CN115734252A - Cognitive wireless energy supply network optimization method based on backscattering relay transmission - Google Patents

Cognitive wireless energy supply network optimization method based on backscattering relay transmission Download PDF

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CN115734252A
CN115734252A CN202211516401.3A CN202211516401A CN115734252A CN 115734252 A CN115734252 A CN 115734252A CN 202211516401 A CN202211516401 A CN 202211516401A CN 115734252 A CN115734252 A CN 115734252A
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energy capture
backscattering
transmitter
energy
backscatter
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CN115734252B (en
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刘晓莹
蔺中葳
王家红
郑可琛
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Zhejiang University of Technology ZJUT
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Zhejiang University of Technology ZJUT
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses a cognitive wireless energy supply network optimization method based on backscattering relay transmission, which is characterized in that the working time slot of a cognitive wireless energy supply network is divided into a relay stage, a backscattering data transmission stage and an energy capture data transmission stage, a main transmitter sends data to a main receiver in the relay stage, a backscattering unit transmitter relays main user data to the main receiver in a backscattering mode, and an energy capture unit transmitter captures energy; in a backscattering data transmission stage, a main transmitter sends data to a main receiver, a backscattering unit transmitter sends data to a backscattering receiver in a backscattering mode, and an energy capture unit transmitter captures energy; during the energy capture data transmission phase, the primary transmitter is dormant and the energy capture element transmitter transmits data to the energy capture receiver using the captured energy. The invention optimizes the duration of each stage and improves the throughput of data transmission.

Description

Cognitive wireless energy supply network optimization method based on backscattering relay transmission
Technical Field
The application belongs to the technical field of cognitive wireless energy supply communication, and particularly relates to a cognitive wireless energy supply network optimization method based on backscattering relay transmission.
Background
In a cognitive wireless power supply network, a primary user and a secondary user are generally included. The master user has the authorization of the frequency spectrum and can access the frequency spectrum at any time. The secondary user does not have the authorized spectrum and can only randomly access the authorized spectrum in the idle period of the authorized spectrum. The primary user typically has a constant power supply, such as a television, cell phone, etc. The secondary user is typically not powered by a fixed power source and therefore needs to capture energy from the surrounding environment and store the captured energy in the secondary user's rechargeable battery. The secondary user stores energy for use in transmitting data.
Furthermore, in recent years, backscatter technology has been extensively studied in cognitive wireless power supply networks due to its low power consumption. The device equipped with the backscatter unit may transmit its own data via a radio frequency signal in the backscatter network, or may act as a backscatter relay device relaying via backscatter.
In this network, how to allocate when and how long secondary users will perform backscatter relaying, energy capture, and secondary data transmission to maximize network throughput is a major consideration.
Disclosure of Invention
In order to improve the throughput in the existing cognitive wireless energy supply network based on the backscatter relay, the method for optimizing the throughput of the cognitive wireless energy supply network based on the backscatter relay transmission is provided. According to the method, the primary user data is relayed by the secondary user, the problem of throughput maximization of the network is effectively solved, the frequency spectrum utilization rate of the network is improved, and the energy consumption of the network is greatly saved by adopting an energy capture technology and a backscattering technology.
In order to achieve the purpose, the technical scheme of the application is as follows:
a cognitive wireless power supply network optimization method based on backscattering relay transmission comprises a main transmitter and a main receiver corresponding to a main user, a secondary transmitter and an energy capture receiver which are provided with energy capture units and correspond to a first secondary user, and a secondary transmitter and a backscattering receiver which are provided with backscattering units and correspond to a second secondary user, the cognitive wireless power supply network optimization method based on backscattering relay transmission comprises the following steps:
dividing the working time slot of the cognitive wireless energy supply network into a relay stage, a backscattering data transmission stage and an energy capture data transmission stage;
the method comprises the steps that a primary transmitter sends data to a primary receiver in a relay stage, a secondary transmitter provided with a backscattering unit relays primary user data to the primary receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
in a backscattering data transmission stage, a main transmitter sends data to a main receiver, a secondary transmitter provided with a backscattering unit sends data to a backscattering receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
during the energy capture data transmission phase, the primary transmitter is dormant and a secondary transmitter equipped with an energy capture unit transmits data to the energy capture receiver using the captured energy.
Further, the cognitive wireless energy supply network optimization method based on the backscatter relay transmission further includes:
the durations of the relay phase, the backscatter data transmission phase and the energy capture data transmission phase are respectively expressed as:
Figure 318676DEST_PATH_IMAGE001
,
Figure 235817DEST_PATH_IMAGE002
and
Figure 828603DEST_PATH_IMAGE003
on the premise of meeting the target throughput of the primary user, an optimization model is constructed by taking the maximization of the total throughput of the secondary user as a target
Figure 223812DEST_PATH_IMAGE004
：
Figure 100501DEST_PATH_IMAGE005
；
The following constraints are satisfied:
Figure 922964DEST_PATH_IMAGE006
；
Figure 501582DEST_PATH_IMAGE007
；
wherein,
Figure 762799DEST_PATH_IMAGE008
is shown in the energy capture data transmission phase
Figure 431678DEST_PATH_IMAGE009
The throughput produced by a secondary transmitter equipped with an energy capture unit,
Figure 238091DEST_PATH_IMAGE010
is shown in the backscatter data transmission phase, first
Figure 992420DEST_PATH_IMAGE011
The throughput produced by a secondary transmitter equipped with a backscatter unit,Mindicating the number of secondary transmitters equipped with a backscatter unit,Nrepresenting the number of secondary transmitters equipped with energy capture units,
Figure 791749DEST_PATH_IMAGE012
；
Figure 956797DEST_PATH_IMAGE013
is shown in the energy capture data transmission phase
Figure 448958DEST_PATH_IMAGE009
The time to which each secondary transmitter equipped with an energy capture unit is allocated;
Figure 690583DEST_PATH_IMAGE014
in the energy capture data transmission phase, the first
Figure 778756DEST_PATH_IMAGE009
A throughput generated by a secondary transmitter equipped with an energy capture unit;
Figure 422227DEST_PATH_IMAGE015
in the backscatter data transmission phase, the first
Figure 819710DEST_PATH_IMAGE011
The throughput produced by a secondary transmitter equipped with a backscatter unit,
Figure 548632DEST_PATH_IMAGE016
the back-scattering coefficient of the light beam,
Figure 939031DEST_PATH_IMAGE017
representing the transmit power of the primary transmitter;
Figure 437008DEST_PATH_IMAGE018
represents the throughput achieved at the primary receiver during the relay phase;
Figure 270972DEST_PATH_IMAGE019
representing the throughput achieved at the primary receiver during the backscatter data transmission phase;
Figure 34660DEST_PATH_IMAGE020
representing the target throughput of a master user in each time slot;
Figure 917165DEST_PATH_IMAGE021
represents the channel bandwidth;
Figure 331966DEST_PATH_IMAGE022
representing the ambient noise power;
Figure 586099DEST_PATH_IMAGE023
denotes the first
Figure 24033DEST_PATH_IMAGE009
Energy captured by a secondary transmitter equipped with an energy capture unit while the licensed spectrum is busy,
Figure 506967DEST_PATH_IMAGE024
represents the energy capture efficiency;
Figure 979537DEST_PATH_IMAGE025
represents from the first
Figure 906036DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit to an energy capture receiver;
Figure 893583DEST_PATH_IMAGE026
represents from the first
Figure 117891DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit to a backscatter receiver;
Figure 490973DEST_PATH_IMAGE027
indicating from the primary transmitter to the secondary
Figure 775324DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit;
Figure 250167DEST_PATH_IMAGE028
indicating from the primary transmitter to the secondary
Figure 91215DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit;
Figure 272798DEST_PATH_IMAGE029
represents from the first
Figure 524788DEST_PATH_IMAGE009
Channel gain of a secondary transmitter to a primary receiver equipped with a backscatter unit;
Figure 424611DEST_PATH_IMAGE030
representing the channel gain from the primary transmitter to the primary receiver;
and solving the optimal solution of the optimization model to obtain the duration of the relay stage, the backscattering data transmission stage and the energy capture data transmission stage.
Further, the solving the optimal solution of the optimization model includes:
will optimize variables
Figure 308165DEST_PATH_IMAGE001
Is converted into
Figure 609833DEST_PATH_IMAGE031
Bringing into the optimization model to obtain the optimization model
Figure 32724DEST_PATH_IMAGE032
：
Figure 419843DEST_PATH_IMAGE033
；
List (a)
Figure 602694DEST_PATH_IMAGE032
Lagrange function of (a), as follows:
Figure 493290DEST_PATH_IMAGE034
wherein:
Figure 352661DEST_PATH_IMAGE035
；
Figure 961497DEST_PATH_IMAGE036
；
Figure 446574DEST_PATH_IMAGE037
；
Figure 519572DEST_PATH_IMAGE038
lagrange multiplier;
by relating to lagrange functions
Figure 487528DEST_PATH_IMAGE002
The first partial derivative of (1) is made zero to obtain
Figure 396710DEST_PATH_IMAGE002
The expression of (c) is as follows:
Figure 108314DEST_PATH_IMAGE039
； （1）
wherein
Figure 35819DEST_PATH_IMAGE040
Is shown if
Figure 174676DEST_PATH_IMAGE041
Then, then
Figure 69688DEST_PATH_IMAGE042
And if not, the step (B),
Figure 319404DEST_PATH_IMAGE043
；
by relating to lagrange functions
Figure 366995DEST_PATH_IMAGE013
The first order partial derivative is made to be zero to obtain
Figure 676753DEST_PATH_IMAGE013
The expression of (c) is as follows:
Figure 560527DEST_PATH_IMAGE044
wherein
Figure 613933DEST_PATH_IMAGE045
；
The expression for the lagrange multiplier update is as follows:
Figure 516030DEST_PATH_IMAGE046
； （3）
Figure 996690DEST_PATH_IMAGE047
； （4）
then solving the optimization model
Figure 600716DEST_PATH_IMAGE032
The method comprises the following steps:
step 4.1: setup initialization
Figure 520130DEST_PATH_IMAGE002
，
Figure 214417DEST_PATH_IMAGE048
And all are greater than or equal to 0, initializing the number of iterations
Figure 679027DEST_PATH_IMAGE049
；
And 4.2: judgment of
Figure 724344DEST_PATH_IMAGE050
If the number exceeds N, if not, updating by adopting a binary search algorithm
Figure 181870DEST_PATH_IMAGE051
By fixing
Figure 730663DEST_PATH_IMAGE002
，
Figure 124430DEST_PATH_IMAGE052
The value of (a) is,
Figure 391463DEST_PATH_IMAGE053
then jump to step 4.2; otherwise, jumping to step 4.3;
step 4.3: by fixing
Figure 652680DEST_PATH_IMAGE054
Updating based on equation (1)
Figure 55979DEST_PATH_IMAGE002
A value of (d);
step 4.4: by fixing
Figure 127972DEST_PATH_IMAGE002
，
Figure 944618DEST_PATH_IMAGE055
Updating based on equation (3)
Figure 681630DEST_PATH_IMAGE056
；
Step 4.5: by fixing
Figure 516600DEST_PATH_IMAGE002
，
Figure 946444DEST_PATH_IMAGE057
Updating based on equation (4)
Figure 984807DEST_PATH_IMAGE058
；
Step 4.6: judging whether all variables are converged, if yes, jumping to a step 4.7; otherwise, jumping to step 4.2;
step 4.7: outputting an optimal solution
Figure 338559DEST_PATH_IMAGE059
Optimal solution
Figure 982030DEST_PATH_IMAGE060
。
Further, the updating by the binary search algorithm
Figure 379513DEST_PATH_IMAGE051
The method comprises the following steps:
step 3.1: inputting an upper bound value
Figure 108435DEST_PATH_IMAGE061
Setting a lower bound value
Figure 498834DEST_PATH_IMAGE062
Will be
Figure 996811DEST_PATH_IMAGE063
Substitution
Figure 565196DEST_PATH_IMAGE051
Into lagrange pairs
Figure 781414DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 476968DEST_PATH_IMAGE064
；
Step 3.2: the number of cycles is set to
Figure 829452DEST_PATH_IMAGE065
Initial value is 1, judge solution
Figure 834317DEST_PATH_IMAGE066
Whether or not less than
Figure 583836DEST_PATH_IMAGE067
，
Figure 4453DEST_PATH_IMAGE067
A very small number, if so, jump to step 3.5, otherwise jump to step 3.3,
Figure 539340DEST_PATH_IMAGE068
；
step 3.3: judgment of
Figure 387210DEST_PATH_IMAGE069
If it is greater than 0, if so, then
Figure 125490DEST_PATH_IMAGE070
And if not, the step (B),
Figure 729559DEST_PATH_IMAGE071
；
step 3.4: will be provided with
Figure 118952DEST_PATH_IMAGE063
Substitution
Figure 403303DEST_PATH_IMAGE051
Into lagrange pairs
Figure 628879DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 391299DEST_PATH_IMAGE069
Jumping to step 3.2;
step 3.5: obtained at this time
Figure 635198DEST_PATH_IMAGE063
Is composed of
Figure 90450DEST_PATH_IMAGE051
The solution of (1).
According to the technical scheme, in the cognitive wireless energy supply network, a transmitter provided with a backscattering unit relays main user data in a backscattering mode, then transmits own data, and the transmitter provided with an energy capturing unit captures radio frequency energy and then transmits the data by using the captured energy. By optimizing the time of relay, energy capture and data transmission, the total throughput of the secondary users is maximized, and therefore the spectrum utilization rate and the energy efficiency of the network are improved. Here, the precondition to be considered is that the primary user needs to satisfy its target throughput in one time slot, and when the primary user is idle, the secondary user can access the licensed spectrum. Then, the problem is transformed through monotonicity analysis of the total throughput problem, the problem is proved to be a convex optimization problem, then Lagrange function is used for solving partial derivatives, optimization change is obtained through a binary search algorithm to obtain an expression, and then the problem is solved through an optimal solution iterative algorithm. Therefore, the primary user is guaranteed to realize the maximization of the total throughput of the secondary user on the premise of meeting the target throughput of the primary user.
Drawings
FIG. 1 is a schematic diagram of a cognitive wireless power supply network in a middle stage;
FIG. 2 is a schematic diagram of a cognitive wireless power supply network in a backscatter data transmission phase according to the present application;
FIG. 3 is a schematic diagram of a cognitive wireless energy supply network of the present application during a data transmission phase of energy capture;
fig. 4 is a diagram illustrating single timeslot division according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As shown in fig. 1 to 3, the cognitive radio-enabled network primary user based on the backscatter relay transmission comprises a primary transmitter and a primary receiver. The secondary users are divided into two types, wherein one type of secondary user comprises N secondary transmitters (called energy capturing unit transmitters) provided with energy capturing units and one secondary receiver (energy capturing receiver), and the energy capturing receiver is only responsible for receiving data of the energy capturing unit transmitters; another type of secondary user comprises M secondary transmitters equipped with backscatter units (called backscatter unit transmitters) and a secondary receiver (backscatter receiver) which is only responsible for receiving the data of the backscatter unit transmitter.
In one embodiment, a cognitive wireless energy supply network optimization method based on backscatter relay transmission comprises the following steps:
dividing the working time slot of the cognitive wireless energy supply network into a relay stage, a backscattering data transmission stage and an energy capture data transmission stage;
the method comprises the steps that a primary transmitter sends data to a primary receiver in a relay stage, a secondary transmitter provided with a backscattering unit relays primary user data to the primary receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
in a backscattering data transmission stage, a main transmitter sends data to a main receiver, a secondary transmitter provided with a backscattering unit sends data to a backscattering receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
during the energy capture data transmission phase, the primary transmitter is dormant and a secondary transmitter, equipped with an energy capture unit, transmits data to the energy capture receiver using the captured energy.
Specifically, as shown in fig. 1 to 3, the relay stage:
the main transmitter sends data to the main receiver, i.e. the spectrum is busy. M secondary transmitters equipped with backscatter units relay primary user data to the primary receiver using backscatter mode. And the N secondary transmitters provided with energy capture units carry out energy capture.
And in a backscattering data transmission stage, the authorized frequency spectrum is busy, and the main transmitter transmits data to the main receiver. M secondary transmitters equipped with backscatter units transmit data to backscatter receivers in backscatter mode. And the N secondary transmitters only provided with the energy capture units carry out energy capture.
Since the secondary transmitter equipped with the backscatter unit relays data of the main transmitter, the main transmitter can satisfy its target throughput in one time slot in advance.
And in the energy capture data transmission phase, the authorized spectrum is in an idle state, and the main transmitter sleeps. The N secondary transmitters equipped with energy capture units transmit data to the energy capture receivers using the captured energy.
The durations of the relay phase, the backscatter data transmission phase and the energy capture data transmission phase of the present embodiment are respectively expressed as:
Figure 301858DEST_PATH_IMAGE001
,
Figure 867968DEST_PATH_IMAGE002
and
Figure 966374DEST_PATH_IMAGE003
as shown in fig. 4. Due to relay phase duration
Figure 592528DEST_PATH_IMAGE001
The increase in (b) may cause the primary user to meet the target throughput more quickly, the licensed spectrum to be busy for a reduced period of time, and the backscatter data transmission phase
Figure 792696DEST_PATH_IMAGE002
Reduce, therefore equipped withThe time for which the secondary transmitter of the energy capturing unit captures energy is reduced.
Figure 896918DEST_PATH_IMAGE001
,
Figure 115410DEST_PATH_IMAGE002
And
Figure 224049DEST_PATH_IMAGE003
there is a trade-off between the need for optimization to get the optimum proportion of time allocation.
In a specific embodiment, the cognitive wireless energy supply network optimization method based on backscatter relay transmission further includes:
step F1, respectively representing the duration of the relay phase, the backscatter data transmission phase and the energy capture data transmission phase as:
Figure 832885DEST_PATH_IMAGE001
,
Figure 68694DEST_PATH_IMAGE002
and
Figure 79375DEST_PATH_IMAGE003
on the premise of meeting the target throughput of the primary user, an optimization model is constructed by taking the maximization of the total throughput of the secondary user as a target
Figure 47331DEST_PATH_IMAGE004
：
Figure 956513DEST_PATH_IMAGE072
Wherein,
Figure 402538DEST_PATH_IMAGE008
is shown in the energy capture data transmission phase
Figure 595622DEST_PATH_IMAGE009
The throughput produced by a secondary transmitter equipped with an energy capture unit,
Figure 734479DEST_PATH_IMAGE010
is shown in the backscatter data transmission phase, the first
Figure 629491DEST_PATH_IMAGE011
The throughput produced by a secondary transmitter equipped with a backscatter unit,Mindicating the number of secondary transmitters equipped with backscatter units,Nrepresenting the number of secondary transmitters equipped with energy capture units.
In the embodiment, the relay stage is optimized through combination on the premise of meeting the target throughput of the master user
Figure 879207DEST_PATH_IMAGE001
The backscatter data transmission phase
Figure 926798DEST_PATH_IMAGE002
And duration of energy capture data transmission phase
Figure 236556DEST_PATH_IMAGE003
And the total throughput of the secondary user is maximized.
During the phase of the transmission of the backscatter data,Ma secondary transmitter equipped with a backscatter unit for transmitting data to a backscatter receiver in a backscatter mode, comprising:Mthe secondary transmitters having backscatter units assigned to transmit data of the same duration, i.e.
Figure DEST_PATH_IMAGE073
。
During the energy capture data transmission phase of the process,Na secondary transmitter equipped with only an energy capture unit for transmitting data to an energy capture receiver using captured energy, comprising:Na secondary transmitter equipped with an energy capture unit transmits data in a Time Division Multiple Access (TDMA) manner. The time to which each transmitter is assigned is represented as:
Figure 120330DEST_PATH_IMAGE074
。
the overall throughput maximization of the secondary users is achieved, and is expressed as the following mathematical optimization model:
Figure 236053DEST_PATH_IMAGE075
the constraint conditions of the optimization model of the embodiment are as follows:
Figure 75833DEST_PATH_IMAGE006
,
Figure 885656DEST_PATH_IMAGE076
. Variables to be optimized:
Figure DEST_PATH_IMAGE077
。
in the above optimization model, the parameters are described as follows:
Figure 240414DEST_PATH_IMAGE001
: the duration of the relay phase, in seconds;
Figure 97511DEST_PATH_IMAGE002
: the duration of the backscatter data transmission, in seconds;
Figure 339268DEST_PATH_IMAGE003
: the duration of the energy capture data transmission phase, in seconds;
Figure 990829DEST_PATH_IMAGE013
: in the energy capture data transmission phase
Figure 364042DEST_PATH_IMAGE009
The time allocated to each secondary transmitter equipped with an energy capture unit is in seconds;
Figure 759251DEST_PATH_IMAGE014
: in the energy capture data transmission phase
Figure 619628DEST_PATH_IMAGE009
The throughput, in bits per second, produced by a secondary transmitter equipped with an energy capture unit;
Figure 707670DEST_PATH_IMAGE015
: in the backscatter data transmission phase, the first
Figure 37020DEST_PATH_IMAGE011
The throughput, in bits per second,
Figure 970341DEST_PATH_IMAGE016
the back-scattering coefficient of the light beam,
Figure 452269DEST_PATH_IMAGE017
representing the transmit power of the primary transmitter;
Figure 711212DEST_PATH_IMAGE018
: in the relay phase, the throughput, in bits per second, achieved at the primary receiver;
Figure 996700DEST_PATH_IMAGE019
: the throughput, in bits per second, achieved at the primary receiver during the backscatter data transmission phase;
Figure 999291DEST_PATH_IMAGE020
: target throughput of primary user in each time slot, unit isBits per second;
Figure 99840DEST_PATH_IMAGE021
: channel bandwidth in hertz;
Figure 264105DEST_PATH_IMAGE022
: ambient noise power in watts;
Figure 568048DEST_PATH_IMAGE023
: first, the
Figure 656220DEST_PATH_IMAGE009
The energy captured by a secondary transmitter provided with an energy capture unit when the authorized spectrum is busy is in joules;
Figure 34112DEST_PATH_IMAGE024
: an energy capture efficiency;
Figure 962754DEST_PATH_IMAGE025
: from the first
Figure 472102DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit to an energy capture receiver;
Figure 816495DEST_PATH_IMAGE026
: from the first
Figure 642369DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit to a backscatter receiver;
Figure 148436DEST_PATH_IMAGE027
: from the main transmitter to the second
Figure 177703DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit;
Figure 856946DEST_PATH_IMAGE028
: from the main transmitter to the second
Figure 209430DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit;
Figure 469422DEST_PATH_IMAGE029
: from the first
Figure 704095DEST_PATH_IMAGE009
Channel gain of a secondary transmitter to a primary receiver equipped with a backscatter unit;
Figure 390291DEST_PATH_IMAGE030
: channel gain from the primary transmitter to the primary receiver.
In the present embodiment, the constraint conditions are explained as follows:
Figure 410331DEST_PATH_IMAGE006
: the embodiment normalizes the length of one time slot to 1 second, and the sum of the time of three stages does not exceed the length of one time slot. For time slots with other lengths, the method provided by the application is still applicable, and only the time lengths of the three stages are obtained in equal proportion according to the length of the time slots;
Figure 523780DEST_PATH_IMAGE078
: the duration of each phase is non-negative;
Figure 776907DEST_PATH_IMAGE079
: in each time slot, the throughput of the primary user at least meets the target throughput.
And F2, solving the optimal solution of the optimization model to obtain the duration of the relay stage, the backscattering data transmission stage and the energy capture data transmission stage.
The embodiment passes through
Figure 1215DEST_PATH_IMAGE004
The problems are respectively related to
Figure 374296DEST_PATH_IMAGE001
，
Figure 658647DEST_PATH_IMAGE002
And
Figure 133491DEST_PATH_IMAGE013
and lists its Hessian matrix, which is found to be semi-negative definite, so one can get
Figure 161490DEST_PATH_IMAGE004
The problem is a convex optimization problem.
Suppose that
Figure 890542DEST_PATH_IMAGE004
The optimal solution to the problem is
Figure 345795DEST_PATH_IMAGE080
When the constraint is satisfied
Figure 307934DEST_PATH_IMAGE081
。
Suppose that
Figure 874045DEST_PATH_IMAGE004
A feasible solution to the problem is
Figure 487298DEST_PATH_IMAGE082
And is and
Figure 113451DEST_PATH_IMAGE083
。
due to the fact that
Figure 31729DEST_PATH_IMAGE004
Problems about
Figure 401530DEST_PATH_IMAGE002
Is greater than 0, so that the throughput achieved by the feasible solution is greater than that of the optimal solution, which contradicts the optimal solution.
Thus, by the above conclusion
Figure 370754DEST_PATH_IMAGE004
Constraint satisfaction when the problem gets the optimal solution
Figure 167809DEST_PATH_IMAGE084
. Optimizing variables at this time
Figure 838962DEST_PATH_IMAGE001
Is converted into
Figure 746875DEST_PATH_IMAGE031
。
Based on the above-mentioned proof that,
Figure 69141DEST_PATH_IMAGE004
problem transformation into
Figure 37097DEST_PATH_IMAGE032
The problem is as follows:
Figure 461125DEST_PATH_IMAGE085
wherein the constraint conditions are as follows:
Figure 172729DEST_PATH_IMAGE086
is converted intoThe optimized variables are:
Figure 850966DEST_PATH_IMAGE002
，
Figure 989823DEST_PATH_IMAGE087
due to the fact that
Figure 369989DEST_PATH_IMAGE004
The problem is a convex optimization problem, thus
Figure 885284DEST_PATH_IMAGE032
The problem is also a convex optimization problem.
When solving, list
Figure 176283DEST_PATH_IMAGE032
The lagrange function of the problem is as follows:
Figure 486041DEST_PATH_IMAGE088
。
the following describes the various parameters in the lagrange function as follows:
Figure 353503DEST_PATH_IMAGE035
；
Figure 406910DEST_PATH_IMAGE036
；
Figure 59739DEST_PATH_IMAGE037
；
Figure 540399DEST_PATH_IMAGE038
: lagrange multipliers.
By relating to lagrange functions
Figure 160736DEST_PATH_IMAGE002
The first order partial derivative of (2) is made zero, so that the first order partial derivative can be obtained
Figure 752254DEST_PATH_IMAGE002
The expression of (c) is as follows:
Figure 23705DEST_PATH_IMAGE039
； (1)
wherein
Figure 675266DEST_PATH_IMAGE040
Is shown if
Figure 517320DEST_PATH_IMAGE041
Then, then
Figure 912529DEST_PATH_IMAGE042
And if not, the step (B),
Figure 539951DEST_PATH_IMAGE043
。
by relating to lagrange functions
Figure 362413DEST_PATH_IMAGE013
The first order partial derivative is made zero to obtain the first order partial derivative
Figure 691763DEST_PATH_IMAGE013
The expression of (c) is as follows:
Figure 890664DEST_PATH_IMAGE044
wherein
Figure 871127DEST_PATH_IMAGE045
The first partial derivative was found to be monotonically decreasing and difficult to obtain due to transcendental functions
Figure 864491DEST_PATH_IMAGE013
Closed type watchExpression, so a binary search algorithm is used to solve
Figure 681137DEST_PATH_IMAGE013
。
The expression for the lagrange multiplier update is as follows:
Figure 683728DEST_PATH_IMAGE089
Figure 20163DEST_PATH_IMAGE090
wherein
Figure 184428DEST_PATH_IMAGE091
The number of iterations is indicated and,
Figure 488370DEST_PATH_IMAGE092
，
Figure 29073DEST_PATH_IMAGE093
to update the step size.
This example
Figure 718549DEST_PATH_IMAGE004
The solution idea of the problem is as follows: firstly, the method is to
Figure 319295DEST_PATH_IMAGE094
Is converted into
Figure 844954DEST_PATH_IMAGE032
A problem is solved; secondly, because of
Figure 189348DEST_PATH_IMAGE094
To convex optimization problem, therefore
Figure 234795DEST_PATH_IMAGE032
The problem is also a convex optimization problem. To solve for
Figure 6442DEST_PATH_IMAGE032
Solving the problem by providing an optimal solution iterative algorithm; respectively updating the variable to be optimized and the Lagrangian multiplier by a block coordinate descent method and a gradient descent method until the variable to be optimized and the Lagrangian multiplier are converged, so as to obtain the solution
Figure 19397DEST_PATH_IMAGE002
，
Figure 901903DEST_PATH_IMAGE003
I.e. by
Figure 571830DEST_PATH_IMAGE094
The global optimum solution of (2).
This example for solving
Figure 514379DEST_PATH_IMAGE032
The problem adopts an optimal solution iterative algorithm, and the steps are as follows:
step 4.1: setup initialization
Figure 14630DEST_PATH_IMAGE002
，
Figure 700826DEST_PATH_IMAGE048
And all are greater than or equal to 0, initializing the number of iterations
Figure 720866DEST_PATH_IMAGE049
。
Step 4.2: judgment of
Figure 834316DEST_PATH_IMAGE050
If the number exceeds N, if not, updating by adopting a binary search algorithm
Figure 821863DEST_PATH_IMAGE051
By fixing
Figure 46171DEST_PATH_IMAGE002
，
Figure 684832DEST_PATH_IMAGE052
The value of (a) is,
Figure 703603DEST_PATH_IMAGE053
and then jumps to step 4.2. Otherwise, go to step 4.3.
Step 4.3: by fixing
Figure 178447DEST_PATH_IMAGE054
Updating based on equation (1)
Figure 206446DEST_PATH_IMAGE002
The value of (c).
Step 4.4: by fixing
Figure 201078DEST_PATH_IMAGE002
，
Figure 656330DEST_PATH_IMAGE055
Updating based on equation (3)
Figure 352891DEST_PATH_IMAGE056
。
Step 4.5: by fixing
Figure 919001DEST_PATH_IMAGE002
，
Figure 532254DEST_PATH_IMAGE057
Updating based on equation (4)
Figure 158407DEST_PATH_IMAGE058
。
Step 4.6: judging whether all variables are converged, if yes, jumping to a step 4.7; otherwise, go to step 4.2.
Step 4.7: outputting an optimal solution
Figure 342264DEST_PATH_IMAGE059
Optimal solution
Figure 712065DEST_PATH_IMAGE060
。
Wherein for solving
Figure 681290DEST_PATH_IMAGE051
The adopted binary search algorithm comprises the following steps:
step 3.1: inputting an upper bound value
Figure 478344DEST_PATH_IMAGE061
Setting a lower bound value
Figure 149497DEST_PATH_IMAGE062
Will be
Figure 57410DEST_PATH_IMAGE063
Substitution
Figure 379676DEST_PATH_IMAGE051
Into lagrange pairs
Figure 347632DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 506081DEST_PATH_IMAGE064
；
Step 3.2: the number of cycles is set to
Figure 217685DEST_PATH_IMAGE065
Initial value is 1, judge solution
Figure 895922DEST_PATH_IMAGE066
Whether or not less than
Figure 34779DEST_PATH_IMAGE067
，
Figure 680524DEST_PATH_IMAGE067
A very small number, if so, jump to step 3.5, otherwise jump to step 3.3,
Figure 195819DEST_PATH_IMAGE068
；
step 3.3: judgment of
Figure 244676DEST_PATH_IMAGE069
If it is greater than 0, if so, then
Figure 554435DEST_PATH_IMAGE070
If not, then,
Figure 687476DEST_PATH_IMAGE071
；
step 3.4: will be provided with
Figure 740883DEST_PATH_IMAGE063
Substitution
Figure 393712DEST_PATH_IMAGE051
Brought into lagrange function pairs
Figure 874372DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 494709DEST_PATH_IMAGE069
Jumping to step 3.2;
step 3.5: obtained at this time
Figure 86227DEST_PATH_IMAGE063
Is composed of
Figure 92098DEST_PATH_IMAGE051
The solution of (1).
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims (4)

1. A cognitive wireless power supply network optimization method based on backscattering relay transmission is characterized in that the cognitive wireless power supply network comprises a main transmitter and a main receiver corresponding to a main user, a secondary transmitter and an energy capture receiver which are provided with energy capture units and correspond to a first secondary user, and a secondary transmitter and a backscattering receiver which are provided with backscattering units and correspond to a second secondary user, and the cognitive wireless power supply network optimization method based on backscattering relay transmission comprises the following steps:
dividing the working time slot of the cognitive wireless energy supply network into a relay stage, a backscattering data transmission stage and an energy capture data transmission stage;
the method comprises the steps that a primary transmitter sends data to a primary receiver in a relay stage, a secondary transmitter provided with a backscattering unit relays primary user data to the primary receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
in a backscattering data transmission stage, a main transmitter sends data to a main receiver, a secondary transmitter provided with a backscattering unit sends data to a backscattering receiver in a backscattering mode, and a secondary transmitter provided with an energy capture unit captures energy;
during the energy capture data transmission phase, the primary transmitter is dormant and a secondary transmitter, equipped with an energy capture unit, transmits data to the energy capture receiver using the captured energy.
2. The backscatter relay transmission-based cognitive wireless power supply network optimization method according to claim 1, wherein the backscatter relay transmission-based cognitive wireless power supply network optimization method further comprises:
the duration of the relay phase, the backscatter data transmission phase and the energy capture data transmission phase are represented as:
Figure 641761DEST_PATH_IMAGE001
,
Figure 421499DEST_PATH_IMAGE002
and
Figure 879025DEST_PATH_IMAGE003
on the premise of meeting the target throughput of the primary user, an optimization model is constructed by taking the maximization of the total throughput of the secondary user as a target
Figure 693397DEST_PATH_IMAGE004
：
Figure 328909DEST_PATH_IMAGE005
；
The following constraints are satisfied:
Figure 658259DEST_PATH_IMAGE006
；
Figure 168744DEST_PATH_IMAGE007
；
wherein,
Figure 837622DEST_PATH_IMAGE008
is shown in the energy capture data transmission phase
Figure 893303DEST_PATH_IMAGE009
The throughput produced by a secondary transmitter equipped with an energy capture unit,
Figure 647632DEST_PATH_IMAGE010
is shown in the backscatter data transmission phase, the first
Figure 384644DEST_PATH_IMAGE011
The throughput produced by a secondary transmitter equipped with a backscatter unit,Mindicating the number of secondary transmitters equipped with backscatter units,Nrepresenting the number of secondary transmitters equipped with energy capture units,
Figure 721079DEST_PATH_IMAGE012
；
Figure 150923DEST_PATH_IMAGE013
is shown in the energy capture data transmission phase
Figure 454866DEST_PATH_IMAGE009
The time to which each secondary transmitter equipped with an energy capture unit is allocated;
Figure 729989DEST_PATH_IMAGE014
in the energy capture data transmission phase, the first
Figure 685044DEST_PATH_IMAGE009
A throughput generated by a secondary transmitter equipped with an energy capture unit;
Figure 20211DEST_PATH_IMAGE015
in the backscatter data transmission phase, first
Figure 811449DEST_PATH_IMAGE011
The throughput produced by a secondary transmitter equipped with a backscatter unit,
Figure 890264DEST_PATH_IMAGE016
the back-scattering coefficient of the light beam,
Figure 935711DEST_PATH_IMAGE017
representing the transmit power of the primary transmitter;
Figure 707358DEST_PATH_IMAGE018
represents the throughput achieved at the primary receiver during the relay phase;
Figure 985893DEST_PATH_IMAGE019
representing the throughput achieved at the primary receiver during the backscatter data transmission phase;
Figure 868398DEST_PATH_IMAGE020
representing the target throughput of a master user in each time slot;
Figure 532467DEST_PATH_IMAGE021
represents the channel bandwidth;
Figure 475015DEST_PATH_IMAGE022
representing the ambient noise power;
Figure 975266DEST_PATH_IMAGE023
denotes the first
Figure 395883DEST_PATH_IMAGE009
Energy captured by a secondary transmitter equipped with an energy capture unit while the licensed spectrum is busy,
Figure 681502DEST_PATH_IMAGE024
representing the efficiency of energy capture;
Figure 794952DEST_PATH_IMAGE025
represents from the first
Figure 516920DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit to an energy capture receiver;
Figure 741228DEST_PATH_IMAGE026
represents from the first
Figure 374029DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit to a backscatter receiver;
Figure 658380DEST_PATH_IMAGE027
indicating from the primary transmitter to the secondary
Figure 133224DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with a backscatter unit;
Figure 161223DEST_PATH_IMAGE028
indicating from the primary transmitter to the secondary
Figure 890275DEST_PATH_IMAGE009
Channel gain of a secondary transmitter equipped with an energy capture unit;
Figure 79948DEST_PATH_IMAGE029
represents from the first
Figure 307667DEST_PATH_IMAGE009
A channel gain from a secondary transmitter to a primary receiver equipped with a backscatter unit;
Figure 873778DEST_PATH_IMAGE030
representing the channel gain from the primary transmitter to the primary receiver;
and solving the optimal solution of the optimization model to obtain the duration of the relay stage, the backscattering data transmission stage and the energy capture data transmission stage.
3. The backscatter relay transmission-based cognitive wireless energy supply network optimization method according to claim 2, wherein the solving of the optimal solution of the optimization model comprises:
will optimize variables
Figure 221451DEST_PATH_IMAGE001
Is converted into
Figure 909922DEST_PATH_IMAGE031
Bringing into the optimization model to obtain the optimization model
Figure 297041DEST_PATH_IMAGE032
：
Figure 479892DEST_PATH_IMAGE033
；
List (a)
Figure 370487DEST_PATH_IMAGE032
Lagrange function of (a), as follows:
Figure 964280DEST_PATH_IMAGE034
wherein:
Figure 573115DEST_PATH_IMAGE035
；
Figure 323771DEST_PATH_IMAGE036
；
Figure 334453DEST_PATH_IMAGE037
；
Figure 99146DEST_PATH_IMAGE038
lagrange multiplier;
by relating to lagrange functions
Figure 8328DEST_PATH_IMAGE002
The first partial derivative of (1) is made zero to obtain
Figure 47828DEST_PATH_IMAGE002
The expression of (c) is as follows:
Figure 913016DEST_PATH_IMAGE039
； （1）
wherein
Figure 97878DEST_PATH_IMAGE040
Is shown if
Figure 681306DEST_PATH_IMAGE041
Then, then
Figure 258918DEST_PATH_IMAGE042
And if not, the step (B),
Figure 244192DEST_PATH_IMAGE043
；
by relating to lagrange functions
Figure 101421DEST_PATH_IMAGE013
The first order partial derivative is made to be zero to obtain
Figure 172145DEST_PATH_IMAGE013
The expression of (c) is as follows:
Figure 287868DEST_PATH_IMAGE044
wherein
Figure 127648DEST_PATH_IMAGE045
；
The expression for the lagrange multiplier update is as follows:
Figure 191331DEST_PATH_IMAGE046
； （3）
Figure 280510DEST_PATH_IMAGE047
； （4）
then solving the optimization model
Figure 137608DEST_PATH_IMAGE032
The method comprises the following steps:
step 4.1: setup initialization
Figure 644943DEST_PATH_IMAGE002
，
Figure 296505DEST_PATH_IMAGE048
And all are greater than or equal to 0, initializing the number of iterations
Figure 404138DEST_PATH_IMAGE049
；
Step 4.2: judgment of
Figure 799347DEST_PATH_IMAGE050
If the number exceeds N, if not, updating by adopting a binary search algorithm
Figure 659725DEST_PATH_IMAGE051
By fixing
Figure 75662DEST_PATH_IMAGE002
，
Figure 342696DEST_PATH_IMAGE052
The value of (a) is set to (b),
Figure 89066DEST_PATH_IMAGE053
then jump to step 4.2; otherwise, jumping to step 4.3;
step 4.3: by fixing
Figure 492366DEST_PATH_IMAGE054
Updating based on equation (1)
Figure 813625DEST_PATH_IMAGE002
A value of (d);
step 4.4: by fixing
Figure 879539DEST_PATH_IMAGE002
，
Figure 616551DEST_PATH_IMAGE055
Updating based on equation (3)
Figure 202253DEST_PATH_IMAGE056
；
Step 4.5: by fixing
Figure 632098DEST_PATH_IMAGE002
，
Figure 421193DEST_PATH_IMAGE057
Updating based on equation (4)
Figure 961896DEST_PATH_IMAGE058
；
Step 4.6: judging whether all variables are converged, if so, jumping to a step 4.7, otherwise, jumping to a step 4.2;
step 4.7: outputting an optimal solution
Figure 667684DEST_PATH_IMAGE059
Optimal solution
Figure 314435DEST_PATH_IMAGE060
。
4. The method for optimizing the cognitive wireless energy supply network based on the backscatter relay transmission according to claim 3, wherein the updating by the binary search algorithm is carried out
Figure 43356DEST_PATH_IMAGE051
The method comprises the following steps:
step 3.1: inputting an upper bound value
Figure 184488DEST_PATH_IMAGE061
Setting a lower bound value
Figure 682465DEST_PATH_IMAGE062
Will be
Figure 1582DEST_PATH_IMAGE063
Substitution
Figure 217800DEST_PATH_IMAGE051
Into lagrange pairs
Figure 162622DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 844269DEST_PATH_IMAGE064
；
Step 3.2: the number of cycles is set to
Figure 786817DEST_PATH_IMAGE065
Initial value is 1, judge solution
Figure 224751DEST_PATH_IMAGE066
Whether or not less than
Figure 707685DEST_PATH_IMAGE067
，
Figure 993304DEST_PATH_IMAGE067
A very small number, if so, jump to step 3.5, otherwise jump to step 3.3,
Figure 841174DEST_PATH_IMAGE068
；
step 3.3: judgment of
Figure 828722DEST_PATH_IMAGE069
If greater than 0, if so, then
Figure 426931DEST_PATH_IMAGE070
And if not, the step (B),
Figure 567057DEST_PATH_IMAGE071
；
step 3.4: will be provided with
Figure 225309DEST_PATH_IMAGE063
Substitution
Figure 637836DEST_PATH_IMAGE051
Into lagrange pairs
Figure 462572DEST_PATH_IMAGE051
In the first partial derivative of (1), obtaining a solution
Figure 644155DEST_PATH_IMAGE069
Jumping to step 3.2;
step 3.5: obtained at this time
Figure 912456DEST_PATH_IMAGE063
Is composed of
Figure 874596DEST_PATH_IMAGE051
The solution of (c).
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