CN113490179A - Unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception - Google Patents

Unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception Download PDF

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CN113490179A
CN113490179A CN202110812521.7A CN202110812521A CN113490179A CN 113490179 A CN113490179 A CN 113490179A CN 202110812521 A CN202110812521 A CN 202110812521A CN 113490179 A CN113490179 A CN 113490179A
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unmanned aerial
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王亚飞
姚媛媛
董瑶瑶
李学华
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Beijing Information Science and Technology University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses an unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception, which comprises the following steps: s1, performing signal-to-drying ratio SINR detection on an unmanned aerial vehicle and a user side, and constructing a probability perception model; s2, collecting horizontal coordinates of a plurality of unmanned aerial vehicles as initial samples to perform Logistic mapping processing to obtain an initial population; s3, updating the position of the initialized population, calculating the current individual fitness and sequencing; s4, calculating the average fitness of the current population, and disturbing the individuals involved in the local optimization by using a random operator to obtain the individuals with the optimal fitness, namely the individuals of the unmanned aerial vehicle with the optimal coverage rate; and S5, obtaining the optimal coverage rate. The method can improve the quality of the initial solution by using the Logistic chaotic sequence and enhance the global search capability of the algorithm. In order to avoid the situation that individuals get into local optimum, two types of random operators are introduced, the local search capacity is improved, the global optimum solution is improved, and the maximum unmanned aerial vehicle network coverage rate is achieved.

Description

Unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception
Technical Field
The invention relates to the technical field of network auxiliary communication of unmanned aerial vehicles, in particular to an unmanned aerial vehicle coverage optimization method based on signal to interference and noise ratio probability perception.
Background
Unmanned Aerial Vehicles (UAVs) are widely used in recent years as high-altitude communication platforms, have flexible mobility and good line-of-sight links, and are not affected by complex ground conditions. In an emergency communication scene, when a base station on the ground is damaged, the unmanned aerial vehicle self-organizing network can be deployed into an aerial base station to build a communication platform for ground users, and particularly to assist post-disaster area recovery communication. However, due to the limited energy consumption of the drone, it is crucial to develop an emergency communication scenario for the drone to deploy the three-dimensional (3D) spatial location of the drone to maximize the coverage of the service area.
Since unmanned aerial vehicle aerial deployment involves multidimensional variables and complex Air-to-Ground (ATG) path loss models, the optimal position cannot be derived from mathematical inferences. Therefore, given a complex set of spatially deployed nodes, finding the best position for these nodes to achieve coverage maximization range is often an NP-hard problem. In order to obtain the optimal solution of the problems, a swarm intelligence algorithm is commonly used in the prior art, however, the swarm intelligence algorithm generally has the problems of easy falling into local optimization and easy falling into the phenomenon of 'precocity', and the quality of an initial swarm in the algorithm also influences the algorithm solving precision and the convergence speed.
Disclosure of Invention
Aiming at the problems, the invention provides an unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception, which can improve the quality of an initial solution by using a Logistic chaotic sequence and enhance the global search capability of an algorithm. In order to avoid the situation that individuals get into local optimum, two types of random operators are introduced, the local search capacity is improved, the global optimum solution is improved, and the maximum unmanned aerial vehicle network coverage rate is achieved.
In order to achieve the purpose, the invention provides the following scheme: the invention provides an unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception, which comprises the following steps:
s1, performing signal-to-dryness ratio SINR detection on an unmanned aerial vehicle and a user side, and constructing a probability perception model based on the signal-to-dryness ratio SINR detection result;
s2, collecting horizontal coordinates of a plurality of unmanned aerial vehicles as initial samples, and performing Logistic mapping processing on the initial samples to obtain an initialization population;
s3, updating the position of the initialization population, calculating the current individual fitness and sequencing;
s4, calculating the average fitness of the current population based on the fitness of the initialized population, and disturbing the individuals involved in the local optimization by using a random operator based on the sequencing result to obtain optimal fitness individuals, wherein the optimal fitness individuals are the unmanned aerial vehicle individuals with the optimal coverage rate;
and S5, inputting the individual position information of the unmanned aerial vehicle with the optimal coverage rate into the probability perception model to obtain the optimal coverage rate.
Preferably, the initialization population comprises a finder position, a follower position and an early-warning person position.
Preferably, in S3, the updating the location of the initialization population includes:
the finder location update is as follows:
Figure BDA0003168936700000031
wherein, i ═ {1,2,3 … … NS }, NS represents the population size; j ═ {1,2,3 … … D }, D represents the dimension of the problem;
Figure BDA0003168936700000032
position information representing the ith individual after t +1 iterations in the jth dimension; r2And ST is the early warning value and the safety value, respectively, where R2∈[0,1]，ST∈[0.5,1](ii) a μ is (0,1)]A random number in between; m is the number of iterations; δ is a random number following a normal distribution; xi is 1 XD sized whole1, matrix;
the follower location update is as follows:
Figure BDA0003168936700000033
in the formula (I), the compound is shown in the specification,
Figure BDA0003168936700000034
representing the global worst position at the t-th iteration; a is a matrix of 1 × D size, the matrix elements being randomly assigned 1 or-1, A+＝AT(AAT)-1；
The forewarning location updates are as follows:
Figure BDA0003168936700000035
in the formula (I), the compound is shown in the specification,
Figure BDA0003168936700000036
is the globally optimal position at the t-th iteration;
Figure BDA0003168936700000037
the global worst position for the t-th iteration; beta is a control step length parameter, and obeys normal distribution with the mean value of 1 and the variance of 0;
Figure BDA0003168936700000041
for the step size control parameter is [ -1,1 [ ]]A random number in between; f. ofiIs the fitness value of the current individual, fgAnd fwIs the global optimum and worst fitness value; ξ is constant to avoid 0 appearing in the denominator.
Preferably, the Logistic mapping is specifically:
Cm+1＝Ω·Cm·(1-Cm)
in the formula, m is the traversal updating times; Ω is a control parameter on (0, 4).
Preferably, the random operator comprises gaussian mutation in LGSSA, cauchy mutation in lcsas.
Preferably, the Gaussian variation X in the LGSSA isnewbestThe method specifically comprises the following steps:
Xnewbest＝Xbest(1+Gaussian(0，1))
in the formula, XbestGaussian (0,1) is a random number that follows a normal distribution, representing the optimal location of the current individual.
Preferably, the cauchy variant XCbest in the LCSSA is specifically:
XCbest＝Xbest(1+tan(π(r-0.5)))
in the formula, XbestRepresenting the optimal location of the current individual; xCbestIs the optimal position of the individual after the Cauchy variation; r is a random number on (0, 1).
Preferably, the specific process of updating the finder position is as follows: randomly setting the early warning value, comparing the early warning value with the safety value, if the difference between the safety value and the early warning value is greater than a preset threshold value, calculating a new individual fitness value, and recording the position of an individual; and if the difference between the safety value and the early warning value is smaller than a preset threshold value, updating the position of the finder, and simultaneously recording the individual position with the worst fitness value.
The invention discloses the following technical effects:
1. the probability perception model based on the SINR detection is constructed for a multi-unmanned aerial vehicle network coverage model, the model determines the perception probability of the unmanned aerial vehicle to the user by detecting the strength of the SINR of the user side, and the coverage quality can be analyzed more accurately.
2. In order to maximize the coverage rate of the unmanned aerial vehicle network, the quality of an initial solution is improved by using a Logistic chaotic sequence, the global search capability of an algorithm is enhanced, two types of random operators are introduced, the diversity of a population is enriched, individuals are prevented from falling into a locally optimal 'precocity' state, the local search capability is improved, and the globally optimal solution is improved.
3. According to the invention, the LGSSA and LCSSA algorithms are adopted to effectively reduce network coverage redundancy and improve the network coverage rate of the unmanned aerial vehicle.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method in an embodiment of the invention;
fig. 2(a) is a model of random deployment of multiple drones in an embodiment of the present invention;
fig. 2(b) is an effective deployment model of multiple drones in the embodiment of the present invention;
FIG. 3 is a flow chart of an algorithm in an embodiment of the present invention;
fig. 4 is a comparison graph of results of LGSSA, LCSSA, SSA, IABC, PSO algorithms when the transmitted power of the drone is 20dBm in the embodiment of the present invention;
fig. 5 is a comparison graph of results of LGSSA and lcsas algorithms under different transmit powers of the drone in the embodiment of the present invention;
FIG. 6 is a diagram illustrating analysis and comparison of different UAVs in LCSSA according to an embodiment of the present invention;
FIG. 7 is a comparison graph of algorithm results of the unmanned aerial vehicle at different heights in the embodiment of the invention;
fig. 8 is a comparison graph of additional energy consumption reduction of the unmanned aerial vehicle at different heights in the embodiment of the present invention;
fig. 9 is a diagram comparing network throughput of drones at different transmission powers in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, the present embodiment provides an unmanned aerial vehicle coverage optimization method based on signal to interference and noise ratio probability sensing, including the following steps:
s1, performing signal-to-dry ratio SINR detection on the unmanned aerial vehicle and the user side, and constructing a probability perception model based on the signal-to-dry ratio SINR detection result.
S2, collecting horizontal coordinates of a plurality of unmanned aerial vehicles as initial samples, and performing Logistic mapping processing on the initial samples to obtain an initialization population; the initialization population comprises a finder position, a follower position and an early-warning person position.
The Logistic mapping is specifically:
Cm+1＝Ω·Cm·(1-Cm)
in the formula, m is the traversal updating times; omega is (0, 4)]When the value range of omega is [3.5699456, 4]]The system is in a chaotic state; when omega is close to 4, C generated by traversing and updating is traversedmThe values are in a pseudo-randomly distributed state, consisting of random numbers evenly distributed between 0 and 1.
And S3, updating the position of the initialized population, calculating the current individual fitness and sequencing.
Updating the location of the initialization population comprises:
the finder location update is as follows:
Figure BDA0003168936700000071
wherein, i ═ {1,2,3 … … NS }, NS represents the population size; j ═ {1,2,3 … … D }, D represents the dimension of the problem;
Figure BDA0003168936700000072
position information representing the ith individual after t +1 iterations in the jth dimension; r2And ST is the early warning value and the safety value, respectively, where R2∈[0,1]，ST∈[0.5,1](ii) a μ is (0,1)]A random number in between; m is the number of iterations; δ is a random number following a normal distribution; xi is a full 1 matrix of size 1 × D.
The specific process of updating the position of the finder is as follows: randomly setting the early warning value, comparing the early warning value with the safety value, if the difference between the safety value and the early warning value is greater than a preset threshold value, calculating a new individual fitness value, and recording the position of an individual; and if the difference between the safety value and the early warning value is smaller than a preset threshold value, updating the position of the finder, and simultaneously recording the individual position with the worst fitness value. The preset threshold is set to 0.75.
The follower location update is as follows:
Figure BDA0003168936700000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003168936700000082
representing the global worst position at the t-th iteration; a is a matrix of 1 × D size, the matrix elements being randomly assigned 1 or-1, A+＝AT(AAT)-1。
The forewarning location updates are as follows:
Figure BDA0003168936700000083
in the formula (I), the compound is shown in the specification,
Figure BDA0003168936700000084
is the globally optimal position at the t-th iteration;
Figure BDA0003168936700000085
the global worst position for the t-th iteration; beta is a control step length parameter, and obeys normal distribution with the mean value of 1 and the variance of 0;
Figure BDA0003168936700000086
for the step size control parameter is [ -1,1 [ ]]A random number in between; f. ofiIs the fitness value of the current individual, fgAnd fwIs the global optimum and worst fitness value; ξ is constant to avoid 0 appearing in the denominator.
And S4, calculating the average fitness of the current population based on the fitness of the initialized population, and disturbing the individuals involved in the local optimization by using a random operator based on the sequencing result to obtain the optimal fitness individuals, wherein the optimal fitness individuals are the unmanned aerial vehicle individuals with the optimal coverage rate.
The random operators include gaussian mutation in LGSSA, cauchy mutation in lcsas, where:
gaussian variation X in LGSSAnewbestThe method specifically comprises the following steps:
Xnewbest＝Xbest(1+Gaussian(0，1))
in the formula, XbestGaussian (0,1) is a random number that follows a normal distribution, representing the optimal location of the current individual. According to the normal distribution characteristic, the Gaussian variation can be locally searched near the current potential optimal individual, and random disturbance is generated near the potential optimal solution.
Compared with Gaussian variation, Cauchy variation can generate larger variation step length, the population diversity can be effectively kept, and the algorithm also has better global search capability. The one-dimensional Cauchy Density function is expressed as follows:
Figure BDA0003168936700000091
where t >0, the Cauchy distribution function is as follows:
Figure BDA0003168936700000092
compared with normal distribution, the variation range of Cauchy distribution is more uniform, and the algorithm is favorable for jumping out of local optimum.
Hence Cauchy variation X in LCSSAChestThe method specifically comprises the following steps:
XCbest＝Xbest(1+tan(π(r-0.5)))
in the formula, XbestRepresenting the optimal location of the current individual; xCbestIs the optimal position of the individual after the Cauchy variation; r is a random number on (0, 1).
The two variation modes can enable the individual to evolve towards non-inferior solution when the evolution of the individual is stagnated, and the ability of the individual to escape from the local optimum point can be effectively improved.
And S5, inputting the individual position information of the unmanned aerial vehicle with the optimal coverage rate into a probability perception model to obtain the optimal coverage rate.
Referring to fig. 2(a) -2 (b), the present embodiment provides a deployment model of an unmanned aerial vehicle when a natural disaster occurs on the ground and a base station cannot work normally; the unmanned aerial vehicle group can provide communication requirements for disaster area users in time by rapidly building an aerial communication platform, the traditional unmanned aerial vehicle random deployment mode shown in fig. 2(a) can cause region coverage redundancy, the region redundancy coverage increases the extra energy consumption of the unmanned aerial vehicle, and the network life cycle of the unmanned aerial vehicle is shortened. Meanwhile, the user receiving end can generate interference from the multiple unmanned aerial vehicles to transmit signals. As shown in fig. 2(b), the position of the unmanned aerial vehicle is reasonably deployed by the proposed algorithm, so that the area coverage rate can be improved, the redundant coverage is reduced, and the additional energy consumption of the unmanned aerial vehicle is reduced; and the interference of the user terminal can be greatly reduced. The following considerations are downlink communications, while assuming that the doppler effect due to the mobility of the drone is well compensated for at the user.
The unmanned aerial vehicle network coverage model has two types, namely Boolean perception and probability perception. The Boolean perception model is an ideal model, only rough approximation can be carried out on actual perception probability, and the coverage quality can be analyzed more accurately by probability perception. In order to research communication transmission closer to a practical scene, a probability perception model based on SINR detection is designed.
In this embodiment, an improved sparrow algorithm is adopted, and the principle of the classical sparrow algorithm is as follows: the method is characterized in that individuals in the sparrow population are simplified into a finder and a follower, the finder is used as an individual with high fitness in the population to be responsible for searching food sources for the whole population, and meanwhile, the finder brings the follower with low fitness. The follower can decide whether to go elsewhere to search for food according to the hunger state of the follower, and otherwise, the follower can search for food near the current optimal position. The early warning sparrows account for 10% -20% of the population, and when the sparrows in the part meet danger, sparrows at the periphery of the population are close to a safe area. Sparrows in the center of the population then walk randomly to get close to other sparrows.
The improved sparrow algorithm adopted by the invention is used for enhancing the global search capability of the algorithm, firstly, a Logistic chaotic sequence is introduced into SSA to set an initial solution of a population, and meanwhile, a random operator is added at the later stage of the algorithm to avoid an individual from falling into local optimum.
And then the positions of sparrows are updated, and the specific updating principle is as follows:
a random number is generated as an early warning value, and the early warning value is compared with a safety value. And when the early warning value is smaller, indicating that no predator appears at the moment, calculating a new fitness value and recording the position information of sparrows. When the early warning value is large, predators appear at the moment, and sparrows need to select safe areas to forage for food. And updating the position according to the discoverer position updating formula, and recording the sparrow position with the minimum fitness value. The fitness function is an objective function optimized in the text.
The position of the follower is updated, and when the sparrow is in a hungry state, namely the fitness value is low, the sparrow needs to fly to other areas to supplement food. Otherwise, sparrows would be foraged around the best discoverer, during which food competition might also occur, making themselves discoverer. And updating the position according to the follower position updating formula, calculating the fitness value and recording the position of the sparrow.
And finally, updating the position of the early-warning person, and randomly generating the position of the early-warning person in the population. Sparrows at the periphery of the population are close to the safe area, and sparrows at the center of the population randomly walk to be close to other sparrows. And updating the position according to the position updating formula of the early-warner.
When the updating iteration is carried out, each iteration is finished, and after the positions of the sparrows are updated, the fitness value f of the current sparrow individual is calculatediAnd the average fitness value f of the populationa. When f isaGreater than fiDisturbing an individual trapped in local optimum by using a random operator, namely adopting Gaussian variation in LGSSA and adopting Cauchy variation in LCSSA; and judging the optimization degree of the generated solution, and if the optimization degree is better than that of the previous individual, carrying out replacement. And (3) repeatedly executing a sparrow optimizing process, and finally outputting a sparrow individual with the highest fitness value when an iteration condition is met, wherein the specific flow is shown in fig. 3.
TABLE 1
Means of Parameter(s) Numerical value
Unmanned aerial vehicle transmitting power Pu 10dBm-21dBm
Speed of light c 3x108m/s
Number of unmanned aerial vehicles E 10
Height of unmanned plane H 15m-40m
Channel noise n0 -120dBm
Unmanned aerial vehicle antenna gain Ga 3dB
Path loss factor n1 2.8
Environmental variable 1 α 5.2
Environmental variable 2 γ 0.35
Line-of-sight link extra loss ζLOS 0.1dB
Extra loss of non line-of-sight link ζNLOS 21dB
Unmanned aerial vehicle carrier frequency f 2GHz
In order to verify the optimization effect of the improved sparrow search algorithm on unmanned aerial vehicle coverage, MATLAB2018 is used for simulating the unmanned aerial vehicle coverage, and an operating system adopted during simulation is Windows 10. The service area range of the unmanned aerial vehicle is 500m multiplied by 500 m. The various parameters of drone communication are given in table 1.
The data in table 1 were simulated to obtain the following results:
when the transmitting power of the unmanned aerial vehicle is 20dBm, the improved LGSSA, LCSSA ratio SSA, IABC and PSO algorithm can effectively improve the network coverage rate. The LCSSA optimized coverage is the highest, and the LGSSA algorithm is the second. Compared with Gaussian variation, Cauchy variation can generate larger variation step length, and the global search capability of the algorithm is improved. After a certain number of iterations, the optimization effect of the lcsas is greater than that of the LGSSA algorithm, and the algorithm effect is stable, as shown in fig. 4.
When the drone is under different transmit powers, the network coverage also increases continuously as the transmit power of the drone increases, as shown in fig. 5. Increasing the transmit power increases the SINR at the user, and at this time, the drone network can cover more users, as can be seen from fig. 5, under the same transmit power, the coverage rate after the lcs sa optimization is higher than the LGSSA, and the implementation effect is stable.
The number of different unmanned aerial vehicles can affect the convergence times of the LCSSA algorithm, and the iteration times are respectively 75 times, 45 times and 30 times when the algorithm is stable. When the number of the unmanned aerial vehicle base stations is increased, the coverage rate is increased, and the number of iterations for achieving stable coverage rate is reduced. This is because as the number of drones increases, the initial uncovered area decreases, and therefore the number of iterations to reach a stable coverage decreases, as shown in fig. 6.
When the drones are at the same altitude, the improved method is obviously superior to other methods in the aspect of optimizing the coverage rate. In addition, as shown in fig. 7, when the height of the drone increases, the path loss between the user and the drone increases, and when the SINR of the user end decreases, the coverage of the drone network decreases.
When the altitude of the drone is larger, after the algorithm iterates 90 times, the additional energy consumption of the drone can be reduced by 15.26% and 16.58% under the LGSSA and LCSSA algorithms, respectively, as shown in fig. 8. It can also be seen from fig. 7 that when the altitude of the drone is small, the difference between the coverage of the network of the drone is small compared with the SSA, lcsa, and this is because as the altitude of the drone decreases, the SINR of the user end increases, the initial coverage of the drone is large, and therefore, when the altitude of the drone decreases, the percentage of additional energy consumption reduction is greatly reduced.
Compared with the PSO, IABC, and SSA algorithms, the method of the present invention can effectively improve throughput, as shown in fig. 9, because by optimizing the aerial deployment of the drone, the interference at the user side can be effectively reduced while increasing the coverage. In addition, when the transmitting power of the unmanned aerial vehicle is increased, the signal received by the user from the unmanned aerial vehicle is greatly enhanced, and therefore the throughput of the system is increased.
The invention discloses the following technical effects:
1. the probability perception model based on the SINR detection is constructed for a multi-unmanned aerial vehicle network coverage model, the model determines the perception probability of the unmanned aerial vehicle to the user by detecting the strength of the SINR of the user side, and the coverage quality can be analyzed more accurately.
2. In order to maximize the coverage rate of the unmanned aerial vehicle network, the quality of an initial solution is improved by using a Logistic chaotic sequence, the global search capability of an algorithm is enhanced, two types of random operators are introduced, the diversity of a population is enriched, individuals are prevented from falling into a locally optimal 'precocity' state, the local search capability is improved, and the globally optimal solution is improved.
3. According to the invention, the LGSSA and LCSSA algorithms are adopted to effectively reduce network coverage redundancy and improve the network coverage rate of the unmanned aerial vehicle.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. An unmanned aerial vehicle coverage optimization method based on signal-to-interference-and-noise ratio probability perception is characterized by comprising the following steps:
s1, performing signal-to-dryness ratio SINR detection on an unmanned aerial vehicle and a user side, and constructing a probability perception model based on the signal-to-dryness ratio SINR detection result;
s2, collecting horizontal coordinates of a plurality of unmanned aerial vehicles as initial samples, and performing Logistic mapping processing on the initial samples to obtain an initialization population;
s3, updating the position of the initialization population, calculating the current individual fitness and sequencing;
s4, calculating the average fitness of the current population based on the fitness of the initialized population, and disturbing the individuals involved in the local optimization by using a random operator based on the sequencing result to obtain optimal fitness individuals, wherein the optimal fitness individuals are the unmanned aerial vehicle individuals with the optimal coverage rate;
and S5, inputting the individual position information of the unmanned aerial vehicle with the optimal coverage rate into the probability perception model to obtain the optimal coverage rate.
2. The unmanned aerial vehicle coverage optimization method based on signal to interference plus noise ratio probability perception according to claim 1, wherein the initialization population comprises a finder position, a follower position and an early-warning position.
3. The method for unmanned aerial vehicle coverage optimization based on signal to interference plus noise ratio probability perception according to claim 2, wherein in the step S3, the updating the position of the initialization population includes:
the finder location update is as follows:
Figure FDA0003168936690000021
wherein, i ═ {1,2,3 … … NS }, NS represents the population size; j ═ {1,2,3 … … D }, D represents the dimension of the problem;
Figure FDA0003168936690000022
position information representing the ith individual after t +1 iterations in the jth dimension; r2And ST is the early warning value and the safety value, respectively, where R2∈[0,1]，ST∈[0.5,1](ii) a μ is (0,1)]A random number in between; m is the number of iterations; δ is a random number following a normal distribution; xi is a full 1 matrix of size 1 × D;
the follower location update is as follows:
Figure FDA0003168936690000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003168936690000024
representing the global worst position at the t-th iteration; a is a matrix of 1 × D size, the matrix elements being randomly assigned 1 or-1, A+＝AT(AAT)-1；
The forewarning location updates are as follows:
Figure FDA0003168936690000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003168936690000026
is the globally optimal position at the t-th iteration;
Figure FDA0003168936690000027
the global worst position for the t-th iteration; beta is controlThe step size parameter follows normal distribution with the mean value of 1 and the variance of 0;
Figure FDA0003168936690000031
for the step size control parameter is [ -1,1 [ ]]A random number in between; f. ofiIs the fitness value of the current individual, fgAnd fwIs the global optimum and worst fitness value; ξ is constant to avoid 0 appearing in the denominator.
4. The unmanned aerial vehicle coverage optimization method based on signal to interference plus noise ratio probability perception according to claim 1, wherein the Logistic mapping specifically comprises:
Cm+1＝Ω·Cm·(1-Cm)
in the formula, m is the traversal updating times; Ω is a control parameter on (0, 4).
5. The method of claim 1, wherein the random operators include gaussian variance in LGSSA, cauchy variance in lcsas.
6. The method of claim 5, wherein the Gaussian variance X in the LGSSA is calculated by the method of UAV coverage optimization based on SINR probability perceptionnewbestThe method specifically comprises the following steps:
Xnewbest＝Xbest(1+Gaussian(0，1))
in the formula, XbestGaussian (0,1) is a random number that follows a normal distribution, representing the optimal location of the current individual.
7. The method according to claim 5, wherein the Cauchy variation X in LCSSA is the coverage optimization method for UAV based on SINR probability perceptionCbestThe method specifically comprises the following steps:
Xcbest＝Xbest(1+tan(π(r-0.5)))
in the formula, XbestRepresenting the optimal location of the current individual; xCbestIs the optimal position of the individual after the Cauchy variation; r is a random number on (0, 1).
8. The method for unmanned aerial vehicle coverage optimization based on signal to interference plus noise ratio probability perception according to claim 3, wherein the specific process of discoverer location update is as follows: randomly setting the early warning value, comparing the early warning value with the safety value, if the difference between the safety value and the early warning value is greater than a preset threshold value, calculating a new individual fitness value, and recording the position of an individual; and if the difference between the safety value and the early warning value is smaller than a preset threshold value, updating the position of the finder, and simultaneously recording the individual position with the worst fitness value.
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