Unmanned aerial vehicle assisted cellular network communication method and device, and storage medium

Abstract

The invention discloses an unmanned aerial vehicle assisted cellular network communication method and device, and a storage medium, and relates to the technical field of communication management. The method comprises the following steps: randomly initializing alliance allocation of unmanned aerial vehicles, and obtaining an initial alliance grouping set; the individual income of each unmanned aerial vehicle in the current unmanned aerial vehicle alliance group is calculated; each unmanned aerial vehicle takes a non-current unmanned aerial vehicle alliance group as a target unmanned aerial vehicle alliance group, and the expected income of the unmanned aerial vehicle in the target unmanned aerial vehicle alliance group is calculated; when it is judged that the expected income meets a preset group switching condition, the unmanned aerial vehicle is allocated to a target unmanned aerial vehicle alliance group; and when no unmanned aerial vehicle meets the preset grouping switching condition, a stable unmanned aerial vehicle alliance grouping set is obtained. According to the invention, by balancing individual interests and overall interests, the stable unmanned aerial vehicle alliance grouping set is obtained, the fairness of the transaction market and the maximization of the network throughput can be considered, and the transaction efficiency is effectively improved.

Description

Technical Field

[0001] This application relates to the field of communication management technology, specifically to a method, device and storage medium for unmanned aerial vehicle (UAV) assisted cellular network communication. Background Technology

[0002] In recent years, drone-assisted cellular network communication has become an important technology for addressing the surge in communication demands in high-density user areas due to the advantages of drones, such as high mobility, ease of deployment, and low cost. Drones can serve as aerial relay nodes, assisting communication between high-density user areas and base stations, effectively improving network throughput. However, the communication capabilities of a single drone are limited and cannot meet the throughput demands of high-density user areas, necessitating the integration of resources through the formation of drone alliances.

[0003] As rational and self-interested individuals, drones in traditional drone alliance formation methods only consider their own benefits when switching alliances, making it difficult to improve the overall benefits of all drones. Existing alliance formation strategies either overemphasize individual interests, such as the self-interested ranking method, or impose overly strict restrictions on cooperation conditions, such as the Pareto ranking method, failing to balance individual and overall benefits and making it difficult to form stable alliances. Furthermore, in the process of building drone alliances, the matching and pricing of drone alliances with high-density user areas must meet multiple economic attributes: individual rationality (e.g., the benefits of both parties in the transaction are not less than 0), budget equilibrium (e.g., the auctioneer's profit is positive), and authenticity (e.g., the bids of both parties reflect the true value). However, existing methods of building drone alliances cannot simultaneously meet these economic attributes. During the transaction process, high-density user areas and drone alliances may lie about their bids or demands in pursuit of their own interests, further exacerbating the complexity of resource allocation and pricing.

[0004] Based on the search of the above information, it is evident that the current drone alliance is unstable and fails to meet multiple economic attributes when matching users, and cannot balance maximizing network throughput with the fairness of the transaction market, resulting in low transaction efficiency and unreasonable resource allocation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application solves the technical problem of how to build a stable drone alliance that can increase total drone revenue while also ensuring network throughput and fairness in the trading market, thereby improving transaction efficiency.

[0006] To achieve the above objectives, in a first aspect, embodiments of this application provide a method for unmanned aerial vehicle (UAV)-assisted cellular network communication, the method comprising the following steps: Randomly initialize the drone alliance assignments and obtain the initial alliance group set; Calculate the individual revenue of each drone in the current drone alliance group; Each drone targets a drone alliance group other than its current drone alliance group, and calculates the expected revenue of the drone in the target drone alliance group. When the expected revenue meets the preset group switching conditions, the drone is assigned to the target drone alliance group; When no drone meets the preset group switching conditions, a stable drone alliance group set is obtained.

[0007] In conjunction with the first aspect, in one implementation, the preset group switching conditions include: the expected revenue of the drone in the target drone alliance group is greater than its individual revenue in the current drone alliance group, and when the drone switches to the target drone alliance group, the sum of the revenues of the other drones in the current drone alliance group does not decrease, and when the drone switches to the target drone alliance group, the sum of the revenues of the other drones in the target drone alliance group does not decrease.

[0008] In conjunction with the first aspect, in one implementation, before calculating the individual revenue of each drone in the current drone alliance group, the final matching result between the drone alliance group and the high-density user area is obtained based on the current drone alliance group, the number of high-density user areas, the throughput demand of the high-density user areas, the bid of the high-density user areas, and the ask price of the drone alliance group. The final matching results will be used to calculate the fees paid to the drone alliance group, the fees charged to high-density user areas, and the revenue per drone.

[0009] In conjunction with the first aspect, in one implementation method, the individual benefit is calculated as follows: ; In the formula, To join the The first high-density user area The revenue from a single drone, The fees paid by ground base stations to the current drone alliance group, For the current drone alliance group to access the first Number of drones in high-density user areas Indicates the first Energy consumption preference coefficient for each drone The current drone alliance is grouped as number 1. The total cost of providing relay communication services to a high-density user area is calculated as follows: ; In the formula, Indicates the first The drone was connected to the first Communication energy consumption in high-density user areas Energy consumption for drone hovering. The energy consumption cost for the drone's flight distance. For the current drone alliance, drones and the first Mean horizontal distance between high-density user areas.

[0010] In conjunction with the first aspect, in one implementation, the method for obtaining the final matching result between the drone alliance group and the high-density user area includes: When combining drone alliance groups with high-density user areas, and determining that the transmission rate provided by the drone alliance group to the high-density user area meets its communication requirements parameters, an adaptation association is established between the drone alliance group and the high-density user area, and an adaptation weight is assigned. After traversing all combinations of drone alliance groups and high-density user regions, a weighted undirected bipartite graph is constructed based on the adaptation association and adaptation weight reconstruction of drone alliance groups and high-density user regions. The Hungarian algorithm is used to solve the weighted undirected bipartite graph by maximum matching, and the initial drone alliance group and high-density user region matching results corresponding to maximizing network throughput are obtained. Based on the initial drone alliance grouping and high-density user area matching results, the high-density user area is sorted by bid and the drone alliance grouping is sorted by ask price. The final matching result and transaction price between drone alliance groups and high-density user areas are determined based on the critical node screening mechanism.

[0011] In conjunction with the first aspect, in one implementation, the formula for calculating the transmission rate provided by the drone alliance group for high-density user areas is: ; ; In the formula, For the first Select the first high-density user area Transmission rate after drone alliance grouping This refers to the group number of the drone alliance. Let be the transmission rate between the ground base station and the i-th UAV. Access to the drone alliance group Number of drones in high-density user areas For the first The drone and the first The transmission rate between high-density user areas is calculated as follows: ; ; ; In the formula, For the first The drone and the first Transmission bandwidth for high-density user areas For the first The drone and the first Signal-to-interference-plus-noise ratio in a high-density user area To join the Number of drones in high-density user areas For total bandwidth resources, This represents the total number of high-density user areas. For the first Transmit power in high-density user areas For the first The drone and the first Small-scale fading coefficients between high-density user areas For noise power spectral density, For the first The transmission power between the drone and the ground base station The self-interference coefficient is... For the first The drone and the first Average channel gain of a high-density user area.

[0012] In conjunction with the first aspect, in one implementation, when determining the final matching result and transaction price between the drone alliance group and the high-density user area based on the critical node screening mechanism, the method of using the critical node screening mechanism includes: STEP 1: Determine the effective transaction benchmark k: In the descending order of the high-density user area bid sequence and the ascending order of the drone alliance group ask sequence, search for the largest index k such that the bid of the kth high-density user area after sorting is greater than or equal to the ask of the kth drone alliance group after sorting. STEP2, Expand the critical high-density user area sequence number a: In the descending order of the high-density user area bid sequence, search for the largest sequence number a, such that the bid of the a-th high-density user area after sorting is greater than or equal to the bid of the k-th drone alliance group after sorting. Extended critical drone alliance group sequence number b: In the ascending order of drone alliance group bids, search for the largest sequence number b such that the bid of the kth high-density user region after sorting is greater than or equal to the bid of the bth drone alliance group after sorting. STEP 3: Count the number of matches between the two sets of critical node pairs. A critical node pair consists of: the bid of the a-th high-density user region after sorting, and the ask price of the k-th drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions with bids ≥ the bid of the group and ask prices ≤ the ask prices of the group and the number of matching pairs with drone alliance groups. Another set of critical node pairs consists of: the bid of the kth high-density user region after sorting and the ask price of the bth drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions and drone alliance groups whose bid is greater than or equal to the bid of the group and whose ask price is less than or equal to the ask price of the group. STEP 4: Determine the transaction price: Select a critical node pair with a larger number of matching nodes, use the bid in the node pair as the unified transaction payment amount for the high-density user area, and use the ask price in the node pair as the unified transaction collection amount for the drone alliance group, thus completing the transaction price determination.

[0013] In conjunction with the first aspect, in one implementation, after determining the final matching result and transaction price between the drone alliance group and the high-density user area, the matched drone alliance group provides relay communication services to the corresponding high-density user area, and the two parties settle the fees according to the transaction price.

[0014] Secondly, embodiments of this application provide a drone-assisted cellular network communication device, the drone-assisted cellular network communication device including a processor, a memory, and a drone-assisted cellular network communication program stored in the memory and executable by the processor, wherein when the drone-assisted cellular network communication program is executed by the processor, it implements the method provided in the first aspect.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium storing a drone-assisted cellular network communication program, which, when executed, implements the method provided in the first aspect.

[0016] Compared with the prior art, the advantages of this application are: (1) By clearly defining the group switching conditions of drones, a stable set of drone alliance groups is obtained. This ensures that the individual benefits of switching drones are improved, while avoiding the negative impact of their switching behavior on the benefits of other members of the original group and the target drone alliance group. This balances individual interests with overall interests, thus avoiding individual decisions from harming overall benefits while improving the flexibility of drone alliance group formation and thereby increasing total benefits.

[0017] (2) By constraining the relationship between the transaction price and the initial bid and ask price, individual rationality is ensured. By screening critical node pairs, the transaction price difference is ensured to be positive, which meets the needs of budget equilibrium. Based on the transmission rate as the judgment benchmark, a weighted undirected bipartite graph is constructed. Then, the Hungarian algorithm is used to solve the maximum matching, ensuring that the matching result can maximize the network throughput and provide reliable support for efficient communication for UAV alliance groups. The transmission rate and service cost are taken as the real value. By clarifying the correspondence between the bid and ask price and the real value, the authenticity is guaranteed, and the false reporting behavior is effectively suppressed. Thus, the fairness of the transaction market and the maximization of network throughput are taken into account, and the transaction efficiency is effectively improved. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first aspect of the embodiments of this application; Figure 2 This is a schematic diagram illustrating the change of the total revenue of the UAV with distance energy consumption coefficient in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the change in the total revenue of the UAV with the self-interference coefficient in the embodiments of this application; Figure 4 This is a diagram illustrating the change in total revenue of the UAV with total bandwidth in the embodiments of this application; Figure 5 This is a diagram illustrating the changes in the revenue, costs, and transaction price of the winning drone alliance as the asking price changes in this embodiment of the application. Figure 6 This is a diagram illustrating how the revenue, true value, and transaction price of the winning high-density user area change with the bid in this embodiment of the application. Figure 7 This is a diagram illustrating how the number of final transaction pairs changes with the number of high-density user areas in this application embodiment; Figure 8 This is a schematic diagram illustrating the change in the number of final transaction pairs as a function of the number of drones in this application embodiment; Figure 9 This is a diagram illustrating how the total revenue of the UAV changes with the number of iterations in the embodiments of this application. Figure 10 This is a connection block diagram of the electronic device in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0023] In a first aspect, embodiments of this application provide a method for unmanned aerial vehicle (UAV)-assisted cellular network communication, the method comprising the following steps: Randomly initialize the drone alliance assignments and obtain an initial alliance group set containing at least two drone alliance groups; Calculate the individual revenue of each drone in the current drone alliance group; Each drone targets a drone alliance group other than its current drone alliance group, and calculates the expected revenue of the drone in the target drone alliance group. Determine whether the expected return meets the preset group switching conditions. If so, perform the target drone alliance group switching and assign the drone to the target drone alliance group; otherwise, reject the alliance group switching. The preset group switching conditions include: the expected return of the drone in the target drone alliance group is greater than its individual return in the current drone alliance group, and when the drone switches to the target drone alliance group, the sum of the returns of the other drones in the current drone alliance group does not decrease, and when the drone switches to the target drone alliance group, the sum of the returns of the other drones in the target drone alliance group does not decrease. When no drone meets the preset group switching conditions, a stable drone alliance group set is obtained.

[0024] Therefore, by clearly defining the conditions for drone group switching, a stable set of drone alliance groups is obtained. This ensures that the individual benefits of switching drones are improved, while avoiding the negative impact of switching behavior on the benefits of other members of the original group and the target drone alliance group. It balances individual interests with overall interests, thus avoiding individual decisions from harming overall benefits, while improving the flexibility of drone alliance group formation and thereby increasing the total benefits.

[0025] In one embodiment, before calculating the individual revenue of each drone in the current drone alliance group, the final matching result between the drone alliance group and the high-density user area is obtained based on the current drone alliance group, the number of high-density user areas, the throughput demand of the high-density user areas, the bid of the high-density user areas, and the ask price of the drone alliance group.

[0026] Based on this, the fees paid to the drone alliance group, the fees charged to high-density user areas, and the revenue of each drone are obtained according to the final matching results, where the revenue includes the individual revenue and expected revenue mentioned above.

[0027] Take individual income as an example.

[0028] The calculation method for individual income is as follows: ; In the formula, To join the The first high-density user area The revenue from a single drone, The fees paid by ground base stations to the current drone alliance group, For the current drone alliance group to access the first Number of drones in high-density user areas Indicates the first Energy consumption preference coefficient for each drone The current drone alliance is grouped as number 1. The total cost of providing relay communication services to a high-density user area is calculated as follows: ; In the formula, Indicates the first The drone was connected to the first Communication energy consumption in high-density user areas Energy consumption for drone hovering. The energy consumption cost for the drone's flight distance. For the current drone alliance, drones and the first Mean horizontal distance between high-density user areas.

[0029] Similarly, the calculation method for expected returns is the same as that for individual returns, the only difference being that the current drone alliance group is replaced with the target drone alliance group.

[0030] This approach distributes the ground base station costs and total costs equally among the number of drones, ensuring fairness in resource allocation and cost sharing, avoiding unreasonable costs or unbalanced benefits for individuals, and achieving a balance between fairness and rationality. By introducing an energy consumption preference coefficient, the cost sharing weight can be adjusted according to the energy consumption sensitivity of different drones. For example, drones that are sensitive to energy consumption will have a higher value, adapting to individual differences, improving the personalization and accuracy of revenue calculation, and aligning with the actual operating characteristics of drones.

[0031] In one embodiment, the method for obtaining the final matching result between drone alliance groups and high-density user areas includes: The drone alliance group is combined with the high-density user area. It is determined whether the transmission rate provided by the drone alliance group to the high-density user area meets its communication requirements. If so, an adaptation association is established between the drone alliance group and the high-density user area (such as establishing an edge) and an adaptation weight is assigned (based on the transmission rate provided by the drone alliance group). Otherwise, the combination is determined to have failed. After traversing all combinations of drone alliance groups and high-density user regions, a weighted undirected bipartite graph is constructed based on the adaptation association and adaptation weight reconstruction of drone alliance groups and high-density user regions. The Hungarian algorithm is used to solve the weighted undirected bipartite graph by maximum matching, and the initial drone alliance group and high-density user region matching results corresponding to maximizing network throughput are obtained. Based on the initial drone alliance grouping and high-density user area matching results, the high-density user area is sorted by bid and the drone alliance grouping is sorted by ask price. The final matching result and transaction price between drone alliance groups and high-density user areas are determined based on the critical node screening mechanism.

[0032] Based on this, the drone alliance groups that have reached a deal will provide relay communication services to the corresponding high-density user areas, and the two parties will settle the fees according to the transaction price.

[0033] Therefore, based on the transmission rate as the criterion, a weighted undirected bipartite graph is constructed, and then the Hungarian algorithm is used to solve for the maximum matching, ensuring that the matching result can maximize the network throughput and provide reliable support for efficient communication for UAV alliance groups.

[0034] In one embodiment, the formula for calculating the transmission rate provided by the drone alliance group for a high-density user area is: ; ; In the formula, For the first Select the first high-density user area Transmission rate after drone alliance grouping This refers to the group number of the drone alliance. Let be the transmission rate between the ground base station and the i-th UAV. Access to the drone alliance group Number of drones in high-density user areas For the first The drone and the first The transmission rate between high-density user areas is calculated as follows: ; ; ; In the formula, For the first The drone and the first Transmission bandwidth for high-density user areas For the first The drone and the first Signal-to-interference-plus-noise ratio in a high-density user area To join the Number of drones in high-density user areas For total bandwidth resources, This represents the total number of high-density user areas. For the first Transmit power in high-density user areas For the first The drone and the first Small-scale fading coefficients between high-density user areas For noise power spectral density, For the first The transmission power between the drone and the ground base station The self-interference coefficient is... For the first The drone and the first The average channel gain of a high-density user area is calculated as follows: ; ; ; In the formula, Channel gain per unit distance For the first The drone and the first The European distance between high-density user areas Additional path loss for the visual link, Additional path loss for non-line-of-sight links, For visible link probability, For non-visual links, where , These are all environmental parameters. For the first The drone and the first The elevation angle of the communication link between high-density user areas and the ground.

[0035] As a detailed explanation, for the first The drone and the first The calculation process for the Euclidean distance between high-density user areas is as follows: Definition of the first The drone and the first The coordinates of the high-density user areas are as follows: and ; Calculate the first The drone and the first Horizontal distance between high-density user areas : ; Then the first The drone and the first The formula for calculating the Euclidean distance between high-density user areas is: ; In the formula, For the first The flight altitude of the drone.

[0036] In one embodiment, when determining the final matching result and transaction price between drone alliance groups and high-density user areas based on the critical node screening mechanism, the method of using the critical node screening mechanism includes: STEP 1: Determine the effective transaction benchmark k: In the descending order of the high-density user area bidding sequence and the ascending order of the drone alliance group asking sequence, search for the largest index k, such that the bid of the kth high-density user area after sorting is greater than or equal to the asking price of the kth drone alliance group after sorting. That is, the user areas and alliance groups with the corresponding indices in the first k groups all meet the basic transaction conditions. STEP2, Expand the critical high-density user area sequence number a: In the descending order of the high-density user area bidding sequence, search for the largest sequence number a (a≥k) such that the bid of the a-th high-density user area after sorting is greater than or equal to the asking price of the k-th drone alliance group after sorting. That is, find the largest sequence number among all high-density user areas that satisfy the condition that the bid is not lower than the asking price of the k-th drone alliance group. Extend the critical drone alliance group number b: In the ascending order of drone alliance group bid sequence, search for the largest number b (b≥k) such that the bid of the kth high-density user area after sorting is greater than or equal to the bid of the bth drone alliance group after sorting. That is, find the largest number among all drone alliance groups that satisfy the condition that the bid is not higher than the bid of the kth high-density user area. STEP 3: Count the number of matches between the two sets of critical node pairs. A critical node pair consists of: the bid of the a-th high-density user region after sorting, and the ask price of the k-th drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions with bids ≥ the bid of the group and ask prices ≤ the ask prices of the group and the number of matching pairs with drone alliance groups. Another set of critical node pairs consists of: the bid of the kth high-density user region after sorting and the ask price of the bth drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions and drone alliance groups whose bid is greater than or equal to the bid of the group and whose ask price is less than or equal to the ask price of the group. STEP 4: Determine the transaction price: Select a critical node pair with a larger number of matching nodes, use the bid in the node pair as the unified transaction payment amount for the high-density user area, and use the ask price in the node pair as the unified transaction collection amount for the drone alliance group, thus completing the transaction price determination.

[0037] Furthermore, the transaction price meets the following criteria: the transaction payment amount in high-density user areas is lower than its initial bid, the transaction receipt amount in drone alliance groups is higher than its initial asking price, and the transaction payment amount is greater than the transaction receipt amount.

[0038] By constraining the relationship between the transaction price and the initial bid and ask prices, individual rationality is ensured. The screening of critical node pairs ensures that the transaction price difference is positive, meeting the needs of budget equilibrium. Transmission rate and service cost are taken as the true value. By clarifying the correspondence between bid, ask prices and true value, authenticity is guaranteed, and false reporting is effectively suppressed. Thus, the fairness of the transaction market and the maximization of network throughput are balanced, effectively improving transaction efficiency.

[0039] The method provided in the first aspect will be illustrated below through a methodological flowchart. See [link to relevant documentation]. Figure 1 As shown, the specific process is as follows: S1. Randomly initialize the drone alliance assignment, obtain the initial alliance group set, and calculate the individual benefit of each drone in the current drone alliance group (see the calculation method of individual benefit above). S2. Each drone targets a drone alliance group other than its current drone alliance group, and calculates the expected revenue of the drone in the target drone alliance group (the calculation method for expected revenue is the same as the calculation method for individual revenue). S3. When the expected revenue meets the preset group switching conditions, the drone is assigned to the target drone alliance group and the drone alliance group is updated. S4. When no drone meets the preset group switching conditions, a stable drone alliance group set is obtained; S5. If it is determined that the transmission rate provided by the drone alliance group to the high-density user area meets its communication requirements, then an edge is established between the drone alliance group and the high-density user area, and an adaptation weight is assigned (based on the transmission rate provided by the drone alliance group). S6. After traversing all drone alliance groups and high-density user area combinations, construct a weighted undirected bipartite graph. S7. Use the Hungarian algorithm to solve the weighted undirected bipartite graph by maximum matching, and obtain the matching results of the initial drone alliance group and high-density user area corresponding to the maximum network throughput. S8. Based on the matching results of the initial drone alliance group and the high-density user area, sort the high-density user area by bid and sort the drone alliance group by ask price. S9. Based on the critical node screening mechanism (see the above-mentioned method for using the critical node screening mechanism), determine the final matching result and transaction price between the drone alliance group and the high-density user area. S10. The drone alliance group that has reached a transaction agreement will provide relay communication services to the corresponding high-density user areas, and the two parties will settle the fees according to the transaction price.

[0040] Secondly, embodiments of this application provide a drone-assisted cellular network communication device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0041] Reference Figure 10 , Figure 10 This is a schematic diagram of the hardware structure of the drone-assisted cellular network communication device involved in the embodiments of this application. In the embodiments of this application, the drone-assisted cellular network communication device may include a processor, a memory, a communication interface, and a communication bus.

[0042] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0043] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the UAV-assisted cellular network communication equipment, as well as interfaces used for interconnecting the UAV-assisted cellular network communication equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0044] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0045] The processor can be a general-purpose processor, which can call the UAV-assisted cellular network communication program stored in the memory and execute the UAV-assisted cellular network communication method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the UAV-assisted cellular network communication program is called can be referred to in the various embodiments of the UAV-assisted cellular network communication method of this application, and will not be repeated here.

[0046] Those skilled in the art will understand that Figure 10 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0047] Thirdly, embodiments of this application also provide a computer-readable storage medium.

[0048] The computer-readable storage medium of this application stores a drone-assisted cellular network communication program, wherein when the drone-assisted cellular network communication program is executed by a processor, it implements the steps of the drone-assisted cellular network communication method as described above.

[0049] The method implemented when the UAV-assisted cellular network communication program is executed can be referred to in various embodiments of the UAV-assisted cellular network communication method of this application, and will not be repeated here.

[0050] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0051] The method for obtaining a stable set of drone alliance groups in this application is defined as bounded cooperative ordering. The advantages of this application will be explained below through comparative testing with existing selfish ordering and Pareto ordering of drones. Test examples are as follows: Test Example 1 A comparison of bounded cooperative ranking, selfish ranking, and Pareto ranking is performed based on the change in total drone revenue with distance energy consumption coefficient. The results are as follows: Figure 2 As shown, compared to selfish sorting and Pareto sorting, the bounded cooperative sorting proposed in this paper improves the total revenue of drones. This is mainly due to the fact that the bounded cooperative sorting proposed in this paper balances selfish sorting and Pareto sorting, taking into account both its own revenue and the total revenue of other drones. Compared to selfish sorting, which tightens the conditions for drones to join other alliances, and Pareto sorting, which relaxes the conditions for drones to join other alliances, the bounded cooperative sorting proposed in this paper can improve the total revenue of drones.

[0052] Test Example 2 A comparison of bounded cooperative ranking, selfish ranking, and Pareto ranking is performed based on the change in total drone revenue with the self-interference coefficient. The results are as follows: Figure 3 As shown, compared to selfish sorting and Pareto sorting, the bounded cooperative sorting proposed in this paper improves the total revenue of the drone. The proposed bounded cooperative ranking method follows this logic: as the self-interference coefficient increases, the total revenue of drones initially increases, then remains constant. This is because an increase in the self-interference coefficient increases the throughput between the drone alliance group and the high-density user area, increasing the real value obtained by the high-density user area, leading to higher bids and higher transaction prices for the drone alliance group, thus increasing the total revenue of drones. However, as the self-interference coefficient continues to increase, the increased throughput between the drone alliance group and the high-density user area leads to an increase in the energy cost of the drone alliance group. The increase in transaction price obtained by the drone alliance group offsets the increase in cost, therefore, the total revenue of drones remains unchanged. Thus, the proposed bounded cooperative ranking method can improve the total revenue of drones.

[0053] Test Example 3 A comparison of bounded cooperative sorting, selfish sorting, and Pareto sorting is performed based on the change in total drone revenue with total bandwidth. The results are as follows: Figure 4As shown, compared to selfish sorting and Pareto sorting, the bounded cooperative sorting proposed in this paper improves the total revenue of UAVs.

[0054] The trend of the bounded cooperative ranking method proposed in this paper is as follows: the total revenue of drones initially increases with the increase of total bandwidth, then remains constant. This is because the increase in total bandwidth increases the throughput between drone alliance groups and high-density user areas, leading to an increase in the real value obtained by high-density user areas, resulting in higher bids and higher transaction prices for drone alliance groups, thus increasing the total revenue of drones. However, as the total bandwidth continues to increase, it does not affect the transaction price and cost of drone alliance groups, therefore the total revenue of drones remains unchanged. Therefore, it can be determined that the bounded cooperative ranking method proposed in this paper can improve the total revenue of drones.

[0055] Test Example 4 A comparison of bounded cooperative sorting, selfish sorting, and Pareto sorting is performed based on the change in total drone revenue with the number of iterations. The results are as follows: Figure 9 As shown, with the increase of the number of iterations, the total revenue of the drones under the three different methods tends to stabilize and no longer changes, indicating that the system can reach a stable state under the three different methods (i.e., all drones no longer change their alliance grouping choices). Compared with selfish sorting and Pareto sorting, the bounded cooperative sorting proposed in this paper improves the total revenue of the drones. Therefore, it can be determined that the bounded cooperative sorting proposed in this paper can improve the total revenue of the drones.

[0056] The method for obtaining the transaction price in this application is defined as a bilateral auction method. The advantages of the bilateral auction method proposed in this application will be explained below through comparative testing. The test examples are as follows: Test Example 5 The winning drone alliance will be selected from the group of critical node pairs that have the most matching points. Figure 5 The chart shows the winning drone alliance's revenue, cost, and transaction price as the asking price changes. It can be seen that as the winning drone alliance's asking price increases, the cost and transaction price remain unchanged. This is because the cost of a drone is only related to the energy consumed by the drone, while the winning drone alliance's asking price does not affect the total energy consumed by the drone. Therefore, the cost remains unchanged. The transaction price of the winning drone alliance is not related to the winning drone alliance's asking price, but to the cost of the winning drone alliance. When the cost of the winning drone alliance remains unchanged, the transaction price remains unchanged.

[0057] The winning drone consortium maximizes its profit when its asking price is less than or equal to its actual cost. When the asking price exceeds the actual cost, the profit is zero. This demonstrates that the two-sided auction method incentivizes drone consortiums to submit their true asking prices, thus ensuring the authenticity of the consortium groupings.

[0058] Test Example 6 The high-density user region among the set of critical node pairs with the largest number of matches will be selected as the winning high-density user region, such as... Figure 6 The chart shows the revenue, true value, and transaction price of the winning high-density user area under different bids. It can be seen that as the bid for the winning high-density user area increases, both the true value and the transaction price remain unchanged. This is because the true value of the winning high-density user area is only related to the congestion level and transmission rate of the high-density user area, while the bid for the winning high-density user area does not affect the congestion level and transmission rate. Therefore, the true value of the winning high-density user area remains unchanged. The transaction price of the winning high-density user area is unrelated to the bid for the high-density user area but is related to its true value. When the true value of the high-density user area remains unchanged, the transaction price remains unchanged.

[0059] When the bid for the winning high-density user region is less than its true value, the profit is 0; when the bid is equal to or greater than its true value, the profit is maximized. This demonstrates that the two-sided auction method incentivizes high-density user regions to submit genuine bids, thus ensuring the authenticity of their bids.

[0060] Test Example 7 The bilateral auction method was compared with the existing McAfee method and candidate critical pair method based on the changes in the number of final transaction pairs under different numbers of high-density user areas. The results are as follows: Figure 7 As shown, when the number of high-density user regions is 3, 4, and 5, the number of final transaction pairs under the proposed bilateral auction method is higher than that under the candidate critical pair method; when the number of high-density user regions is 6 and 7, the number of final transaction pairs under the proposed bilateral auction method is higher than that under the McAfee method. Therefore, it can be determined that the final critical node pairs determined by the proposed bilateral auction method are more effective.

[0061] Test Example 8 The bilateral auction method was compared with the existing McAfee method and the candidate critical pair method based on the changes in the number of final transaction pairs under different numbers of drones. The results are as follows: Figure 8 As shown, when the number of drones is 8 and 9, the number of final transaction pairs under the proposed two-way auction method is higher than that under the candidate critical pair method; when the number of drones is 10, 11, and 12, the number of final transaction pairs under the proposed two-way auction method is equal to or higher than that under the other two methods. Therefore, it can be determined that the final critical node pairs determined by the proposed two-way auction method are more effective.

[0062] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0063] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0064] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0065] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0067] The above are merely specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the scope of the claims.

Claims

1. A method for unmanned aerial vehicle (UAV)-assisted cellular network communication, characterized in that, The method includes the following steps: Randomly initialize the drone alliance assignments and obtain the initial alliance group set; Calculate the individual revenue of each drone in the current drone alliance group; Each drone targets a drone alliance group other than its current drone alliance group, and calculates the expected revenue of the drone in the target drone alliance group. When the expected revenue meets the preset group switching conditions, the drone is assigned to the target drone alliance group; When no drone meets the preset group switching conditions, a stable drone alliance group set is obtained.

2. The UAV-assisted cellular network communication method as described in claim 1, characterized in that: The preset group switching conditions include: the expected revenue of the drone in the target drone alliance group is greater than its individual revenue in the current drone alliance group, and when the drone switches to the target drone alliance group, the sum of the revenues of the other drones in the current drone alliance group does not decrease, and when the drone switches to the target drone alliance group, the sum of the revenues of the other drones in the target drone alliance group does not decrease.

3. The UAV-assisted cellular network communication method as described in claim 1, characterized in that: Before calculating the individual revenue of each drone in the current drone alliance group, the final matching result between the drone alliance group and the high-density user area is obtained based on the current drone alliance group, the number of high-density user areas, the throughput demand of the high-density user areas, the bid of the high-density user areas, and the ask price of the drone alliance group. The final matching results will be used to calculate the fees paid to the drone alliance group, the fees charged to high-density user areas, and the revenue per drone.

4. The UAV-assisted cellular network communication method as described in claim 3, characterized in that: The calculation method for individual income is as follows: ； In the formula, To join the The first high-density user area The revenue from a single drone, The fees paid by ground base stations to the current drone alliance group, For the current drone alliance group to access the first Number of drones in high-density user areas Indicates the first Energy consumption preference coefficient for each drone The current drone alliance is grouped as number 1. The total cost of providing relay communication services to a high-density user area is calculated as follows: ； In the formula, Indicates the first The drone was connected to the first Communication energy consumption in high-density user areas Energy consumption for drone hovering. The energy consumption cost for the drone's flight distance. For the current drone alliance, drones and the first Mean horizontal distance between high-density user areas.

5. The UAV-assisted cellular network communication method as described in claim 3, characterized in that: The methods for obtaining the final matching results between drone alliance groups and high-density user areas include: When combining drone alliance groups with high-density user areas, and determining that the transmission rate provided by the drone alliance group to the high-density user area meets its communication requirements parameters, an adaptation association is established between the drone alliance group and the high-density user area, and an adaptation weight is assigned. After traversing all combinations of drone alliance groups and high-density user regions, a weighted undirected bipartite graph is constructed based on the adaptation association and adaptation weight reconstruction of drone alliance groups and high-density user regions. The Hungarian algorithm is used to solve the weighted undirected bipartite graph by maximum matching, and the initial drone alliance group and high-density user region matching results corresponding to maximizing network throughput are obtained. Based on the initial drone alliance grouping and high-density user area matching results, the high-density user area is sorted by bid and the drone alliance grouping is sorted by ask price. The final matching result and transaction price between drone alliance groups and high-density user areas are determined based on the critical node screening mechanism.

6. The UAV-assisted cellular network communication method as described in claim 5, characterized in that: The formula for calculating the transmission rate provided by the drone alliance group for high-density user areas is: ； ； In the formula, For the first Select the first high-density user area Transmission rate after drone alliance grouping This refers to the group number of the drone alliance. Let be the transmission rate between the ground base station and the i-th UAV. Access to the drone alliance group Number of drones in high-density user areas For the first The drone and the first The transmission rate between high-density user areas is calculated as follows: ； ； ； In the formula, For the first The drone and the first Transmission bandwidth for high-density user areas For the first The drone and the first Signal-to-interference-plus-noise ratio in a high-density user area To join the Number of drones in high-density user areas For total bandwidth resources, This represents the total number of high-density user areas. For the first Transmit power in high-density user areas For the first The drone and the first Small-scale fading coefficients between high-density user areas For noise power spectral density, For the first The transmission power between the drone and the ground base station The self-interference coefficient is... For the first The drone and the first Average channel gain of a high-density user area.

7. The UAV-assisted cellular network communication method as described in claim 5, characterized in that: The method for determining the final matching result and transaction price between drone alliance groups and high-density user areas based on the critical node screening mechanism includes: STEP 1: Determine the effective transaction benchmark k: In the descending order of the high-density user area bid sequence and the ascending order of the drone alliance group ask sequence, search for the largest index k such that the bid of the kth high-density user area after sorting is greater than or equal to the ask of the kth drone alliance group after sorting. STEP2, Expand the critical high-density user area sequence number a: In the descending order of the high-density user area bid sequence, search for the largest sequence number a, such that the bid of the a-th high-density user area after sorting is greater than or equal to the bid of the k-th drone alliance group after sorting. Extended critical drone alliance group sequence number b: In the ascending order of drone alliance group bids, search for the largest sequence number b such that the bid of the kth high-density user region after sorting is greater than or equal to the bid of the bth drone alliance group after sorting. STEP 3: Count the number of matches between the two sets of critical node pairs. A critical node pair consists of: the bid of the a-th high-density user region after sorting, and the ask price of the k-th drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions with bids ≥ the bid of the group and ask prices ≤ the ask prices of the group and the number of matching pairs with drone alliance groups. Another set of critical node pairs consists of: the bid of the kth high-density user region after sorting and the ask price of the bth drone alliance group after sorting. The number of matching pairs is: the number of high-density user regions and drone alliance groups whose bid is greater than or equal to the bid of the group and whose ask price is less than or equal to the ask price of the group. STEP 4: Determine the transaction price: Select a critical node pair with a larger number of matching nodes, use the bid in the node pair as the unified transaction payment amount for the high-density user area, and use the ask price in the node pair as the unified transaction collection amount for the drone alliance group, thus completing the transaction price determination.

8. The UAV-assisted cellular network communication method as described in claim 1, characterized in that: After determining the final matching results and transaction price between the drone alliance group and the high-density user area, the matched drone alliance group will provide relay communication services to the corresponding high-density user area, and the two parties will settle the fees according to the transaction price.

9. A drone-assisted cellular network communication device, characterized in that, The drone-assisted cellular network communication device includes a processor, a memory, and a drone-assisted cellular network communication program stored in the memory and executable by the processor, wherein when the drone-assisted cellular network communication program is executed by the processor, it implements the steps of the drone-assisted cellular network communication method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a drone-assisted cellular network communication program, wherein when the drone-assisted cellular network communication program is executed, it implements the steps of the drone-assisted cellular network communication method as described in any one of claims 1 to 8.

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