CN109120317B - Equal power user scheduling method based on GRMES iteration - Google Patents

Equal power user scheduling method based on GRMES iteration Download PDF

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CN109120317B
CN109120317B CN201811043385.4A CN201811043385A CN109120317B CN 109120317 B CN109120317 B CN 109120317B CN 201811043385 A CN201811043385 A CN 201811043385A CN 109120317 B CN109120317 B CN 109120317B
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CN109120317A (en
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黄学军
章笛
黄秋实
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Jiangsu Hengxin Technology Co Ltd
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • 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
    • H04W72/12—Wireless traffic scheduling
    • H04W72/121—Wireless traffic scheduling for groups of terminals or users
    • 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 invention discloses an equal power user scheduling method based on GRMES iteration, which is applied to a large-scale MIMO system for realizing user signal enhancement and interference elimination functions by adopting an MMSE precoding mode, and the method comprises the steps of firstly setting channel characteristics H of users in the large-scale MIMO system, and calculating a coding matrix W of the MMSE precoding mode by adopting a GMRES iterative algorithm based on the channel characteristics H; resetting the expected rate R of users to power transmission in a massive MIMO systemECombined with a desired rate RECalculating a minimum power covariance matrix P required by each user according to the channel characteristics H; sequencing the lowest power covariance matrix P of each user from large to small to obtain a set P', setting a user group number G, and sequentially setting preset users in each user group number G according to a minimum variance standard; adjusting the number G of each user group entering a preset user according to the constraint condition of SIC superposition coding to obtain the lowest packet power variance
Figure DDA0001792656910000011
Modifying the coding matrix W based on constraint conditions; the invention can ensure that the total power of each group of users in a large-scale MIMO system is equal.

Description

Equal power user scheduling method based on GRMES iteration
Technical Field
The invention belongs to the technical field of user power scheduling, and relates to an application and large-scale MIMO (Multiple-Input Multiple-Output) system, in particular to a uniform power user scheduling algorithm based on GRMES (Generalized minimum residual error) iteration.
Background
Large-scale MIMO is recognized as one of the key technologies of 5G mobile communication, and can meet the requirements of high system capacity, high energy efficiency, high user experience throughput, and the like. Under the same time-frequency resource, the large-scale MIMO utilizes the high spatial freedom degree interference to eliminate and schedule more users through a large number of antennas equipped at the transmitting end of the base station, thereby improving the system performance. The precoding technology, which is one of the key technologies in massive MIMO, is an important way to further improve system performance and reduce interference. Due to the influence of the number of antennas and the channel hardening effect, the higher complexity of the nonlinear precoding is difficult to be applied, and the linear precoding is usually adopted to eliminate the interference and improve the system capacity. The scheduling problem of users is an important research topic in large-scale MIMO, the quality of a user scheduling algorithm directly affects the effective resource integration of a system, and a user scheduling scheme is a non-deterministic polynomial problem. Conventional scheduling algorithms typically have: random user scheduling, orthogonal user scheduling algorithm, determinant user scheduling algorithm and the like. However, for massive MIMO, a part of scheduling algorithms applied directly will generate higher computational complexity. Therefore, it is important to design a simple and effective scheduling scheme that can increase the sum rate of the system as much as possible or reduce the power consumption of the base station as much as possible while ensuring the fairness of the users.
Disclosure of Invention
The invention mainly aims to provide an equal power user scheduling method based on GRMES iteration, which is simple and effective, has low power consumption, is convenient for the detection of a receiving end of a large-scale MIMO system, is used for solving the problems of complex calculation, high power consumption and low system processing rate in the traditional scheduling method, and has the following specific technical scheme:
a equal power user scheduling method based on GRMES iteration is applied to a large-scale MIMO system, the large-scale MIMO system adopts an MMSE precoding mode to realize the functions of user signal enhancement and interference elimination, and the scheduling method comprises the following steps:
s1, setting channel characteristics H of users in the massive MIMO system, and calculating a coding matrix W of the MMSE precoding mode by adopting a GMRES iterative algorithm based on the channel characteristics H;
s2, setting the expected rate R of the users to the power transmission in the massive MIMO systemEIn combination with said desired rate RECalculating a minimum power covariance matrix P required by each user according to the channel characteristics H;
s3, sequencing the lowest power covariance matrix P of each user in a big-to-small mode to obtain a set P', setting a user group number G, and sequentially arranging preset users in each user group number G according to a minimum variance standard;
s4, adjusting the number G of each user group in which preset users are arranged according to constraint conditions of SIC superposition codingObtaining the lowest packet power variance
Figure GDA0002939460820000021
And S5, correcting the coding matrix W by adopting orthogonalization processing based on the constraint condition.
Preferably, a ring channel model is constructed in the massive MIMO system, and the ring channel model is formed by a formula
Figure GDA0002939460820000022
And representing channel parameters of a kth user, wherein m and p represent two designated antennas in the base station, delta represents an angle expansion amount, D (m-p) represents an antenna m-band antenna, and theta represents an azimuth angle of the user relative to the base station.
Preferably, the channel characteristic H is represented by the formula H ═ H1,h2,...hk-1,hk]Is represented by the formula (I) in which hkRepresents the channel characteristic H of a k-th user; and the coding matrix W is represented by the formula W ═ H (H)HH+αI)-1。
Preferably, the transmission signal rate of the base station in the massive MIMO system is greater than or equal to the expected rate REAnd the desired rate R of the kth userE，kThe transmission rate with the base station satisfies a condition RE,k≤log2(1+SINRk) Wherein, in the step (A),
Figure GDA0002939460820000031
in the formula, wkSatisfy | | W for the kth column vector of the precoding matrix W k1, wherein pkFor the power allocated to the kth user, the power covariance matrix is P ═ diag (P)1,p2,...,pK)，
Figure GDA0002939460820000032
White noise power, h, for the kth userkIs the channel of the kth user.
Preferably, the users include high priority users and low priority users, wherein the users are selected from the group consisting of a plurality of users, and a plurality of usersThe constraint conditions are as follows:
Figure GDA0002939460820000033
in the formula (I), the compound is shown in the specification,
Figure GDA0002939460820000034
HP represents the high-priority user, LP represents the low-priority user, the high-priority user corresponds to a high-power user, and the low-priority user corresponds to a low-power user;
wherein, PLPIs the power allocated to the Low Priority user (LP), PHPIs the power allocated to the High Priority user (HP)wTotal power of the group in which the LP users are located, PvTotal power of the group in which the HP users are located, ΓLPAnd ΓHPSignal-to-noise ratio constraints for LP and HP users respectively,
Figure GDA0002939460820000041
and
Figure GDA0002939460820000042
the gaussian white noise variance values for LP and HP users, respectively.
Preferably, step S4 further includes the steps of:
s41, correcting the coding matrix of the high-power user and correcting the power of the corresponding user according to the corrected coding matrix;
and S42, reordering according to the corrected power of each user from big to small. The invention adopts the mode of superposition coding to carry out linear superposition on the user signals, and the receiving end adopts the serial successive interference elimination technology, thus being capable of realizing multi-user signal detection more simply; meanwhile, the used scheduling scheme is simpler and is convenient for the base station to realize; under the condition of ensuring that the power of each group of users is equal, the transmitting power of the base station is reduced as much as possible, so that the power consumption of the system is reduced, and the energy efficiency is improved; compared with the prior art, the method and the device have the advantages that the user is rescheduled and calculated on the basis of MMSE (Minimum Mean Square Error) precoding, so that a better power distribution scheme and scheduling strategy are obtained, and the problem that the dynamic range of the antenna transmitting power is overlarge due to the fact that different powers are required to be adopted for each antenna when a transmitting end processes signals in the MMSE precoding scheme for obtaining the channel capacity is considered; the operation of solving the inverse matrix in the traditional MMSE is avoided, so that the computational complexity is reduced, meanwhile, the scheduling process is a linear processing process, and the generation of additional complexity caused by nonlinear operation in the base station processing process is avoided.
Drawings
FIG. 1 is a schematic flow chart of a uniform power user scheduling method based on GRMES iteration according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a relationship between the user desired rate and the total power in the embodiment of the present invention;
fig. 3 is a diagram illustrating a relationship between the average user rate and the total power in the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
With reference to fig. 1 to fig. 3, in an embodiment of the present invention, a GRMES iteration-based equal power user scheduling method is provided, which is applied to a large-scale MIMO system, where the large-scale MIMO system adopts an MMSE precoding manner to implement user signal enhancement and interference cancellation functions, and the scheduling method includes the steps of:
s1, setting channel characteristics H of users in the massive MIMO system, and calculating a coding matrix W of the MMSE precoding mode by adopting a GMRES iterative algorithm based on the channel characteristics H;
in the specific embodiment, the present invention adopts a large-scale MIMO system in which a ring channel model is constructed by formula
Figure GDA0002939460820000051
Representing the channel parameters of the kth user in a ring channel model, wherein m and p represent two antennas in a base station, delta represents the angle expansion, and D (m-p) refers to an antenna m bandAn antenna, theta represents the azimuth angle of the user relative to the base station; and the loop channel model is provided with antennas, if the number of users is K, the number of antennas is NtThen the condition N is satisfiedt>>K, where the channel characteristic H of the user is represented by the formula H ═ H1,h2,...hk-1,hk]Is represented bykRepresenting the channel characteristics H of the kth user, the expression W of the coding matrix W is obtained from H (H)HH+αI)-1(ii) a Since the matrix W is H (H)HH+αI)-1In order to obtain a symmetrical positive definite sparse matrix, the invention adopts a cholesky decomposition mode to solve the matrix W-H (H)HH+αI)-1Wherein the matrix W ═ H (H)HH+αI)-1After cholesky decomposition, (H)HH+αI)＝LHAnd L is a lower triangular matrix in the formula, and the processing efficiency of the power in the annular channel model can be effectively improved and the calculation rate is accelerated by processing the small triangular matrix.
S2, setting the expected rate R of the users to the power transmission in the massive MIMO systemEIn combination with said desired rate RECalculating a minimum power covariance matrix P required by each user according to the channel characteristics H;
in an embodiment, the transmission signal rate of the base station is greater than or equal to the desired rate R in a massive MIMO systemEWith RE，kRepresents the desired rate for the kth user and the corresponding base station transmission rate is log2(1+SINRk) Then based on the Shannon formula, the two can satisfy the condition RE,k≤log2(1+SINRk) Wherein, in the step (A),
Figure GDA0002939460820000061
in the formula, wkSatisfy | | W for the kth column vector of the precoding matrix W k1, wherein pkFor the power allocated to the kth user, the power covariance matrix is P ═ diag (P)1,p2,...,pK)，
Figure GDA0002939460820000062
White noise power for the kth user; this is achieved byThe power of all users can be obtained by solving the power of one user.
In addition, the invention can obtain the lowest power of all users by infinite step power substituting calculation or obtain the lowest power of all users by proving, wherein the lowest power is obtained by a formula
Figure GDA0002939460820000063
It is shown that, among others,
Figure GDA0002939460820000064
Figure GDA0002939460820000071
s3, sequencing the lowest power covariance matrix P of each user in a big-to-small mode to obtain a set P', setting a user group number G, and sequentially arranging preset users in each user group number G according to a minimum variance standard;
in the invention, because SIC superposition coding is needed to be used subsequently, but the decoding of the SIC superposition coding has time delay, the invention preferably takes 3 users as a group, and then the total packet number is known to be
Figure GDA0002939460820000072
Putting the first G users in the P' into groups, then dividing the G + 1-2G users into the same group of users with the previously-put users in reverse order, calculating the power of each group after superposition, and sequencing the users from large to small again; finally, the rest users are substituted into the groups in sequence after reversing the order, and the power P of each group at the moment is calculatedGAnd returns packet information.
S4, adjusting the number G of each user group of preset users according to the constraint condition of SIC superposition coding to obtain the lowest grouping power variance
Figure GDA0002939460820000073
In the massive MIMO system of the present invention, users include high priority users and low priority users,in order to adjust the user group number G by using SIC superposition coding, the user needs to satisfy the SIC constraint condition:
Figure GDA0002939460820000074
in the formula (I), the compound is shown in the specification,
Figure GDA0002939460820000075
HP represents the high priority user, LP represents the low priority user, the high priority user corresponds to the high power user, the low priority user corresponds to the low power user, and in order to ensure that all users meet constraint conditions
Figure GDA0002939460820000081
That is, the signal strength of the high power user on the low power user channel is greater than or equal to the signal strength of the low power user, the invention needs to adjust the power of all users, and specifically includes the following steps:
firstly correcting a coding matrix of a high-power user and correcting the power of the corresponding user according to the corrected coding matrix, wherein the correction formula of the high-power user is w'HP＝λwLP+wHPWherein λ is a minimum weight correction coefficient, which is represented by the following formula
Figure GDA0002939460820000082
And obtaining solution, and accordingly, the method can promote the power of the high-power user to P'HP＝PHP·||w′HP| whereby the formula is combined
Figure GDA0002939460820000083
And formula P'HP＝PHP·||w′HPObtaining power value P 'of each group of users | |'GAnd variance corresponding to total power value
Figure GDA0002939460820000084
Then, the power values P 'of all groups of users are sequentially sorted from large to small'GTo ensure each user in each user groupThe power distribution method comprises three users, and each user group is distributed to L, M and H according to the power of each user, namely low, medium and high layer users.
Then selecting two groups with maximum and second maximum total power in all the distributed user groups, and interacting two groups of users with maximum and second maximum power, according to formula
Figure GDA0002939460820000085
And formula P'HP＝PHP·||w′HPCalculating corresponding lambda, returning to the condition that the sum of the minimum lambda sets generated in the exchange process is minimum, and returning the new grouping to the original group; if the lambda set is consistent with the initial value, the next operation is carried out, otherwise, two groups with the maximum total power and the second maximum total power in all the user groups are continuously selected, and the steps are carried out; wherein, the next operation is as follows: selecting two groups with maximum and minimum power in all user groups, exchanging the power of the corresponding layers of the users in the user groups, selecting the user with the lowest power in the group with the highest power if the lambda set is consistent with the initial value under the condition that the two groups have the closest power, combining the user with the current lowest power, returning the current group if the total power can be reduced under the condition of combination, selecting two groups with the maximum and next maximum total power in all the user groups again, interacting the users in the two groups with the maximum and next maximum power, and carrying out interaction according to a formula w'HP＝λwLP+wHP、
Figure GDA0002939460820000091
And formula P'HP＝PHP·||w′HPAnd if not, testing the next group of users until the conditions are not met, and ending iteration.
Finally, the power of each packet is returned, and the average power and the variance σ at that time are calculated2And back.
And S5, correcting the coding matrix W by adopting orthogonalization processing based on the constraint condition.
According to the grouping condition in step S4 and the obtained power of each group, the precoding of each user is adjusted, and in consideration of the correlation of some users, the present invention further reduces the power of some groups through further linear processing, or obtains better system performance under the condition of constant power.
When the coefficient lambda is considered, the whole occupation of the low-power user precoding is adopted before, and actually, the condition of meeting the constraint condition can be obtained at the receiving end through partial channels
Figure GDA0002939460820000101
Correction formula w'HP＝λwLP+wHPAnd lambda expression
Figure GDA0002939460820000102
The method specifically comprises the following steps: first, an interference matrix of each user is calculated
Figure GDA0002939460820000103
Wherein HiWhich represents the channel of the packet or packets, ihrepresenting user channels within the same group, wherein kh＝hkw′k，gkDenotes the kth user in the g group, glThe last user in the group; then orthogonalizing the interference matrix, preferably, the invention adopts a Householder orthogonalization scheme to carry out orthogonalization processing on the interference matrix, and the algorithm is as follows:
Figure GDA0002939460820000104
For k＝1,2,...m
If k>1compute rk:＝Pk-1Pk-2...P1 h k
Compute
Figure GDA0002939460820000105
Compute rk:＝Pkrkwith Pk＝I-2τkτk T
Compute qk＝P1P2...Pkek
End Do
finally using the obtained qiM where m is the maximum number of iterations, is substituted into the formula
Figure GDA0002939460820000106
Thereby calculating a weight correction coefficient
Figure GDA0002939460820000107
Thereby obtaining a corrected precoding increment of
Figure GDA0002939460820000108
Modified precoding is
Figure GDA0002939460820000111
And each user is processed in turn to obtain the modified precoding after all users are scheduled.
In other embodiments of the present invention, different orthogonalization schemes may be used to pair the interference matrix H-gi≥kOrthogonalization is performed, for example, Gram-Schmidt orthogonalization, Householder orthogonalization, etc., for which the present invention is not limited and fixed, and can be selected according to actual circumstances. The invention adopts the mode of superposition coding to carry out linear superposition on the user signals, and the receiving end adopts the serial successive interference elimination technology, thus being capable of realizing multi-user signal detection more simply; meanwhile, the used scheduling scheme is simpler and is convenient for the base station to realize; under the condition of ensuring that the power of each group of users is equal, the transmitting power of the base station is reduced as much as possible, so that the power consumption of the system is reduced, and the energy efficiency is improved; compared with the prior art, the invention reschedules and calculates the user on the basis of MMSE (Minimum Mean Square Error) precoding so as to obtain a better power distribution scheme and scheduling strategy, and the MMSE precoding party for obtaining the channel capacity is consideredWhen a scheme processes signals at a sending end, different powers are required to be adopted for each antenna, so that the problem that the dynamic range of the transmitting power of the antenna is too large is caused; the operation of solving the inverse matrix in the traditional MMSE is avoided, so that the computational complexity is reduced, meanwhile, the scheduling process is a linear processing process, and the generation of additional complexity caused by nonlinear operation in the base station processing process is avoided.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing detailed description, or equivalent changes may be made in some of the features of the embodiments described above. All equivalent structures made by using the contents of the specification and the attached drawings of the invention can be directly or indirectly applied to other related technical fields, and are also within the protection scope of the patent of the invention.

Claims (5)

1. A equal power user scheduling method based on GRMES iteration is applied to a large-scale MIMO system, and the large-scale MIMO system adopts an MMSE precoding mode to realize the functions of user signal enhancement and interference elimination, and is characterized in that the scheduling method comprises the following steps:
s1, setting channel characteristics H of users in the massive MIMO system, and calculating a coding matrix W of the MMSE precoding mode by adopting a GMRES iterative algorithm based on the channel characteristics H;
s2, setting the expected rate R of the users to the power transmission in the massive MIMO systemEIn combination with said desired rate RECalculating a minimum power covariance matrix P required by each user according to the channel characteristics H;
s3, sequencing the lowest power covariance matrix P of each user in a big-to-small mode to obtain a set P', setting a user group number G, and sequentially arranging preset users in each user group number G according to a minimum variance standard;
s4, adjusting the number G of each user group of preset users according to the constraint condition of SIC superposition coding to obtain the lowest grouping workVariance of rate
Figure FDA0002939460810000011
S5, correcting the coding matrix W by adopting orthogonalization processing based on the constraint condition;
the users comprise high-priority users and low-priority users, wherein the constraint conditions are as follows:
Figure FDA0002939460810000012
in the formula (I), the compound is shown in the specification,
Figure FDA0002939460810000013
HP represents the high-priority user, LP represents the low-priority user, the high-priority user corresponds to a high-power user, and the low-priority user corresponds to a low-power user;
wherein, PLPFor power allocated to low priority users, PHPIs the power allocated to the high priority user; pwTotal power of the group in which the LP users are located, PvThe total power of the group where the HP user is located; gamma-shapedLPAnd ΓHPSignal-to-noise ratio constraints for LP and HP users respectively,
Figure FDA0002939460810000021
and
Figure FDA0002939460810000022
the gaussian white noise variance values for LP and HP users, respectively.
2. The GRMES iteration-based equal power user scheduling method as claimed in claim 1, wherein a circular channel model is constructed in the massive MIMO system, and the circular channel model is represented by a formula
Figure FDA0002939460810000023
Denotes channel parameters of the k-th user, where m, p denote two designated in the base stationThe antenna is a root antenna, delta represents the angle expansion, D (m-p) represents the antenna m-band antenna, and theta represents the azimuth angle of a user relative to the base station.
3. The GRMES iteration-based equal power user scheduling method of claim 1, wherein the channel characteristic H is represented by the formula H ═ H [ H ═ H1,h2,...hk-1,hk]Is represented by the formula (I) in which hkRepresents the channel characteristic H of a k-th user; and the coding matrix W is represented by the formula W ═ H (H)HH+αI)-1。
4. The equal power user scheduling method based on GRMES iteration of claim 1, wherein the transmission signal rate of the base station in the massive MIMO system is greater than or equal to the desired rate REAnd the desired rate R of the kth userE，kThe transmission rate with the base station satisfies a condition RE,k≤log2(1+SINRk) Wherein, in the step (A),
Figure FDA0002939460810000024
in the formula, wkSatisfy | | W for the kth column vector of the precoding matrix Wk1, wherein pkFor the power allocated to the kth user, the power covariance matrix is P ═ diag (P)1,p2,...,pK)，
Figure FDA0002939460810000031
White noise power, h, for the kth userkIs the channel of the kth user.
5. The GRMES iteration-based equal power user scheduling method according to any one of claims 1 to 4, wherein the step S4 further comprises the steps of:
s41, correcting the coding matrix of the high-power user and correcting the power of the corresponding user according to the corrected coding matrix;
and S42, reordering according to the corrected power of each user from big to small.
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