CN115085781A - MIMO IC chain interference alignment method based on maximum independent set - Google Patents

MIMO IC chain interference alignment method based on maximum independent set Download PDF

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CN115085781A
CN115085781A CN202210658264.0A CN202210658264A CN115085781A CN 115085781 A CN115085781 A CN 115085781A CN 202210658264 A CN202210658264 A CN 202210658264A CN 115085781 A CN115085781 A CN 115085781A
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receiving end
interference alignment
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CN115085781B (en
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刘伟
石凌涛
冯智超
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Chengdu Yaguang Electronic Co ltd
Xidian University
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
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    • 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
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Abstract

The invention discloses a MIMO IC chain interference alignment method based on a maximum independent set, aiming at solving the condition limitation that the channel state information matrix is selected fixedly and the antenna utilization rate of a transceiving node is low in the prior art. The method comprises the following implementation steps: setting system parameters; constructing a partial connection model of the MIMO interference channel; generating a sending end set of each receiving end; generating an interference alignment scheme of each receiving end; constructing a conflict graph representing an interference alignment scheme; generating a maximum independent set of conflict graphs; constructing an interference alignment chain equation; generating a decoding matrix; each sending end simultaneously sends data streams; eliminating interference signals of a receiving end; and obtaining a non-interference expected signal at each receiving end, and finishing interference alignment. The invention increases the aligned interference range, improves the utilization rate of the antenna and increases the degree of freedom of the system and the transmission rate of the channel.

Description

MIMO IC chain interference alignment method based on maximum independent set
Technical Field
The invention belongs to the technical field of communication, and further relates to a MIMO (multiple input multiple output) partial connection interference channel IC (interference channel) medium-chain interference alignment method based on a maximum independent set in the technical field of wireless communication. The method can be used for interference channels connected with the MIMO part, and the precoding matrix and the decoding matrix are solved by designing the interference alignment chain, so that the degree of freedom of the system is improved, and the purpose of interference alignment is achieved.
Background
The idea of interference alignment is to divide a signal space into two parts, namely an expected signal subspace and an interference signal subspace, and align interference at a receiving end through a precoding technology, so that the signal dimension occupied by the interference is compressed, the influence of the interference on an expected signal is reduced, and the purpose of improving the transmission rate of a system is achieved. The Jafar professor has theoretically proved that, in the wireless interference channel of K users, each user can obtain 1/2 which is equivalent to the total spectrum resource of only one user at most, and the spectrum resource which can be obtained by K users is K/2 times of that of only one user by the interference alignment technology.
A Chain Interference Alignment method in MIMO full-link Interference channel is proposed in the paper "Alignment Chain-Based Closed-Form IA Solution for Multiple User MIMO Interference Networks" ("IEEE Transactions on Vehicular Technology" 2021, 70 (2): 1518-. The method comprises the following implementation steps: the first step is as follows: constructing an MIMO full-connection interference channel model, and the second step: determining the alignment sequence of the pre-coding submatrices so as to design an interference alignment chain, and the third step is as follows: determining an interference alignment equation according to the interference alignment chain, and the fourth step: acquiring the reachable freedom degree and the channel state information matrix of the receiving end, and the fifth step: and solving the precoding matrix and the decoding matrix through a zero forcing algorithm according to the interference alignment equation, the reachable freedom and the channel state information matrix. The method has the disadvantages that only a channel state information matrix can be selected fixedly, only the minimum number of antennas in each transmitting and receiving node in the MIMO system is used, and the redundant number of antennas in the transmitting and receiving nodes is not utilized effectively.
Disclosure of Invention
The invention aims to provide a MIMO IC chain interference alignment method based on a maximum independent set aiming at the defects in the prior art, and aims to solve the condition limitations that the channel state information matrix is selected fixedly and the antenna utilization rate of a receiving and transmitting node is low in the prior art.
The technical idea of the invention is as follows: the invention obtains the conflict-free interference alignment scheme by constructing the conflict graph of the interference alignment scheme to obtain the maximum independent set of the conflict graph, describes the conflict relationship of the interference alignment scheme, and selects the channel state information matrix, thereby solving the problems of fixed selection of the channel state information matrix, reduced aligned interference range, insufficient system interference alignment and low channel transmission rate. According to the invention, the interference which can be borne by the antenna of each receiving node is determined by obtaining the spatial dimension which needs to be compressed of each receiving end and the interference alignment scheme obtained by the maximum independent set, and a chain interference alignment equation is designed, so that the problems of low utilization rate of the antenna of the receiving and transmitting node, low degree of freedom of the system and low channel transmission rate are solved.
In order to achieve the purpose, the invention mainly comprises the following steps:
step 1, setting system parameters:
setting a MIMO interference channel system comprising K pairs of receiving and transmitting terminals, each receiving terminal being configured with N R Root antenna, configuring each transmitting end with M T A root antenna, wherein M T ≥2,N R Not less than 2 and N R ＞M T The transmitting end transmits d data streams to the receiving end,
Figure BDA0003689306580000021
step 2, constructing a partial connection model of the MIMO interference channel:
setting an interference link when P is larger than or equal to eta to be 1, setting the interference link when P is smaller than eta to be 0, neglecting the influence of the interference link with 0 on system transmission, and obtaining a partial connection model of an MIMO interference channel with the connection relation between a sending end and a receiving end; wherein P represents the sum of interference powers from a plurality of interfering links, and η represents an interference threshold set according to transmission requirements of the MIMO interfering channel;
step 3, generating a sending end set of each receiving end:
generating a sending end set having a connection relation with each receiving end according to the connection relation between the sending end and the receiving end in a partial connection model of the MIMO interference channel;
step 4, generating an interference alignment scheme of each receiving end:
step 4.1, judging whether each receiving end in the partial connection model meets the requirement
Figure BDA0003689306580000022
If yes, executing the step 4.2, otherwise, executing the step 5; wherein the content of the first and second substances,
Figure BDA0003689306580000023
representing a sending end set which has a connection relation with a jth receiving end, | · | represents base number taking operation;
step 4.2, use
Figure BDA0003689306580000031
Formula, calculating the interference space dimension to be compressed for each receiving end, wherein N j Representing the interference spatial dimension that the jth receiver needs to compress,
Figure BDA0003689306580000032
represents a rounding up operation;
step 4.3, generating the ordered sequence of each sending end selected by each receiving end by the set of each receiving end, and merging all the ordered sequences of the sending ends of the same receiving end to obtain the ordered sequence set of the receiving end;
step 4.4, selecting the ordered sequences with different left indexes and different right indexes of the sending end from the ordered sequence set of each receiving end to form an interference alignment scheme;
step 5, constructing a conflict graph representing the interference alignment scheme:
constructing all conflict graphs of each receiving end, wherein each vertex in each conflict graph represents an interference alignment scheme, two vertices meeting the conflict condition of the interference alignment scheme are connected by an edge, and each edge represents that a conflict relation exists between the interference alignment schemes represented by the two vertices connected by the edge;
step 6, generating the maximum independent set of the conflict graph:
calculating the maximum collision groups of all collision graphs of each receiving end by using a Bron-Kerbosch algorithm, merging all the maximum collision groups to obtain a maximum group, and forming a maximum independent set by vertexes which do not belong to the maximum group in all vertexes in the collision graphs;
step 7, constructing an interference alignment chain equation:
step 7.1, enumerating interference alignment schemes represented by all vertexes contained in the maximum independent set, and enumerating all ordered sequences contained in the interference alignment schemes;
step 7.2, constructing d-1 interference alignment equations for aligning the pre-coding column vectors by each ordered sequence;
step 7.3, the interference alignment equations containing the same pre-coding column vectors are combined to obtain an interference alignment chain equation set;
step 8, generating a decoding matrix:
step 8.1, solving each interference alignment chain equation set through a zero forcing algorithm, and forming a precoding matrix of a complex field by solving all the interference alignment chain equation sets;
step 8.2, selecting elements from the complex domain by using an interference alignment constraint formula to generate a decoding matrix;
step 9, each sending end simultaneously sends data streams:
step 9.1, performing precoding operation on the transmission vector to be transmitted by each transmitting end by using a precoding formula;
9.2, each sending end sends the pre-coded sending vector to the corresponding receiving end;
step 10, eliminating interference signals of a receiving end:
step 10.1, each receiving end receives the pre-coded expected signal vector sent by the corresponding sending end and simultaneously receives the pre-coded interference signal vectors sent by other sending ends;
step 10.2, decoding operation is carried out on the signal vector received by each receiving end by using a decoding formula, and an expected signal vector is separated from an interference signal vector;
and 11, obtaining an interference-free expected signal at each receiving end, and finishing interference alignment.
Compared with the prior art, the invention has the following advantages:
firstly, the invention constructs a conflict graph of an interference alignment scheme, obtains a maximum group of the conflict graph by using a Bron-Kerbosch algorithm, obtains a non-conflict interference alignment scheme by obtaining a maximum independent set of the conflict graph, and thus describes a conflict graph relation between the interference alignment schemes, and solves the problems of fixed selected channel state information matrixes, reduced aligned interference range, insufficient system interference alignment and low channel transmission rate in the prior art, so that the invention increases the selection range of the channel state information matrixes, increases the aligned interference range, fully aligns the interference of the system and improves the channel transmission rate.
Secondly, the invention obtains a non-conflicted interference alignment scheme by obtaining the spatial dimension required to be compressed of each receiving node and the maximum independent set, determines the interference which can be borne by the antenna of each receiving node, designs a chain interference alignment equation to obtain a pre-coding matrix and a decoding matrix, and overcomes the problems of low antenna utilization rate, low system degree of freedom and low channel transmission rate of the receiving and transmitting nodes in the prior art, so that the invention improves the antenna utilization rate and increases the system degree of freedom and the channel transmission rate.
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FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a diagram of a partial connection model of a MIMO interference channel.
FIG. 3 is a graph of simulation results of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
The implementation steps of the present invention are described in further detail with reference to fig. 1 and an embodiment.
Step 1, setting system parameters.
The MIMO interference channel system provided in the embodiment of the invention comprises K pairs of transmitting and receiving ends, wherein K is 8, and each receiving end is configured with N R Root antenna, N R Each transmitting end is configured with M34 T Root antenna, M T 29, wherein M T ,N R Not less than 2 and N R ＞M T The transmitting end transmits d data streams to the receiving end,
Figure BDA0003689306580000051
d＝6。
and 2, constructing a partial connection model of the MIMO interference channel.
Referring to fig. 2, a detailed structure of a partial connection model for constructing a MIMO interference channel is described as follows.
Tx i in fig. 2 represents the ith transmitting end in the MIMO interference channel, Rx j represents the jth receiving end in the MIMO interference channel, i, j ∈ {1,2.. K }, and ∈ represents belonging to a symbol.
Setting an interference link when P is larger than or equal to eta to be 1, setting the interference link when P is smaller than eta to be 0, neglecting the influence of the interference link 0 on system transmission, and obtaining a partial connection model of an MIMO interference channel with the connection relation between a sending end and a receiving end; where P represents the sum of the interference powers from multiple interfering links and η represents the interference threshold set according to the transmission requirements of the MIMO interfering channel.
And 3, generating a sending end set which has a connection relation with each receiving end according to a partial connection model diagram of the MIMO interference channel.
Figure BDA0003689306580000052
Figure BDA0003689306580000053
Wherein the content of the first and second substances,
Figure BDA0003689306580000054
indicating a sender set having a connection relationship with a jth receiver,
Figure BDA0003689306580000055
it shows that the 1 st receiving end has connection relation with the 5 th, 6 th, 7 th and 8 th sending ends.
And 4, generating an interference alignment scheme of each receiving end.
Step 4.1, judge whether each receiving end in the partial connection model of MIMO interference channel satisfies
Figure BDA0003689306580000056
If so, judging that the number of the antennas of the receiving end is not enough to bear interference space dimensionality and useful signal space dimensionality, and executing a step 4.2, otherwise, judging that the number of the antennas of the receiving end is enough to bear interference subspace dimensionality and useful signal space dimensionality, and executing a step 5; wherein, | - | represents a radix operation;
step 4.2, use
Figure BDA0003689306580000061
Determining the interference space dimension, N, to be compressed for each receiver 4 ＝N 5 ＝N 6 ＝1,N 7 ＝2,N 8 (ii) wherein, N j Representing the interference spatial dimension that the jth receiver needs to compress,
Figure BDA0003689306580000062
represents a rounding up operation;
step 4.3, obtaining the ordered sequence L of each receiving end, and forming an ordered sequence set, where the ordered sequence L of the jth receiving end is { j, i ═ j 1 ,i 2 }，
Figure BDA0003689306580000063
i 1 Indicating the left index of the transmitting end,i 2 representing the sender right index, the 5 th receiver ordered set of sequences is {5,1,3}, {5,1,6}, {5,1,7}, {5,1,8}, {5,3,1} …, {5,8,6}, {5,8,7 };
step 4.4, selecting an ordered sequence L from the ordered sequence set of each receiving end to form an interference alignment scheme, wherein the jth receiving end needs to select N j An ordered sequence. In order to avoid contradiction in precoding column vector solution caused by subsequent generation of crossed alignment chains, the left indexes of the transmitting ends of each ordered sequence are different from each other, and the right indexes of the transmitting ends of each ordered sequence are different from each other in the same interference alignment scheme.
And 5, constructing a conflict graph representing the interference alignment scheme.
And constructing a conflict graph, wherein each vertex in the conflict graph represents an interference alignment scheme, two vertices meeting the conflict condition of the interference alignment scheme are connected by an edge, and each edge represents that a conflict relationship exists between the interference alignment schemes represented by the two vertices connected by the edge. The interference alignment scheme conflict condition refers to a situation that any one of the following three conditions is satisfied:
in the condition 1, a left index of a transmitting end of an ordered sequence in an interference alignment scheme represented by a vertex is the same as a left index of a transmitting end in an interference alignment scheme represented by another vertex;
condition 2, a right index of a transmitting end of an ordered sequence in the interference alignment scheme represented by the vertex is the same as a right index of a transmitting end in the interference alignment scheme represented by another vertex;
and 3, the interference alignment scheme represented by the two vertexes is the interference alignment scheme of the same receiving end.
And 6, generating the maximum independent set of the conflict graph.
Acquiring the maximum collision groups of all collision graphs of each receiving end by using a Bron-Kerbosch algorithm, merging all the maximum collision groups to obtain a maximum group, and forming a maximum independent set by vertexes which do not belong to the maximum group in all vertexes in the collision graphs;
and 7, constructing an interference alignment chain equation.
Step 7.1, enumerating all interference alignment schemes contained in the maximum independent set, and enumerating all ordered sequences according to the interference alignment schemes;
step 7.2, constructing 5 interference alignment equations by the ordered sequence L,
Figure BDA0003689306580000071
wherein H ji1 Denotes the ith 1 A channel state information matrix from a transmitting end to a jth receiving end,
Figure BDA0003689306580000072
denotes the ith 2 A channel state information matrix from a transmitting end to a jth receiving end,
Figure BDA0003689306580000073
representing a precoding matrix
Figure BDA0003689306580000074
The kth precoded column vector of (1);
and 7.3, combining interference alignment equations containing the same precoding column vectors to obtain an interference alignment chain equation set.
And 8, generating a decoding matrix.
Step 8.1, solving each interference alignment chain equation set through a zero forcing algorithm, and forming a precoding matrix of a complex field by solving all the interference alignment chain equation sets;
step 8.2, using the formula (U) j ) H H ji V i 0, selecting elements from the complex domain to generate a decoding matrix, wherein U j Denotes the jth decoding matrix, H ji Denotes a channel state information matrix, V, from the ith transmitting end to the jth receiving end i Representing the ith precoding matrix.
And 9, each sending end simultaneously sends the data streams.
Step 9.1, use formula s i ＝V i x i Performing a precoding operation on a transmission vector to be transmitted by each transmitting end, wherein s i Indicates the ith hairTransmitting vector x after precoding at transmitting end i Representing the transmit vector for the ith sender.
And 9.2, each transmitting end transmits the precoded transmission vector to the corresponding receiving end.
And step 10, eliminating the interference signal of the receiving end.
Step 10.1, each receiving end receives the pre-coded expected signal vector sent by the corresponding sending end, and simultaneously receives the pre-coded interference signal vectors sent by other sending ends.
Step 10.2, using the formula y j ＝(U j ) H p j Performing a decoding operation on the signal vector received by each receiving end to separate its desired signal vector from the interference signal vector, wherein y j Denotes the desired signal vector at jth receiver, H denotes the conjugate transpose operation, p j Representing the signal vector received by the jth receiver.
And 11, obtaining an interference-free expected signal at each receiving end, and finishing interference alignment.
The effect of the invention is further explained by combining simulation experiments as follows:
1. simulation experiment conditions are as follows:
the hardware platform of the simulation experiment of the invention is as follows: the processor is an InterXeon Silver 4208 CPU, the main frequency is 2.1GHz, and the memory is 128G.
The software platform of the simulation experiment of the invention is as follows: windows10 operating system python3.7 emulation software.
2. Simulation content and result analysis thereof:
the simulation experiment of the invention is to simulate a part of a connection network of a multi-input multi-output MIMO interference channel by programming in python3.7 simulation software, each transmitter in the network is respectively provided with 29 antennas, each receiver is respectively provided with 34 antennas, each transmitter transmits 6 data streams, when the number of the transmitting and receiving ends is respectively 6,7,8, 9, 10, 11 and 12 by adopting the invention and the prior art (chain interference alignment method in the MIMO interference channel), the total freedom degree of the network is simulated, and the result is shown in figure 3.
In the simulation experiment, one prior art adopted means:
w Liu et al, in its published article "Alignment Chain-Based Closed-Form IA Solution for Multiple User MIMO Interference Networks" ("IEEE Transactions on Vehicular Technology" 2021, 70 (2): 1518-.
The effect of the present invention will be further described with reference to the simulation diagram of fig. 3.
Fig. 3 is a comparison diagram of total degrees of freedom of networks respectively obtained by the method of the present invention and the method of the prior art under the same configuration of the MIMO interference channel partial connection network. The abscissa in fig. 3 represents the number of transceiving ends in a MIMO interfering channel partial connectivity network. The ordinate represents the total degree of freedom of the MIMO interfering channel part connecting the network. The curves marked with circles in fig. 3 represent the curves of the simulation results using the prior art, and the curves marked with stars represent the curves of the simulation results using the method of the present invention.
As can be seen from the two simulation curves in fig. 3, the total degree of freedom of the MIMO interference channel partial connection network obtained by the present invention is significantly higher than that of the MIMO interference channel partial connection network obtained by the prior art.
The simulation experiment results show that under the condition that the number of the transmitting and receiving ends in the network is the same, the total degree of freedom of the MIMO interference channel part connecting network obtained by the method is obviously higher than that of the MIMO interference channel part connecting network obtained by the prior art. The invention increases the selection range of the channel state information matrix, increases the aligned interference range and fully aligns the interference of the system; the problems of low antenna utilization rate, low system freedom degree and low channel transmission rate of the receiving and transmitting node in the prior art are solved, so that the antenna utilization rate is improved, and the system freedom degree and the channel transmission rate are increased.

Claims (5)

1. A chain interference alignment method in a multi-input multi-output MIMO part connection interference channel based on a maximum independent set is characterized in that an interference alignment chain equation is constructed by obtaining a maximum independent set representing a conflict graph of an interference alignment scheme, and the interference alignment chain equation is solved to obtain a precoding matrix and a decoding matrix, and the alignment method comprises the following steps:
step 1, setting system parameters:
setting a MIMO interference channel system comprising K pairs of receiving and transmitting terminals, each receiving terminal being configured with N R Root antenna, configuring each transmitting end with M T A root antenna, wherein M T ≥2,N R Not less than 2 and N R ＞M T The transmitting end transmits d data streams to the receiving end,
Figure FDA0003689306570000011
step 2, constructing a partial connection model of the MIMO interference channel:
setting an interference link when P is larger than or equal to eta to be 1, setting the interference link when P is smaller than eta to be 0, neglecting the influence of the interference link with 0 on system transmission, and obtaining a partial connection model of an MIMO interference channel with the connection relation between a sending end and a receiving end; wherein P represents the sum of interference powers from a plurality of interfering links, and η represents an interference threshold set according to transmission requirements of the MIMO interfering channel;
step 3, generating a sending end set of each receiving end:
generating a sending end set having a connection relation with each receiving end according to the connection relation between the sending end and the receiving end in a partial connection model of the MIMO interference channel;
step 4, generating an interference alignment scheme of each receiving end:
step 4.1, judging whether each receiving end in the partial connection model meets the requirement
Figure FDA0003689306570000012
If yes, executing the step 4.2, otherwise, executing the step 5; wherein the content of the first and second substances,
Figure FDA0003689306570000013
is represented by the j-thEach receiving end has a sending end set with connection relation, | · | represents radix taking operation;
step 4.2, use
Figure FDA0003689306570000014
Formula, calculating the interference space dimension needing to be compressed for each receiving end, wherein N j Representing the interference spatial dimension that the jth receiver needs to compress,
Figure FDA0003689306570000021
represents a rounding up operation;
step 4.3, generating the ordered sequence of each sending end selected by each receiving end by the set of each receiving end, and merging all the ordered sequences of the sending ends of the same receiving end to obtain the ordered sequence set of the receiving end;
step 4.4, selecting the ordered sequences with different left indexes and different right indexes of the sending end from the ordered sequence set of each receiving end to form an interference alignment scheme;
step 5, constructing a conflict graph representing an interference alignment scheme:
constructing all conflict graphs of each receiving end, wherein each vertex in each conflict graph represents an interference alignment scheme, two vertices meeting the conflict condition of the interference alignment scheme are connected by an edge, and each edge represents that a conflict relation exists between the interference alignment schemes represented by the two vertices connected by the edge;
step 6, generating the maximum independent set of the conflict graph:
calculating the maximum collision groups of all collision graphs of each receiving end by using a Bron-Kerbosch algorithm, merging all the maximum collision groups to obtain a maximum group, and forming a maximum independent set by vertexes which do not belong to the maximum group in all vertexes in the collision graphs;
step 7, constructing an interference alignment chain equation:
step 7.1, enumerating interference alignment schemes represented by all vertexes contained in the maximum independent set, and enumerating all ordered sequences contained in the interference alignment schemes;
step 7.2, constructing d-1 interference alignment equations for aligning the pre-coding column vectors by each ordered sequence;
step 7.3, the interference alignment equations containing the same pre-coding column vectors are combined to obtain an interference alignment chain equation set;
step 8, generating a decoding matrix:
step 8.1, solving each interference alignment chain equation set through a zero forcing algorithm, and forming a precoding matrix of a complex field by solving all the interference alignment chain equation sets;
step 8.2, selecting elements from the complex domain by using an interference alignment constraint formula to generate a decoding matrix;
step 9, each sending end simultaneously sends data streams:
step 9.1, performing precoding operation on the transmission vector to be transmitted by each transmitting end by using a precoding formula;
9.2, each sending end sends the pre-coded sending vector to the corresponding receiving end;
step 10, eliminating interference signals of a receiving end:
step 10.1, each receiving end receives the pre-coded expected signal vector sent by the corresponding sending end and simultaneously receives the pre-coded interference signal vectors sent by other sending ends;
step 10.2, decoding operation is carried out on the signal vector received by each receiving end by using a decoding formula, and an expected signal vector is separated from an interference signal vector;
and 11, obtaining an interference-free expected signal at each receiving end, and finishing interference alignment.
2. The method of claim 1, wherein the interference alignment scheme collision condition in step 5 is a condition that satisfies any one of the following three conditions:
in the condition 1, a left index of a transmitting end of an ordered sequence in an interference alignment scheme represented by a vertex is the same as a left index of a transmitting end in an interference alignment scheme represented by another vertex;
condition 2, a right index of a transmitting end of an ordered sequence in the interference alignment scheme represented by the vertex is the same as a right index of a transmitting end in the interference alignment scheme represented by another vertex;
and 3, the interference alignment scheme represented by the two vertexes is the interference alignment scheme of the same receiving end.
3. The method of claim 1, wherein the interference alignment constraint in step 8.2 is defined as: (U) j ) H H ji V i 0, wherein U j Denotes the jth decoding matrix, H denotes the conjugate transpose operation, H ji Denotes a channel state information matrix, V, from the ith transmitting end to the jth receiving end i Representing the ith precoding matrix.
4. The method of claim 1, wherein the precoding formula in step 9.1 is as follows: s i ＝V i x i Wherein s is i Represents the transmission vector, x, of the ith transmitting end after precoding i Representing the transmit vector for the ith sender.
5. The method of claim 1, wherein the decoding formula in step 10.2 is: y is j ＝(U j ) H p j Wherein, y j Representing the desired signal vector, p, of the jth receiver j Representing the signal vector received by the jth receiver.
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