CN110213815B - Relay system power control method based on statistical channel state information - Google Patents

Relay system power control method based on statistical channel state information Download PDF

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CN110213815B
CN110213815B CN201910299338.4A CN201910299338A CN110213815B CN 110213815 B CN110213815 B CN 110213815B CN 201910299338 A CN201910299338 A CN 201910299338A CN 110213815 B CN110213815 B CN 110213815B
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power control
relay
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CN110213815A (en
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谢磊
陈惠芳
赵越
王匡
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Zhejiang University ZJU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/46—TPC being performed in particular situations in multi hop networks, e.g. wireless relay networks

Abstract

The invention discloses a relay system power control method based on statistical channel state information. Firstly, establishing a multi-relay cooperative communication system framework with path delay, and establishing a power control optimization problem model in the form of symbolic programming by taking the interruption probability of a relay system as a target; then carrying out variable substitution, linear approximation and piecewise linear approximation on the symbolic programming problem, and converting the original problem into a mixed integer linear programming problem which is easier to solve; and finally, obtaining the optimal power control coefficients of all the relay nodes by using a global optimal algorithm. Aiming at a multi-relay cooperative communication system with time delay, the method obtains the optimal solution of power control based on the statistical channel state information of all links, has small communication overhead and strong robustness to fast fading channels.

Description

Relay system power control method based on statistical channel state information
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a relay system power control method based on statistical channel state information.
Background
For the fading characteristics of wireless channels, diversity technology is a commonly used method for resisting fading and improving the receiving performance. In a relay system, a plurality of relay nodes assist a source node to forward a signal to a destination node in a cooperative manner, so that cooperative diversity is realized, and the relay system has the advantages of low transmission power, large communication range, wide application scene and the like.
Power control is a classical method of optimizing the performance of a communication system. However, the existing power control methods for the relay system generally consider how to improve the system capacity, and these methods are based on the instantaneous channel state information of the system, and each communication node performs optimization of the upper limit of the system capacity under the condition of perfect synchronization in the time domain. Due to the particularity of the multi-relay system, in the process that different relay nodes receive source signals and forward the source signals to a destination node, time delay difference between paths may exist, so that multipath effect is caused, and the reliability of the system is reduced. In addition, the power control method based on the instantaneous channel state information has high real-time requirements, and the node power switching is easy to be over-fast in a fast fading environment. Meanwhile, the system performance is obviously reduced under the condition that the channel state cannot be accurately acquired in real time.
Disclosure of Invention
The invention aims to provide a relay system power control method based on statistical channel state information aiming at a multi-relay cooperative communication system with time delay difference, and the interruption probability of the system is minimized by designing the transmission power of a relay node under the condition of giving an upper limit of node power. The design method requires that the statistical channel state information of each link is known, so that the power control algorithm can work off-line after the channel state information is collected.
The relay system power control method solves the power control problem of a multi-relay system under the condition of path delay difference on one hand, and ensures the system performance in a fast fading environment by utilizing statistical channel state information on the other hand.
The method comprises the following specific steps:
step 1, collecting key parameters of a relay system, specifically comprising: channel fading parameter, relay transmission power upper limit
Figure BDA0002027708380000011
Decision threshold gamma of interrupted signal-to-noise ratiothAn algorithm convergence judgment threshold epsilon; the probability of communication interruption represents the probability that the signal-to-noise ratio of the receiving end is lower than the interruption decision threshold, and is expressed as
Figure BDA0002027708380000012
Wherein f isγ(x) The specific form of probability density for the received signal-to-noise ratio depends on the channel fading distribution.
Step 2, initializing, constructing an optimization problem which minimizes the communication interruption probability, simplifying the original problem into a symbolic programming problem, respectively performing linear approximation and piecewise linear approximation on an objective function and a constraint condition, and obtaining a starting point of iteration of a power control algorithm after solving; the method specifically comprises the following steps:
(2-1) according to a specific channel fading model, calculating to obtain a communication interruption probability PoutEstablishing a power control problem that minimizes the outage probability, i.e. the first problem:
Figure BDA0002027708380000021
wherein η ═ η1，η2，…，ηN]Representing a relay power control coefficient, wherein N is the number of relay nodes;
(2-2) converting the first problem into a symbolic programming problem, namely a second problem, by linear variable substitution according to the expression for calculating the interrupt probability:
Figure BDA0002027708380000022
wherein M represents the number of variables after variable substitution, and M ≧ N, T represents the number of terms of the symbolic expression, α represents the coefficient of the symbolic expression, rtj]Expressing the indexes of the symbolic expressions;
Figure BDA00020277083800000210
is a real number domain; a and c represent linear variable substitutions; x represents the value space of the power control coefficient,xthe lower bound of the value space is represented,
Figure BDA00020277083800000211
representing a value space upper bound;
(2-3) solving a boundary problem for each variable according to the constraint condition of the second problem, and reducing the value space of the variables;
(2-4) recording the reduced variable value space as X0；
(2-5) carrying out exponential transformation on the variables in the second problem to obtain a new optimization problem, namely a third problem:
Figure BDA0002027708380000023
where ψ represents a coefficient vector of the objective function after linear approximation,
Figure BDA0002027708380000024
ξ is the constant part of the objective function after linear approximation,
Figure BDA0002027708380000025
and is
Figure BDA0002027708380000026
Figure BDA0002027708380000027
And
Figure BDA0002027708380000028
respectively representing the upper and lower bounds of the objective function in the new value space Y,
Figure BDA0002027708380000029
(2-6) further performing piecewise linear approximation on the constraint condition of the nonlinear equation, setting the number of the separation points to be K, and converting the third problem into a mixed integer linear programming problem, namely a fourth problem:
Figure BDA0002027708380000031
wherein P ═ Pij]K×MExpressed in a value space X0A constant matrix composed of the separation points of the piecewise linear function; lambda ═ lambdaij]K×MIs a newly introduced variable matrix for constructing a linear function between two adjacent separation points, β ═ βij](K-1)×MIs a matrix of binary variables to ensure η and y are piecewise linear functions, ΛiAnd βiRepresenting rows i of matrices Λ and β, respectively, B is a Toeplitz matrix of K × (K-1) with row 1 elements [ 10 … 0 ]]The column 1 element is [ 110 … 0 ]]T(ii) a diag (X) represents a vector formed by diagonal elements of matrix X; []TRepresenting a matrix transposition;
(2-7) solving the fourth problem to obtain a feasible power control solution η meeting the constraint condition0And the corresponding objective function value LB (X)0) Record η0＝[e1e2… eM]T，LB(X0)＝ψ·ln(η0) + ξ, and η0Substituting the second problem to obtain a function value UB (η)0) Wherein
Figure BDA0002027708380000032
(2-8) let Q denote the active set of nodes and F denote the set of feasible solutions, i.e., Q ═ X0}，F＝{η0}；
(2-9) let K be 1, active node X currently operatingk＝X0Memory for recording
Figure BDA0002027708380000033
Step 3, segmenting a value space, obtaining two child nodes of the initial point by using a binary tree algorithm, and solving an upper bound and a lower bound of the optimal solution of the problem corresponding to each child node; the method specifically comprises the following steps:
(3-1) from vector η ═ η1，η2，…，ηM]Selecting a variable ηpWherein
Figure BDA0002027708380000034
(3-2) by mixing ηpValue range of
Figure BDA0002027708380000035
Bisection to obtain a node XkTwo child nodes of
Figure BDA0002027708380000036
And
Figure BDA0002027708380000037
a and b are child node identifiers;
(3-3) respectively pairing the child nodes
Figure BDA0002027708380000038
And
Figure BDA0002027708380000039
and (5) carrying out transformation of the steps (2-5) and (2-6).
(3-4) at child node
Figure BDA00020277083800000310
And
Figure BDA00020277083800000311
the fourth problem is solved to obtain the corresponding relation of each sub-node
Figure BDA00020277083800000312
Figure BDA00020277083800000313
And
Figure BDA00020277083800000314
(3-5) adding child nodes to the sets F and Q.
Step 4, contracting the feasible set, and deleting nodes which cannot have the optimal solution according to the updated upper bound and lower bound of the optimal solution; the method specifically comprises the following steps:
(4-1) updating the upper and lower bounds, UBk＝min{UB(η),η∈F}，LBk＝min{LB(X),X∈Q}。
(4-2) obtaining current optimal solution η*，
Figure BDA0002027708380000041
(4-3) deleting nodes from the set for which the optimal solution does not exist: q \ X LB (X) UBk,X∈Q},F＝F\{η|LB(X)＞UBk,X∈Q}。
Step 5, checking the convergence of the algorithm, outputting an optimal solution if the convergence is reached, and returning to the step 3 to continue iteration if the convergence is not reached; the method specifically comprises the following steps:
(5-1) updating the set Q according to the convergence threshold epsilon, the objective function can not be reduced continuouslyThe nodes of value are deleted from the set, Q \ X: "UBk-LB(X)≤ε,X∈Q}。
(5-2) if the set Q is empty, the algorithm is converged;
otherwise, k is k +1, and a new operation node is set
Figure BDA0002027708380000042
And returning to the step 3.
(5-3) output Power control optimal solution η*And its corresponding probability of communication interruption Pout＝UBk。
Compared with the prior power control method, the invention has the advantages that:
1. most of the existing power control methods are only suitable for time domain synchronous communication systems, and the multipath effect of a multi-relay system caused by synchronous deviation is not considered; the invention takes the multipath effect as a precondition, can solve the power control problem when the multi-relay system has random phase deviation, and effectively improves the performance of the relay system.
2. Most of the traditional power control methods are based on instantaneous channel state information, and the methods have high requirements on instantaneity and high communication overhead; when the channel state changes rapidly, large fluctuation of node power is caused, and channel state information lag is easy to occur, thereby causing system performance degradation. The invention is a method based on statistical channel state information, which has low requirement on real-time performance and low communication overhead, can ensure that nodes can keep stable power in a plurality of time slots, and has better robustness on fast fading channels.
3. In the problem solving process, compared with direct search, the method carries out linear approximation on the original problem, reduces the value space of the variable and improves the solving efficiency.
Drawings
FIG. 1 is a system diagram of a method for controlling power of a relay system based on statistical channel state information;
fig. 2 is a flowchart of an algorithm of a relay system power control method based on statistical channel state information.
Detailed Description
The invention is further described in detail below by way of examples and with reference to the accompanying drawings.
The system structure of the relay system power control method based on statistical channel state information is shown in figure 1, for a rhombus relay system with N +2 nodes, a source node S firstly broadcasts signals to a relay node R1,R2,…,RN(ii) a N relay nodes RiAfter the signals are decoded, the correctly decoded signals are simultaneously forwarded to a destination node D; the node D receives the multipath superposed signals with random phase difference, judges after outputting the local received signal-to-noise ratio, and judges if the signal-to-noise ratio is lower than a preset threshold gammathAn interrupt occurs.
In this example, based on the algorithm flowchart shown in fig. 2, the relay system obtains a channel fading factor by collecting statistical channel state information of the communication link, and finally minimizes the communication interruption probability of a diamond-shaped relay system (relay number N is 2) by controlling the transmission power of the relay node, thereby achieving the purpose of improving the communication reliability.
This example was specifically achieved by the following steps:
step 1, collecting key parameters of a relay system, specifically comprising: channel fading parameters (m, b, omega), relay transmit power ceiling
Figure BDA0002027708380000057
Decision threshold gamma of interrupted signal-to-noise ratiothAnd an algorithm convergence judgment threshold epsilon.
In the channel fading parameters, m is a shape factor of channel fading, which describes the strength of channel multipath, and b and Ω are scale factors of channel fading, which respectively represent the energy of multipath components and direct components of the wireless channel. The link S-R in this example1And S-R2For shadow rice fading, these three parameters are simultaneously available, where the link S-R1Parameter (d) of
Figure BDA0002027708380000051
Indicating a mild fade; shadow rice fading S-R2Parameter (d) of
Figure BDA0002027708380000052
Indicating a moderate fade; link R1-D and R2D is Nakagami-m fading, with only two parameters of m and Ω, where link R is1Parameters of-D
Figure BDA0002027708380000053
Link R2Parameters of-D
Figure BDA0002027708380000054
Other system parameters that are set include: upper limit of relay transmission power
Figure BDA0002027708380000055
Decision threshold gamma of interrupted signal-to-noise ratioth5dB, and an algorithm convergence decision threshold e 10-5。
And 2, an initialization stage. The method specifically comprises the following steps:
2-1, calculating to obtain the system communication interruption probability according to the probability distribution of the shadow Rice fading and Nakagami-m fading mixed model, and establishing the power control problem of the minimized interruption probability as follows:
Figure BDA0002027708380000056
s.t.0≤ηi≤1,i＝1,2
Figure BDA0002027708380000061
2-2, converting variable substitution into symbolic programming problem according to the expression of communication interruption probability as follows:
Figure BDA0002027708380000062
α denotes a sign coefficienttj]Expressing the indexes of the symbolic expressions;
Figure BDA0002027708380000063
is a real number domain; x represents the value space of the power control coefficient,xthe lower bound of the value space is represented,
Figure BDA0002027708380000064
representing a value space upper bound;
Figure BDA0002027708380000065
b is 0, α represents a symbolic formula coefficient, and specifically:
Figure BDA0002027708380000066
2-3, reducing a variable value space according to constraint conditions:
for(m＝1to3,m++)do
Figure BDA0002027708380000067
Figure BDA0002027708380000068
|end for；
2-4, recording the reduced variable value space as X0；
2-5, carrying out exponential transformation on variables:
Figure BDA0002027708380000069
wherein
Figure BDA00020277083800000610
Figure BDA0002027708380000071
2-6, further performing piecewise linear approximation on the constraint condition of the nonlinear equation, setting 5 separation points in a value space, and converting the problem (P.3) into a mixed integer linear programming problem as follows:
Figure BDA0002027708380000072
wherein P ═ Pij]5×3Expressed in a value space X0A constant matrix composed of the separation points of the piecewise linear function; lambda ═ lambdaij]5×3Is a newly introduced variable matrix for constructing a linear function between two adjacent separation points, β ═ βij]4×4Is a matrix of binary variables to ensure η and y are piecewise linear functions, ΛiAnd βiRepresenting the ith row of matrices Λ and β, respectively, B is a 5 × 4 Toeplitz matrix with row 1 element of [ 1000]Column 1 element is [ 11000 ]]T。
2-7, get solution η of problem (P.4)0And the corresponding objective function value LB (X)0) Record η0＝[e1e2e3]T，LB(X0)＝ψ·ln(η0) + ξ, step η0Substituting the question (P.2) to obtain a function value UB (η)0) Wherein
Figure BDA0002027708380000073
2-8. let Q be the active set of nodes and F denote the set of feasible solutions, i.e., Q ═ { X }0}，F＝{η0}。
2-9, let K equal to 1, active node X currently operatingk＝X0。
Step 3, segmenting a value space, specifically:
3-1. slave vector η ═ η1，η2，η3]Selecting a variable ηpWherein
Figure BDA0002027708380000074
3-2. passing ηpValue range of
Figure BDA0002027708380000075
Bisection to obtain a node XkTwo child nodes of
Figure BDA0002027708380000076
And
Figure BDA0002027708380000077
3-3. respectively pair sub-nodes
Figure BDA0002027708380000078
And
Figure BDA0002027708380000079
performing the transformation from step 2-5 to step 2-6;
3-4. at child node
Figure BDA00020277083800000710
And
Figure BDA00020277083800000711
the problem (P.4) is solved separately to obtain the corresponding sub-node
Figure BDA00020277083800000712
Figure BDA00020277083800000713
And
Figure BDA0002027708380000081
3-5, updating the sets F and Q,
Figure BDA0002027708380000082
and 4, contracting the feasible set, specifically comprising the following steps:
4-1. update the upper and lower bounds, UBk＝min{UB(η),η∈F}，LBk＝min{LB(X),X∈Q}；
4-2, obtaining the current optimal solution
Figure BDA0002027708380000083
And 4-3, deleting the nodes without the optimal solution from the set, wherein Q is Q \ X:LB(X)＞UBk,X∈Q},F＝F\{η|LB(X)＞UBk,X∈Q}。
and 5, outputting an optimal solution, specifically:
5-1, updating the set Q according to the convergence threshold epsilon, wherein Q is Q \ X is UBk-LB(X)≤ε,X∈Q}；
Figure BDA0002027708380000084
To step 5.3
Else
Let k equal k +1
A new current operation node is set up and,
Figure BDA0002027708380000085
go back to step 3-1;
5-3. optimal solution η for output power control*And corresponding interruption probability UBk。

Claims (1)

1. A relay system power control method based on statistical channel state information is characterized by comprising the following steps:
step (1), collecting key parameters of a relay system, specifically comprising: channel fading parameter, relay transmission power upper limit
Figure FDA0002434720630000011
Decision threshold gamma of interrupted signal-to-noise ratiothAn algorithm convergence judgment threshold epsilon; the probability of communication interruption represents the probability that the signal-to-noise ratio of the receiving end is lower than the interruption decision threshold, and is expressed as
Figure FDA0002434720630000012
Wherein f isγ(x) Probability density for received signal-to-noise ratio;
initializing, namely constructing an optimization problem which minimizes the communication interruption probability, simplifying the original problem into a symbolic programming problem, performing linear approximation and piecewise linear approximation on an objective function and a constraint condition respectively, and solving to obtain a starting point of iteration of a power control algorithm; the specific method comprises the following steps:
(2-1) according to a specific channel fading model, calculating to obtain a communication interruption probability PoutEstablishing a power control problem that minimizes the outage probability, i.e. the first problem:
Figure FDA0002434720630000013
η thereiniRepresenting the power control coefficient of the relay node i, wherein N is the number of the relay nodes;
(2-2) converting the first problem into a symbolic programming problem, namely a second problem, by linear variable substitution according to the expression for calculating the interrupt probability:
Figure FDA0002434720630000014
wherein M represents the variable number after variable substitution, M is more than or equal to N, T represents the number of terms of the symbolic expression, αtRepresenting the t-term symbolic coefficient; [ r ] oftj]Expressing the indexes of the symbolic expressions;
Figure FDA0002434720630000015
is a real number domain; a and c represent linear variable substitutions; x represents the value space of the power control coefficient,xthe lower bound of the value space is represented,
Figure FDA0002434720630000016
representing a value space upper bound;
(2-3) solving a boundary problem for each variable according to the constraint condition of the second problem, and reducing the value space of the variables;
(2-4) recording the reduced variable value space as X0；
(2-5) carrying out exponential transformation on the variables in the second problem to obtain a new optimization problem, namely a third problem:
Figure FDA0002434720630000017
where ψ represents a coefficient vector of the objective function after linear approximation,
Figure FDA0002434720630000021
ξ is the constant part of the objective function after linear approximation,
Figure FDA0002434720630000022
and is
Figure FDA0002434720630000023
Yt uAnd Yt lRespectively representing the upper and lower bounds of the objective function in the new value space Y,
Figure FDA0002434720630000024
(2-6) further performing piecewise linear approximation on the constraint condition of the nonlinear equation, setting the number of the separation points to be K, and converting the third problem into a mixed integer linear programming problem, namely a fourth problem:
Figure FDA0002434720630000025
wherein P ═ Pij]K×MExpressed in a value space X0A constant matrix composed of the separation points of the piecewise linear function; lambda ═ lambdaij]K×MIs a newly introduced variable matrix for constructing a linear function between two adjacent separation points, β ═ βij](K-1)×MIs a matrix of binary variables to ensure η and y are piecewise linear functions, ΛiAnd βiRepresenting rows i of matrices Λ and β, respectively, B is a Toeplitz matrix of K × (K-1) with row 1 elements [ 10 … 0 ]]The column 1 element is [ 110 … 0 ]]T(ii) a diag (X) represents a vector formed by diagonal elements of matrix X; []TRepresenting a matrix transposition;
(2-7) solving the fourth problem to obtain a feasible power control solution η meeting the constraint condition0And the corresponding objective function value LB (X)0) Record η0＝[e1e2… eM]T，LB(X0)＝ψ·ln(η0) + ξ, and η0Substituting into the second problemObtain a function value UB (η)0) Wherein
Figure FDA0002434720630000026
(2-8) let Q denote the active set of nodes and F denote the set of feasible solutions, i.e., Q ═ X0}，F＝{η0}；
(2-9) let K be 1, active node X currently operatingk＝X0Memory for recording
Figure FDA0002434720630000027
Step (3), segmenting a value space, obtaining two child nodes of the initial point by using a binary tree algorithm, and solving an upper bound and a lower bound of the optimal solution of the problem corresponding to each child node; the specific method comprises the following steps:
(3-1) from vector η ═ η1，η2，…，ηM]Selecting a variable ηpWherein
Figure FDA0002434720630000028
(3-2) by mixing ηpValue range of
Figure FDA0002434720630000029
Bisection to obtain a node XkTwo child nodes of
Figure FDA00024347206300000210
And
Figure FDA00024347206300000211
a and b are child node identifiers;
(3-3) respectively pairing the child nodes
Figure FDA0002434720630000031
And
Figure FDA0002434720630000032
carrying out the step (2-5)) And (2-6);
(3-4) at child node
Figure FDA0002434720630000033
And
Figure FDA0002434720630000034
the fourth problem is solved to obtain the corresponding relation of each sub-node
Figure FDA0002434720630000035
Figure FDA0002434720630000036
And
Figure FDA0002434720630000037
(3-5) adding child nodes to the sets F and Q;
step (4), contracting the feasible set, and deleting nodes which cannot have the optimal solution according to the updated upper bound and lower bound of the optimal solution; the specific method comprises the following steps:
(4-1) updating the upper and lower bounds, UBk＝min{UB(η),η∈F}，LBk＝min{LB(X),X∈Q}；
(4-2) obtaining current optimal solution η*，
Figure FDA0002434720630000038
(4-3) deleting nodes from the set for which the optimal solution does not exist: q \ X LB (X) UBk,X∈Q},F＝F\{η|LB(X)＞UBk,X∈Q}；
Step (5), checking the convergence of the algorithm, outputting an optimal solution if the convergence is reached, and returning to the step (3) to continue iteration if the convergence is not reached; the specific method comprises the following steps:
(5-1) updating a set Q according to a convergence threshold epsilon, and deleting nodes which can not continuously reduce the objective function value from the set, wherein Q is Q \ X is UBk-LB(X)≤ε,X∈Q}；
(5-2) if the set Q is empty, the algorithm is converged;
otherwise, k is k +1, and a new operation node is set
Figure FDA0002434720630000039
Returning to the step (3);
(5-3) output Power control optimal solution η*And its corresponding probability of communication interruption Pout＝UBk。
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CN117560049A (en) * 2023-05-11 2024-02-13 武汉能钠智能装备技术股份有限公司四川省成都市分公司 Satellite ground station relay forwarding system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101931961A (en) * 2009-06-23 2010-12-29 华为技术有限公司 Method, system and equipment for realizing return link control channel transmission of relay system
CN104981008A (en) * 2015-05-12 2015-10-14 江苏省邮电规划设计院有限责任公司 Interference-limited relay user power control method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2456086B1 (en) * 2010-11-17 2016-07-20 NTT DoCoMo, Inc. Apparatus and method for allocating resources to nodes in a communication system using an update of iteration resource weights
CN105376844B (en) * 2015-08-25 2018-11-20 浙江工业大学 A kind of Poewr control method based on monotonicity optimization and simulated annealing in cognition wireless network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101931961A (en) * 2009-06-23 2010-12-29 华为技术有限公司 Method, system and equipment for realizing return link control channel transmission of relay system
CN104981008A (en) * 2015-05-12 2015-10-14 江苏省邮电规划设计院有限责任公司 Interference-limited relay user power control method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
《广义衰落信道下中继系统高效率合作传输技术研究》;赵越;《中国博士学位论文全文数据库 信息科技辑》;20190615;1-5 *
P.Shen等.《Accelerating Method of Global Optimization for Signomial Geometric Programming》.《Journal of Computational and Applied Mathematics》.2008, *
R.PoRn等.《Global Solution of optimization Problems with Signomial Parts》.《Discrete optimization》.2008, *

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