CN114222318B - Robust optimization method for cognitive wireless power supply backscatter communication network - Google Patents
Robust optimization method for cognitive wireless power supply backscatter communication network Download PDFInfo
- Publication number
- CN114222318B CN114222318B CN202111490922.1A CN202111490922A CN114222318B CN 114222318 B CN114222318 B CN 114222318B CN 202111490922 A CN202111490922 A CN 202111490922A CN 114222318 B CN114222318 B CN 114222318B
- Authority
- CN
- China
- Prior art keywords
- representing
- receiver
- transmitter
- constraint
- transmission
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004891 communication Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000005457 optimization Methods 0.000 title claims abstract description 20
- 230000001149 cognitive effect Effects 0.000 title claims abstract description 19
- 230000005540 biological transmission Effects 0.000 claims abstract description 48
- 238000013468 resource allocation Methods 0.000 claims abstract description 13
- 230000009977 dual effect Effects 0.000 claims abstract description 9
- 238000006467 substitution reaction Methods 0.000 claims abstract description 8
- 230000008054 signal transmission Effects 0.000 claims abstract description 6
- 238000003306 harvesting Methods 0.000 claims description 14
- 239000000654 additive Substances 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 2
- 238000010168 coupling process Methods 0.000 claims description 2
- 238000005859 coupling reaction Methods 0.000 claims description 2
- 238000004088 simulation Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000005562 fading Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000011426 transformation method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a robust optimization method for a cognitive wireless power supply backscatter communication network, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: establishing a signal transmission model based on an underlying cognitive wireless power supply backscatter communication network; s2: constructing a resource allocation problem of maximizing the throughput of a 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; s3: modeling a robust resource allocation problem in consideration of parameter uncertainty; s4: the original problem is converted into an equivalent convex optimization form by using a Q function and a variable substitution method, and an analytic solution of transmission time, transmission power and reflection coefficient is obtained by using a Lagrange dual method. The invention improves the transmission rate and the robustness of the secondary system.
Description
Technical Field
The invention belongs to the technical field of wireless communication, relates to the technical field of wireless network resource allocation, and in particular 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 devices, massive nodes can be connected into the internet of things, and how to prolong the operation period of the energy-limited nodes becomes one of the problems to be solved in large-scale deployment of the internet of things. In recent years, a technology of backscatter communication has been proposed by scholars to solve the above-mentioned problems. The backscatter communication reflects and modulates the incident radio frequency wave for data transmission by the backscatter device, 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.
The backscatter communication system allows the internet of things node to transmit data by reflecting and modulating in signals without the node itself processing the data. Compared with the traditional wireless node, the back scattering node has no complex high-power-consumption radio frequency component, and the power consumption of the information sending node is greatly reduced. Therefore, the backscattering node can be made into miniature hardware with extremely low power consumption, so that the large-scale deployment is convenient and flexible, the network coverage rate and coverage area are improved, and the power consumption of communication is effectively reduced. In order to further break through the problems of limited energy and short service life of the traditional Internet of things node, the service life of the Internet of things node is effectively prolonged, and the problem that the node is too dependent on battery supply is relieved, a method for improving the robustness of the cognitive wireless power supply backscatter communication network is needed.
Disclosure of Invention
Therefore, the present invention aims to provide a robust optimization method for a cognitive wireless power supply backscatter communication network, which combines backscatter communication and a collection-transmission mode to improve the transmission rate and robustness of a secondary network.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a robust optimization method for a cognitive radio power supply backscatter communication network, wherein the cognitive radio power supply backscatter 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 based on an underlying cognitive wireless power supply backscatter communication network;
the secondary transmitter performs energy collection in the first stage; reflecting signals to corresponding secondary receivers in a time division multiple access protocol at the secondary transmitter of the second stage; the secondary transmitter in the third stage actively transmits data to the corresponding secondary receiver using the energy collected in the first two stages.
S2: constructing a resource allocation problem of maximizing the throughput of a 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;
s3: modeling a robust resource allocation problem in consideration of parameter uncertainty;
s4: and (3) 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 the transmission time, the transmission power and the reflection coefficient by using a Lagrange dual method.
Further, in step S1, the signal transmission model established specifically includes: definition P 0 For the transmission power of the main transmitter, in the energy harvesting phase t 1 The energy collected by the nth secondary transmitter is:
wherein eta n ∈[0,1]Representing the energy harvesting efficiency of the secondary transmitter n; g n Representing the channel gain of the primary transmitter to the secondary transmitter n;
in the backscattering stage t 2 The secondary transmitter n adopts a time division multiple access mode to reflect information to the secondary receiver n; thus in reflection time slot τ n Inner secondary transmitter nReflection Rate to secondary receiver nExpressed as:
wherein W represents bandwidth, beta n Representing the reflection coefficient, h, of the secondary transmitter n n Representing the channel gain of secondary transmitter n to secondary receiver n,representing the background noise power of the secondary receiver n; in the energy harvesting stage t 1 And reflection time slot tau n Total energy collected by the nth secondary transmitter>The method comprises the following steps:
in the active transmission stage t 3 Since the plurality of secondary transmitters transmit information to the secondary receiver by using a time division multiple access mode, the information is transmitted in the time slot alpha n Data transmission rate R from inner secondary transmitter n to secondary receiver n n The method comprises the following steps:
wherein P is n Representing time slot alpha n The transmit power of the inner secondary transmitter n.
Further, in step S2, the expression of the resource allocation problem of maximizing the throughput of the constructed secondary system is:
where g is the channel gain from the primary transmitter to the primary receiver,channel gain for secondary transmitter n to primary receiver +.>Representing the noise power of the primary receiver; c (C) 1 Representing the minimum rate constraint of the secondary receiver n during the backscatter information phase>Representing a minimum rate threshold; c (C) 2 Representing the minimum rate constraint of the secondary receiver n during the active transmission phase,representing a minimum rate threshold; c (C) 3 And C 4 Representing the service quality constraint of the main receiver, guaranteeing the service quality of the main receiver, gamma min Representing a minimum quality of service threshold for the primary receiver; c (C) 5 Representing that the collected energy is greater than the sum of the energy consumed by the circuit itself and the energy consumed by the active transmission information phase; c (C) 6 ～C 8 Representing a transmission slot constraint; c (C) 9 Representing the reflection coefficient constraint of the secondary transmitter n.
Further, in step S3, modeling the robust resource allocation optimization problem specifically includes: it is difficult to obtain perfect channel state information due to factors such as channel fading, parameter uncertainty, etc. in a wireless communication system. Thus, an additive model of the uncertainty parameter is considered, and it is assumed that the channel estimation error follows a gaussian distribution, i.e
Wherein,and->Representing a set of uncertainties; />Representing estimated channel gains for the nth secondary transmitter through the nth secondary receiver; />Representing the estimated channel gain of the nth secondary transmitter to the primary receiver; />Representing channel estimation errors from the nth secondary transmitter to the nth secondary receiver, obeying a mean value of zero and a variance of +.>Is a gaussian distribution of (c); />Representing the channel estimation error from the nth secondary transmitter to the primary receiver, obeying a mean value of zero and a variance of +.>Is a gaussian distribution of (c);
the robust resource allocation problem P2 corresponding to P1 can be expressed as
Wherein,indicated in the reflection time slot tau n Interrupt probability constraint for secondary receiver n reflection rate, < >>Representing the secondary receiver n reflection rate threshold, ω n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />Representing the active transmission time slot alpha n Interrupt probability constraint for active transmission of information by secondary receiver n,/->Indicating the threshold of the active transmission rate, v n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />And->Representing outage probability constraints of the primary receiver, gamma min Representing the quality of service threshold of the primary receiver ζ ε [0,1 ]]Representing an outage probability threshold for protecting a minimum quality of service requirement of the primary receiver; pr {. Cndot. } represents outage probability.
Further, in step S4, the problem constructed in step S3 is converted into an equivalent convex optimization form and solved, and specifically includes the following steps:
s41: converting the interrupt probability constraint into a deterministic constraint by using a Q function;
s42: based on variable substitution method, introducing auxiliary variableProcessing existing coupling variable constraints;
s43: and solving an analytical solution of the convex optimization problem by adopting a Lagrangian dual theory.
The invention has the beneficial effects that: the invention improves the transmission rate and the robustness of the secondary system, thereby improving the robustness and the throughput of the cognitive wireless power supply backscatter communication network system to a certain extent.
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 objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a system model diagram of a cognitive wireless powered backscatter communications network of the present invention;
FIG. 2 is a flow chart of a robust optimization method for a cognitive wireless power supply backscatter communication network of the present invention;
FIG. 3 is a graph showing the actual outage probability of the algorithm of the present invention versus the non-robust algorithm under different algorithms;
fig. 4 is a relationship between uncertainty and throughput of a secondary system under different algorithms.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Referring to fig. 1 to 4, the robust optimization method for a cognitive wireless power supply backscatter communication network provided by the invention includes a main transmitter, a main receiver, a plurality of secondary transmitters and secondary receivers. The method specifically comprises the following steps:
s1: establishing a signal transmission model based on an underlying cognitive wireless power supply backscatter communication network; the secondary transmitter performs energy collection in the first stage; reflecting signals to corresponding secondary receivers in a time division multiple access protocol at the secondary transmitter of the second stage; in the third phase the secondary transmitter actively transmits data to the corresponding secondary receiver based on the energy collected in the first two phases.
The invention considers an underlying cognitive wireless power supply backscatter communication network scenario, and a system model is shown in figure 1. The network scene consists of a main network and a secondary network, wherein the main network consists of 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 asThe primary transmitter, primary receiver, secondary transmitter and secondary receiver are equipped with a single antenna and all secondary transmitters have a radio frequency energy harvesting module, a backscatter communication module and an active data transmission module, so that secondary transmitter n can communicate with secondary receiver n by selecting either a backscatter mode or an active transmission information mode, but both modes cannot transmit data simultaneously. It is assumed that the main transmitter is broadcasting a signal all the time, so the main channel will be busy for the transmission time frame T. Dividing the time frame T into three parts, respectively an energy harvesting phase T 1 Backscattering information phase t 2 Active transmission phase t 3 And satisfy->At t 1 An energy harvesting phase, wherein all secondary transmitters harvest energy; at t 2 And in the backscattering information stage, the secondary transmitter n uses a time division multiple access mode to reflect information to the secondary receiver n. At each reflection time slot τ n In that only one secondary transmitter n reflects a signal to the secondary receiver n and satisfies +.>The remaining secondary transmitters remain silent; at t 3 And in the active transmission stage, the plurality of secondary transmitters actively transmit information to the secondary receivers in a time division multiple access mode. In time slot alpha n In that the secondary transmitter n actively transmits information to the secondary receiver n and satisfies +.>The remaining secondary transmitters remain silent. It is assumed that all channels meet the block fading channel, i.e. remain unchanged for a small time slot, time-varying over the whole time course.
Definition P 0 For the transmission power of the main transmitter, in the energy harvesting phase t 1 The energy collected by the nth secondary transmitter is
Wherein eta n ∈[0,1]Representing the energy harvesting efficiency of the secondary transmitter n; g n Representing the channel gain of the primary transmitter to the secondary transmitter n.
In the backscattering stage t 2 The secondary transmitter n reflects information to the secondary receiver n using time division multiple access. Thus in reflection time slot τ n The reflection rate of the inner secondary transmitter n to the secondary receiver n can be expressed as
Wherein W represents bandwidth; beta n Representing the reflection coefficient of the secondary transmitter n; h is a n Representing the channel gain of the secondary transmitter n to the secondary receiver n;representing the background noise power of the secondary receiver n.
In the energy harvesting stage t 1 And reflection time slot tau n The total energy collected by the nth secondary transmitter is
In the active transmission stage t 3 Since the plurality of secondary transmitters transmit information to the secondary receiver by using a time division multiple access mode, the information is transmitted in the time slot alpha n The data transmission rate from the inner secondary transmitter n to the secondary receiver n is
Wherein P is n Representing time slot alpha n The transmit power of the inner secondary transmitter n.
S2: the resource allocation problem of maximizing secondary system throughput takes into account secondary receiver transmission rate constraints, primary receiver quality of service constraints, energy harvesting constraints, reflection coefficients, and time constraints.
Assuming that the channel gain from the primary transmitter to the primary receiver is g, the channel gain from the secondary transmitter n to the primary receiver isIn perfect channel state information, the optimization problem can be expressed as:
wherein,representing the noise power of the primary receiver; c (C) 1 Representing the minimum rate constraint of the secondary receiver n during the backscatter information phase,/i>Representing a minimum rate threshold; c (C) 2 Representing the minimum rate constraint of the secondary receiver n during the active transmission phase,/>Representing a minimum rate threshold; c (C) 3 And C 4 Ensure the service quality of the main receiver, gamma min Representing a minimum quality of service threshold for the primary receiver; c (C) 5 Representing that the collected energy is greater than the sum of the energy consumed by the circuit itself and the energy consumed by the active transmission information phase; c (C) 6 ～C 8 Representing a transmission slot constraint; c (C) 9 Representing the reflection coefficient constraint of the secondary transmitter n.
S3: and modeling the problem of optimizing the distribution of the robust resources by considering the uncertainty of the channel parameters.
It is difficult to obtain perfect channel state information due to factors such as channel fading, parameter uncertainty, etc. in a wireless communication system. Thus, an additive model of the uncertainty parameter is considered, and it is assumed that the channel estimation error follows a gaussian distribution, i.e
Wherein,representing estimated channel gains for the nth secondary transmitter through the nth secondary receiver; />Representing the estimated channel gain of the nth secondary transmitter to the primary receiver; />Representing channel estimation errors from the nth secondary transmitter to the nth secondary receiver, with a mean of zero and a variance of +.> Representing the channel estimation error from the nth secondary transmitter to the primary receiver, with a mean value of zero and a variance of +.>
The problem of robust resource allocation corresponding to P1 can be expressed as
Wherein,indicated in the reflection time slot tau n Interrupt probability constraint for secondary receiver n reflection rate, < >>Representing the secondary receiver n reflection rate threshold, ω n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />Representing the active transmission time slot alpha n Interrupt probability constraint for active transmission of information by secondary receiver n,/->Representing an active transmission rate threshold, v n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />And->Representing outage probability constraints of the primary receiver, gamma min Representing quality of service of primary receiverThreshold, ζ ε [0,1 ]]Representing an outage probability threshold, the constraint being used to protect the minimum quality of service requirements of the primary receiver.
Wherein,representation concerning Δh n Is a cumulative distribution function of (a). Therefore, we have->Wherein (1)>Q -1 (. Cndot.) represents the inverse of the Q function. According to formula (8), will->Conversion to deterministic constraints
By using a transformation method similar to the one of the formula (8) and the formula (9)And->Conversion to deterministic constraints
P2 can be expressed again as follows from formulas (9) to (12)
S4: the original problem is converted into an equivalent convex optimization form by using a Q function and a variable substitution method, and an analytic solution of transmission time, transmission power and reflection coefficient is obtained by using a Lagrange dual theory.
P3 has been transformed into a deterministic, non-convex optimization problem, and it is still difficult to find an analytical solution.
Definition of the definitionP3 can be re-expressed as
According to the variable substitution method, P4 is a convex optimization problem, and can be solved by the Lagrange dual theory. Definition of the definitionThe Lagrangian function of P4 is
Wherein χ is n ,ε n ,φ n ,κ n ,μ,v,θ,/>ξ n Representing non-nessNegative lagrangian multiplier. Formula (15) can be re-expressed as
Wherein,
the dual problem of formula (18) is
Wherein the dual function is
From the karuss-Kuhn-turner (KKT) condition, the following closed-form solution can be obtained
Wherein [ x ]]+＝max(0,x)； Optimizing variables based on gradient descent methodAnd Lagrangian multiplier update expression as follows
μ l+1 ＝[μ l -Δμ×(T-t 1 -t 2 -t 3 )] + (30)
Wherein l represents the number of iterations; Δτ n ,Δα n ,Δχ n ,Δε n ,Δφ n ,Δκ n ,Δμ,Δν,Δθ,/>Δξ n A step size greater than zero. According to the variable substitution method, the +.>
Verification experiment: the application effect of the present invention will be described in detail with reference to simulation.
1) Simulation conditions
It is assumed that there is one primary transmitter in the primary network and one primary receiver and two secondary transmitters in the secondary network and two secondary receivers. The distance between the main transmitter and the main receiver is 9m, the distances from the two secondary transmitters to the main receiver are 7m and 6m, and the distances from the two secondary transmitters to the two secondary receivers are 4m and 5m. The antenna gain of the base station and the antenna gain of the secondary transmitter are set to 6dBi. The channel model isWherein d is n Is the distance between the transmitting end and the receiving end, χ=3 is the path loss index. Other parameters are shown in table 1.
Table 1 simulation parameters
2) Simulation results
FIG. 3 depicts the standard deviation E of the actual outage probability and the channel estimation error R Is a relationship of (3). 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 standard deviation epsilon R The actual outage probability of the algorithm of the present invention is lower than the non-robust algorithm and does not exceed the outage probability threshold ζ. Because the non-robust algorithm ignores the channel estimation error, the actual outage probability is higher than that of the algorithm, and therefore the algorithm has better robustness. Fig. 4 depicts the relationship of the overall throughput of the secondary system to the channel estimation error under different algorithms. The results show that with sigma h The overall throughput of the inventive algorithm, the pure backscatter algorithm and the pure collect-transmit algorithm is reduced. In contrast, the overall throughput remains unchanged since the non-robust algorithm ignores the uncertainty of the channel. Furthermore, the overall throughput of the algorithm is higher than that of the pure backscatter algorithm and the pure collect-transmit algorithm, further illustrating the effectiveness of the algorithm.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. A robust optimization method for a cognitive wireless power supply backscatter communication network is characterized by comprising the following steps:
s1: establishing a signal transmission model based on an underlying cognitive wireless power supply backscatter communication network;
the cognitive wireless power supply backscatter communication network comprises a main transmitter, a main receiver, N secondary transmitters and N secondary receivers; the secondary transmitter performs energy collection in the first stage; in the second stage the secondary transmitter transmits to the corresponding secondary in a time division multiple access protocolA machine reflection signal; the secondary transmitter actively transmits data to the corresponding secondary receiver by using the energy collected in the first two stages in the third stage; the established signal transmission model specifically comprises the following steps: definition P 0 For the transmission power of the main transmitter, in the energy harvesting phase t 1 The energy collected by the nth secondary transmitter is:
wherein eta n ∈[0,1]Representing the energy harvesting efficiency of the secondary transmitter n; g n Representing the channel gain of the primary transmitter to the secondary transmitter n;
in the backscattering stage t 2 The secondary transmitter n adopts a time division multiple access mode to reflect information to the secondary receiver n; in the reflection time slot tau n Reflection rate of an inner secondary transmitter n to a secondary receiver nExpressed as:
wherein W represents bandwidth, beta n Representing the reflection coefficient, h, of the secondary transmitter n n Representing the channel gain of secondary transmitter n to secondary receiver n,representing the background noise power of the secondary receiver n; in the energy harvesting stage t 1 And reflection time slot tau n Total energy collected by the nth secondary transmitter>The method comprises the following steps:
in the active transmission stage t 3 In time slot alpha n Data transmission rate R from inner secondary transmitter n to secondary receiver n n The method comprises the following steps:
wherein P is n Representing time slot alpha n The transmit power of the inner secondary transmitter n;
s2: the resource allocation problem of the secondary system throughput maximization is constructed 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, and the expression is as follows:
where g is the channel gain from the primary transmitter to the primary receiver,for the channel gain of the secondary transmitter n to the primary receiver,representing the noise power of the primary receiver; c (C) 1 Representing minimum rate constraints for secondary receiver n during the backscatter information phaseRepresenting a minimum rate threshold; c (C) 2 Representing the minimum rate constraint of the secondary receiver n during the active transmission phase,/>Representing a minimum rate threshold; c (C) 3 And C 4 Representing the quality of service constraints of the primary receiver, gamma min Representing a minimum quality of service threshold for the primary receiver; c (C) 5 Representing that the collected energy is greater than the sum of the energy consumed by the circuit itself and the energy consumed by the active transmission information phase; c (C) 6 ～C 8 Representing a transmission slot constraint; c (C) 9 Representing the reflection coefficient constraint of the secondary transmitter n;
s3: modeling the robust resource allocation problem by considering parameter uncertainty specifically includes: consider an additive model of uncertainty parameters and assume that the channel estimation error follows a gaussian distribution, i.e
Wherein,and->Representing a set of uncertainties; />Representing estimated channel gains for the nth secondary transmitter through the nth secondary receiver; />Representing the estimated channel gain of the nth secondary transmitter to the primary receiver; />Representing channel estimation errors from the nth secondary transmitter to the nth secondary receiver, obeying a mean value of zero and a variance of +.>Is a gaussian distribution of (c);representing the channel estimation error from the nth secondary transmitter to the primary receiver, subject to zero mean and varianceIs a gaussian distribution of (c);
the robust resource allocation problem P2 corresponding to P1 is that
Wherein,indicated in the reflection time slot tau n Interrupt probability constraint for secondary receiver n reflection rate, < >>Representing the secondary receiver n reflection rate threshold, ω n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />Representing the active transmission time slot alpha n Interrupt probability constraint for active transmission of information by secondary receiver n,/->Indicating the threshold of the active transmission rate, v n ∈[0,1]Representing an outage probability threshold for the secondary receiver n; />And->Representing outage probability constraints of the primary receiver, gamma min Representing the quality of service threshold of the primary receiver ζ ε [0,1 ]]Representing an outage probability threshold for protecting a minimum quality of service requirement of the primary receiver; pr {. Cndot. } represents outage probability;
s4: the problem constructed in the step S3 is converted into an equivalent convex optimization form by utilizing a Q function and a variable substitution method, and an analytic solution of transmission time, transmission power and reflection coefficient is obtained by utilizing a Lagrange dual method, and the method specifically comprises the following steps:
s41: converting the interrupt probability constraint into a deterministic constraint by using a Q function;
s42: based on variable substitution method, introducing auxiliary variableProcessing existing coupling variable constraints;
s43: and solving an analytical solution of the convex optimization problem by adopting a Lagrangian dual theory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111490922.1A CN114222318B (en) | 2021-12-08 | 2021-12-08 | Robust optimization method for cognitive wireless power supply backscatter communication network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111490922.1A CN114222318B (en) | 2021-12-08 | 2021-12-08 | Robust optimization method for cognitive wireless power supply backscatter communication network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114222318A CN114222318A (en) | 2022-03-22 |
| CN114222318B true CN114222318B (en) | 2023-12-12 |
Family
ID=80700208
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111490922.1A Active CN114222318B (en) | 2021-12-08 | 2021-12-08 | Robust optimization method for cognitive wireless power supply backscatter communication network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114222318B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115734252A (en) * | 2022-11-30 | 2023-03-03 | 浙江工业大学 | Cognitive wireless energy supply network optimization method based on backscattering relay transmission |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109618383A (en) * | 2018-12-18 | 2019-04-12 | 中国科学院深圳先进技术研究院 | Robust applied to environment backscattering wireless sensor network waits for an opportunity Routing Protocol |
| WO2020102813A1 (en) * | 2018-11-16 | 2020-05-22 | Totemic Labs, Inc. | System and method for processing multi-directional wireless backscattered signals |
| CN112512085A (en) * | 2020-12-21 | 2021-03-16 | 郑州大学产业技术研究院有限公司 | Robust opportunity constraint optimization method based on WPBC system security |
| CN112954736A (en) * | 2019-12-10 | 2021-06-11 | 深圳先进技术研究院 | Policy-based computation offload of wireless energy-carrying internet-of-things equipment |
-
2021
- 2021-12-08 CN CN202111490922.1A patent/CN114222318B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020102813A1 (en) * | 2018-11-16 | 2020-05-22 | Totemic Labs, Inc. | System and method for processing multi-directional wireless backscattered signals |
| CN109618383A (en) * | 2018-12-18 | 2019-04-12 | 中国科学院深圳先进技术研究院 | Robust applied to environment backscattering wireless sensor network waits for an opportunity Routing Protocol |
| CN112954736A (en) * | 2019-12-10 | 2021-06-11 | 深圳先进技术研究院 | Policy-based computation offload of wireless energy-carrying internet-of-things equipment |
| CN112512085A (en) * | 2020-12-21 | 2021-03-16 | 郑州大学产业技术研究院有限公司 | Robust opportunity constraint optimization method based on WPBC system security |
Non-Patent Citations (3)
| Title |
|---|
| Sahar Imtiaz;Sebastian Schiessl;Georgios P. Koudouridis ; James Gross.Coordinates-Based Resource Allocation Through Supervised Machine Learning.《IEEE Transactions on Cognitive Communications and Networking ( Volume: 7, Issue: 4, December 2021)》.2021,全文. * |
| Yongjun Xu;Xiaohui Zhao;Ying-Chang Liang.Robust Power Control and Beamforming in Cognitive Radio Networks: A Survey.《 IEEE Communications Surveys & Tutorials ( Volume: 17, Issue: 4, Fourthquarter 2015)》.2015,全文. * |
| 反向散射辅助的无线供能通信网络中用户能效公平性研究;叶迎晖;施丽琴;卢光跃;;通信学报(07);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114222318A (en) | 2022-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | Optimal resource allocation in full-duplex ambient backscatter communication networks for wireless-powered IoT | |
| CN109587690B (en) | Multi-point cooperative transmission method for unmanned aerial vehicle auxiliary cognitive satellite ground communication network | |
| CN109995413B (en) | Relay-assisted environment backscattering communication method | |
| CN113613273B (en) | Robust energy efficiency optimization method for intelligent super-surface auxiliary wireless power supply network | |
| CN106605378A (en) | Antenna-radiation-pattern selection for reduced interference | |
| CN109640371A (en) | Wireless energy-carrying relay communication method and network based on backscattering transmission | |
| CN113825159B (en) | Robust resource allocation method for wireless energy-carrying communication system based on intelligent reflecting surface | |
| CN114024640B (en) | Robust relay node selection method in full duplex energy collection relay system | |
| CN109586759B (en) | A kind of novel full duplex symbiosis communication system | |
| Shin et al. | Cooperative wireless powered communication networks with interference harvesting | |
| CN114222318B (en) | Robust optimization method for cognitive wireless power supply backscatter communication network | |
| CN113709687A (en) | Intelligent reflector assisted resource allocation method for wireless sensor network | |
| Tran et al. | RF energy harvesting: an analysis of wireless sensor networks for reliable communication | |
| CN115915362A (en) | STAR-RIS assisted NOMA system uplink low-power-consumption transmission method | |
| Bhowal et al. | RIS-aided communications in indoor and outdoor environments: Performance analysis with a realistic channel model | |
| Deepan et al. | On the performance of wireless powered communication networks over generalized κ− μ fading channels | |
| Ahmed et al. | Cooperative backscatter NOMA with imperfect SIC: towards energy efficient sum rate maximization in sustainable 6G networks | |
| CN114039706A (en) | Space-time coding sending method based on novel reconfigurable intelligent surface | |
| Du et al. | Sum rate maximization for UAV-enabled wireless powered NOMA systems | |
| Lagunas et al. | Power allocation for in-band full-duplex self-backhauling | |
| Zhou | Max-min throughput in hybrid of wireless powered NOMA and backscatter communications | |
| Yang et al. | Optimal resource allocation in full-duplex ambient backscatter communication networks for green IoT | |
| Fan et al. | Sum Rate Optimization for UAV-assisted NOMA-based Backscatter Communication System | |
| Zhang et al. | Performance analysis of relay based NOMA cooperative transmission under cognitive radio network | |
| Ye et al. | Total transmission time minimization in wireless powered hybrid passive-active communications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231117 Address after: 066000 No.50 Haiyang Road, Haigang District, Qinhuangdao City, Hebei Province Applicant after: QINHUANGDAO POWER SUPPLY COMPANY OF STATE GRID JIBEI ELECTRIC POWER Co.,Ltd. Address before: No. 135 Luozhuang, Shuanglu Village, Gucheng Town, Huaiyuan County, Bengbu City, Anhui Province, 233000 Applicant before: Zhao Chenglong Effective date of registration: 20231117 Address after: No. 135 Luozhuang, Shuanglu Village, Gucheng Town, Huaiyuan County, Bengbu City, Anhui Province, 233000 Applicant after: Zhao Chenglong Address before: 400065 Chongqing Nan'an District huangjuezhen pass Chongwen Road No. 2 Applicant before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |