CN108449790B - Time and power distribution method of cognitive wireless network based on differential evolution algorithm - Google Patents
Time and power distribution method of cognitive wireless network based on differential evolution algorithm Download PDFInfo
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Abstract
The invention provides a time and power distribution method of a cognitive wireless network based on a differential evolution algorithm. The method adopts a differential evolution algorithm, forms a temporary population through continuous variation and cross operation, and compares objective function values of individuals in the temporary population with objective function values of individuals in a current population corresponding to the objective function values through selection operation according to a greedy rule; and when the objective function value of the temporary population individual is large, the temporary population individual replaces the corresponding individual to enter the next generation population, otherwise, the temporary population individual is kept. Before reaching the maximum evolution algebra G, the population needs to carry out mutation, intersection and selection operations in a circulating way to finally form an optimal population, an optimal time distribution value is searched, and the optimal transmitting power is calculated to enable the utility function of the cognitive user to reach the maximum value. The invention can effectively improve the throughput of the cognitive user and the spectrum utilization rate and energy efficiency of the cognitive wireless network.
Description
Technical Field
The invention relates to a power and time distribution method of a cognitive radio network based on a differential evolution algorithm under the condition of energy collection, which mainly adopts the differential evolution algorithm to search and find out an optimal time distribution value so as to calculate the optimal cognitive user transmitting power and enable the utility function of a cognitive user to reach the maximum value, and belongs to the technical field of radio communication.
Background
With the rapid development of wireless communication services, the existing frequency spectrum becomes more and more scarce, statistics shows that the utilization rate of the authorized frequency band is very low, and the problem is well solved by the cognitive radio network.
Cognitive radio is an effective technology for realizing coexistence of primary and secondary networks and enabling an authorization system to share a frequency spectrum band with other systems. The proposal improves the network life and the frequency spectrum utilization rate of the communication system, and simultaneously does not influence the performance of the master user in the bottom mode.
However, the battery-powered wireless system has short service life and inevitable problems of replacement, charging and the like, and energy collection is an effective way for reducing the problems. It can provide nearly permanent energy supply, which helps to solve the resource constraint problem of wireless network, especially radio frequency energy collection, and its continuous energy supply is more flexible and convenient than general solar energy and wind energy collection. The key idea of energy collection is as follows: the wireless node may collect the radio frequency signal transmitted by the source node, charge its battery or super capacitor, and then use for signal processing or transmission. The proposal of the energy acquisition scheme reduces the operation expenditure cost and reduces the emission of greenhouse gases.
In any wireless network, the quality of received signals is affected by various loss factors such as fading, multipath, distance and the like, and in order to overcome the deterioration of the signals, a relay forwarding node is needed between a source node and a destination node to forward the signals. The relay communication technology improves the network system performance of the cognitive wireless network, simultaneously enlarges the coverage area, and improves the transmission efficiency and the transmission reliability. For energy-limited applications, relay communication reduces overall transmission efficiency and reduces interference to other users. In cognitive radio networks, cognitive users may also act as relays to assist in the transfer of information between primary users. In return, the cognitive user may collect energy from the received primary user signal and send its own signal to the cognitive user receiver using the same frequency band, the cognitive user acquiring energy and spectrum from the primary system, which may use low energy consumption and low cost equipment in existing and upcoming networks. Energy efficiency and spectral efficiency are two basic design indexes of a radio network for coping with environmental problems and wireless service requirements, and a cognitive radio network based on energy harvesting is just an effective way to solve the problems.
The differential evolution algorithm (DE) is an effective method for optimizing a real-valued multi-modal objective function, and has good convergence characteristics and parallelization applicability. The important idea of the differential evolution algorithm is that: a scheme for generating trial parameter vectors. In general, DE generates a new parameter vector by adding a weighted difference between two overall vectors to a third vector. If the objective function value of the parameter vector is higher than that of the corresponding current population individual, the newly generated parameter vector will enter the next generation, otherwise, the previous vector is retained.
The operation procedure of the differential evolution algorithm mainly comprises the following steps: 1) and (5) initializing a population. To establish an initial point for an optimization search, the population needs to be initialized. The initialization population is chosen randomly and should cover as much of the entire parameter space as possible. 2) And (5) carrying out mutation. Mutation is the addition of the weighted difference between two global vectors to a third vector to create a new parametric variable. 3) And (4) crossing. The interleaving is to increase the diversity of the interference parameters. And mixing the new parameter vector with the determined target vector according to a certain rule to generate a test vector to form a temporary population. 4) And (4) selecting. And comparing the test vector with the target vector in the current population by a differential evolution algorithm through a greedy criterion, namely comparing the target function value of each vector in the temporary population with the target function value of each vector corresponding to the current population, if the target function value of a certain individual in the temporary population is larger than the target function value of the individual corresponding to the current population, entering the individual in the temporary population into the next generation of population, and otherwise, keeping the original individual, and finally forming the next generation of population. 5) Before reaching the maximum evolution algebra G, we need to loop the steps 2), 3) and 4) to finally obtain the optimal individual.
Disclosure of Invention
The technical problem is as follows: the invention aims to overcome the defects of the prior art and provide a power allocation method of a cognitive radio network based on an adaptive differential evolution algorithm. According to the invention, the energy utilization rate and the utility function of the cognitive user are improved by optimizing the time allocation value in one time slot.
The technical scheme is as follows: according to the time and power distribution method of the cognitive radio network based on the differential evolution algorithm under the energy collection, firstly, the time distribution of one time slot of the cognitive radio network is regarded as a population. Then, according to boundary conditions, an initialization population is obtained, and then a temporary population is obtained through mutation and intersection. And comparing the objective function of the individual in the temporary population with the objective function of the individual of the current population corresponding to the temporary population according to a greedy criterion, and then forming a next generation population, so as to circulate until the evolution algebra reaches a maximum value, and finding the optimal individual, namely the optimal time distribution value, so as to calculate the optimal cognitive user transmitting power and maximize the utility function of the cognitive user.
The invention discloses a time and power distribution method of a cognitive wireless network based on a differential evolution algorithm, which comprises the following steps of:
step 1) at time t1The internal main user transmits radio frequency signals to the cognitive user, and the cognitive user collects energy from the radio frequency signals transmitted by the main user PT and stores the energy in a super capacitor of the cognitive user; the energy collection efficiency of the kth cognitive user is ξk，PaIs the transmit power of the primary user source node PT,
is the channel gain from PT to the k-th cognitive user, the energy E collected by the k-th cognitive userkComprises the following steps:
step 2) at time t2-t3In the method, a master user destination node PR receives a message from a PT and a relay forwarding node STkThe information of (a); the channel gain of PT to PR is
Noise gain is N0The signal-to-noise ratio received by the destination node PR from the source node PT is:
at the same time, the user can select the desired position,primary user selection as STkIts own optimal relay forwarding point at time t3Internal, cognitive user STkAssisting the PT in transferring information to the PR; cognitive user STkIs transmitted with a power of
The channel gain to PR is
The received signal-to-noise ratio of PR through relay forwarding is:
the total signal-to-noise ratio received by the PR is:
the total signal-to-noise ratio of PR is larger than the threshold value SNR specified by the cognitive radio network systemth；
Step 3) at time t4In the method, a master user provides idle frequency bands for a cognitive user STk，STkTo the corresponding destination node SRkTransmitting the information; the transmission power of the cognitive user is still kept at
STk→SRkThe channel gain of the link is
SRkThe received noise is white Gaussian noise and the noise power is N0(ii) a Node SRkThe received signal-to-noise ratio is:
at t3To t4In time of (2), cognitive user STkThe collected energy is completely used up,
cognitive user STkUtility function U ofkIs that
B is the channel bandwidth of the communication channel; the expression of the utility function is used as a target function of the self-adaptive differential evolution algorithm;
step 4), the cognitive wireless network optimizes the time allocation of one time slot by using a self-adaptive differential evolution algorithm to realize the maximization of the utility function, which is as follows:
according to the channel condition of each link and the condition of cognitive user energy collection, the cognitive radio network regards the time value of one time slot as a group, and one individual t is equal to (t)1、t2、t3、t4) (ii) a Continuously screening by using a self-adaptive differential evolution algorithm according to a target function, selecting the time distribution which enables the utility function to be maximum when the evolution algebra is increased to the maximum evolution algebra, and further calculating the transmitting power
The value of (d), i.e. the optimum power value;
wherein the content of the first and second substances,
and the target function value, wherein the population individual with the maximum target function value is the best transmission power.
The maximum evolutionary algebra is preset.
The maximum evolutionary algebra is 100.
The step 4) is specifically as follows:
step 4.1) initializing parameters of the population by the system model, wherein the parameters comprise: the method comprises the following steps of (1) carrying out population NP, a self-adaptive mutation operator F, a transaction operator CR and a maximum evolution number G; the cognitive radio network generates an initial population through random selection according to a boundary value of time and a constraint condition of a signal-to-noise ratio, and performs objective function calculation on the initial population; wherein each time slot is composed of four parts, the dimension D being equal to 4; the initialization population is generated by the following method:
wherein i represents the sequence of an individual in a population, i 1,2,3, … …, NP, j 1,2,3,4, and
respectively represent tjMaximum and minimum values of; after the initial population is generated, calculating an objective function value of the initial population;
step 4.2) the cognitive wireless network performs mutation and cross operation to obtain a temporary population; in the mutation operation, for each target vector ti,gThe self-adaptive differential evolution algorithm generates a variation vector through random selection as follows:
wherein r is1、r2、r3Is a randomly selected sequence number, i is the target vector sequence number, and r1≠r2≠r3Not equal to i; f is an adaptive mutation operator, which is generated by the following method:
F＝F0·2λ
where lambda is an exponential function on the evolution algebra,
g is the current evolution algebra; in the crossover operation, the test variables were generated by:
ui,G+1＝(u1i,G+1,u2i,G+1,...,uDi,G+1)
randb (j) represents a j-th estimated value randomly generated in [0,1], when the estimated value is less than or equal to a crossover operator, the j-th individual in the temporary population is an individual obtained through mutation operation, if the estimated value is greater than the crossover operator, the j-th individual in the temporary population is a corresponding j-th individual in the current population, and the temporary population of the system model is finally obtained through mutation and crossover construction;
step 4.3) the cognitive radio network uses an algorithm to select the obtained temporary population, calculates the target function of the temporary population and then carries out the target vector t with the current populationi,gComparing the target functions, wherein the individuals with relatively large target function values enter the next generation of population, so that a new population is obtained;
step 4.4) increasing the evolution algebra G by 1, and judging whether G reaches the maximum value G: if so, terminating the evolution, wherein the optimal individual in the population is the final optimal individual, namely the optimal time allocation of the cognitive radio network in a time slot; if not, continue with step 4.2).
Has the advantages that: compared with other schemes, the technical scheme adopted by the invention has the following technical effects:
1) global search probability increases: according to the scheme, population individuals are randomly generated by using a self-adaptive differential evolution algorithm, different mutation operators are set according to the searching condition of the algorithm and the population evolution algebra, and the probability of searching the global optimal solution is increased.
2) And (3) convergence is accelerated: compared with the common differential evolution algorithm, under the same convergence condition, the self-adaptive differential evolution algorithm is faster to converge than the common differential evolution algorithm.
Description of the drawings:
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a cognitive radio system model.
The specific implementation method comprises the following steps:
the technical scheme of the invention is further explained in detail by combining the attached drawings:
in the cognitive radio network, in order to improve the energy utilization rate and enlarge the communication range, the master user transmitting node PT can provide energy for the cognitive user transmitting node ST, and meanwhile, the cognitive user transmitting node ST can be used as a relay forwarding node of the PT to assist the PT in transmitting information to the master user destination node PR. The cognitive radio network reasonably allocates the time of one time slot according to the channel condition and the energy acquisition condition.
The scheme is improved on the basis of a differential evolution algorithm. Firstly, a population is randomly generated as an initial population according to the boundary condition of the cognitive radio network model. Meanwhile, different mutation operators are set according to the gradual increase of the evolution algebra, so that the diversity of individuals is ensured, the phenomenon of early maturity is avoided, the diversity of population individuals is favorably ensured, and the probability of searching a global optimum value is increased. The utility function test result in the cognitive radio network proves that: the adaptive differential evolution algorithm is superior to the general differential evolution algorithm.
Fig. 2 is a cognitive radio system model. The model consists of a pair of master users and K pairs of cognitive users. The working principle is as shown in the figure: one time slot T may be divided into four parts: t is t1、t2、t3、t4。t1In time, K cognitive users collect the energy of radio frequency signals from a main user source node PT; t is t2In time, a main user source node PT selects a cognitive user STkAs the best relay node of itself and transmit information to it; t is t3Within time, STkAs a relay forwarding node, transmitting information to a master user destination node PR in a decoding forwarding mode; t is t4Within time, STkTransmitting own information to corresponding cognitive user destination node SRk。
Explained in connection with fig. 2, the primary user transmitting node PT is at time t1And meanwhile, a plurality of cognitive users ST collect energy from PT radio frequency signals and store the energy in self super capacitors. At time t2In this case, the PT selects an ST as the best relay forwarding node while still transmitting information to the PR. At time t3Internal, cognitive user STkAssist the PT as the selected relay forwarding node in transferring information to the PR. At time t4Inter, PT providing white space to STk，STkTransmitting information to corresponding cognitive user destination node SRk. In STkDuring the transmission of information, i.e. at time t3～t4Internal, cognitive user STkAll consumed is t1Energy collected over time. The cognitive wireless network allocates t in a time slot through an adaptive differential evolution algorithm1、t2、t3、t4Thereby calculating the optimum transmission power
The utility function is maximized.
Each time value in a time slot is a coefficient to be optimized by the model, and the utility function is an objective function. The individuals in the initial population are randomly generated according to the boundary conditions of the model, and the evolution algebra g is 1. Through mutation and cross operation, the system generates a temporary population. During the mutation operation, the mutation operator is adaptive, and changes along with the increase of evolution generations. And calculating the objective function values of the temporary population and the current population, comparing the objective function values, and taking the population with the larger objective function value as the current population to enter next generation evolution, namely g + 1. Before the maximum evolution number G is reached, the cognitive radio network needs to perform cycle operation until the evolution number reaches G, and the corresponding population individuals are the time allocation with the optimal time slot.
The time and power distribution method of the cognitive wireless network based on the differential evolution algorithm under the energy collection comprises the following steps:
fig. 1 is a flowchart of a method of the present invention, and is a method for allocating time and power of a cognitive radio network based on a differential evolution algorithm when a cognitive user collects energy, and the method is characterized by comprising the following steps:
step 1) at time t1The inner main user transmits radio frequency signals to the cognitive user, and meanwhile, the cognitive user collects energy from the radio frequency signals transmitted by the main user PT and stores the energy in the super capacitor of the cognitive user. The energy collection efficiency of the kth cognitive user is ξk，PaIs the transmit power of the primary user source node PT,
is the channel gain from PT to the k-th cognitive user, the energy E collected by the k-th cognitive userkComprises the following steps:
step 2) at time t2-t3In the method, a master user destination node PR receives a message from a PT and a relay forwarding node STkThe information of (1). The channel gain of PT to PR is
Noise gain is N0The signal-to-noise ratio received by the destination node PR from the source node PT is:
at the same time, the primary user selects as STkIts own optimal relay forwarding point at time t3Internal, cognitive user STkAssisting the PT in transferring information to the PR. Cognitive user STkIs transmitted with a power of
The channel gain to PR is
The received signal-to-noise ratio of PR through relay forwarding is:
the total signal-to-noise ratio received by the PR is:
the total signal-to-noise ratio of PR is larger than the threshold value SNR specified by the cognitive radio network systemth。
Step 3) at time t4In, the master user provides idle frequency bandCognitive user STk，STkTo the corresponding destination node SRkThe information is transmitted. The transmission power of the cognitive user is still kept at
STk→SRkThe channel gain of the link is
SRkThe received noise is white Gaussian noise and the noise power is N0. Node SRkThe received signal-to-noise ratio is:
at t3To t4In time of (2), cognitive user STkThe collected energy is completely used up,
cognitive user STkUtility function U ofkIs that
B is the channel bandwidth of the communication channel. The expression of the utility function is used as the target function of the adaptive differential evolution algorithm.
Step 4), the cognitive radio network optimizes the time allocation of one time slot by using a self-adaptive differential evolution algorithm to realize the maximization of the utility function, which is as follows:
according to the channel condition of each link and the condition of cognitive user energy collection, the cognitive radio network regards the time value of one time slot as a group, and one individual t is equal to (t)1、t2、t3、t4). Continuously screening according to the objective function by using a self-adaptive differential evolution algorithm, finally selecting the time distribution which enables the utility function to be maximum, and further calculating the transmitting power
I.e. the optimum power value.
Step 4.1), initializing key parameters of the population by the system model, wherein the key parameters comprise: the system comprises a population NP, a self-adaptive mutation operator F, a transaction operator CR and a maximum evolution number G. The cognitive radio network generates an initial population through random selection according to a boundary value of time and a constraint condition of a signal-to-noise ratio, and performs objective function calculation on the initial population. Where each slot consists of four parts and the dimension D is equal to 4. The initialization population is generated by the following method:
wherein i represents the sequence of an individual in a population, i 1,2,3, …, NP, j 1,2,3,4, and
respectively represent tjMaximum and minimum values of. After the initial population is generated, objective function values of the initial population are calculated.
And 4.2) performing mutation and cross operation on the cognitive radio network to obtain a temporary population. In the mutation operation, for each target vector ti,g(i ═ 1,2, …, NP), the adaptive differential evolution algorithm generates the variant vectors by random selection as:
wherein r is1,r2,r3Is a randomly selected sequence number, i is the target vector sequence number, and r1≠r2≠r3Not equal to i. F is an adaptive mutation operator, which is generated by the following method:
F＝F0·2λ
where lambda is an exponential function on the evolution algebra,
g is the current evolution algebra.
In the crossover operation, the test variables were generated by:
ui,G+1＝(u1i,G+1,u2i,G+1,...,uDi,G+1)
andb (j) represents a j-th estimated value randomly generated in [0,1], when the estimated value is less than or equal to the crossover operator, the j-th individual in the temporary population is an individual obtained through mutation operation, if the estimated value is greater than the crossover operator, the j-th individual in the temporary population is a corresponding j-th individual in the current population, and the temporary population of the system model is finally obtained through mutation and crossover construction.
Step 4.3) the cognitive radio network uses an algorithm to select the obtained temporary population, calculates the target function of the temporary population and then carries out the target vector t with the current populationi,gThe target functions of the two groups are compared, and the individual with the larger target function value enters the next generation of population, so that a new population is obtained.
Step 4.4) and judging the size of the evolution algebra, wherein the operations of the steps 4.2) and 4.3) are circularly performed before the maximum evolution number G is reached, and the evolution algebra G is increased by one. When the evolution algebra G is G, the evolution is terminated, and the optimal individual value is obtained, namely the optimal time distribution of the cognitive wireless network in a time slot is obtained.
The foregoing is only a partial disclosure of the present invention, and it should be understood that modifications may be made by those skilled in the art without departing from the spirit of the present invention, and such modifications are to be considered as within the scope of the present invention.
Claims (4)
1. A time and power distribution method of a cognitive wireless network based on a differential evolution algorithm is characterized by comprising the following steps:
step 1) at time t1The internal main user transmits radio frequency signals to the cognitive user, and the cognitive user collects energy from the radio frequency signals transmitted by the main user PT and stores the energy in a super capacitor of the cognitive user; the energy collection efficiency of the kth cognitive user is ξk，PaIs the transmit power of the primary user source node PT,
is the channel gain from PT to the k-th cognitive user, the energy E collected by the k-th cognitive userkComprises the following steps:
step 2) at time t2-t3In the method, a master user destination node PR receives a message from a PT and a relay forwarding node STkThe information of (a); the channel gain of PT to PR is
Noise gain is N0The signal-to-noise ratio received by the destination node PR from the source node PT is:
at the same time, the master user selects STkAs its best relay forwarding point, at time t3Internal, cognitive user STkAssisting the PT in transferring information to the PR; cognitive user STkIs transmitted with a power of
The channel gain to PR is
The received signal-to-noise ratio of PR through relay forwarding is:
the total signal-to-noise ratio received by the PR is:
the total signal-to-noise ratio of PR is larger than the threshold value SNR specified by the cognitive radio network systemth；
Step 3) at time t4In the method, a master user provides idle frequency bands for a cognitive user STk，STkTo the corresponding destination node SRkTransmitting the information; the transmission power of the cognitive user is still kept at
STk→SRkThe channel gain of the link is
SRkThe received noise is white Gaussian noise and the noise power is N0(ii) a Node SRkThe received signal-to-noise ratio is:
at t3To t4In time of (2), cognitive user STkThe collected energy is completely used up,
cognitive user STkUtility function U ofkIs that
B is the channel bandwidth of the communication channel; the expression of the utility function is used as a target function of the self-adaptive differential evolution algorithm;
step 4), the cognitive wireless network optimizes the time allocation of one time slot by using a self-adaptive differential evolution algorithm to realize the maximization of the utility function, which is as follows:
according to the channel condition of each link and the condition of cognitive user energy collection, the cognitive radio network regards the time value of one time slot as a group, and one individual t is equal to (t)1、t2、t3、t4) (ii) a Continuously screening by using a self-adaptive differential evolution algorithm according to a target function, selecting the time distribution which enables the utility function to be maximum when the evolution algebra is increased to the maximum evolution algebra, and further calculating the transmitting power
The value of (d), i.e. the optimum power value;
the step 4) is specifically as follows:
step 4.1) initializing parameters of the population by the system model, wherein the parameters comprise: the method comprises the following steps of (1) carrying out population NP, a self-adaptive mutation operator F, a transaction operator CR and a maximum evolution algebra G; the cognitive radio network generates an initial population through random selection according to a boundary value of time and a constraint condition of a signal-to-noise ratio, and performs objective function calculation on the initial population; wherein each time slot is composed of four parts, the dimension D being equal to 4; the initialization population is generated by the following method:
wherein i represents the sequence of an individual in a population, i 1,2,3, … …, NP, j 1,2,3,4, and
respectively represent tjMinimum and maximum values of; after the initial population is generated, calculating an objective function value of the initial population;
step 4.2) the cognitive wireless network performs mutation and cross operation to obtain a temporary population; in the mutation operation, for each target vector ti,gAdaptive differential evolution algorithm by randomThe machine selection and the generation of the variation vector are as follows:
wherein r is1、r2、r3Is a randomly selected sequence number, i is the target vector sequence number, and r1≠r2≠r3Not equal to i; f is an adaptive mutation operator, which is generated by the following method:
F＝F0·2λ
where lambda is an exponential function on the evolution algebra,
g is the current evolution algebra; in the crossover operation, the test variables were generated by:
ui,g+1＝(u1i,g+1；u2i,g+1；…；uDi,g+1)
randb (j) represents a j-th estimated value randomly generated in [0,1], when the estimated value is less than or equal to a crossover operator, the j-th individual in the temporary population is an individual obtained through mutation operation, if the estimated value is greater than the crossover operator, the j-th individual in the temporary population is a corresponding j-th individual in the current population, and the temporary population of the system model is finally obtained through mutation and crossover construction;
step 4.3) the cognitive radio network uses an algorithm to select the obtained temporary population, calculates the target function of the temporary population and then carries out the target vector t with the current populationi,gComparing the target functions, wherein the individuals with relatively large target function values enter the next generation of population, so that a new population is obtained;
step 4.4) increasing the evolution algebra G by 1, and judging whether G reaches the maximum value G: if so, terminating the evolution, wherein the optimal individual in the population is the final optimal individual, namely the optimal time allocation of the cognitive radio network in a time slot; if not, continue with step 4.2).
2. The differential evolution algorithm-based time and power distribution method for the cognitive wireless network according to claim 1, wherein the population with the largest objective function value is the population with the best transmission power.
3. The method of claim 1, wherein the maximum evolution algebra is preset.
4. The method for time and power allocation of cognitive wireless network based on differential evolution algorithm as claimed in claim 1, wherein the maximum evolution algebra is 100.
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