CN112600593A - NOMA-based beam selection method - Google Patents

NOMA-based beam selection method Download PDF

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CN112600593A
CN112600593A CN202011473464.6A CN202011473464A CN112600593A CN 112600593 A CN112600593 A CN 112600593A CN 202011473464 A CN202011473464 A CN 202011473464A CN 112600593 A CN112600593 A CN 112600593A
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channel
users
matrix
representing
beam selection
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张文彬
刘玲芝
吴少川
李壮
王德昊
朱秋玥
张立妍
苏佳琪
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Beijing Mechanical And Electrical Engineering General Design Department
Harbin Institute of Technology
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Harbin Institute of Technology
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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
    • H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
    • 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
    • H04B7/0426—Power distribution
    • 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

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Abstract

A beam selection method based on NOMA can improve the performance and communication quality of a large-scale MIMO system, and belongs to the technical field of spectrum power distribution. The invention comprises the following steps: s1, establishing a channel model of the large-scale MIMO system, and converting the channel model into a channel model in a beam space; s2, clustering the users in the channel model, if two rows of the two users corresponding to the same channel vector with the maximum amplitude are the same, taking the two rows as the users in the same cluster, and forming a cluster by the users without pairing to obtain a channel matrix in a beam space; s3, based on the channel matrix in the beam space, obtaining the channel matrix after the beam selection by using an improved maximum signal-to-interference-and-noise ratio beam selection algorithm or an improved maximum capacity selection algorithm; s4, performing zero forcing pre-coding according to the channel matrix after the wave beam selection to obtain a pre-coded matrix, performing distributed power distribution on users in different clusters, and introducing a non-orthogonal multiple access technology among a plurality of users in the same cluster to perform distributed power distribution.

Description

NOMA-based beam selection method
Technical Field
The invention relates to a beam selection method in a large-scale MIMO system based on NOMA, belonging to the technical field of spectrum power distribution.
Background
In recent years, as mobile devices have become more commonly used, the amount of wireless data has increased rapidly, and the demand for wireless network capacity has increased. In the latest 5G communication, multi-antenna Massive MIMO (Multiple-input Multiple-output, Multiple-input Multiple-output technology)) under millimeter waves has great advantages in the aspects of improving spectrum utilization rate, controlling interference and the like, and will take an important position in the future 5G communication.
However, millimeter wave Massive MIMO puts higher requirements on the power consumption of the hardware of the transmitting end. Therefore, the industry and the academia turn attention to the research of the beam selection algorithm in the beam space field, the Massive MIMO beam forming technology is applied to the beam space, and the transmitting end adopts the hybrid beam forming, so that the hardware complexity and the power consumption can be reduced to a great extent, and the approximately optimal communication performance is achieved. In the beam space, the beams in the physical channel model are converted into the angle domain, so that each radio frequency link in B-MIMO corresponds to a beam in a certain direction angle, and does not correspond to one antenna. In this case, it can be considered that different single-antenna users can meet their own communication requirements by selecting beams in several directions, which is sparsity of beam space channels. Therefore, it can be understood that by using DLA (Discrete Lens Array) which causes only a part of negligible performance loss, the conventional physical space channel model can be converted into a beam space channel model in an angle domain, and at this time, the sparsity of the channel matrix can be utilized to complete the communication process by selecting only a small part of beams without causing excessive loss to the overall system performance, and this selection finally further reduces the number of radio frequency links. In order to solve the above problems, a technique of antenna selection is proposed, but with the proposal of the beam space concept, a beam selection algorithm is then widely studied. The beam selection algorithm can be seen as the processing of antenna selection in beam space. The proposed various beam selection algorithms make performance analysis for different algorithms more important, and the existing beam selection algorithms still need to be improved for improving the performance and communication quality of a large-scale MIMO system.
Disclosure of Invention
In view of the above disadvantages, the present invention provides a NOMA-based beam selection method capable of improving the performance and communication quality of a massive MIMO system.
The invention discloses a beam selection method based on NOMA, which comprises the following steps:
s1, establishing a channel model of the large-scale MIMO system, and converting the channel model into a channel model in a beam space;
s2, clustering the users in the channel model, if two rows of the two users with the maximum corresponding channel vector amplitude are the same, taking the two users as the users in the same cluster, and the users without pairing form a cluster to obtain a channel matrix H in the beam spaceb；
S3 channel matrix H based on beam spacebObtaining the channel matrix after the wave beam selection by using the improved maximum signal-to-interference-and-noise ratio wave beam selection algorithm
Figure BDA0002836767510000021
The method specifically comprises the following steps:
s31, let matrix C be Hb，F＝C(CHC)-1F represents normalizedChannel matrix, ClThe representation matrix C removes the channel matrix after the ith beam, NcDetermining the number of co-selected beams, wherein n represents the total number of beams;
s32, calculating deltajJ takes a value from 1 to N-NcAccording to deltajObtain set D ═ δ1,…,δn-NGet the solution according to the set D
Figure BDA0002836767510000022
δjThe method comprises the following steps: solving the value of j from 1 to n-j +1 according to the current value of j
Figure BDA0002836767510000023
According to the obtained alpha(l)Calculating deltaj＝argmax{|α(l)|2}；
Figure BDA0002836767510000024
Represents HbThe mth row of (1), wherein the value of m should satisfy not belonging to the set D; d represents the set of deleted beams after beam selection; deltajA row indicating a channel which maximizes a signal to interference and noise ratio; f(l)Representing the normalized channel matrix after the first beam is removed; tr () represents the trace of the matrix; ρ represents the signal power of the user; alpha is alpha(l)A control factor representing a precoding matrix;
s33, according to
Figure BDA0002836767510000025
Determining a post-beam selection channel matrix
Figure BDA0002836767510000026
S4, selecting the channel matrix according to the wave beam
Figure BDA0002836767510000027
Zero forcing pre-coding is carried out to obtain a pre-coding matrix, distributed power distribution is carried out on users in different clusters, and a non-orthogonal multiple access technology is introduced among a plurality of users in the same cluster for distributionThe power distribution of equation (iv).
The invention also provides a beam selection method based on NOMA, which comprises the following steps:
s1, establishing a channel model of the large-scale MIMO system, and converting the channel model into a channel model in a beam space;
s2, clustering the users in the channel model, if two rows of the two users with the maximum corresponding channel vector amplitude are the same, taking the two users as the users in the same cluster, and the users without pairing form a cluster to obtain a channel matrix H in the beam spaceb；
S3 channel matrix H based on beam spacebObtaining a channel matrix after beam selection by using an improved maximum capacity selection algorithm
Figure BDA0002836767510000028
The method specifically comprises the following steps:
s31, let K equal H matrixbγ denotes the signal-to-noise ratio, NcDetermining the number of co-selected beams, wherein n represents the total number of beams;
s32, calculating deltajJ takes a value from 1 to N-NcAccording to deltajObtain set D ═ δ1,…,δn-NGet the solution according to the set D
Figure BDA0002836767510000031
Wherein deltajThe method comprises the following steps: solving the value of j from 1 to n-j +1 according to the current value of j
Figure BDA0002836767510000032
Wherein the initial value of the channel iteration matrix B is B ═ I + gamma KHK)-1Updating the channel iteration matrix as j varies
Figure BDA0002836767510000033
According to the obtained omega(l)Calculating deltaj＝arg max{Ω(l)}；
Figure BDA0002836767510000034
Represents HbThe mth row of (1), wherein the value of m should satisfy not belonging to the set D;
Figure BDA0002836767510000035
δ of the expression matrix KjA row;
d represents the set of deleted beams after beam selection; deltajAn index indicating a row of a channel that maximizes system capacity; k is a radical oflRepresenting a vector obtained after the matrix K takes the l-th row; omega(l)ΩlRepresenting the system capacity factor after the first beam is removed;
s33, according to
Figure BDA0002836767510000036
Determining a post-beam selection channel matrix
Figure BDA0002836767510000037
S4, selecting the channel matrix according to the wave beam
Figure BDA0002836767510000038
And performing zero forcing precoding to obtain a precoding matrix, performing distributed power distribution on users in different clusters, and introducing a non-orthogonal multiple access technology among a plurality of users in the same cluster to perform distributed power distribution.
The invention has the beneficial effects that: according to the invention, through changing the flow sequence of selecting the wave beam by the user and pairing the user, namely, firstly, the user is paired according to the wave beam corresponding to the maximum amplitude value, and secondly, the maximum signal-to-interference-and-noise ratio selection or maximum capacity selection algorithm is analyzed, the expression of the optimization target is changed along with the change of the channel matrix, so that the number of the final transmitting end transmitting wave beams is influenced. The invention improves the wave beam selection algorithm, and the precoding mode adopts a zero-forcing precoding algorithm based on the strong user channel vector corresponding to the wave beam selection algorithm. The invention provides performance simulation graphs of channel models corresponding to different beam selection algorithms, and through result analysis, the spectrum efficiency and the energy efficiency of the improved algorithm based on the maximum signal-to-interference-and-noise ratio selection and the maximum capacity selection fixed energy capture ratio are improved when the number of users is large. When the number of the selected beams is fixed, the spectrum efficiency is reduced when the number of the users is large, and the energy efficiency is generally improved, because the algorithm of the invention reduces the number of the selected beams, the concept of reducing the power consumption of the transmitting end and improving the energy efficiency by using the beam selection algorithm in the beam space is conformed.
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FIG. 1 is a graph showing the comparison of the spectral efficiency of the improved selection algorithm of the maximum signal to interference plus noise ratio and the conventional selection algorithm of the maximum signal to interference plus noise ratio;
FIG. 2 is a graph showing the energy efficiency comparison of the improved selection algorithm of the maximum signal to interference plus noise ratio and the conventional algorithm of the maximum signal to interference plus noise ratio;
FIG. 3 is a graph of the spectral efficiency of a system using the improved maximum capacity selection algorithm of the present invention versus a conventional maximum capacity selection algorithm;
FIG. 4 is a graph of energy efficiency versus system efficiency using the improved maximum capacity selection algorithm of the present invention and a conventional maximum capacity selection algorithm;
FIG. 5 is a graph showing the comparison of the spectral efficiency of a system at a fixed energy capture ratio using the improved maximum SINR selection algorithm of the present invention and a conventional maximum SINR algorithm;
FIG. 6 is a graph of energy efficiency versus fixed energy capture ratio using the improved maximum signal to interference plus noise ratio selection algorithm of the present invention and a conventional maximum signal to interference plus noise ratio algorithm;
FIG. 7 is a graph of the spectral efficiency of a system at a fixed energy capture ratio using the improved maximum capacity selection algorithm of the present invention and a conventional maximum capacity selection algorithm;
FIG. 8 is a graph of energy efficiency versus fixed energy capture ratio using the improved maximum capacity selection algorithm of the present invention and a conventional maximum capacity selection algorithm;
FIG. 9 is a schematic flow chart 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.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
As shown in fig. 9, the NOMA-based beam selection method according to the present embodiment includes the steps of:
step one, establishing a channel model of a large-scale MIMO system, and converting the channel model into a channel model in a beam space;
the channel model of the embodiment is a widely-used Saleh-Vallenzuela channel model, only a time-invariant channel is considered, in order to be suitable for a more real channel condition, the number of multipath is 2, a Rice factor is set to be 5, and the parameters are matched with the real millimeter wave channel condition;
according to the characteristics of a Discrete Lens Array (DLA), a downlink physical channel between a base station and a mobile user is converted into a beam space channel through a unitary matrix U, and beam space conversion of a Saleh-Vallenzuela model is completed;
step two, clustering the users in the channel model, if the beam positions corresponding to the channel vectors with the maximum amplitude values of the two users are the same, taking the two users as the users in the same cluster, wherein the users without pairing form a cluster, namely, the situation that one or two users exist in one cluster exists, and obtaining a channel matrix H under the beam spaceb；
Step three, based on the channel matrix H under the beam spacebObtaining the channel matrix after the wave beam selection by using an improved maximum signal-to-interference-and-noise ratio wave beam selection algorithm or an improved maximum capacity selection algorithm
Figure BDA0002836767510000051
Obtaining actual channels in wave beam spaces of a transmitting end and a receiving end;
step four, selecting the channel matrix according to the wave beam
Figure BDA0002836767510000052
Zero forcing precoding is carried out to obtain a precoding matrix, distributed power distribution is carried out on users in different clusters, a Non-orthogonal multiple access (NOMA) technology power distribution algorithm is introduced among a plurality of users in the same cluster, and the purposes of reducing the number of communication links and reducing power consumption are achieved.
The idea of the traditional maximum signal-to-interference-and-noise ratio algorithm is to optimize the performance index of the signal-to-interference-and-noise ratio. The selection of the maximum SINR is for the ith user, and the SINR is defined as follows
Figure BDA0002836767510000053
Wherein σ2In order to be able to measure the power of the noise,
Figure BDA0002836767510000054
is HbThe (c) th column of (a),
Figure BDA0002836767510000055
is FbColumn i, FbIs a zero-forcing precoding matrix without power limitation. Since ZF precoding is used here, if the base station can acquire complete channel information, the base station can acquire the complete channel information
Figure BDA0002836767510000056
And is
Figure BDA0002836767510000057
The above equation can therefore be simplified to:
Figure BDA0002836767510000058
wherein γ is ρ/σ2Is the signal-to-noise ratio SNR. Alpha is a control factor of the precoding matrix, and as can be seen from the above equation, if the SINR is to be maximized, in the case of a fixed SNR, | alpha | needs to be maximized2And max. If it is decided to co-select NcFor each beam, an exhaustive search of the channel matrix is required to calculate | α2The maximum beam combination is too large in computation amount and too high in complexity in practical engineering application, and in the embodiment, the channel matrix after the beam selection is obtained by using the improved maximum signal-to-interference-and-noise-ratio beam selection algorithm in the step three
Figure BDA0002836767510000059
The improved maximum signal-to-interference-and-noise ratio algorithm of the embodiment can reduce the operation complexity, so that the method has practical application value.
Improved maximum signal-to-interference-and-noise ratio selection algorithm
Figure BDA00028367675100000510
Figure BDA0002836767510000061
The maximum capacity selection is maximized for the capacity index, first defining the capacity
Figure BDA0002836767510000062
The capacity when the system drops the ith beam can be expressed as
Figure BDA0002836767510000063
Wherein
Figure BDA0002836767510000064
After the above formula is simplified
Figure BDA0002836767510000065
Then maximizing the capacity after beam selection is needed
Figure BDA0002836767510000066
The term is minimized.
Similar to the maximum sir selection algorithm in terms of large computation, step three of the present embodiment obtains the channel matrix after beam selection using the improved maximum capacity selection algorithm
Figure BDA0002836767510000067
The method can reduce the complexity of operation, thereby having practical application value. First define a set
Figure BDA0002836767510000068
Maximum capacity selection algorithm
Figure BDA0002836767510000069
Figure BDA0002836767510000071
Also, according to the above algorithm, output
Figure BDA0002836767510000072
I.e. the channel matrix after beam selection. It can be seen that the beam space channel matrix HbThe effect of (a) is very large,the maximum capacity selection algorithm also varies the number of selected beams if it is based on the channel matrix after clustering.
In a preferred embodiment, the steps of this embodiment are based on a widely-used Saleh-valeazulia channel model, and only a time-invariant model is considered, wherein the number of multipath is 2, and the rice factor is set to 5, and these parameters are more suitable for the real millimeter wave channel condition; in this embodiment, only the time invariant channel is considered. First, a control vector is defined
a(θ)＝[e-j2πθq]q∈Γ(N)
Where the dimension of a (θ) is N × 1, representing a discrete, complex spatial sinusoid, whose spatial angle is represented by θ ═ 0.5sin (Φ), corresponding to the direction angle Φ ∈ [ -pi/2, pi/2 in physical space]The phi angle is the physical direction angle.
Figure BDA0002836767510000073
Is a symmetric set centered around 0, and j represents an imaginary symbol.
The MP (multipath) multipath component channel vector of the kth user in the Saleh-Vallenzuela channel model can be derived as
Figure BDA0002836767510000074
Wherein, thetai,qRepresenting the spatial angle, beta, of the ith useri,qAnd the complex path loss corresponding to different paths of the ith user is shown.
Since the millimeter wave wavelength is very short, the width of the transmitted beam is very narrow, and LoS is the main propagation mode. The channel vector after the LoS direct path component is represented as
Figure BDA0002836767510000081
βi,0a(θi,0) The term represents the direct path component, the magnitude of the multipath component | βi,qL in generalSpecific LoS component | βi,0L is 5 to 10dB smaller.
The channel model is then transformed by a unitary matrix to an expression in the beam space, which requires a specific expression of the unitary matrix U in order to be transformed to the beam space for processing. The columns of the matrix U correspond to n control vectors of fixed spatial angle and have a fixed pitch
Figure BDA0002836767510000082
Figure BDA0002836767510000083
Figure BDA0002836767510000084
At this point, the beam space transformation for the Saleh-Vallenzuela model is completed.
In a preferred embodiment, step four of this embodiment is to perform improved zero-forcing precoding, where the precoding method uses a corresponding zero-forcing precoding algorithm based on a strong user channel vector, that is, when a precoding matrix is calculated, the channel matrix should participate in calculation by using a channel matrix after beam selection, so as to suppress interference of users between different beams.
The distributed power distribution is carried out based on the non-orthogonal multiple access technology (NOMA), and the aim of maximizing the spectrum efficiency of the system is achieved. In the process of power allocation, the algorithm has the advantages that fairness of users with low signal-to-noise ratio is fully considered and decoding complexity of a receiver is reduced, although the algorithm is a local optimization algorithm, in the embodiment, the power allocation adopts a distributed algorithm, and the two algorithms just meet the idea that complexity reduction is required in the embodiment.
The power expression of user i is
Figure BDA0002836767510000085
Where P represents the sum of the energies of all users, U represents the set of users, gs(i)/ns(i) Representing the signal-to-noise ratio, g, of user is(k)/ns(k) Representing the signal-to-noise ratio, alpha, of user kftpa∈[0,1]Denotes the power division factor, and it can be seen that when α isftpaWhen equal to 0, it means equal power distribution among users, and likewise, with alphaftpaWith this increase, users with low signal-to-noise ratios can also obtain more power. It is noted that different power attenuation factors alpha are chosenftpaDifferent allocation strategies are corresponded, and the difference is large.
The embodiment further comprises a fifth step of respectively simulating the maximum signal-to-interference-and-noise ratio algorithm and the maximum capacity selection algorithm according to the power distribution result, comparing the performance of the improved algorithm with that of the original algorithm, and verifying the improvement of the improved algorithm on the system performance.
Fig. 1 shows a graph comparing spectral efficiencies of systems using the improved maximum sir selection algorithm and the conventional maximum sir algorithm according to the present embodiment in the model of the present embodiment. As can be seen from fig. 1, the improved maximum sir selection algorithm has a large spectral efficiency at a number of users less than 46, which is most advantageous at a moderate number of users, but has a lower spectral efficiency than the original algorithm at a number of users of 50, which does not necessarily prove to be poor. The reason that the spectral efficiency will be reduced is that the algorithms of this section reduce the number of selected beams, i.e. the number of radio links, so that energy efficiency is more of a concern.
Fig. 2 shows a graph of energy efficiency versus system efficiency using the improved maximum signal to interference plus noise ratio selection algorithm of the present embodiment and a conventional maximum signal to interference plus noise ratio algorithm. It can be seen that the energy efficiency of the improved algorithm is improved, which is proved by the trend of improving the spectrum efficiency, but the corresponding system energy efficiency is still larger up to 50 users, because the spectrum efficiency is low when the number of users is 50, and the final energy efficiency is relatively increased due to the reduction of the transmission power consumption.
It can be seen from fig. 1 and fig. 2 that, for the maximum sir selection algorithm, the significance of the beam selection algorithm is to reduce the number of transmission links and improve energy efficiency, so that the algorithm of this embodiment is effective to improve the maximum sir selection algorithm.
Fig. 3 is a graph of the spectral efficiency versus the system using the improved maximum capacity selection algorithm of the present embodiment and a conventional maximum capacity selection algorithm. It can be seen that when the number of users is large, the spectral efficiency corresponding to the improved algorithm has a more obvious decreasing trend than that in the first graph, and then, by observing fig. 4, which shows the energy efficiency comparison graph of the system using the improved maximum capacity selection algorithm of the present embodiment and the conventional maximum capacity selection algorithm, it can be seen that although the spectral efficiency of the improved algorithm starts to be lower when the number of users reaches 38, and even decreases as the number of users increases, the energy efficiency only starts to be lower when the number of users increases to 46, and when the number of users is moderate or low, the improved algorithm can also improve the energy efficiency. This also proves that the improved algorithm can reduce the number of selected beams, i.e. the number of radio frequency links, but in practical communication, when the maximum capacity selection algorithm is used to fix the number of selected beams, the algorithm should be reasonably selected according to the communication requirements.
Fig. 5 and fig. 6 show the spectral efficiency and energy efficiency contrast curves of the system when the maximum signal to interference plus noise ratio selection algorithm improved by the embodiment is used for fixing the energy capture ratio and the traditional algorithm. It can be seen that both the spectral efficiency and the energy efficiency start to be improved gradually as the number of users increases, and the performance is not improved greatly when the number of users is small.
Fig. 7 and 8 show the spectral efficiency and energy efficiency comparison graphs of the system when the improved maximum capacity selection algorithm of the present embodiment is used to fix the energy capture ratio, and the traditional maximum capacity selection algorithm. It can be seen that similar to the maximum signal to interference plus noise ratio selection algorithm, the improved algorithm will slightly improve the spectrum efficiency and energy efficiency of the system when the number of users is large.
The simulation results show that: different beam selection algorithms should be used according to different system requirements, and since the theoretical basis of the different beam selection algorithms is different and the maximized target is different, the influence of the different beam selection algorithms on the algorithm or the model provided by the embodiment is different.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It is to be understood that features described in different dependent claims and in this embodiment may be combined in ways other than those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.

Claims (6)

1. A NOMA based beam selection method, said method comprising:
s1, establishing a channel model of the large-scale MIMO system, and converting the channel model into a channel model in a beam space;
s2, clustering the users in the channel model, if two rows of the two users with the maximum corresponding channel vector amplitude are the same, taking the two users as the users in the same cluster, and the users without pairing form a cluster to obtain a channel matrix H in the beam spaceb；
S3 channel matrix H based on beam spacebObtaining the channel matrix after the wave beam selection by utilizing the wave beam selection algorithm of the maximum signal-to-interference-and-noise ratio
Figure FDA0002836767500000011
The method specifically comprises the following steps:
s31, let matrix C be Hb，F＝C(CHC)-1F denotes a normalized channel matrix, ClThe representation matrix C removes the channel matrix after the ith beam, NcDetermining the number of co-selected beams, wherein n represents the total number of beams;
s32, calculating deltajJ takes a value from 1 to N-NcAccording to deltajObtain set D ═ δ1,…,δn-NGet the solution according to the set D
Figure FDA0002836767500000012
δjThe method comprises the following steps: solving the value of j from 1 to n-j +1 according to the current value of j
Figure FDA0002836767500000013
According to the obtained alpha(l)Calculating deltaj＝argmax{|α(l)|2}；
Figure FDA0002836767500000014
Represents HbThe mth row of (1), wherein the value of m should satisfy not belonging to the set D;
d represents the set of deleted beams after beam selection;
δja row indicating a channel which maximizes a signal to interference and noise ratio;
F(l)representing the normalized channel matrix after the first beam is removed;
tr () represents the trace of the matrix;
ρ represents the signal power of the user;
α(l)a control factor representing a precoding matrix;
s33, according to
Figure FDA0002836767500000015
Determining a post-beam selection channel matrix
Figure FDA0002836767500000016
S4, selecting the channel matrix according to the wave beam
Figure FDA0002836767500000017
And performing zero forcing precoding to obtain a precoding matrix, performing distributed power distribution on users in different clusters, and introducing a non-orthogonal multiple access technology among a plurality of users in the same cluster to perform distributed power distribution.
2. The NOMA-based beam selection method of claim 1, wherein said S1 comprises: establishing a Saleh-Vallenzuela channel model of a large-scale MIMO system, wherein the number of multipath is 2, the Rice factor is 5, and the multipath component channel vector of the kth user in the Saleh-Vallenzuela channel model is as follows:
Figure FDA0002836767500000021
wherein, thetai,qRepresenting the spatial angle, beta, of the ith useri,qRepresents the complex path loss, a (theta), corresponding to different paths of the ith useri,q) Which represents the control vector(s) of the control,
Figure FDA0002836767500000022
q represents the qth element in the set Γ (N), j represents an imaginary symbol, Γ (N) = { l-N-1)/2: 1, N-1 is a symmetric set centered around 0; n is a radical ofpRepresenting the number of multipath strips;
the Saleh-Valencuela channel model is converted to the beam space by a unitary matrix, wherein the columns of the unitary matrix U correspond to n control vectors with fixed spatial angles and have fixed distances
Figure FDA0002836767500000023
Figure FDA0002836767500000024
Figure FDA0002836767500000025
3. The NOMA-based beam selection method of claim 2, wherein in S4, distributed power allocation is performed based on a non-orthogonal multiple access technique, and power allocated to user i is:
Figure FDA0002836767500000026
where P represents the sum of the energies of all users, U represents the set of users, gs(i)/ns(i) Representing the signal-to-noise ratio, g, of user is(k)/ns(k) Representing the signal-to-noise ratio, alpha, of user kftpa∈[0,1]Representing the power allocation factor.
4. A NOMA based beam selection method, said method comprising:
s1, establishing a channel model of the large-scale MIMO system, and converting the channel model into a channel model in a beam space;
s2, clustering the users in the channel model, if two rows of the two users with the maximum corresponding channel vector amplitude are the same, taking the two users as the users in the same cluster, and the users without pairing form a cluster to obtain a channel matrix H in the beam spaceb；
S3 channel matrix H based on beam spacebObtaining the channel matrix after the wave beam selection by utilizing the maximum capacity selection algorithm
Figure FDA0002836767500000027
The method specifically comprises the following steps:
s31, let K equal H matrixbγ denotes the signal-to-noise ratio, NcIs to decide to select togetherThe number of selected beams, n representing the total number of beams;
s32, calculating deltajJ takes a value from 1 to N-NcAccording to deltajObtain set D ═ δ1,…,δn-NGet the solution according to the set D
Figure FDA0002836767500000031
Wherein deltajThe method comprises the following steps: solving the value of j from 1 to n-j +1 according to the current value of j
Figure FDA0002836767500000032
Wherein the initial value of the channel iteration matrix B is B ═ I + gamma KHK)-1Updating the channel iteration matrix as j varies
Figure FDA0002836767500000033
According to the obtained omega(l)Calculating deltaj＝argmax{Ω(l)}；
Figure FDA0002836767500000034
Represents HbThe mth row of (1), wherein the value of m should satisfy not belonging to the set D;
Figure FDA0002836767500000035
δ of the expression matrix KjA row;
d represents the set of deleted beams after beam selection;
δjan index indicating a row of a channel that maximizes system capacity;
klrepresenting a vector obtained after the matrix K takes the l-th row;
Ω(l)Ωlrepresenting the system capacity factor after the first beam is removed;
s33, according to
Figure FDA0002836767500000036
Determining a post-beam selection channel matrix
Figure FDA0002836767500000037
S4, selecting the channel matrix according to the wave beam
Figure FDA0002836767500000038
And performing zero forcing precoding to obtain a precoding matrix, performing distributed power distribution on users in different clusters, and introducing a non-orthogonal multiple access technology among a plurality of users in the same cluster to perform distributed power distribution.
5. The NOMA-based beam selection method of claim 4, wherein the S1 includes: establishing a Saleh-Vallenzuela channel model of a large-scale MIMO system, wherein the number of multipath is 2, the Rice factor is 5, and the multipath component channel vector of the kth user in the Saleh-Vallenzuela channel model is as follows:
Figure FDA0002836767500000039
wherein, thetai,qRepresenting the spatial angle, beta, of the ith useri,qRepresents the complex path loss, a (theta), corresponding to different paths of the ith useri,q) Which represents the control vector(s) of the control,
Figure FDA00028367675000000310
q represents the qth element in the set Γ (N), j represents an imaginary symbol, Γ (N) ═ { l- (N-1)/2: 1, N-1 is a symmetric set centered around 0; n is a radical ofpRepresenting the number of multipath strips;
the Saleh-Valencuela channel model is converted to the beam space by a unitary matrix, wherein the columns of the unitary matrix U correspond to n control vectors with fixed spatial angles and have fixed distances
Figure FDA00028367675000000311
Figure FDA0002836767500000041
Figure FDA0002836767500000042
6. The NOMA-based beam selection method as claimed in claim 5, wherein in S4, distributed power allocation is performed based on non-orthogonal multiple access technology, and the power allocated to user i is:
Figure FDA0002836767500000043
where P represents the sum of the energies of all users, U represents the set of users, gs(i)/ns(i) Representing the signal-to-noise ratio, g, of user is(k)/ns(k) Representing the signal-to-noise ratio, alpha, of user kftpa∈[0,1]Representing the power allocation factor.
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