CN114143852A - Anti-interference communication link selection method applied to unmanned aerial vehicle cluster - Google Patents
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Abstract
The invention discloses an anti-interference communication link selection method applied to an unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle communication network link selection. The method comprises a link state calculation process and a communication link selection process, wherein the link state calculation is to obtain the channel state and the information transmission rate of each link based on initial external environment information and channel information, the communication link selection is to solve the time consumed by information transmission on each link according to the fixed transmission data volume on the basis of the known link information transmission rate, and finally solve the optimal communication link path based on a dynamic programming algorithm. The invention can actively avoid the communication path of link degradation in the interfered communication environment, adaptively find a better link and ensure the reliable transmission of information data.
Description
Technical Field
The invention relates to the technical field of link selection of unmanned aerial vehicle communication networks, in particular to an anti-interference communication link selection method applied to an unmanned aerial vehicle cluster.
Background
At present, along with unmanned aerial vehicle equipment's rapid development, its bearing and flight duration also obtain promoting fast, and academic and industry take more and more attention to the research and the exploration of using in unmanned aerial vehicle cluster communication network's each side. Through long-term development, through continuous practice attempts in the civil and military fields, the practical application of the unmanned aerial vehicle in various industries is more mature, and the production efficiency of the industries is obviously improved. However, with the expansion of the applicable scenes of the unmanned aerial vehicle communication network, some problems are gradually revealed, in the traditional single unmanned aerial vehicle communication guarantee system, the unmanned aerial vehicle directly realizes the communication guarantee function to the ground communication coverage area, and the unmanned aerial vehicle is used as an aerial base station to support the emergency communication in a certain area. However, in a complex aerial guarantee task, a plurality of unmanned aerial vehicles are needed to form an aerial mobile network in the air to guarantee communication of ground users. However, in the multi-unmanned aerial vehicle communication network, except for considering the cooperation of the communication network of the unmanned aerial vehicle, the non-uniformity of the coverage area of the whole communication network deployment is faced, that is, the environment of each unmanned aerial vehicle communication node in the communication process of the unmanned aerial vehicle network is different, and the interference suffered by each node and the gap of the ground guarantee communication load capacity are large. In addition, because the distribution of ground users does not have regularity, the distribution of unmanned aerial vehicles in the air also does not have regularity, and mutual communication between unmanned aerial vehicles also can mutually interfere. These factors can make the data distribution of the drone communication network less efficient in coordination.
Along with the application of unmanned aerial vehicles in communication in recent years, more and more communication devices are mounted on unmanned aerial vehicles to guarantee the communication of dense crowd areas, and now, unmanned aerial vehicle flight control systems and communication technologies are more mature, in order to better guarantee ground communication, the optimization problem of links in unmanned aerial vehicle cooperative communication is researched, and the application scenes of the unmanned aerial vehicles are further expanded. Based on the background, the decision of the communication link of the unmanned aerial vehicle is a key technical problem of optimizing the communication network of the unmanned aerial vehicle at present and ensuring efficient data transmission in the network.
In the process of guaranteeing a ground communication task, the geographic environments and the electromagnetic environments of different unmanned aerial vehicle nodes have large differences, in the actual communication task, a certain user on the ground generally needs to communicate with other nodes in a network, and an information packet can pass through a plurality of aerial unmanned aerial vehicle relay nodes in the process from a source node to a destination node. The communication conditions of each drone in the communication path may also vary. If the data is directly transmitted by adopting the path planned by the routing protocol, the communication effect of the whole network system can be greatly reduced without considering the change of the real-time external situation environment, and the loss of the data packet is caused. Therefore, a simple routing protocol algorithm cannot be adopted to decide a final communication link, the currently optimal link needs to be judged by combining external real-time state information, the calculated optimal link information is transmitted to a networking system for information transmission, the efficient operation of the network is ensured, and the benefit maximization of network transmission is realized.
Disclosure of Invention
In view of this, the present invention provides an anti-interference communication link selection method applied to an unmanned aerial vehicle cluster, which can select a reliable transmission communication link of an unmanned aerial vehicle in an interfered environment.
The technical scheme adopted by the invention is as follows:
an anti-interference communication link selection method applied to an unmanned aerial vehicle cluster comprises a link state calculation process and a communication link selection process;
the link state calculation process includes the steps of:
(101) receiving external input information, including: power p of jammersAnd the channel state h between the jammer and the node unmanned aerial vehicle 1-Ns，1，hs，2，…，hs,NAdditive white Gaussian noise
Communication transmitting power p of node unmanned aerial vehicle 1-N1，p2，…，pNCommunication bandwidth W;
(102) calculating the link communication rate: calculating a link communication rate of the link in the current state based on the external input information;
the communication link selection procedure comprises the steps of:
(201) calculating the communication transmission time between the links: calculating the communication transmission time between any two links under the condition of transmitting fixed data packets according to the link communication rate;
(202) calculating the shortest path: under the condition of external interference, according to the communication transmission time between any two links in the step (201), a path with the shortest total communication transmission time from the source node unmanned aerial vehicle to the destination node unmanned aerial vehicle is solved based on a dynamic programming algorithm, so that the path degraded by the interference is bypassed.
Further, the specific manner of step (102) is as follows:
(1021) based on external input information, calculating the signal-to-noise ratio of a communication link between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle on an information transmission link:
wherein SINR is the signal to interference plus noise ratio, hk,k+1K is more than or equal to 1 and less than or equal to N-1 for the channel state between the unmanned aerial vehicles of the corresponding nodes;
(1022) calculating a link communication rate between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle in a communication network link based on the signal-to-noise ratio in the step (1021):
Rk.k+1＝Wlog2(1+αk，k+1)
wherein k is more than or equal to 1 and less than or equal to N-1.
Further, the specific manner of step (201) is as follows:
assuming that the size of the fixed unit data packet is Q, calculating the time required for the k-th node unmanned aerial vehicle and the (k + 1) -th node unmanned aerial vehicle to pass through the data packet, namely the communication transmission time t between the k-th node unmanned aerial vehicle and the (k + 1) -th node unmanned aerial vehiclek,k+1：
The specific mode of the step (202) is as follows:
(2021) assuming that there are M communication nodes in a communication network link, after the communication transmission time from the kth node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is known, the communication transmission time cost (t) from the 1 st node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is calculatedk,k+1) Comprises the following steps:
then, in the communication link where the nodes 1 to M are located, the total communication transmission time is:
Ttotal＝cost(tM-1，M)
(2022) obtaining total communication transmission time T of all linkstotalAnd then, calculating a link path with the shortest total communication transmission time from the source node unmanned aerial vehicle S to the destination node unmanned aerial vehicle D based on the communication transmission time between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle, namely solving the problem of the TSP (short message service) traveler:
minTtotal
(2023) solving the TSP (service provider) traveler problem by adopting a genetic algorithm and a dynamic programming algorithm to obtain a path with the shortest total communication transmission time.
Further, the specific manner of step (2023) is as follows:
(20231) Defining an unmanned aerial vehicle node set V, and storing a destination node unmanned aerial vehicle D in the node set V; starting from the destination node unmanned aerial vehicle D, solving the node S with the minimum time consumption and capable of directly communicating with the node DmAnd then S ismAdding the node into a node set V;
(20232) In the node space, removing nodes in the node set V, and finding one node S in the rest nodeskSo that the slave SkThe time consumed from the node in V to the node D is minimized, and S is addedkAdding the node into a node set V;
(20233) Repeating the step (20232) until only the starting node S remains, adding the node S to the node set V;
(20234) And forming a communication link path by the nodes in the node set V according to the adding sequence, namely forming the solved path.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention improves the efficiency of data transmission in the whole network and reduces the packet loss rate.
2. The invention improves the anti-interference performance of the whole topological network, ensures the self-adaptive path finding of the network in the interference environment, and can be suitable for the communication scene of the unmanned aerial vehicle topological network in the complex interference environment.
3. The invention adopts the electromagnetic situation information-based communication link with anti-interference self-adaption generation, can improve the reliability of transmission and ensure the accuracy of ground user data transmission.
Drawings
FIG. 1 is a general flow chart of a method according to an embodiment of the present invention.
FIG. 2 is a detailed flow chart of a method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an aerial drone communication link in an embodiment of the present invention.
Fig. 4 is a flowchart of link communication rate calculation according to an embodiment of the present invention.
Fig. 5 is a flow chart of an over-the-air communication link selection in an embodiment of the present invention.
Fig. 6 is a flowchart of the selection of the unmanned aerial vehicle air communication link based on the dynamic programming algorithm in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples.
An anti-interference communication link selection method applied to an unmanned aerial vehicle cluster comprises a link state calculation process and a communication link selection process, and is shown in fig. 1. Through the ground external environment information (including the interference power of the jammers in the environment, the channel condition and the mutual interference condition of surrounding nodes) which is predicted in advance, the communication condition, namely the communication rate, in all the links in the network is calculated, and the optimal communication link is selected according to the total time consumed by communication. Schematic diagram of air communication link of the drone is shown in fig. 3, there are multiple links that can transmit data in the air, but the two links are interfered to different degrees. The link state calculation is to obtain the channel state and the information transmission rate of each link based on the initial external environment information and the channel information, and the communication link selection is to solve the communication transmission time of each link according to the fixed transmission data volume on the basis of the known link information transmission rate and finally solve the optimal communication link path based on a dynamic programming algorithm.
As shown in fig. 2, the method mainly includes the following steps:
step (1), data input: the input external environment situation information comprises the electromagnetic interference environment and the distribution situation of the unmanned aerial vehicles in the topological network.
Step (2), calculating the signal-to-noise ratio and the signal-to-interference-and-noise ratio: and (4) calculating the packet loss rate of data transmission on the communication link based on the network flow measurement function, starting to automatically find a switching path when the packet loss rate of the data exceeds a threshold value of 0.3, and entering the step (3).
Step (3), communication rate calculation: based on the collected and input electromagnetic information and the unmanned aerial vehicle position information, the error rate and the packet loss rate between any two connected links in the network are calculated, and finally the communication rate is calculated.
Step (4), calculating communication transmission time: the elapsed time for each link for the fixed packet Q to travel from the source node S to the destination node D is calculated.
And (5) calculating the shortest path of the total link time: and solving the minimum total time of the data packet sent from the source node S to the destination node D based on a dynamic programming algorithm, and selecting the communication link with the minimum total time consumption as the final communication link for transmitting data.
The following is a more specific example:
as shown in fig. 1, a method for interference-free communication link path selection applied to a cluster of drones includes a link state calculation process and a communication link selection process.
The link state calculation process refers to calculating the current interfered state of all the air communication links in the initial state, including the signal-to-noise ratio, the signal-to-interference-and-noise ratio and the communication rate between any two nodes. Finally, the time consumption for transmitting from the source node to the destination node under the condition of limiting the size of the fixed data packet can be calculated according to the communication rate between any two links. The purpose of the link state calculation process is to introduce all external initial data into the system for processing, and the communication state of the whole communication network link can be obtained in detail from a physical perspective, so that a basis is provided for later decision making. As shown in fig. 4, the specific steps are as follows:
(1) data input: the initial link calculation module needs to obtain the communication environment state of the external environment. The data types mainly include: power p of jammersChannel state h between jammer and nodes 1-Ns，1，hs，2，…，hs，NAdditive white Gaussian noise
Communication transmission power p of communication nodes 1-N1，p2，…，pNAnd a communication bandwidth W.
(2) And (3) calculating the signal-to-noise ratio: based on the input data, calculating the signal-to-interference-and-noise ratio of the communication between the k node unmanned aerial vehicle and the (k + 1) th node as follows:
(3) calculating the link communication rate: based on the signal-to-noise ratio, calculating the link communication rate between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle in the communication network link:
Rk.k+1＝Wlog2(1+αk，k+1)
the communication link selection process is to calculate the communication time required by the unit fixed data packet size to pass through the link according to the communication rate of the unmanned aerial vehicle aerial communication link. And finally, solving the optimal path in the current interference state according to the consumed time of communication transmission between any two links. As shown in fig. 5, the process mainly includes two steps:
calculating the link communication transmission time: under the limiting condition of the size Q of a fixed unit data packet, calculating the time required by the data packet between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle, namely the communication transmission time t between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehiclek,k+1：
(1) And (3) selecting an optimal link: assuming that there are M communication nodes in a communication network link, after the communication transmission time from the kth node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is known, the communication transmission time cost (t) from the 1 st node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is calculatedk,k+1) Comprises the following steps:
then, in the communication link where the nodes 1 to M are located, the total communication transmission time is:
Ttotal＝cost(tM-1，M)
(2) after the communication transmission time among all the nodes is obtained, the problem is the TSP traveler problem. The final question is then:
minTtotal (1)
in the conventional method for solving the TSP problem, a genetic algorithm and a dynamic programming algorithm may be used to solve the shortest path problem, i.e., the link that consumes the least total time. As shown in fig. 6, the steps of solving the shortest communication link path by the dynamic programming algorithm are as follows:
a: and defining an unmanned aerial vehicle node set V, and storing D into the set V. Calculating from the fixed path end point, i.e. point D in the graph, and solving for the node S with the minimum time consumption capable of directly communicating with the node DmAnd then S ismAnd adding into the set V.
b: in the node space, the nodes in the node set V are removed, and a node S which can minimize the consumption time of the nodes passing through in the past, namely formula (1), is found in the rest nodeskAnd then S iskAnd adding the node into the node set V.
c: and repeating b until only the initial node S remains, and adding the node S into the running traversal point set V.
d: and sequentially sending the data according to the sequence of the communication link paths according to the sequence of the point set combination V.
In a word, the packet loss rate condition in the initial communication link is calculated based on the external electromagnetic interference state and the electromagnetic interference condition of the unmanned aerial vehicle in the network. Then, after the packet loss rate exceeds a certain threshold, the communication link path searching and switching function is automatically started. And calculating the signal noise, the communication transmission rate and the packet loss rate of each sub-link in the whole network based on the electromagnetic interference information. And finally, on the basis of a link meeting the packet loss rate of the basic requirement, calculating the optimal path with the shortest total communication time length from the source node to the destination node based on the genetic algorithm and the ant colony algorithm. The invention can actively avoid the communication path of link degradation in the interfered communication environment, adaptively find a better link and ensure the reliable transmission of information data.
Claims (4)
1. An anti-interference communication link selection method applied to an unmanned aerial vehicle cluster is characterized by comprising a link state calculation process and a communication link selection process;
the link state calculation process includes the steps of:
(101) receiving external input information, including: power p of jammersAnd the channel state h between the jammer and the node unmanned aerial vehicle 1-Ns，1，hs，2，…，hs，NAdditive white Gaussian noise
Communication transmitting power p of node unmanned aerial vehicle 1-N1，p2，…，pNCommunication bandwidth W;
(102) calculating the link communication rate: calculating a link communication rate of the link in the current state based on the external input information;
the communication link selection procedure comprises the steps of:
(201) calculating the communication transmission time between the links: calculating the communication transmission time between any two links under the condition of transmitting fixed data packets according to the link communication rate;
(202) calculating the shortest path: under the condition of external interference, according to the communication transmission time between any two links in the step (201), a path with the shortest total communication transmission time from the source node unmanned aerial vehicle to the destination node unmanned aerial vehicle is solved based on a dynamic programming algorithm, so that the path degraded by the interference is bypassed.
2. The method for selecting the anti-jamming communication link applied to the cluster of unmanned aerial vehicles according to claim 1, wherein the step (102) is implemented in a specific manner as follows:
(1021) based on external input information, calculating the signal-to-interference-and-noise ratio of a communication link between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle on an information transmission link:
wherein, SINR is signal and interference plus noiseSound ratio, hk，k+1K is more than or equal to 1 and less than or equal to N-1 for the channel state between the unmanned aerial vehicles of the corresponding nodes;
(1022) calculating a link communication rate between the kth node unmanned aerial vehicle and the (k + 1) th node unmanned aerial vehicle in a communication network link based on the signal-to-noise ratio in the step (1021):
Rk.k+1＝Wlog2(1+αk，k+1)
wherein k is more than or equal to 1 and less than or equal to N-1.
3. The method for selecting the anti-jamming communication link applied to the unmanned aerial vehicle cluster according to claim 2, wherein the step (201) is specifically performed by:
assuming that the size of the fixed unit data packet is Q, calculating the time required for the k-th node unmanned aerial vehicle and the (k + 1) -th node unmanned aerial vehicle to pass through the data packet, namely the communication transmission time t between the k-th node unmanned aerial vehicle and the (k + 1) -th node unmanned aerial vehiclek，k+1：
The specific mode of the step (202) is as follows:
(2021) assuming that there are M communication nodes in a communication network link, after the communication transmission time from the kth node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is known, the communication transmission time cost (t) from the 1 st node unmanned aerial vehicle to the (k + 1) th node unmanned aerial vehicle is calculatedk，k+1) Comprises the following steps:
then, in the communication link where the nodes 1 to M are located, the total communication transmission time is:
Ttotal＝cost(tM-1，M)
(2022) obtaining total communication transmission time T of all linkstotalAfterwards, based on between k node unmanned aerial vehicle and k +1 node unmanned aerial vehicleThe communication transmission time of (2) is calculated, and the link path with the shortest total communication transmission time from the source node unmanned aerial vehicle S to the destination node unmanned aerial vehicle D is the TSP (traffic service gateway) traveler problem:
minTtotal
(2023) solving the TSP (service provider) traveler problem by adopting a genetic algorithm and a dynamic programming algorithm to obtain a path with the shortest total communication transmission time.
4. A method for selection of an anti-jamming communication link for a cluster of drones according to claim 3, characterized in that the step (2023) is implemented in the following way:
(20231) Defining an unmanned aerial vehicle node set V, and storing a destination node unmanned aerial vehicle D in the node set V; starting from the destination node unmanned aerial vehicle D, solving the node S with the minimum time consumption and capable of directly communicating with the node DmAnd then S ismAdding the node into a node set V;
(20232) In the node space, removing nodes in the node set V, and finding one node S in the rest nodeskSo that the slave SkThe time consumed from the node in V to the node D is minimized, and S is addedkAdding the node into a node set V;
(20233) Repeating the step (20232) until only the starting node S remains, adding the node S to the node set V;
(20234) And forming a communication link path by the nodes in the node set V according to the adding sequence, namely forming the solved path.
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| CN112243252A (en) * | 2020-09-08 | 2021-01-19 | 西北工业大学 | Safety transmission enhancement method for relay network of unmanned aerial vehicle |
| CN113382381A (en) * | 2021-05-30 | 2021-09-10 | 南京理工大学 | Unmanned aerial vehicle cluster network intelligent frequency hopping method based on Bayesian Q learning |
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| CN115774460A (en) * | 2023-02-10 | 2023-03-10 | 四川腾盾科技有限公司 | Unmanned aerial vehicle group topology control method based on interference avoidance |
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