CN109039974A - Direction modulation signal synthesis method based on PSO-GA hybrid algorithm - Google Patents
Direction modulation signal synthesis method based on PSO-GA hybrid algorithm Download PDFInfo
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Abstract
The present invention provides a kind of direction modulation signal synthesis methods based on PSO-GA hybrid algorithm, construct the low complex degree multiple objective function based on quaternary phased array direction modulation communication system signal synthesis, in conjunction with the strong feature of particle swarm optimization algorithm fast convergence rate and genetic algorithm ability of searching optimum, devise the hybrid algorithm based on PSO-GA, the defect of single optimization algorithm is compensated for, simulation result confirms the validity of proposed method.
Description
Technical field
The present invention relates to a kind of direction modulation signal synthesis methods.
Background technique
Traditional wireless communication transmitter signal is completed to modulate in base band, then up-conversion and power amplification to radio frequency domains.This
Kind transmitting signal is only that power is different, and the format of planisphere is held essentially constant in all directions.Therefore, listener-in utilizes
Efficient and sensible receiver is easy to restore information in undesired direction, this is extremely unfavorable for safety of physical layer transmission.Direction tune
System is identified as a kind of secure transmission technique of great future, is the effective way for solving secure wireless communication.
Direction modulation technique emits digital modulation information along preassigned direction, while making star in the other direction
Seat figure Severe distortion, which has very strong directionality, so as to effectively improve the safety of wireless transmission.It is sent out in tradition
It penetrates in machine, radiation pattern beamwidth represents the power direction of wave beam；In the modulation transmitter of direction, information beam angle
Indicate the direction message of wave beam.
" M.P.Daly, J.T.Bernhard.Directional modulation the technique for of document 1
phased arrays.IEEE Trans.Antennas Propag.,vol.57,no.9,pp.2633-2640,Sep.2009”
Middle introduction phased array antenna, based on single-goal function with the comprehensive direction modulated signal of genetic algorithm, but the integrated approach is only
Consider that baseband modulation signal keeps identical constellation format in the transmit direction, does not consider the distortion of planisphere in undesired direction
Degree.
" Hong Tao, Song Maozhong, the Liu Yu.Design of directional modulation of document 2
signal based on multi-objective genetic algorithm for physical layer secure
Communication.Journal of Applied Sciences.vol.32, pp.51-56,2014 " are based on multiple objective function
With the comprehensive direction modulated signal of genetic algorithm, but the convergence rate of optimization algorithm is not considered.
" Ding Y, the Fusco V.Directional modulation transmitter synthesis of document 3
using particle swarm optimization.IEEE Antennas and Propagation Conference.,
Phase-shift value is arranged according to error rate of system performance in vol.9, no.6, pp.500-503,2014 ", comprehensive with particle swarm optimization algorithm
Direction modulated signal is closed, but does not consider the ability of searching optimum of optimization algorithm.
Above-mentioned document is each to have advantage and deficiency using different target function and the comprehensive direction modulated signal of optimization method.
Summary of the invention
For overcome the deficiencies in the prior art, the present invention provides a kind of direction modulated signal based on PSO-GA hybrid algorithm
Integrated approach constructs multiple objective function and a kind of hybrid optimization algorithm on the basis of modulation technique in phased array direction to realize direction
Modulated signal is comprehensive, in conjunction with the strong feature of particle swarm optimization algorithm fast convergence rate and genetic algorithm ability of searching optimum, reduces
Objective function complexity, so that direction modulation has better security performance.
The technical solution adopted by the present invention to solve the technical problems the following steps are included:
Step 1, in being based on quaternary phased array direction modulation communication system, i-th of the transmitting of direction modulation transmitter
QPSK symbol reach far field receiver electric vector beWherein, anIndicate antenna
The excitation amplitude of array element n, ψn(i) excitation phase of bay n is indicated, θ is the deflection about z-axis, and k=2 π/λ is electromagnetism
Propagation Constants, λ are carrier wavelengths, and intended receivers are located at far field, in terms of amplitude, approximate processing Rn≈R0, in terms of phase,The distance of item is approximately Rn≈R0-ndsinθ；Assuming that the amplitude excitation of all array elements is all identical, and it is normalized to a0
=a1=a2=a3=1, the antenna directivity function f of each array elemente(θ, φ) is indicated, then spoke of the direction modulation transmitter at P point
Conjunction field strength is penetrated to be expressed as
The constellation point of QPSK modulation is expressed as againIn order to maintain desired orientation θsOn
Planisphere, objective functionObjective functionWherein, θcIt is a constant, value is 10 °, and step is step
Into value 0.01；
Therefore, Model for Multi-Objective Optimization is designed asIn order to right
Than algorithm performance, another Model for Multi-Objective Optimization is established
Step 2, definition of search region are 16 dimension spaces, and the value range of every dimension space is -180 ° to 180 °, search essence
Degree is set as 0.01 °, is encoded using 16bit, as item chromosome；PSO uses Global Optimization Model xid k+1=xid k+vid k+1,
vid k+1=ω vid k+c1·rand1()·(pbest_id k-xid k)+c2·rand2()·(gbest_id k-xid k),
In, vidIt is the speed of particle i, xidIt is the position of particle i, pbest_id kIt is the desired positions that particle i is lived through, gbest_id kIt is grain
The global desired positions that sub- i is lived through；Subscript k indicates kth time iteration, and subscript d indicates d dimension；rand1() and rand2()
It is stochastic variable, is uniformly distributed in [0,1]；c1And c2It is accelerated factor, value 1.5；ω is inertia weight, and value range is
0.9 to 0.4；
The scale K of population is set as 10000, and maximum number of iterations is set as 100；Population Size that genetic algorithm uses and
The population size of PSO is consistent；
The mixed strategy of selection operator design the use ratio selection and optimal save strategy of genetic algorithm, what individual i was selected
Probability isWherein, FiIt is the fitness of individual i；
The crossover operator of genetic algorithm uses single point crossing, and a crossover location is randomly generated first；Then two father's dyes
Colour solid intercourses the chromosome of crossover location leading portion using crossover location as boundary；In the process by crossover probability PcIt is set as 0.8, PcCertainly
Whether determining crossover operation implementation；
Two variable positions are randomly generated in the generation of the mutation operator of genetic algorithm first；Then two variable positions are exchanged
On value；Mutation probability is set as P in the processmIt is 0.05, PmDetermine whether mutation operation carries out；
The specific implementation steps are as follows for hybrid optimization algorithm:
(1) each parameter is initialized.According to the coding rule of problem, individual is generated at random, forms initial population, it will be every
Individual substitutes into objective function, obtains corresponding fitness；
(2) fitness function is evaluated, particle optimal solution p itself is recordedbest_idWith the current optimal solution g of populationbest_id；
(3) according to vid k+1=ω vid k+c1·rand1()·(pbest_id k-xid k)+c2·rand2()·(gbest_id k-
xid k) particle rapidity is updated, according to xid k+1=xid k+vid k+1Particle position is updated, after reaching maximum number of iterations, output is initial
Optimize population；
(4) according to formulaSelection individual, by crossover probability PcCrossover operation is carried out, new individual is generated；
(5) with mutation probability PmMutation operation is carried out, new individual is generated and is added in progeny population；
(6) when the iterated conditional for meeting setting, then stop, exporting optimized individual as optimized results, otherwise, jump to
Step (4)；
Step 3 solves multiple objective function using hybrid algorithm, obtains globally optimal solution.
The beneficial effects of the present invention are: constructing based on the low multiple of quaternary phased array direction modulation communication system signal synthesis
Miscellaneous degree multiple objective function, in conjunction with the strong feature of particle swarm optimization algorithm fast convergence rate and genetic algorithm ability of searching optimum, if
The hybrid algorithm based on PSO-GA has been counted, the defect of single optimization algorithm is compensated for, simulation result confirms having for mentioned method
Effect property.
Detailed description of the invention
Fig. 1 is the direction modulation transmitter based on quaternary phased array；
Fig. 2 is the realization block diagram of the hybrid optimization algorithm based on PSO-GA；
The planisphere that Fig. 3 is optimal solution and worst solution at 60 °, 55 ° and 65 ° of direction；
Fig. 4 is the ber curve figure in different orientations of the comprehensive direction modulated signal out of four kinds of different methods；
Fig. 5 is the convergence curve figure of three kinds of optimization algorithms.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples, and the present invention includes but are not limited to following implementations
Example.
The present invention provides a kind of direction modulation signal synthesis method based on PSO-GA hybrid algorithm, in conjunction with particle group optimizing
The feature that algorithm the convergence speed is fast and genetic algorithm ability of searching optimum is strong, has complementary advantages, constructs a kind of hybrid algorithm, real
Existing direction modulated signal is comprehensive, so that direction modulation has better security performance.
The direction modulation transmitter based on quaternary phased array that the present invention uses, functional block diagram are as shown in Figure 1.Antenna array
It is quaternary linear microstrip panel-shaped antenna, is divided into half-wavelength between array element.Emit signal to modulate using QPSK.Assuming that intended receivers
Positioned at the far-field region of antenna array.Then the far field electric vector of i-th of QPSK symbol arrival receiver of transmitter transmitting is
Wherein, anIndicate the excitation amplitude of bay, ψn(i) excitation phase of bay, i.e. n-th of phase shift are indicated
It plants.θ is the deflection about z-axis.K=2 π/λ is Electromagnetic Wave Propagation constant, and λ is carrier wavelength.
In overwhelming majority application, far-field signal is only considered, therefore a little approximate processings can be done.In the width of electric field intensity
Degree aspect, the range difference of different antenna element to intended receivers can be ignored, approximate representation Rn≈R0.Meanwhile we limit
Intended receivers are located at x-z-plane, then electric field intensity is unrelated with φ, and φ can take arbitrary value.Distance in can be close
Seemingly it is expressed as
Rn≈R0-nd sinθ (2)
For quaternary even linear array, it is assumed that the amplitude excitation of all array elements is all identical, and is normalized, then has
a0=a1=a2=a3=1 (3)
The antenna directivity function f of each array elemente(θ, φ) unified representation, while ignoring the coupling between array element, return
One turns to 1.
To sum up, field strength of the direction modulation transmitter at P point is considered as the mutual folded of 4 array element transmitting signal electric vectors
Add.Therefore, the radiation at P point is closed field strength and can be expressed as
It therefore, can be by the way that suitable phase-shift value { ψ be arranged0(i),ψ1(i),ψ2(i),ψ3(i) } in desired orientation θsIt is upper comprehensive
Close conventional baseband modulated signal.
The constellation point of QPSK modulation is expressed as
In order to maintain desired orientation θsOn planisphere, an objective function is defined as follows
The objective function only considers that in the planisphere of desired orientation be reference format, to the planisphere of undesired direction
Distortion degree does not make any constraint, and single object optimization result may make approximate test constellation lattice occur in certain undesired directions
Formula reduces the safety of direction modulation.Therefore, in order to obtain maximum information beam angle, increase direction and modulate safety, examine
Consider near the undesired direction that planisphere distortion degree reaches maximum as far as possible in a certain range, while in order to reduce operation complexity
It spends and is convenient for hardware realization, another objective function is defined as follows
Wherein, θcIt is a constant, value is 10 °, and step is stepping, value 0.01.
Therefore, Model for Multi-Objective Optimization is designed as
The Pareto optimal solution for defining multiple objective function (8) is multiple objective function optimal solution.In order to compare algorithm performance, build
Another Model for Multi-Objective Optimization is found
The Pareto optimal solution for defining multiple objective function (9) simultaneously is the worst solution of multiple objective function.
Because optimization task is a multiple target and nonlinearity problem, classical optimization algorithm is easy to cause part most
Excellent solution and be no longer appropriate for the solution of the problem.The present invention is devised mixed based on PSO-GA to preferably realize global convergence
Close optimization algorithm.The algorithm carries out the optimization of previous stage first with the fast convergence characteristic of particle swarm optimization algorithm, obtains one
Determine the initial population of degree evolution；Then the optimization that the latter half is carried out with genetic algorithm, obtains globally optimal solution.The mixing is excellent
The realization block diagram for changing algorithm is as shown in Figure 2.
Comprehensive for the direction modulated signal of quaternary phased array, the parameter designing of the hybrid optimization algorithm is as follows.
Region of search is 16 dimension spaces, and the value range of every dimension space is -180 ° to 180 °.Search precision is set as
0.01°.Therefore, every dimension space is encoded using 16bit, as item chromosome, while corresponding to the particle in particle swarm algorithm.
For PSO, the present invention uses Global Optimization Model, and concrete form is as follows:
vid k+1=ω vid k+c1·rand1()·(pbest_id k-xid k)+c2·rand2()·(gbest_id k-xid k) (10)
xid k+1=xid k+vid k+1 (11)
Wherein, vidIt is the speed of particle i, xidIt is the position of particle i, pbest_id kIt is the desired positions that particle i is lived through,
gbest_id kIt is the global desired positions that particle i is lived through.Subscript k indicates kth time iteration, and subscript d indicates d dimension.
rand1() and rand2() is stochastic variable, is uniformly distributed in [0,1].c1And c2It is accelerated factor, value is
1.5.ω is inertia weight, and with the number of iterations linear change, value range is 0.9 to 0.4.
vidAnd xidAbsolute value may evolve to sufficiently large and particle is made to fly out solution space.Therefore, v is limitedidMaximum value
For vmax, xidMaximum value be xmax。
The scale K of population is set as 10000, and maximum number of iterations is set as 100.
For GA, in order to keep the continuity of algorithm interface, the Population Size that genetic algorithm is used is designed as with PSO's
Population size is consistent.
The mixed strategy of selection operator design the use ratio selection and optimal save strategy of genetic algorithm.What individual i was selected
Probability is
Wherein, FiIt is the fitness of individual i.
The crossover operator of genetic algorithm uses single point crossing, realizes that steps are as follows:
(1) crossover location is randomly generated；
(2) two father's chromosomes intercourse the chromosome of crossover location leading portion using crossover location as boundary.
(3) crossover probability PcIt is set as 0.8, PcWhether determining that crossover operation is implemented.
The mutation operator of genetic algorithm realizes that steps are as follows:
(1) two variable positions are randomly generated；
(2) value on two variable positions is exchanged；
(3) mutation probability is set as PmIt is 0.05, PmDetermine whether mutation operation carries out.
In conclusion the specific implementation steps are as follows for hybrid optimization algorithm:
(1) each parameter is initialized.According to the coding rule of problem, individual is generated at random, forms initial population, it will be every
Individual substitutes into objective function, obtains corresponding fitness；
(2) fitness function is evaluated, particle optimal solution p itself is recordedbest_idWith the current optimal solution g of populationbest_id；
(3) particle rapidity is updated according to formula (10), updates particle position according to formula (11), after reaching maximum number of iterations,
Export initial optimization population；
(4) individual is selected according to formula (11), by crossover probability PcCrossover operation is carried out, new individual is generated；
(5) with mutation probability PmMutation operation is carried out, new individual is generated and is added in progeny population；
(6) when meeting iterated conditional, then stop, exporting optimized individual as optimized results, otherwise, jump to step
(4)。
In step (3), when updating particle position according to formula (11), particle position is likely to become decimal, does not meet coding
Definition needs decimal value to be immediate integer.
Table 1 uses the phase-shift value of hybrid optimization algorithm comprehensive QPSK symbol out on desired orientation (60 °)
According to step optimized as above, the phase-shift value of comprehensive QPSK symbol out is as shown in table 1 on desired orientation (60 °).
The planisphere of optimal solution and worst solution at 60 °, 55 ° and 65 ° of direction is as shown in Figure 3.From figure 3, it can be seen that in addition to expectation side
Outer to 60 °, different degrees of distortion occurs for the planisphere in other directions.The planisphere of optimal solution transmitter is at 55 ° of direction
Severe distortion, and distortion of the planisphere of worst solution transmitter at 55 ° of direction is smaller, and listener-in is received using highly sensitive
Machine is easy to recover information.Thus illustrate that the hybrid optimization algorithm has validity.
Such as Fig. 4 of the bit error rate performance in different orientations institute of the comprehensive direction modulated signal out of four kinds of different methods
Show.Four kinds of used integrated approach of emulation are the direction modulation transmitter, excellent using population using genetic algorithm optimization respectively
Change the direction modulation transmitter of algorithm optimization, using the direction modulation transmitter of hybrid algorithm optimization and the direction tune of document [2]
Transmitter processed.As can be drawn from Figure 4, four kinds of transmitters are almost the same in 60 ° of desired orientation of the bit error rate, and hybrid optimization algorithm
Bit error rate deterioration degree of the direction modulation transmitter near 60 ° is substantially better than other transmitters, and information beam angle is narrower, peace
Full property is higher.
It is respectively adopted standard genetic algorithm, the comprehensive direction modulated signal of particle swarm optimization algorithm and hybrid optimization algorithm, three
The convergence curve of kind algorithm is as shown in Figure 5.It can be obtained by Fig. 5, the convergence rate of hybrid optimization algorithm is better than genetic algorithm, is inferior to grain
Subgroup optimization algorithm.From total result, hybrid algorithm has preferably optimization performance.
In order to make those of ordinary skill in the art understand the present invention, and to the present invention have been described in detail, but can think
Arrive, do not depart from claim of the invention it is covered in the range of can also make other change and modification, these variation
It is within the scope of the present invention with modifying.
Claims (1)
1. a kind of direction modulation signal synthesis method based on PSO-GA hybrid algorithm, it is characterised in that include the following steps:
Step 1, based in the modulation communication system of quaternary phased array direction, i-th of QPSK of direction modulation transmitter transmitting is accorded with
Number reach far field receiver electric vector beWherein, anIndicate bay n's
Excitation amplitude, ψn(i) excitation phase of bay n is indicated, θ is the deflection about z-axis, and k=2 π/λ is Electromagnetic Wave Propagation
Constant, λ are carrier wavelengths, and intended receivers are located at far field, in terms of amplitude, approximate processing Rn≈R0, in terms of phase,The distance of item is approximately Rn≈R0-ndsinθ；Assuming that the amplitude excitation of all array elements is all identical, and it is normalized to a0
=a1=a2=a3=1, the antenna directivity function f of each array elemente(θ, φ) is indicated, then spoke of the direction modulation transmitter at P point
Conjunction field strength is penetrated to be expressed as
The constellation point of QPSK modulation is expressed as againIn order to maintain desired orientation θsOn
Planisphere, objective functionObjective functionWherein, θcIt is a constant, value is 10 °, and step is step
Into value 0.01；
Therefore, Model for Multi-Objective Optimization is designed asIt is calculated to compare
Method performance establishes another Model for Multi-Objective Optimization
Step 2, definition of search region are 16 dimension spaces, and the value range of every dimension space is -180 ° to 180 °, and search precision is set
It is 0.01 °, is encoded using 16bit, as item chromosome；PSO uses Global Optimization Model xid k+1=xid k+vid k+1, vid k+1
=ω vid k+c1·rand1()·(pbest_id k-xid k)+c2·rand2()·(gbest_id k-xid k), wherein vidIt is particle i
Speed, xidIt is the position of particle i, pbest_id kIt is the desired positions that particle i is lived through, gbest_id kIt is complete to be that particle i is lived through
Office's desired positions；Subscript k indicates kth time iteration, and subscript d indicates d dimension；rand1() and rand2() is stochastic variable,
It is even to be distributed in [0,1]；c1And c2It is accelerated factor, value 1.5；ω is inertia weight, and value range is 0.9 to 0.4；
The scale K of population is set as 10000, and maximum number of iterations is set as 100；Population Size that genetic algorithm uses and PSO's
Population size is consistent；
The mixed strategy of selection operator design the use ratio selection and optimal save strategy of genetic algorithm, individual i selected probability
ForWherein, FiIt is the fitness of individual i；
The crossover operator of genetic algorithm uses single point crossing, and a crossover location is randomly generated first；Then two father's chromosome
Using crossover location as boundary, the chromosome of crossover location leading portion is intercoursed；In the process by crossover probability PcIt is set as 0.8, PcIt determines to hand over
Whether fork operation is implemented；
Two variable positions are randomly generated in the generation of the mutation operator of genetic algorithm first；Then it exchanges on two variable positions
Value；Mutation probability is set as P in the processmIt is 0.05, PmDetermine whether mutation operation carries out；
The specific implementation steps are as follows for hybrid optimization algorithm:
(1) each parameter is initialized.According to the coding rule of problem, individual is generated at random, forms initial population, it will be per each and every one
Body substitutes into objective function, obtains corresponding fitness；
(2) fitness function is evaluated, particle optimal solution p itself is recordedbest_idWith the current optimal solution g of populationbest_id；
(3) according to vid k+1=ω vid k+c1·rand1()·(pbest_id k-xid k)+c2·rand2()·(gbest_id k-xid k)
Particle rapidity is updated, according to xid k+1=xid k+vid k+1Particle position is updated, after reaching maximum number of iterations, exports initial optimization
Population；
(4) according to formulaSelection individual, by crossover probability PcCrossover operation is carried out, new individual is generated；
(5) with mutation probability PmMutation operation is carried out, new individual is generated and is added in progeny population；
(6) when the iterated conditional for meeting setting, then stop, exporting optimized individual as optimized results, otherwise, jump to step
(4)；
Step 3 solves multiple objective function using hybrid algorithm, obtains globally optimal solution.
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