CN106027206A - search method and communication method for calculating and forwarding coding coefficient vector in bidirectional cooperative relay channel - Google Patents

Abstract

The invention discloses a search method and a communication method for calculating and forwarding a coding coefficient vector in a bidirectional cooperative relay channel. In view of the characteristics of a working mode of calculating a forwarding code in the bidirectional cooperative relay channel, the method is used for modeling a coding coefficient vector optimization problem of a relay node into an integer quadratic programming model with quadratic constraints; and aiming at the characteristics of the optimization problem, the original optimization problem is converted into a new relaxation planning problem that can be resolved more easily by improvement, convex relaxation, cutting plane generation and other steps, and the relaxation planning problem is resolved to effectively acquire the optimal solution of the original problem. The selection of the coding coefficient vector has important influence on network reachability indexes, and by adopting the method provided by the invention, an optimal coefficient vector combination under the current channel state can be effectively acquired, and a good optimization foundation is laid for the application of calculating, forwarding and coding.

Description

Bidirectional cooperative relay channel calculation forwarding coding coefficient vector searching method and communication method

Technical Field

The invention belongs to the technical field of wireless communication, relates to a cooperative communication technology under physical layer network coding, and provides a bidirectional cooperative relay channel calculation forwarding coding coefficient vector searching method.

Background

In a wireless communication system, a transmission signal is transmitted in a physical layer by an electromagnetic wave. In a wireless multi-source communication network, the broadcast characteristic of a transmitting node makes it possible for a receiving node to receive transmission information from a plurality of different source nodes in the same time slot, which causes mutual interference among different transmission signals, thereby affecting the performance of the whole network. Therefore, how to effectively deal with the problem of mutual interference between multiple received signals at the receiving end is a major challenge in the research of wireless communication technology.

In recent years, linear network coding techniques have achieved significant research results in wired network applications. The network coding has strong compatibility and information extraction capability, which makes it possible to solve the above-mentioned interference problem between multi-user signals. Most of the traditional network coding schemes operate in the MAC layer, and in order to reduce the corresponding modifications to the software, hardware, and devices and protocols of the existing wireless communication system, the resources of the MAC layer and the user scheduling algorithm are generally adopted to reduce the interference as much as possible. However, conventional network coding methods are still inefficient when transmitting multi-source data. In a wireless network, how to effectively utilize the broadcast characteristics of a transmitting node to improve the wireless channel capacity is more important.

The calculation forwarding network coding scheme based on the nested Lattice can solve the decoding problem at the relay node under high-order modulation, and can approach the AWGN bidirectional relay channel capacity. The structural characteristics of Lattice coding enable the superposed signal vector to be still a code word, and the relay node only needs to decode the linear combination of each code word. The destination node can effectively decode the sending information of the source node by acquiring the linear combination information forwarded by each relay node.

Disclosure of Invention

The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a bidirectional cooperative relay channel calculation forwarding code coefficient vector searching method and a communication method.

The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:

in a bidirectional cooperation relay channel calculation forwarding coding scheme, a calculation forwarding coding coefficient vector optimization problem of a relay node is modeled into an integer quadratic programming model with quadratic constraint. And introducing an auxiliary variable according to the forwarding coding coefficient vector of the relay node to promote the quadratic term in the integer quadratic programming model to a new high-dimensional space. And converting the integer quadratic programming model promoted to a new high-dimensional space into a new relaxation problem through the processes of convex and relaxation, solving the relaxation problem, and obtaining the optimal solution of the original problem if the obtained optimal solution meets the integer requirement. Otherwise, defining a secant plane constraint condition according to the introduced auxiliary variable and the convex and loose constraint conditions of the previous step, adding the secant plane constraint condition into a constraint set of the loose problem to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new loose planning problem. And repeating the processes until an integer optimal solution is obtained.

The method specifically comprises the following steps:

step 1: in a calculation forwarding coding strategy of a bidirectional cooperative relay channel, an optimization target function of a coefficient vector a of a relay node is obtained.

Step 2: introducing an auxiliary variable A according to a coefficient vector a of the relay node_ijAnd (4) promoting the quadratic term of the optimization objective function of the coefficient vector a of the relay node to a new high-dimensional space to obtain the new optimization objective function of the coefficient vector a of the relay node.

And step 3: and (3) carrying out convex relaxation on the non-convex constraint in the original problem in the step (2) by adopting a PSD relaxation method, and rewriting the optimized objective function of the coefficient vector a of the new relay node obtained in the step (2) into a relaxation problem.

And 4, step 4: and (5) solving the relaxation problem in the step (3), if the obtained optimal solution meets the integer requirement, the optimal solution is the optimal solution of the original problem, and the step (6) is skipped. Otherwise jump to step 4.

And 5: and (3) adding a secant plane constraint condition in the constraint set of the relaxation problem obtained in the step (3) according to the introduced auxiliary variable and the convex and relaxation constraint conditions of the previous step so as to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new relaxation planning problem.

Step 6: and returning and outputting the optimal solution.

The optimization objective function of the coefficient vector a of the relay node obtained in step 1 is as follows:

a＝arg min(a^TGa)

s.t.||a||²≤1+P||h||²

a₁≠0,a₂≠0

where a denotes the coefficient vector of the relay node, i.e. the integer coefficients of the network coding linear combination,a₁represents a node S₁Integer coefficients of network coding, a₂Represents a node S₂The integer coefficients of the network coding are,the transpose of the matrix is represented,p represents constrained power, h ═ h₁,h₂]^T，Is a node S₁、S₂And real-valued channel gain between relays, I denotes an identity matrix.

The optimization objective function of the coefficient vector a of the new relay node in step 2 is as follows:

a＝arg min<G,A>

and is

Wherein I is an identity matrix of 2 × 2,a set of symmetric matrices of 2 × 2, b ═ 1+ P | | | h | | survival². The symmetric matrix a may be referred to as a ═ aa^T。<A,B>Denotes the Frobenius inner product of the symmetric matrices A and B, i.e. tr (A)^TB) In that respect The quadratic term a in the original problem^TGa may be represented by<G,aa^T>。

The relaxation problem obtained in the step 3 is as follows:

$a=\arg min<\tilde{G},\tilde{A}>$

$\begin{array}{cc}s.t.& <\widetilde{G_1},\tilde{A}>\&le;0\end{array}$

$v^T\tilde{A}v\&GreaterEqual;0$

wherein A ≧ 0 denotes A as a symmetric semi-positive definite matrix.

v denotes an arbitrary real number vector.

The cut plane constraint obtained in step 5 is expressed as:

$v_k^T\tilde{A}{v}_k\&GreaterEqual;0.$

in, v_kRespectively representThe feature vector corresponding to the feature value of (b),to representK is 1,2 at any point in the variable space.

A bidirectional cooperative relay channel communication method comprises the following steps:

step (1), in a bidirectional cooperative relay channel calculation forwarding coding scheme, a source node maps respective information from a finite field to a nested Lattice code word.

And (2) the source node simultaneously sends the mapped code word information to the relay node.

And (3) the relay node receives the composite signals from the source nodes and decodes the received information into a linear combination equation of the Lattice code word.

Step (4), the relay node broadcasts the Lattice equation to the first source node S₁And a second source node S₂And each source node maps the Lattice code back to the finite field and completes decoding by utilizing the information stored by the source node.

Has the advantages that: compared with the prior art, the bidirectional cooperative relay channel calculation forwarding coding coefficient vector searching method and the communication method provided by the invention have the following beneficial effects:

aiming at the characteristics of a working mode of calculating forwarding codes in a bidirectional cooperative relay channel, the invention models the problem of optimization of coding coefficient vectors of relay nodes into an integer quadratic programming model with quadratic constraint; aiming at the characteristics of the optimization problem, the original optimization problem is converted into a new relaxation planning problem which is easy to solve through the steps of lifting, convex relaxation, generating a cutting plane and the like, and the optimal solution of the original problem is effectively obtained through the solution of the relaxation planning problem. The selection of the coding coefficient vector has important influence on the network reachable performance index; through simulation verification, the method provided by the invention can effectively obtain the optimal coefficient vector combination in the current channel state, and provides a good optimization basis for calculating the application of forwarding codes.

Drawings

FIG. 1 is a block diagram of a bidirectional relaying channel calculation and forwarding strategy in the present invention;

FIG. 2 is a graph of the effect of coding coefficient vectors on achievable computational rate in the present invention;

FIG. 3 is a comparison of the computation rates of different coefficient vector search schemes in accordance with the present invention;

fig. 4 shows the effect of channel coefficient on achievable computation rate when P is 10dB in the present invention;

fig. 5 shows the effect of channel coefficient on achievable computation rate when P is 20dB in the present invention;

fig. 6 shows the effect of channel coefficient on achievable computation rate when P is 30 dB;

fig. 7 is a schematic diagram of a bidirectional cooperative relaying channel communication method.

Detailed Description

The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.

A bidirectional cooperative relay channel calculation and forwarding coding coefficient vector searching method is disclosed, as shown in figure 1, in a bidirectional cooperative relay channel calculation and forwarding coding scheme, a problem of optimization of a calculation and forwarding coding coefficient vector of a relay node is modeled into an integer quadratic programming model with quadratic constraint. And introducing an auxiliary variable according to the forwarding coding coefficient vector of the relay node to promote the quadratic term in the integer quadratic programming model to a new high-dimensional space. And converting the integer quadratic programming model promoted to a new high-dimensional space into a new relaxation problem through the processes of convex and relaxation, solving the relaxation problem, and obtaining the optimal solution of the original problem if the obtained optimal solution meets the integer requirement. Otherwise, defining a secant plane constraint condition according to the introduced auxiliary variable and the convex and loose constraint conditions of the previous step, adding the secant plane constraint condition into a constraint set of the loose problem to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new loose planning problem. And repeating the processes until an integer optimal solution is obtained.

As shown in FIG. 1, the relay node receives the signals acquired during the multiple access phaseThe estimated value of the linear combination equation of (a) is broadcast to each source node. Each source node passes through a decoderMapping the received code word back to the finite field, and subtracting the information stored in the finite field to recover the required estimation information

The method specifically comprises the following steps:

step 1: in the calculation forwarding coding strategy of the bidirectional cooperative relay channel, an optimization target function of a coefficient vector a of a relay node is obtained, if coefficient vectors of linear equations recovered by the relay node are different, the achievable calculation rates of all the nodes are also different, and in order to maximize the system capacity, the calculation forwarding coding coefficient vector optimization problem of the relay node is modeled into an integer quadratic programming model with quadratic constraint. The optimization objective function of the coefficient vector a of the relay node is as follows:

a＝argmin(a^TGa)

where a denotes the coefficient vector of the relay node, i.e. the integer coefficients of the network coding linear combination,a₁represents a node S₁Integer coefficients of network coding, a₂Represents a node S₁The integer coefficients of the network coding are,the transpose of the matrix is represented,p represents constrained power, h ═ h₁,h₂]^T，Is a node S₁、S₂And real-valued channel gain between relays, I denotes an identity matrix.

Step 2: introducing an auxiliary variable A according to a coefficient vector a of the relay node_ijAnd (3) promoting the quadratic term of the optimized target function formula (1) of the coefficient vector a of the relay node to a new high-dimensional space. Let A_ij＝a_ia_j(1 ≦ i, j ≦ 2), the symmetric matrix A may be raised to be A ≦ aa^T，<A,B>Denotes the Frobenius inner product of the symmetric matrices A and B, i.e. tr (A)^TB) In that respect The quadratic term a in the original problem (equation (1)) is^TGa may be represented by<G,aa^T>. Thus, an optimized objective function of the coefficient vector a of the new relay node is obtained:

and is

Wherein I is an identity matrix of 2 × 2,a set of symmetric matrices of 2 × 2, b ═ 1+ P | | | h | | survival². The symmetric matrix a may be referred to as a ═ aa^T。<A,B>Denotes the Frobenius inner product of the symmetric matrices A and B, i.e. tr (A)^TB) In that respect The quadratic term a in the original problem^TGa may be represented by<G,aa^T>。

As can be seen from the above formula (2), the improved minimization problem is converted from the original quadratic function and quadratic constraint into a linear integer optimization problem related to (a, A), and the solving difficulty is reduced.

And step 3: and (3) carrying out convex relaxation on the non-convex constraint in the original problem in the step (2) by adopting a PSD relaxation method, and rewriting the optimized objective function of the coefficient vector a of the new relay node obtained in the step (2) into a relaxation problem.

In the original problem formula (1), the quadratic constraint is a convex constraint. But the constraint condition A introduced in the lifting process is aa^TNon-convex, the optimization problem is convex using a suitable relaxation method. The original problem formula (1) can be written as the following equivalent form

Wherein,to representThe convex envelope of (a).

Irrespective of A ═ aa^TAndtwo constraints, the problem constraint can be expressed as follows:

let A ≧ 0 denote A as a symmetric semi-positive definite matrix, where A ═ aa^TAvailable A-aa^TNot less than 0, therefore, the PSD relaxation method can be adopted to carry out convex relaxation on the non-convex constraint in the original problem on the basis.

For theSatisfies the following conditions:

thus, the linear semi-positive inequalityIs suitable forThe PSD constraint is defined as follows:

$PSD=\{(a,A):\tilde{A}\&GreaterEqual;0\}$

after the constraint of the PSD relaxation is increased,

order to

$\tilde{G}=\left(\begin{array}{cc}0& 0\\ 0& G\end{array}\right),\widetilde{G_1}=\left(\begin{array}{cc}-b& 0\\ 0& I\end{array}\right)$

The relaxed objective function (2) can be expressed as:

$a=\arg min<\tilde{G},\tilde{A}>$

$\begin{array}{cc}s.t.& <\widetilde{G_1},\tilde{A}>\&le;0\\ & \tilde{A}\&GreaterEqual;0\end{array}---\left(3\right)$

due to the fact thatIs a semi-positive definite matrix, namely:

the PSD constraint in equation (3) can be replaced by the following form (relaxation problem):

$a=\arg min<\tilde{G},\tilde{A}>$

$\begin{array}{cc}s.t.& <\widetilde{G_1},\tilde{A}>\&le;0\end{array}$

$v^T\tilde{A}v\&GreaterEqual;0$

where v represents an arbitrary real number vector.

And 4, step 4: and (5) solving the relaxation problem in the step (3), if the obtained optimal solution meets the integer requirement, the optimal solution is the optimal solution of the original problem, and the step (6) is skipped. Otherwise jump to step 4.

And 5: and (3) adding a secant plane constraint condition in the constraint set of the relaxation problem obtained in the step (3) according to the introduced auxiliary variable and the convex and relaxation constraint conditions of the previous step so as to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new relaxation planning problem.

Order toTo representAt any point in the variable space. Can pass throughIs determined by feature decompositionWhether it is within the constrained region of the PSD cone (PSD cone). Let lambda_kAnd v_kRespectively representK is 1,2, and its corresponding eigenvector. To avoid loss of generality, let λ be assumed₁≤λ₂，λ_t<0≤λ_t+1T ∈ 0,1, 2. if t is 0, all the characteristic values are non-negative,is a semi-positive definite matrix; if t ≠ 0, thenk 1, t, which cannot be satisfiedIs a requirement of positive definite matrix.

Therefore, the following formula can be used as a secant plane constraint condition, and added to the original constraint set to solve a new relaxation planning problem.

$v_k^T\tilde{A}{v}_k\&GreaterEqual;0---\left(4\right)$

Wherein v is_kRespectively representThe feature vector corresponding to the feature value of (b),to representK is 1,2 at any point in the variable space.

Step 6: and returning and outputting the optimal solution.

A bidirectional cooperative relaying channel communication method, as shown in fig. 7, includes the following steps:

step (1), in a bidirectional cooperative relay channel calculation forwarding coding scheme, a source node maps respective information from a finite field to a nested Lattice code word.

And (2) the source node simultaneously sends the mapped code word information to the relay node.

And (3) the relay node receives the composite signals from the source nodes and decodes the received information into a linear combination equation of the Lattice code word.

Step (4), the relay node broadcasts the Lattice equation to the first source node S₁And a second source node S₂And each source node maps the Lattice code back to the finite field and completes decoding by utilizing the information stored by the source node.

FIG. 2 is a graph of the effect of coding coefficient vectors on achievable computation rates in the present invention. The search problem of the code word linear combination coefficient vector is a key problem for calculating the forwarding coding strategy. Under the condition that the channel coefficient is fixed, different coding coefficient vectors have important influence on the network reachable rate index. It is assumed that the transmission rate of each source node of the bidirectional cooperative relay network is symmetrical, namely R₁＝R₂. Let channel coefficient vector h be [1, h ═ h₂]^T，h₂∈[0,1]The power constraint P is 10 dB. The effect of the coefficient vectors of the linear combination of the different codewords on the information rate is shown in fig. 2. Herein compare a ═ 0,1, respectively]^T、a＝[-1,-1]^T、a＝[1,2]^TThe achievable computation rate case for the case of three coefficient vectors. As can be seen from the figure, the achievable computation rate and codingThe selection of the code coefficient vector is closely related. When the coding coefficient vector matches the current channel coefficient, the network achievable computation rate can be maximized. When h is generated₂When 1, a [ -1, -1 ═ 1]^TThe matching state with the channel coefficient is optimal, so that the maximum calculation rate can be achieved. Therefore, in order to ensure the maximization of the system computation rate, an optimal selection of the coding coefficient vector needs to be made.

Fig. 3 is a comparison of the computation rates of different coefficient vector search schemes in the present invention. 1000 random experiments were performed using the Monte-Carlo method, and the results were statistically averaged. Node S₁、S₂And the real-valued channel gain h between the relays₁,h₂Independent of each other and obeyThe distribution, power constraint P, varies from 0dB to 30 dB. FIG. 3 shows the comparison of achievable computation rates for the method, the upper bound of achievable computation rates, the poor search algorithm, and the no-coefficient vector search PNC under several different scenarios. The upper bound on the achievable computation rate for computing the forwarding policy may be expressed asThe finite search algorithm meets the requirement that the solution with the largest current calculation rate is the optimal coefficient vector under the current channel state through all integer combinations of the coding coefficient vectors in the finite search constraint range. The LRCP algorithm represents the coefficient vector search algorithm proposed herein. The coding-free coefficient vector search PNC scheme is defined in that the relay node directly decodes and forwards information x to each source node without coefficient vector search₁+x₂. It can be seen from the figure that the difference of the achievable computation rates obtained by different coding strategies is obvious. The solution obtained by the poor search algorithm is the optimal coefficient vector, so that the solution is closest to the theoretical upper bound. The result obtained by the LRCP algorithm is consistent with the poor search algorithm, which shows that the algorithm provided by the invention can effectively obtain the optimal coefficient vector combination of the network. The no coefficient vector search PNC scheme performs the worst performance because no corresponding adjustment of the coefficient vector is made for the change of the channel coefficientThere is a large performance gap at high signal-to-noise ratio.

Fig. 4, 5, 6 show the effect of channel coefficient changes on the achievable computation rate for power constraints P of 10dB, 20dB, 30dB, respectively. Let channel coefficient vector h be [1, h ═ h₂]^T，h₂∈[0,1]Firstly, an optimal coefficient vector is obtained through the method provided by the text, and then the reachable computation rate under the optimal coefficient vector is computed. It can be seen from the figure that under the condition of low signal-to-noise ratio, the performances of the method, the poor search algorithm and the coefficient-free vector search PNC scheme are relatively close, and are all close to the upper bound of the achievable computation rate. But as the signal-to-noise ratio increases, the performance of the method and the poor search algorithm proposed herein will be significantly better than the coefficient-free vector search PNC scheme. It is to be noted that the default coding coefficient vector of the no-coding-coefficient-vector search PNC scheme is a ═ 1,1]^TWhen h is present₂When the value is more than or equal to 0.8, the matching degree of the scheme and the channel coefficient is improved, so the performance of the scheme is equivalent to that of the method and the poor search algorithm. At the same time, when h₂The method presented herein can reach a theoretical upper bound on the calculation rate at 0.5.

The simulation results show that the selection of the coding coefficient vector has important influence on the network reachable performance index, and the method provided by the invention can effectively obtain the optimal coefficient vector combination in the current channel state, and provides a good optimization basis for the application of calculating the forwarding code.

The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (7)

1. A bidirectional cooperative relay channel calculation forwarding coding coefficient vector searching method is characterized in that: in a bidirectional cooperative relay channel calculation forwarding coding scheme, modeling a calculation forwarding coding coefficient vector optimization problem of a relay node as an integer quadratic programming model with quadratic constraint; introducing an auxiliary variable according to a forwarding coding coefficient vector of the relay node to promote a quadratic term in the integer quadratic programming model to a new high-dimensional space; converting an integer quadratic programming model promoted to a new high-dimensional space into a new relaxation problem through a process of convex and relaxation, solving the relaxation problem, and if the obtained optimal solution meets the integer requirement, obtaining the optimal solution of the original problem; otherwise, defining a secant plane constraint condition according to the introduced auxiliary variable and the convex and loose constraint conditions of the previous step, adding the secant plane constraint condition into a constraint set of the loose problem to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new loose planning problem; and repeating the processes until an integer optimal solution is obtained.

2. The method for searching the vector of the forward coding coefficient calculated by the bidirectional cooperative relaying channel according to claim 1, comprising the steps of:

step 1: in a calculation forwarding coding strategy of a bidirectional cooperative relay channel, obtaining an optimized target function of a coefficient vector a of a relay node;

step 2: introducing an auxiliary variable A according to a coefficient vector a of the relay node_ijPromoting the quadratic term of the optimization objective function of the coefficient vector a of the relay node to a new high-dimensional space to obtain the new optimization objective function of the coefficient vector a of the relay node;

and step 3: carrying out convex relaxation on the non-convex constraint in the original problem in the step 2 by adopting a PSD relaxation method, and rewriting the optimized objective function of the coefficient vector a of the new relay node obtained in the step 2 into a relaxation problem;

and 4, step 4: solving the relaxation problem in the step 3, if the obtained optimal solution meets the integer requirement, the optimal solution is the optimal solution of the original problem, and skipping to the step 6; otherwise, jumping to the step 4;

and 5: adding a secant plane constraint condition in the constraint set of the relaxation problem obtained in the step 3 according to the introduced auxiliary variable and the convex and relaxation constraint conditions of the previous step to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain, and then solving a new relaxation planning problem;

step 6: and returning and outputting the optimal solution.

3. The method for searching the vector of the forward coding coefficient calculated by the bidirectional cooperative relaying channel as claimed in claim 1, wherein: the optimization objective function of the coefficient vector a of the relay node obtained in step 1 is as follows:

a＝arg min(a^TGa)

s.t. ‖a‖²≤1+P‖h‖²

a₁≠0,a₂≠0

wherein a represents a network encoded integer coefficient vector of the relay node,a₁represents a node S₁Integer coefficients of network coding, a₂Represents a node S₂The integer coefficients of the network coding are,the transpose of the matrix is represented,p represents constrained power, h ═ h₁,h₂]^T，Is a node S₁、S₂And real-valued channel gain between relays, I denotes an identity matrix.

4. The method for searching the vector of the forward coding coefficient calculated by the bidirectional cooperative relaying channel as claimed in claim 1, wherein: the optimization objective function of the coefficient vector a of the new relay node in step 2 is as follows:

a＝arg min〈G,A〉

and is

Wherein I is an identity matrix of 2 × 2,is a symmetric matrix set of 2 × 2, b ═ 1+ P | h |²(ii) a The symmetric matrix a may be referred to as a ═ aa^T(ii) a < A, B > represents the Frobenius inner product of the symmetric matrices A and B, i.e. tr (A)^TB)；

The quadratic term a in the original problem^TGa may be represented as < G, aa^T>。

5. The method for searching the vector of the forward coding coefficient calculated by the bidirectional cooperative relaying channel as claimed in claim 1, wherein: the relaxation problem obtained in the step 3 is as follows:

$a=\mathrm{argmin}<\tilde{G},\tilde{A}>$

$\begin{array}{cc}s.t.& <{\tilde{G}}_1,\tilde{A}>\&le;0\end{array}$

$v^T\tilde{A}v\&GreaterEqual;0$

wherein,representing that A is a symmetric semi-positive definite matrix;

v denotes an arbitrary real number vector.

6. The method for searching the vector of the forward coding coefficient calculated by the bidirectional cooperative relaying channel as claimed in claim 1, wherein: the cut plane constraint obtained in step 5 is expressed as:

$v_k^T\tilde{A}{v}_k\&GreaterEqual;0;$

wherein v is_kRespectively representThe feature vector corresponding to the feature value of (b),to representK is 1,2 at any point in the variable space.

7. A bidirectional cooperative relaying channel communication method based on the bidirectional cooperative relaying channel calculation forwarding coding coefficient vector search method of any one of claims 1 to 6, comprising the steps of:

step (1), in a bidirectional cooperative relay channel calculation forwarding coding scheme, a source node maps respective information from a finite field to a nested Lattice code word; (ii) a

Step (2), the source node simultaneously sends the mapped code word information to the relay node;

step (3), the relay node receives the composite signals from each source node and decodes the received information into a linear combination equation of the Lattice code word;

step (4), the relay node broadcasts the Lattice equation to the first source node S₁And a second source node S₂And each source node maps the Lattice code back to the finite field and completes decoding by utilizing the information stored by the source node.