CN107359928B - Optimized power distribution algorithm of multi-user single-relay cooperative communication system - Google Patents

Optimized power distribution algorithm of multi-user single-relay cooperative communication system Download PDF

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CN107359928B
CN107359928B CN201710588550.3A CN201710588550A CN107359928B CN 107359928 B CN107359928 B CN 107359928B CN 201710588550 A CN201710588550 A CN 201710588550A CN 107359928 B CN107359928 B CN 107359928B
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power
user
relay
source node
optimization problem
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CN107359928A (en
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王仕果
曹姝
李志军
姚志强
邓清勇
许海霞
全世齐
王梦蛟
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Xiangtan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/155—Ground-based stations
    • H04B7/15592—Adapting at the relay station communication parameters for supporting cooperative relaying, i.e. transmission of the same data via direct - and relayed path
    • 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
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • 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
    • H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
    • H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range

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Abstract

The invention discloses an optimized power distribution algorithm of a multi-user single-relay cooperative communication system, which comprises the following steps: establishing a generalized optimal solution frame structure model; establishing a source node user total power optimization problem model; establishing a system total power optimization problem model; and solving a source node user total power optimization problem model and a system total power optimization problem model based on a generalized optimal solution framework structure model to realize power distribution. The invention solves the source node user total power optimization problem model and the system total power optimization problem model on the basis of two proposed theorems and one theorem, respectively minimizes the power consumption of each source node user and the total power consumption of the system under the condition of ensuring that each user obtains the target signal-to-noise ratio of each user, can optimize and solve the similar optimization problems conforming to the constraint conditions of the theorems and the theorem by using the algorithm, has wide application range, can meet the communication service quality of the users and can reduce the power consumption of the system.

Description

Optimized power distribution algorithm of multi-user single-relay cooperative communication system
Technical Field
The invention relates to an optimized power distribution algorithm of a multi-user single-relay cooperative communication system.
Background
The wireless broadband green communication is a development trend of future communication and a current research hotspot, the reduction of energy consumption on the premise of ensuring the communication quality is a key point for realizing the broadband green communication, and relay cooperation is an effective way for achieving the aim.
In recent years, the power allocation of the relay cooperative system has attracted much attention, and patent CN101282199A discloses a method for adaptively selecting and allocating power of a relay strategy for multi-relay cooperative communication; CN103068027A discloses an optimal power distribution method for multiple relays under a frequency flat fading channel, and for a two-hop parallel direct Amplification (AF) relay network, the method considers the circuit processing power of a node per se under the frequency flat fading environment, and provides an optimal power distribution method under the mixed power constraint; patent CN104093210A discloses an optimized power allocation algorithm for a total power limited double-hop full duplex decoding forwarding relay system. However, these systems are all directed to single-user relay or multi-relay systems, and are not suitable for network scenarios where multiple users share a single relay and compete with each other for relay power.
Disclosure of Invention
In order to solve the technical problem, the invention provides an optimized power allocation algorithm of a multi-user single-relay cooperative communication system, which is suitable for the situation that the power is limited and has a simple algorithm.
The technical scheme for solving the problems is as follows: an optimized power allocation algorithm for a multi-user single-relay cooperative communication system, comprising the following steps:
the method comprises the following steps: establishing a generalized optimal solution frame structure model;
step two: establishing a source node user total power optimization problem model;
step three: establishing a system total power optimization problem model;
step four: based on a generalized optimal solution framework structure model, when a relay is powered by a fixed power supply, a source node user total power optimization problem model is solved, and power distribution is realized; when the relay does not have a fixed power supply to supply power or is a common mobile user, the model of the system total power optimization problem is solved, and power distribution is realized.
The optimized power allocation algorithm of the multi-user single-relay cooperative communication system comprises the following specific steps:
for a set of n elements phi { (S)1,D1),(S2,D2),…,(Sn,Dn)}，SnFor the nth source node user, D1,D2,…,DnFor n source node users S1,S2,…,SnEach source node user and the corresponding target node user form a user pair, and the user pairs carry out signal transmission through the same relay node R, gi(xi,yi) Is an element (S)i,Di) Of the objective function of (1), it is for yiIs differentiable, i ═ 1,2, …, n; and xi＝fi(yi) Is a monotonically increasing function of yi, where xi>0，yi>0; the objective function of a system consisting of n elements is:
Figure BDA0001354174760000021
in the second step, the model of the problem of optimizing the total power of the source node users is as follows:
Figure BDA0001354174760000032
Figure BDA0001354174760000033
Figure BDA0001354174760000034
wherein the content of the first and second substances,
Figure BDA0001354174760000035
and
Figure BDA0001354174760000036
for normalizing the channel gain, the expression is
Figure BDA0001354174760000037
And
Figure BDA0001354174760000039
for corresponding channel coefficients, σ2Power of additive noise, ΓiIs a user pair (S)i,Di) The target signal-to-noise ratio of (c),is a user pair (S)i,Di) The maximum transmit power that can be provided,
Figure BDA00013541747600000311
maximum transmit power for the relay;
regardless of the direct link between the source node user and the destination node user, the signal is transmitted through the relay channel,respectively source node subscriber S1,S2,…,SnThe transmit power of (a);
Figure BDA00013541747600000313
respectively allocated to source node users S for relays1,S2,…,SnThe relay forwarding power of.
In the third step, the model of the system total power optimization problem is as follows:
Figure BDA00013541747600000314
Figure BDA00013541747600000316
Figure BDA00013541747600000317
in the fourth step, the source node user total power optimization problem model and the system total power optimization problem model are solved based on theorem 1, theorem 2 and theorem 1;
theorem 1 is:
under the constraint condition
Figure BDA0001354174760000041
Wherein C is a constant greater than zero, provided thatThe system overall optimizes the objective function G (x)1，y1，x2，y2，…，xn，yn) In thatTaking an extreme value;
theorem 2 is:
is provided with
Figure BDA0001354174760000044
Obtaining the extreme point for the system objective function in theorem 1, if the requirement satisfies the constraint condition xi≤xi,maxWherein x isi,maxIs a given constant, then, when
Figure BDA0001354174760000045
Then, only take xi＝xi,maxWhen, G (x)1,y1,…,xn,yn) The extreme value can be obtained;
the introduction 1 is: for xy ═ U where x and y are variables and U is a constant greater than zero, the constraint condition 0 is required to be satisfied<x≤xmaxAnd y>0, wherein xmaxIs a constant greater than zero; if it is
Figure BDA0001354174760000046
When x is equal to xmaxWhen x + y reaches a minimum value.
In the fourth step, the solving of the source node user total power optimization problem model, that is, the minimization of the source node user total power consumption, is implemented as follows:
step 1): initializing parameters: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0, and remaining relay power PΔFor maximum transmission power of relay
Figure BDA0001354174760000047
Namely, it is
Step 2): if Flag is true, go to step 3);
step 3): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1And
Figure BDA00013541747600000410
and each user is respectively allocated with power to the source node and the relay
Figure BDA0001354174760000051
And
Figure BDA0001354174760000052
step 4): for each user pair in Φ, suppose the power provided by the source node user is less than the power of equal ratio, i.e.
Figure BDA0001354174760000053
Then according to theorem 2, the transmission power is obtainedAnd
Figure BDA0001354174760000055
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is 0, and the step 3) is returned to, and the steps 3) to 4) are executed in a loop until all the user pairs are allocated with power.
In the fourth step, the solving of the system total power optimization problem model, that is, the minimizing of the system total power consumption, is implemented by the following steps:
step (1): initializing parameters: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0;
step (2): obtaining the highest energy efficiency distribution power of each user pair according to the channel parameters of the user pairs without considering the transmission power limit of the user source node and the relay
Figure BDA0001354174760000056
And
Figure BDA0001354174760000057
namely, it is
Figure BDA0001354174760000058
And
Figure BDA0001354174760000059
and (3): for each user pair of Φ, according to
Figure BDA00013541747600000510
And
Figure BDA00013541747600000511
by dividing phi into two subsets Ψ and Ω, each element in Ψ satisfying
Figure BDA00013541747600000512
Each element of Ω satisfies
Figure BDA00013541747600000513
And (4): according to theorem 1, for an element of Ψ, let
Figure BDA00013541747600000514
For the elements in omega, let
Figure BDA00013541747600000515
And then find out
Figure BDA00013541747600000516
And (5): if the total relay power required is greater than or equal to the maximum power that the relay can provide, i.e. the total relay power required is less than or equal to
Figure BDA00013541747600000517
Let Flag be 0 and relay power P remainΔFor maximum transmission power of relay
Figure BDA00013541747600000518
Namely, it is
Figure BDA00013541747600000519
Entering the step (6) to carry out power distribution;
and (6): if Flag is true, go to step (7);
and (7): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1And
Figure BDA0001354174760000062
and each user is respectively allocated with power to the source node and the relay
Figure BDA0001354174760000063
And
Figure BDA0001354174760000064
and (8): for each user pair in Φ, suppose the power provided by the source node user is less than the power of equal ratio, i.e.
Figure BDA0001354174760000065
Then according to theorem 2, the transmission power is obtained
Figure BDA0001354174760000066
And
Figure BDA0001354174760000067
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is equal to 0, the step (7) is returned, and the steps (7) to (8) are executed in a loop mode until all the user pairs are allocated with power.
The invention has the beneficial effects that: the method comprises the steps of firstly establishing a generalized optimal solution framework structure model, then establishing a source node user total power optimization problem model and a system total power optimization problem model, then solving the source node user total power optimization problem model and the system total power optimization problem model on the basis of the generalized optimal solution framework structure model on the basis of two proposed theorems and one theorem, realizing power distribution, and respectively enabling the power consumption of each source node user and the total power consumption of the system to be minimum under the condition that each user is ensured to obtain a target signal-to-noise ratio. The similar optimization problems meeting theorem and theorem constraint conditions can be optimized and solved by the algorithm, the application range is wide, the communication service quality of a user can be met, and the power consumption of a system can be reduced.
Drawings
Fig. 1 is a schematic structural diagram of a multi-user single-relay cooperative communication system.
Figure 2 shows that when the user pairs are two pairs,followed byTrend graph of change.
Fig. 3 is a trend graph of power consumption of a source node user as a function of power of a relay node when the user number is 10.
FIG. 4 is a drawing showing
Figure BDA00013541747600000610
And (4) a trend graph of the power consumption of the source node user along with the position change of the relay node.
Figure 5 shows that when the user pairs are two pairs,
Figure BDA0001354174760000071
followed by
Figure BDA0001354174760000072
And
Figure BDA0001354174760000073
trend graph of change.
Fig. 6 is a trend graph of the power consumption of the source node user as a function of the power of the relay node when the user number is 10.
FIG. 7 is a drawing showing
Figure BDA0001354174760000074
When it comes toAnd (3) a trend graph of the power consumption of the node user along with the position change of the relay node.
Detailed Description
The invention is further described below with reference to the figures and examples.
An optimized power allocation algorithm for a multi-user single-relay cooperative communication system, comprising the following steps:
the method comprises the following steps: and establishing a generalized optimal solution framework structure model.
As shown in fig. 1, for a set of n elements phi { (S)1,D1),(S2,D2),…,(Sn,Dn)}，SnFor the nth source node user, D1,D2,…,DnFor n source node users S1,S2,…,SnEach source node user and the corresponding target node user form a user pair, and the user pairs carry out signal transmission through the same relay node R, gi(xi,yi) Is an element (S)i,Di) Of the objective function of (1), it is for yiIs differentiable, i ═ 1,2, …, n; and xi＝fi(yi) Is yiA monotonically increasing function of (2), wherein xi>0，yi>0; the objective function of a system consisting of n elements is:
Figure BDA0001354174760000075
step two: and establishing a source node user total power optimization problem model.
The model of the problem of optimizing the total power of the source node user is as follows:
Figure BDA0001354174760000082
Figure BDA0001354174760000083
Figure BDA0001354174760000084
wherein the content of the first and second substances,
Figure BDA0001354174760000085
and
Figure BDA0001354174760000086
for normalizing the channel gain, the expression is
Figure BDA0001354174760000087
Figure BDA0001354174760000088
And
Figure BDA0001354174760000089
for corresponding channel coefficients, σ2Power of additive noise, ΓiIs a user pair (S)i,Di) The target signal-to-noise ratio of (c),
Figure BDA00013541747600000810
is a user pair (S)i,Di) The maximum transmit power that can be provided,
Figure BDA00013541747600000811
maximum transmit power for the relay;
regardless of the direct link between the source node user and the destination node user, the signal is transmitted over a relay channel.
Figure BDA00013541747600000812
Respectively source node subscriber S1,S2,…,SnThe transmit power of (a);
Figure BDA00013541747600000813
respectively allocated to source node users S for relays1,S2,…,SnThe relay forwarding power of. Channel gains between source node user and relay, and between relay and destination node user are used respectively
Figure BDA00013541747600000814
And
Figure BDA00013541747600000815
and (4) showing.
Step three: and establishing a system total power optimization problem model.
The model of the system total power optimization problem is as follows:
Figure BDA00013541747600000816
Figure BDA00013541747600000818
step four: when the relay is powered by a fixed power supply, the model of the source node user total power optimization problem is solved, and power distribution is realized; when the relay does not have a fixed power supply to supply power or is a common mobile user, the model of the system total power optimization problem is solved, and power distribution is realized.
Solving a source node user total power optimization problem model and a system total power optimization problem model on the basis of theorems 1,2 and 1;
theorem 1 is: under the constraint condition
Figure BDA0001354174760000091
Wherein C is a constant greater than zero, provided that
Figure BDA0001354174760000092
The system overall optimizes the objective function G (x)1，y1，x2，y2，…，xn，ynIs either) at
Figure BDA0001354174760000093
The point takes an extreme value.
And (3) proving that: syndrome differentiation method
For any two elements/users i and j, G (x) is assumed when (1) holds1,y1,…,xn,yn) In that
Figure BDA0001354174760000094
An extreme value is reached.
Figure BDA0001354174760000095
Thus, the element (S)i,Di) In that
Figure BDA0001354174760000096
Dot
Figure BDA00013541747600000911
To pair
Figure BDA00013541747600000912
Rate of change and element (S)j,Dj) In thatDot
Figure BDA00013541747600000913
To pairFor a slight amount η, due to the fact that
Figure BDA0001354174760000098
Is constant, can order
Figure BDA0001354174760000099
Since (1) is established, it can be obtained
Figure BDA00013541747600000910
I.e. by reassignment, G (x) can be made1,y1,…,xn,yn) Become larger or smaller. Therefore, when (1) is established, G (x)1,y1,…,xn,yn) The extreme value cannot be reached. Theorem 1 proves that the process is finished.
If g isi(xi,yi) Obtaining a maximum value by a monotone increasing function; if g isi(xi,yi) Is a monotone decreasing function and obtains a minimum value. Using this theorem, it is possible to obtain that the constraint x is not consideredi>0 and yi>Extreme value at 0.
Theorem 2 is: is provided with
Figure BDA0001354174760000101
Obtaining the extreme point for the system objective function in theorem 1, if the requirement satisfies the constraint condition xi≤xi,maxWherein x isi,maxIs a given constant, then, when
Figure BDA0001354174760000102
Then, only take xi＝xi,maxWhen, G (x)1,y1,…,xn,yn) The extreme value can be taken.
And (3) proving that: due to the fact that
Figure BDA0001354174760000103
Is an equivalence ratio point based on theorem 1, thereforeThis is true. According to
Figure BDA0001354174760000105
Is equal to xi,max(i e {1,2, …, n }) the relationship can divide the elements/users into two sets ψ and Ω, i.e., (S)i,Di) E.g. psi
Figure BDA0001354174760000106
(Sj,Dj) E is satisfied with omegaFor any element (S)i,Di) E.g. psi, order
Figure BDA0001354174760000108
Wherein delta1A minor amount greater than 0; order to
Figure BDA0001354174760000109
Due to xi＝fi(yi) Is yiMonotonically decreasing function of, and xi>0 and yi>0, so xiAnd yiIs in a one-to-one correspondence, i.e. xi＝fi(yi) Is reversible. Thus, the objective function gi(xi,yi) Can be expressed as gi(fi -1(yi),yi). For xi,maxSmall change delta in1There is another satisfaction
Figure BDA00013541747600001010
A small increment delta of2. Due to the fact that
Figure BDA00013541747600001011
Without loss of generality, small increments delta2Can be seen as reducing the elements/users (S)j,Dj) E.g. omega, i.e. obtaining
Figure BDA00013541747600001012
Thus, for element/user (S)i,Di) E.g. psi and (S)j,Dj) E Ω and their target function variance can be expressed as shown in (2).
Figure BDA00013541747600001014
If the optimization objective has a minimum value, then
Figure BDA0001354174760000111
So Δ>0. Instant game
Figure BDA0001354174760000112
The value of the objective function becomes large. Meanwhile, if the target has the maximum value, then
Figure BDA0001354174760000113
So Δ<0. Instant game
Figure BDA0001354174760000114
The objective function value becomes small. In summary, for (S)i,Di) E element/user in ψ, only if xi＝xi,maxThe system can only take the extreme value. Theorem 2 proves that the process is finished.
The introduction 1 is: for xy ═ U where x and y are variables and U is a constant greater than zero, the constraint condition 0 is required to be satisfied<x≤xmaxAnd y>0, if
Figure BDA0001354174760000115
When x is equal to xmaxWhen x + y reaches a minimum value.
And (3) proving that:
for two points (x) of a variable pair (x, y)2,y2) And (x)3,y3) Let x2＝xmaxAnd x2y2U, thus y2＝U/x2＝U/xmax. Let x be3＝x2- δ and y3＝U/x3Wherein 0 is<δ<x2. Thus, it is possible to provide
Due to the fact that
Figure BDA0001354174760000117
And xmax-δ>0, then (x)2+y2)-(x3+y3)<0 when x ═ xmaxWhen x + y reaches a minimum value. And finishing the certification of the leading theory 1.
The problem of minimizing the total power consumption of the source node can be solved by using a source node user total power optimization problem model, if the source node user total power optimization problem model has an effective solution, the optimal power distribution can be obtained based on theorems 1 and 2, namely the source node user total power consumption minimization implementation step is as follows:
step 1): initializing parameters: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0, and remaining relay power PΔFor maximum transmission power of relay
Figure BDA0001354174760000118
Namely, it is
Step 2): if Flag is true, go to step 3);
step 3): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1And
Figure BDA0001354174760000122
and each user is respectively allocated with power to the source node and the relay
Figure BDA0001354174760000123
And
Figure BDA0001354174760000124
step 4): for each user pair in phiIf the power provided by the source node user is less than the power of equal rate, i.e. the source node user provides
Figure BDA0001354174760000125
Then according to theorem 2, the transmission power is obtained
Figure BDA0001354174760000126
And
Figure BDA0001354174760000127
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is 0, and the step 3) is returned to, and the steps 3) to 4) are executed in a loop until all the user pairs are allocated with power.
For a cooperative communication system with only two pairs of users sharing the same relay, the equation is1＝Γ2＝10dB、
Figure BDA0001354174760000128
User pair (S)1,D1) And (S)2,D2) The coordinate points of (0.1,0.8) and (0.2,0.95) are respectively,
Figure BDA0001354174760000129
with P1 RAs shown in fig. 2. When the user logarithm is 10, the power consumption of the source node user is shown in fig. 3 along with the change of the relay node power;
Figure BDA00013541747600001210
the power consumption of the source node user as a function of the relay node location is shown in fig. 4.
The problem of taking the total power consumption of the system as a target can be solved by using a total power optimization problem model of the system, if the total power optimization problem model of the system has a feasible solution, the optimal power distribution can be obtained based on theorem 1, theorem 2 and theorem 1, namely the minimum realization step of the total power consumption of the system is as follows:
step (1): parameter(s)Initialization: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0;
step (2): obtaining the highest energy efficiency distribution power of each user pair according to the channel parameters of the user pairs without considering the transmission power limit of the user source node and the relay
Figure BDA00013541747600001211
And
Figure BDA00013541747600001212
namely, it is
Figure BDA00013541747600001213
And
Figure BDA0001354174760000131
and (3): for each user pair of Φ, according toAnd
Figure BDA0001354174760000133
by dividing phi into two subsets Ψ and Ω, each element in Ψ satisfying
Figure BDA0001354174760000134
Each element of Ω satisfies
And (4): according to theorem 1, for an element of Ψ, let
Figure BDA0001354174760000136
For the elements in omega, let
Figure BDA0001354174760000137
And then find out
Figure BDA0001354174760000138
And (5): if the total relay power required is greater than or equal to the maximum power that the relay can provide, i.e. the total relay power required is less than or equal to
Figure BDA0001354174760000139
Let Flag be 0 and relay power P remainΔFor maximum transmission power of relay
Figure BDA00013541747600001310
Namely, it is
Figure BDA00013541747600001311
Entering the step (6) to carry out power distribution;
and (6): if Flag is true, go to step (7);
and (7): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1
Figure BDA00013541747600001312
And
Figure BDA00013541747600001313
and each user is respectively allocated with power to the source node and the relay
Figure BDA00013541747600001314
And
Figure BDA00013541747600001315
and (8): for each user pair in Φ, suppose the power provided by the source node user is less than the power of equal ratio, i.e.
Figure BDA00013541747600001316
Then according to theorem 2, the transmission power is obtained
Figure BDA00013541747600001317
And
Figure BDA00013541747600001318
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is equal to 0, the step (7) is returned, and the steps (7) to (8) are executed in a loop mode until all the user pairs are allocated with power.
For a cooperative communication system with only two pairs of users sharing the same relay, the equation is1＝Γ2＝10dB、
Figure BDA00013541747600001319
User pair (S)1,D1) And (S)2,D2) The coordinate points are (0.1,0.8) and (0.2,0.95) respectively,
Figure BDA00013541747600001320
with P1 RAndthe variation is shown in figure 5. When the user logarithm is 10, the power consumption of the source node user is shown in fig. 6 along with the change of the relay node power;
Figure BDA0001354174760000141
the source node user power consumption as a function of relay node location is shown in fig. 7.

Claims (3)

1. An optimized power allocation algorithm for a multi-user single-relay cooperative communication system, comprising the following steps:
the method comprises the following steps: establishing a generalized optimal solution frame structure model; the method comprises the following specific steps:
for a set of n elements phi { (S)1,D1),(S2,D2),…,(Sn,Dn)}，SnFor the nth source node user, D1,D2,…,DnFor n source node users S1,S2,…,SnCorresponding target node users, each source nodeThe user and the corresponding target node user form a user pair, and the user pair carries out signal transmission through the same relay node R, gi(xi,yi) Is an element (S)i,Di) Of the objective function of (1), it is for yiIs differentiable, i ═ 1,2, …, n; and xi＝fi(yi) Is yiA monotonically increasing function of (2), wherein xi>0，yi>0; the objective function of a system consisting of n elements is:
Figure FDA0002239599230000011
step two: establishing a source node user total power optimization problem model; the model of the problem of optimizing the total power of the source node user is as follows:
Figure FDA0002239599230000013
Figure FDA0002239599230000014
Figure FDA0002239599230000015
wherein the content of the first and second substances,
Figure FDA0002239599230000016
andfor normalizing the channel gain, the expression is
Figure FDA0002239599230000018
Figure FDA0002239599230000019
And
Figure FDA00022395992300000110
for corresponding channel coefficients, σ2Power of additive noise, ΓiIs a user pair (S)i,Di) The target signal-to-noise ratio of (c),
Figure FDA00022395992300000111
is a user pair (S)i,Di) The maximum transmit power that can be provided,maximum transmit power for the relay;
regardless of the direct link between the source node user and the destination node user, the signal is transmitted through the relay channel,
Figure FDA0002239599230000021
respectively source node subscriber S1,S2,…,SnThe transmit power of (a);
Figure FDA0002239599230000022
respectively allocated to source node users S for relays1,S2,…,SnThe relay forwarding power of (a);
step three: establishing a system total power optimization problem model; the model of the system total power optimization problem is as follows:
Figure FDA0002239599230000023
step four: based on a generalized optimal solution framework structure model, when a relay is powered by a fixed power supply, a source node user total power optimization problem model is solved, and power distribution is realized; when the relay does not have a fixed power supply to supply power or is a common mobile user, carrying out model solution on the system total power optimization problem to realize power distribution;
solving a source node user total power optimization problem model and a system total power optimization problem model on the basis of theorems 1,2 and 1;
theorem 1 is:
under the constraint condition
Figure FDA0002239599230000024
Wherein C is a constant greater than zero, provided that
Figure FDA0002239599230000025
The system overall optimizes the objective function G (x)1，y1，x2，y2，…，xn，yn) In thatTaking an extreme value;
theorem 2 is:
is provided with
Figure FDA0002239599230000027
Obtaining the extreme point for the system objective function in theorem 1, if the requirement satisfies the constraint condition xi≤xi,maxWherein x isi,maxIs a given constant, then, when
Figure FDA0002239599230000031
Then, only take xi＝xi,maxWhen, G (x)1,y1,…,xn,yn) The extreme value can be obtained;
the introduction 1 is: for xy ═ U where x and y are variables and U is a constant greater than zero, the constraint condition 0 is required to be satisfied<x≤xmaxAnd y>0, wherein xmaxIs a constant greater than zero; if it is
Figure FDA0002239599230000032
When x is equal to xmaxWhen x + y reaches a minimum value.
2. The optimized power distribution algorithm for multi-user single-relay cooperative communication system as claimed in claim 1, wherein in the fourth step, the solution of the source node user total power optimization problem model, i.e. the minimization of the source node user total power consumption, is implemented by:
step 1): initializing parameters: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0, and remaining relay power PΔFor maximum transmission power of relay
Figure FDA0002239599230000033
Namely, it is
Figure FDA0002239599230000034
Step 2): if Flag is true, go to step 3);
step 3): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1
Figure FDA0002239599230000035
And
Figure FDA0002239599230000036
and each user is respectively allocated with power to the source node and the relay
Figure FDA0002239599230000037
And
step 4): for each user pair in Φ, suppose the power provided by the source node user is less than the power of equal ratio, i.e.
Figure FDA0002239599230000039
Then according to theorem 2, the transmission power is obtained
Figure FDA00022395992300000310
And
Figure FDA00022395992300000311
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is 0, and the step 3) is returned to, and the steps 3) to 4) are executed in a loop until all the user pairs are allocated with power.
3. The optimized power distribution algorithm for multi-user single-relay cooperative communication system as claimed in claim 2, wherein in the fourth step, the solution of the system total power optimization problem model, i.e. the minimization of the system total power consumption, is implemented by:
step (1): initializing parameters: set phi { (S) for user pairs1,D1),…,(Sn,Dn) Let initialization Flag be 0;
step (2): obtaining the highest energy efficiency distribution power of each user pair according to the channel parameters of the user pairs without considering the transmission power limit of the user source node and the relay
Figure FDA0002239599230000041
And
Figure FDA0002239599230000042
namely, it is
Figure FDA0002239599230000043
And
Figure FDA0002239599230000044
and (3): for each user pair of Φ, according to
Figure FDA0002239599230000045
Andby dividing phi into two subsets Ψ and Ω, each element in Ψ satisfyingEach element of Ω satisfies
Figure FDA0002239599230000048
And (4): according to theorem 1, for an element of Ψ, let
Figure FDA0002239599230000049
For the elements in omega, let
Figure FDA00022395992300000410
And then find out
Figure FDA00022395992300000411
And (5): if the total relay power required is greater than or equal to the maximum power that the relay can provide, i.e. the total relay power required is less than or equal to
Figure FDA00022395992300000412
Let Flag be 0 and relay power P remainΔFor maximum transmission power of relay
Figure FDA00022395992300000413
Namely, it is
Figure FDA00022395992300000414
Entering the step (6) to carry out power distribution;
and (6): if Flag is true, go to step (7);
and (7): let Flag 1, for each user pair in Φ, get equal ratio distribution power according to theorem 1
Figure FDA00022395992300000415
And
Figure FDA00022395992300000416
and each user is respectively allocated with power to the source node and the relay
Figure FDA00022395992300000417
And
Figure FDA00022395992300000418
and (8): for each user pair in Φ, suppose the power provided by the source node user is less than the power of equal ratio, i.e.
Figure FDA00022395992300000419
Then according to theorem 2, the transmission power is obtained
Figure FDA00022395992300000420
And
Figure FDA00022395992300000421
and delete the user pair from Φ, i.e., Φ: ═ Φ - (S)i,Di) Updating the remaining relay power and the flag state, i.e. P, simultaneouslyΔ:＝PΔ-Pi RAnd Flag is equal to 0, the step (7) is returned, and the steps (7) to (8) are executed in a loop mode until all the user pairs are allocated with power.
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