A UAV swarm intelligence spectrum sensing incentive method based on contract theory

Abstract

The present invention relates to the field of wireless communication technology, and in particular to a method for motivating unmanned aerial vehicle (UAV) swarm intelligence spectrum perception based on contract theory, including constructing a multi-UAV collaborative spectrum perception framework, establishing utility functions of UAVs and utility functions of secondary users respectively; constructing a set of "detection probability-perception reward" contract pairs for asymmetric position information between UAVs and authorized users, maximizing the expected utility of secondary users under the conditions of ensuring individual rationality of UAVs and incentive compatibility constraints; solving optimization problems to obtain optimal contracts and optimal number of UAVs by analyzing feasibility conditions of contracts and simplifying constraints. The present invention uses contract theory to assist secondary users in designing incentive mechanisms under conditions of unknown UAV spectrum perception positions, so as to improve the accuracy and efficiency of swarm intelligence spectrum perception.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for motivating unmanned aerial vehicle (UAV) swarm intelligence spectrum perception based on contract theory. Background Art

[0002] In the era of computing paradigm, edge computing is highly consistent with the needs of the Internet of Everything. Edge computing has begun to play a vital role, especially mobile edge computing in mobile cellular networks (Abbas N, Zhang Y, Taherkordi A, et al. Mobile Edge Computing: A Survey [J]. IEEE Internet of Things Journal, 2018, 5 (1): 450–465.). Edge computing deploys communication, computing, and storage resources at the edge of the network to provide services such as offloading, caching, and crowd intelligence perception. However, as the number of edge computing network users continues to increase, the problem of spectrum scarcity has become increasingly prominent.

[0003] Unmanned aerial vehicles (UAVs) were first used in the military as reconnaissance and combat aircraft. With the improvement of industrial and technological levels, UAVs have become more and more diverse in size, weight, load capacity, flight speed, flight altitude, and endurance, and the types of tasks they can perform are becoming more and more diverse. Civilian UAVs are also gradually becoming popular. The low cost, flexible deployment, and high maneuverability of UAVs make them play an excellent role in many fields. When UAVs are combined with wireless communications, they can maintain a high probability of line-of-sight signals with other communication nodes (Matolak DW, Sun R. Unmanned Aircraft Systems: Air-Ground Channel Characterization for Future Applications [J]. IEEE Vehicular Technology Magazine, 2015, 10 (2): 79–85.). With the rapid development of wireless communication technology, UAVs are widely used in scenarios such as data collection, mobile relay, and secure communication. In addition, in today's era of the Internet of Everything, the application of UAVs in edge computing scenarios has attracted widespread attention.

[0004] Cognitive radio technology allows secondary users (SU) to access and use spectrum resources during the idle time of authorized primary users (PU), improving spectrum utilization and alleviating spectrum scarcity. However, the perception ability of a single user is limited and there are location limitations. Collaborative spectrum sensing can break the limitations of single-user perception and bring better perception performance. In the drone-assisted edge computing scenario, secondary users can use drones as perception nodes to complete collaborative spectrum sensing, improve perception accuracy and achieve efficient dynamic spectrum access.

[0005] Most existing studies are based on the optimistic assumption that drones provide services free of charge. However, drones incur time and energy losses during the service process. In reality, this assumption is not true. Therefore, secondary users need to design a crowd-sensing spectrum sensing incentive mechanism to issue crowd sensing tasks to drones to encourage drones to participate in collaborative spectrum sensing. In addition, the flight trajectory and sensing position of drones are private information and are not broadcast to the public. That is, the sensing distance between the drone and the authorized user is unknown to the secondary user. When designing the incentive mechanism, the asymmetric information problem also needs to be solved. Summary of the invention

[0006] In order to motivate drones to honestly participate in collaborative spectrum sensing under asymmetric information, solve the incentive design problem when the sensing distance is unknown, and improve the accuracy of secondary users' dynamic spectrum access, the present invention proposes a drone swarm intelligence spectrum sensing incentive method based on contract theory, which specifically includes the following steps:

[0007] S1, secondary users classify drones based on the perceived distance between them and authorized users;

[0008] S2. Based on the contract theory, the secondary user solves the optimal number of drones N with the goal of maximizing the expected utility of the secondary user, and designs a first contract pair consisting of detection probability and perception reward for each type of drone. Then all contract pairs and task information are broadcast to the drones;

[0009] S3, the drone selects the second contract pair that can obtain the highest utility from all the contract pairs broadcasted by the secondary user, and feeds back the second contract pair as the preferred contract to the secondary user;

[0010] S4. The secondary user selects and signs the corresponding preferred contracts with the N drones with the highest utility according to the drone feedback information;

[0011] S5. After signing the contract, the drone performs the perception task according to the agreed detection probability and sends the local judgment result to the secondary user;

[0012] S6. If the secondary user transmission requirement is completed, the process ends; otherwise, the process returns to step S2.

[0013] Preferably, the expected utility of the secondary user is maximized by selecting N drones and signing corresponding preferred contracts with them respectively. The expected utility of the secondary user is maximized as:

[0014] Constraints:

[0015] Among them, U SU is the expected utility of the secondary user when incentivizing N drones to participate in cooperative spectrum sensing; Ω is the set of contract pairs of all types of drones, expressed as P d (θ k ) represents the target detection probability of type k UAV, R(θ k ) represents the reward obtained by the type k UAV for completing the perception task, Θ is the set of maximum Euclidean distances between the UAV and the authorized user, expressed as Θ = {θ1,…,θ k ,…,θ K}, θ k The first contract pair of k types of UAVs and the maximum; U UAV (θ k ) is the utility of the type k UAV for participating in cooperative spectrum sensing according to the first contract; U UAV (θ k′ ) is the utility of type k UAV participating in cooperative spectrum sensing according to the second contract, θ k′ It represents the maximum distance between the authorized user and the parameter cooperative spectrum sensing according to the second contract; K is the number of drone classification categories; I represents the number of drones distributed within the communication range of the secondary user.

[0016] Preferably, the expected utility USU when the secondary user incentivizes N drones to participate in cooperative spectrum sensing is expressed as:

[0017]

[0018] Where β is the unit throughput benefit; E(C) is the expected transmission rate of the secondary user; ρ k represents the probability that the drone is of type k; It represents the probability that the secondary user detects that the spectrum occupancy state is idle when the authorized user exists; l is the penalty for the secondary user to access the spectrum occupied by the authorized user.

[0019] Preferably, the expected transmission rate E(C) of the secondary user is expressed as:

[0020]

[0021] Among them, P(H1) represents the probability that the target frequency band is occupied; represents the fusion detection probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the authorized user is occupied; C1 represents the transmission rate of the secondary user when the authorized user occupies the spectrum; P(H0) represents the probability that the target frequency band is idle; It represents the fusion false alarm probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the authorized user is not occupied; C0 represents the transmission rate of the secondary user when the authorized user is not occupying the spectrum.

[0022] Preferably, when N UAVs participate in cooperative spectrum sensing, the fusion detection probability of the secondary user is and the fusion false alarm probability of secondary users Respectively expressed as:

[0023]

[0024] in, Indicates rounding up, that is, the smallest integer not less than x; represents the number of combinations, that is, the number of all combinations of taking b elements from a different elements and forming a group; ρ k represents the probability that the drone is of type k; P d (θ k ) represents the target detection probability of type k UAV; P f (θ k ) represents the false alarm probability of type k UAV.

[0025] Prioritize the utility U of type k UAV participating in cooperative spectrum sensing UAV (θ k ) is expressed as:

[0026] U UAV (θ k )=R(θ k )-α×τ(θ k ,P d (θ k ))

[0027] Among them, R(θ k ) represents the reward obtained by the drone when it completes the perception task when it selects the second contract pair of type k; α represents the cost per unit time of the drone’s perception; τ(θ k ,P d (θ k )) represents the detection time function when a k-type UAV selects a k-type second contract pair.

[0028] Prior to this, the secondary user globally integrates the status information uploaded by the drone and decides whether to access it based on the integration results, including:

[0029] The drone uses an energy detection method to calculate the signal energy it receives in the target frequency band during the sensing time, and compares it with the preset threshold to determine the spectrum status of the authorized spectrum, determine the spectrum occupancy status of the authorized user in the frequency band, and feedback it to the secondary user;

[0030] After the secondary user receives the spectrum occupancy status of all drones, when no less than half of the drones return the status as occupied, the secondary user determines that the frequency band is in an occupied state, otherwise it is in an idle state;

[0031] The secondary user accesses the spectrum if and only if the global aggregation result is an idle state.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention can solve the incentive problem when there is information asymmetry between secondary users and drones in the drone-assisted crowd-intelligence spectrum sensing scenario, that is, when the secondary users only know the type distribution of the drone's perception distance, a set of suitable contracts can be designed to make the drones honestly participate in collaborative spectrum sensing and maximize the secondary user utility. Compared with traditional pricing methods, the present invention's contract-based drone crowd-intelligence spectrum sensing incentive mechanism can enable secondary users to achieve efficient spectrum access. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the UAV swarm intelligence spectrum sensing incentive method based on contract theory of the present invention;

[0035] Figure 2 A diagram showing the change in utility when different types of drones are selected for different contract pairs in an example of the present invention;

[0036] Figure 3 This is a comparison chart of the detection probabilities of different types of drones incentivized by the contract-based drone swarm spectrum sensing incentive method and the traditional linear pricing method in an example of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The present invention proposes a UAV swarm intelligence spectrum sensing incentive method based on contract theory, which specifically includes the following steps:

[0039] S1, secondary users classify drones based on the perceived distance between them and authorized users;

[0040] S2. Based on the contract theory, the secondary user solves the optimal number of drones N with the goal of maximizing the expected utility of the secondary user, and designs a first contract pair consisting of detection probability and perception reward for each type of drone, and broadcasts all contract pairs and task information to the drones;

[0041] S3, the drone selects the second contract pair that can obtain the highest utility from all the contract pairs broadcasted by the secondary user, and feeds back the second contract pair as the preferred contract to the secondary user;

[0042] S4. The secondary user selects and signs the corresponding preferred contracts with the N drones with the highest utility according to the drone feedback information;

[0043] S5. After signing the contract, the drone performs the perception task according to the agreed detection probability and sends the local judgment result to the secondary user;

[0044] S6. If the secondary user transmission requirement is completed, the process ends; otherwise, the process returns to step S2.

[0045] In the present invention, the secondary user classifies the drones according to the perceived distance between the drone and the authorized user, as follows:

[0046] The sensing distance of the drone is private information and is not disclosed to the public. The secondary user classifies the randomly distributed drones in the area according to the Euclidean distance between the drone and the authorized user.

[0047] The drones are divided into K types in ascending order, denoted as K = {1,…,k,…,K}, and the corresponding distance size set is recorded as: Θ = {θ1,…,θ k ,…,θ K}, where θ k represents the maximum distance between a k-type UAV and an authorized user, That is: θ1<…<θ k <…<θ K ;

[0048] According to prior knowledge, the probability that a drone is of type k is given by ρ k Indicates that there is

[0049] In this embodiment, the secondary user designs a corresponding {detection probability, perception reward} contract pair for each type of drone based on contract theory, and broadcasts the task information and the optimal contract to the drone, as follows:

[0050] (I) First, define the utility of UAVs participating in collaborative spectrum sensing.

[0051] The utility function of the UAV is modeled, and the utility of the UAV is defined as the difference between the benefit obtained from completing the sensing task and the task cost. The utility equation of k types of UAVs participating in cooperative spectrum sensing can be expressed as:

[0052] U UAV (θ k )=R(θ k )-α×τ(θ k ,P d (θ k ))

[0053] Among them, U UAV (θ k ) is the utility of selecting the first contract pair of type k as the second contract pair for k-type UAVs to participate in cooperative spectrum sensing. In the present invention, the first contract pair is the optimal contract pair of each type of UAV optimized with the goal of maximizing the expected utility of the secondary user. The UAV selects the contract pair with the highest utility, i.e., the second contract pair, from all the first contract pairs broadcast by the secondary user and takes the contract pair as the preferred contract; R(θ k ) represents the reward obtained by the drone when it completes the perception task when it selects the second contract pair of type k; α represents the cost per unit time of the drone’s perception; τ(θ k ,P d (θ k )) represents the detection time function when k-type UAV selects k-type second contract pair, P d (θ k ) represents the target detection probability when the k-type UAV selects the k-type second contract pair.

[0054] (ii) Secondly, define the expected transmission rate of the secondary user.

[0055] The secondary user is modeled and the expected transmission rate of the secondary user is defined as:

[0056]

[0057] Among them, P(H1) represents the probability that the target frequency band is occupied; represents the fusion detection probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the authorized user occupies the target frequency band; C1 represents the transmission rate of the secondary user when the authorized user occupies the spectrum; P(H0) represents the probability that the target frequency band is idle; It represents the fusion false alarm probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the target frequency band is idle; C0 represents the transmission rate of the secondary user when the target frequency band is idle.

[0058] (iii) Then, the utility of the secondary user is calculated based on the drone utility and the secondary user's expected transmission rate.

[0059] In this embodiment, the utility of the secondary user is defined as the difference between the benefit obtained from signal transmission and the reward paid to the drone. When the secondary user incentivizes N drones to participate in cooperative spectrum sensing, its expected utility can be expressed as:

[0060]

[0061] Among them, β is the unit throughput benefit; It represents the probability that the secondary user detects that the spectrum occupancy state is idle when the authorized user exists; l is the penalty for the secondary user to access the spectrum occupied by the authorized user.

[0062] (iv) Next, secondary users design mechanisms based on contract theory.

[0063] In this embodiment, K contract pairs are designed for K types of drones. The {detection probability, perception reward} contract pair corresponding to the k type is expressed as (P d (θ k ),R(θ k ), the contract established by the secondary user can be defined as:

[0064] Define the individual rationality (IR) constraint of the drone, that is, the drone can obtain positive utility when choosing the contract pair corresponding to its true type, which is expressed as:

[0065]

[0066] Define the incentive compatibility (IC) constraint of the drone, that is, when the drone chooses the contract pair corresponding to its true type, the utility it can choose is no less than any type, which can be expressed as:

[0067]

[0068] Among them, R(θ k′ ) represents the reward obtained by the drone when it completes the perception task when it selects the second contract pair of type k′, θ k′ represents the maximum distance between the k′ type UAV and the authorized user; τ(θ k ,P d (θ k′ )) represents the detection time function of the k-type UAV selecting the k′-type second contract, P d (θ k′) represents the target detection probability when a k-type UAV selects a k′-type second contract pair.

[0069] Secondary users optimize the number of incentive drones N and the contract Ω according to the constraints of individual rationality and incentive compatibility of drones to maximize their expected utility, and then formulate the optimal contract Ω * The mission information is broadcast to the drones in the area. The optimization problem of maximizing the expected utility of secondary users can be expressed as:

[0070]

[0071] Constraint C1:

[0072] Constraint C2:

[0073] Constraint C3: N ≤ I

[0074] Among them, the constraints C1 and C2 are the individual rationality constraints and incentive compatibility constraints of drones, respectively, and the constraint C3 is the total number of incentive drones. The secondary user uses mathematical methods to analyze the feasibility of the contract, simplify the constraints, and combine the optimization algorithm to solve the optimal number of incentive drones N * and the optimal contract Ω * , maximize one's expected utility.

[0075] After designing the corresponding optimal contract for each type of drone, the secondary user broadcasts the optimal contract and mission information to the drones in the area. The drones calculate the actual utility of each contract pair and take the contract pair with the highest actual utility as the preferred contract. Drone i selects the contract pair of type k (P d (θ k ),R(θ k ), the actual utility of its participation in collaborative spectrum sensing can be expressed as:

[0076]

[0077] Among them, d i Represents the actual distance between UAV i and the authorized user. When there is a contract pair corresponding to the positive utility in the contract broadcast by the secondary user, the UAV returns the contract pair corresponding to the highest utility as the preferred contract to the secondary user.

[0078] In this embodiment, the secondary user selects N drones with the highest utility based on the information returned by the drones, and signs a corresponding preferred contract with the selected drones. The drones perform the perception task and upload the local binary judgment according to the signed contract, as follows:

[0079] After the contract is signed, the drone uses the energy detection method to calculate the signal energy it receives in the target frequency band during the sensing time;

[0080] The drone compares the received signal energy with the preset threshold and makes a local binary judgment on the spectrum status of the authorized spectrum. That is, if the received energy is greater than the set threshold, the spectrum occupancy status on the frequency band is judged to be idle. Otherwise it is in occupied state

[0081] In this embodiment, the secondary user globally aggregates the judgment results uploaded by the drone and decides whether to access based on the aggregation results, specifically:

[0082] After receiving the binary judgment of the drone, the secondary user globally aggregates the local judgment results of each drone according to the majority rule. That is, when no less than half of the drones return an occupied state, the secondary user determines that the frequency band is occupied. If and only if the global aggregation result is an idle state, the secondary user accesses the spectrum.

[0083] Assuming that the spectrum sensing decisions of all drones are independent of each other, according to the probability distribution of drone distance types, when N drones participate in cooperative spectrum sensing, the detection probability of secondary users after aggregation is and false alarm probability Respectively expressed as:

[0084]

[0085]

[0086] in, Indicates rounding up, that is, the smallest integer not less than x; represents the number of combinations, that is, the number of all combinations of taking b elements from a different elements and forming a group; ρ k represents the probability that the drone is of type k; P f (θ k ) represents the false alarm probability of type k UAV. The detection probability and false alarm probability of the UAV are related to its position and sensing time when performing spectrum sensing tasks. Those skilled in the art obtain or set the parameters according to the prior art, and the present invention does not make other limitations on this.

[0087] When the secondary user's transmission demand is completed, the process ends. Otherwise, it is necessary to continue broadcasting the mission information and contract to encourage drones to participate in a new round of collaborative spectrum sensing until the transmission demand is completed.

[0088] like Figure 1This embodiment proposes a specific implementation process of a UAV swarm intelligence spectrum sensing incentive method based on contract theory, which specifically includes the following steps:

[0089] 101. The secondary user classifies the drones according to the sensing distance, and divides I drones into K categories. The sensing distance is the Euclidean distance between the authorized user and the drone;

[0090] 102. An optimization problem is established with the goal of maximizing the expected utility of the secondary user. The secondary user solves the number N of drones that need to be incentivized and the optimal contract, and broadcasts the task information and contract pair to the drones;

[0091] 103. The drone selects a contract pair that maximizes the drone's utility based on the broadcasted mission information and contract, and sends the contract pair to the secondary user as the preferred contract;

[0092] 104. The secondary user receives the preferred contracts of each drone, and selects N drones with the highest utility based on the received feedback, and signs the corresponding preferred contracts with these N drones;

[0093] 105. All drones perform the sensing task according to the preferred contract, determine whether the target frequency band is occupied or idle, and send the local binary judgment of the target frequency band status to the secondary user;

[0094] 106. The secondary user globally aggregates the binary judgments uploaded by the drones. If no less than half of the binary judgments of the drones indicate that the target frequency band is occupied, the frequency band is judged to be occupied and the secondary user chooses not to access the spectrum. Otherwise, the secondary user accesses the spectrum.

[0095] 107. Determine whether the task requirement of the secondary user is completed. If it is completed, end the process; otherwise, return to step 102.

[0096] like Figure 2 As shown, different types of drones obtain the maximum positive utility when selecting the contract pair corresponding to the real type. That is, this embodiment can ensure the individual rationality and incentive compatibility constraints of drones while maximizing the expected utility of secondary users and improving the perception accuracy of secondary users.

[0097] like Figure 3 As shown in the figure, the detection probability of drones gradually decreases with the increase of sensing distance, that is, drones with short sensing distance can bring better detection performance. Compared with linear pricing, the drone swarm intelligence spectrum sensing incentive method based on contract theory of the present invention can encourage drones of various types to participate in collaborative spectrum sensing with a higher detection probability.

[0098] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV swarm intelligence spectrum sensing incentive method based on contract theory, characterized in that: The specific steps include: S1, secondary users classify drones based on the perceived distance between them and authorized users; S2. Based on the contract theory, the secondary user solves the optimal number of drones N with the goal of maximizing the expected utility of the secondary user, and designs a first contract pair consisting of detection probability and perception reward for each type of drone, broadcasting all contract pairs and task information to the drones; S3, the drone selects the second contract pair that can obtain the highest utility from all the contract pairs broadcasted by the secondary user, and feeds back the second contract pair as the preferred contract to the secondary user; S4. The secondary user selects and signs the corresponding preferred contracts with the N drones with the highest utility according to the drone feedback information; S5. After signing the contract, the drone performs the perception task according to the agreed detection probability and sends the local judgment result to the secondary user; S6. If the secondary user transmission requirement is completed, the process ends; otherwise, the process returns to step S2.

2. According to the contract theory-based UAV swarm intelligence spectrum sensing incentive method of claim 1, it is characterized in that: Maximizing the expected utility of secondary users selects N drones and signs corresponding preferred contracts with them respectively. The expected utility of maximizing secondary users is expressed as: Constraints: Among them, U SU is the expected utility of the secondary user when incentivizing N drones to participate in cooperative spectrum sensing; Ω is the set of contract pairs of all types of drones, expressed as P d (θ k ) represents the target detection probability of type k UAV, R(θ k ) represents the reward obtained by the type k UAV for completing the perception task, Θ is the set of maximum Euclidean distances between the UAV and the authorized user, expressed as Θ = {θ1,…,θ k ,…,θ K }, θ k represents the first contract pair and the maximum of k types of drones, K is the number of drone classification categories; U UAV (θ k ) is the utility of the type k UAV for participating in cooperative spectrum sensing according to the first contract; U UAV (θ k′ ) is the utility of type k UAV participating in cooperative spectrum sensing according to the second contract, θ k′ It represents the maximum distance between the authorized user and the parameter cooperative spectrum sensing according to the second contract; I represents the number of drones distributed within the communication range of the secondary user.

3. According to the contract theory-based UAV swarm intelligence spectrum sensing incentive method of claim 2, it is characterized in that: The expected utility U when the secondary user incentivizes N drones to participate in cooperative spectrum sensing SU It is expressed as: Where β is the unit throughput benefit; E(C) is the expected transmission rate of the secondary user; ρ k represents the probability that the drone is of type k; It represents the probability that the secondary user detects that the spectrum occupancy state is idle when the authorized user exists; l is the penalty for the secondary user to access the spectrum occupied by the authorized user.

4. According to the contract theory-based UAV swarm intelligence spectrum sensing incentive method of claim 3, it is characterized in that: The expected transmission rate E(C) of the secondary user is expressed as: Among them, P(H1) represents the probability that the target frequency band is occupied; represents the fusion detection probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the authorized user is occupied; C1 represents the transmission rate of the secondary user when the authorized user occupies the spectrum; P(H0) represents the probability that the target frequency band is idle; It represents the fusion false alarm probability of the secondary user, that is, the probability that the secondary user is determined to be occupied when the authorized user is not occupied; C0 represents the transmission rate of the secondary user when the authorized user is not occupying the spectrum.

5. The UAV swarm intelligence spectrum sensing incentive method based on contract theory according to claim 4 is characterized in that: When N UAVs participate in cooperative spectrum sensing, the fusion detection probability of secondary users and the fusion false alarm probability of secondary users Respectively expressed as: in, Express Round up; Represents the number of combinations, that is, taking any of N different elements The number of all combinations of elements that are grouped together; ρ k represents the probability that the drone is of type k; P d (θ k ) represents the target detection probability of type k UAV; P f (θ k ) represents the false alarm probability of type k UAV.

6. The UAV swarm intelligence spectrum sensing incentive method based on contract theory according to claim 2 is characterized in that: The utility of type k UAV participating in cooperative spectrum sensing UAV (θ k ) is expressed as: U UAV (i k )=R(θ k )-α×τ(θ k ,P d (i k )) Among them, R(θ k ) represents the reward obtained by the drone when it completes the perception task when it selects the second contract pair of type k; α represents the cost per unit time of the drone’s perception; τ(θ k ,P d (θ k )) represents the detection time function when a k-type UAV selects a k-type second contract pair.

7. The UAV swarm intelligence spectrum sensing incentive method based on contract theory according to claim 1 is characterized in that: The secondary user globally integrates the status information uploaded by the drone and decides whether to access based on the integration results, including: The drone uses an energy detection method to calculate the signal energy it receives in the target frequency band during the sensing time, and compares it with the preset threshold to determine the spectrum status of the authorized spectrum, determine the spectrum occupancy status of the authorized user in the frequency band, and feedback it to the secondary user; After the secondary user receives the spectrum occupancy status of all drones, when no less than half of the drones return the status as occupied, the secondary user determines that the frequency band is in an occupied state, otherwise it is in an idle state; The secondary user accesses the spectrum if and only if the global aggregation result is an idle state.

8. The UAV swarm intelligence spectrum sensing incentive method based on contract theory according to claim 1 is characterized in that: The secondary user classifies the drones according to the perceived distance between the drone and the authorized user. That is, according to the Euclidean distance between the drone and the authorized user, the I drones above the authorized user are divided into K categories and arranged in ascending order according to the Euclidean distance between the drone and the authorized user. The set of drone categories is expressed as The maximum distance set between each category of drones and authorized users is expressed as Θ = {θ1,…,θ k ,…,θ K } and satisfies θ1<…<θ k <…<θ K ,θ k Indicates the maximum distance between type k UAV and secondary users.

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