Channel parameter estimation method based on spider bee optimization algorithm

Abstract

The invention discloses a channel parameter estimation method based on a spider bee optimization algorithm. The method comprises the following steps: step 1, generating a channel impulse response based on channel measurement; step 2, initializing spider bee optimization algorithm parameters based on the channel parameters; step 3, initializing channel estimation parameters; step 4, iteratively estimating channel parameters by using a spider bee optimization algorithm, wherein the channel parameters comprise time delay and complex amplitude; and step 5, calculating population fitness. According to the method disclosed by the invention, aiming at the problem of estimating the channel parameters, the channel parameters on each path are estimated by utilizing a spider bee optimization algorithm, and the algorithm is high in search capability, so that the influence of a complex environment on channel parameter estimation is reduced, and the accuracy of channel parameter estimation is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a channel parameter estimation method based on a spider bee optimization algorithm in this field. Background Art

[0002] With the rapid development of wireless communication technology, the performance requirements of modern communication systems are constantly increasing. In wireless communication, the characteristics of the channel have a crucial impact on the performance of the system. The wireless channel is a multipath propagation environment. During the propagation process, the signal will experience reflection, refraction, scattering and diffraction, resulting in signal fading, delay and Doppler shift. In order to design and optimize wireless communication systems, it is necessary to accurately understand and estimate the parameters of the channel.

[0003] Due to the significant impact of the environment on positioning, distance-based positioning methods that estimate channel parameters through actual measurements are gaining more and more attention. This method can more accurately reflect the real environmental characteristics and improve positioning accuracy and reliability. There are many methods for channel parameter estimation, such as MUSIC, ESPRIT, and Space Alternating Generalized Expectation (SAGE). Among them, the MUSIC algorithm is relatively complex; the ESPRIT algorithm has a large amount of calculation; the SAGE algorithm is greatly affected by the multipath effect, is too dependent on the channel model, and has a large positioning error.

[0004] Therefore, there is an urgent need for a parameter estimation method that can target dense multipath environments to solve the above-mentioned problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a channel parameter estimation method based on the spider bee optimization algorithm, which can accurately estimate the channel parameters, reduce positioning errors, and improve the estimation accuracy of the channel parameters.

[0006] The present invention adopts the following technical solution:

[0007] A channel parameter estimation method based on spider bee optimization algorithm, the improvement of which is that it includes the following steps:

[0008] Step 1, generating a channel impulse response based on channel measurement;

[0009] Step 2, initializing the parameters of the spider bee optimization algorithm based on the channel parameters;

[0010] Step 3, initialize channel estimation parameters;

[0011] Step 4, using the spider bee optimization algorithm to iteratively estimate channel parameters, including delay and complex amplitude;

[0012] Step 5, calculate the fitness of the population, and determine the optimal delay and complex amplitude based on the fitness value after each iteration. When the algorithm end condition is met, return the optimal delay and complex amplitude estimated by the algorithm, which is the estimated channel parameters, otherwise return to step 4.

[0013] Furthermore, in step 1, the generated wideband channel impulse response model is expressed as:

[0014]

[0015] In the above formula, L(t) is the number of paths at time t, θ l (t) α l (t),ψ l (t), τ l (t) are the azimuth, elevation, amplitude, phase and delay of the lth path respectively, is the guiding vector of the lth path, l = 1,…,L(t), δ(τ-τ l (t)) is τ l The impulse function at (t);

[0016] The number of path estimates at time t is L, so the channel impulse response at time t is:

[0017]

[0018] In the above formula, τ is the delay corresponding to the channel impulse response at time t, θ l , α l , l , τ l are the azimuth, elevation, amplitude, phase and delay of the lth path at time t, is the guidance vector of the lth path at time t, l = 1,…,L, δ(τ-τ l ) is the time tτ l The impulse function at ;

[0019] Performing discrete Fourier transform on the above equation gives the impulse response of the channel:

[0020]

[0021] Furthermore, in step 4, the spider bee optimization algorithm is used to estimate the delay and complex amplitude of the channel, so as to calculate its fitness. The delay and complex amplitude corresponding to the optimal fitness are the delay and complex amplitude of the channel, which specifically include:

[0022] Channel parameter setting: Assuming there are L paths, when estimating the channel parameters of all paths for the first time, the hidden data space X of the nth pathn for:

[0023]

[0024]

[0025] In the above formula, n = 1, ..., L;

[0026] When all path channel parameters are estimated for the first time, the hidden data space X of the nth path n for:

[0027]

[0028] F=||X n *S * || 2 (4)

[0029] In the above formula, Z is the impulse response of the snapshot, which is the state record of the received signal at a certain moment, and H l is the channel impulse response of the lth path. When the channel parameters of all paths are estimated for the first time, l=1,…,L-1. When the channel parameters of all paths are estimated for a non-first time, l=1,…,L, and l≠n, n is the current nth estimated path, θ l is the azimuth of the lth path, is the elevation angle of the lth path, is the guiding vector of the lth path, α l is the amplitude of the lth path, ψ l is the phase of the lth path, τ l is the delay of the lth path, f is the signal frequency, S is the transmitted data symbol, and the algorithm fitness function is F, S * represents the complex conjugate of S, ||X n *S * || 2 Represents X n *S * The square of the Frobenius norm of .

[0030] Furthermore, the spider bee optimization algorithm is based on the behavior modeling of spider bees, including:

[0031] Hunting and Nesting Behavior:

[0032] (1) Search phase: This phase simulates the behavior of the female bee looking for the most suitable spider to feed the larvae. The calculation is as follows:

[0033] sw i (x+1)=sw i (x)+μ1*(sw a (x)-swb (x)) (5)

[0034] sw i (x+1)=sw c (x)+μ2*(L+r2*(HL)) (6)

[0035] In the above formula, a and b are two indices randomly selected from the population, r2 is a randomly generated number between [0,1], and c is an index randomly selected from the population;

[0036] μ1=|rn|*r1 (7)

[0037] μ2=B*cos(2πl) (8)

[0038] B=1 / (1+exp(l)) (9)

[0039] In the above formula, r1 is a randomly generated number between 0 and 1, rn is also a random number, but it is generated using a normal distribution, and l is a randomly generated number between [-2,1];

[0040]

[0041] r3 and r4 are random numbers between [0,1];

[0042] (2) Following and fleeing stage:

[0043] sw i (x+1)=sw i (x)+C*(2*r5*sw a (x)-sw i (x)) (11)

[0044]

[0045] In the above formula, x and x max Represent the current calculation and the maximum number of calculations respectively; r5 is a vector representing a randomly generated value in the interval [0,1]; r6 is a random number in the interval [0,1];

[0046] sw i (x+1)=sw i (x+1)*vc (13)

[0047] k=1-(x / x max ) (14)

[0048] In the above formula, vc is a vector generated between k and -k according to the normal distribution;

[0049] The trade-off between these two computations is stochastically implemented as shown in the following equation:

[0050]

[0051] The exchange between the search phase and the subsequent mechanism is adjusted according to the following formula:

[0052]

[0053] In the above formula, p is a randomly generated number between [0,1];

[0054] (3) Nesting behavior:

[0055] sw i (x+1)=sw * (x)+cos(2πl)*(sw * (x)-sw i (x)) (17)

[0056] In the above formula, sw * Representing the best solution so far, the second equation will build the nest within the location of a female spider randomly selected from the population, using an additional step size, designed as follows:

[0057] sw i (x+1)=sw a (x)+r3*|γ|*(sw a (x)-sw i (x)+(1-r3)*U*

[0058] (sw b (x)-sw c (x))(18)In the above formula, γ is a number generated according to the levy flight; U is a binary vector, and U is calculated according to the following formula:

[0059]

[0060] Formulas (13) and (14) are randomly swapped according to the following formula:

[0061]

[0062] The trade-off between hunting and nesting behavior is implemented by equation (20);

[0063]

[0064] N represents the number of individuals in the population;

[0065] Mating behavior:

[0066] swi (x+1)=Crossover(sw i (x),sw m (x),CR) (22)

[0067] In the above formula, Crossover means that in the solution sw i (x) and sw m The uniform crossover operator applied between (x) is called the crossover rate CR; sw m (x) and sw i (x) are two vectors, representing male and female spider bees respectively. The difference between the male and female spider bees generated by the algorithm is as follows:

[0068] sw i (x+1)=sw i (x)+e l *|β|*v1+(1-e l )*|β1|*v2 (23)

[0069] In the above formula, β and β1 are two numbers randomly generated according to the normal distribution, e is the exponential constant, and v1 and v2 are generated according to the following formula:

[0070]

[0071]

[0072] In the above formula, f() is the fitness value of the population;

[0073] Reduce population and save memory behavior:

[0074] N=N min +(NN min )×k (26)

[0075] In the above formula, N min Represents the minimum population size.

[0076] The beneficial effects of the present invention are:

[0077] The method disclosed by the present invention aims at estimating channel parameters and uses the spider bee optimization algorithm to estimate the channel parameters on each path. Since the algorithm has a strong search capability, the influence of complex environments on the estimated channel parameters is reduced, and the accuracy of the estimated channel parameters is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic flow diagram of the method of the present invention;

[0079] Figure 2This is a comparison chart between the Spider Bee Optimization Algorithm and the Cramer-Rao Lower Bound Algorithm. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] Embodiment 1, this embodiment discloses a channel parameter estimation method based on spider bee optimization algorithm, such as Figure 1 As shown, the following steps are included:

[0082] Step 1, generating a channel impulse response based on channel measurement;

[0083] Step 2, initializing the parameters of the Spider Bee Optimization algorithm (SWO) based on the channel parameters;

[0084] Step 3, initialize channel estimation parameters;

[0085] Step 4, using the spider bee optimization algorithm to iteratively estimate channel parameters, including delay and complex amplitude;

[0086] Step 5, calculate the population fitness according to the hidden data space of the path, and determine the optimal delay and complex amplitude based on the fitness value after each iteration. When the algorithm end condition is met, return the optimal delay and complex amplitude estimated by the algorithm, which is the estimated channel parameters, that is, the delay and complex amplitude corresponding to the optimal fitness are the delay and complex amplitude of the channel, otherwise return to step 4.

[0087] In step 1, the generated wideband channel impulse response model is expressed as:

[0088]

[0089] In the above formula, L(t) is the number of paths at time t, θ l (t) α l (t),ψ l (t), τ l (t) are the azimuth, elevation, amplitude, phase and delay of the lth path respectively, is the guiding vector of the lth path, l = 1,…,L(t), δ(τ-τ l (t)) is τ l The impulse function at (t);

[0090] The number of path estimates at time t is L, so the channel impulse response at time t is:

[0091]

[0092] In the above formula, τ is the delay corresponding to the channel impulse response at time t, θ l , α l , l , τ l are the azimuth, elevation, amplitude, phase and delay of the lth path at time t, is the guidance vector of the lth path at time t, l = 1,…,L, δ(τ-τ l ) is the time tτ l The impulse function at ;

[0093] Performing discrete Fourier transform (DFT) on the above equation gives the impulse response of the channel:

[0094]

[0095] In step 4, the spider bee optimization algorithm is used to estimate the delay and complex amplitude of the channel, so as to calculate its fitness. The delay and complex amplitude corresponding to the optimal fitness are the delay and complex amplitude of the channel, which specifically include:

[0096] Channel parameter setting: Assuming there are L paths, when estimating the channel parameters of all paths for the first time, the hidden data space X of the nth path n for:

[0097]

[0098]

[0099] In the above formula, n = 1, ..., L;

[0100] When all path channel parameters are estimated for the first time, the hidden data space X of the nth path n for:

[0101]

[0102] F=||X n *S * || 2 (4)

[0103] In the above formula, Z is the impulse response of the snapshot, which is the state record of the received signal at a certain moment, and H l is the channel impulse response of the lth path. When the channel parameters of all paths are estimated for the first time, l=1,…,L-1. When the channel parameters of all paths are estimated for a non-first time, l=1,…,L, and l≠n, n is the current nth estimated path, θ l is the azimuth of the lth path, is the elevation angle of the lth path, is the guiding vector of the lth path, α l is the amplitude of the lth path, ψ l is the phase of the lth path, τ l is the delay of the lth path, f is the signal frequency, S is the transmitted data symbol, and the algorithm fitness function is F, S * represents the complex conjugate of S, ||X n *S * || 2 Represents X n *S * The square of the Frobenius norm of .

[0104] Figure 2 This is a comparison chart between the Spider Bee Optimization Algorithm and the Cramer-Rao Lower Bound Algorithm. The Spider Bee Optimization Algorithm is based on the behavior modeling of the Spider Bee, including:

[0105] Hunting and Nesting Behavior:

[0106] (1) Search phase: This phase simulates the behavior of the female bee looking for the most suitable spider to feed the larvae. The calculation is as follows:

[0107] sw i (x+1)=sw i (x)+μ1*(sw a (x)-sw b (x)) (5)

[0108] sw i (x+1)=sw c (x)+μ2*(L+r2*(HL)) (6)

[0109] In the above formula, a and b are two indices randomly selected from the population, r2 is a randomly generated number between [0,1], c is an index randomly selected from the population, which is used to determine the exploration direction followed by the female bee; and μ1 is used to determine the direction of movement, and its formula is as follows:

[0110] μ1=|rn|*r1 (7)

[0111] μ2=B*cos(2πl) (8)

[0112] B=1 / (1+exp(l)) (9)

[0113] In the above formula, r1 is a randomly generated number between 0 and 1, rn is also a random number, but it is generated using a normal distribution, and l is a randomly generated number between [-2,1];

[0114]

[0115] r3 and r4 are random numbers between [0,1];

[0116] (2) Following and fleeing stage:

[0117] sw i (x+1)=sw i (x)+C*(2*r5*sw a (x)-sw i (x)) (11)

[0118]

[0119] In the above formula, a is an indicator randomly selected from the population; x and x max Represent the current calculation and the maximum number of calculations respectively; r5 is a vector representing a randomly generated value in the interval [0,1]; r6 is a random number in the interval [0,1];

[0120] sw i (x+1)=sw i (x+1)*vc (13)

[0121] k=1-(x / x max ) (14)

[0122] In the above formula, vc is a vector generated between k and -k according to the normal distribution to gradually increase the distance between the female bee and the spider;

[0123] The trade-off between these two computations is stochastically implemented as shown in the following equation:

[0124]

[0125] The exchange between the search phase and the subsequent mechanism is adjusted according to the following formula:

[0126]

[0127] In the above formula, p is a randomly generated number between [0,1];

[0128] (3) Nesting behavior:

[0129] sw i (x+1)=sw * (x)+cos(2πl)*(sw * (x)-sw i (x)) (17)

[0130] In the above formula, sw *Representing the best solution so far, the second equation will build the nest within the location of a female spider randomly selected from the population, and use an extra step size to avoid building two nests in the same location. The design is as follows:

[0131] sw i (x+1)=sw a (x)+r3*|γ|*(sw a (x)-sw i (x)+(1-r3)*U*

[0132] (sw b (x)-sw c (x))(18)

[0133] In the above formula, r3 is a random number created in the interval [0,1]; γ is a number generated according to the levy flight; a, b, c are the indices of three solutions randomly selected from the population; U is a binary vector used to determine when to apply a step to avoid building two nests in the same location; U is calculated according to the following formula:

[0134]

[0135] In the above formula, r4 and r5 are two vectors representing random values ​​in the interval [0,1]. Formulas (13) and (14) are randomly swapped according to the following formula:

[0136]

[0137] The trade-off between hunting and nesting behavior is implemented by equation (20);

[0138]

[0139] N represents the number of individuals in the population;

[0140] Mating behavior:

[0141] sw i (x+1)=Crossover(sw i (x),sw m (x),CR) (22)

[0142] In the above formula, Crossover means that in the solution sw i (x) and sw m The uniform crossover operator applied between (x) is called the crossover rate CR; sw m (x) and sw i (x) are two vectors, representing male and female spider bees respectively. The difference between the male and female spider bees generated by the algorithm is as follows:

[0143] sw i (x+1)=sw i (x)+e l *|β|*v1+(1-e l )*|β1|*v2 (23)

[0144] In the above formula, β and β1 are two numbers randomly generated according to the normal distribution, e is the exponential constant, and v1 and v2 are generated according to the following formula:

[0145]

[0146]

[0147] In the above formula, f() is the fitness value of the population;

[0148] Crossing over is used to recombinate the genetic material of parental spider wasps to produce offspring (eggs) with characteristics of their parents. The trade-off between hunting and mating behaviors is based on a predefined factor called the trade-off ratio (TR).

[0149] Reduce population and save memory behavior:

[0150] N=N min +(NN min )×k (26)

[0151] In the above formula, N min It represents the minimum population size used to avoid falling into the local minimum at different stages of the optimization process.

[0152] In summary, the spider bee optimization algorithm is used to estimate the channel parameters (delay, complex amplitude), generate a population based on the channel impulse response, calculate the fitness value, use the hunting and nesting method, and the breeding method to calculate the update position, and return the estimated parameters when the return conditions are met. The specific steps include the following:

[0153] Step S1: Initialize the parameters of the spider bee optimization algorithm: U, p, K, r1, r2, r3, r4, r5, r6, rn;

[0154] Step S2: generating a channel impulse response based on channel parameters;

[0155] Step S3: Select different methods to calculate the population position based on p, K, r3, r4, r5, r6;

[0156] Step S4: Iteratively calculate the solution based on the optimal position;

[0157] Step S5: Iteratively estimate channel parameters: delay, complex amplitude, and return the parameters when conditions are met.

[0158] In order to verify the effectiveness of the method proposed in the present invention, the application effect of the present invention is described in detail in combination with the simulation results. In the specific simulation, two paths are set, the real amplitude of the first path is set to 1, the real delay is set to 1, and the phase is set to 0, the real amplitude of the second path is set to 0.5, the real delay is set to 2.5, the phase is set to 40 degrees, and the number of estimations of all path parameters is set to 2.

[0159] Delay Estimation Root Mean Square Error M is the Monte Carlo number, which is set to 300 in the experiment, τ is the actual path delay, is the Mth estimated path delay.

Claims

1. A channel parameter estimation method based on spider bee optimization algorithm, characterized in that: The steps include: Step 1, generating a channel impulse response based on channel measurement; Step 2, initializing the parameters of the spider bee optimization algorithm based on the channel parameters; Step 3, initialize channel estimation parameters; Step 4, using the spider bee optimization algorithm to iteratively estimate channel parameters, including delay and complex amplitude; Step 5, calculate the fitness of the population, and determine the optimal delay and complex amplitude based on the fitness value after each iteration. When the algorithm end condition is met, return the optimal delay and complex amplitude estimated by the algorithm, which is the estimated channel parameters, otherwise return to step 4.

2. The channel parameter estimation method based on spider bee optimization algorithm according to claim 1, characterized in that: In step 1, the generated wideband channel impulse response model is expressed as: In the above formula, L(t) is the number of paths at time t, θ l (t) α l (t),ψ l (t), τ l (t) are the azimuth, elevation, amplitude, phase and delay of the lth path respectively, is the guiding vector of the lth path, l = 1,…,L(t), δ(τ-τ l (t)) is τ l The impulse function at (t); The number of path estimates at time t is L, so the channel impulse response at time t is: In the above formula, τ is the delay corresponding to the channel impulse response at time t, θ l , α l , l , τ l are the azimuth, elevation, amplitude, phase and delay of the lth path at time t, is the guidance vector of the lth path at time t, l = 1,…,L, δ(τ-τ l ) is the time tτ l The impulse function at ; Performing discrete Fourier transform on the above equation gives the impulse response of the channel:

3. The channel parameter estimation method based on spider bee optimization algorithm according to claim 1, characterized in that: In step 4, the spider bee optimization algorithm is used to estimate the delay and complex amplitude of the channel, so as to calculate its fitness. The delay and complex amplitude corresponding to the optimal fitness are the delay and complex amplitude of the channel, which specifically include: Channel parameter setting: Assuming there are L paths, when estimating the channel parameters of all paths for the first time, the hidden data space X of the nth path n for: In the above formula, n = 1, ..., L; When all path channel parameters are estimated for the first time, the hidden data space X of the nth path n for: F=||X n *S * || 2 (4) In the above formula, Z is the impulse response of the snapshot, which is the state record of the received signal at a certain moment, and H l is the channel impulse response of the lth path. When the channel parameters of all paths are estimated for the first time, l=1,…,L-1. When the channel parameters of all paths are estimated for a non-first time, l=1,…,L, and l≠n, n is the current nth estimated path, θ l is the azimuth of the lth path, is the elevation angle of the lth path, is the guiding vector of the lth path, α l is the amplitude of the lth path, ψ l is the phase of the lth path, τ l is the delay of the lth path, f is the signal frequency, S is the transmitted data symbol, and the algorithm fitness function is F, S * represents the complex conjugate of S, ||X n *S * || 2 Represents X n *S * The square of the Frobenius norm of .

4. The channel parameter estimation method based on spider bee optimization algorithm according to claim 1, characterized in that: The spider bee optimization algorithm is based on the behavior modeling of spider bees, including: Hunting and Nesting Behavior: (1) Search phase: This phase simulates the behavior of the female bee looking for the most suitable spider to feed the larvae. The calculation is as follows: sw i (x+1)=sw i (x)+μ1*(sw a (x)-sw b (x)) (5) sw i (x+1)=sw c (x)+μ2*(L+r2*(H-L)) (6) In the above formula, a and b are two indices randomly selected from the population, r2 is a randomly generated number between [0,1], and c is an index randomly selected from the population; μ1=|rn|*r1 (7) μ2=B*cos(2πl) (8) B=1 / (1+exp(l)) (9) In the above formula, r1 is a randomly generated number between 0 and 1, rn is also a random number, but it is generated using a normal distribution, and l is a randomly generated number between [-2,1]; r3 and r4 are random numbers between [0,1]; (2) Following and fleeing stage: sw i (x+1)=sw i (x)+C*(2*r5*sw a (x)-sw i (x)) (11) In the above formula, x and x max Represent the current calculation and the maximum number of calculations respectively; r5 is a vector representing a randomly generated value in the interval [0,1]; r6 is a random number in the interval [0,1]; sw i (x+1)=sw i (x+1)*vc (13) k=1-(x / x max ) (14) In the above formula, vc is a vector generated between k and -k according to the normal distribution; The trade-off between these two computations is stochastically implemented as shown in the following equation: The exchange between the search phase and the subsequent mechanism is adjusted according to the following formula: In the above formula, p is a randomly generated number between [0,1]; (3) Nesting behavior: sw i (x+1)=sw * (x)+cos(2πl)*(sw * (x)-sw i (x)) (17) In the above formula, sw * Representing the best solution so far, the second equation will build the nest within the location of a female spider randomly selected from the population, using an additional step size, designed as follows: sw i (x+1)=sw a (x)+r3*|γ|*(sw a (x)-sw i (x)+(1-r3)*U* (sw b (x)-sw c (x))(18) In the above formula, γ is a number generated according to the levy flight; U is a binary vector, and U is calculated according to the following formula: Formulas (13) and (14) are randomly swapped according to the following formula: The trade-off between hunting and nesting behavior is implemented by equation (20); N represents the number of individuals in the population; Mating behavior: sw i (x+1)=Crossover(sw i (x),sw m (x),CR) (22) In the above formula, Crossover means that in the solution sw i (x) and sw m The uniform crossover operator applied between (x) is called the crossover rate CR; sw m (x) and sw i (x) are two vectors, representing male and female spider bees respectively. The difference between the male and female spider bees generated by the algorithm is as follows: sw i (x+1)=sw i (x)+e l *|β|*v1+(1-e l )*|β1|*v2 (23) In the above formula, β and β1 are two numbers randomly generated according to the normal distribution, e is the exponential constant, and v1 and v2 are generated according to the following formula: In the above formula, f() is the fitness value of the population; Reduce population and save memory behavior: N=N min +(N-N min )×k (26) In the above formula, N min Represents the minimum population size.

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