative user set K is copied into the selected user set S, copying the mth user in the alternative user set K into the to-be-selected set Q if the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit, and entering step 007; otherwise, the assumption that the mth user in the alternative user set K is copied and moved into the selected user set S is abandoned, and the process proceeds to step 007.

In the above steps 004 to 006, the low-rate and low-complexity token of the user is obtained by using an iterative recursion method, and it is assumed that the number of users served at the same time is | S |, and the total power of the system is P₀In the base station MU-MIMO system, the power is equally distributed on each transmitting antenna, so that the data rate of the user j can be adjustedExpressed as:

$\begin{array}{c}{R}_j^{\left(\right|S\left|\right)}\&ap;{\log }_2{\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)}^{min(N_j,{n_j}^{\left(\right|S\left|\right)})}+{\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\\ =min(N_j,{n_j}^{\left(\right|S\left|\right)}){\log }_2\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right){\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\end{array}---\left(19\right)$

wherein sigma²Which represents the power of a gaussian white noise, the representation corresponds to a set of selected usersOf user j in SThe product of all non-zero eigenvalues of; p₀Represents the total transmit power of the base station;the product matrix, min (N), of the equivalent channel matrix of user j in the selected user set S and its conjugate matrix_j,n_j ^(|S|)) Equivalent channel matrix representing any user j in selected user set SRank ofFurther obtainThe values with the addition of new users are:

de-interference precoding matrix V for user j at the (| S | +1) th iteration_j ^(|S|+1)The value can be obtained by an iterative recursive method:

${V_j}^{\left(\right|S|+1)}={V_j}^{\left(\right|S\left|\right)}{G_j}^{\left(\right|S|+1)}---\left(21\right)$

whereby the concept of product angle from subspace can be derivedAndthe relationship between them is:

in the formula, theta_lRepresentation spaceAndthe ith pair of basis vectors e_lAnd a_lThe base angle therebetween.Can be defined as spaceAnd spaceThe angle of the product between.I.e. a low complexity rate characterizing quantity when the user capacity is | S |.

In addition from top to bottomData rate of user jBy analyzing the data rate difference after the user j iterates twice, the data rate change in the real environment can be obtained as follows:

$\begin{array}{c}{\mathrm{\&Delta;R}}_j^{\left(\right|S|+1)}=R_j^{\left(\right|S\left|\right)}-R_j^{\left(\right|S|+1)}\\ \&ap;{\log }_2{\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)}^{{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S\left|\right)}}-{\log }_2{\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)}^{{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S|+1)}}+{\log }_2\left({\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\right)-{\log }_2\left({\mathrm{\&mu;}}_j^{\left(\right|S\left|\mathrm{+1}\right)}\right)\\ =\&lsqb;{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S\left|\right)}-{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S|+1)}\&rsqb;{\log }_2\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)+{\log }_2\left(\frac{{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}}{{\mathrm{\&mu;}}_j^{\left(\right|S|+1)}}\right)\\ =\&lsqb;{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S\left|\right)}-{\stackrel{\&OverBar;}{L}}_j^{\left(\right|S|+1)}\&rsqb;{\log }_2\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)+{\log }_2\frac{1}{{\cos }^2{\mathrm{\&Phi;}}_j^{\left(\right|S|+1)}}\end{array}---\left(35\right)$

in the above formula, becauseThus it is possible toThrough analysis, the rank of the equivalent channel matrix of the user j is known as the user joinsOnly possible fall, i.e.Thus, it is possible to obtainThis equation indicates that each time the system accesses a new user, the new user will occupy a part of the space resources of the partitioned system, resulting in a possibility that the data rate of the user that has been accessed by the original system will be impaired.

In particular, when the MIMO channel is in a rich scattering environment, equation (38) may become:

under the condition of rich scattering, due to the limitation condition of user capacity, the situation is impossible to occurThe case (1). As can be seen from equation (36), when the number of effective receiving antennas of each user in the system is the same, the selection of the (I S I +1) th new user only affectsIn the expressionItem, at this timeIs characterized by low complexity of the rate change amountWhereas in a non-rich scattering environment,watch (A)The expression is (35).

Step 007, judging whether the traversal for all the users in the alternative user set K is completed, if so, entering the step 008; otherwise, add 1 to the value corresponding to m, assign the result to m, and then return to step 004.

Step 008, selecting a user with the largest user selection reference from the to-be-selected set Q, moving the user into the selected user set S, deleting the user from the alternative user set K, emptying the to-be-selected set Q, and then entering step 009.

In the above step 008, one user that maximizes the user selection criterion is selected from the candidate set Q and moved into the selected user set S, and the user is deleted from the candidate user set K and the candidate user set K, and the candidate set Q is cleared, where the values of the following formulas corresponding to the users are obtained for each user Q in the candidate set Q, respectively:selecting the user corresponding to the maximum value, namely the user with the maximum reference, moving the user into the selected user set S, deleting the user from the alternative user set K, and emptying the to-be-selected set Q; wherein, P_avgRepresenting the average transmitted power per transmit antenna of the base station, i.e.

Step 009, calculating a transition matrix of each original user in the selected user set S according to the channel matrix of the newly added user in the selected user set S and the precoding matrix of each original user in the selected user set S, updating the precoding matrix of each corresponding user in the selected user set S according to the transition matrix of each original user in the selected user set S, and returning to step 003.

And 010, calculating to obtain a precoding matrix of each user in the selected user set S according to the channel matrix of each user in the selected user set S, and eliminating the interference among the users in the selected user set S by using a block diagonalization method.

In step 010, for any user j in the alternative user set K, the user j is determined to passFor eliminating interference from other users, precoding matrix V_jIf there is a non-zero solution, the number of equations that are required to be satisfied is less than the number of variables, i.e.:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<M,\&ForAll;j=1,2,...,\left|S\right|---\left(4\right)$

therefore, for any user accessing the base station, the precoding matrix V can exist only if the rank of the joint channel matrix of other users is smaller than the sum M of the number of the transmitting antennas of the base station_jEach user is guaranteed not to be interfered by other users, which is also the limit of the user capacity | S | of the selected user set S when the block diagonalization technique is applied in the real environment.

In a rich scattering environment, due toThe upper limit of the capacity of the set of selected users becomes:this is the user capacity limitation condition of the MIMO system using BD for precoding that is ubiquitous in the existing research; in a low scattering environment, due toSo the user capacity at this time: | S | O_{Low scattering}≥|S|_{Rich scattering}. Therefore, if the channel under the low scattering environment is processed according to the rich scattering environment, the upper limit of the selected user set capacity is affected.

The MU-MIMO system real environment low complexity user selection method designed by the technical scheme obtains the dynamic capacity upper limit selected by the user in the non-rich scattering environment by analyzing based on the real wireless transmission environment (both rich scattering and non-rich scattering exist), specifically analyzes the change of the user equivalent channel matrix dimension and rank along with the increase of user selection iteration, and applies the change to the user selection reference; in addition, the invention takes the product of the nonzero eigenvalue of the matrix obtained by multiplying the equivalent channel matrix of the user by the conjugate matrix thereof, namely the low-complexity eigenvalue as the selection reference of the user, so that the low-complexity eigenvalue can represent the available rate of the user to a certain extent, the product value can be obtained by the recursion of the last iteration value, the calculation complexity of the algorithm is reduced, and meanwhile, the effective throughput of the system is improved on the basis of refining and distinguishing the QoS (quality of service).

The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (6)

1. A low-complexity user selection method for a MU-MIMO system real environment is provided, which is used for selecting a user aiming at a base station with an MU-MINO system; the method is characterized by comprising the following steps:

step 001, initializing a selected user set S as an empty set, initializing an alternative user set K comprising real-time users and non-real-time users in a downlink of a base station MU-MINO system, randomly generating channel matrixes respectively corresponding to the users in the alternative user set K, initializing a to-be-selected set Q as an empty set, setting a parameter m to be 1, and then entering the step 002;

step 002, calculating the determinant of the product matrix of the channel matrix and the conjugate matrix of the channel matrix of the user respectively for each user in the alternative user set K, and obtaining the determinant value corresponding to the determinant, and further obtaining the determinant value of each user in the alternative user set K, then moving the user corresponding to the maximum determinant value in the alternative user set K into the selected user set S, and calculating the null space of the user, and deleting the user in the alternative user set K, and then entering step 003;

step 003, judge whether there is a user in the user set S selected, the rank of the joint channel matrix of all users except that in the user set S selected is greater than the antenna number of the base transceiver station, if yes, the user chooses to finish; otherwise, go to step 004;

step 004, after the mth user in the alternative user set K is copied and moved into the selected user set S, performing block diagonalization on each user in the selected user set S, calculating a precoding matrix of the user newly added into the selected user set S, and calculating an equivalent channel matrix of the user by combining the channel matrix of the user according to the precoding matrix of the user; then, according to the product matrix determinant of the equivalent channel matrix and the conjugate matrix of the user, obtaining the low-speed and low-complexity characterization quantity of the user; then, go to step 005;

step 005, continuously calculating equivalent channel matrixes of other users except the user in the selected user set S on the basis of supposing that the mth user in the alternative user set K is copied and moved into the selected user set S, calculating low-complexity characteristic quantities of rate variation corresponding to the users respectively according to the equivalent channel matrixes of the users and the transition matrixes of the users, updating the low-complexity characteristic quantities of the rate variation corresponding to the users respectively, and entering step 006;

step 006, continuing to judge whether the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit on the basis of supposing that the mth user in the alternative user set K is copied into the selected user set S, copying the mth user in the alternative user set K into the to-be-selected set Q if the rate low complexity token of all real-time users in the selected user set S is larger than the preset rate low complexity token lower limit, and entering step 007; otherwise, abandoning the assumption that the mth user in the alternative user set K is copied and moved into the selected user set S, and entering step 007;

step 007, judging whether the traversal for all the users in the alternative user set K is completed, if so, entering the step 008; otherwise, adding 1 to the value corresponding to m, giving the result to m, and then returning to the step 004;

step 008, selecting a user with the largest user selection reference from the to-be-selected set Q, moving the user into the selected user set S, deleting the user from the alternative user set K, emptying the to-be-selected set Q, and then entering step 009;

step 009, calculating a transition matrix of each original user in the selected user set S according to the channel matrix of the newly added user in the selected user set S and the precoding matrix of each original user in the selected user set S, updating the precoding matrix of each corresponding user in the selected user set S according to the transition matrix of each original user in the selected user set S, and returning to step 003.

2. The MU-MIMO system real environment low complexity user selection method according to claim 1, wherein: step 010 is as follows, in step 003, after the user finishes selecting, step 010 is entered;

and 010, calculating to obtain a precoding matrix of each user in the selected user set S according to the channel matrix of each user in the selected user set S, and eliminating the interference among the users in the selected user set S by using a block diagonalization method.

3. The MU-MIMO system real environment low complexity user selection method according to claim 2, wherein: in the step 010, for any user j in the alternative user set S, the step is performedFor eliminating interference from other users, precoding matrix V_jIf there is a non-zero solution, the number of equations that are required to be satisfied is less than the number of variables, i.e.:

${{\hat{L}}_j}^{\left(\right|S\left|\right)}<M,\&ForAll;j=1,2,...,\left|S\right|---\left(4\right)$

therefore, for any user accessing the base station, the precoding matrix V can exist only if the rank of the joint channel matrix of other users is smaller than the sum M of the number of the transmitting antennas of the base station_jEach user is guaranteed not to be interfered by other users, which is also the limit of the user capacity | S | of the selected user set S when the block diagonalization technique is applied in the real environment.

4. The method of claim 3, wherein the method comprises: in the steps 004 to 006, it is assumed that one user in the alternative user set K is copied and moved into the selected user set S, and in the step 008, one user which makes the user selection reference maximum is selected from the to-be-selected set Q and moved into the selected user set S, and when the user channel is respectively in the rich scattering environment and the non-rich scattering environment, the influence of the newly added user in the selected user set S on the equivalent channel matrix dimension and the rank of the served user is analyzed. Precoding matrix V for any user j in set S_jHas the dimension ofBy n_j ^(S)To representWhen the user capacity of the selected user set S is | S |, the number of spatial degrees of freedom which can not generate interference among users j is taken as n in a real environment with rich scattering and low scattering_j ^(S)The expression of (a) is:

${n_j}^{\left(\right|S\left|\right)}=M-{{\hat{L}}_j}^{\left(\right|S\left|\right)}\&GreaterEqual;M-\underset{k=1,k\&NotEqual;j}{\overset{\left|S\right|}{\&Sigma;}}{N}_k---\left(5\right)$

the equivalent channel matrix of user j isFrom the above to the precoding matrix V_jThe analysis of the dimension can know that,thenRank ofSuppose that the number of users that have been served by the system at a certain time is | S | in the process of performing the user selection algorithm, letA joint channel matrix representing all users in the set S of selected users, then Ne_SRepresenting a joint channel matrix He_SRank of (1), Ne_SAndthere is a relationship between: specifically, the value is taken according to the following formula;

thereby obtaining

5. The method of claim 4, wherein the method comprises: in the steps 004 to 006, the low-rate and low-complexity characterization quantity of the user is obtained by using an iterative recursion method, and the assumption is made that the number of users served at the same time is | S |, and the total power of the system is P₀In the base station MU-MIMO system, the power is equally distributed on each transmitting antenna, so that the data rate of the user j can be adjustedExpressed as:

$\begin{array}{c}{R}_j^{\left(\right|S\left|\right)}\&ap;{\log }_2{\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)}^{\min (N_j,{n_j}^{\left(\right|S\left|\right)})}+{\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}+\\ =\min (N_j,{n_j}^{\left(\right|S\left|\right)}){\log }_2\left(\frac{P_0}{{\mathrm{\&sigma;}}^{2}M}\right)+{\log }_2{\mathrm{\&mu;}}_j^{\left(\right|S\left|\right)}\end{array}$

wherein sigma²Which represents the power of a gaussian white noise, the representation corresponding to a user j in the set S of selected usersThe product of all non-zero eigenvalues of; p₀Represents the total transmit power of the base station;the product matrix, min (N), of the equivalent channel matrix of user j in the selected user set S and its conjugate matrix_j,n_j ^(S)) Equivalent channel matrix representing any user j in selected user set SRank ofFurther obtainThe values with the addition of new users are:

de-interference precoding matrix V for user j at the (| S | +1) th iteration_j ^(|S|+1)The value can be obtained by an iterative recursive method:

V_j ^(|S|+1)＝V_j ^(|S|)G_j ^(|S|+1)(21)

whereby the concept of product angle from subspace can be derivedAndthe relationship between them is:

in the formula, theta_lRepresentation spaceAndthe ith pair of basis vectors e_lAnd a_lThe base angle therebetween.Can be defined as spaceAnd spaceThe angle of the product between.

6. The method of claim 5, wherein the method comprises: in the step 008, a user with the largest user selection reference is selected from the candidate set Q and moved into the selected user set S, and the user is deleted from the candidate user set K, and the candidate set Q is cleared, wherein values of the following formulas corresponding to the users are obtained for the users Q in the candidate set Q respectively:selecting the user corresponding to the maximum value, namely the user with the maximum reference, moving the user into the selected user set S, deleting the user from the alternative user set K, and emptying the to-be-selected set Q; wherein, P_avgRepresenting the average transmitted power per transmit antenna of the base station, i.e.