CN114222318A - Robustness optimization method for cognitive wireless power supply backscatter communication network - Google Patents

Robustness optimization method for cognitive wireless power supply backscatter communication network Download PDF

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CN114222318A
CN114222318A CN202111490922.1A CN202111490922A CN114222318A CN 114222318 A CN114222318 A CN 114222318A CN 202111490922 A CN202111490922 A CN 202111490922A CN 114222318 A CN114222318 A CN 114222318A
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receiver
representing
transmitter
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communication network
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CN114222318B (en
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徐勇军
杨浩克
陈前斌
周继华
陈量
叶荣飞
黄东
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Zhao Chenglong
Qinhuangdao Power Supply Co of State Grid Jibei Electric Power Co Ltd
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/22—Scatter propagation systems, e.g. ionospheric, tropospheric or meteor scatter
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a cognitive wireless power supply backscattering communication network robustness optimization method, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: establishing a signal transmission model of a cognitive wireless power supply backscatter communication network based on a lower cushion type; s2: considering the transmission rate constraint of a secondary receiver, the service quality constraint of a main receiver, the energy collection constraint, the reflection coefficient and the time constraint, and constructing a resource allocation problem of maximizing the throughput of a secondary system; s3: modeling the robust resource allocation problem by considering parameter uncertainty; s4: and converting the original problem into an equivalent convex optimization form by using a Q function and variable substitution method, and obtaining an analytic solution of transmission time, emission power and a reflection coefficient by using a Lagrange dual method. The invention improves the transmission rate and robustness of the secondary system.

Description

Robustness optimization method for cognitive wireless power supply backscatter communication network
Technical Field
The invention belongs to the technical field of wireless communication, relates to the technical field of wireless network resource allocation, and particularly relates to a robust optimization method for a cognitive wireless power supply backscatter communication network.
Background
With the development of communication technology and the rapid growth of wireless equipment, a large number of nodes can be accessed into the internet of things, and how to prolong the operation cycle of energy-limited nodes becomes one of the problems to be solved urgently in deploying the internet of things on a large scale. In recent years, the technique of backscatter communication has been proposed by scholars to solve the above-mentioned problems. Backscatter communication reflects and modulates incident radio frequency waves by a backscatter device for data transmission, and therefore, the backscatter device does not need to generate an active radio frequency signal and perform analog-to-digital conversion, thereby reducing power consumption.
Backscatter communication systems allow internet of things nodes to transmit data by reflecting and modulating in signals without the nodes themselves processing the data. Compared with the traditional wireless node, the backscattering node has no complex high-power-consumption radio frequency assembly, and the power consumption of the information sending node is greatly reduced. Therefore, the backscattering node can be manufactured into micro hardware with extremely low power consumption, large-scale deployment is facilitated flexibly, network coverage rate and coverage area are improved, and power consumption of communication is effectively reduced. In order to further break through the problems of limited energy of nodes of the traditional internet of things, high power consumption and short service life of node equipment, effectively prolong the service life of the nodes of the internet of things and relieve the problem that the nodes depend on battery supply too much, a method capable of improving the robustness of a cognitive wireless power supply backscatter communication network is urgently needed.
Disclosure of Invention
In view of this, the present invention provides a robust optimization method for a cognitive radio-powered backscatter communication network, which improves the transmission rate and robustness of a secondary network by combining a backscatter communication mode and a collection-transmission mode.
In order to achieve the purpose, the invention provides the following technical scheme:
a robustness optimization method for a cognitive radio power supply backscattering communication network is provided, wherein the cognitive radio power supply backscattering communication network comprises a main transmitter, a main receiver, N secondary transmitters and N secondary receivers. The method specifically comprises the following steps:
s1: establishing a signal transmission model of a cognitive wireless power supply backscatter communication network based on a lower cushion type;
energy collection is carried out by the secondary transmitter in the first stage; in the second stage, the secondary transmitter reflects signals to the corresponding secondary receiver by using a time division multiple access protocol; in the third phase, the secondary transmitter actively transmits data to the corresponding secondary receiver by using the energy collected in the first two phases.
S2: considering the transmission rate constraint of a secondary receiver, the service quality constraint of a main receiver, the energy collection constraint, the reflection coefficient and the time constraint, and constructing a resource allocation problem of maximizing the throughput of a secondary system;
s3: modeling the robust resource allocation problem by considering parameter uncertainty;
s4: and (4) converting the problem constructed in the step S3 into an equivalent convex optimization form by using a Q function and a variable substitution method, and obtaining an analytic solution of transmission time, transmission power and a reflection coefficient by using a Lagrange dual method.
Further, in step S1, the established signal transmission model specifically includes: definition P0For the transmitting power of the main transmitter, during the energy collection phase t1The energy collected by the nth secondary transmitter is:
Figure BDA0003399312270000021
wherein eta isn∈[0,1]Represents the energy collection efficiency of the secondary transmitter n; gnIndicating primary to secondary transmittersChannel gain of stage transmitter n;
in the backscatter phase t2The secondary transmitter n adopts a time division multiple access mode to reflect information to the secondary receiver n; thus in the reflection time slot taunReflection rate of internal secondary transmitter n to secondary receiver n
Figure BDA0003399312270000022
Expressed as:
Figure BDA0003399312270000023
wherein W represents the bandwidth, βnRepresenting the reflection coefficient, h, of a secondary transmitter nnRepresenting the channel gain of the secondary transmitter n to the secondary receiver n,
Figure BDA0003399312270000024
representing the background noise power of the secondary receiver n; during the energy collection phase t1And a reflection time slot taunTotal collected energy of inner nth secondary transmitter
Figure BDA0003399312270000025
Comprises the following steps:
Figure BDA0003399312270000026
in the active transmission phase t3Since a plurality of secondary transmitters transmit information to the secondary receivers using TDMA access, the time slots alpha are usednData transmission rate R from inner secondary transmitter n to secondary receiver nnComprises the following steps:
Figure BDA0003399312270000027
wherein, PnRepresenting a time slot alphanThe transmit power of the inner secondary transmitter n.
Further, in step S2, the expression of the resource allocation problem with maximized throughput of the constructed secondary system is:
Figure BDA0003399312270000031
where g is the channel gain from the primary transmitter to the primary receiver,
Figure BDA0003399312270000032
for the channel gain of the secondary transmitter n to the primary receiver,
Figure BDA0003399312270000033
representing the noise power of the primary receiver; c1Indicating the minimum rate constraint of the secondary receiver n during the backscatter information phase
Figure BDA0003399312270000034
Represents a minimum rate threshold; c2Indicating the minimum rate constraint of the secondary receiver n,
Figure BDA0003399312270000035
represents a minimum rate threshold; c3And C4Representing quality of service constraints of the primary receiver, ensuring the quality of service of the primary receiver, gammaminRepresents a minimum quality of service threshold for the primary receiver; c5The collected energy is larger than the sum of the energy consumed by the self circuit and the energy consumed in the active information transmission phase; c6～C8Represents a transmission slot constraint; c9Representing the reflection coefficient constraint of the secondary transmitter n.
Further, in step S3, modeling the robust resource allocation optimization problem, specifically including: due to channel fading, uncertainty of parameters, etc. in a wireless communication system, it is difficult to obtain perfect channel state information. Therefore, an additive model of the uncertainty parameter is considered and the channel estimation error is assumed to follow a gaussian distribution, i.e.
Figure BDA0003399312270000036
Wherein the content of the first and second substances,
Figure BDA0003399312270000037
and
Figure BDA0003399312270000038
representing a set of uncertainties;
Figure BDA0003399312270000039
representing the estimated channel gain from the nth secondary transmitter to the nth secondary receiver;
Figure BDA00033993122700000310
representing the estimated channel gain of the nth secondary transmitter to the primary receiver;
Figure BDA00033993122700000311
representing the channel estimation error from the nth secondary transmitter to the nth secondary receiver, subject to a mean of zero and a variance of
Figure BDA00033993122700000312
(ii) a gaussian distribution of;
Figure BDA00033993122700000313
representing the channel estimation error of the nth secondary transmitter to the primary receiver, subject to a mean of zero and a variance of
Figure BDA00033993122700000314
(ii) a gaussian distribution of;
the robust resource allocation problem P2 corresponding to P1 can be expressed as
Figure BDA0003399312270000041
Wherein the content of the first and second substances,
Figure BDA0003399312270000042
is indicated in the reflection time slot taunThe outage probability constraint of the secondary receiver n reflection rate,
Figure BDA0003399312270000043
representing the n-reflection rate threshold, omega, of the secondary receivern∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure BDA0003399312270000044
indicating that in the active transmission time slot alphanThe outage probability constraint that the secondary receiver n actively transmits information,
Figure BDA0003399312270000045
representing an active transmission rate threshold, upsilonn∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure BDA0003399312270000046
and
Figure BDA0003399312270000047
representing the outage probability constraint, gamma, of the primary receiverminRepresents the quality of service threshold of the primary receiver, ζ ∈ [0,1 ]]Representing an outage probability threshold that protects the primary receiver's minimum quality of service requirements; pr {. cndot.) represents the outage probability.
Further, in step S4, converting the problem constructed in step S3 into an equivalent convex optimization form and solving the problem, specifically including the following steps:
s41: converting the interrupt probability constraint into a certainty constraint by using a Q function;
s42: introducing auxiliary variables based on variable substitution
Figure BDA0003399312270000048
Handling existing coupled variable constraints;
s43: and solving the analytic solution of the convex optimization problem by adopting a Lagrange dual theory.
The invention has the beneficial effects that: the invention improves the robustness and the throughput of the cognitive wireless power supply backscatter communication network system to a certain extent by improving the transmission rate and the robustness of the secondary system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a system model diagram of a cognitive wireless power-supplied backscatter communications network of the present invention;
FIG. 2 is a flow chart of a method for optimizing the robustness of a cognitive radio-powered backscatter communication network according to the present invention;
FIG. 3 is a relationship of actual outage probability of the algorithm of the present invention versus a non-robust algorithm under different algorithms;
fig. 4 is a graph of the relationship between uncertainty and throughput of a secondary system under different algorithms.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 4, the method for robust optimization of a cognitive radio-powered backscatter communication network provided by the present invention includes a primary transmitter and a primary receiver, and a plurality of secondary transmitters and secondary receivers. The method specifically comprises the following steps:
s1: establishing a signal transmission model of a cognitive wireless power supply backscatter communication network based on a lower cushion type; energy collection is carried out by the secondary transmitter in the first stage; in the second stage, the secondary transmitter reflects signals to the corresponding secondary receiver by using a time division multiple access protocol; in the third phase, the secondary transmitter actively transmits data to the corresponding secondary receiver according to the energy collected in the first two phases.
The invention considers a cognitive wireless power supply backscattering communication network scene based on an underlying type, and a system model is shown as figure 1. The network scene comprises a main network and a secondary network, wherein the main network comprises a main transmitter and a main receiver; the secondary network is composed of N secondary transmitters and N secondary receivers, and the set of the secondary transmitters and the secondary receivers is defined as
Figure BDA0003399312270000051
The primary transmitter, the primary receiver, the secondary transmitter and the secondary receiver are provided with a single antenna, and all the secondary transmitters are provided with a radio frequency energy collection module, a backscatter communication module and an active data transmission module, so that the secondary transmitter n can communicate with the secondary receiver n by selecting a backscatter mode or an active information transmission mode, but the two modes cannot simultaneously transmit data. It is assumed that the main transmitter is broadcasting the signal all the time, and thus the main channel will be busy for the transmission time frame T. The time frame T is divided into three parts, respectively an energy harvesting phase T1Phase t of backscatter information2And an active transmission phase t3And satisfy
Figure BDA0003399312270000052
At t1An energy collection stage, wherein all secondary transmitters collect energy; at t2In the back scattering information stage, the secondary transmitter n adopts the mode of time division multiple accessThe stage receiver n performs reflection information. In each reflection time slot taunIn that only one secondary transmitter n reflects a signal to a secondary receiver n, and that
Figure BDA0003399312270000053
The remaining secondary transmitters remain silent; at t3And in the active transmission stage, a plurality of secondary transmitters actively transmit information to a secondary receiver in a time division multiple access mode. In time slot alphanIn which a secondary transmitter n actively transmits information to a secondary receiver n, and satisfies
Figure BDA0003399312270000061
The remaining secondary transmitters remain silent. All channels are assumed to satisfy block fading channels, i.e. remain unchanged for a small time slot, being time-varying over the course of time.
Definition P0For the transmitting power of the main transmitter, during the energy collection phase t1The nth secondary transmitter collects energy of
Figure BDA0003399312270000062
Wherein eta isn∈[0,1]Represents the energy collection efficiency of the secondary transmitter n; gnRepresenting the channel gain from the primary transmitter to the secondary transmitter n.
In the backscatter phase t2And the secondary transmitter n reflects information to the secondary receiver n by adopting a time division multiple access mode. Thus in the reflection time slot taunThe reflection rate of the inner secondary transmitter n to the secondary receiver n can be expressed as
Figure BDA0003399312270000063
Wherein W represents a bandwidth; beta is anRepresenting the reflection coefficient of the secondary transmitter n; h isnRepresenting the channel gain of the secondary transmitter n to the secondary receiver n;
Figure BDA0003399312270000064
representing the background noise power of the secondary receiver n.
During the energy collection phase t1And a reflection time slot taunThe total collected energy of the inner nth secondary transmitter is
Figure BDA0003399312270000065
In the active transmission phase t3Since a plurality of secondary transmitters transmit information to the secondary receivers using TDMA access, the time slots alpha are usednThe data transmission rate from the inner secondary transmitter n to the secondary receiver n is
Figure BDA0003399312270000066
Wherein, PnRepresenting a time slot alphanThe transmit power of the inner secondary transmitter n.
S2: and (3) resource allocation problem for maximizing the throughput of the secondary system by considering the transmission rate constraint of the secondary receiver, the service quality constraint of the primary receiver, the energy collection constraint, the reflection coefficient and the time constraint.
Assuming a channel gain g from the primary transmitter to the primary receiver and a channel gain g from the secondary transmitter n to the primary receiver
Figure BDA0003399312270000067
Under perfect channel state information, the optimization problem can be expressed as:
Figure BDA0003399312270000071
wherein the content of the first and second substances,
Figure BDA0003399312270000072
representing the noise power of the primary receiver; c1At the backscattering information levelSegment, minimum rate constraint of secondary receiver n,
Figure BDA0003399312270000073
represents a minimum rate threshold; c2Indicating the minimum rate constraint of the secondary receiver n,
Figure BDA0003399312270000074
represents a minimum rate threshold; c3And C4Quality of service, gamma, of the primary receiver is guaranteedminRepresents a minimum quality of service threshold for the primary receiver; c5The collected energy is larger than the sum of the energy consumed by the self circuit and the energy consumed in the active information transmission phase; c6～C8Represents a transmission slot constraint; c9Representing the reflection coefficient constraint of the secondary transmitter n.
S3: and modeling the robust resource allocation optimization problem by considering the uncertainty of the channel parameters.
Due to channel fading, uncertainty of parameters, etc. in a wireless communication system, it is difficult to obtain perfect channel state information. Therefore, an additive model of the uncertainty parameter is considered and the channel estimation error is assumed to follow a gaussian distribution, i.e.
Figure BDA0003399312270000075
Wherein the content of the first and second substances,
Figure BDA0003399312270000076
representing the estimated channel gain from the nth secondary transmitter to the nth secondary receiver;
Figure BDA0003399312270000077
representing the estimated channel gain of the nth secondary transmitter to the primary receiver;
Figure BDA0003399312270000078
representing channels from nth secondary transmitter to nth secondary receiverError is estimated, and mean is zero and variance is
Figure BDA0003399312270000079
Figure BDA00033993122700000710
Represents the channel estimation error of the nth secondary transmitter to the primary receiver, and has a mean value of zero and a variance of
Figure BDA00033993122700000711
The robust resource allocation problem corresponding to P1 can be expressed as
Figure BDA0003399312270000081
Wherein the content of the first and second substances,
Figure BDA0003399312270000082
is indicated in the reflection time slot taunThe outage probability constraint of the secondary receiver n reflection rate,
Figure BDA0003399312270000083
representing the n-reflection rate threshold, omega, of the secondary receivern∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure BDA0003399312270000084
indicating that in the active transmission time slot alphanThe outage probability constraint that the secondary receiver n actively transmits information,
Figure BDA0003399312270000085
indicating an active transmission rate threshold, vn∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure BDA0003399312270000086
and
Figure BDA0003399312270000087
to representInterruption probability constraint of the primary receiver, gammaminRepresents the quality of service threshold of the primary receiver, ζ ∈ [0,1 ]]Representing a outage probability threshold, which constraint is used to protect the minimum quality of service requirements of the primary receiver.
Figure BDA0003399312270000088
Wherein the content of the first and second substances,
Figure BDA0003399312270000089
is expressed with respect to Δ hnThe cumulative distribution function of (a). Therefore, we have
Figure BDA00033993122700000810
Wherein the content of the first and second substances,
Figure BDA00033993122700000811
Q-1(. cndot.) represents the inverse of the Q function. According to the formula (8), the
Figure BDA00033993122700000812
Into a deterministic constraint of
Figure BDA00033993122700000813
By using a similar transformation method to that of formula (8) and formula (9), the
Figure BDA00033993122700000814
And
Figure BDA00033993122700000815
conversion to deterministic constraints
Figure BDA00033993122700000816
Figure BDA0003399312270000091
Figure BDA0003399312270000092
From equations (9) to (12), P2 can be re-expressed as
Figure BDA0003399312270000093
S4: and converting the original problem into an equivalent convex optimization form by using a Q function and a variable substitution method, and obtaining an analytic solution of transmission time, emission power and a reflection coefficient by using a Lagrange dual theory.
P3 has been transformed into a deterministic non-convex optimization problem, and it is still difficult to solve the solution analytically.
Definition of
Figure BDA0003399312270000094
P3 may be re-denoted as
Figure BDA0003399312270000095
According to the variable replacement method, P4 is a convex optimization problem and can be solved through a Lagrangian dual theory. Definition of
Figure BDA0003399312270000096
The Lagrangian function of P4 is
Figure BDA0003399312270000101
Wherein, χn,εn,φn,
Figure BDA0003399312270000102
κn,μ,v,θ,
Figure BDA0003399312270000103
ξnRepresenting a non-negative lagrange multiplier. Equation (15) may be re-expressed as
Figure BDA0003399312270000104
Wherein the content of the first and second substances,
Figure BDA0003399312270000105
the dual problem of formula (18) is
Figure BDA0003399312270000106
Wherein the dual function is
Figure BDA0003399312270000107
According to the Karush-Kuhn-Tucker (KKT) conditions, the following closed solutions can be obtained
Figure BDA0003399312270000108
Figure BDA0003399312270000109
Figure BDA00033993122700001010
Wherein, [ x ]]+＝max(0,x)；
Figure BDA00033993122700001011
Figure BDA0003399312270000111
Based on the gradient descent method, the optimization variables and the Lagrange multiplier update expression are as follows
Figure BDA0003399312270000112
Figure BDA0003399312270000113
Figure BDA0003399312270000114
Figure BDA0003399312270000115
Figure BDA0003399312270000116
Figure BDA0003399312270000117
Figure BDA0003399312270000118
μl+1＝[μl-Δμ×(T-t1-t2-t3)]+ (30)
Figure BDA0003399312270000119
Figure BDA00033993122700001110
Figure BDA00033993122700001111
Figure BDA00033993122700001112
Wherein l represents the number of iterations; delta taun,Δαn,Δχn,Δεn,Δφn,
Figure BDA00033993122700001113
Δκn,Δμ,Δν,Δθ,
Figure BDA00033993122700001114
ΔξnA step size greater than zero. According to the method of variable substitution, can be derived
Figure BDA00033993122700001115
And (3) verification experiment: the application effect of the present invention will be described in detail with reference to the simulation.
1) Simulation conditions
It is assumed that there is one primary transmitter and one primary receiver in the primary network and two secondary transmitters and two secondary receivers in the secondary network. The distance between the main transmitter and the main receiver is 9m, the distance between the two secondary transmitters and the main receiver is 7m and 6m, and the distance between the two secondary transmitters and the two secondary receivers is 4m and 5 m. The antenna gain of the base station and the antenna gain of the secondary transmitter are set to 6 dBi. The channel model is
Figure BDA0003399312270000121
Wherein d isnIs the distance between the transmitting end and the receiving end, and χ is 3, the path loss exponent. Other parameters are shown in table 1.
TABLE 1 simulation parameters
Figure BDA0003399312270000122
2) Simulation result
FIG. 3 depicts the standard deviation e of the actual outage probability and the channel estimation errorRThe relationship (2) of (c). Under different algorithms, the actual outage probability of the primary receiver is related to the channel estimation error of the secondary transmitter n to the primary receiver. With the standard deviation ∈RThe actual outage probability of the algorithm of the present invention is lower than that of the non-robust algorithm and does not exceed the outage probability threshold ζ. The non-robust algorithm ignores the channel estimation error, so that the actual interruption probability is higher than that of the algorithm, and the algorithm has better robustness. Fig. 4 depicts the overall throughput of the secondary system versus channel estimation error under different algorithms. The results show that with σhThe overall throughput of the inventive algorithm, the pure backscatter algorithm and the pure gather-transmit algorithm is reduced. In contrast, the overall throughput remains unchanged, since the non-robust algorithm ignores the uncertainty of the channel. In addition, the total throughput of the algorithm is higher than that of a pure backscattering algorithm and a pure collection-transmission algorithm, and the effectiveness of the algorithm is further explained.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (6)

1. A robustness optimization method for a cognitive wireless power supply backscattering communication network is characterized by specifically comprising the following steps:
s1: establishing a signal transmission model of a cognitive wireless power supply backscatter communication network based on a lower cushion type;
s2: considering the transmission rate constraint of a secondary receiver, the service quality constraint of a main receiver, the energy collection constraint, the reflection coefficient and the time constraint, and constructing a resource allocation problem of maximizing the throughput of a secondary system;
s3: modeling the robust resource allocation problem by considering parameter uncertainty;
s4: and (4) converting the problem constructed in the step S3 into an equivalent convex optimization form by using a Q function and a variable substitution method, and obtaining an analytic solution of transmission time, transmission power and a reflection coefficient by using a Lagrange dual method.
2. The method for robust optimization of cognitive radio-powered backscatter communication network of claim 1, wherein in step S1, the cognitive radio-powered backscatter communication network comprises a primary transmitter, a primary receiver, N secondary transmitters, and N secondary receivers; energy collection is carried out by the secondary transmitter in the first stage; in the second stage, the secondary transmitter reflects signals to the corresponding secondary receiver by using a time division multiple access protocol; in the third phase, the secondary transmitter actively transmits data to the corresponding secondary receiver by using the energy collected in the first two phases.
3. The method for robust optimization of a cognitive wireless power supply backscatter communication network according to claim 2, wherein the signal transmission model established in step S1 specifically includes: definition P0For the transmitting power of the main transmitter, during the energy collection phase t1The energy collected by the nth secondary transmitter is:
Figure FDA0003399312260000011
wherein eta isn∈[0,1]Represents the energy collection efficiency of the secondary transmitter n; gnRepresents the channel gain from the primary transmitter to the secondary transmitter n;
in the backscatter phase t2The secondary transmitter n adopts a time division multiple access mode to reflect information to the secondary receiver n; in the reflection time slot taunReflection rate of internal secondary transmitter n to secondary receiver n
Figure FDA0003399312260000012
Expressed as:
Figure FDA0003399312260000013
wherein W represents the bandwidth, βnRepresenting the reflection coefficient, h, of a secondary transmitter nnRepresenting the channel gain of the secondary transmitter n to the secondary receiver n,
Figure FDA0003399312260000014
representing the background noise power of the secondary receiver n; during the energy collection phase t1And a reflection time slot taunTotal collected energy of inner nth secondary transmitter
Figure FDA0003399312260000015
Comprises the following steps:
Figure FDA0003399312260000016
in the active transmission phase t3In time slot alphanData transmission rate R from inner secondary transmitter n to secondary receiver nnComprises the following steps:
Figure FDA0003399312260000021
wherein, PnRepresenting a time slot alphanThe transmit power of the inner secondary transmitter n.
4. The method for robust optimization of cognitive radio-powered backscatter communication network according to claim 3, wherein in step S2, the expression of the resource allocation problem for maximizing the throughput of the secondary system is:
Figure FDA0003399312260000022
where g is the channel gain from the primary transmitter to the primary receiver,
Figure FDA0003399312260000023
for the channel gain of the secondary transmitter n to the primary receiver,
Figure FDA0003399312260000024
representing the noise power of the primary receiver; c1Indicating the minimum rate constraint of the secondary receiver n during the backscatter information phase
Figure FDA0003399312260000025
Represents a minimum rate threshold; c2Indicating the minimum rate constraint of the secondary receiver n,
Figure FDA0003399312260000026
represents a minimum rate threshold; c3And C4Representing quality of service constraints of the primary receiver, gammaminRepresents a minimum quality of service threshold for the primary receiver; c5The collected energy is larger than the sum of the energy consumed by the self circuit and the energy consumed in the active information transmission phase; c6～C8Represents a transmission slot constraint; c9Representing the reflection coefficient constraint of the secondary transmitter n.
5. The method for robust optimization of a cognitive radio power supply backscatter communication network of claim 4, wherein in step S3, modeling a robust resource allocation optimization problem specifically comprises: additive models taking into account uncertainty parameters and assuming that the channel estimation error follows a Gaussian distribution, i.e.
Figure FDA0003399312260000027
Wherein the content of the first and second substances,
Figure FDA0003399312260000028
and
Figure FDA0003399312260000029
representing a set of uncertainties;
Figure FDA00033993122600000210
representing the estimated channel gain from the nth secondary transmitter to the nth secondary receiver;
Figure FDA00033993122600000211
representing the estimated channel gain of the nth secondary transmitter to the primary receiver;
Figure FDA00033993122600000212
representing the channel estimation error from the nth secondary transmitter to the nth secondary receiver, subject to a mean of zero and a variance of
Figure FDA00033993122600000213
(ii) a gaussian distribution of;
Figure FDA0003399312260000031
representing the channel estimation error of the nth secondary transmitter to the primary receiver, subject to a mean of zero and a variance of
Figure FDA0003399312260000032
(ii) a gaussian distribution of;
p1 robust resource allocation problem P2 is
Figure FDA0003399312260000033
Wherein the content of the first and second substances,
Figure FDA0003399312260000034
is indicated in the reflection time slot taunThe outage probability constraint of the secondary receiver n reflection rate,
Figure FDA0003399312260000035
representing the n-reflection rate threshold, omega, of the secondary receivern∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure FDA0003399312260000036
indicating that in the active transmission time slot alphanThe outage probability constraint that the secondary receiver n actively transmits information,
Figure FDA0003399312260000037
representing an active transmission rate threshold, upsilonn∈[0,1]Representing the outage probability threshold of the secondary receiver n;
Figure FDA0003399312260000038
and
Figure FDA0003399312260000039
representing the outage probability constraint, gamma, of the primary receiverminRepresents the quality of service threshold of the primary receiver, ζ ∈ [0,1 ]]Representing an outage probability threshold that protects the primary receiver's minimum quality of service requirements; pr {. cndot.) represents the outage probability.
6. The method for robust optimization of a cognitive radio power supply backscatter communication network of claim 5, wherein in step S4, the problem constructed in step S3 is converted into an equivalent convex optimization form and solved, and the method specifically comprises the following steps:
s41: converting the interrupt probability constraint into a certainty constraint by using a Q function;
s42: introducing auxiliary variables based on variable substitution
Figure FDA00033993122600000310
Handling existing coupled variable constraints;
s43: and solving the analytic solution of the convex optimization problem by adopting a Lagrange dual theory.
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