CN106027206B - A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means - Google Patents
A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means Download PDFInfo
- Publication number
- CN106027206B CN106027206B CN201610616663.5A CN201610616663A CN106027206B CN 106027206 B CN106027206 B CN 106027206B CN 201610616663 A CN201610616663 A CN 201610616663A CN 106027206 B CN106027206 B CN 106027206B
- Authority
- CN
- China
- Prior art keywords
- coefficient vector
- relaxation
- node
- forwarding
- constraint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000013598 vector Substances 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000004364 calculation method Methods 0.000 title claims abstract description 27
- 238000004891 communication Methods 0.000 title claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 24
- 230000002457 bidirectional effect Effects 0.000 claims description 23
- 239000011159 matrix material Substances 0.000 claims description 21
- 239000002131 composite material Substances 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims 1
- 230000017105 transposition Effects 0.000 claims 1
- 238000010845 search algorithm Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000012887 quadratic function Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0076—Distributed coding, e.g. network coding, involving channel coding
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0076—Distributed coding, e.g. network coding, involving channel coding
- H04L1/0077—Cooperative coding
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Radio Relay Systems (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means, the code coefficient Vector Optimization Problems of relay node are modeled as the integer quadratic programming model with quadratic constraints by this method is for forwarding coding work mode is calculated in two-way cooperating relay channel the characteristics of；The characteristics of for the optimization problem, by promotion, convex relaxation, generate cutting plane and etc. convert former optimization problem to the new loose planning problem for being easier to solve, by the solution to relaxation planning problem effectively to obtain the optimal solution of former problem.The selection of code coefficient vector has an important influence network reachability energy index, and method proposed by the present invention can effectively obtain the combination of the optimal coefficient vector under current channel condition, provides good optimization basis to calculate the application of forwarding coding.
Description
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 auxiliary variables according to coefficient vector a of relay nodeQuantity AijAnd (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＝argmin(aTGa)
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＝argmin<G,A>
and is
Where I is a 2X 2 identity matrix, S2Is a 2 × 2 set of symmetric matrices, b ═ 1+ P | h |2. The symmetric matrix a may be referred to as a ═ aaT。<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 problemTGa may be represented by<G,aaT>。
The relaxation problem obtained in the step 3 is as follows:
wherein,and A is a symmetric semi-positive definite matrix.
v denotes an arbitrary real number vector.
The cut plane constraint obtained in step 5 is expressed as:
wherein v iskRespectively 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(aTGa)
s.t.‖a‖2≤1+P‖h‖2
a1≠0,a2≠0
wherein a representsThe 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.
Step 2: introducing an auxiliary variable A according to a coefficient vector a of the relay nodeijLifting 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 to enable Aij＝aiaj(1 ≦ i, j ≦ 2), the symmetric matrix A may be raised to be A ≦ aaT，<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)) isTGa may be represented by<G,aaT>. Thus, an optimized objective function of the coefficient vector a of the new relay node is obtained:
and is
Where I is a 2X 2 identity matrix, S2Is a 2 × 2 set of symmetric matrices, b ═ 1+ P | h |2. The symmetric matrix a may be referred to as a ═ aaT。<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 problemTGa may be represented by<G,aaT＞。
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 aaTNon-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 ═ aaTAndtwo constraints, the problem constraint can be expressed as follows:
order toA is a symmetric semi-positive definite matrix, and A is aaTCan obtain the productTherefore, the PSD relaxation method can be adopted on the basis to carry out convex relaxation on the non-convex constraint in the original problem.
For theSatisfies the following conditions:
thus, the linear semi-positive inequalityIs suitable forThe PSD constraint is defined as follows:
after the constraint of the PSD relaxation is increased,
order to
The relaxed objective function (2) can be expressed as:
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):
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 be used for dredgingFor treatingIs determined by feature decompositionWhether it is within the constrained region of the PSD cone (PSD cone). Let lambdakAnd vkRespectively representK is 1,2, and its corresponding eigenvector. To avoid loss of generality, let λ be assumed1≤2，λt＜0≤λt+1And t ∈ 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.
Wherein v iskRespectively 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 is closely related to the selection of the coding coefficient vector. When the coding coefficient vector matches the current channel coefficient, the netThe maximum can be achieved only if the computation rate is reached. 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 PNC scheme without coefficient vector search has the worst performance because no corresponding adjustment of the coefficient vector is performed for the change of the channel coefficient, and a large performance gap exists under the condition of 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 (2)
1. A bidirectional cooperative relay channel calculation forwarding coding coefficient vector searching method is characterized by comprising the following steps:
step 1: in a calculation forwarding coding strategy of a bidirectional cooperative relay channel, obtaining an optimization objective function of a forwarding coding coefficient vector a of a relay node; modeling the calculation forwarding coding coefficient vector optimization problem of the relay node into an integer quadratic programming model with quadratic constraint;
wherein a represents a forwarding coding coefficient vector of the relay node,a1represents a node S1Integer coefficients of network coding, a2Represents a node S2Integer coefficients of the network coding, T denotes a matrix transposition,p represents constrained power, h ═ h1,h2]T，Is a node S1、S2And the real-valued channel gain between relays, I denotes the identity matrix;
step 2: introducing an auxiliary variable A according to a forwarding coding coefficient vector a of the relay nodeijPromoting the quadratic term in the integer quadratic programming model obtained in the step 1 to a new high-dimensional space to obtain an optimized objective function of a new forwarding coding coefficient vector a of the relay node;
and is
Wherein I is a 2 x 2 identity matrix,is a 2 × 2 set of symmetric matrices, b ═ 1+ P | h |2(ii) a The symmetric matrix A is lifted and expressed as A ═ aaT；<A,B>Represents the Frobenius inner product of the symmetric matrix A and the symmetric matrix B and is marked as tr (A)TB) (ii) a Then the formula (II)1) Second order term a inTGa is represented by<G,aaT>；
And step 3: carrying out convex relaxation on the non-convex constraint in the optimized objective function of the forwarding coding coefficient vector a of the new relay node obtained in the step 2 by adopting a PSD relaxation method, and rewriting the optimized objective function of the forwarding coding coefficient vector a of the new relay node obtained in the step 2 into a relaxation problem;
for theSatisfies the following conditions:
thus, the linear semi-positive inequalityIs suitable forTo representThe PSD constraint is defined as follows:
after the constraint of the PSD relaxation is increased,
order to
The relaxed objective function equation (2) is expressed as:
due to the fact thatIs a semi-positive definite matrix:
the PSD constraint in equation (3) is replaced by a relaxation problem of the form:
wherein v represents any real number vector;
and 4, step 4: solving the relaxation problem obtained in the step 3, if the obtained optimal solution meets the integer requirement, obtaining the optimal solution of the optimization objective function of the new relay node forwarding coding coefficient vector a obtained in the step 2, and skipping to the step 6; otherwise, jumping to the step 5;
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 in the step 3 to cut off a part of feasible solutions which do not meet the requirements, reducing the feasible domain to obtain a new relaxation planning problem, and then solving the new relaxation planning problem;
the cut plane constraint obtained in step 5 is expressed as:
wherein v iskRespectively representThe feature vector corresponding to the feature value of (b),to representK is 1, 2;
step 6: and returning and outputting the optimal solution.
2. 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 claim 1, 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;
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610616663.5A CN106027206B (en) | 2016-07-29 | 2016-07-29 | A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610616663.5A CN106027206B (en) | 2016-07-29 | 2016-07-29 | A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106027206A CN106027206A (en) | 2016-10-12 |
| CN106027206B true CN106027206B (en) | 2019-03-19 |
Family
ID=57115369
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610616663.5A Active CN106027206B (en) | 2016-07-29 | 2016-07-29 | A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106027206B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110971286B (en) * | 2019-10-22 | 2022-03-22 | 浙江理工大学 | Network coding method and system based on Lattice coding amplification forwarding |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544551A (en) * | 2013-11-12 | 2014-01-29 | 北京宜能高科科技有限公司 | Method and device for operation optimization of steam power system |
-
2016
- 2016-07-29 CN CN201610616663.5A patent/CN106027206B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544551A (en) * | 2013-11-12 | 2014-01-29 | 北京宜能高科科技有限公司 | Method and device for operation optimization of steam power system |
Non-Patent Citations (3)
| Title |
|---|
| Compute-and-Forward: Harnessing Interference Through Structured Codes;Bobak Nazer，Michael Gastpar;《IEEE TRANSACTIONS ON INFORMATION THEORY》;20111006;第57卷(第10期);全文 |
| Tian Liang, Wei Heng, Chao Meng, Guodong Zhang.Cooperative Power Allocation Based on Multi- Cooperative Power Allocation Based on Multi-objective Intelligent Optimization for Multi-source Multi-Relay Networks.《IEICT Transactions on communication》.2014,1938-1946. |
| 基于协作通信的中继计算转发网络编码策略研究;魏宇明;《万方数据库》;20150817;全文 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106027206A (en) | 2016-10-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109495881B (en) | Wireless relay network safety transmission method using cooperative interference | |
| CN105263135B (en) | A kind of robust security design method of MIMO communication system | |
| CN111446992B (en) | Method for allocating resources with maximized minimum energy efficiency in wireless power supply large-scale MIMO network | |
| CN110311715A (en) | The nonopiate unicast multicast transmission power distribution method of the optimal extensive MIMO of efficiency | |
| WO2015081277A1 (en) | Method and apparatus for downlink transmission in a cloud radio access network | |
| CN112333702A (en) | Optimization method for delay minimization based on safe NOMA moving edge calculation | |
| CN106712816B (en) | A kind of design method of the safe beam forming based on energy harvesting cognition network | |
| CN108848563A (en) | A kind of cooperation resource allocation methods of the NOMA system down link based on efficiency | |
| Wang et al. | Optimal beamforming in MIMO two-way relay channels | |
| CN105636188A (en) | Power allocation method of cognitive decode-and-forward relay system | |
| CN117938276B (en) | Unified communication perception integrated signal design method and system | |
| CN108418651B (en) | Safe transmission method of bidirectional wireless power supply relay system | |
| CN106027206B (en) | A kind of two-way cooperating relay channel calculation forwarding code coefficient vector search method and communication means | |
| Alajmi et al. | Semi-centralized optimization for energy efficiency in IoT networks with NOMA | |
| CN117202171A (en) | Minimum safe rate resource allocation method for RIS (radio resource allocation system) auxiliary RSMA (RSMA) system | |
| Du et al. | Outage analysis and optimization for four-phase two-way transmission with energy harvesting relay | |
| Wang et al. | Energy Efficiency Optimization Algorithm of CR‐NOMA System Based on SWIPT | |
| Steinberg | Channels with cooperation links that may be absent | |
| CN105827294B (en) | A kind of method of uplink extensive MIMO combined optimization antenna for base station number and user emission power | |
| Lei et al. | SCMA-OFDM codebook design based on IRM | |
| Waqar et al. | On the ergodic capacities of decode‐and‐forward MIMO relay network with simultaneous wireless information and power transfer | |
| Mejri et al. | Practical implementation of integer forcing linear receivers in MIMO channels | |
| Chen et al. | Energy-Efficient Resource Allocation for IRS-Assisted SWIPT-enabled D2D Communications with Imperfect CSI | |
| Liang et al. | Dynamic programming based power control algorithm with primary user QoS guarantee for cognitive radio networks | |
| Ling et al. | Joint wireless information and energy transfer in two-way relay channels |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |