WO2020215801A1 - Method and device for relay network energy efficiency optimized distribution, terminal, and storage medium - Google Patents

Abstract

Disclosed are a method and device for relay network energy efficiency optimized distribution, a terminal, and a storage medium. The method comprises: dividing an information transmission process from a source end to a receiving end on the basis of a preset distribution scheme; acquiring a relevant power parameter, a relevant channel gain parameter, a relevant circuit loss parameter in an information transmission process, and additive white Gaussian noise parameters of a relay, of the receiving end, and of a tapping end; then respectively calculating the energy collected by the relay, the rate of transmission from the source end to the receiving end, and the total energy consumption; constructing and solving a nonconvex optimization problem for maximum energy efficiency on the basis of the transmission rate and of the total energy consumption, and converting the problem into one D.C. optimization problem, thus calculating an optimal solution. The present invention, by acquiring the relevant parameters in the information transmission process, constructing and solving the nonconvex optimization problem for maximum energy efficiency, and then converting the problem into one D.C. optimization problem, thus producing the optimal solution, further complies with practical applications and, with the problem of tapping being taken into consideration, increases security.

Description

Relay network energy efficiency optimal distribution method, device, terminal and storage medium Technical field

The present invention relates to the technical field of resource optimization of wireless energy-carrying relay networks, and in particular to a method, device, terminal and storage medium for optimal allocation of energy efficiency of a relay network.

Background technique

With the continuous development of wireless communication networks and communication technologies, as well as the continuous consumption of limited resources such as energy, communication devices with energy harvesting functions are considered to be able to provide self-sustainable development for energy-constrained communication systems. Substitute. Wireless energy-carrying communication combines communication technology and wireless energy harvesting technology. The purpose is to realize the simultaneous transmission of information and energy, which can bring huge benefits in terms of spectrum efficiency, energy consumption, and interference management. At the same time, nodes in wireless networks can Use wireless energy-carrying communication to collect energy to extend its service life.

At present, as people pay more and more attention to energy and environmental issues, the concept of green communication is increasingly accepted by people. While ensuring that the communication network can provide users with high-speed communication services, the energy consumption of the network is reduced as much as possible, and the operation cost and energy consumption of the communication network are reduced, which is beneficial to the protection of the environment and the realization of green development. However, in the optimal configuration of communication network resources, the energy efficiency of the system is optimized only by using a simple linear energy receiving model, and the obtained results have a large deviation from the actual situation. Moreover, in the existing energy efficiency optimization schemes, eavesdropping is not considered. The existing problems of the user reduce the security of the communication network.

Summary of the invention

The present invention provides a relay network energy efficiency optimal distribution method, device, terminal and storage medium, so as to solve the problem that the maximum energy efficiency optimization scheme of the existing relay communication network has large actual deviation and low safety.

In order to solve the above problems, the present invention provides a method for optimal distribution of energy efficiency in a relay network, which is applied to the relay of a relay network system. The relay network system further includes a source end, a receiver end and an eavesdropping section; the method includes:

Receive the information and energy sent by the source based on a preset distribution method and forward the information to the receiving end;

Obtain the relevant power parameters, relevant channel gain parameters, relevant circuit loss parameters, and additive white Gaussian noise parameters of itself, the receiving end and the eavesdropping end during information transmission from the source to the receiving end;

Calculate the collected energy E according to related power parameters and related channel gain parameters;

Calculate the transmission rate R from the source end to the receiver end according to the relevant power parameters, relevant channel gain parameters and additive white Gaussian noise parameters;

Calculate total energy consumption E _tot according to relevant power parameters, relevant circuit loss parameters and energy E;

According to the transmission rate R and the total energy consumption E _tot, construct a non-convex optimization problem with the maximum energy efficiency maxEE as the goal;

By introducing variables and according to the theory of nonlinear fractional programming, the non-convex optimization problem is transformed into a D.C. optimization problem, and the optimal solution is calculated.

As a further improvement of the present invention, the preset allocation method includes a time allocation method or a power allocation method.

As a further improvement of the present invention, when the preset distribution mode is a time distribution mode, the steps of receiving the information and energy sent by the source terminal based on the preset distribution mode and forwarding the information to the receiving terminal include:

The transmission time from the source end to the receiving end is divided into three time periods: α ₁ T, α ₂ T, and α ₃ T, where α ₁ +α ₂ +α ₃ =1, T is the total transmission time, and α ₁ T time Collect the energy sent by the source in the period, receive the information sent by the source in the period α ₂ T, and forward the information to the receiving end in the period α ₃ T;

The steps of obtaining the information sent by the receiving source and the related power parameters, related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of the receiving end and the eavesdropping end when forwarding the information to the receiving end include:

Get the transmit power of the source on the nth subcarrier when collecting the energy sent by the source

The transmit power on the nth subcarrier when the receiving source sends information

The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

Where n ∈ N, N is the number of subcarriers;

Obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

Obtain the circuit loss when the source sends energy to itself

The circuit loss when the source sends information to itself

Circuit loss when transmitting information

And the circuit loss of the receiving end when receiving information

Get the variance of its own additive Gaussian white noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

As a further improvement of the present invention, when the preset allocation method is a power allocation method, the steps of receiving the information and energy sent by the source end based on the preset allocation method and forwarding the information to the receiving end include:

Set the power distribution ratio of the source to send information and energy to θ, and in the first T/2 time, the source sends information and energy to itself, and in the next T/2 time, it forwards the information to the receiving end. Is the total transmission time;

The steps of obtaining the information sent by the receiving source and the related power parameters, related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of the receiving end and the eavesdropping end when forwarding the information to the receiving end include:

The transmit power of the source on the nth subcarrier when obtaining the information and energy sent by the source

The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

n ∈ N, N is the number of subcarriers;

Obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

Obtain the circuit loss when the source sends information and energy

Circuit loss when transmitting information

Circuit loss at the receiving end when receiving information

Get the variance of its own additive Gaussian white noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

In order to solve the above problems, the present invention also provides a relay network energy efficiency optimal distribution device, which includes:

The dividing module is used to receive the information and energy sent by the source terminal based on a preset distribution method and forward the information to the receiving terminal;

The parameter acquisition module is used to acquire relevant power parameters, relevant channel gain parameters, relevant circuit loss parameters, and additive white Gaussian noise parameters of itself, the receiving end and the eavesdropping end during information transmission from the source end to the receiving end;

The collected energy calculation module is used to calculate the collected energy E according to related power parameters and related channel gain parameters;

The transmission rate calculation module is used to calculate the transmission rate R from the source end to the receiving end according to the relevant power parameters, the relevant channel gain parameters and the additive white Gaussian noise parameters;

The energy consumption calculation module is used to calculate the total energy consumption E _tot according to related power parameters, related circuit loss parameters and energy E;

The building module is used to construct a non-convex optimization problem with the maximum energy efficiency maxEE as the goal according to the transmission rate R and the total energy consumption E _tot ;

The transformation module is used to transform the non-convex optimization problem into a D.C. optimization problem by introducing variables and according to the nonlinear fractional programming theory, and calculate the optimal solution.

As a further improvement of the present invention, the preset allocation method includes a time allocation method or a power allocation method.

As a further improvement of the present invention, when the preset allocation method is time allocation, the dividing module is used to divide the transmission time from the source end to the receiving end into three times: α ₁ T, α ₂ T, and α ₃ T Segment, where α ₁ + α ₂ + α ₃ =1, T is the total transmission time, the energy sent by the source is collected in the α ₁ T period, the information sent by the source is received in the α ₂ T period, and α ₃ T is time Forward information within the segment to the receiving end;

The parameter acquisition module includes:

The first power parameter obtaining unit is used to obtain the transmission power of the source on the nth subcarrier when the energy sent by the source is collected

The transmit power on the nth subcarrier when the receiving source sends information

The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

Where n ∈ N, N is the number of subcarriers;

The first channel gain parameter obtaining unit is used to obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

The first circuit loss parameter acquisition unit is used to acquire the circuit loss when the source sends energy to itself

The circuit loss when the source sends information to itself

Circuit loss when transmitting information

And the circuit loss of the receiving end when receiving information

The first noise parameter obtaining unit is used to obtain the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

As a further improvement of the present invention, when the preset distribution mode is the power distribution mode, the dividing module is used to set the power distribution ratio of the source to send information and energy to θ, and within the first T/2 time, the source Send information and energy to itself, and forward the information to the receiving end within the next T/2, where T is the total transmission time;

The parameter acquisition module includes:

The second power parameter obtaining unit is used to obtain the transmission power of the source end on the nth subcarrier when the information and energy sent by the source end are received by itself

The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

n ∈ N, N is the number of subcarriers;

The second channel gain parameter obtaining unit is used to obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

The second circuit loss parameter acquisition unit is used to acquire the circuit loss when the source sends information and energy

Circuit loss when transmitting information

Circuit loss at the receiving end when receiving information

The second noise parameter acquisition unit is used to acquire additive white Gaussian noise parameters, and the additive white Gaussian noise parameters include: the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

In order to solve the above problems, the present invention also provides a terminal, which includes a memory and a processor, the processor is coupled to the memory, and a computer program that can run on the processor is stored in the memory;

When the processor executes the computer program, it implements the steps in any one of the above-mentioned methods for optimal distribution of energy efficiency in a relay network.

In order to solve the above-mentioned problems, the present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above-mentioned methods for optimal distribution of energy efficiency of a relay network are realized.

The invention obtains the relevant power parameters, relevant channel gain parameters, relevant circuit loss parameters in the process of transmitting information and energy from the source end to the relay and relaying the information to the receiving end, as well as the additivity of the relay itself, the receiving end and the eavesdropping end. Gaussian white noise parameters, and calculate the energy collected by the relay itself, the transmission rate of information sent from the source to the receiving end, and the total energy consumption in the process of sending information from the source to the receiving end based on the above parameters, and then construct a The non-convex optimization problem with maximum energy efficiency as the goal, and then through the introduction of variables and based on the nonlinear fractional programming theory, the non-convex optimization problem is transformed into a DC optimization problem, that is, a more realistic nonlinear energy receiving model is obtained. In addition, the eavesdropping end is also taken into consideration, while optimizing the energy efficiency of the system from the source end to the receiving end, the security performance in the information transmission process is improved.

Description of the drawings

Figure 1 is a schematic block diagram of an embodiment of a relay network system according to the present invention;

2 is a schematic flowchart of a first embodiment of a method for optimal distribution of energy efficiency in a relay network according to the present invention;

3 is a schematic flowchart of a second embodiment of a method for optimal distribution of energy efficiency in a relay network according to the present invention;

4 is a schematic flowchart of a third embodiment of a method for optimal distribution of energy efficiency in a relay network according to the present invention;

5 is a schematic diagram of functional modules of a first embodiment of a device for optimally assigning energy efficiency to a relay network according to the present invention;

6 is a schematic diagram of functional modules of a second embodiment of a device for optimal distribution of energy efficiency in a relay network according to the present invention;

FIG. 7 is a schematic diagram of functional modules of a third embodiment of a device for optimal distribution of energy efficiency in a relay network according to the present invention;

Fig. 8 is a schematic block diagram of an embodiment of a terminal of the present invention.

Detailed ways

In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

First, as shown in Figure 1, the method for optimal distribution of energy efficiency in a relay network provided by the present invention relates to a relay network system, which includes a source terminal 10, a relay terminal 11, a receiving terminal 12, and an eavesdropping terminal 13. Among them, the relay 11 has the ability to collect energy. First, the source 10 sends a signal to the relay 11, and the relay 11 performs energy harvesting. Then the source 10 sends information to the relay 11, and finally the relay 11 uses the collected energy to forward The information from the source end 10 arrives at the receiving end 12, and the eavesdropping end 13 can steal information from the relay 11 during the information transmission process. Assuming that the relay 11 has no initial available energy, consider that the channels from the source 10 to the relay 11, the relay 11 to the receiving end 12, and the relay 11 to the eavesdropping end 13 are all multi-carrier transmission, and the number of available subcarriers is N.

Fig. 2 shows the first embodiment of the optimal distribution method for energy efficiency of the relay network of the present invention. As shown in FIG. 2, in this embodiment, the optimal distribution method for energy efficiency of the relay network includes:

Step S1, receiving the information and energy sent by the source terminal based on a preset distribution method and forwarding the information to the receiving terminal.

It should be noted that the preset allocation method includes a time allocation method or a power allocation method.

Step S2: Obtain related power parameters, related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of the source end to the receiving end during information transmission.

Specifically, when the relay receives the information and energy sent by the source end and forwards the information to the receiving end, the relevant power parameters, the relevant channel gain parameters, the relevant circuit loss parameters, and the additivity of itself, the receiving end and the eavesdropping end are obtained. Gaussian white noise parameters, where the relevant power parameters include the transmission power between the source, relay and the receiver, and the relevant channel gain parameters include the channel gains from the source to the relay, from the relay to the receiver, and from the relay to the eavesdropping section. Related circuit loss parameters include the circuit loss when the source sends information and energy, the circuit loss when relaying and forwarding information, the circuit loss when the receiving end receives information, and the additive white Gaussian noise parameters include relay, receiving, and wiretapping. The variance of the additive white Gaussian noise with the mean value of the segment being 0.

Step S3: Calculate the collected energy E according to the relevant power parameter and the relevant channel gain parameter.

Specifically, the relay has the ability to collect energy from the surrounding environment. In this embodiment, after obtaining the relevant power parameter and the relevant channel gain parameter, the energy E collected by the relay can be calculated.

Step S4: Calculate the transmission rate R from the source end to the receiving end according to the relevant power parameter, the relevant channel gain parameter and the additive white Gaussian noise parameter.

Specifically, the achievable transmission rate when the source terminal sends information to the relay and the achievable transmission rate when the relay transmits information to the receiving terminal are calculated through the relevant power parameters, the relevant channel gain parameters and the additive white Gaussian noise parameters, and Taking into account the interference of the eavesdropping channel, combined with the additive white Gaussian noise parameter of the eavesdropping end, the achievable transmission rate R from the source end to the receiving end under security conditions can be obtained.

Step S5: Calculate the total energy consumption E _tot according to the relevant power parameters, the relevant circuit loss parameters and the energy E.

Specifically, in this embodiment, both the source and the relay need to send information, so they are equivalent to the transmitter. In an actual wireless network, the transmitter sends signals not only because of the transmission power consumption, but also the actual The consumption of the circuit, including frequency modulation and amplitude modulation, AD/DA conversion, filtering and power amplification, etc. When the transmitting power of the transmitter P>0, we consider the transmitter to be in the "on" state. When P=0, the transmitter is in the "on" state. "off" state, P _C,on ≥0, P _C,off ≥0 respectively represent the actual circuit losses in the "on" and "off" states, so the actual power consumption model for the wireless transmitter can be expressed as

Among them, ε is a multiplicative constant used to reflect the low efficiency of the radio frequency circuit. P _total represents the total energy consumed by the transmitter. In fact, PC _{,off is} much smaller than PC _,on , so for simplicity, we can consider P _C,on ＞0, and P _C,off =0, so the actual power consumption model of the wireless transmitter can be expressed as P _total =εP+P _C,on , so as to calculate the value in the process of sending information from the source to the receiving end Total energy consumption E _tot .

Step S6, construct a non-convex optimization problem with the maximum energy efficiency maxEE as the target according to the transmission rate R and the total energy consumption E _tot .

Specifically, based on the calculated transmission rate R from the source to the receiving end and the total energy consumption E _{tot of the} source, relay, and receiving end, and then comprehensively consider the limitation of transmission power, time allocation, and communication quality requirements, etc., construct A non-convex optimization problem targeting the maximum energy efficiency maxEE can be expressed as:

In step S7, the non-convex optimization problem is transformed into a D.C. optimization problem by introducing variables and according to the nonlinear fractional programming theory, and the optimal solution is calculated.

Specifically, through the introduction of variables, combined with nonlinear fractional programming theory, the above non-convex optimization problem with the maximum energy efficiency maxEE as the goal is transformed into a DC optimization problem, and then it is approximated at each feasible point to obtain a convex Optimize the problem, and then solve the convex optimization problem to get the optimal solution.

In this embodiment, the related power parameters, related channel gain parameters, related circuit loss parameters in the process of acquiring the source end sending information and energy to the relay and relaying the information to the receiving end, as well as the increase of the relay itself, the receiving end and the eavesdropping end Gaussian white noise parameters, and calculate the energy collected by the relay itself, the transmission rate of information sent from the source to the receiving end, and the total energy consumption in the process of sending information from the source to the receiving end based on the above parameters, and then construct a The non-convex optimization problem with maximum energy efficiency as the goal, and then through the introduction of variables and based on the nonlinear fractional programming theory, the non-convex optimization problem is transformed into a DC optimization problem, that is, a more realistic nonlinear energy receiving model is obtained In addition, the eavesdropping terminal is also taken into consideration to optimize the energy efficiency of the system from the source terminal to the receiving terminal while improving the security performance in the information transmission process.

Further, as shown in Fig. 3, Fig. 3 shows a second embodiment of the optimal distribution method for energy efficiency of a relay network of the present invention. In this embodiment, the mode under the preset conditions is the mode of time distribution, and the method for optimal distribution of energy efficiency of the relay network includes the following steps:

In step S10, the transmission time from the source end to the receiving end is divided into three time periods α ₁ T, α ₂ T, and α ₃ T.

It should be noted that α ₁ + α ₂ + α ₃ =1, T is the total transmission time, the energy sent by the source is collected during the period α ₁ T, and the information sent by the source is received during the period α ₂ T, α ₃ The information is forwarded to the receiving end within T time period.

Step S11: Obtain the transmit power of the source on the nth subcarrier when the energy sent by the source is collected

The transmit power on the nth subcarrier when the receiving source sends information

The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

It should be noted that the channels from the source end to the relay, the relay to the receiving end, and the relay to the eavesdropping end are all multi-carrier transmissions, and the number of available subcarriers is N, where n represents the first subcarrier, n∈N.

Step S12: Obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

Step S13: Obtain the circuit loss when the source sends energy to itself

The circuit loss when the source sends information to itself

Circuit loss when transmitting information

And the circuit loss of the receiving end when receiving information

Step S14, obtain the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

Step S15: Calculate the collected energy E according to the relevant power parameter and the relevant channel gain parameter.

Specifically, the energy sent by the source is collected in the α ₁ T time period, specifically according to the transmission power of the source on the nth subcarrier when the energy sent by the source is collected

And the channel gain of the nth subcarrier from source to relay

And according to the time distribution method, the energy E=α ₁ TΦ is calculated, where,

a, b, and M are preset constants, which can be obtained by data fitting of actual measurement data.

Step S16: Calculate the transmission rate R from the source end to the receiver end according to the relevant power parameter, the relevant channel gain parameter and the additive white Gaussian noise parameter.

Specifically, in the α ₂ T time period, the information sent by the source is received, and the transmission power on the nth subcarrier when the information is sent by the receiving source is

The channel gain of the nth subcarrier from the source to itself

Variance of repeating additive white Gaussian noise

Calculate the achievable transmission rate from the source to the relay

among them,

Forward the information to the receiving end in the α ₃ T time period, according to the transmit power of the source end on the nth subcarrier when the information is forwarded to the receiving end

Channel gain from repeater to receiver

Variance of additive white Gaussian noise at the receiving end

Calculate the achievable transmission rate of the relay forwarding information to the receiving end

among them,

At this time, considering the interference of the eavesdropping channel, therefore, the transmission rate that can be finally achieved from the source to the receiver

among them,

Step S17: Calculate the total energy consumption E _tot according to the relevant power parameters, the relevant circuit loss parameters and the energy E.

Specifically, it can be seen from the above embodiment that the actual power consumption model can be expressed as P _total =εP+PC _,on , where ε is a multiplication constant used to reflect the low efficiency of the radio frequency circuit, and in this embodiment, There are circuit losses at the source, relay, and receiver. According to the above consumption model, we know:

The energy consumed by the source to send information and energy is

The energy consumed by the relay is

The receiving end only needs to receive information, but does not need to send information. Therefore, the energy consumed by the receiving end is

Thus, combined with the energy E collected by the relay, the total energy consumption in the process of sending information from the source to the sink

Step S18, construct a non-convex optimization problem with the maximum energy efficiency maxEE as the target according to the transmission rate R and the total energy consumption E _tot .

Specifically, considering the transmission power limitation, time allocation, and communication quality requirements, a non-convex optimization problem with the maximum energy efficiency maxEE as the goal is constructed according to the transmission rate R and the total energy consumption E _tot , which can be expressed as:

Among them, st is the constraint condition of the maximum energy efficiency maxEE expression. Constraints (1) and (2) indicate that the transmission power of energy and information sent by the source must not exceed its maximum available power threshold P _MAX , and constraint (3) indicates not considering In order to ensure that the communication is not interrupted, the relay must meet the requirement that the collected energy is greater than or equal to the energy consumed when forwarding information. Constraint (4) indicates that in order to ensure communication quality, the safe transmission rate must be higher than the set Threshold R _Q.

In step S19, the non-convex optimization problem is transformed into a D.C. optimization problem by introducing variables and according to the nonlinear fractional programming theory, and the optimal solution is calculated.

Specifically, it can be seen from Formula 1 that all optimization variables are divided into two categories, so we have a variable set Α={α ₁ ,α ₂ ,α ₃ },

Moreover, it can be seen that the above optimization problem is a non-convex optimization problem and is difficult to solve. Therefore, this embodiment proves

with

And introduce the variables y and z, and then define new variables according to the theory of nonlinear fractional programming

Thus, the non-convex optimization problem is transformed into a DC optimization problem, where the variable y satisfies

So as to get

Variable z satisfies

And α ₁ TΦ≥z, so that

Therefore, the above-mentioned problem of maximizing energy efficiency can be described as:

The objective function of the transformed optimization problem is

It is a DC function for a given value of q. The 4th, 5th, 6th, 7th, 11th and 12th constraint conditions in the constraint conditions also belong to the DC function, that is, the form is f(x)-g(x), where x Is a vector composed of all variables, f(x) and g(x) are convex functions, so the problem belongs to the DC optimization problem, and the idea of solving the DC optimization problem is to approximate it at each feasible point to obtain a convex Optimize the problem, and then solve the convex optimization problem to get the optimal solution. This iterative step continues until convergence. The specific solution process is as follows:

1: Set the calculation accuracy of the algorithm Δ, given the initial value of q, set the number of iterations i=0;

2: Given an initial value X(0,0) that satisfies the variable constraints, set the number of iterations k=0, where X represents the vector formed by all variables in the optimization problem;

3: Let i=i+1;

4: Let k=k+1;

5: Solve the approximate convex optimization problem and get the optimal solution X(i,k);

6: Judge whether the absolute value of the difference between the objective function values of steps k and k-1 is less than or equal to the set accuracy, if less than or equal, proceed to step 7, otherwise skip to step 4;

7: Update the value of q,

8: Judge whether the difference between the q value of the i-th step and the i-1 step is less than or equal to the set precision, if it is less than or equal to the set precision, the solution obtained in the i-th step is determined to be the final solution of the algorithm, and then the q obtained in the i-th step The value is applied to formula 2 to solve and obtain the optimal solution, otherwise skip to step 3 to continue execution.

Further, as shown in FIG. 4, FIG. 4 shows a third embodiment of the method for optimal distribution of energy efficiency in a relay network of the present invention. In this embodiment, the way under the preset conditions is the way of power distribution, and the method for optimal distribution of energy efficiency of the relay network includes the following steps:

Step S20: Set the power distribution ratio of the source to send information and energy to θ, and within the first T/2, the source sends information and energy to itself, and within the next T/2, it forwards the information to the receiver. end.

Specifically, where 0<θ<1, T is the total transmission time.

Step S21: Obtain the transmit power of the source on the nth subcarrier when the information and energy sent by the source are received by itself

The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

It should be noted that the channels from the source end to the relay, the relay to the receiving end, and the relay to the eavesdropping end are all multi-carrier transmissions, and the number of available subcarriers is N, where n represents the first subcarrier, n∈N.

Step S22: Obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

Step S23: Obtain the circuit loss when the source sends information and energy

Circuit loss when transmitting information

Circuit loss at the receiving end when receiving information

Step S24, obtain the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

Step S25: Calculate the collected energy E sent by the source end according to the relevant power parameter and the relevant channel gain parameter.

Specifically, in the first T/2 time, the source sends information and energy to itself, and relays the received energy

among them,

a, b, and M are preset constants, which can be obtained by data fitting of actual measurement data.

Step S26: Calculate the transmission rate R from the source end to the receiver end according to the relevant power parameter, the relevant channel gain parameter and the additive white Gaussian noise parameter.

Specifically, in the first T/2 time, the source sends a signal to the relay, and the transmission rate from the source to the relay at this time is

among them,

In the last T/2 time, the relay uses the collected energy to send the received information to the receiving end. At this time, the transmission rate from the relay to the receiving end is

among them,

At this time, considering the interference of the eavesdropping channel, therefore, the transmission rate that can be finally achieved from the source to the receiver

among them,

Step S27: Calculate the total energy consumption E _tot according to the relevant power parameters, the relevant circuit loss parameters and the energy E.

Specifically, it can be seen from the above embodiment that the actual power consumption model can be expressed as P _total =εP+PC _,on , where ε is a multiplication constant used to reflect the low efficiency of the radio frequency circuit, and in this embodiment, There are circuit losses at the source, relay, and receiver. According to the above consumption model, we know:

The energy consumed by the source to send information and energy is

The energy consumed by the relay is

The receiving end only needs to receive information, but does not need to send information. Therefore, the energy consumed by the receiving end is

Thus, combined with the energy E collected by the relay, the total energy consumption in the process of sending information from the source to the sink

Step S28, construct a non-convex optimization problem with the maximum energy efficiency maxEE as the target according to the transmission rate R and the total energy consumption E _tot .

Specifically, considering the transmission power limitation, time allocation, and communication quality requirements, a non-convex optimization problem with the maximum energy efficiency maxEE as the goal is constructed according to the transmission rate R and the total energy consumption E _tot , which can be expressed as:

Among them, st is the constraint condition of the maximum energy efficiency maxEE expression. Constraint (1) means that the transmission power of the source and information must not exceed its maximum available power threshold P _MAX , and constraint (2) means that the initial energy of the relay is not considered In order to ensure uninterrupted communication, the relay must satisfy that the collected energy is greater than or equal to the energy consumed when forwarding information. Constraint (3) indicates that in order to ensure communication quality, the safe transmission rate must be higher than the set threshold R _Q.

In step S29, the non-convex optimization problem is transformed into a D.C. optimization problem by introducing variables and according to the nonlinear fractional programming theory, and the optimal solution is calculated.

Specifically, according to the above formula 3, it is proved that

And introduce the variables y and z, and then define new variables according to the theory of nonlinear fractional programming

Thus, the non-convex optimization problem is transformed into a DC optimization problem. Therefore, the above-mentioned problem of maximizing energy efficiency can be described as:

Refer to the solution process of the D.C. optimization problem in the second embodiment to obtain the optimal solution. For details, please refer to the solution process of the D.C. optimization problem in the second embodiment, which will not be repeated here.

Fig. 5 shows an embodiment of the optimal distribution device for energy efficiency of the relay network of the present invention. In this embodiment, the relay network energy efficiency optimal distribution device includes a dividing module 10, a parameter obtaining module 11, a collected energy calculation module 12, a transmission rate calculation module 13, an energy consumption calculation module 14, a construction module 15 and a conversion module 16.

Among them, the dividing module 10 is used to receive the information and energy sent by the source end based on a preset allocation method and forward the information to the receiving end; the parameter obtaining module 11 is used to obtain related power parameters, Related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of itself, the receiving end and the eavesdropping end; the collected energy calculation module 12 is used to calculate the collected energy E according to the related power parameters and the related channel gain parameters; transmission The rate calculation module 13 is used to calculate the transmission rate R from the source end to the receiving end according to the relevant power parameters, the relevant channel gain parameters and the additive white Gaussian noise parameters; the energy consumption calculation module 14 is used to calculate the relevant power parameters and the relevant circuit loss parameters And energy E to calculate the total energy consumption E _tot ; the construction module 15 is used to construct a non-convex optimization problem with the maximum energy efficiency maxEE as the goal according to the transmission rate R and the total energy consumption E _tot ; the transformation module 16 is used to introduce variables According to the theory of nonlinear fractional programming, the non-convex optimization problem is transformed into a DC optimization problem, and the optimal solution is calculated.

On the basis of the foregoing embodiment, in other embodiments, the preset allocation method includes a time allocation method or a power allocation method.

On the basis of the foregoing embodiment, in other embodiments, as shown in FIG. 6, when the preset allocation method is a time allocation method, the dividing module is used to divide the transmission time from the source end to the sink end as α ₁ T , Α ₂ T, α ₃ T three time periods, where α ₁ +α ₂ +α ₃ =1, T is the total transmission time, the energy sent by the source is collected during the α ₁ T time period, and the α ₂ T time period Receive the information sent by the source, and forward the information to the receiving end within the α ₃ T time period;

The parameter acquisition module 11 includes a first power parameter acquisition unit 1100, a first channel gain parameter acquisition unit 1101, a first circuit loss parameter acquisition unit 1102, and a first noise parameter acquisition unit 1103.

Wherein, the first power parameter obtaining unit 1100 is configured to obtain the transmit power of the source on the nth subcarrier when the energy sent by the source is collected

The transmit power on the nth subcarrier when the receiving source sends information

The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

Where n ∈ N, N is the number of subcarriers; the first channel gain parameter acquisition unit 1101 is used to acquire the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

The first circuit loss parameter acquisition unit 1102 is used to acquire the circuit loss when the source sends energy to itself

The circuit loss when the source sends information to itself

Circuit loss when transmitting information

And the circuit loss of the receiving end when receiving information

The first noise parameter obtaining unit 1103 is configured to obtain the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

On the basis of the foregoing embodiment, in other embodiments, as shown in FIG. 7, when the preset allocation method is the power allocation method, the dividing module is used to set the power allocation ratio of the source to send information and energy to θ, And in the first T/2, the source sends information and energy to itself, and in the latter T/2, it forwards the information to the receiving end, and T is the total transmission time;

The parameter acquisition module 11 includes a second power parameter acquisition unit 1110, a second channel gain parameter acquisition unit 1111, a second circuit loss parameter acquisition unit 1112, and a second noise parameter acquisition unit 1113.

Wherein, the second power parameter obtaining unit 1110 is used to obtain the transmit power of the source end on the nth subcarrier when it receives the information and energy sent by the source end.

The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

n ∈ N, N is the number of subcarriers; the second channel gain parameter obtaining unit 1111 is used to obtain the channel gain of the nth subcarrier from the source to itself

Channel gain from self to receiver

And the channel gain from itself to the eavesdropper

The second circuit loss parameter acquisition unit 1112 is used to acquire the circuit loss when the source sends information and energy

Circuit loss when transmitting information

Circuit loss at the receiving end when receiving information

The second noise parameter obtaining unit 1113 is configured to obtain additive white Gaussian noise parameters, and the additive white Gaussian noise parameters include: the variance of its own additive white Gaussian noise

Variance of additive white Gaussian noise at the receiving end

And the variance of the additive white Gaussian noise

For other details of the implementation of the technical solutions of the modules of the device for optimal distribution of energy efficiency in the relay network in the foregoing embodiment, reference may be made to the description of the method for optimal distribution of energy efficiency of the relay network in the foregoing embodiment, which is not repeated here.

FIG. 8 shows a schematic block diagram of a terminal provided by another embodiment of the present invention. Referring to FIG. 8, the terminal in this embodiment includes: one or at least two processors 80, a memory 81, and the A computer program 810 running on the processor 80. When the processor 80 executes the computer program 810, it implements the steps in the method for optimal distribution of energy efficiency of the relay network described in the foregoing embodiment, for example: step S1-step S7 shown in FIG. 2. Or, when the processor 80 executes the computer program 810, it realizes the functions of the modules/units in the above-mentioned embodiment of the device for optimal distribution of energy efficiency in a relay network based on multi-mode integration, for example: the functions of module 10-module 16 shown in FIG. 5 .

The computer program 810 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 81 and executed by the processor 80 to complete the application. One or more modules/units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 810 in the terminal.

The terminal includes but is not limited to a processor 80 and a memory 81. Those skilled in the art can understand that FIG. 8 is only an example of the terminal, and does not constitute a limitation on the terminal. It may include more or less components than shown in the figure, or combine some components, or different components, such as a terminal. It can also include input devices, output devices, network access devices, buses, and so on.

The processor 80 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), ready-made Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The memory 81 can be a read-only memory, a static storage device that can store static information and instructions, a random access memory, or a dynamic storage device that can store information and instructions, or it can be an electrically erasable programmable read-only memory or a read-only optical disk. , Or other optical disk storage, optical disk storage, magnetic disk storage media or other magnetic storage devices. The memory 81 and the processor 80 may be connected through a communication bus, or may be integrated with the processor 80.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail or recorded in an embodiment, reference may be made to related descriptions of other embodiments.

A person of ordinary skill in the art may be aware that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of modules or units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another device, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

In addition, the functional units in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.

An embodiment of the present application also provides a storage medium for storing a computer program, which contains program data designed for executing the foregoing embodiment of the method for optimal distribution of energy efficiency in a relay network of the present application. By executing the computer program stored in the storage medium, the optimal distribution method for the energy efficiency of the relay network provided in this application can be realized.

If the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, this application implements all or part of the processes in the above-mentioned embodiment methods, and can also be completed by instructing relevant hardware through a computer program 810. The computer program 810 can be stored in a computer-readable storage medium. When executed by the processor 80, 810 may implement the steps of the foregoing method embodiments. The computer program 810 includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. Computer-readable media may include: any entity or device capable of carrying computer program code, recording media, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access Memory (RAM, Random Access Memory), electric carrier signal, telecommunications signal, software distribution medium, etc. It should be noted that the content contained in computer-readable media can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, computer-readable media does not include It is electric carrier signal and telecommunication signal.

It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. For the same and similar parts between the various embodiments, refer to each other. can. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment.

The specific embodiments of the invention are described in detail above, but they are only examples, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions made to the invention are also within the scope of the invention. Therefore, equivalent changes, modifications and improvements made without departing from the spirit and principle of the invention Etc., should be covered within the scope of the present invention.

Claims (10)

1. A method for optimal distribution of energy efficiency in a relay network, characterized in that it is applied to the relay of a relay network system, the relay network system further includes a source end, a receiver end and an eavesdropping section; the method includes:

   Receive the information and energy sent by the source based on a preset distribution method and forward the information to the receiving end;

   Obtain the relevant power parameters, relevant channel gain parameters, relevant circuit loss parameters, and additive white Gaussian noise parameters of itself, the receiving end and the eavesdropping end during information transmission from the source to the receiving end;

   Calculating the collected energy E according to the relevant power parameter and the relevant channel gain parameter;

   Calculating the transmission rate R from the source end to the receiving end according to the relevant power parameter, the relevant channel gain parameter and the additive white Gaussian noise parameter;

   Calculate the total energy consumption E _tot according to the relevant power parameter, the relevant circuit loss parameter and the energy E;

   Construct a non-convex optimization problem with the maximum energy efficiency maxEE as the target according to the transmission rate R and the total energy consumption E _tot ;

   The non-convex optimization problem is transformed into a D.C. optimization problem by introducing variables and according to the nonlinear fractional programming theory, and the optimal solution is calculated.
2. The method for optimal allocation of energy efficiency in a relay network according to claim 1, wherein the preset allocation method includes a time allocation method or a power allocation method.
3. The method for optimal distribution of energy efficiency in a relay network according to claim 2, wherein when the preset distribution mode is a time distribution mode, the information sent by the source end is received based on the preset distribution mode The steps of combining energy and forwarding information to the receiving end include:

   The transmission time from the source end to the receiving end is divided into three time periods α ₁ T, α ₂ T, and α ₃ T, where α ₁ +α ₂ +α ₃ =1, and T is the total transmission time. The α ₁ collecting period T an energy source transmitted, the period [alpha] ₂ T receiving information sent by the source, the time period α ₃ T forwarding information to the receiving end;

   The step of obtaining the information sent by the receiving source and the related power parameters, related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of the receiving end and the eavesdropping end when forwarding the information to the receiving end includes:

   Get the transmit power of the source on the nth subcarrier when collecting the energy sent by the source

   

   The transmit power on the nth subcarrier when the receiving source sends information

   

   The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

   

   Where n ∈ N, N is the number of subcarriers;

   Obtain the channel gain of the nth subcarrier from the source to itself

   

   Channel gain from self to receiver

   

   And the channel gain from itself to the eavesdropper

   

   Obtain the circuit loss when the source sends energy to itself

   

   The circuit loss when the source sends information to itself

   

   Circuit loss when transmitting information

   

   And the circuit loss of the receiving end when receiving information

   

   Get the variance of its own additive Gaussian white noise

   

   Variance of additive white Gaussian noise at the receiving end

   

   And the variance of the additive white Gaussian noise

   
4. The method for optimal distribution of energy efficiency in a relay network according to claim 2, wherein when the preset distribution mode is a power distribution mode, the information sent by the source terminal is received based on the preset distribution mode The steps of combining energy and forwarding information to the receiving end include:

   Set the power distribution ratio of the source to send information and energy to θ, and in the first T/2 time, the source sends information and energy to itself, and in the next T/2 time, it forwards the information to the receiving end. Is the total transmission time;

   The step of obtaining the information sent by the receiving source and the related power parameters, related channel gain parameters, related circuit loss parameters, and additive white Gaussian noise parameters of the receiving end and the eavesdropping end when forwarding the information to the receiving end includes:

   The transmit power of the source on the nth subcarrier when obtaining the information and energy sent by the source

   

   The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

   

   n ∈ N, N is the number of subcarriers;

   Obtain the channel gain of the nth subcarrier from the source to itself

   

   Channel gain from self to receiver

   

   And the channel gain from itself to the eavesdropper

   

   Obtain the circuit loss when the source sends information and energy

   

   Circuit loss when transmitting information

   

   Circuit loss at the receiving end when receiving information

   

   Get the variance of its own additive Gaussian white noise

   

   Variance of additive white Gaussian noise at the receiving end

   

   And the variance of the additive white Gaussian noise

   
5. An optimal distribution device for energy efficiency of a relay network, which is characterized in that it comprises:

   The dividing module is used to receive the information and energy sent by the source terminal based on a preset distribution method and forward the information to the receiving terminal;

   The parameter acquisition module is used to acquire relevant power parameters, relevant channel gain parameters, relevant circuit loss parameters, and additive white Gaussian noise parameters of itself, the receiving end and the eavesdropping end during information transmission from the source end to the receiving end;

   A collected energy calculation module, configured to calculate the collected energy E according to the relevant power parameter and the relevant channel gain parameter;

   A transmission rate calculation module, configured to calculate the transmission rate R from the source end to the receiver end according to the relevant power parameter, the relevant channel gain parameter, and the additive white Gaussian noise parameter;

   An energy consumption calculation module, configured to calculate total energy consumption E _tot according to the relevant power parameter, the relevant circuit loss parameter, and the energy E;

   A construction module for constructing a non-convex optimization problem with the maximum energy efficiency maxEE as the goal according to the transmission rate R and the total energy consumption E _tot ;

   The transformation module is used to transform the non-convex optimization problem into a D.C. optimization problem by introducing variables and according to the theory of nonlinear fractional programming, and calculate the optimal solution.
6. The device for optimally allocating energy efficiency in a relay network according to claim 5, wherein the preset allocation method includes a time allocation method or a power allocation method.
7. The device for optimal distribution of energy efficiency in a relay network according to claim 6, wherein when the preset distribution mode is a time distribution mode, the division module is used to send information from the source to the receiver. The transmission time is divided into three time periods of α ₁ T, α ₂ T, and α ₃ T, where α ₁ +α ₂ +α ₃ =1, and T is the total transmission time. The collection source sends in the α ₁ T time period. The information sent by the source end is received within the α ₂ T time period, and the information is forwarded to the receiving end within the α ₃ T time period;

   The parameter acquisition module includes:

   The first power parameter obtaining unit is used to obtain the transmission power of the source on the nth subcarrier when the energy sent by the source is collected

   

   The transmit power on the nth subcarrier when the receiving source sends information

   

   The transmit power of the source on the nth subcarrier when forwarding information to the receiving end

   

   Where n ∈ N, N is the number of subcarriers;

   The first channel gain parameter obtaining unit is used to obtain the channel gain of the nth subcarrier from the source to itself

   

   Channel gain from self to receiver

   

   And the channel gain from itself to the eavesdropper

   

   The first circuit loss parameter acquisition unit is used to acquire the circuit loss when the source sends energy to itself

   

   The circuit loss when the source sends information to itself

   

   Circuit loss when transmitting information

   

   And the circuit loss of the receiving end when receiving information

   

   The first noise parameter obtaining unit is used to obtain the variance of its own additive white Gaussian noise

   

   Variance of additive white Gaussian noise at the receiving end

   

   And the variance of the additive white Gaussian noise

   
8. The device for optimal distribution of energy efficiency in a relay network according to claim 6, wherein when the preset distribution mode is a power distribution mode, the division module is used to set the source to send information and energy The power distribution ratio of is θ, and within the first T/2, the source sends information and energy to itself, and in the latter T/2, it forwards the information to the receiving end, and T is the total transmission time;

   The parameter acquisition module includes:

   The second power parameter obtaining unit is used to obtain the transmission power of the source end on the nth subcarrier when the information and energy sent by the source end are received by itself

   

   The transmit power of the source end on the nth subcarrier when it forwards information to the receiving end

   

   n ∈ N, N is the number of subcarriers;

   The second channel gain parameter obtaining unit is used to obtain the channel gain of the nth subcarrier from the source to itself

   

   Channel gain from self to receiver

   

   And the channel gain from itself to the eavesdropper

   

   The second circuit loss parameter acquisition unit is used to acquire the circuit loss when the source sends information and energy

   

   Circuit loss when transmitting information

   

   Circuit loss at the receiving end when receiving information

   

   The second noise parameter acquiring unit is configured to acquire the additive white Gaussian noise parameter, where the additive white Gaussian noise parameter includes: the variance of its own additive white Gaussian noise

   

   Variance of additive white Gaussian noise at the receiving end

   

   And the variance of the additive white Gaussian noise

   
9. A terminal, characterized in that it includes a memory and a processor, the processor is coupled to the memory, and the memory stores a computer program that can run on the processor;

   When the processor executes the computer program, the steps in the method for optimal distribution of energy efficiency of the relay network according to any one of claims 1 to 4 are realized.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the method for optimal distribution of energy efficiency of a relay network according to any one of claims 1-4.

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