Multi-unmanned aerial vehicle communication and sensing integrated network resource optimization system
By building a multi-UAV synaesthesia integrated network system model and potential energy game module, the UAV communication and perceived resource allocation are optimized, and the problems of uneven resource allocation and interference management are solved, and network performance and energy efficiency are improved.
Patent Information
- Application Number
- CN202510497989.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-21
- Publication Date
- 2025-07-25
AI Technical Summary
There are problems in the integrated network of multi-UAV synaesthesia networks with uneven resource allocation, difficulty in interference management and low energy efficiency, especially in complex environments, UAV communication and perceived resource scheduling are difficult to efficiently manage.
Build a multi-UAV synaesthesia integrated network system model, combine the potential energy game module, considering energy consumption constraints, power limits, trajectory constraints and flight range constraints, and optimize the allocation of UAV communication and perception resources.
The communication and perception performance of multi-UAV networks is improved, local optimization is avoided, network mutual information is significantly improved, resource scheduling efficiency is optimized, and overall network performance is improved under energy consumption constraints.
Smart Images
Figure CN120379053A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) communication and sensing, and particularly to a multi-UAV integrated communication and sensing network resource optimization system. Background Art
[0002] As a typical representative of mobile platforms, UAVs provide an ideal platform support for the application of integrated sensing and communication (ISAC) devices with their highly flexible deployment capabilities and low-cost characteristics. Although the combination of UAVs and ISAC shows great potential in theory, it still faces many challenges in practical applications. First, UAV communication is vulnerable to interference from malicious signal sources, resulting in a significant decline in communication quality and a weakening of sensing performance. Second, in multi-UAV cooperative operations, especially in resource-constrained distributed networks, the co-channel interference problem is particularly prominent. This interference not only reduces the utilization efficiency of communication and sensing resources but also further increases system energy consumption and complexity. In addition, with the continuous increase in the number of UAVs, the distributed characteristics of the network architecture and the diversification of user requirements also pose higher requirements for the resource scheduling and optimization of communication and sensing. Therefore, how to efficiently manage the communication and sensing resources of multi-UAV networks in complex environments has become a key problem to be solved urgently. Summary of the Invention
[0003] The present invention aims to solve the problems of resource allocation, interference management, and energy efficiency in multi-UAV integrated communication and sensing networks, and proposes a system in which UAVs can efficiently manage communication and sensing resources in complex environments.
[0004] To achieve the above object, the present invention provides the following technical solutions:
[0005] A multi-UAV integrated communication and sensing network resource optimization system includes a set Q of ground users, a set J of malicious interference sources, a UAV cluster M, the flight altitude H of UAVs uav , the flight time T, the channel gain of the communication link, the channel gain of the radar sensing link, and the signal-to-noise ratio. The system constructs a system model of the multi-UAV integrated communication and sensing network based on ground users, malicious interference sources, UAVs, the flight altitude of UAVs, the flight time, the channel gain of the communication link, the channel gain of the radar sensing link, and the signal-to-noise ratio. A potential game module is set in the system model, and the system model and the potential game module mutually demonstrate and calculate the optimal solution of the communication and sensing network resource allocation of UAVs under the conditions of energy consumption constraint, power limit, trajectory constraint, and flight range constraint.
[0006] Furthermore, the number of the ground user set, malicious jammer set, and UAV cluster is the same, i.e., M = J = Q. To reduce the in-channel interference among UAVs, each UAV uses an independent communication channel.
[0007] Furthermore, the communication link channel gain is the communication link channel gain between the UAV and the base station BS, and the communication link channel gain is expressed as:
[0008]
[0009] where m represents the UAV m ∈ {1, 2, …, M}, t represents the flight time t ∈ {1, 2, …, T}, G uav and G base respectively represent the gains of the UAV transmitting antenna and the base station receiving antenna, λ represents the wavelength of the signal, d uav,m,BS (t) is the distance between the UAV m and the base station, and this distance is calculated as follows:
[0010]
[0011] where (x uav,m (t), y uav,m (t), z uav,m (t)) is the coordinate of the UAV m at time t, and (x BS , y BS , z BS ) is the fixed coordinate of the base station.
[0012] Furthermore, the radar sensing link channel gain is the channel gain of the radar sensing link between the UAV and the ground user, and the radar sensing link channel gain is expressed as:
[0013]
[0014] where q represents the ground user, q ∈ {1, 2, …, Q}; G uav is the UAV transmitting antenna gain; G radar is the radar receiving antenna gain; η is the radar cross section of the target; d radar,q,m (t) is the distance between the UAV and the ground user, and its calculation formula is:
[0015]
[0016] where (x q , y q , z q ) is the coordinate of the ground user q.
[0017] Further, the signal-to-noise ratio includes the signal-to-noise ratio of the communication link and the signal-to-noise ratio of the radar sensing link. The signal-to-noise ratio of the communication link is expressed as
[0018]
[0019] The signal-to-noise ratio of the radar sensing link is expressed as
[0020]
[0021] where p m (t) represents the transmission power of UAV m, and σ 2 is the power of additive white Gaussian noise, and respectively represent the interference caused by malicious jammers to the communication link and the radar sensing link;
[0022]
[0023] where j represents a malicious jammer, j ∈ {1, 2,..., J}; p j represents the transmission power of jammer j, and J c,t is the set of malicious jammers active on the channel at time t, and respectively represent the channel gains between jammer j and the base station, and between jammer j and UAV m.
[0024] Further, the energy consumption constraint is expressed as
[0025]
[0026] where E m,max represents the maximum energy consumption limit of UAV m, P(ν h , ν ν ) is the mechanical power consumption of the UAV, c p is the heat of combustion of the fuel, and μ fuel is the average thermodynamic efficiency of the gas turbine;
[0027] The mechanical power consumption of the UAV
[0028]
[0029] where v h is the horizontal velocity of the UAV, v v is the vertical velocity of the UAV, v H is the induced velocity during UAV hovering, W represents the weight of the UAV, ρ is the air density, A is the rotor disk area of the UAV, c is the incremental correction coefficient, S blade is the blade area of the UAV, and v tipis the tip speed of the drone rotor, r d is the drag ratio of the drone fuselage.
[0030] Furthermore, the power limit is where p min and p max represent the minimum and maximum power limits of the drone m, respectively.
[0031] Furthermore, the trajectory constraint is expressed as
[0032]
[0033] where the initial left side of the drone m is expressed as (x uav,m (0), y uav,m (0), z uav,m (0)), the final coordinates of the drone m (x uav,m (T), y uav,m (T), z uav,m (T)), and H uav is the flight altitude of the drone.
[0034] Furthermore, the flight range constraint is expressed as
[0035]
[0036] where represents the radar sensing performance of the drone m during the flight time t, represents the communication rate at which the drone m transmits data to the base station;
[0037] The radar sensing performance
[0038]
[0039] where β represents the allocation factor of the sensing flight time;
[0040] The communication rate
[0041]
[0042] Furthermore, the potential game module is expressed as
[0043]
[0044] where represents the set of game participants (coalitions) in the network, and the strategy space represents (p m (t), {x m (t), y m (t), z m(t)}, that is, the strategy of the UAV m at time slot t is to select the transmission power p m (t) and the trajectory {x m (t), y m (t), z m (t)}. The utility function of the UAV m is expressed as U m (s m , s -m );
[0045] The total mutual information of the radar sensing and communication link is expressed as
[0046]
[0047] where s -m represents the strategy selection of all other UAVs except the UAV m, is the sensing link rate between the UAV and the sensing target, expressed as the mutual information of the sensing link; is the communication link rate from the UAV m to the base station BS, expressed as the mutual information of the communication link, specifically as follows:
[0048]
[0049] In the potential game, the strategy combination makes the strategy of each UAV m optimal, that is, given that the strategies of other UAVs remain unchanged, its utility function reaches the maximum value, expressed as:
[0050]
[0051] The advantages of the present invention are as follows: By constructing a system model and a potential game module, fully considering the dynamic characteristics of UAVs and energy consumption constraints, power limitations, trajectory constraints, and flight range constraints, a comprehensive system framework is provided for the optimization of multi-UAV communication and sensing integration networks, overcoming the limitations of the prior art; mutually verifying with the potential game module to seek the optimal solution for communication and sensing integration resource allocation, effectively solving the resource scheduling problem, and performing better than traditional algorithms (such as the bat algorithm, crow search algorithm, etc.) in improving communication and sensing performance, being able to quickly increase the network mutual information and avoid falling into local optima, significantly improving the optimization efficiency; at the same time, it can significantly improve the communication and sensing performance of the multi-UAV network under energy consumption constraints, and further balance communication and sensing resources by optimizing parameters such as flight altitude and time slot allocation factor, realizing the overall performance optimization of the network, and providing an effective solution for practical applications. Brief Description of the Drawings
[0052] Figure 1 It is a schematic diagram of a multi-UAV communication and sensing network. Detailed Embodiments
[0053] A multi-UAV communication and sensing integrated network resource optimization system, including a set Q of ground users, a set J of malicious interference sources, a UAV cluster M, and a UAV flight altitude H uav , flight time T, communication link channel gain, radar sensing link channel gain, and signal-to-noise ratio. The system constructs a system model of the multi-UAV communication and sensing integrated network based on ground users, malicious interference sources, UAVs, UAV flight altitude, flight time, communication link channel gain, radar sensing link channel gain, and signal-to-noise ratio. In this network resource optimization system, a group of fixed ground users are served by multiple UAVs. Each UAV is responsible for sensing the status of the ground users assigned to it and transmitting the collected data to a centralized base station. The UAVs performing sensing tasks are interfered by malicious signal sources, which may originate from environmental factors or hostile devices. To reduce the in-channel interference between UAVs, each UAV uses an independent communication channel, that is, the number of the set of ground users, the set of malicious interference sources, and the UAV cluster is the same, that is, M = J = Q. To ensure the stability and reliability of information transmission and minimize the interference between UAVs at the same time, the flight altitude of each UAV is fixed, which can ensure the efficient operation of the network and optimize the performance of the communication and sensing network. A potential game module is set in the system model. The system model and the potential game module mutually demonstrate and calculate the optimal solution of the communication and sensing network resource allocation of the UAVs under the conditions of energy consumption constraint, power limit, trajectory constraint, and flight range constraint. The potential game module is an exact potential game, and there is at least one pure strategy Nash equilibrium, and the optimal solution calculated by the network resource optimization system corresponds to the Nash equilibrium solution of the potential game.
[0054] The communication link channel gain is the communication link channel gain between the UAV uav and the base station BS, and the communication link channel gain is expressed as:
[0055]
[0056] where m represents the UAV m ∈ {1, 2,..., M}, t represents the flight time t ∈ {1, 2,..., T}, G uav and G base respectively represent the gains of the UAV transmitting antenna and the base station receiving antenna, λ represents the wavelength of the signal, d uav,m,BS (t) is the distance between the UAV m and the base station, and this distance is calculated as follows:
[0057]
[0058] where (x uav,m (t), y uav,m (t), z uav,m (t)) is the coordinate of the UAV m at time t, (x BS , yBS , z BS ) is the fixed coordinate of the base station.
[0059] The channel gain of the radar sensing link is the channel gain of the radar sensing link between the UAV and the ground user, and the channel gain of the radar sensing link is expressed as:
[0060]
[0061] where q represents the ground user, q ∈ {1, 2, …, Q}; G uav is the gain of the UAV transmitting antenna; G radar is the gain of the radar receiving antenna; η is the radar cross section of the target; d radar,q,m (t) is the distance between the UAV and the ground user, and its calculation formula is:
[0062]
[0063] where (x q , y q , z q ) are the coordinates of the ground user q.
[0064] The signal-to-noise ratio includes the signal-to-noise ratio of the communication link and the signal-to-noise ratio of the radar sensing link, and the signal-to-noise ratio of the communication link is expressed as
[0065]
[0066] The signal-to-noise ratio of the radar sensing link is expressed as
[0067]
[0068] where p m (t) represents the transmission power of UAV m, σ 2 is the power of additive white Gaussian noise, and respectively represent the interference caused by malicious jammers to the communication link and the radar sensing link;
[0069]
[0070] where j represents the malicious jammer, j ∈ {1, 2, …, J}; p j represents the transmission power of jammer j, J c,t is the set of malicious jammers active on the channel at time t, and respectively represent the channel gains between jammer j and the base station, and between jammer j and UAV m.
[0071] The energy consumption constraint is expressed as
[0072]
[0073] Among them, E m,max represents the maximum energy consumption limit of the unmanned aerial vehicle m, and P(ν h , ν ν ) is the mechanical power consumption of the unmanned aerial vehicle, c p is the calorific value of fuel combustion, and μ fuel is the average thermodynamic efficiency of the gas turbine;
[0074] The mechanical power consumption of the unmanned aerial vehicle
[0075]
[0076] Among them, v h is the horizontal speed of the unmanned aerial vehicle, v v is the vertical speed of the unmanned aerial vehicle, v H is the induced speed when the unmanned aerial vehicle hovers, W represents the weight of the unmanned aerial vehicle, ρ is the air density, A is the rotor disk area of the unmanned aerial vehicle, c is the incremental correction coefficient, S blade is the blade area of the unmanned aerial vehicle, v tip is the tip speed of the rotor of the unmanned aerial vehicle, and r d is the drag ratio of the fuselage of the unmanned aerial vehicle.
[0077] The power limit is Among them, p min and p max respectively represent the minimum and maximum power limits of the unmanned aerial vehicle m. The power of the unmanned aerial vehicle limits the speed of its task execution to ensure efficient operation.
[0078] The trajectory constraint ensures that the unmanned aerial vehicle returns to the initial position or meets specific task requirements at the end of the task. The trajectory constraint is expressed as
[0079]
[0080] Among them, the initial position of the unmanned aerial vehicle m is expressed as (x uav,m (0), y uav,m (0), z uav,m (0)), the final coordinates of the unmanned aerial vehicle m are (x uav,m (T), y uav,m (T), z uav,m (T)), and H uav is the flight altitude of the unmanned aerial vehicle.
[0081] The flight range constraint ensures that the unmanned aerial vehicle remains within a predefined operation area when performing communication and sensing tasks, thereby maintaining the integrity of the network and minimizing interference. The flight range constraint is expressed as
[0082]
[0083] Among them, represents the radar sensing performance of the unmanned aerial vehicle m during the flight time t, represents the communication rate at which the unmanned aerial vehicle m transmits data to the base station;
[0084] The said radar sensing performance
[0085]
[0086] Among them, β represents the allocation factor of the sensing flight time;
[0087] The said communication rate
[0088]
[0089] The potential game module in the network resource optimization system demonstrates the allocation result of the integrated communication and sensing resources of the system. There is at least one pure strategy Nash equilibrium in the potential game module, and the optimal solution of the potential game corresponds to the Nash equilibrium solution of this game. The accuracy of the allocation result obtained by the network resource optimization system is proved through mutual demonstration. The said potential game module is expressed as
[0090]
[0091] Among them, represents the set of game participants (coalitions) in the network, and the strategy space represents (p m (t), {x m (t), y m (t), z m (t)}), that is, the strategy of the unmanned aerial vehicle m during the flight time t is to select the transmission power p m (t) and the trajectory {x m (t), y m (t), z m (t)}, and the utility function of the unmanned aerial vehicle m is expressed as U m (s m , s -m );
[0092] The total mutual information of the said radar sensing and communication link is expressed as
[0093]
[0094] Among them, s -m represents the strategy choices of all other unmanned aerial vehicles except the unmanned aerial vehicle m, is the sensing link rate between the unmanned aerial vehicle and the sensing target, and is expressed as the mutual information of the sensing link; is the communication link rate from the UAV m to the base station BS, expressed as the mutual information of the communication link, specifically as follows:
[0095]
[0096] In the potential game, the strategy combination makes the strategy of each UAV m optimal, that is, given the strategies of other UAVs remain unchanged, the maximum value of its utility function is expressed as:
[0097]
[0098] In the potential game module, define Φ(s) as the sum of the utility functions of all UAVs, then
[0099]
[0100] where, U m (s m ,s -m ) represents the utility function of UAV m, which depends on its own strategy s m and the strategies s -m of other UAVs. If UAV m unilaterally changes its own strategy from s m =(s m,1 ,s m,2 ,…,s m,T ) to s' m =(s' m,1 ,s' m,2 ,…,s' m,T ), then the change in the utility function of this coalition is:
[0101]
[0102] Since the strategies of other UAVs remain unchanged, their utility functions will not be affected, and the change in the utility function is only related to the change in the strategy of UAV m itself.
[0103] On the other hand, the change in the potential function caused by UAV m unilaterally changing its own strategy is:
[0104] Φ(s′ m ,s -m )-Φ(s m ,s -m ) = U m (s' m ,s -m )-U m (s m ,s -m )+∑ n≠m (Un (s' n , s -n ) - U n (s n , s -n ))
[0105] Since the strategy s of other unmanned aerial vehicles n≠m -m has not changed, its utility function remains unchanged, that is:
[0106] U n (s′ n , s -n ) - U n (s n , s -n ) = 0
[0107] Therefore,
[0108] U m (s′ m , s -m ) - U m (s m , s -m ) = Φ(s′ m , s -m ) - Φ(s m , s -m )
[0109] This shows that the change in the utility function of unmanned aerial vehicle m is equal to the change in its potential function, thus demonstrating the consistency between the game result and the optimization result.
[0110] Although the specific implementation of the present invention has been described and explained in detail above, it should be pointed out that various changes and modifications can be made to the above implementation, but these do not depart from the spirit of the present invention and the scope defined by the appended claims.
Claims
1. A multi-UAV integrated communication and sensing network resource optimization system, characterized in that It includes a set Q of ground users, a set J of malicious interference sources, a UAV cluster M, a UAV flight altitude H uav , a flight time T, a communication link channel gain, a radar sensing link channel gain, and a signal-to-noise ratio. The system constructs a system model of an integrated communication and sensing multi-UAV network based on ground users, malicious interference sources, UAVs, UAV flight altitude, flight time, communication link channel gain, radar sensing link channel gain, and signal-to-noise ratio. A potential game module is set in the system model, and the system model mutually demonstrates and calculates the optimal solution of the communication and sensing network resource allocation of UAVs under the conditions of energy consumption constraint, power limitation, trajectory constraint, and flight range constraint.
2. The multi-UAV communication and sensing integrated network resource optimization system according to claim 1, wherein, The number of the ground user set, malicious interference source set, and UAV cluster is the same.
3. The multi-UAV communication-sensing integrated network resource optimization system according to claim 2, characterized in that, The communication link channel gain is the communication link channel gain between the UAV and the base station BS, and the communication link channel gain is expressed as: Among them, m represents the unmanned aerial vehicle, m ∈ {1, 2, …, M}, t represents the flight time, t ∈ {1, 2,..., T}, G uav and G base respectively represent the gains of the transmitting antenna of the unmanned aerial vehicle and the receiving antenna of the base station, λ represents the wavelength of the signal, d uav,m,BS (t) is the distance between the unmanned aerial vehicle m and the base station, and this distance is calculated as follows: Among them, (x uav,m (t), y uav,m (t), z uav,m (t)) is the coordinate of the unmanned aerial vehicle m at time t, and (x BS , y BS , z BS ) is the fixed coordinate of the base station.
4. The multi-UAV communication and sensing integrated network resource optimization system according to claim 3, characterized in that, The radar sensing link channel gain is the channel gain of the radar sensing link between the UAV and the ground user, and the radar sensing link channel gain is expressed as: where \(q\) represents a ground user, \(q\in\{1,2,\ldots,Q\}\); \(G\) uav is the gain of the UAV transmitting antenna; \(G\) radar is the gain of the radar receiving antenna; \(\eta\) is the radar cross section of the target; \(d\) radar,q,m (t) is the distance between the UAV and the ground user, and its calculation formula is: where (x q , y q , z q ) are the coordinates of the ground user q.
5. The multi-UAV communication and sensing integrated network resource optimization system according to claim 4, characterized in that, The signal-to-noise ratio includes the communication link signal-to-noise ratio and the radar sensing link signal-to-noise ratio. The communication link signal-to-noise ratio is expressed as The radar sensing link signal-to-noise ratio is expressed as Among them, p m (t) represents the transmission power of the UAV m, and σ 2 is the power of additive white Gaussian noise. And respectively represent the interference caused by malicious jammers to the communication link and the radar sensing link. Among them, j represents a malicious interferer, j ∈ {1, 2, …, J}; p j represents the transmission power of interferer j, and J c,t is the set of malicious interferers active on the channel at time t, and represent the channel gains between interferer j and the base station, and between interferer j and UAV m, respectively.
6. The integrated communication and sensing network resource optimization system for multiple UAVs according to claim 5, characterized in that The energy consumption constraint is expressed as Among them, E m,max represents the maximum energy consumption limit of the unmanned aerial vehicle m, P(ν h , ν ν ) is the mechanical power consumption of the unmanned aerial vehicle, c p is the calorific value of fuel combustion, and μ fuel is the average thermodynamic efficiency of the gas turbine; The mechanical power consumption of the UAV Among them, v h is the horizontal velocity of the UAV, v v is the vertical velocity of the UAV, v H is the induced velocity when the UAV hovers, W represents the weight of the UAV, ρ is the air density, A is the rotor disk area of the UAV, c is the incremental correction coefficient, S blade is the blade area of the UAV, v tip is the tip speed of the UAV rotor, r d is the drag ratio of the UAV fuselage.
7. The multi-UAV communication and sensing integrated network resource optimization system according to claim 6, wherein The power limit is where p min and p max represent the minimum and maximum power limits of the drone m, respectively.
8. A multi-UAV communication, sensing, and integration network resource optimization system according to claim 7, characterized in that The trajectory constraint is expressed as Among them, the initial left side of the drone m is represented as (x uav,m (0), y uav,m (0), z uav,m (0)), and the final coordinates of the drone m are (x uav,m (T), y uav,m (T), z uav,m (T)), and H uav is the flight altitude of the drone.
9. The multi-UAV communication, sensing, and integration network resource optimization system according to claim 8, wherein, The flight range constraint is expressed as Among them, represents the radar sensing performance of the drone m during the flight time t, represents the communication rate at which the drone m transmits data to the base station; The radar sensing performance where β represents the allocation factor of the sensing flight time; The communication rate 10. A multi-UAV communication and sensing integrated network resource optimization system according to claim 9, characterized in that, The potential game module is expressed as Among them, represents the set of game participants (coalitions) in the network, and the strategy space represents (p m (t), {x m (t), y m (t), z m (t)}), that is, the strategy of UAV m at time slot t is to select the transmission power p m (t) and the trajectory {x m (t), y m (t), z m (t)}, and the utility function of UAV m is expressed as U m (s m , s -m ); The total mutual information of the radar sensing and communication links is expressed as where s -m represents the strategy selections of all other UAVs except UAV m, is the sensing link rate between the UAV and the sensing target, expressed as the mutual information of the sensing link; is the communication link rate from UAV m to the base station BS, expressed as the mutual information of the communication link, specifically as follows: In the potential game, the strategy profile makes the strategy of each drone m optimal, that is, given the strategies of other drones unchanged, its utility function reaches the maximum value, expressed as: