Multi-unmanned aerial vehicle track and power joint optimization method in interference environment, equipment and medium

Abstract

The invention discloses a multi-unmanned aerial vehicle track and power joint optimization method and device in an interference environment and a medium, and relates to the field of communication resource allocation, and the method comprises the steps: constructing a multi-unmanned aerial vehicle relay communication model in the interference environment; establishing a target function for balancing fairness and throughput by taking the hovering time of the unmanned aerial vehicle and the maximum distance of a path point as limitation, taking the path of the unmanned aerial vehicle, a ground user, a channel and power distribution as a joint optimization target and introducing a throughput ratio coefficient; and according to the objective function, adopting an optimization method combining a crown porcupine algorithm and a Kuhn-Munkres algorithm to obtain a joint optimization result of the track and the power of the unmanned aerial vehicle. According to the invention, the total communication throughput and the service fairness can be improved.

Description

Technical Field

[0001] The present application relates to the field of communication resource allocation, and in particular to a method, device and medium for joint optimization of trajectories and powers of multiple UAVs in an interference environment. Background Art

[0002] Due to the advantages of unmanned aerial vehicles (UAVs), such as low cost, high flexibility, and good air-to-ground channel transmission characteristics, the use of UAVs as mobile relays to assist wireless communications has received widespread attention in recent years. Multi-unmanned aerial vehicle (MUAV) relay networks are considered to be ideal auxiliary tools for ground cellular networks, especially in the face of complex electromagnetic frequency equipment and shortage of spectrum resources in emergency communication scenarios. UAVs can restore regional communication capabilities through rapid and flexible deployment. Compared with traditional static relays, mobile relay communications provide new degrees of freedom to improve performance by carefully designing UAV trajectories. Due to the continuous reduction in UAV costs and the miniaturization of communication equipment, the actual deployment of high-mobility wireless relays is more feasible than ever before. First, UAV relay communication systems are more cost-effective and can be deployed more quickly, making them particularly suitable for sudden or temporary events such as emergency response and military operations. In addition, the high mobility of UAV relay communications provides new opportunities to improve performance by dynamically adjusting the position to best adapt to the communication environment. This technology is particularly suitable for real-time applications such as periodic sensing and big data upload / download.

[0003] NOMA (Non-Orthogonal Multiple Access) technology is a new type of multiple access technology, which is mainly used to improve the spectrum efficiency and user experience of wireless communication systems. It allows multiple users to share the same frequency band at the same time, and through coding and modulation technology, the signals of different users overlap in the frequency domain and time domain, but can still be separated and decoded by the receiving end. Applying NOMA technology to MUAV relay communication is an important research trend in MUAV relay communication. An existing paper (DANG VH, NGUYEN LMD, VO VN, et al. Throughput Optimization for Noma Energy Harvesting Cognitive Radio With Multi-UAV-Assisted Relaying Under Security Constraints[J]. IEEE Transactions on Cognitive Communications and Networking, 2023, 9(1):82–98) analyzes the use of a multi-antenna UAV-assisted NOMA relay system to support finite block length communications between multiple user pairs. Taking into account the influence of line-of-sight probability, communication environment, and drone speed, the exact and asymptotic closed-form expressions of the average block error rate and throughput of the multi-antenna UAV-assisted NOMA relay system under study are derived. Another paper (HU H, AN Q, HUANG Y, et al. Resource optimization for energy-efficient NOMA-based multi-UAV-enabled relaying networks[J]. IET Communications, 2023, 17(11): 1288-1299) studied the throughput of the NOMA-based cognitive radio system under system performance and security constraints in the presence of MUAV-assisted relaying.In another paper (CHEN R, CHENG W, DING Y, et al. QoS-Guaranteed Multi-UAV Coverage Scheme for IoT Communications With Interference Management [J]. IEEE Internet of Things Journal, 2024, 11 (3): 4116-4126.), in order to improve the energy efficiency of the UAV relay system, an optimization problem of user grouping, UAV trajectory design and resource allocation was proposed in a NOMA-based multi-UAV relay system under the constraints of information causality constraints, UAV maximum service capability, maximum transmission power constraints and different communication rate requirements of mobile users.

[0004] Due to the limited spectrum resources and the differences in user service quality requirements, the trajectory and resource optimization of MUAV relay communication are challenged. The MUAV coverage scheme with interference management function and guaranteed service quality determines the deployment of UAVs and the allocation of spectrum resources to cover all ground users with different service quality requirements. A resilient UAV network design using modern portfolio theory enhances the stability of the overall service by jointly optimizing bandwidth allocation, UAV-user association and UAV trajectory, and achieves a better balance between total rate and user rate fluctuation. A MUAV joint mission planning and spectrum resource optimization algorithm uses an improved genetic algorithm combined with continuous convex approximation to solve the mission planning subproblem, and applies the block coordinate descent method to achieve the optimal strategy of mission planning, channel allocation and power control. In the scenario where MUAV provides communication services to ground users as an aerial base station, in order to ensure the fairness of user communication, spectrum reuse and co-channel interference management are considered, a method for optimizing the maximum-minimum average rate is proposed, and the alternating optimization method is used to iteratively optimize user association, subchannel allocation and power allocation until convergence, which effectively improves the transmission rate and ensures the fairness of user communication.

[0005] MUAV Relay in Interference Environment, One technique studies the use of MUAVs to communicate with a group of ground terminals in the presence of potential jammers. The throughput of the entire ground terminal is maximized by jointly optimizing the trajectory of the UAV, the scheduling and power allocation of the ground terminals. One technique considers a weighted total throughput objective function to achieve various trade-offs between total throughput performance and user fairness. One technique explores the MUAV deployment problem in NOMA wireless networks in the presence of co-channel interference. Considering that the UAV is an aerial base station for downlink communication, the energy efficiency and coverage are maximized by jointly optimizing the user power allocation and UAV location. One technique focuses on the uplink performance with and without repeater support in NOMA access scenarios, and proposes a reinforcement learning method to improve link performance in order to cope with the uncertain environment associated with interference attacks.

[0006] It can be seen from the above-mentioned existing technologies that there is relatively little research on the service capabilities of MUAVs based on NOMA in interference environments. In particular, how to enable UAVs to maximize their service capabilities through joint optimization of resources within a limited time and ensure the fairness of services as much as possible is a problem that needs to be studied and solved at present. Summary of the invention

[0007] The purpose of this application is to provide a method, device and medium for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment, which can improve service capabilities and fairness of services, and can increase the overall communication throughput.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] In a first aspect, the present application provides a method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment, and the method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment comprises:

[0010] Construct a multi-UAV relay communication model in an interference environment; the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with ground users; the ground users use NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power;

[0011] A target function that balances fairness and throughput is established by taking the drone's hovering time and the maximum distance between the path points as constraints, the drone's path, ground user matching, channel allocation, and power allocation as joint optimization goals, and introducing the throughput share coefficient.

[0012] According to the objective function, the optimization method combining the crown porcupine algorithm and the Kuhn-Munkres algorithm is adopted to obtain the joint optimization result of the UAV trajectory and power.

[0013] In a second aspect, the present application provides a device for jointly optimizing the trajectories and power of multiple UAVs in an interference environment, wherein the device for jointly optimizing the trajectories and power of multiple UAVs in an interference environment comprises:

[0014] A model building module is used to build a multi-UAV relay communication model in an interference environment; the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with the ground user; the ground user uses NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power;

[0015] The objective function establishment module is used to establish an objective function that balances fairness and throughput, with the drone's hovering time and the maximum distance between the path points as constraints, the drone's path, ground user matching, channel allocation, and power allocation as joint optimization goals, and introduces the throughput share coefficient;

[0016] The matching module is used to obtain the joint optimization result of the UAV trajectory and power by adopting the optimization method combining the crown porcupine algorithm and the Kuhn-Munkres algorithm according to the objective function.

[0017] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application provides a method, device and medium for joint optimization of multi-UAV trajectory and power in an interference environment. The hovering time of the UAV and the flight time of the path point are limited by the objective function, and the UAV trajectory and the path point are approximately fitted by the crown porcupine optimizer; the maximum weight matching of the UAV, the ground user and the channel is obtained by Kuhn-Munkres matching through the individual position information of the UAV and the ground user position information; the flight trajectory of the UAV is optimized by optimizing the path point, and the fairness of the ground users is improved while ensuring the total throughput of all ground users. The present application improves the service capability and fairness of the service, and can improve the total communication throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a flow chart of a method for joint optimization of multi-UAV trajectories and powers in an interference environment provided by an embodiment of the present application;

[0023] Figure 2 Schematic diagram of multi-UAV relay communication model in interference environment;

[0024] Figure 3 Initialize the schematic diagram for the UAV path;

[0025] Figure 4 Adjust the schematic for UAV waypoints;

[0026] Figure 5 Schematic diagram of the impact of different interference channels on the algorithm;

[0027] Figure 6 Schematic diagram of algorithm performance verification.

[0028] Figure 7 Schematic diagram of a single channel serving two terrestrial users;

[0029] Figure 8 Schematic diagram for performance comparison of algorithms;

[0030] Fig. 9 Schematic diagram of the algorithm's anti-interference performance;

[0031] Fig.10 Schematic diagram of the impact of throughput coefficient. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] In an exemplary embodiment, Figure 1 As shown, a method for joint optimization of multi-UAV trajectory and power in an interference environment is provided, and the method includes the following S101 to S103. Among them:

[0035] S101, construct a multi-UAV relay communication model in an interference environment; Figure 2 As shown, the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with the ground user; the ground user uses NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power;

[0036] like Figure 2 As shown in the figure, in combat scenarios, the electromagnetic environment is complex, and the increase in frequency-using devices has caused a shortage of spectrum resources. Multiple UAVs depart from different UAV platform vehicles at the same time, quickly move and deploy as temporary air base stations, and cooperate with each other to provide communication services for ground user equipment, and return to their respective starting points at a specified time. The UAV set can be represented as The set of ground users can be expressed as The available frequency bandwidth in the region is B, which is divided into N mutually orthogonal channels. The set of channels is expressed as Consider that the control center manages the UAV's spectrum resources in a centralized manner and matches each channel with the user. Each user accesses the channel in a NOMA manner. There is an enemy jammer J in the mission area. We have learned its location, and the control center can obtain the interference power P through spectrum sensing. J Considering the combat cost, we do not eliminate interference by physical destruction, but optimize the UAV path to reduce the impact of the jammer on us.

[0037] In NOMA, a single channel can access multiple ground users in one time slot. However, as the number of ground users accessing the channel increases, the power domain decoding complexity of Successive Interference Cancellation (SIC) also increases. Therefore, the number of ground users that can access each channel at the same time is limited. The number of ground users served by each channel is expressed as K. mu . is the matching factor between UAV m in time slot t and ground user k in channel n. If ground user k communicates with UAV m through channel n in time slot t, then otherwise Assume that in a time slot t, all ground users communicate with the UAV through a unique channel, that is, one channel can serve multiple ground users at the same time, while a ground user can only be served by a certain channel of a UAV in a time slot, which can be expressed as:

[0038]

[0039] In the NOMA downlink system, drone m is the transmitter of communication and uses superposition coding technology. The information transmitted on the nth channel in time slot t is:

[0040]

[0041] in and They represent the signal data and transmission power transmitted by UAV m to ground user k through channel n at time slot t, and satisfy

[0042] A hybrid channel gain model is adopted between UAV m and ground user k, and the distance between UAV and ground user is: q m represents the horizontal coordinate of the drone m, w k represents the horizontal coordinate of the ground user k, and H represents the flight altitude of the UAV. represents the channel coefficient between UAV m and ground user k. Generally speaking, Can be decomposed into Among them, β k represents large-scale fading effects such as path loss and shadowing, Usually a random complex-valued variable representing small-scale fading and In addition, for UAV-ground user links, large-scale attenuation is usually modeled as random variables for line-of-sight (LoS) and non-line-of-sight (NLoS) propagation. Specifically, β k It can be written as:

[0043]

[0044] Where β0 is the path loss at the reference distance, is the path loss exponent, and κ < 1 is the additional attenuation factor due to NLoS. The LoS probability depends on the propagation environment and the statistical model of building density and height. A common approach is to model the LoS probability between the UAV and the ground user as a logarithmic function of the elevation angle P k,LoS , expressed as:

[0045]

[0046] Among them, D and E are parameters that depend on the propagation environment. is the elevation angle in degrees. Obviously, the LoS probability P k,LoS depends on the UAV location. Therefore, the channel is a random variable with two different degrees of randomness, namely the random occurrence of LoS and NLoS links. The expected channel power gain after averaging these two randomnesses is:

[0047]

[0048] Then the signal received by ground user k corresponding to drone m on channel n is expressed as:

[0049]

[0050] Among them, the first part represents the superimposed signal received by the ground user k through the channel n and the UAV m, the second part is the interference of the jammer to the UAV ground user on the same subcarrier, and the third part is the additive white Gaussian noise.

[0051] At the receiver, SIC technology is used to eliminate the interference caused by subcarrier sharing. Ground users with higher power will be given priority. Therefore, the SINR of ground user k in drone m on subcarrier n is:

[0052]

[0053] The interference caused by the jammer to ground users, is additive white Gaussian noise, is the co-channel interference from other NOMA ground users, which is:

[0054]

[0055] in, is the constraint condition after considering the SIC principle, that is, the ground users that decode later will cause interference to the ground users that decode earlier. Considering the normalized bandwidth, the corresponding data rate is:

[0056]

[0057] S102, establishing a target function that balances fairness and throughput by taking the drone's hovering time and the maximum distance of the path points as constraints, the drone's path, ground user matching, channel allocation matching, and power allocation as joint optimization goals, and introducing a throughput share coefficient;

[0058] The coefficient C of the proportion of ground users with the minimum throughput is introduced to balance fairness and the proportion of system throughput. When C = 0, the proportion of system throughput is 0, and the optimization goal becomes maximizing the throughput of the minimum ground user, that is, only considering fairness; when C = 1, the optimization goal becomes maximizing the throughput of all ground users, that is, considering the system throughput without considering the fairness of ground users; when C∈(0,1), it means that while optimizing the throughput of all ground users, the throughput of the ground user with the minimum throughput is improved. The smaller the value of C, the higher the proportion of the throughput of the ground user with the minimum throughput, and the more prominent the optimization focus. Since the discrete points optimized on a trajectory that provides communication services in a continuous flight cannot represent the trajectory information, for this reason, the UAV is made to travel between each trajectory point with the same time T. f ′Fly, and make the UAV hover at each optimized trajectory point to provide services, and the total hovering time is T S Much longer than the total flight time T f , the total flight path time T = T S +T f In this way, the service capabilities provided by each path point are basically the same as those of each path point, and the path points can be mapped to paths. The objective function specifically includes the following formula:

[0059]

[0060] Where X is the trajectory of the UAV, Φ is the ground user matching matrix, P is the power allocation matrix, B is the channel allocation matrix, T is the entire flight path time, T = T S +T f , T f is the total flight time, T S is the total hovering time, T S >>T f , is the data rate between drone m in time slot t and ground user k in channel n, M is the number of drones, K is the number of ground users, N is the number of mutually orthogonal channels, C is the proportion coefficient of users with minimum throughput, is the matching factor between UAV m in time slot t and ground user k in channel n, is the transmission power of UAV m in time slot t and ground user i in channel n, is the total power of UAV m in channel n, is the position of UAV m at time slot t, is the position of UAV m at time slot t+1, Ω is the set of ground users occupying channel n among the ground users served by UAV m at time slot t, K mu is the number of ground users served by each channel, S is the maximum flight distance of a flight cycle, S′ is the maximum interval of path nodes, C1 means that a ground user can only be served by one UAV on one channel in a time slot, C2 means that all ground users have a unique channel and UAV matching in a time slot, C3 means the matching of UAV m, channel n, and ground user k in time slot t, Otherwise, it is 0; C4 means that the sum of the power of each ground user in the same channel of drone m in a time slot is equal to the total power of the channel; C5 means that for any channel occupied by drones in a time slot, the number of ground users served at the same time is K mu ; C6 means that the maximum flight distance of the UAV in one flight cycle does not exceed the flight distance of one flight cycle, and C7 means that the distance between two path nodes does not exceed the maximum interval of the path nodes.

[0061] S103, according to the objective function, a combined crested porcupine algorithm and a Kuhn-Munkres algorithm are used to obtain the maximum weighted matching between the optimal path of the UAV and the ground users, channels, and power.

[0062] Crested Porcupine Optimizer (CPO) is a new type of intelligent optimization algorithm. The crested porcupine (CP) is a large rodent that lives on all continents except Antarctica and is distributed in forests, deserts, rock outcrops and hillsides. CPO simulates four defense strategies of crested porcupines: visual, sound, smell and physical attack. These strategies are ranked from the least aggressive to the most aggressive. In CPO, four different strategies correspond to different defense levels. The first stage is visual defense, where CP stays away from the predator and CP creates a threat to the predator through visual effects. The second stage is the implementation of the second defense strategy when the predator is not afraid of the first stage defense mechanism and moves towards the predator, mainly by making sounds to threaten the predator. The third stage is used to implement the third defense strategy. When the predator is not afraid of the second and third defense mechanisms and still moves towards the CP, the strategy is activated to defend by emitting smell. The last stage is used to implement the last defense strategy. In the last area, after all previous defense mechanisms have failed, CP will attack the predator, incapacitate it, or even kill it to protect itself.

[0063] As a single-point optimization algorithm, CPO initializes the population by directly randomly selecting points within the feasible solution range. Since the UAV path is composed of multiple points, it cannot be directly applied to the UAV path optimization process. Therefore, a method for initializing the UAV path population is designed.

[0064] First, since different UAVs serve different ground users, the UAVs will each fly towards their own proximal ground users according to the characteristics of the algorithm to avoid collisions. At the same time, in order to prevent UAVs from crossing and colliding, each UAV is allowed to serve surrounding ground users with a minimum flight distance S within the area. The flight distance S of a UAV flight cycle is determined based on the flight range and number of the UAVs, and then the time T of a flight cycle is calculated based on the flight distance S and flight speed of the UAVs. f For example, suppose there are 4 UAVs providing services in an area of ​​1000m×1000m. In order to better cover the mission area and avoid hard competition, the distance of each UAV in a flight cycle is limited to no more than 1200m, that is, S=1200. At the same time, assume that the maximum flight speed of the UAV is V max is 20m / s, then T f =S / V max =1200 / 20=60s.

[0065] According to the flight time T f , you can set the number of path nodes x and the single node stay time T S ′, and ensure the node stay time xT S′>>T f , then the maximum interval between each path node S′=T f ·V max / x. In order to initialize the path of the UAV, the location information of the ground users in the area has been obtained at the current location of the UAV, and the relative location information between the UAV and the ground users is obtained, such as Figure 3 As shown, the UAV calculates the angle θ between the UAV and the edge ground user at position 1 11 ,θ 12 , and at θ 12 -θ 11 Randomly select a direction within the angle range and move forward S′. At position 2, at θ 22 -θ 21 Randomly select a direction within the angle range and move forward S′. At each position point t, the UAV calculates its Euclidean distance S from the starting point t and the remaining flight time T left , when S t ≤T left ·V max And S t-1 ≥(T left +1)·V max When , the UAV flies directly back to the starting point and evenly sets the path points on the straight path, thus completing the initialization of a cycle path.

[0066] Then, individuals are initialized according to the number of UAVs, the combination of the paths of all UAV individuals generated according to the plan forms a population individual, and then the population is set according to the population number.

[0067] The cyclic population reduction (CPR) technique is an important innovation of the CPO algorithm, which can not only speed up the convergence speed, but also keep the population diverse. Under this strategy, not all CPs activate the defense mechanism, but only those CPs that are threatened activate the defense mechanism. Therefore, in this strategy, some CPs are obtained from the population during the optimization process to speed up the convergence speed, and they are reintroduced into the population to improve diversity and avoid falling into local minima; the loop is based on the loop variable T′ to determine the number of times the process is executed during the optimization process.

[0068]

[0069] Where T′ is a variable that determines the number of loops, t is the current function evaluation, and T max is the maximum number of function evaluations, % represents the remainder or modulo operator, N min is the minimum number of individuals in the newly generated population, so the population size cannot be less than N minAs the number of current function evaluations increases, the population size gradually decreases. When the population size reaches the lower limit, the population size is maximized again and then gradually decreases until the end of the optimization process is reached.

[0070] The defense strategies include the following:

[0071] First defense strategy

[0072] When the CP becomes aware of the predator, it starts to lift and fan its feathers to give the predator a deeper impression. Therefore, the predator has two options, either move towards it or move away from it. In the first option, the distance between the predator and the CP decreases as the predator moves towards the CP. This option encourages exploration of the area between the predator and the CP to speed up convergence. In contrast, in the second option, the distance between the predator and the CP is maximized as the predator chooses to move away. This option encourages exploration of distant areas to identify unvisited areas, which may involve the desired solution. These options are simulated mathematically using a normal distribution to generate random values. If these random values ​​are less than 1 or greater than -1, moving closer to the CP is encouraged. Otherwise, the predator will move away from the CP. In general, this behavior is simulated mathematically as follows:

[0073]

[0074] in is the best solution for evaluating function t, is a vector generated between the current CP and a randomly selected CP from the population, used to represent the position of the predator at iteration t, τ1 is a random number based on a normal distribution, and τ2 is a random value in the interval [0, 1]. The mathematical formula is as follows:

[0075]

[0076] Where r is a random number between [1, N].

[0077] Second defense strategy

[0078] In this strategy, the CP uses vocal methods to make noise and threaten the predator. When the predator approaches the porcupine, the porcupine's voice becomes louder. In order to mathematically model this behavior, the following formula was proposed:

[0079]

[0080] Where r1 and r2 are two random integers between [1, N] and τ3 is a generated random value between 0 and 1.

[0081] The third defense strategy

[0082] In this strategy, the CP secretes a foul odor that spreads in the area around it to prevent predators from approaching it. To mathematically simulate this behavior, the following formula is proposed:

[0083]

[0084] Where r3 is a random number between [1, N] and δ is a parameter used to control the search direction. is the position of the ith individual at iteration t, γ t is the defense factor. τ3 is a random value in the interval [0, 1], is to use the odor diffusion factor. As shown below:

[0085]

[0086] in, represents the objective function value of the ith individual at iteration t, ε is a small value to avoid division by zero, rand is a vector of randomly generated numbers between 0 and 1, rand is a variable containing randomly generated numbers between 1 and 0, N is the population size, t is the number of the current iteration, and t max is the maximum number of iterations. Vectors are used to simulate three possible scenarios in this strategy:

[0087] (1) When If it is equal to 0, CP will stop the scent diffusion because the predator will stop moving due to fear of CP, so the distance between the predator and CP remains unchanged;

[0088] (2) When When it is equal to 1, CP will emit a significant smell because the predator is nearby;

[0089] (3) When is a combination of 0 and 1, the predator keeps a safe distance from the CP, so there is no need to release its scent widely.

[0090] Fourth Defense Strategy

[0091] The last strategy is physical attack. When the predator is very close to it and attacks it with short and thick feathers, the CP will take physical attack. During the physical attack, the two objects merge strongly, representing a one-dimensional inelastic collision. In order to express its physical attack behavior with a mathematical formula, the following formula is proposed:

[0092]

[0093] in, is the best solution obtained, indicating The position of the i-th individual at iteration t represents the predator at that position, α is the convergence speed factor, τ4 is a random value in the interval [0, 1], is the average force affecting the CP of the i-th predator. It is given by the law of inelastic collisions and defined by:

[0094]

[0095] Among them, m i is the mass of the ith individual (predator) at iteration t, f(·) represents the objective function, is the final velocity of the ith individual at the next iteration t+1, and is allocated based on selecting random solutions from the current population, is the initial velocity of the i-th individual at iteration t, Δt is the number of the current iteration, and τ6 is a vector consisting of random values ​​generated between 0 and 1. In the equation, the average force of the CP is calculated based on the numerator divided by the current iteration, which increases linearly during the optimization process. Therefore, the impact of the average force of the CP is gradually minimized. In fact, small values ​​of this factor are not detrimental to the performance of CPO, as they may not help to exploit the various areas around the best solution so far to find a better solution. Therefore, by removing the numerator and relying only on the denominator, as shown in the formula. This approach will help create a wide range of values ​​within the search space, thereby providing a more comprehensive examination of the area around the best solution so far.

[0096]

[0097] After the position of the UAV is adjusted through the four defense mechanisms, it is inevitable that the UAV time slot position point will fly out of the mission area or the distance between two time slot points will exceed the maximum flight distance of the UAV in one time slot. Therefore, it is necessary to adjust the position of the CP individual after the defense mechanism is adopted to avoid an infeasible solution.

[0098] (1) When the position of the CP exceeds the limit of the area range, the position coordinates of the CP are adjusted to the limit value of the area range.

[0099] (2) In order to prevent the distance between two time slots of a CP individual from exceeding the maximum distance S′ of one flight time slot of the UAV, the time slot distance of the individual is adjusted in turn after each update of the individual.

[0100] First, starting from the starting point, each time slot point is adjusted one by one. When the time slot interval is greater than the maximum distance S′ of a UAV flight time slot (such as Figure 4 When the maximum reliable distance is set to 10 in the example above, delete the points with a distance greater than the limit value of 10, and then add a new path point in the original direction with the maximum reliable distance of 10. Then check the next point until the second to last point (because the end point cannot be changed).

[0101] Then, starting from the end point, adjust each time slot point one by one in the opposite direction until the second point (because the starting point cannot be changed).

[0102] After each adjustment, the total path distance will be shortened accordingly (more obvious in the closed-loop path). Since the total path distance is restricted, after the forward adjustment, except for the distances of the last two time slot points which may not meet the requirements, all time slot distances are within the limited range. In the reverse adjustment process, due to the total path restriction, it is easy to achieve a feasible solution after the CP individual adjustment.

[0103] According to the location information of the UAV and the location information of the ground user, the problem is converted into a bipartite graph matching problem by adding virtual nodes, and the best match is obtained by using the Kuhn-Munkres matching method; the core idea is to convert the weight into a feasible top label, and then use the Kuhn-Munkres algorithm to find a complete match. If a complete match cannot be found, the feasible top label is adjusted until it is found. The complete match at this time is the best match, that is, the UAV-ground user match.

[0104] The matched UAV and ground user pairs and channels are again transformed into a bipartite graph matching problem by adding virtual nodes, and the Kuhn-Munkres matching method is used to complete the optimal matching of UAV and ground user pairs and channels.

[0105] The joint optimization of the crown porcupine algorithm and the Kuhn-Munkres algorithm is shown in Table 1:

[0106] Table 1

[0107]

[0108]

[0109] By jointly optimizing UAV paths, ground user matching, channel allocation, and power allocation, the crown porcupine algorithm is used as the main body and combined with the Kuhn-Munkres algorithm to obtain a method for improving the throughput of UAV under interference conditions while ensuring fairness. It is compared with the classic particle swarm algorithm and two baseline algorithms for joint optimization under fixed channel allocation conditions, and simulation analysis is performed through multiple groups of randomly generated ground users and interference locations. The simulation parameter settings are shown in Table 2:

[0110] Table 2

[0111]

[0112] Since the jammer channel changes in ms and the UAV position changes in s during the algorithm, the UAV position change can be ignored within a ms time slot. Since the time slots of the path points in the algorithm are not in the same order of magnitude as the time slots of the jammer interference channel change, the impact of the algorithm channel selection on the UAV path under any two different interference conditions is verified, such as Figure 5 As shown in the figure, 12 relatively regular ground user points and jammer positions are set, the number of path points x = 10, S′ = 150, and two groups of interference channels are randomly generated, where the interference channel is required to randomly select a channel in any time slot for interference, and the impact of the algorithm on the UAV path under different interference conditions is tested. Figure 5 It can be seen that the paths under two different interference conditions Figure 5 Part (a) and Figure 5 The (c) part of Figure 5 Part (b) and Figure 5 From the (d) part, we can see that 1 indicates that the channel is interfered, and 0 indicates that the channel is not interfered. Although the channels interfered by the jammer in different time slots under the two interference conditions are different, the UAVs occupy the interference channels in exactly the same way under the two interference conditions, that is, UAV1 occupies the interfered channel only in the 3rd time slot, UAV2 occupies the interfered channel in the 2nd and 10th time slots, UAV3 occupies the interfered channel in the 4th, 5th and 7th time slots, and UAV4 occupies the interference channel in the 1st and 9th time slots. It can be concluded that the path of the UAV obtained under the algorithm has nothing to do with the channel interfered by the jammer. This is because when the channel matching is performed at each time slot point of the UAV path, no matter how the jammer interferes with the channel, the way each UAV selects the channel remains unchanged, and the interference channel will be occupied by a certain UAV to maintain the performance of the system, regardless of which specific channel is interfered.

[0113] In order to intuitively verify the feasibility of the algorithm, two groups of relatively regular ground users were selected, and only 6 path points (including the starting point and the end point) were selected. The distance between the path points did not exceed 400m, and the interference power was set to 0 to verify the flight path point situation of the UAV. Figure 6 As shown in part (a) of , when a UAV serves two ground users in one time slot, each UAV flight path point basically coincides with the ground user position point due to the limitation of the number of path points to maximize the objective function value. Figure 6 From part (b), we can see that a UAV can serve three ground users in one time slot. Each UAV will select its nearest ground user for matching. Due to the dual requirements of throughput maximization and fairness, the UAV will pass through each ground user location as much as possible and improve the overall throughput.

[0114] According to the scenario with 4 UAVs, 8 randomly distributed ground users and 1 jammer, K mu =2, S′=200m, throughput coefficient C=0.1, and the paths of each UAV are obtained according to the proposed algorithm. Figure 7 As shown in part (a) of . Due to the dual constraints of the ground user throughput and the minimum ground user rate, Figure 7 From part (b), we can see that during the entire UAV path service process, the ground user 4 has the lowest throughput, and the throughput of all ground users except ground user 1 has achieved greater fairness. Ground user 1 has the highest throughput, which is because ground user 1 is closer to the jammer and the ground users around it are sparse. UAV1 occupies the interference channel in two time slots, which can still make its throughput larger. UAV3 serves a dense number of ground users and is far away from the jammer. Its non-use of the interference channel can greatly improve the overall ground user throughput. Figure 7 As can be seen from part (c), UAV3 did not occupy the interference channel during the whole process. UAV2 and UAV4 served ground users relatively stably, occupying interference channels in different time slots. UAV1 was close to the interference and the surrounding ground users were sparse. Its two time slots occupied the interference channel, which ensured the throughput of ground user 1 and improved the throughput of all other ground users.

[0115] In order to verify the performance of the algorithm, this application is compared with the classic particle swarm algorithm (PSO) and fixed channel allocation. Since each CPO individual is updated once for one iteration, and the PSO population is updated once for one iteration, the number of PSO populations is set to 200, the number of iterations is set to 1000, and the number of CPO iterations is set to 200000, the two algorithms are equivalent to being compared at the same iteration level. Figure 8 The performance comparison between the algorithm (CPO) of this application and two benchmark algorithms is shown. A group of ground users and a group of 12 ground users and jammers are randomly generated, and a single channel is used to serve 2 ground users and 3 ground users for comparison. The jammer randomly interferes with a channel with an interference power of 1W in any time slot, and the throughput coefficient C = 0.1. Figure 8 It can be seen that the channel allocation algorithm adopted in this application, whether combined with the CPO architecture or the PSO architecture, has a significantly higher algorithm fitness value than the fixed channel allocation algorithm. Compared with the PSO algorithm, the CPO algorithm has a more stable convergence result and a higher convergence value.

[0116] In order to verify the anti-interference ability of the algorithm, the interference power of a group of data is set to 0W, 0.5W, 1W, 2W, and 2W to interfere with two channels at the same time. Starting from the second group of data, the interference increases exponentially, such as Fig. 9As shown in the figure: Under the condition of only one interference channel, when the interference power is 0.5W, 1W, and 2W respectively, the total ground user throughput, fitness value, and minimum ground user throughput have a slight decrease, reflecting that the algorithm has a strong anti-interference ability. There is a big jump in the ground user throughput, fitness value, and minimum ground user throughput for interference numbers 1 to 2 and 4 to 5. This is because interference 1 to 2 is the process from no interference to interference, and 4 to 5 is the process from one channel to two channels. It can only be avoided by optimizing the path and channel selection, so the jump occurs.

[0117] In order to verify the impact of the throughput coefficient in the objective function on the ground user throughput and ground user fairness, the ground user throughput, objective function, and minimum ground user rate in a set of data are compared when the ground user throughput ratio C is 0, 0.1, 0.5, and 1 respectively. Fig.10 As shown in Figure 1, when C = 0, the optimization goal is equivalent to maximizing the throughput of the minimum ground user without considering the throughput, and the ground user's fitness value is the same as the value of the minimum ground user's throughput. When C = 1, the optimization goal is equivalent to maximizing the throughput without considering the fairness of ground users, and the ground user's throughput is equal to the fitness value. Fig.10 It can be seen that when only fairness is considered, the total ground user throughput is relatively low, and when only the total ground user throughput is considered, the ground user fairness is poor. When C = 0.1, since the number of ground users is 8, the minimum ground user throughput is approximately equal to 1 / 10 of the total throughput, and the ground user fairness and total throughput are basically the same, and can be optimized at the same time. Compared with C = 1, while ensuring the overall ground user throughput, the minimum ground user throughput is greatly increased, and the fairness of ground users is improved; compared with C = 0, while ensuring the minimum ground user throughput, the overall throughput of all ground users is improved.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a device for jointly optimizing the trajectories and power of multiple UAVs in an interference environment for implementing the method for jointly optimizing the trajectories and power of multiple UAVs in an interference environment. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for jointly optimizing the trajectories and power of multiple UAVs in an interference environment provided below can be referred to the limitations of the method for jointly optimizing the trajectories and power of multiple UAVs in an interference environment above, and will not be repeated here.

[0119] In an exemplary embodiment, a device for joint optimization of multi-UAV trajectories and powers in an interference environment is provided, comprising:

[0120] A model building module is used to build a multi-UAV relay communication model in an interference environment; the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with the ground user; the ground user uses NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power;

[0121] The objective function establishment module is used to establish an objective function that balances fairness and throughput, with the drone's hovering time and the maximum distance between the path points as constraints, the drone's path, ground user matching, channel allocation, and power allocation as joint optimization goals, and introduces the throughput share coefficient;

[0122] The matching module is used to obtain the maximum weighted matching between the UAV trajectory and the ground user, channel, and power according to the objective function by using the combined crown porcupine algorithm and Kuhn-Munkres algorithm.

[0123] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for joint optimization of multi-UAV trajectory and power in an interference environment is implemented.

[0124] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0125] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0128] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0129] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for joint optimization of multi-UAV trajectory and power in an interference environment, characterized in that: The method for jointly optimizing the trajectories and powers of multiple UAVs under interference environment includes: Construct a multi-UAV relay communication model in an interference environment; the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with ground users; the ground users use NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power; A target function that balances fairness and throughput is established by taking the drone's hovering time and the maximum distance between the path points as constraints, the drone's path, ground user matching, channel allocation, and power allocation as joint optimization goals, and introducing the throughput share coefficient. According to the objective function, the optimization method combining the crown porcupine algorithm and the Kuhn-Munkres algorithm is adopted to obtain the joint optimization result of the UAV trajectory and power.

2. The method for joint optimization of multi-UAV trajectories and power in an interference environment according to claim 1 is characterized in that: In the multi-UAV relay communication model under interference environment, the UAV acts as the transmitter and adopts superposition coding technology; a mixed channel gain model is adopted between the UAV and the ground user; at the receiver end, SIC technology is used to eliminate the interference caused by subcarrier sharing.

3. The method for joint optimization of multi-UAV trajectories and power in an interference environment according to claim 1 is characterized in that: The objective function specifically includes the following formula: Where X is the trajectory of the UAV, Φ is the ground user matching matrix, P is the power allocation matrix, B is the channel allocation matrix, T is the entire flight path time, T = T S +T f , T f is the total flight time, T S is the total hovering time, T S >>T f , is the data rate between drone m in time slot t and ground user k in channel n, M is the number of drones, K is the number of ground users, N is the number of mutually orthogonal channels, C is the proportion coefficient of users with minimum throughput, is the matching factor between UAV m in time slot t and ground user k in channel n, is the transmission power of UAV m in time slot t and ground user i in channel n, is the total power transmitted by UAV m in channel n, is the position of UAV m at time slot t, is the position of UAV m at time slot t+1, Ω is the set of ground users occupying channel n among the ground users served by UAV m at time slot t, K mu is the number of ground users served by each channel, S is the maximum flight distance of a flight cycle, S′ is the maximum interval of path nodes, C1 means that a ground user can only be served by one drone on one channel in a time slot, C2 means that all ground users have a unique channel and drone matching in a time slot, C3 means that when drone m, channel n, and ground user k match in time slot t, Otherwise, it is 0; C4 means that the sum of the power of each ground user in the same channel of drone m in a time slot is equal to the total power of the channel; C5 means that for any channel occupied by drones in a time slot, the number of ground users served at the same time is K mu ; C6 means that the maximum flight distance of the UAV in one flight cycle does not exceed the flight distance of one flight cycle, and C7 means that the distance between two path nodes does not exceed the maximum interval of the path nodes.

4. The method for joint optimization of multi-UAV trajectories and power in an interference environment according to claim 1 is characterized in that: According to the objective function, the combined crown porcupine algorithm and Kuhn-Munkres algorithm are used to obtain the maximum weight matching between the drone and the ground user, channel, and power, specifically including: According to the location information of the UAV and the location information of the ground user, the problem is transformed into a bipartite graph matching problem by adding virtual nodes, and the best match is obtained using the Kuhn-Munkres matching method. The matched UAV and ground user pairs and channels are transformed into a bipartite graph matching problem again, and the Kuhn-Munkres matching method is used to complete the optimal matching of UAV and ground user pairs and channels.

5. A device for joint optimization of multi-UAV trajectory and power in an interference environment, characterized in that: The multi-UAV trajectory and power joint optimization device in the interference environment includes: A model building module is used to build a multi-UAV relay communication model in an interference environment; the multi-UAV relay communication model in the interference environment includes a UAV set, a ground user set, a channel set and a jammer; the UAVs perform centralized spectrum resource management and match each channel with the ground user; the ground user uses NOMA technology to access the channel; the jammer transmits an interference signal with a fixed power; The objective function establishment module is used to establish an objective function that balances fairness and throughput, with the drone's hovering time and the maximum distance between the path points as constraints, the drone's path, ground user matching, channel allocation, and power allocation as joint optimization goals, and introduces the throughput share coefficient; The matching module is used to obtain the maximum weight matching between the UAV and the ground user and the channel according to the objective function by using the combined crested porcupine optimizer and the Kuhn-Munkres algorithm.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for jointly optimizing the trajectories and powers of multiple UAVs in an interference environment described in any one of claims 1 to 4 is implemented.

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