Unmanned aerial vehicle communication resource allocation method and device based on interference link

Abstract

The application relates to a method, device and equipment for allocating communication resources of unmanned aerial vehicles (UAVs) based on interference links. The method comprises the following steps: constructing an UAV communication resource management model according to preset resource allocation conditions. The resource allocation conditions comprise interruption conditions and target signal identification conditions. The communication mode of UAV users is determined according to the interruption conditions and the target signal identification conditions, and the optimal transmission power of the transmission end of each pair of UAV users is obtained according to the communication mode. The optimal transmission power is input into the UAV communication resource management model to perform cost operation, so as to obtain an optimal power allocation matrix. According to the optimal power allocation matrix, the communication resources of an UAV cluster composed of UAV users in a multiplexing interference link are allocated. The method can ensure the continuity of UAV link data transmission and the quality of communication services in a disturbed communication link environment.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method, apparatus, and device for allocating UAV communication resources based on interference links. Background Technology

[0002] Unmanned aerial vehicle (UAV) communication is fundamental for UAV swarms to collaboratively complete tasks, but several challenging and unresolved key research issues remain, among which spectrum resource shortage, physical layer transmission security, and power supply are particularly prominent. Under conditions of communication link disruption, spectrum resources for UAV communication networks are extremely valuable. Besides spectrum resource shortages, UAV communication is also susceptible to interference and eavesdropping by malicious attackers, which severely impacts the communication security and reliability of UAVs.

[0003] Currently, dynamic spectrum sharing technology combined with encryption algorithms can solve the problems of spectrum resource shortage and physical layer transmission security. However, dynamic spectrum sharing may lead to spectrum conflicts and interference, requiring precise spectrum management and coordination mechanisms. Furthermore, encryption and authentication technologies consume additional computing resources and increase communication latency. In particular, under conditions of communication link disruption, the problems of reduced transmission rate and poor reliability of UAV communication links are more prominent. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, and device for allocating UAV communication resources based on interference links, which can ensure the continuity of UAV link data transmission and the quality of communication services in environments where communication links are disrupted.

[0005] A method for allocating UAV communication resources based on interference links, the method comprising:

[0006] A UAV communication resource management model is constructed based on preset resource allocation conditions. The resource allocation conditions include: interruption conditions and target signal recognition conditions.

[0007] The communication mode of the drone users is determined based on the interruption conditions and target signal identification conditions, and the optimal transmission power of the transmitter of each pair of drone users is obtained based on the communication mode.

[0008] The optimal transmit power is input into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link.

[0009] In one embodiment, it further includes:

[0010]

[0011] st

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] Where, x * For the optimal channel allocation matrix, p * For the optimal power allocation result, x = {x} (1) ,x (2)} represents the channel allocation matrix for communication among drone swarms composed of drone users. For drone user i operating on idle spectrum k, the idle transmission results are as follows: Let p be the multiplexing transmission result of drone user i in the jammed link, where p is the transmit power of drone user i. i For the transmit power of drone user i, For the maximum transmit power of the drone user, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j Let ξ be the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from drone user j, and let ξ be the SIR of the interference signal from drone user i to drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, Pr{·} is the interruption probability, p0 is the threshold of the interruption condition, and R... i,k R represents the transmission rate of drone user i operating on idle spectrum k. i,j To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

[0019] In one embodiment, the preset interruption condition is:

[0020]

[0021] Wherein, Pr{ξ i,j ≤ξ min} represents the preset interruption condition, ξ minFor the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, p0 is the threshold for the interruption condition, and i is the current user of the drone. For the logarithm of drone users.

[0022] In one embodiment, the communication modes include: idle transmission mode and multiplexing transmission mode.

[0023] The preset target recognition conditions are:

[0024]

[0025] Where, ξ j p represents the signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. i p is the transmit power of drone user i. j To interfere with the transmission power of drone user j, σ 2 For the noise power spectral density, g j To interfere with the channel gain from the drone user's transmitter to the drone user's receiver, To interfere with the channel gain of the line-of-sight channel between the drone user's transmitter and receiver, To compensate for the channel gain of the non-line-of-sight channel between the drone user's transmitter and receiver, K is the Rice factor. The maximum distance between the drone user and the target drone is determined based on the target signal identification conditions.

[0026]

[0027] Where, d max The maximum distance between drone user i and the target drone. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, p j To interfere with the transmission power of drone user j, σ 2 Let β0 be the noise power spectral density, d0 be the channel gain power at a reference distance d0 = 1 meter, and h be the channel gain power at a reference distance d0 = 1 meter. i,j The scattering component during signal propagation between the drone user and the interfering drone user, ||·|| 2 Let K be the Euclidean 2-norm and K be the Rice factor. Determine the minimum distance between the drone user and the interfering drone user based on the interruption conditions:

[0028]

[0029] Where, d min ξ is the minimum distance between drone user i and interfering drone user j. min For drone users, the minimum signal-to-interference-plus-noise ratio, p i p is the transmit power of drone user i. j To interfere with the transmit power of drone user j, p0 is the threshold for the interruption condition, σ 2 Let K be the noise power spectral density, K be the Rice factor, and d be the noise power spectral density. i Let d be the distance from the transmitter to the receiver of drone user i. If the distance d between drone user i and interfering drone user i is... i,j Greater than d max And less than d min If the communication mode of the drone users is determined to be multiplexed transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using a closed-form expression that maximizes the traversal capacity:

[0030]

[0031] in, For the optimal transmit power of the transmitter for each pair of drone users, p i p is the transmit power of drone user i. j To interfere with the transmission power of drone user j, d i,j σ represents the distance between the drone user and the interfering drone user, β0 represents the channel gain power at the reference distance d0 = 1 meter, and σ represents the distance between the drone user and the interfering drone user. 2 For the noise power spectral density, d i Let be the distance between the transmitter and receiver of drone user i. Conversely, if the drone user's communication mode is idle transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using the maximum transmission rate of each pair of drone users:

[0032]

[0033] in, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p i For the transmit power of drone user i, g i Let σ be the channel gain from the transmitter to the receiver of drone user i. 2 This represents the noise power spectral density.

[0034] In one embodiment, the method further includes: the UAV communication resource management model uses a power control algorithm to optimize the Hungarian algorithm to obtain the transmission rate of the UAV user.

[0035]

[0036] in, To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p j To interfere with the transmit power of UAV user j, β0 is the channel gain power at a reference distance d0 = 1 meter, σ 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum.

[0037] In one embodiment, the method further includes: inputting the optimal transmit power into the UAV communication resource management model to obtain the transmission rate r of the UAV user, expanding the matrix range of the UAV user's transmission rate r to τ×τ, and obtaining the maximum transmission rate r of the UAV user. max Where τ = max(M,N). The maximum transmission rate r max Substituting the values ​​into the UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained:

[0038]

[0039] st

[0040]

[0041]

[0042]

[0043] Where, x * Assign the optimal channel matrix. For drone user i operating on idle spectrum k, the idle transmission results are as follows: For drone user i to reuse the multiplexed transmission result of interfering drone user j under the interfered link, g i Let p be the channel gain from the transmitter of UAV user i to the receiver of UAV user i. j To interfere with the transmission power of drone user j, σ represents the maximum transmit power of the drone user, β0 is the channel gain power at a reference distance d0 = 1 meter, and σ is the maximum transmit power of the drone user. 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, This represents the logarithm of drone users. Communication resources are allocated to the drone swarm consisting of drone users in the multiplexed interference link based on the optimal power allocation matrix.

[0044] A UAV communication resource allocation device based on interference links, the device comprising:

[0045] The communication resource model construction module is used to build a UAV communication resource management model based on preset resource allocation conditions. These conditions include interruption conditions and target signal identification conditions.

[0046] The optimal transmit power acquisition module is used to determine the communication mode of the UAV users based on the interruption conditions and target signal identification conditions, and to acquire the optimal transmit power of the transmitter of each pair of UAV users based on the communication mode.

[0047] The resource allocation module is used to input the optimal transmit power into the UAV communication resource management model for cost calculation, obtain the optimal power allocation matrix, and allocate communication resources to the UAV cluster composed of UAV users in the multiplexed interference link according to the optimal power allocation matrix.

[0048] In one embodiment, the communication resource model construction module is further used for

[0049] st

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] Where, x * For the optimal channel allocation matrix, p * For the optimal power allocation result, x = {x} (1) ,x (2)} represents the channel allocation matrix for communication among drone swarms composed of drone users. For drone user i operating on idle spectrum k, the idle transmission results are as follows: Let p be the multiplexing transmission result of drone user i in the jammed link, where p is the transmit power of drone user i. iFor the transmit power of drone user i, For the maximum transmit power of the drone user, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, Pr{·} is the interruption probability, p0 is the threshold of the interruption condition, and R... i,k R represents the transmission rate of drone user i operating on idle spectrum k. i,j To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

[0057] In one embodiment, the resource allocation module is further configured to input the optimal transmit power into the UAV communication resource management model to obtain the transmission rate r of the UAV user, and expand the matrix range of the UAV user's transmission rate r to τ×τ to obtain the maximum transmission rate r of the UAV user. max Where τ = max(M,N). The maximum transmission rate r max Substituting the values ​​into the UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained:

[0058]

[0059] st

[0060]

[0061]

[0062]

[0063] Where, x * Assign the optimal channel matrix. For drone user i operating on idle spectrum k, the idle transmission results are as follows: For drone user i to reuse the multiplexed transmission result of interfering drone user j under the interfered link, g i Let p be the channel gain from the transmitter of UAV user i to the receiver of UAV user i. j To interfere with the transmission power of drone user j, σ represents the maximum transmit power of the drone user, β0 is the channel gain power at a reference distance d0 = 1 meter, and σ is the maximum transmit power of the drone user. 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, This represents the logarithm of drone users. Communication resources are allocated to the drone swarm consisting of drone users in the multiplexed interference link based on the optimal power allocation matrix.

[0064] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0065] A UAV communication resource management model is constructed based on preset resource allocation conditions. The resource allocation conditions include: interruption conditions and target signal recognition conditions.

[0066] The communication mode of the drone users is determined based on the interruption conditions and target signal identification conditions, and the optimal transmission power of the transmitter of each pair of drone users is obtained based on the communication mode.

[0067] The optimal transmit power is input into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link.

[0068] The aforementioned UAV communication resource allocation method, apparatus, and equipment based on interference links construct a UAV communication resource management model according to preset resource allocation conditions, including interruption conditions and target signal identification conditions. Control begins from the signal access of the UAV user, thereby determining the UAV user's communication mode. This allows the UAV to select a mode that matches the current communication mode and obtain the corresponding optimal transmission power. This effectively avoids network resource waste, reduces information transmission time on the communication link, improves signal transmission rate, and allows for flexible scheduling of any resource in the entire network system to achieve optimal data transmission quality. The optimal transmission power is input into the UAV communication resource management model for cost calculation. A comprehensive evaluation can be performed based on indicators such as communication resource utilization efficiency, power consumption, and interference, ensuring the continuity of UAV link data transmission and network service quality. Attached Figure Description

[0069] Figure 1This is an application scenario diagram of a UAV communication resource allocation method based on interference links in one embodiment;

[0070] Figure 2 This is a flowchart illustrating a UAV communication resource allocation method based on interference links in one embodiment.

[0071] Figure 3 This is a schematic diagram comparing the performance of a UAV communication resource allocation method based on interference links and a random allocation algorithm in one embodiment.

[0072] Figure 4 This is a structural block diagram of a UAV communication resource allocation device based on an interference link in one embodiment;

[0073] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0075] The UAV communication resource allocation method based on interference links provided in this application can be applied to, for example... Figure 1 The diagram illustrates the application environment of a UAV communication network model with an interference link. The area where a UAV swarm's communication is disrupted is designated as a denial zone. Multiple ground-based jamming UAV user fixed stations are located within this zone, and the jamming signals emitted by each station occupy the same interference airspace. Based on the communication distance from the jamming fixed stations, the interference airspace is further divided into a communication / GPS blocking zone and a communication / GPS jammed zone.

[0076] In one embodiment, such as Figure 2 As shown, a method for allocating UAV communication resources based on interference links is provided, which can be applied to... Figure 1 Taking the UAV communication network model with interference links as an example, the following steps are included:

[0077] Step 202: Construct a UAV communication resource management model based on preset resource allocation conditions.

[0078] Within the denial zone, drone nodes can communicate not only using idle spectrum resources but also reusing spectrum resources occupied by interfering drone users. Furthermore, the communication network has F idle spectrum resources and M spectrum resources already occupied by interfering drone users, with corresponding sets as follows: and Within the network coverage area, there are N pairs of drone users. Each pair of drone users includes the drone's transmitter and receiver, and their set is as follows: Furthermore, in this network, drones can operate on either idle spectrum or reused spectrum resources. Moreover, drone users exist in D2D pairs; under the cluster distribution model, drone user receivers are uniformly distributed within a circle centered on the drone user transmitter with radius r. Due to spectrum constraints in the denial environment...

[0079] Specifically, the Ricean channel model is used to represent the communication links between multiple pairs of UAVs and the communication links between each pair of UAVs and the user fixed station of the interfering UAV:

[0080]

[0081] Among them, g i,j The channel gain between UAV node i and interfering UAV user node j. Let be the channel gain of the signal between UAV node i and interfering UAV user node j in a line-of-sight (LOS) channel model. Let K be the channel gain of the signal between UAV node i and interfering UAV user node j in a line-of-sight (NLOS) channel model, and K be the Rice factor. Furthermore,

[0082]

[0083] Where, d i,j To account for the distance between the fixed UAV user station and the UAV user receiver, α (usually taken as 2) is the path loss exponent, and β0 is the channel power gain (-30dB) at the reference distance d0 = 1 meter. 2 Let q represent the Euclidean 2 norm. i q represents the horizontal coordinate of the drone user receiver. j H represents the horizontal coordinates of the user's fixed station that interferes with the drone. i This represents the drone's flight altitude. Assume the altitude of the user's fixed station interfering with the drone is 0. Furthermore,

[0084]

[0085] Among them, h i,j This represents the scattered components of the signal during propagation, and follows a complex Gaussian distribution with mean 0 and variance 1.

[0086] Furthermore, the channel gain g of the line-of-sight (LOS) channel from the transmitter to the receiver of the UAV user i. i :

[0087]

[0088] Where, d i Let α be the distance between the drone user's transmitter and receiver, α (usually taken as 2) be the path loss exponent, and β0 be the channel power gain (-30dB) at a reference distance d0 = 1 meter. ||·|| represents the Euclidean norm. This indicates the horizontal coordinates of the drone user's transmitter. This represents the horizontal coordinates of the drone user's receiver. This indicates the horizontal coordinates of the drone user's transmitter. This represents the horizontal coordinates of the drone user's receiver.

[0089] In addition, the preset interruption condition is:

[0090]

[0091] Wherein, Pr{ξ i,j ≤ξ min} represents the preset interruption condition, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, p0 is the threshold for the interruption condition, and i is the current user of the drone. For the logarithm of drone users.

[0092] Furthermore, a UAV communication resource management model is constructed based on preset resource allocation conditions:

[0093]

[0094] st

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] Resource allocation conditions include: interruption conditions Target signal identification and maximum power limit conditions x * For the optimal channel allocation matrix, p * For the optimal power allocation result, x = {x} (1) ,x (2)} represents the channel allocation matrix for communication among drone swarms composed of drone users. For drone user i operating on idle spectrum k, the idle transmission results are as follows: Let p be the multiplexing transmission result of UAV user i in the interference link, where p is the transmit power of the UAV user. i For the transmit power of drone user i, For the maximum transmit power of the drone user, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, Pr{·} is the interruption probability, p0 is the threshold of the interruption condition, and R... i,k R represents the transmission rate of drone user i operating on idle spectrum k. i,j To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

[0102] Step 204: Determine the communication mode of the UAV users based on the interruption conditions and target signal identification conditions, and obtain the optimal transmission power of the transmitter of each pair of UAV users based on the communication mode.

[0103] Based on the number of drone users and their environment, the communication modes of drone users are divided into three transmission modes: dedicated mode drone node signal transmission, multiplexing mode drone node signal transmission, and interference drone user-to-drone signal transmission. Accordingly, three transmission modules are generated in the drone communication resource management model:

[0104] The signal transmission module of the dedicated mode drone node ensures that when drone user i is operating in the idle spectrum k, its communication link is not interfered with by any other users, and its signal-to-noise ratio (SNR) ξ is optimal. i,k for:

[0105]

[0106] Its corresponding transmission rate is:

[0107] R i,k =Blog2(1+ξ i,k )

[0108] Where B is the channel bandwidth, σ 2 For noise power spectral density, Let n be the transmit power of the drone user.

[0109] The signal transmission model of a drone node in multiplexing mode is as follows: When a drone user reuses the resources of an interfering user, the drone communication link will be interfered with by the interfering signal. After interference, the signal-to-interference-plus-noise ratio (SIR) of the drone communication link is:

[0110]

[0111] Where, p i For the transmit power of drone users, p j This refers to the transmission power of the fixed station.

[0112] Its corresponding transmission rate R i,j for:

[0113] R i,j =Blog2(1+ξ i,j )

[0114] Since the spectral bandwidth is given, the bandwidth is normalized.

[0115] The signal transmission model from user i to user j interfering with the drone is as follows: Under interference-free conditions, the signal-to-noise ratio (SNR) of user i receiving signal from user j interfering with the drone is:

[0116]

[0117] The signal-to-noise ratio of the detected interference signal meets the target signal identification condition:

[0118]

[0119] Target drone pairs can be identified. This represents the minimum signal-to-noise ratio required to identify the target signal.

[0120] In addition, the preset interruption condition is:

[0121]

[0122] Wherein, Pr{ξ i,j ≤ξ min} represents the preset interruption condition, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξi,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, p0 is the threshold for the interruption condition, and i is the current user of the drone. For the logarithm of drone users.

[0123] Furthermore, the maximum distance between each pair of drone users and the target drone is determined based on the target signal identification conditions:

[0124]

[0125] Where, d max The maximum distance between drone user i and the target drone. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, p j To interfere with the transmission power of drone user j, σ 2 Let β0 be the noise power spectral density, d0 be the channel gain power at a reference distance d0 = 1 meter, and h be the channel gain power at a reference distance d0 = 1 meter. i,j The scattering component during signal propagation between the drone user and the interfering drone user, ||·|| 2 Let K be the Euclidean 2-norm and K be the Rice factor.

[0126] Furthermore, the minimum distance between each pair of drone users and the interfering drone user is determined based on the interruption conditions:

[0127]

[0128] Where, d min ξ is the minimum distance between drone user i and interfering drone user j. min For drone users, the minimum signal-to-interference-plus-noise ratio, p i p is the transmit power of drone user i. j To interfere with the transmit power of drone user j, p0 is the threshold for the interruption condition, σ 2 Let K be the noise power spectral density, K be the Rice factor, and d be the noise power spectral density. i Let d be the distance from the transmitter to the receiver of drone user i. If the distance d between drone user i and interfering drone user i is... i,j Greater than d max And less than d min If the communication mode of the drone users is determined to be multiplexed transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using a closed-form expression that maximizes the traversal capacity:

[0129]

[0130] in, For the optimal transmit power of the transmitter for each pair of drone users, p i p is the transmit power of drone user i. j To interfere with the transmission power of drone user j, d i,j σ represents the distance between the drone user and the interfering drone user, β0 represents the channel gain power at the reference distance d0 = 1 meter, and σ represents the distance between the drone user and the interfering drone user. 2 For the noise power spectral density, d i Let be the distance between the transmitter and receiver of drone user i. Conversely, if the drone user's communication mode is idle transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using the maximum transmission rate of each pair of drone users:

[0131]

[0132] in, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p i For the transmit power of drone user i, g i Let σ be the channel gain from the transmitter to the receiver of drone user i. 2 This represents the noise power spectral density.

[0133] Step 206: The optimal transmit power is input into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link.

[0134] Specifically, the UAV communication resource management model uses a power control algorithm to optimize the Hungarian algorithm to obtain the transmission rate for UAV users:

[0135]

[0136] in, To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p j To interfere with the transmit power of UAV user j, β0 is the channel gain power at a reference distance d0 = 1 meter, σ 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum.

[0137] Furthermore, the optimal transmit power is input into the UAV communication resource management model to obtain the transmission rate r of the UAV user. The matrix range of the transmission rate r of the UAV user is then expanded to τ×τ to obtain the maximum transmission rate r of the UAV user. max Where τ = max(M,N). The maximum transmission rate r max Substituting the values ​​into the UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained:

[0138]

[0139] st

[0140]

[0141]

[0142]

[0143] Where, x * Assign the optimal channel matrix. For drone user i operating on idle spectrum k, the idle transmission results are as follows: For drone user i to reuse the multiplexed transmission result of interfering drone user j under the interfered link, g i Let p be the channel gain from the transmitter of UAV user i to the receiver of UAV user i. j To interfere with the transmission power of drone user j, σ represents the maximum transmit power of the drone user, β0 is the channel gain power at a reference distance d0 = 1 meter, and σ is the maximum transmit power of the drone user. 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, This represents the logarithm of drone users. Communication resources are allocated to the drone swarm consisting of drone users in the multiplexed interference link based on the optimal power allocation matrix.

[0144] The aforementioned UAV communication resource allocation method based on interference links constructs a UAV communication resource management model according to preset resource allocation conditions, including interruption conditions and target signal identification conditions. Control begins from the signal access of the UAV user, determining the user's communication mode. This allows the user to select a matching mode and obtain the corresponding optimal transmission power, effectively avoiding network resource waste, reducing information transmission time on the communication link, improving signal transmission rate, and flexibly scheduling any resource in the entire network system to optimize data transmission quality. The optimal transmission power is input into the UAV communication resource management model for cost calculation, allowing for comprehensive evaluation based on communication resource utilization efficiency, power consumption, interference, and other indicators, ensuring the continuity of UAV link data transmission and network service quality.

[0145] In one embodiment,

[0146]

[0147] st

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] Where, x * For the optimal channel allocation matrix, p * For the optimal power allocation result, x = {x} (1) ,x (2)} represents the channel allocation matrix for communication among drone swarms composed of drone users. For drone user i operating on idle spectrum k, the idle transmission results are as follows: Let p be the multiplexing transmission result of drone user i in the jammed link, where p is the transmit power of drone user i. i For the transmit power of drone user i, For the maximum transmit power of the drone user, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,jξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, Pr{·} is the interruption probability, p0 is the threshold of the interruption condition, and R... i,k R represents the transmission rate of drone user i operating on idle spectrum k. i,j To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

[0155] The spectrum resources allocated to one drone user are restricted to be reused only by one pair of drone users. Resource allocation conditions. Each drone is limited to using only one spectrum resource. (Constraints and resource allocation conditions are also mentioned.) Maximum transmit power for drone users is limited. Interruption conditions. Utilizing interruption probability to ensure the reliability of UAV communication links. Target signal identification conditions. The signal-to-noise ratio of the identified target signal is limited to >10dB. Therefore, while ensuring the QoS requirements of the UAV communication link, the goal is to maximize the sum rate of all UAV users. Constraints are used to restrict access to only one pair of UAV users per idle spectrum resource, restrict the spectrum resource of a target user to be reused only by one pair of UAV users, limit each UAV to using one spectrum resource, and limit the maximum transmit power of each UAV user. The probability of interruption is used to ensure the reliability of the UAV communication link.

[0156] In one embodiment, the preset interruption condition is:

[0157]

[0158] Wherein, Pr{ξ i,j ≤ξ min} represents the preset interruption condition, ξ min For the minimum signal-to-interference-plus-noise ratio of drone users, ξ i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, p0 is the threshold of the interruption condition, and i is the current user of the drone. For the logarithm of drone users.

[0159] In one embodiment, the communication modes include: idle transmission mode and multiplexing transmission mode.

[0160] The preset target recognition conditions are:

[0161]

[0162] Where, ξ j p represents the signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. i p is the transmit power of drone user i. j To interfere with the transmission power of drone user j, σ 2 For the noise power spectral density, g j To interfere with the channel gain from the drone user's transmitter to the drone user's receiver, To interfere with the channel gain of the line-of-sight channel between the drone user's transmitter and receiver, To compensate for the channel gain of the non-line-of-sight channel between the drone user's transmitter and receiver, K is the Rice factor. The maximum distance between the drone user and the target drone is determined based on the target signal identification conditions.

[0163]

[0164] Where, d max The maximum distance between drone user i and the target drone. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, p j To interfere with the transmission power of drone user j, σ 2 Let β0 be the noise power spectral density, d0 be the channel gain power at a reference distance d0 = 1 meter, and h be the channel gain power at a reference distance d0 = 1 meter. i,j The scattering component during signal propagation between the drone user and the interfering drone user, ||·|| 2 Let K be the Euclidean 2-norm and K be the Rice factor. Determine the minimum distance between the drone user and the interfering drone user based on the interruption conditions:

[0165]

[0166] Where, d min ξ is the minimum distance between drone user i and interfering drone user j. min For drone users, the minimum signal-to-interference-plus-noise ratio, p i p is the transmit power of drone user i. j To interfere with the transmit power of drone user j, p0 is the threshold for the interruption condition, σ 2Let K be the noise power spectral density, K be the Rice factor, and d be the noise power spectral density. i Let d be the distance from the transmitter to the receiver of drone user i. If the distance d between drone user i and interfering drone user i is... i,j Greater than d max And less than d min If the communication mode of the drone users is determined to be multiplexed transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using a closed-form expression that maximizes the traversal capacity:

[0167]

[0168] in, For the optimal transmit power of the transmitter for each pair of drone users, p i p is the transmit power of drone user i. j To interfere with the transmission power of drone user j, d i,j σ represents the distance between the drone user and the interfering drone user, β0 represents the channel gain power at the reference distance d0 = 1 meter, and σ represents the distance between the drone user and the interfering drone user. 2 For the noise power spectral density, d i Let be the distance between the transmitter and receiver of drone user i. Conversely, if the drone user's communication mode is idle transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using the maximum transmission rate of each pair of drone users:

[0169]

[0170] in, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p i For the transmit power of drone user i, g i Let σ be the channel gain from the transmitter to the receiver of drone user i. 2 This represents the noise power spectral density.

[0171] It is worth noting that, based on meeting the access control conditions, the optimal transmit power of the UAV is obtained by using UAV communication power control algorithms under different communication modes, including the optimal power control method for UAVs operating on idle spectrum resources and the optimal power control method in multiplexing mode.

[0172] In one embodiment, the UAV communication resource management model uses a power control algorithm to optimize the Hungarian algorithm to obtain the transmission rate for UAV users:

[0173]

[0174] in, To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, To obtain the optimal transmit power based on the maximum transmission rate of the drone user, p j To interfere with the transmit power of UAV user j, β0 is the channel gain power at a reference distance d0 = 1 meter, σ 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum.

[0175] In one embodiment, the optimal transmit power is input to the UAV communication resource management model to obtain the transmission rate r of the UAV user. The matrix range of the UAV user's transmission rate r is then expanded to τ×τ to obtain the maximum transmission rate r of the UAV user. max Where τ = max(M,N). The maximum transmission rate r max Substituting the values ​​into the UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained:

[0176]

[0177] st

[0178]

[0179]

[0180]

[0181] Where, x * Assign the optimal channel matrix. For drone user i operating on idle spectrum k, the idle transmission results are as follows: For drone user i to reuse the multiplexed transmission result of interfering drone user j under the interfered link, g i Let p be the channel gain from the transmitter of UAV user i to the receiver of UAV user i. j To interfere with the transmission power of drone user j, σ represents the maximum transmit power of the drone user, β0 is the channel gain power at a reference distance d0 = 1 meter, and σ is the maximum transmit power of the drone user. 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, This represents the logarithm of drone users. Communication resources are allocated to the drone swarm consisting of drone users in the multiplexed interference link based on the optimal power allocation matrix.

[0182] In one embodiment, the performance of this method was compared with that of a random allocation algorithm through simulation experiments. The random allocation algorithm, which allows drones in a drone swarm to randomly select spectrum resources for communication, was used as the benchmark algorithm for comparison. Simulation environment settings: area 2km*2km, number of drones 15, number of interfering drone users 10. Figure 3 As shown, the sum rate obtained by different methods at drone altitudes of 1km, 2km, and 3km were compared. The comparison revealed that the performance of the proposed method is significantly better than that of the random allocation algorithm. Furthermore, the closer the drone cluster is to the interfering drone user, the greater the interference and the smaller the sum rate.

[0183] In one embodiment, a method for controlling UAV communication power under different communication modes is provided, including the following steps:

[0184] Step S1: When the drone user operates in different communication modes, the mathematical model description of the corresponding power control problem is also different. When drone user i operates on idle resource k, there is no co-channel interference problem. In this case, the drone's maximum transmission rate can be achieved at its maximum transmit power, and its expression is as follows:

[0185]

[0186] Step S2, when UAV user i reuses the resources of interfering user j, the optimization problem of maximizing the transmission rate of UAV users under the constraints of maximum transmission power and interruption probability of UAV users is expressed as follows:

[0187]

[0188] st

[0189]

[0190]

[0191]

[0192] Step S3: To make the power control optimization problem discussed in step S2 easier to handle, a closed-form expression for the traversal capacity of the UAV node is derived to simplify formula (1), thereby reducing computational complexity. Note that the traversal capacity in formula (1) is a convex function of X, and a closed-form expression for the traversal capacity can be derived using Jensen's inequality.

[0193] According to Jensen's inequality, the closed-form expression for the traversal capacity in formula (1) is the lower bound of the original formula, and its expression is as follows:

[0194]

[0195] Step S4: To reduce algorithm complexity, the optimal transmit power for the user is obtained by maximizing the closed-form expression of the traversal capacity, as shown below:

[0196]

[0197] Step S5, since the logarithmic function in the above formula is about p i Since it is a monotonically increasing function, its optimal solution can be obtained at its maximum value within its feasible region.

[0198] In one embodiment, a one-to-one matching resource allocation method based on an improved Hungarian algorithm is provided, comprising the following steps:

[0199] Step S1: Using a lower-bound-based power control algorithm, the optimal power allocation result in closed-form can be obtained for all possible combinations of different spectrum resources used by the drone user. Substituting these power allocation results into the original optimization problem, the original problem can be simplified to a spectrum resource allocation problem with a single 0-1 variable x, as follows:

[0200]

[0201] st

[0202]

[0203]

[0204]

[0205] The optimization problem described above can be viewed as a one-to-one assignment problem, which can be solved using the classic Hungarian algorithm. The Hungarian algorithm not only has low computational complexity but can also obtain the exact optimal solution to the assignment problem.

[0206] Step S2, the main idea of ​​the Hungarian algorithm is to select N elements from the cost matrix C (N×N) to maximize the sum of the coefficient weights, and only one element can be selected from each row and column of the cost matrix. Let the cost matrix C be composed of c i,j The matrix formed:

[0207]

[0208] According to the Hungarian algorithm, the number of rows and columns in the cost matrix must be equal. Therefore, we further assume that $M = N$, meaning that matrix C is an N×N cost matrix. The implementation of the Hungarian algorithm is mainly based on the following two fundamental theorems: 1. If subtracting the smallest element in any row (or column) of the assignment problem cost matrix C yields a new cost matrix C... * Then through C * The optimal solution to the assignment problem is the optimal solution to the original assignment problem.

[0209] The cost matrix C is transformed into a new cost matrix C after elementary transformations. * When C * When the maximum number of independent 0 elements in the matrix is ​​equal to the minimum number of lines covering all 0 elements in the matrix, the x-coordinates of the corresponding positions of the independent 0 elements are... i,j Set to 1, and the rest of the positions x i,j Setting it to zero yields the optimal solution X for the assignment problem. * In this context, an independent zero element refers to a zero element in the cost matrix that has only one zero element. In this case, the zero element in the row (column) containing only one zero element is circled, and all other zero elements in the same column (row) are crossed out.

[0210] The transmission rate matrix for UAVs using idle / non-idle spectrum can be obtained from the optimal power control algorithm as follows:

[0211]

[0212] in,

[0213]

[0214] To find the optimal solution to optimization problem (2), we actually select n = min(M,N) elements from r to maximize the sum of the proportional fairness functions of all users, and only one element can be selected from each row and column.

[0215] Step S4: However, the traditional Hungarian algorithm requires the number of rows and columns of the cost matrix to be equal. In practical UAV swarm communication systems, the number of UAV users and the number of spectrum resources are not necessarily equal. Therefore, the size of matrix r is expanded from N×M to τ×τ, where τ=max(M,N). Elements in idle positions are set to 0. Typically, the Hungarian algorithm can obtain the minimum value of the cost function; therefore, the Hungarian algorithm is improved to obtain the maximum value of the cost function.

[0216] It should be understood that, although Figure 2The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0217] In one embodiment, such as Figure 4 As shown, a UAV communication resource allocation device based on an interference link is provided, comprising: a communication resource model construction module 402, an optimal transmission power acquisition module 404, and a resource allocation module 406, wherein:

[0218] The communication resource model construction module 402 is used to construct a UAV communication resource management model based on preset resource allocation conditions. The resource allocation conditions include: interruption conditions and target signal identification conditions.

[0219] The optimal transmission power acquisition module 404 is used to determine the communication mode of the UAV user based on the interruption condition and the target signal identification condition, and to acquire the optimal transmission power of the transmitter of each pair of UAV users based on the communication mode.

[0220] The resource allocation module 406 is used to input the optimal transmission power into the UAV communication resource management model for cost calculation, obtain the optimal power allocation matrix, and allocate communication resources to the UAV cluster composed of UAV users in the multiplexed interference link according to the optimal power allocation matrix.

[0221] In one embodiment, the communication resource model construction module 402 is further used for

[0222]

[0223]

[0224]

[0225]

[0226]

[0227]

[0228] Where, x *For the optimal channel allocation matrix, p * For the optimal power allocation result, x = {x} (1) ,x (2)} represents the channel allocation matrix for communication among drone swarms composed of drone users. For drone user i operating on idle spectrum k, the idle transmission results are as follows: Let p be the multiplexing transmission result of UAV user i in the interference link, where p is the transmit power of the UAV user. i The transmit power of the drone user i For the maximum transmit power of the drone user, ξ min ξ represents the minimum signal-to-interference-plus-noise ratio for the drone user. i,j ξ is the signal-to-interference-plus-noise ratio (SIR) of the interference link after drone user i receives the interference signal from interfering drone user j. j The signal-to-interference-plus-noise ratio (SIR) of the interference signal received by drone user i from drone user j. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target UAV signal, Pr{·} is the interruption probability, p0 is the threshold of the interruption condition, and R... i,k R represents the transmission rate of drone user i operating on idle spectrum k. i,j To allow drone user i to reuse the transmission rate of interfering drone user j under an interfering link, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

[0229] In one embodiment, the resource allocation module 406 is further configured to input the optimal transmission power to the UAV communication resource management model to obtain the transmission rate r of the UAV user, expand the matrix range of the UAV user's transmission rate r to τ×τ, and obtain the maximum transmission rate r of the UAV user. max Where τ = max(M,N). The maximum transmission rate r max Substituting the values ​​into the UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained:

[0230]

[0231] st

[0232]

[0233]

[0234]

[0235] Where, x *Assign the optimal channel matrix. For drone user i operating on idle spectrum k, the idle transmission results are as follows: For drone user i to reuse the multiplexed transmission result of interfering drone user j under the interfered link, g i Let p be the channel gain from the transmitter of UAV user i to the receiver of UAV user i. j To interfere with the transmission power of drone user j, σ represents the maximum transmit power of the drone user, β0 is the channel gain power at a reference distance d0 = 1 meter, and σ is the maximum transmit power of the drone user. 2 For the noise power spectral density, d i,j d represents the distance between the drone user and the interfering drone user. i Let k be the distance between the transmitter and receiver of drone user i, and k be the available spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, This represents the logarithm of drone users. Communication resources are allocated to the drone swarm consisting of drone users in the multiplexed interference link based on the optimal power allocation matrix.

[0236] Specific limitations regarding the UAV communication resource allocation device based on interference links can be found in the limitations of the UAV communication resource allocation method based on interference links mentioned above, and will not be repeated here. Each module in the aforementioned UAV communication resource allocation device based on interference links can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0237] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figures 4-5As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for allocating UAV communication resources based on interference links. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0238] Those skilled in the art will understand that Figures 4-5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0239] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps:

[0240] A UAV communication resource management model is constructed based on preset resource allocation conditions. The resource allocation conditions include: interruption conditions and target signal recognition conditions.

[0241] The communication mode of the drone users is determined based on the interruption conditions and target signal identification conditions, and the optimal transmission power of the transmitter of each pair of drone users is obtained based on the communication mode.

[0242] The optimal transmit power is input into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link.

[0243] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0244] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0245] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for allocating UAV communication resources based on interference links, characterized in that, The method includes: A UAV communication resource management model is constructed based on preset resource allocation conditions; the resource allocation conditions include: interruption conditions and target signal identification conditions; The communication mode of the UAV user is determined based on the interruption condition and the target signal identification condition, and the optimal transmission power of the transmitter of each pair of UAV users is obtained based on the communication mode. The preset interruption condition is: in, The preset interruption condition. The minimum signal-to-interference-plus-noise ratio for the drone user. For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference link after the interference signal, The threshold value for the interruption condition. For current drone users, The logarithm of drone users; The preset target recognition conditions are: in, For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference signal. For the users of the interference drone The transmission power, For noise power spectral density, The channel gain from the transmitter of the interfering drone user to the receiver of the drone user. The channel gain of the line-of-sight channel from the transmitter of the interfering drone user to the receiver of the drone user. The channel gain of the non-line-of-sight channel from the transmitter of the interfering drone user to the receiver of the drone user. Rice factor; If the distance between the drone user and the interfering drone user Greater than and less than If the communication mode of the drone users is determined to be multiplexed transmission mode, then the optimal transmission power of the transmitter for each pair of drone users is obtained by using a closed-form expression that maximizes the traversal capacity: in, For each pair of drone users, the optimal transmission power of the transmitter is determined. For the drone user The transmission power, For the users of the interference drone The transmission power, The distance between the drone user and the interfering drone user. For reference distance Channel gain power at meters, For noise power spectral density, For the drone user The distance between the sender and receiver; Conversely, if the communication mode of the drone users is idle transmission mode, then the optimal transmission power of the transmitter of each pair of drone users is obtained by using the maximum transmission rate of each pair of drone users: in, The optimal transmit power is obtained based on the maximum transmission rate of the drone user. For the drone user The transmission power, For the drone user The transmitter to the drone user The channel gain at the receiver, The noise power spectral density; The optimal transmission power is input into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link.

2. The method according to claim 1, characterized in that, A UAV communication resource management model is constructed based on preset resource allocation conditions, including: st in, Assign the optimal channel matrix. For optimal power allocation results, The channel allocation matrix for communication of the drone swarm composed of the aforementioned drone users. For drone users Operating in idle spectrum The idle transmission result on For the drone user Multiplexing interference drone users under interfered links The multiplexed transmission result For drone users' transmit power, For the drone user The transmission power, The maximum transmit power for drone users, The minimum signal-to-interference-plus-noise ratio for the drone user. For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference link after the interference signal, For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference signal. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, For interruption probability, The threshold value for the interruption condition. For drone users Operating in idle spectrum The transmission rate on For the drone user Multiplexing of jamming drone users under jamming links transmission rate, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

3. The method according to claim 2, characterized in that, The communication modes include: idle transmission mode and multiplexing transmission mode; The communication mode of the UAV users is determined based on the interruption condition and the target signal identification condition. The optimal transmission power of the transmitter for each pair of UAV users is obtained based on the communication mode, including: Determine the maximum distance between the drone user and the target drone based on the target signal identification conditions: in, For the drone user Maximum distance to the target drone To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, For the users of the interference drone The transmission power, For noise power spectral density, For reference distance Channel gain power at meters, The scattered component during signal propagation between the drone user and the interfering drone user. For the Euclidean 2 norm, Rice factor; Determine the minimum distance between the drone user and the interfering drone user based on the interruption condition: in, For the drone user With the interference drone user The minimum distance, The minimum signal-to-interference-plus-noise ratio for the drone user. For the drone user The transmission power, For the users of the interference drone The transmission power, The threshold value for the interruption condition. For noise power spectral density, Rice factor, For the drone user The distance between the sender and the receiver.

4. The method according to claim 3, characterized in that, Before the step of inputting the optimal transmission power into the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix, the method further includes: The UAV communication resource management model uses a power control algorithm to optimize the Hungarian algorithm to obtain the transmission rate of the UAV user: in, For the drone user Multiplexing of jamming drone users under jamming links transmission rate, The optimal transmit power is obtained based on the maximum transmission rate of the drone user. For the users of the interference drone The transmission power, For reference distance Channel gain power at meters, For noise power spectral density, The distance between the drone user and the interfering drone user. For the drone user The distance between the sender and receiver. This is the idle spectrum.

5. The method according to claim 4, characterized in that, The optimal transmit power is input to the UAV communication resource management model for cost calculation to obtain the optimal power allocation matrix. Based on the optimal power allocation matrix, communication resources are allocated to the UAV cluster composed of UAV users in the multiplexed interference link, including: The optimal transmission power is input to the UAV communication resource management model to obtain the transmission rate of the UAV user. The transmission rate of the drone user The matrix range is extended to The maximum transmission rate of the drone user is obtained. ;in, ; The maximum transmission rate Substituting the values ​​into the aforementioned UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained: st in, Assign the optimal channel matrix. For drone users Operating in idle spectrum The idle transmission result on For the drone user Multiplexing interference drone users under interfered links The multiplexed transmission result For the drone user The transmitter to the drone user The channel gain at the receiver, For the users of the interference drone The transmission power, The maximum transmit power for drone users, For reference distance Channel gain power at meters, For noise power spectral density, The distance between the drone user and the interfering drone user. For the drone user The distance between the sender and receiver. This is the idle spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, The logarithm of drone users; The communication resources of the UAV cluster composed of UAV users in the multiplexed interference link are allocated according to the optimal power allocation matrix.

6. A UAV communication resource allocation device based on an interference link, characterized in that, The apparatus for implementing the method according to any one of claims 1 to 5, the apparatus comprising: A communication resource model construction module is used to construct a UAV communication resource management model based on preset resource allocation conditions; the resource allocation conditions include: interruption conditions and target signal identification conditions; The optimal transmission power acquisition module is used to determine the communication mode of the UAV user based on the interruption condition and the target signal identification condition, and to acquire the optimal transmission power of the transmitter of each pair of UAV users based on the communication mode. The resource allocation module is used to input the optimal transmission power into the UAV communication resource management model for cost calculation, obtain the optimal power allocation matrix, and allocate communication resources to the UAV cluster composed of UAV users in the multiplexed interference link according to the optimal power allocation matrix.

7. The apparatus according to claim 6, characterized in that, The communication resource model construction module is also used for st in, Assign the optimal channel matrix. For optimal power allocation results, The channel allocation matrix for communication of the drone swarm composed of the aforementioned drone users. For drone users Operating in idle spectrum The idle transmission result on For the drone user Multiplexing interference drone users under interfered links The multiplexed transmission result For drone users' transmit power, For the drone user The transmission power, The maximum transmit power for drone users, The minimum signal-to-interference-plus-noise ratio for the drone user. For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference link after the interference signal, For the drone user The user receiving the interference drone The signal-to-interference-plus-noise ratio of the interference signal. To identify the minimum signal-to-interference-plus-noise ratio (SIR) of the target drone signal, For interruption probability, The threshold value for the interruption condition. For drone users Operating in idle spectrum The transmission rate on For drone users Multiplexing of jamming drone users under jamming links transmission rate, For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, For the logarithm of drone users.

8. The apparatus according to claim 6, characterized in that, The resource allocation module is also used to input the optimal transmission power into the UAV communication resource management model to obtain the transmission rate of the UAV user. The transmission rate of the drone user The matrix range is extended to The maximum transmission rate of the drone user is obtained. ;in, ; The maximum transmission rate Substituting the values ​​into the aforementioned UAV communication resource management model and performing iterative calculations, the optimal power allocation matrix is ​​obtained: st in, Assign the optimal channel matrix. For drone users Operating in idle spectrum The idle transmission result on For the drone user Multiplexing interference drone users under interfered links The multiplexed transmission result For the drone user The transmitter to the drone user The channel gain at the receiver, For the users of the interference drone The transmission power, The maximum transmit power for drone users, For reference distance Channel gain power at meters, For noise power spectral density, The distance between the drone user and the interfering drone user. For the drone user The distance between the sender and receiver. This is the idle spectrum. For idle spectrum resources, For the spectrum resources occupied by the interfered drone users, The logarithm of drone users; The communication resources of the UAV cluster composed of UAV users in the multiplexed interference link are allocated according to the optimal power allocation matrix.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

Images