Completely distributed unmanned aerial vehicle cluster communication method and system, medium and product

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a completely distributed unmanned aerial vehicle cluster communication method and system, a medium and a product, and the method comprises the steps: obtaining an initial condition, and calculating the transmission information between a bth unmanned aerial vehicle and a # imgabs0 # unmanned aerial vehicle through an undirected graph; the method comprises the following steps: defining an undirected graph, nodes of which represent unmanned aerial vehicles; defining initial parameters according to the undirected graph; generating a solving model according to the initial parameters and the constraint conditions; solving information is obtained through calculation according to the solving model; obtaining a new bth unmanned aerial vehicle from the unmanned aerial vehicle cluster, defining the transmission information of the previous bth unmanned aerial vehicle as an initial condition, and calculating new transmission information; and the unmanned aerial vehicles in the unmanned aerial vehicle cluster are traversed. According to the fully distributed communication method, the purpose of decentration can be achieved, and the communication quality is ensured on the basis of reducing the post-transmission signaling overhead.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) communication, and particularly to a fully distributed UAV cluster communication method, system, medium, and product. Background Art

[0002] When UAVs perform complex tasks, such as wide-area reconnaissance, disaster monitoring, agricultural monitoring, low-altitude logistics, signal coverage, etc., large-scale UAV clusters need to work collaboratively. Therefore, higher requirements are currently placed on the communication capabilities of UAV clusters.

[0003] However, current UAV clusters still face some challenges when providing communication services to users. For example, the communication links of UAV clusters based on high frequency bands (such as millimeter waves, terahertz, etc.) are easily blocked by obstacles.

[0004] Currently, traditional solutions usually involve deploying a larger number of UAVs to enhance the signal coverage range and using a larger-scale array antenna (i.e., MIMO) to compensate for severe propagation losses. However, as the number of UAV clusters increases and the dimension of the MIMO channel increases, traditional centralized UAV cluster communication systems need to send all information to the ground station for processing, which will cause a sharp increase in both backhaul signaling overhead and computational complexity, especially in large-scale UAV cluster communication scenarios.

[0005] In addition, patent application CN119341678A discloses an interference power allocation method based on multi-agent deep reinforcement learning, which includes: establishing a cooperative interference model of multiple interference devices against multiple UAV targets in a "multi-to-multi" communication confrontation scenario; constructing a distributed partially observable Markov decision process and modeling the interference power allocation problem as a multi-agent fully cooperative model; designing a "decentralized training, distributed decision-making" multi-agent framework to reduce the collaborative decision-making dimension of interference power resources; designing an extractor to preprocess the input state information of agents to improve training efficiency; and implementing an efficient exploration of the power allocation strategy based on the discrete flexible actor-critic algorithm.

[0006] Patent application CN113316091A discloses an information sending method and device applied to UAV clusters. This information sending method mainly solves the problems of unstable links and packet loss caused by frequent topological changes in UAV clusters. In this regard, the method can calculate the node degree of each UAV itself based on its own attribute information and the attribute information of multiple surrounding neighbor UAVs received, and determine the UAV cluster head and divide the clusters using the node degree.

[0007] However, the applicant has noticed that these distributed communication methods are difficult to communicate reliably in complex urban environments. Summary of the Invention

[0008] The object of the present invention is to provide a fully distributed UAV cluster communication method, system, medium and product, which partly solves or alleviates the above deficiencies in the prior art, can reduce the loss in the information transmission process, and ensure the communication quality at the same time. Specifically, for the sum rate optimization problem of a fully distributed UAV cluster millimeter-wave MIMO communication system, the present application provides a beamforming design scheme based on the alternating direction method of multipliers, models the sum rate maximization problem of a centralized UAV cluster millimeter-wave MIMO communication system into a distributed consensus optimization problem, so that only a small amount of backhaul signaling exchange is required between adjacent UAVs to complete the local update and local calculation of beamforming, and there is no need to interact with the ground station for information. Under the framework of the alternating direction method of multipliers, the present invention adopts the block gradient descent algorithm and the manifold optimization algorithm to optimize and design the hybrid beamformer of the UAV and the synthesizer of the user respectively. Compared with the centralized communication system, the fully distributed UAV cluster millimeter-wave MIMO communication system proposed by the present invention has basically the same sum rate performance, and can greatly reduce the computational complexity and the backhaul signaling overhead.

[0009] Specifically, the present invention provides a fully distributed UAV cluster communication method, including the steps of:

[0010] S1, obtaining the initial conditions, and using an undirected graph to calculate the transfer information between the b-th UAV and the -th UAV; wherein, S1 includes the steps of: S11, defining an undirected graph for describing the UAV communication relationship, the nodes of the undirected graph representing the UAVs, and one node being connected to another node through at least one edge; S12, defining the initial parameters according to the undirected graph, the initial parameters including: channel, undirected graph, incidence matrix, signed incidence matrix, analog beamforming, digital beamforming, user combiner; S13, defining the constraint conditions according to the initial parameters: ; wherein, and are the user combiners of the b-th and non-b-th UAVs respectively, is the UAV cluster; S14, generating a solution model according to the initial parameters and the constraint conditions; S15, calculating the solution information according to the solution model, the solution information including: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, the optimal sum rate; S16, determining the transfer information according to the solution information; S2, obtaining a new b-th UAV from the UAV cluster, and defining the transfer information of the previous b-th UAV as the initial conditions; S3, returning to S1 until all the UAVs in the UAV cluster are traversed to complete the communication in the current round.

[0011] In some embodiments, S15 includes: setting the relationship between the UAV index b and the number of iterations t: , where mod represents the modulo operator and B is the number of UAVs; the b-th UAV receives the signaling information sent by the -th UAV through the backhaul link and ; updating the auxiliary variable and the auxiliary variable generated during the fractional transformation process, where represents the user set; the b-th UAV updates the backhaul signaling information: and ; where is the complex number at the (t + 1)-th iteration, is the complex number at the t-th iteration, , are the conjugate transpose and digital beamforming vector of the combiner for the j-th user at the t-th iteration of the b-th UAV, is the channel, is the analog beamforming at the t-th iteration; is the second backhaul signaling information excluding the b-th UAV at the (t + 1)-th iteration, is the combiner for all users of the b-th UAV at the t-th iteration, is a complex matrix; using the block gradient descent algorithm to optimize the hybrid beamforming matrix , analog beamforming matrix , digital beamforming matrix of the b-th UAV; the b-th UAV updates the combiner vectors of K users; updating the dual variable generated during the alternating direction multiplier method; the b-th UAV updates the backhaul signaling information: and , and sends it to the adjacent UAVs; after the solution process converges, the output solution information includes: the optimal analog beamforming, digital beamforming, optimal combiner vectors for all users, and optimal sum rate of the b-th UAV.

[0012] In some embodiments, S11 includes: constructing an undirected graph according to the connection relationship between UAVs, where the undirected graph has B nodes, and the nodes represent the UAVs, and the edge represents the backhaul link between the UAVs; defining the incidence matrix as the incidence matrix of , where the rows and columns of the incidence matrix correspond to the nodes and the edges; converting the incidence matrix into a signed incidence matrix , and for the e-th connection, given an edge , so as to be defined as: ; where b is the index of the UAV, is a complex space.

[0013] In some embodiments, S11 further includes: calculating the communication obstacle degree of at least one communication partition, where a communication partition includes: at least one node; selecting the number of edges connected by the node according to the communication obstacle degree; generating or updating the undirected graph according to the number of edges.

[0014] In some embodiments, S11 further includes: when the communication obstacle degree of the communication partition is greater than a preset first level, a first node in the communication partition is connected to a second number of second nodes by using a second number of edges; and the second nodes are selected from the communication partitions with a communication obstacle degree less than a preset second level.

[0015] In some embodiments, the communication partition includes multiple nodes. Correspondingly, S11 further includes: setting a first sub-number of edges for a first node located in the central area of the communication partition; setting a second sub-number of edges for a first node located in the edge area of the communication partition, and the first sub-number is greater than or equal to the second sub-number.

[0016] In some embodiments, the solution model includes: ;

[0017] where is the objective function, , are the analog beamforming and digital beamforming of the b-th UAV, is the transmission power of the b-th UAV, is the element in the i1-th row and j1-th column of the analog beamforming matrix of the b-th UAV, and i1 and j1 are the row number and column number of the element in the matrix respectively, is the i2-th element in the user combining vector locally calculated by the b-th UAV, and i2 is the serial number of the element in the vector, , , , , are two types of introduced auxiliary variables.

[0018] The present invention also provides a fully distributed UAV cluster communication method, including the steps:

[0019] A decentralized module for obtaining initial conditions and using an undirected graph to calculate the transmission information between the b-th unmanned aerial vehicle (UAV) and other UAVs. Among them, the decentralized module includes: a graph definition unit for defining an undirected graph used to describe the communication relationships of multiple UAVs, where the nodes of the undirected graph represent the UAVs, and one node is connected to another node through at least one edge; a parameter definition unit for defining initial parameters according to the undirected graph, the initial parameters including: channels, undirected graph, incidence matrix, signed incidence matrix, analog beamforming, digital beamforming, user combiners; a constraint definition unit for defining constraint conditions according to the initial parameters: ; where, ; among them, and are the user combiners of the b-th UAV and the non-b-th UAV respectively, is the UAV cluster; a solution definition unit for generating a solution model according to the initial parameters and the constraint conditions; a solution unit for calculating solution information according to the solution model, the solution information including: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, the optimal sum rate; a result unit for determining the transmission information according to the solution information; an update module for obtaining a new b-th UAV from the UAV cluster, defining the transmission information of the previous b-th UAV as the initial conditions, and entering the decentralized solution module, a traversal module, returning to the decentralized module to traverse the UAVs in the UAV cluster to complete the communication in the current round.

[0020] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in any one of the embodiments is implemented. The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a computer, the method described in any one of the embodiments is implemented.

[0021] Beneficial technical effects: It is worth noting that the traditional centralized communication method requires all UAVs to perform centralized communication with a ground base station. On the contrary, the present invention provides a distributed communication method (or a decentralized communication method) that can perform pairwise communication in a decentralized manner (i.e., without relying on a ground base station).

[0022] Especially in complex scenarios with dense high-rise buildings (such as city centers, high-density residential areas), during the operation of a UAV cluster, the communication between UAVs and ground stations may be blocked due to the occlusion of high-rise buildings, significantly increasing the difficulty of UAV communication. In this regard, the present application can adopt the method of pairwise communication between UAVs to reduce the dependence on ground base stations.

[0023] Specifically, in this embodiment, a method is provided for solving a path based on the definition of a backhaul link undirected graph, and using the user combiner as a guiding constraint condition to ensure that pairwise communication does not generate conflicts. Among them, in order to ensure that the UAVs complete the overall signal update of the UAV cluster through pairwise communication, this application focuses on providing a method that can be based on limited specific factors (such as UAV beamforming, user combiner) and uses the user combiner consistency as the dominant constraint condition to quickly iterate the communication scheme of the UAV cluster, thereby achieving the purpose of complete decentralization. In particular, this application simplifies the iterative process at least in the following aspects:

[0024] 1) By means of the undirected graph defined by the UAV communication link, simplify the consistency constraint conditions in the beamforming optimization process. This constraint condition focuses on using local UAV information (such as ) and the capabilities of the UAV cluster (such as a complex matrix of dimensions) to constrain the communication results between pairwise UAVs during the iterative process. When the communication results between pairwise UAVs satisfy this constraint condition, it means that the pairwise UAVs have successfully shaken hands;

[0025] 2) Restrictively optimize the communication content between adjacent UAVs. Specifically, during the iterative solution process, the b-th UAV only needs to consider its own specific information (such as its own channel information) and the backhaul information updated by the previous UAV. Furthermore, through the dual-dimensional constraints of the communication content between adjacent UAVs and the dimension of local UAV calculation content, the computational pressure of the iterative process can be reduced and the iterative efficiency can be improved.

[0026] Furthermore, the present invention uses a communication relationship with differential communication density to set or optimize the undirected graph, which can improve the communication ability of this decentralized communication method in a complex communication environment without causing excessive communication pressure. Especially in the "low-altitude economy" application environment, such as deploying a large number of UAVs for food delivery in the city, the dense downtown area may pose great challenges to UAV communication. However, the decentralized communication scheme based on differential communication density in this embodiment can coordinate the contradiction between communication ability and communication pressure to a certain extent.

[0027] Furthermore, for nodes in different regions such as the center and periphery of the same communication partition, differential communication density can also be used for communication to ensure that nodes with higher local communication difficulty attempt to communicate with multiple nodes, and the reliability of the communication information solution of a single node can be improved through multi-party verification.

[0028] Especially when large-scale UAV clusters are scattered to perform flight tasks in urban building complexes, this differential communication density design for local areas can not only improve the anti-interference communication ability of local UAVs, but also avoid imposing excessive communication pressure on the UAV cluster due to changes in communication density.

[0029] From another perspective, this application selects a completely decentralized method and only completes the signal update of the entire UAV cluster through the mutual communication of adjacent UAVs (i.e., an adjacent communication mechanism). Moreover, in order to improve the transmission efficiency and accuracy under this adjacent communication mechanism, this application focuses on selecting limited dual factors such as the local information of UAVs and the backhaul signaling information of adjacent UAVs to centrally optimize the beamforming of UAVs during the iteration process. Thus, on the one hand, this application can perform collaborative calculations through the local information of UAVs, that is, the backhaul signaling information of adjacent UAVs (such as the previous UAV), to ensure the error-free adjacent communication process during the iteration process (able to avoid information loss); at the same time, optimize and solve the current beamforming based on the local information and the backhaul signaling information of adjacent UAVs, and then find the optimal communication solution through the cyclic iteration process.

[0030] Furthermore, it is worth noting that through simulation tests, it can be seen that the method of quickly iterating the communication scheme of the UAV cluster based on limited specific factors adopted in this application can not only avoid the interference of obstacles to the communication system, but also the communication performance is almost the same as that of the centralized communication scheme, that is, the communication performance will not be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale. Obviously, the following described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1a Schematic diagram of the communication method process in an exemplary embodiment of the present invention;

[0033] Figure 1b Schematic diagram of the communication method process in a preferred embodiment of the present invention;

[0034] Figure 2 Schematic diagram of the centralized communication mode of the millimeter-wave MIMO communication system for UAV clusters;

[0035] Figure 3Schematic diagram of the distributed communication mode of the millimeter-wave MIMO communication system for the UAV swarm;

[0036] Figure 4 Comparison chart of the sum rate performance of the centralized and fully distributed millimeter-wave MIMO communication systems for the UAV swarm;

[0037] Figure 5 Schematic diagram of the module structure of the communication system in an exemplary embodiment of the present invention. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. In this document, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of describing the present invention, and they have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] As used herein, "and / or" includes any and all combinations of one or more of the listed related items. As used herein, "a plurality" means two or more, that is, it includes two, three, four, five, etc. As used in this specification, the term "about" typically represents + / -5% of the value, more typically + / -4% of the value, more typically + / -3% of the value, more typically + / -2% of the value, even more typically + / -1% of the value, and even more typically + / -0.5% of the value. In this specification, certain embodiments may be disclosed in a format within a certain range. It should be understood that this kind of description "within a certain range" is only for convenience and brevity, and should not be construed as a rigid limitation on the disclosed range. Therefore, the description of the range should be considered to have specifically disclosed all possible sub-ranges and the individual numerical values within this range. For example, the description of the range 1-6 should be regarded as having specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and the individual numbers within this range, such as 1, 2, 3, 4, 5, and 6. The above rules apply regardless of the breadth of the range.

[0040] Embodiment 1: Refer to Figure 1a andFigure 1b As shown in the figure, the present invention provides a fully distributed UAV cluster communication method, including the steps of:

[0041] S1, obtaining initial conditions, and using an undirected graph to calculate the transfer information between the b-th UAV and the -th UAV; where the -th UAV refers to the UAVs in the UAV cluster other than the b-th UAV.

[0042] For example, the UAV cluster includes a group of UAVs, and the group of UAVs communicates with a group of users. Among them, the group of UAVs includes UAVs, the group of users includes users, and each UAV is equipped with N array antennas and N RF radio frequency chains, and each user is equipped with M array antennas and 1 radio frequency chain; correspondingly, the initial conditions may include the parameters for describing the UAV cluster communication relationship, such as the number of UAVs, the number of users, the number of array antennas, the number of radio frequency chains, and so on.

[0043] Among them, S1 includes the steps of:

[0044] S11, defining an undirected graph for describing the UAV communication relationship, where the nodes of the undirected graph represent the UAVs, and one node is connected to another node through at least one edge;

[0045] That is to say, the present invention selects to use the undirected graph formed by the backhaul links between the UAVs (or called the backhaul link undirected graph).

[0046] S12, defining initial parameters according to the undirected graph, where the initial parameters include: channels, undirected graphs, incidence matrices, signed incidence matrices, analog beamforming, digital beamforming, user combiners;

[0047] Among them, the signal refers to the channel between the UAV and the user. In some embodiments, the initial parameters can be obtained through the initial conditions.

[0048] In this embodiment, the undirected graph, incidence matrix, and signed incidence matrix are determined by the undirected graph defined by the backhaul links between the UAVs, while the analog beamforming, digital beamforming, and user combiners can have the initial parameters obtained from the initial conditions.

[0049] S13, defining constraint conditions according to the initial parameters: ; where , are the user combiners of the b-th and non-b-th UAVs respectively, For the UAV cluster;

[0050] Among them, the constraint condition is used to promote the consistency of communication between two UAVs (such as the b-th UAV and the -th UAV), that is, the user information (such as the user combiner) calculated by the two is consistent.

[0051] For example, the user information can be the number of enabled array antennas (which can be described or defined by the user combiner). That is to say, the UAV actually provides a guiding opinion on how to set the usage details of the antenna. And the constraint condition in this embodiment is to ensure that the guiding opinions output between two UAVs are consistent.

[0052] S14. Generate a solution model according to the initial parameters and the constraint conditions;

[0053] Among them, according to the constraint conditions, the centralized system optimization problem is transformed into a distributed communication system optimization problem, that is, a decentralized optimization problem. Decentralization means that each UAV does not have to communicate with a central base station, but can communicate pairwise with adjacent UAVs to complete the update of communication in the entire UAV cluster.

[0054] For example, in some embodiments, the generated solution model is:

[0055] ;

[0056] Among them, is the objective function set by the user for the UAV cluster communication system, , are the analog beamforming and digital beamforming of the b-th UAV, is the transmission power of the b-th UAV, is the element in the i1-th row and j1-th column of the analog beamforming matrix of the b-th UAV, where i1 and j1 are the row number and column number of the element in the matrix respectively, [[ID=3\7]] is the i2-th element in the user combining vector locally calculated by the b-th UAV, where i2 is the serial number of the element in the vector, , , [[ID=4\2]] , , are two types of introduced auxiliary variables.

[0057] S15. Calculate the solution information (or the optimal solution) according to the solution model. The solution information includes: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, and the optimal sum rate;

[0058] S16. Determine the transmission information according to the solution information;

[0059] For example, the current solution information can be used as the transmission information and transmitted to the next drone (i.e., the new b-th drone) for the initial condition of the next round of calculation. Alternatively, the transmission information can also include other conditional parameters of the current b-th drone when calculating the optimal solution. Specifically, the selection and reading of data types can be combined with the actual communication solution process.

[0060] S2. Obtain a new b-th drone from the drone cluster and define the transmission information of the previous b-th drone as the initial condition;

[0061] S3. Return to S1 until all the drones in the drone cluster are traversed to complete the communication of the current round.

[0062] It is worth noting that in the traditional centralized communication method, all drones need to communicate centrally with a ground base station. In contrast, the present invention provides a distributed communication method (or a decentralized communication method) that can directly perform pairwise communication in a decentralized manner (i.e., without relying on a ground base station).

[0063] Specifically, in this embodiment, a solution path is defined based on the backhaul link undirected graph, and the user combiner is used as the guiding constraint condition to ensure that pairwise communication does not cause conflicts.

[0064] In some embodiments, S15 includes:

[0065] Set the relationship between the drone index b and the iteration number t: , where mod represents the modulo operator and B is the number of drones;

[0066] The b-th drone receives the signaling information sent by the -th drone through the backhaul link and ;

[0067] Update the auxiliary variables and the auxiliary variable , where represents the user set (or user group);

[0068] The b-th drone updates the backhaul signaling information: and ; where is the complex number at the (t + 1)-th iteration , is the complex number at the t-th iteration, , is the conjugate transpose of the combiner for the b-th UAV at the t-th iteration for the j-th user, and the digital beamforming vector, is the channel, is the analog beamforming at the t-th iteration; is the second backhaul message excluding the b-th UAV at the (t + 1)-th iteration, is the combiner for all users of the b-th UAV at the t-th iteration, is a complex matrix;

[0069] Optimize the hybrid beamforming matrix of the b-th UAV using the block gradient descent algorithm , the analog beamforming matrix , and the digital beamforming matrix ;

[0070] The b-th UAV updates the combiner vectors of K users ;

[0071] Update the dual variables generated during the alternating direction method of multipliers ;

[0072] The b-th UAV updates the backhaul signaling information: and , and sends it to adjacent UAVs;

[0073] After the solution process converges, the output solution information includes: the optimal analog beamforming, digital beamforming, the optimal combining vectors for all users, and the optimal sum rate of the b-th UAV.

[0074] In summary, the present application proposes a pairwise communication mechanism to achieve a fully decentralized communication mode. Moreover, on the one hand, by restricting the communication content between pairwise UAVs and simplifying the constraint conditions of the iterative process, the operation efficiency of the iterative process is improved on the basis of ensuring the effectiveness of the iteration; on the other hand, through the above limited communication content and constraint conditions, the beamforming of the UAV cluster is quickly optimized, so as to ensure that the final beamforming result can effectively improve the throughput (or sum rate) of the communication system under the pairwise communication mechanism, and at the same time ensure that the necessary communication content under the pairwise communication mechanism can be accurately transmitted.

[0075] In some embodiments, S11 includes:

[0076] Construct an undirected graph according to the connection relationship between UAVs , where the undirected graph has B nodes, and the nodes represent the UAVs, and the edge represents the backhaul link between the UAVs;

[0077] Define the incidence matrix as the incidence matrix of , where the rows and columns of the incidence matrix correspond to the nodes and edges of respectively; convert the incidence matrix to a signed incidence matrix , and for the e-th connection, given the edge , define as:

[0078] ; where b is the index of the drone, is the complex space.

[0079] In some embodiments, S11 further includes:

[0080] (1) Calculate the communication obstacle degree of at least one communication partition, where a communication partition includes: at least one node;

[0081] For a drone cluster with B drones, it correspondingly generates an undirected graph with B nodes, which can be divided into multiple communication partitions, and a communication partition can have one or more nodes. Among them, the communication obstacle degree is used to define the communication quality or communication difficulty of a communication partition.

[0082] For example, in some embodiments, the communication obstacle degree can be represented by the number of communication obstacles in a communication partition. For example, there may be multiple high-rise buildings in a communication partition, and the high-rise buildings may form a communication obstacle between two drones. Correspondingly, the levels of communication obstacles can be defined in sequence according to the number of communication obstacles, such as level three, level two, level one, etc.

[0083] (2) Select the number of edges connected to the node according to the communication obstacle degree;

[0084] For example, in some embodiments, the higher the communication obstacle degree of the communication partition where a node is located, the more edges it can be connected to (the edge is used to represent that communication can be established between two drones), and different communication partitions can also adopt different communication relationships. That is to say, when the communication obstacle degree is higher, the communication density of the corresponding communication partition is greater.

[0085] (3) Generate or update the undirected graph according to the number of edges.

[0086] Preferably, in this embodiment, the communication obstacle degrees of different communication partitions of an undirected graph may be different, and different communication densities can be set for different communication partitions.

[0087] In summary, in this embodiment, the undirected graph is set or optimized by using a differential communication relationship, which can improve the communication ability of the decentralized communication method in a complex communication environment without causing excessive communication pressure. Especially in the application environment of the "low-altitude economy", such as calling a large number of drones for food delivery in a city, it may pose a great challenge to drone communication in the downtown area with dense buildings. However, the decentralized communication scheme set based on differential communication density in this embodiment can coordinate the contradiction between communication ability and communication pressure to a certain extent.

[0088] In some embodiments, S11 further includes:

[0089] When the communication obstacle degree of the communication partition is greater than a preset first level, the first node in the communication partition is connected to a second number of second nodes by a second number of edges; and the second nodes are selected from the communication partitions with a communication obstacle degree less than a preset second level.

[0090] For example, in some embodiments, the first node can be connected to a first number of first nodes by a first number of edges, and at the same time be connected to a second number of second nodes by a second number of edges, that is, the first node can communicate with nearby nodes (i.e., nodes in the current partition) and far nodes (i.e., nodes in external partitions) at the same time to improve the reliability of communication.

[0091] For example, in some embodiments, the current communication partition can be used as the first partition, and another communication partition (such as a communication partition adjacent to the first partition) can be selected as the second partition; the first partition includes a plurality of first nodes, and the second partition includes a plurality of second nodes. When the communication obstacle degree of the first partition is relatively high, preferably, communication with second nodes outside the first partition can be increased.

[0092] In some embodiments, the communication partition includes a plurality of nodes. Correspondingly, S11 further includes:

[0093] Set a first sub-number of edges for the first node located in the central area of the communication partition;

[0094] Set a second sub-number of edges for the first node located in the edge area (or, peripheral area) of the communication partition, and the first sub-number is greater than or equal to the second sub-number.

[0095] Preferably, for nodes in different areas such as the center and periphery of the same communication partition, differential communication density can also be used for communication to ensure that nodes with relatively high local communication difficulty attempt to communicate with multiple nodes, and the reliability of solving communication information of a single node can be improved through multi-party verification.

[0096] For example, in some embodiments, when multiple nodes are connected in sequence, the edges of the geometric figure formed by the connection can be recognized as edge regions (for example, the nodes on the outermost connection line can be recognized as the edge nodes). Correspondingly, the remaining regions can be recognized as central regions.

[0097] In some embodiments, according to the number of the UAV cluster or according to the size of the communication partition, the division ranges of the central region and the edge region can be adjusted.

[0098] Next, in order to more clearly show the communication optimization process for the decentralized design in the present invention, an attempt is made below to obtain the initial conditions for S1 and use an undirected graph to calculate the transmission information between the b-th UAV and the -th UAV in detail, which includes the steps:

[0099] S101, Provide a communication system, the communication system includes: a group of UAVs, and the group of UAVs communicates with a group of users, wherein, the group of UAVs includes UAVs, the group of users includes users, and each UAV is equipped with N array antennas and N RF radio frequency chains, and each user is equipped with M array antennas and 1 radio frequency chain;

[0100] S102, Define an undirected graph representing the communication relationship of the communication system, wherein, the undirected graph has B nodes, and the nodes represent the UAVs, and the edges represent the backhaul links between the UAVs. When the original incidence matrix is defined as the incidence matrix of the rows and columns of the original incidence matrix correspond to the nodes and the edges of respectively; convert the original incidence matrix to a signed incidence matrix and for the e-th connection, given the edge define

[0101] ; where, b is the index of the UAV, is the complex space;

[0102] S103, Solve the fully distributed UAV cluster communication model according to the initial parameters of the communication system; wherein, the initial parameters include: the channels between the UAVs and the users , the undirected graph formed by the backhaul links between the UAVs , incidence matrix , incidence matrix , initial number of iterations , initial analog beamforming , initial digital beamforming , initial user combiner ;

[0103] Usually, when the UAV is used as a transmitter, beamforming technology (beamforming, also known as beamforming and spatial filtering) is used. It is a signal processing technology that uses an array antenna to transmit and receive signals in a directional manner, that is, a technology that realizes beam focusing by adjusting the phase and amplitude of each antenna element. The user uses combining technology (that is, a combiner) as a receiver for signal processing. Similarly, it can realize beam focusing by adjusting the phase and amplitude of the transmitted signal.

[0104] Among them, the specific expansion content of the fully distributed UAV cluster communication model (abbreviated as the solution model) also includes:

[0105] (1) Constraint conditions:

[0106] ;

[0107] Among them, is the defined matrix , is the identity matrix of dimension is a complex matrix of dimension is a complex matrix of dimension and satisfies the condition , is a complex vector of dimension is the combiner of all users locally calculated by the b-th UAV; it can be understood that the constraint condition in this embodiment is the specific mathematical expression form of the constraint condition in S13.

[0108] (2) Alternating update calculation conditions set for the solution model:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] wherein, is the t-th iteration, is the transpose operation, is the conjugate transpose operation, is the analog beamforming, is the digital beamforming, is the complex space, and are the analog beamforming and digital beamforming matrices of the b-th unmanned aerial vehicle (UAV) respectively, and , is the K-th column vector of the matrix , is the combining vector of all users, is the combiner of all users locally calculated by the b-th UAV, is at the (t + 1)-th iteration, is the combiner of all users not locally calculated by the b-th UAV, is set at the t-th iteration, and are the first auxiliary variable and the second auxiliary variable generated during the fractional programming transformation respectively, is the dual variable at the t-th iteration, is the hybrid beamforming matrix of the b-th UAV, is the beamforming matrix set at the t-th iteration, is the beamforming matrix set at the (t + 1)-th iteration, is the beamforming matrix set of the b-th UAV, is at the (t + 1)-th iteration, is the beamforming matrix set of non-b-th UAVs, is at the t-th iteration, is the penalty coefficient, is the communication rate weight of the k-th user, is the operation of taking the real part of a complex number, is the second auxiliary variable conjugate transpose of the k-th element of, is the conjugate transpose of the combining vector of the k-th user, is the millimeter-wave channel between the b-th UAV and the k-th user, and , is the digital beamforming vector of the b-th UAV for the k-th user, is the digital beamforming vector of the b-th UAV for the j-th user, is the noise variance, is the augmented Lagrangian function, is the objective function; is the Lagrange multiplier used by the b-th UAV, is a custom parameter, and , is the matrix 's Frobenius norm; and represent the indicator function. For example, if is satisfied, equals 0, otherwise .

[0116] S104. After the solution process of S103 converges, the currently calculated result is output as the optimal condition, and the optimal condition includes: , , , ; where is the optimal analog beamforming matrix of the b-th UAV, is the optimal digital beamforming matrix of the b-th UAV, is the optimal combining vector calculated by the b-th UAV for all users locally, is the optimal sum rate of the communication system.

[0117] In this embodiment, a beamforming design method in a fully distributed UAV swarm millimeter-wave MIMO communication system is actually proposed, which improves the throughput of the communication system by designing the hybrid beamformer of the UAV and the synthesizer of the user.

[0118] For example, consider a fully distributed UAV swarm millimeter-wave MIMO communication scenario in the downlink, where a group of UAVs communicate with a group of users . Each UAV is equipped with N array antennas and N RF radio frequency chains, while each user is equipped with M array antennas and 1 radio frequency chain. Since the communication system is fully distributed, there is no need for a ground station to perform coordination and calculation tasks among the UAVs. The signaling information shared among the UAVs is transmitted through the backhaul link, and all baseband signal processing is completed locally at the UAVs. Considering the limitations on hardware cost and power consumption, each UAV adopts a hybrid beamforming structure, satisfying and At this time, the signal received by the k-th user is: ;

[0119] where, represents the additive white Gaussian noise at the k-th user that follows distribution, represents the millimeter-wave channel between the b-th UAV and the k-th user, represents the symbol of the j-th user, represents the symbol vector of K users, represents the analog beamforming of the b-th UAV, represents the digital beamforming of the b-th UAV, represents the combining vector used by the k-th user.

[0120] In addition, it is assumed that each UAV sends K data streams to all users, and the transmitted signal from the b-th UAV can be expressed as: ; where represents the symbol vector and satisfies , is the identity matrix of dimension. and respectively represent the analog beamforming matrix and the digital beamforming matrix of the b-th UAV. The transmission power of the b-th UAV satisfies .

[0121] According to the millimeter-wave MIMO communication model of the UAV swarm, the received signal of the k-th user is the superposition of the signals transmitted from B UAVs, which can be expressed as ; where, represents the additive white Gaussian noise at the k-th user that follows distribution, represents the millimeter-wave channel between the b-th UAV and the k-th user. Due to the sparse characteristics of the millimeter-wave channel, the Saleh-Valenzuela channel model with a small number of scattering paths is adopted in the present invention. For simplicity of notation, a uniform linear array is used to define as: ; where, is the number of propagation paths, is the complex gain of the l-th path between the b-th UAV and the k-th user. In particular, is mainly determined by propagation attenuation and molecular absorption in the high-frequency band. In addition, and represent the azimuth angle of arrival and the azimuth angle of departure of the l-th path. and respectively represent the antenna array response vectors of the b-th UAV and the k-th user.

[0122] Subsequently, the k-th user uses the combining vector to further process the received signal, and can be obtained;

[0123] where the final received signal of the k-th user is divided into three parts: desired signal, interference signal, and noise.

[0124] Based on the above formula, the signal-to-interference-plus-noise ratio (SINR) of the k-th user can be expressed as: ;

[0125] where represents the effective noise variance, and is the original noise variance. Given the SINR, the sum rate of the UAV swarm millimeter-wave MIMO communication system can be written as: ;

[0126] where represents the WSR weight and satisfies the constraint .

[0127] Furthermore, for a fully distributed UAV swarm millimeter-wave MIMO communication system, to ensure that all UAVs reach a consensus on the combiners of K users. Specifically, for the consensus constraint, it is set to satisfy the condition , where represents the combiners of K users locally calculated by the b-th UAV. Thus, the optimization problem (i.e., the solution model) is set as:

[0128]

[0129] where and .

[0130] It should be noted that in a centralized UAV swarm millimeter-wave MIMO communication system, a large amount of information interaction is required between the UAV swarm and the ground station. Especially as the number of UAVs and the dimension of the MIMO channel increase, this will lead to a sharp increase in both the backhaul signaling overhead and the computational complexity.

[0131] To solve this problem, the present invention proposes a fully distributed UAV swarm communication system, which uses the alternating direction method of multipliers to transform the centralized communication system optimization problem into a distributed consensus optimization problem, and then uses the block gradient descent algorithm and the popular optimization algorithm to optimize the beamforming matrix of the UAVs and the combining vectors of the users respectively, finally achieving the maximization of the sum rate of the communication system.

[0132] Further, to illustrate the solution process of the solution information calculated according to the solution model, the solution process of step S102 will be introduced in detail below:

[0133] For example, in some embodiments, S102 includes steps: S201, obtaining initial parameters in the current communication state, where the initial parameters include: the channel between the drone and the user , the undirected graph formed by the backhaul links between the drones , the incidence matrix corresponding to the undirected graph , the initial number of iterations , the initial analog beamforming , the initial analog beamforming , the initial user combiner ;

[0134] S202, when the number of iterations , loop to execute steps S203 - S216;

[0135] S203, set the relationship between the drone index and the number of iterations: ; where is the drone index, and mod represents the modulo operator;

[0136] S204, the b-th drone receives the first backhaul signaling information and the second backhaul signaling information , where is the dimensional signaling matrix at the t-th iteration , is the dimensional signaling vector at the t-th iteration ;

[0137] S205, update the first auxiliary variable: ;

[0138] S206, update the second auxiliary variable: ;

[0139] S207, the b-th drone updates the backhaul signaling information: , where is the complex number at the (t + 1)-th iteration and satisfies , is the complex number at the t-th iteration and satisfies , is the conjugate transpose of the combiner of the b-th drone for the j-th user at the t-th iteration, is the simulated beamforming at the t-th iteration, is the conjugate transpose of the combiner for the j-th user by the b-th UAV at the t-th iteration, is the digital beamforming vector for the j-th user by the b-th UAV at the t-th iteration;

[0140] S208, the b-th UAV updates the post-transmission signaling information: , is the second post-transmission signaling information without the b-th UAV at the (t + 1)-th iteration, is the combiner for all users by the b-th UAV at the t-th iteration;

[0141] S209, the b-th UAV updates the hybrid beamforming matrix: , is the hybrid beamforming matrix at the (t + 1)-th iteration;

[0142] S210, the b-th UAV updates the simulated beamforming matrix: , is the simulated beamforming matrix at the (t + 1)-th iteration;

[0143] S211, according to the optimized variable set the b-th UAV updates the digital beamforming matrix: , is the digital beamforming matrix at the (t + 1)-th iteration;

[0144] S212, according to the updated hybrid beamforming matrix, simulated beamforming matrix, and digital beamforming matrix, the b-th UAV updates the combiner for all users: , is the user combiner by the b-th UAV at the (t + 1)-th iteration;

[0145] S213, update the auxiliary variable: ;

[0146] S214, the b-th UAV updates the post-transmission signaling information: ;

[0147] S215, the b-th UAV updates the post-transmission signaling information: ;

[0148] S216, send the updated post-transmission signaling information and to adjacent UAVs;

[0149] S217, calculate the sum rate of the current communication system If the difference between the current sum rate and the sum rate in the previous iteration process is less than the set difference degree, it is considered that the solution process converges. Among them, the sum rate is the total amount of data that the communication system can transmit per unit time. The sum rate refers to the sum of the communication rates between all UAVs and all users, that is, the communication capacity of the entire system.

[0150] It should be noted that the distributed technology proposed in this application can not only compress the data dimension and data volume of information transmission, but also maintain excellent sum rate performance during the transmission process of limited information.

[0151] Specifically, this application adopts a completely distributed transmission scheme to achieve the purpose of decentralization, that is, to avoid the communication tasks between UAVs and ground stations. Further, in this application, through the distributed consensus optimization problem, only a small amount of feedback signaling exchange is required between adjacent UAVs to complete the local update and local calculation of beamforming, so as to maximize the communication capacity.

[0152] In some embodiments, the communication system meets the following requirements: , .

[0153] In some embodiments, the first auxiliary variable and the second auxiliary variable are calculated using the following model: ; ;

[0154] ; ;

[0155] And ;

[0156] Among them, is the after optimization for the k-th user, is the signal-to-interference-plus-noise ratio of the k-th user, is the after optimization for the k-th user, is the conjugate transpose of the combiner for the k-th user by the b-th UAV, is the first feedback signaling information element of the k-th row and k-th column, is the first feedback signaling information element of the k-th row and j-th column.

[0157] To further solve the fully distributed hybrid beamforming problem, the b-th UAV can also locally optimize multiple beamforming matrices (such as , and ), the present invention designs the analog beamforming and digital beamforming of the UAV by using the BCD algorithm, and the specific implementation process is as follows.

[0158] 1) Optimization : Given a fixed variable set , such that the augmented Lagrangian function with respect to is defined as ; where is the hybrid beamforming matrix of the b-th UAV, is the Lagrange multiplier used by the b-th UAV, is the transmit power of the b-th UAV, is the penalty coefficient; its Karush-Kuhn-Tucker (KKT) conditions can be expressed as:

[0159] , , , ;

[0160] where is the Frobenius norm of the matrix ;

[0161] After algebraic operations, the closed-form solution of can be expressed as: ;

[0162] 2) Optimization : Considering that and are fixed, the minimization problem of the b-th UAV regarding the analog beamforming design is defined as: ;

[0163] where the new objective function with respect to is expressed as:

[0164] ;

[0165] where is .

[0166] For the non-convex problem under the constant modulus constraint, a popular optimization algorithm can be used to solve it. By mapping this constraint to a smooth Riemannian manifold and introducing , the above optimization problem can be rewritten as:

[0167] ; where

[0168] ;

[0169]

[0170]

[0171] ;

[0172] Among them, is the column vectorization of the matrix . is the objective function with respect to the vector . is the conjugate transpose of the vector . is the conjugate transpose of the matrix . is the conjugate transpose of the matrix . is the conjugate of the vector . is the transpose of the vector . is , is the transpose of the matrix . is the identity matrix of dimension is the column vectorization of the matrix ;

[0173] Furthermore, is defined as a complex circular Riemannian manifold, and can be specifically written as

[0174] ; Among them, is the first element of the vector , is the -th element of the vector ;

[0175] For a given point , the Riemannian gradient can be calculated by projecting the Euclidean gradient onto the tangent space of the corresponding point on the Riemannian manifold . Therefore, can be expressed as

[0176] ; Among them, represents the tangent vector at .

[0177] The Euclidean gradient at the point can be expressed as ;

[0178] In Euclidean space, the Riemannian gradient belongs to the tangent vector and represents the steepest descent direction of the function. Then, can be expressed as: ;

[0179] For the Riemannian gradient, the following step is the contraction operation, which ensures that the required tangent vector at the considered point is projected onto the specified Riemannian manifold. Based on the above analysis, the contraction operation can be expressed as: ; where the superscript i is the iteration index, is the step size of the MO algorithm. In addition, the step size selection belongs to the category of the Riemannian gradient descent (RGD) algorithm, and the classical RGD step size selection strategy is the Armijo backtracking line search. More specifically, the Armijo backtracking line search method iteratively reduces the tentative step size through a controllable parameter (usually 0.5 or 0.8) until a preset condition is reached. For a specific iteration, the well-known Armijo-Goldstein condition can be expressed as:

[0180] ;

[0181] where, represents a constant determined according to the actual application, and the above condition can achieve a rapid descent on the Riemannian manifold. is an empirical parameter between 0 and 1 and needs to be adjusted according to the actual application; is the iteration step size of the MO algorithm and needs to be adjusted according to the actual application; is the vector at the i-th iteration in the MO algorithm;

[0182] 3) Optimization : Fix the optimized variable set , then the digital beamforming design of is defined as an unconstrained minimization problem, so that the objective function can be written as: ; where, ; By introducing [[ID=_56]]and the problem related to is further rewritten as:

[0183] where, is the first auxiliary parameter,

[0184] is the second auxiliary parameter, is the third auxiliary parameter,

[0185] is the fourth auxiliary parameter, is the fifth auxiliary parameter,

[0186] is the sixth auxiliary parameter,

[0187] is the seventh auxiliary parameter, is the eighth auxiliary parameter.

[0188] It should be noted that the above problem is defined as an unconstrained convex problem and includes a quadratic objective function. Therefore, by setting the derivative to zero, the optimal digital beamforming can be obtained as follows:

[0189] ;

[0190] Regarding the analog combiner design for K users can be written as:

[0191] where,

[0192] ;

[0193] Furthermore, in this embodiment, the above design is simplified to a more compact form as follows:

[0194]

[0195] where, is the ninth auxiliary parameter,

[0196] is the tenth auxiliary parameter, is the eleventh auxiliary parameter,

[0197] is the twelfth auxiliary parameter, is the thirteenth auxiliary parameter,

[0198] is the fourteenth auxiliary parameter, is the fifteenth auxiliary parameter,

[0199] is the sixteenth auxiliary parameter, is the seventeenth auxiliary parameter,

[0200] is the eighteenth auxiliary parameter, is the nineteenth auxiliary parameter.

[0201] Specifically, the above optimization problem belongs to a non-convex quadratic constraint quadratic programming problem with a quadratic objective function and a constant modulus constraint. Therefore, the existing popular optimization algorithms can be used to solve this optimization problem well.

[0202] Furthermore, referring to Figures 3 - 4 as shown, the present invention also conducts a simulation test on the reliability of the communication system. Specifically, Figure 2 shows a schematic diagram of the centralized communication relationship of the UAV swarm millimeter-wave MIMO communication system, Figure 3 and is a schematic diagram of the distributed communication relationship of the UAV swarm millimeter-wave MIMO communication system. Taking Figure 3 as an example, in this embodiment, an actual UAV swarm millimeter-wave MIMO communication system is simulated. All UAVs and users are randomly distributed within a range of 400×400 square meters. The heights of the UAVs and users are set to 6 meters and 2 meters respectively (where the links between UAVs are set as backhaul links, and the links between UAVs and users are set as wireless links). Since millimeter-wave communication links are easily blocked by obstacles, it is assumed that there is always 1 line-of-sight path and 3 non-line-of-sight paths between each UAV and each user. The complex gain of the line-of-sight path is mainly determined by the free-space loss, while the complex gain of the non-line-of-sight path is calculated by the multipath loss. The departure azimuth angle and arrival azimuth angle of the millimeter-wave channel are selected from , following a uniform distribution. The number of array antennas equipped for each UAV and each user is N = 64 and M = 8 respectively. In addition, the operating frequencies and noise powers of the centralized and distributed UAV swarm millimeter-wave MIMO communication systems are respectively configured as GHz, dBm. It can be obtained from Figure 3 that the sum-rate performance gap between the centralized and fully distributed UAV swarm millimeter-wave MIMO communication systems is small, and this performance gap can be ignored in actual communication scenarios. The above simulation process can reflect that the computational complexity and backhaul signaling overhead of the fully distributed communication system in this application are not affected by the number of UAV swarms and the MIMO channel dimension, and are applicable to the application scenarios of large-scale UAV swarms.

[0203] Moreover, compared with the centralized UAV swarm millimeter-wave MIMO communication system, the method protected by this application can significantly reduce the computational complexity and backhaul signaling overhead of the network, and ensure that the sum-rate performance of the network remains basically unchanged, which will make the fully distributed communication system easier to expand to the application scenarios of large-scale UAV swarms.

[0204] Referring to Figure 4As shown in the figure, the pairwise communication mechanism in this application can not only avoid the interference of obstacles to the communication efficiency, but also, under the same UAV transmission power, the sum rate performance of this application is almost the same as that of the traditional centralized UAV cluster millimeter-wave MIMO communication system, that is, the avoidance of obstacles in this application will not reduce the communication speed of the UAV cluster.

[0205] Embodiment 2: Refer to Figure 5 As shown in the figure, the present invention also provides a fully distributed UAV cluster communication system, including: a decentralized module 01, configured to obtain initial conditions and calculate the transfer information between the b-th UAV and the UAVs by using an undirected graph; wherein, the decentralized module includes: a graph definition unit 011, configured to define an undirected graph for describing the communication relationships of multiple UAVs, the nodes of the undirected graph representing the UAVs, and one node being connected to another node through at least one edge; a parameter definition unit 012, configured to define initial parameters according to the undirected graph, the initial parameters including: channels, undirected graphs, incidence matrices, signed incidence matrices, analog beamforming, digital beamforming, user combiners; a constraint definition unit 013, configured to define constraint conditions according to the initial parameters: ; wherein, , are the user combiners of the b-th UAV and the non-b-th UAV respectively, is the UAV cluster; a solution definition unit 014, configured to generate a solution model according to the initial parameters and the constraint conditions; a solution unit 015, configured to calculate solution information according to the solution model, the solution information including: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, and the optimal sum rate; a result unit 016, configured to determine the transfer information according to the solution information; an update module 02, configured to obtain a new b-th UAV from the UAV cluster, define the transfer information of the previous b-th UAV as the initial conditions, and enter the decentralized solution module; a traversal module 03, configured to return to the decentralized module to traverse the UAVs in the UAV cluster to complete the communication in the current round.

[0206] In some embodiments, the communication system meets the following requirements: , Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in any one of the embodiments is implemented. Furthermore, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a computer, the method described in any one of the embodiments is implemented. In some embodiments, the communication system is a millimeter-wave MIMO communication system. In some embodiments, the communication link between at least one of the users and at least one of the drones is blocked by an obstacle.

[0207] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including that element. [[ID=,4]]

[0208] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, 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), and includes several instructions to enable a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0209] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A fully distributed UAV cluster communication method, characterized in that, Including the steps: S1. Obtain the initial conditions and calculate the transfer information between the b-th drone and the -th drone using an undirected graph; where S1 includes the steps: S11. Define an undirected graph for describing the communication relationships of the UAVs. The nodes of the undirected graph represent the UAVs, and one node is connected to another node through at least one edge; S12. Define initial parameters according to the undirected graph. The initial parameters include: channel, undirected graph, incidence matrix, signed incidence matrix, analog beamforming, digital beamforming, user combiner; S13. Define a constraint condition according to the initial parameters: ; where and are the user combiners of the b-th drone and the non-b-th drone respectively, is the drone cluster; S14. Generate a solution model according to the initial parameters and the constraint conditions; S15. Calculate solution information according to the solution model. The solution information includes: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, and the optimal sum rate; S16. Determine the transfer information according to the solution information; S2. Obtain a new b-th UAV from the UAV cluster, and define the transfer information of the previous b-th UAV as the initial condition; S3. Return to S1 until all the UAVs in the UAV cluster are traversed to complete the communication in the current round.

2. The method according to claim 1, characterized in that, S15 includes: Set the relationship between the UAV index b and the number of iterations t: , where mod represents the modulo operator and B is the number of UAVs; The b-th UAV receives, via the backhaul link, the signaling information sent by the -th UAV and ; Update the auxiliary variables generated during the fractional transformation and the auxiliary variables , where represents the user set; The b-th UAV transmits signaling information after update: and ; where is a complex number at the (t + 1)-th iteration , is a complex number at the t-th iteration, , are the conjugate transpose and digital beamforming vector of the combiner for the j-th user by the b-th UAV at the t-th iteration, is the channel, is the analog beamforming at the t-th iteration; is the second post-transmission signaling information excluding the b-th UAV at the (t + 1)-th iteration, is the combiner for all users by the b-th UAV at the t-th iteration, is a complex matrix; Optimize the hybrid beamforming matrix of the b-th unmanned aerial vehicle using the block gradient descent algorithm , simulated beamforming matrix , digital beamforming matrix ; The b-th UAV updates the combiner vectors of K users ; Update the dual variables generated during the alternating direction method of multipliers ; The b-th drone transmits signaling information after update: and , and sends it to adjacent drones; After the solution process converges, the output solution information includes: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, and the optimal sum rate.

3. The method according to claim 1, wherein S11 includes: Construct an undirected graph based on the connection relationship between drones , where there are B nodes in the undirected graph, and the nodes represent the drones, and the edges represent the backhaul links between the drones; Define the incidence matrix as 's incidence matrix, where the rows and columns of the incidence matrix correspond to the nodes and edges of respectively; convert the incidence matrix to a signed incidence matrix , and for the e-th connection, given the edge , define as: ; where b is the index of the drone, is a complex space.

4. The method according to any one of claims 1 to 3, characterized in that, S11 further includes: Calculate the communication obstacle degree of at least one communication partition. One communication partition includes: at least one node; Select the number of edges connected by the nodes according to the communication obstacle degree; Generate or update the undirected graph according to the number of edges.

5. The method according to claim 4, wherein S11 further includes: When the communication obstacle degree of the communication partition is greater than a preset first level, the first node in the communication partition uses a second number of edges to be respectively connected to a second number of second nodes; and the second nodes are selected from the communication partitions with a communication obstacle degree less than a preset second level.

6. The method according to claim 5, wherein There are multiple nodes in the communication partition. Correspondingly, S11 further includes: Set a first sub-number of edges for the first node located in the central area of the communication partition; Set a second sub-number of edges for the first node located in the edge area of the communication partition, and the first sub-number is greater than or equal to the second sub-number.

7. The method according to claim 4, wherein The solution model includes: ； Among them, is the objective function, , are the analog beamforming and digital beamforming of the b-th unmanned aerial vehicle (UAV), is the transmission power of the b-th UAV, is the element in the i1-th row and j1-th column of the analog beamforming matrix of the b-th UAV, where i1 and j1 are the row number and column number of the element in the matrix respectively, is the i2-th element in the user combining vector locally calculated by the b-th UAV, where i2 is the serial number of the element in the vector, , , , , are two types of introduced auxiliary variables.

8. A fully distributed UAV cluster communication system, characterized in that, Including the steps: The decentralized module is used to obtain the initial conditions and calculate the transfer information between the b-th drone and the drone by using an undirected graph; wherein, the decentralized module includes: A graph definition unit for defining an undirected graph for describing the communication relationships of multiple UAVs. The nodes of the undirected graph represent the UAVs, and one node is connected to another node through at least one edge; A parameter definition unit for defining initial parameters according to the undirected graph. The initial parameters include: channel, undirected graph, incidence matrix, signed incidence matrix, analog beamforming, digital beamforming, user combiner; A constraint definition unit for defining constraint conditions according to the initial parameters: ; wherein, and are user combiners of the b-th drone and non-b-th drones respectively, is the drone cluster; A solution definition unit for generating a solution model according to the initial parameters and the constraint conditions; A solution unit for calculating solution information according to the solution model. The solution information includes: the optimal analog beamforming, digital beamforming of the b-th UAV, the optimal combining vectors of all users, and the optimal sum rate; A result unit for determining the transfer information according to the solution information; An update module, configured to obtain a new b-th drone from the drone cluster, define the transfer information of the previous b-th drone as the initial condition, and enter a decentralized solution module. A traversal module, configured to return to the decentralized module to traverse the drones in the drone cluster to complete communication in the current round.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, the method according to any one of claims 1-7 is implemented.

10. A computer program product, characterized in that, It includes a computer program, and when the computer program is executed by a computer, the method according to any one of claims 1-7 is implemented.

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