A low-interference communication method for satellite cognitive radio based on multi-objective optimization

Abstract

The present invention discloses a satellite cognitive radio low-interference communication method based on multi-objective optimization, and the present invention relates to the field of wireless communication technology, including: step 1, collecting the coordinate position of the satellite, the coordinate position of the primary user, the coordinate position of the secondary user, and the number of drones used to perform beamforming and their initial positions; step 2, establishing an objective function; step 3, obtaining the optimal solution of the objective function according to an improved multi-objective dragonfly optimization algorithm, obtaining the optimal satellite transmission power, the optimal drone antenna excitation current weight, the optimal position of the drone and the secondary user sequence number; step 4, after all drones fly to the optimal position, they establish a communication connection with the satellite, the satellite sends a signal to the drone according to the optimal transmission power and communicates normally with the primary user, and sets the drone antenna excitation current weight to the optimal weight and then forwards the received satellite signal to the secondary user. The present invention has the characteristic of improving communication efficiency.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more specifically, to a satellite cognitive radio low-interference communication method based on multi-objective optimization. Background Art

[0002] With the emergence of 6G communication technology, wireless communication has entered a new era. The integrated communication network of air, land, and sea, which is integrated by ground networks, satellites, near-Earth space platforms, drones, and aircraft, will realize the seamless coverage of all-round Internet of Everything and wireless networks. Compared with 5G networks, 6G networks will have larger information capacity, higher transmission rate, lower transmission delay, higher connection density, higher spectrum efficiency, and higher energy efficiency. However, this will be accompanied by a surge in demand for communication resources. Since radio spectrum resources are very scarce, cognitive radio technology is used to improve the utilization of spectrum resources and optimize communication performance.

[0003] Cognitive radio technology is an intelligent spectrum sharing technology that can dynamically sense the radio environment. It uses an efficient and flexible dynamic spectrum access method to enable unauthorized users to obtain spectrum resources and achieve normal communication without hindering the normal communication of authorized users. Satellite cognitive radio networks apply cognitive radio technology to networks composed of satellites, drones, and ground facilities. Through spectrum sharing, all terminals in the network can communicate to avoid interference between signals. However, the channel state of satellite communication links changes rapidly and the communication distance is long. Although the sensing equipment can detect spectrum holes in real time, feedback delay is also an unavoidable problem. This not only increases the complexity of the system but also causes errors in resource allocation.

[0004] In the cognitive radio network scenario, the primary user and secondary users in the network complete communication by sharing spectrum resources. Common sharing modes include interleaving, padding and overlaying. The interleaving spectrum sharing mode is also called protective spectrum sharing. The padding and overlaying spectrum sharing modes belong to aggressive spectrum sharing. When the interleaving mode is used to share spectrum, the secondary user is not allowed to work in the same frequency band as the primary user at the same time. In this mode, in order to avoid interference to the primary user, the secondary user needs to constantly explore spectrum holes. If the primary user communicates for a long time or the communication frequency is high, the secondary user will have few spectrum holes available, and communication services will be difficult to carry out. In the padding sharing mode, the secondary user is allowed to work in the same frequency band as the primary user at the same time. The secondary user and the primary user operate on the same frequency band, but the transmission power of the secondary user is required to meet certain constraints, otherwise long-distance and high-power communication cannot be carried out. When the overlay mode is used to share the spectrum, the secondary user and the primary user are also allowed to operate in the same frequency band at the same time. However, in this mode, in order to avoid interference, the secondary user needs to master the information of the primary user, including complete channel information, etc. For limited computing power, this undoubtedly increases the cost of information processing. In addition, when responding to sudden communication needs, such as disaster relief, temporary emergency communications and other scenarios that do not have spectrum perception conditions, it is often impossible to accurately and effectively coordinate and schedule spectrum resources. For these problems, the relevant technology has no effective solutions yet.

[0005] A typical scenario is that ground networks share satellite spectrum resources through cognitive radio technology. Although this effectively solves the problem of spectrum resource shortage, a major problem is signal interference, which is unavoidable in cognitive radio and needs to be solved urgently. Since satellite communications have a wide coverage range and when ground network terminals are densely distributed, interference between users will increase, seriously affecting information transmission. Therefore, drone arrays can be used to use collaborative beamforming technology to forward satellite signals to the target terminal through a synthesized beam, and by controlling the position of array elements and excitation current and other parameters, the beam level in the main lobe is maximized, while the levels in other side lobes are minimized, thereby achieving a high-speed communication with interference protection. This not only reduces interference between users, but also overcomes the poor communication conditions caused by geographical factors and increases the communication distance range.

[0006] Collaborative beamforming technology is a commonly used communication method. Array elements will eventually form various beams through different arrangements. Due to the limited onboard energy of drones, in order to find the best position, they must continue to fly and explore. Moreover, when the array communicates with different target terminals, all array elements need to readjust their positions. The resulting power consumption is a factor that cannot be ignored.

[0007] At present, the relevant technical solutions in cognitive radio networks only focus on using different spectrum sharing modes to complete communication tasks, and improve the spectrum sensing related algorithms to reasonably allocate spectrum resources to avoid interference. However, the channel state of satellite communication links changes rapidly, so the sensing end is required to be more sensitive and timely, so the complexity of the system increases accordingly. In addition, many algorithms require the processing center to have certain learning and computing capabilities, but the on-board processing capabilities are not mature enough and the computing power is limited, which will lead to errors in spectrum sensing and resource allocation. Summary of the invention

[0008] The purpose of the present invention is to design and develop a satellite cognitive radio low-interference communication method based on multi-objective optimization, which simultaneously meets the three goals of high communication rate, low interference and low power consumption, and ultimately achieves a high-speed communication link with interference protection with low energy consumption.

[0009] The purpose of the present invention is to design and develop a satellite cognitive radio low-interference communication method based on multi-objective optimization, which improves the accuracy by solving the optimization problem separately by continuous values ​​and discrete values.

[0010] The technical solution provided by the present invention is:

[0011] A satellite cognitive radio low-interference communication method based on multi-objective optimization comprises the following steps:

[0012] Step 1: Based on the Cartesian coordinate system, collect the coordinate position of the satellite, the coordinate position of the primary user, the coordinate position of the secondary user, the initial position of the drone, and the number of drones;

[0013] Step 2: Establish the objective function:

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] In the formula, For multi-objective optimization problems, is the first optimization goal, is the second optimization goal, For the third optimization goal, To solve the set, For the The excitation current weight of each drone, is the number of drones, and , is the total number of drones, The transmission power allocated by the satellite to the secondary user link, Assigning a transmission power cap to the satellite on the secondary user link, For the The position coordinates of the drones, For drones on the horizontal plane The minimum distance from the starting point in the axis direction, Drone in horizontal plane The maximum distance from the starting point in the axis direction, For drones on the horizontal plane The minimum distance from the starting point in the axis direction, For drones on the horizontal plane The maximum distance from the starting point in the axis direction, The minimum altitude of the drone. The maximum altitude at which the drone flies. is the target user serial number set, is the secondary user sequence number of the current communication, is the set of all secondary user serial numbers, is the total number of users, for For any two adjacent drones, is the distance between adjacent UAVs, The minimum distance between drones to avoid collision. is the wavelength, It is the minimum distance between two drones to avoid collision and prevent antenna coupling;

[0023] Step 3: Obtain the optimal solution of the objective function according to the improved multi-objective dragonfly optimization algorithm to obtain the optimal satellite transmission power, the optimal UAV antenna excitation current weight, the optimal position of the UAV and the secondary user number;

[0024] Step 4: After all drones fly to the optimal position, they establish a communication connection with the satellite. The satellite sends signals to the drones according to the optimal transmission power. At the same time, the satellite communicates normally with the primary user, sets the drone antenna excitation current weight to the optimal weight, and then forwards the received satellite signal to the secondary user.

[0025] Preferably, the first optimization objective, the second optimization objective and the third optimization objective satisfy:

[0026] ;

[0027] ;

[0028] ;

[0029] In the formula, is the actual data receiving rate of the secondary user, is the strength of the main lobe, is the azimuth of the main lobe, is the pitch angle of the main lobe, The side lobe is The intensity in the direction of the primary user, The side lobe is facing The azimuth of the primary user, The side lobe is facing The elevation angle of the primary user, is the number of primary users, For the The energy consumption of a drone is the number of drones.

[0030] Preferably, the actual data receiving rate of the secondary user satisfies:

[0031] ;

[0032] In the formula, is the actual data receiving rate of the secondary user, The total transmission rate of the beam synthesized by the drone array toward the secondary user, The beam synthesized for the drone array is oriented toward The transmission rate of the primary user.

[0033] Preferably, the total transmission rate of the beam synthesized by the drone array toward the secondary user satisfies:

[0034] ;

[0035] In the formula, The communication rate between the synthesized beams of the UAV array and the ground users, for or ,when for When is the secondary user, for When is the primary user, is the transmission bandwidth, Transmit power for the drone array, is the transmit gain of the drone array, is the path loss from the UAV to the secondary user, is the noise power.

[0036] Preferably, the path loss from the drone to the secondary user satisfies:

[0037] ;

[0038] In the formula, is the loss coefficient constant, is the distance from the array center to the target user, is the loss index, is the probability that the communication link is a line-of-sight link, is the attenuation factor under line-of-sight link, is the probability that the communication link is a non-line-of-sight link, is the attenuation factor of the non-line-of-sight link.

[0039] Preferably, the transmission power of the drone array satisfies:

[0040] ;

[0041] In the formula, is the satellite antenna gain, is the receiving gain of the drone array antenna, is the distance between the center of the drone array and the satellite, is the conversion rate, is the ratio of pi.

[0042] Preferably, the transmission gain of the drone array satisfies:

[0043] ;

[0044] In the formula, is the azimuth of the synthetic beam, is the elevation angle of the synthetic beam, is the array factor, is the directional pattern function of the drone antenna unit, is the azimuth of the beam toward the current communicating ground user, is the elevation angle of the beam toward the current communicating ground user, is the array factor in the direction of the current communicating ground user, is the size of the far-field beam pattern in the direction of the current communicating ground user, is the array efficiency, and ;

[0045] The array factor satisfies:

[0046] ;

[0047] In the formula, is an imaginary unit, is the initial phase, represents the phase constant, and , It is The location coordinates of the drone.

[0048] Preferably, the The energy consumption of each drone satisfies:

[0049] ;

[0050] In the formula, is the communication time between the drone and the secondary user, For the quality of the drone, is the acceleration due to gravity, For drones The flight speed at the moment, Indicates A drone flies in a horizontal plane at a speed Power consumption during propulsion, For drones The horizontal flight speed at that moment, is the initial speed of the drone, For drones The height of the moment, is the initial height of the drone;

[0051] The said A drone flies in a horizontal plane at a speed The power consumption during propulsion meets the following requirements:

[0052] ;

[0053] In the formula, is the blade power of the UAV rotor, is the rotor induced power of the UAV rotor, is the rotor tip speed, It represents the average rotor induced speed of the UAV in the hovering state. is the fuselage drag ratio, is the solid resistance, is the air density, is the rotor blade area.

[0054] Preferably, the step three specifically includes the following steps:

[0055] Step 1: Initialize the population using the chaotic mapping initialization strategy and calculate the fitness value of the initial population;

[0056] Wherein, the chaos map initialization strategy is:

[0057] ;

[0058] In the formula, For the initial iteration Individual Dimension value, For Dimension The upper bound of For Dimension The lower bound of is the chaos value;

[0059] The chaos value satisfies:

[0060] ;

[0061] In the formula, is the current value of the chaotic sequence, is the updated value of the chaotic sequence, is the first control factor, is the second control factor;

[0062] The fitness value is an objective function;

[0063] Step 2: Update the individuals in the population and update the fitness value. Select the best individual through non-dominated sorting and store it in the Pareto solution set to obtain the continuous value of the solution.

[0064] Among them, individual updates satisfy:

[0065] ;

[0066] In the formula, for After the iteration, the newly generated individuals are For the The individuals generated by the iterations are is the step length;

[0067] Step 3: Update the discrete value of the solution through elite reverse learning and differential evolution algorithm to obtain the serial number of the target user;

[0068] Step 4: Optimization ends until the maximum number of iterations or the objective function converges. The discrete and continuous values ​​of the solution are merged, and the optimal solution is obtained from the elite solution set to obtain the optimal satellite and UAV transmission power, UAV position coordinates, excitation current weight, and user serial number.

[0069] Preferably, the step 3 specifically includes:

[0070] If χ<0.5, the elite reverse learning algorithm is used to update the discrete value:

[0071] ;

[0072] In the formula, χ is a decision variable, and χ is a random number between 0 and 1. is the newly generated discrete value, is the current iteration number, For the The old discrete value of the solution, is a random number, is the upper bound of the discrete values ​​in the Pareto solution set, It is the lower bound of the discrete values ​​in the Pareto solution set;

[0073] If χ≥0.5, the differential evolution algorithm is used to update the discrete value:

[0074] ;

[0075] In the formula, For the The discrete value variation of the solution, is a random number between 0 and 1. is the crossover rate, is the fitness value when using discrete value mutation value, is the fitness value when using the old discrete value;

[0076] The said The discrete value variation value of a solution satisfies:

[0077] ;

[0078] In the formula, is the scaling factor, , and is between 1 and Randomly selected between Different distinct integers, , and are three different discrete values ​​randomly selected from the current population.

[0079] The beneficial effects of the present invention are:

[0080] (1) The present invention designs and develops a satellite cognitive radio low-interference communication method based on multi-objective optimization, which simultaneously meets the three objectives of high communication rate, low interference and low power consumption. The multi-objective optimization algorithm is used to obtain the optimal solution, and ultimately a high-speed communication link with interference protection is achieved with lower energy consumption.

[0081] (2) The satellite cognitive radio low-interference communication method based on multi-objective optimization designed and developed by the present invention utilizes collaborative beamforming technology to suppress the overall sidelobe level and reduce interference between signals. In addition, the overall power consumption is low, and the cost and difficulty are greatly advantageous compared to spectrum sensing strategies. At the same time, the application capability of this open-loop mode communication scenario is also strong and is less affected by geographical factors.

[0082] (3) The present invention designs and develops a satellite cognitive radio low-interference communication method based on multi-objective optimization, which is flexible and convenient and is a low-cost and easier to deploy interference protection communication measure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 The present invention is a schematic diagram of the device structure of the satellite cognitive radio low-interference communication method based on multi-objective optimization.

[0084] Figure 2 This is a flow chart of the improved multi-objective dragonfly optimization algorithm described in the present invention.

[0085] Figure 3 This is an individual schematic diagram of the improved multi-objective dragonfly optimization algorithm described in the present invention. DETAILED DESCRIPTION

[0086] The present invention is further described in detail below so that those skilled in the art can implement it according to the description.

[0087] like Figure 1 As shown, the present invention provides a satellite cognitive radio low-interference communication method based on multi-objective optimization, comprising the following steps:

[0088] Step 1: Based on the Cartesian coordinate system, the coordinate position of the satellite, the coordinate position of the primary user, the coordinate position of the secondary user, and the number and initial positions of the drones used to perform beamforming are collected;

[0089] The primary user and the secondary user may be a device end or a base station. In this embodiment, the primary user is the device end, and the secondary user is the base station.

[0090] Step 2: Establish the objective function:

[0091] ;

[0092] In the formula, For multi-objective optimization problems, is the first optimization goal, is the second optimization goal, For the third optimization goal, is the solution set, corresponding to the population in the optimization algorithm;

[0093] The constraints are:

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] In the formula, For multi-objective optimization problems, is the first optimization goal, is the second optimization goal, For the third optimization goal, for, For the The excitation current weight of each drone, is the number of drones, and , is the total number of drones, The transmission power allocated by the satellite to the secondary user link, Assigning a transmission power cap to the satellite on the secondary user link, For the The position coordinates of the drones, For drones on the horizontal plane The minimum distance from the starting point in the axis direction, For drones on the horizontal plane The maximum distance from the starting point in the axis direction, For drones on the horizontal plane The minimum distance from the starting point in the axis direction, For drones on the horizontal plane The maximum distance from the starting point in the axis direction, The minimum altitude of the drone. The maximum altitude at which the drone flies. is the target user serial number set, is the secondary user sequence number of the current communication, is the set of all secondary user serial numbers, is the total number of users, and For any two adjacent drones, is the distance between adjacent UAVs, The minimum distance between drones to avoid collision. is the wavelength, It is the minimum distance between two drones to avoid collision and prevent antenna coupling;

[0102] in, , , ,The first optimization goal is to save as much power as possible on the secondary user link (satellite--UAV--ground secondary user) allocated by the satellite under the condition of ensuring normal communication between the satellite and the primary user, and to maximize the transmission rate on the secondary user communication link. Since the UAV is closer to the satellite than the ground user, the channel quality is better, so it is only necessary to focus on optimizing the communication rate between the UAV and the ground secondary user. The first optimization goal is:

[0103] ;

[0104] In the formula, is the three-dimensional space coordinate of the drone, is the excitation current weight set, that is , is the actual data receiving rate of the secondary user;

[0105] The second optimization goal is to minimize the interference of the beam to the primary user, that is, to minimize the beam sidelobe ratio, so that:

[0106] ;

[0107] In the formula, is the strength of the main lobe, is the azimuth of the main lobe, is the pitch angle of the main lobe, The side lobe is The intensity in the direction of the primary user, The side lobe is facing The azimuth of the primary user, The side lobe is facing The elevation angle of the primary user, is the number of primary users;

[0108] The third optimization goal is to minimize the energy loss of the drone during the entire communication process, specifically satisfying:

[0109] ;

[0110] In the formula, For the The energy consumption of a drone is the number of drones.

[0111] The actual data receiving rate of the secondary user satisfies:

[0112] ;

[0113] In the formula, is the actual data receiving rate of the secondary user, The total transmission rate of the beam synthesized by the drone array toward the secondary user, The beam synthesized for the drone array is oriented toward The transmission rate of the primary user.

[0114] According to Shannon's formula, the transmission rate between the UAV array and all ground users (i.e. and ) can be calculated as follows:

[0115] ;

[0116] In the formula, The communication rate between the synthesized beams of the UAV array and the ground users, for or , representing the secondary user or the primary user, is the transmission bandwidth, Transmit power for the drone array, is the transmit gain of the drone array, is the path loss from the UAV to the secondary user, is the noise power;

[0117] The path loss from the UAV to the secondary user satisfies:

[0118]

[0119] In the formula, is the loss coefficient constant, is the distance from the array center to the target user, is the loss index, is the probability that the communication link is a line-of-sight link, is the attenuation factor under line-of-sight link, is the probability that the communication link is a non-line-of-sight link, is the attenuation factor of the non-line-of-sight link;

[0120] The transmission power of the drone array meets the following requirements:

[0121] ;

[0122] In the formula, is the satellite antenna gain, is the receiving gain of the drone array antenna, is the distance between the center of the drone array and the satellite, is the conversion rate, is the ratio of pi.

[0123] The transmission gain of the UAV array satisfies:

[0124] ;

[0125] In the formula, is the azimuth of the synthetic beam, is the elevation angle of the synthetic beam, is the array factor, is the directional pattern function of the drone antenna unit, is the azimuth of the beam toward the current communicating ground user, is the elevation angle of the beam toward the current communicating ground user, is the array factor in the direction of the current communicating ground user, is the size of the far-field beam pattern in the direction of the current communicating ground user, is the array efficiency, and ;

[0126] The array factor satisfies:

[0127] ;

[0128] In the formula, is an imaginary unit, is the initial phase, represents the phase constant, and , It is The location coordinates of the drone.

[0129] The said The energy consumption of each drone satisfies:

[0130] ;

[0131] In the formula, is the communication time between the drone and the secondary user, For the quality of the drone, is the acceleration due to gravity, For drones The flight speed at the moment, Indicates A drone flies in a horizontal plane at a speed Power consumption during propulsion, For drones The horizontal flight speed at that moment, is the initial speed of the drone, For drones The height of the moment, is the initial height of the drone;

[0132] The said A drone flies in a horizontal plane at a speed The power consumption during propulsion meets the following requirements:

[0133] ;

[0134] In the formula, is the blade power of the UAV rotor, is the rotor induced power of the UAV rotor, is the rotor tip speed, It represents the average rotor induced speed of the UAV in the hovering state. is the fuselage drag ratio, is the solid resistance, is the air density, is the rotor blade area.

[0135] Step 3: Obtain the optimal solution of the objective function through the improved multi-objective dragonfly optimization algorithm to obtain the optimal satellite transmission power, the optimal UAV antenna excitation current weight, the optimal position of the UAV and the secondary user number;

[0136] Among them, the multi-objective dragonfly optimization algorithm is based on the dragonfly optimization algorithm. The optimal solution of each round is stored in the Pareto solution set through non-dominated sorting, and then the Pareto solution set is iteratively updated until the maximum number of iterations is reached or the objective function has converged. At this time, the Pareto solution set is the optimal solution set. The meaning of the non-dominated sorting is that when an individual All target values ​​(function values) are better than individual Good, then Dominate , the set of all dominant solutions is the Pareto solution set,

[0137] like Figure 2 As shown, the improved multi-objective dragonfly optimization algorithm specifically includes the following steps:

[0138] Step 1: Use log-tent mapping (chaotic mapping) to initialize the population, that is, initialize the transmission power allocated by the satellite to the secondary user link, the position of the drone, the excitation current weight and the sequence number of the target user (secondary user), and calculate the fitness value of the initial population;

[0139] Among them, a population is composed of many individuals, and the number of individuals is also called the population size. , individuals are composed of multidimensional attributes, such as Figure 3 As shown, it corresponds to a solution of the objective function, and the fitness value is the objective function value calculated by the current solution (individual), that is, .

[0140] Since each column (dimension) of the above individuals (solutions) represents different practical meanings and has different ranges of values, the random initialization strategy of the original algorithm regards all dimensions as having the same attributes and processes them uniformly, which cannot guarantee the diversity of each dimension. Therefore, the improved multi-objective dragonfly optimization algorithm first replaces the random initialization strategy with the chaotic mapping initialization strategy;

[0141] The random initialization population strategy in the original algorithm is as follows:

[0142] ;

[0143] In the formula, For the initial iteration Individual Dimension value, and The dimensions are The upper and lower bounds of is a random value between 0 and 1.

[0144] The chaos map initialization strategy is:

[0145] ;

[0146] In the formula, is the chaos value, used to update the The dimension of a solution;

[0147] The chaos value satisfies:

[0148] ;

[0149] In the formula, is the current value of the chaotic sequence, is the updated value of the chaotic sequence, is the first control factor, is the second control factor;

[0150] Step 2: Update the individuals in the population, and then update the population and fitness value. Select the best individual through non-dominated sorting and store it in the Pareto solution set to obtain the continuous value of the solution, that is, the transmission power allocated by the satellite to the secondary user link, the position of the drone and the excitation current weight;

[0151] Among them, individual updates satisfy:

[0152] ;

[0153] In the formula, for After the iteration, the newly generated individuals are For the The individuals generated by the iterations are is the step length;

[0154] Wherein, the step length satisfies:

[0155] ;

[0156] In the formula, It means that the individual takes separation action. It is the individual who takes the alignment action, indicates that individuals form alliances with their neighbors, is the separation weight, is the alignment weight, For alliance weight, is the food weight factor, is the natural enemy weight factor, is the inertia weight, It means catching food. It means avoiding natural enemies and satisfying:

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] ;

[0162] In the formula, and Represent food sources and natural enemies, respectively. For the The individuals generated by the iterations are for The number of adjacent individuals, For the Neighboring individuals, For the The speed of adjacent individuals.

[0163] Step 3: Update the discrete value of the solution through elite reverse learning and differential evolution algorithm to obtain the serial number of the target user (secondary user);

[0164] Since the solution to the multi-objective optimization problem is a mixed variable, that is, the three dimensions of satellite power, excitation current weight, and drone position coordinates are continuous values, and the ordinal dimension of the target user (sub-user) is a discrete value, and the original algorithm can only handle the optimization problem of continuous variables, the improved multi-objective dragonfly optimization algorithm adopts a mixed solution update strategy to complete the iterative update of the entire population, that is, the continuous value part of the current solution is updated using the original dragonfly optimization algorithm, and the discrete value part is updated using elite reverse learning and differential evolution algorithms, and then the updated continuous value part and discrete value part are combined into a new solution;

[0165] Therefore, the update of discrete values ​​satisfies:

[0166] If χ<0.5, the elite reverse learning algorithm is used to update the discrete value:

[0167] ;

[0168] In the formula, χ is a decision variable, and χ is a random number between 0 and 1. is the newly generated discrete value, is the current iteration number, For the The old discrete value of the solution, is a random number, is the upper bound of the discrete values ​​in the Pareto solution set, It is the lower bound of the discrete values ​​in the Pareto solution set;

[0169] If χ≥0.5, the differential evolution algorithm is used to update the discrete value:

[0170] ;

[0171] In the formula, For the The discrete value variation of the solution, is a random number between 0 and 1. is the crossover rate, is the fitness value when using discrete value mutation value, is the fitness value when using the old discrete value;

[0172] The said The discrete value variation value of a solution satisfies:

[0173] ;

[0174] In the formula, is the scaling factor, , and is between 1 and Randomly selected between Different distinct integers, , and are the discrete values ​​of three different individuals randomly selected from the current population.

[0175] Step 4: When the number of iterations reaches the set value or all objective functions have converged, the optimization ends, the discrete and continuous values ​​of the solution are merged, and the optimal solution is obtained from the elite solution set, which is the transmission power of the best satellite and UAV, the position coordinates of the UAV, the excitation current weight and the user serial number.

[0176] The framework of the improved multi-objective dragonfly optimization algorithm is as follows:

[0177]

[0178] Input: Population size , number of iterations , the size of the Pareto solution set

[0179] Output: Pareto solution set , the optimal fitness value

[0180] 1 Initializing the population using chaotic mapping

[0181] 2 Initialization step size

[0182] 3 Settings

[0183] 4 for do / /

[0184] 5 Calculate the fitness values ​​of all individuals

[0185] 6Find all Pareto solutions

[0186] 7 Update the Pareto solution set according to the non-dominated sorting method

[0187] 8 Full

[0188] 9 According to the roulette algorithm from Delete an element and delete Corresponding value

[0189] 10. Introducing the New Pareto Liberation At the same time, the fitness value of the corresponding solution is placed in

[0190] 11 end if

[0191] 12 Selecting food sources from the Pareto solution set

[0192] 13 Selecting natural enemies from the Pareto solution set

[0193] 14 Update step size

[0194] 15 Update the continuous value of the solution

[0195] 16 is a random number

[0196] 17Use elite reverse learning to update the discrete value of the solution

[0197] 18else

[0198] 19Using differential evolution algorithm to update the discrete value of the solution

[0199] 20endif

[0200] 21 Combine the discrete and continuous values ​​of the solution to generate new individuals

[0201] 22end for

[0202] twenty three

[0203] Step 4: After all drones fly to the optimal position, they establish a communication connection with the satellite. The satellite sends signals to the drones according to the optimal transmission power. At the same time, the satellite communicates normally with the primary user, sets the drone antenna excitation current weight to the optimal weight, and then forwards the received satellite signal to the target user, realizing long-distance communication with interference protection.

[0204] In this embodiment, =100, =500, =30, =1 MHz, , =0.1W, = 50W, =1dBi, =1dBi, =0.951, =2, =2.7, =3dB, =23dB, =1 dBi, =2kg, =200m / s, =0.3, =0.05N / m 3 , =1.225kg / m 3 , =0.79m 2 , =0m, =100m, =0m, =100m, =75m, =90m, =100W.

[0205] The present invention designs and develops a satellite cognitive radio low-interference communication method based on multi-objective optimization, which is a new mode different from the traditional spectrum sensing method. It uses collaborative beamforming technology to suppress the overall sidelobe level, which not only improves the spectrum utilization rate, but also does not require complex perception to reduce signal interference, saves system overhead, and has low overall power consumption. The cost and difficulty have great advantages over the spectrum sensing strategy. At the same time, the application capability of this open-loop mode communication scenario is also strong, and it is less affected by geographical factors. It can respond to different communication scenarios in real time and can be quickly deployed even without spectrum sensing conditions. It has flexible and convenient characteristics and is a low-cost, easier to deploy interference protection communication measure.

[0206] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A satellite cognitive radio low-interference communication method based on multi-objective optimization, characterized in that: The steps include: Step 1: Based on the Cartesian coordinate system, collect the coordinate position of the satellite, the coordinate position of the primary user, the coordinate position of the secondary user, the initial position of the drone, and the number of drones; Step 2: Establish the objective function: ； ； ； ； ； ； ； ； In the formula, For multi-objective optimization problems, is the first optimization goal, is the second optimization goal, For the third optimization goal, To solve the set, For the The excitation current weight of each drone, is the number of drones, and , is the total number of drones, The transmission power allocated by the satellite to the secondary user link, Assigning a transmission power cap to the satellite on the secondary user link, For the The position coordinates of the drones, For drones on the horizontal plane The minimum distance from the starting point in the axis direction, For drones on the horizontal plane The maximum distance from the starting point in the axis direction, Drone in horizontal plane The minimum distance from the starting point in the axis direction, For drones on the horizontal plane The maximum distance from the starting point in the axis direction, The minimum altitude of the drone. The maximum altitude at which the drone can fly. is the target user serial number set, is the secondary user sequence number of the current communication, is the set of all secondary user serial numbers, is the total number of users, for For any two adjacent drones, is the distance between adjacent UAVs, The minimum distance between drones to avoid collision. is the wavelength, It is the minimum distance between two drones to avoid collision and prevent antenna coupling; The first optimization objective, the second optimization objective and the third optimization objective satisfy: ； ； ； In the formula, is the actual data receiving rate of the secondary user, is the strength of the main lobe, is the azimuth of the main lobe, is the pitch angle of the main lobe, The side lobe is The intensity in the direction of the primary user, The side lobe is facing The azimuth of the primary user, The side lobe is facing The elevation angle of the primary user, is the number of primary users, For the The energy consumption of a drone is the number of drones; Step 3: Obtain the optimal solution of the objective function according to the improved multi-objective dragonfly optimization algorithm to obtain the optimal satellite transmission power, the optimal UAV antenna excitation current weight, the optimal position of the UAV and the secondary user number; The step three specifically includes the following steps: Step 1: Initialize the population using the chaotic mapping initialization strategy and calculate the fitness value of the initial population; Wherein, the chaos map initialization strategy is: ； In the formula, For the initial iteration Individual Dimension value, For Dimension The upper bound of For Dimension The lower bound of is the chaos value; The chaos value satisfies: ； In the formula, is the current value of the chaotic sequence, is the updated value of the chaotic sequence, is the first control factor, is the second control factor; The fitness value is an objective function; Step 2: Update the individuals in the population and update the fitness value. Select the best individual through non-dominated sorting and store it in the Pareto solution set to obtain the continuous value of the solution. Among them, individual updates satisfy: ； In the formula, for After the iteration, the newly generated individuals are For the The individuals generated by the iterations are is the step length; Step 3: Update the discrete value of the solution through elite reverse learning and differential evolution algorithm to obtain the serial number of the target user; The step 3 specifically includes: like , the elite reverse learning algorithm is used to update the discrete value: ； In the formula, is a decision variable, and is a random number between 0 and 1. is the newly generated discrete value, is the current iteration number, For the The old discrete value of the solution, is a random number, is the upper bound of the discrete values ​​in the Pareto solution set, It is the lower bound of the discrete values ​​in the Pareto solution set; like , then the differential evolution algorithm is used to update the discrete value: ； In the formula, For the The discrete value variation of the solution, is a random number between 0 and 1. is the crossover rate, is the fitness value when using discrete value mutation value, is the fitness value when using the old discrete value; The said The discrete value variation value of a solution satisfies: ； In the formula, is the scaling factor, , and is between 1 and Randomly selected between Different distinct integers, , and are three different discrete values ​​randomly selected from the current population; Step 4: until the maximum number of iterations or the objective function converges, the optimization ends, the discrete value and the continuous value of the solution are merged, the optimal solution is obtained from the elite solution set, and the transmission power of the best satellite and UAV, the position coordinates of the UAV, the excitation current weight and the user serial number are obtained; Step 4: After all drones fly to the optimal position, they establish a communication connection with the satellite. The satellite sends signals to the drones according to the optimal transmission power. At the same time, the satellite communicates normally with the primary user, sets the drone antenna excitation current weight to the optimal weight, and then forwards the received satellite signal to the secondary user.

2. The satellite cognitive radio low-interference communication method based on multi-objective optimization according to claim 1, characterized in that: The actual data receiving rate of the secondary user satisfies: ； In the formula, is the actual data receiving rate of the secondary user, The total transmission rate of the beam synthesized by the drone array toward the secondary user, The beam synthesized for the drone array is oriented toward The transmission rate of each primary user.

3. The satellite cognitive radio low-interference communication method based on multi-objective optimization as claimed in claim 2, characterized in that: The total transmission rate of the beam synthesized by the drone array toward the secondary user satisfies: ； In the formula, The communication rate between the synthesized beams of the UAV array and the ground users, for or ,when for When is the secondary user, for When is the primary user, is the transmission bandwidth, Transmit power for the drone array, is the transmit gain of the drone array, is the path loss from the UAV to the secondary user, is the noise power.

4. The satellite cognitive radio low-interference communication method based on multi-objective optimization as claimed in claim 3, characterized in that: The path loss from the drone to the secondary user satisfies: ； In the formula, is the loss coefficient constant, is the distance from the center of the array to the target user, is the loss index, is the probability that the communication link is a line-of-sight link, is the attenuation factor under line-of-sight link, is the probability that the communication link is a non-line-of-sight link, is the attenuation factor of the non-line-of-sight link.

5. The satellite cognitive radio low-interference communication method based on multi-objective optimization as claimed in claim 4, characterized in that: The transmission power of the drone array meets the following requirements: ； In the formula, is the satellite antenna gain, is the receiving gain of the drone array antenna, is the distance between the center of the drone array and the satellite, is the conversion rate, is the ratio of pi.

6. The satellite cognitive radio low-interference communication method based on multi-objective optimization as claimed in claim 5, characterized in that: The transmission gain of the UAV array satisfies: ； In the formula, is the azimuth of the synthetic beam, is the elevation angle of the synthetic beam, is the array factor, is the directional pattern function of the drone antenna unit, is the azimuth of the beam toward the current communicating ground user, is the elevation angle of the beam toward the current communicating ground user, is the array factor in the direction of the current communicating ground user, is the size of the far-field beam pattern in the direction of the current communicating ground user, is the array efficiency, and ; The array factor satisfies: ； In the formula, is an imaginary unit, is the initial phase, represents the phase constant, and , It is The position coordinates of .

7. The satellite cognitive radio low-interference communication method based on multi-objective optimization as claimed in claim 6, characterized in that: The said The energy consumption of each drone satisfies: ； In the formula, is the communication time between the drone and the secondary user, For the quality of the drone, is the acceleration due to gravity, For drones The flight speed at the moment, Indicates A drone flies in a horizontal plane at a speed Power consumption during propulsion, For drones The horizontal flight speed at that moment, is the initial speed of the drone, For drones The height of the moment, is the initial height of the drone; The said A drone flies in a horizontal plane at a speed The power consumption during propulsion meets the following requirements: ； In the formula, is the blade power of the UAV rotor, is the rotor induced power of the UAV rotor, is the rotor tip speed, It represents the average rotor induced speed of the UAV in the hovering state. is the fuselage drag ratio, is the solid resistance, is the air density, is the rotor blade area.

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