CN114679702A - Deployment of multiple access edge computing cellular networks in unmanned environments - Google Patents
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Abstract
The invention discloses deployment of a multi-address edge computing cellular network in an unmanned environment, which comprises an unmanned aerial vehicle parking apron, a base station, a GS (global system for Mobile communications) deployed on the ground, an unmanned aerial vehicle, a wireless remote control device and an unmanned aerial vehicle edge computing device, wherein the unmanned aerial vehicle MECs are divided into two types of networked MECs and airborne MECs, the GS comprises an RTK (real-time kinematic), a power management module and a GPRS (general packet radio service) communication module, and the GS is also provided with communication equipment and computing equipment; the invention is used for communication and management of unmanned aerial vehicles in autonomous combat service, provides a distributed selective transmission protocol, fully utilizes the advantages of networked MECs and airborne MECs, develops an improved unmanned aerial vehicle cellular network, is used for the unmanned aerial vehicle cellular network for communication and management of unmanned aerial vehicles in autonomous combat service, and can obtain optimal MEC and optimal calculation data under the conditions of unknown MEC calculation capability, unknown unmanned aerial vehicle cellular communication network and unknown sensing data size through the distributed selective transmission protocol.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to deployment of a multi-access edge computing cellular network in an unmanned environment.
Background
The unmanned aerial vehicle plays an important role in a fifth generation 5G communication network system. The unmanned aerial vehicle can be used for a high-altitude autonomous operation terminal pod, and the next generation unmanned aerial vehicle network requires that an unmanned aerial vehicle can be autonomously controlled and integrated in an unmanned environment. With the gradual maturity of unmanned aerial vehicle control and autonomous flight technique, unmanned aerial vehicle with its mobility high, easily control, communication real-time good, the field of vision characteristics such as open, the wide military and civilian fields such as electric power detection, freight, traffic control of being used in. When performing tasks, drones often face complex environments. When performing autonomous tasks, the drones typically need to find and perceive task targets and transmit the perception data back to the base station in real time. To address these issues, the third generation partnership project proposed a method of deploying a communication network for drones, namely the drone cellular internet, that facilitates drone communication and allows drones to transmit awareness data.
The method comprises the steps that under the condition that an unmanned aerial vehicle completely autonomously flies in an unmanned environment, the autonomous flying performance of the unmanned aerial vehicle calculated by an edge in a cellular network changes, and the unmanned aerial vehicle and a mobile edge calculate communicate, so that deployment of a multi-address edge calculation cellular network in the unmanned environment is provided.
Disclosure of Invention
The present invention is directed to a deployment of a multiple access edge computing cellular network in an unmanned environment to solve the problems set forth in the background above.
In order to achieve the purpose, the invention provides the following technical scheme:
the deployment of the multi-address edge computing cellular network in the unmanned environment comprises an Unmanned Aerial Vehicle (UAV) parking apron, a base station, a ground-deployed GS, an UAV, a wireless remote control device and an UAV edge computing device, wherein the UAV MECs are divided into two types of networked MECs and airborne MECs, the GS comprises a real-time kinematic (RTK), a power management module and a General Packet Radio Service (GPRS) communication module, the GS is also provided with communication equipment and computing equipment, the UAV is provided with a camera, an airborne computing platform Jetson TX2 and various types of sensing devices, the UAV acquires sensing data through the camera and numbers the data, the UAV transmits the numbered data to the Jetson TX2 and the GS for computing so as to identify a tracking target and obtain relative position information between the target and the UAV, then the UAV recovers the number of the data, and the Jetson TX2 and the GS send respective computing results back to the UAV for the UAV to filter the data, identifying and tracking the pedestrian by adopting a distributed selection sending protocol to perform edge calculation through visual sensing equipment; the single chip microcomputer imports the filtered data into a motion estimation model, and outputs unmanned aerial vehicle control information through fusion with airborne sensing calculation of the unmanned aerial vehicle.
Preferably, the channel bandwidth of the communication device is 8MHz, the frequency band is 750MHz, the video transmission delay in the test range is 0.47 second, and the computing device includes an Intel Xeon E5-2678V 3 processor and a 2080Ti graphics card.
Preferably, the wireless remote control device selects the unmanned aerial vehicle edge computing device, and the unmanned aerial vehicle edge computing device comprises A, B, C three modes, wherein the mode A supports onboard edge computing device transmission and network edge computing device transmission; mode B allows onboard edge computing device transmissions only; mode C only allows network edge computing devices to transmit.
Preferably, the Jetson-TX2 tracks the pedestrian with an image refresh rate of 3-5 fps. In the test, subaerial pedestrian slowly moves to ensure that aerial camera can catch the target, let unmanned aerial vehicle follow in the air, switch the A, B, C three kinds of modes of unmanned aerial vehicle edge computing equipment, at the in-process of switching edge computing equipment at random, unmanned aerial vehicle can correctly track ground target.
Preferably, the distributed selective transmission protocol includes the following stages: data awareness, edge processing, and data selection.
Preferably, the data perception is that the unmanned aerial vehicle acquires perception data from the sensing equipment, the unmanned aerial vehicle carries GNSS equipment with a timing function during outdoor operation, the error of the timing function is within 25 nanoseconds, a timestamp is added to each group of perception data to indicate the generation time of the data, the perception frequency of the GNSS equipment is 10-20hz, the sampling frequency of the data perception is greater than the perception frequency of the GNSS equipment, the data are marked by the same timestamp and form a unique time sequence in sequence, so that the data synchronization is realized, and then the unmanned aerial vehicle distributes the data to an airborne computing platform and a ground sensor.
Preferably, the edge processing step is: after receiving the data, the airborne computing platform and the ground sensor respectively process the sensing data; extracting the time sequence of the perception data to obtain only required data, and solving the time sequence of the data; screening, discarding and circulating the sensing data, and putting the data into a queue according to a time sequence to ensure the consistency of the data; and adding the original data number to the processed data, attaching the edge computing equipment information to the data, and then sending the processed data back to the unmanned aerial vehicle micro central processing unit.
Preferably, the data selection is: and the UAV MCU receives data sent back by the GS and the airborne MECs, the UAV obtains the data sequence according to the data number, sequentially reads and processes the data, and the UVA discards the received data, wherein the data number is later than the number of the processed data.
Preferably, the unmanned aerial vehicle performs edge calculation on the mass data and is divided into three stages: data perception, data transmission and data processing, the data sensing stage is: at this stage, the drone is aimed at performing tasks with the sensing devices it carries; the airborne equipment classifies the data, performs local processing on the simple data, and sends the complex data to the GS through a special transmission channel; then, the GS adopts high-performance edge computing equipment to process the received data;
The data transmission stage comprises the following steps: at this stage, the unmanned aerial vehicle establishes a plurality of communication channels, detects the communication channels, finds an idle channel and transmits data; the unmanned aerial vehicle preferentially establishes a transmission channel which can be directly connected to the ground sensor; if the drone cannot directly contact the ground sensor, it will search for nearby communicating drones and then create relay communications;
the data processing stage comprises the following steps: at this stage, the GS processes the received perception data. Because the real-time property of data transmission is difficult to realize and time delay exists; therefore, in some cases, prediction needs to be performed on part of the data in the data processing process; and after data processing, transmitting the result back to the terminal unmanned aerial vehicle.
Preferably, the data transmission environment includes massive data transmission, long-distance communication and severe channel interference when the unmanned aerial vehicle executes a task, and the unmanned aerial vehicle can carry multi-channel transmission equipment during massive data transmission and utilize a deep Q-network to perform communication channel switching; use a plurality of unmanned aerial vehicle collaborative work during long distance communication, one of them unmanned aerial vehicle is as data sensing's operation terminal, and other unmanned aerial vehicles carry communications facilities to communicate, need manage the resource in the unmanned aerial vehicle cellular network when serious channel interference, including user association, power management, subchannel distribution etc..
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is used for communication and management of unmanned aerial vehicles in autonomous combat service, provides a distributed selective transmission protocol, fully utilizes the advantages of networked MECs and airborne MECs, develops an improved unmanned aerial vehicle cellular network, is used for the unmanned aerial vehicle cellular network for communication and management of unmanned aerial vehicles in autonomous combat service, and can obtain optimal MEC and optimal calculation data under the conditions of unknown MEC calculation capability, unknown unmanned aerial vehicle cellular communication network and unknown sensing data size through the distributed selective transmission protocol.
2. Time synchronization is carried out by using global navigation satellite system equipment, and a proper edge computing device is selected by adopting a distributed selection sending protocol; taking unmanned aerial vehicle autonomous target tracking as an example, a method for reducing edge calculation time loss by using an unmanned aerial vehicle dynamic model is provided, and experimental verification is carried out.
Drawings
Fig. 1 is a schematic diagram of a fully autonomous drone cellular network of the present invention;
FIG. 2 is a schematic diagram of a distributed selective transmission protocol of the present invention;
fig. 3 is a schematic diagram of a frame of the unmanned aerial vehicle vision testing system of the present invention;
FIG. 4 is a diagram showing the test results of mode A, mode B and mode C according to the present invention;
FIG. 5 is a diagram illustrating the tracking results of mode A, mode B and mode C according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1 to 5, the present invention provides a technical solution: the deployment of the multi-address edge computing cellular network under the unmanned environment comprises an unmanned aerial vehicle parking apron, a base station, a GS (unmanned aerial vehicle) deployed on the ground, an unmanned aerial vehicle, a wireless remote control device and an unmanned aerial vehicle edge computing device, wherein the unmanned aerial vehicle MECs are divided into two types of networked MECs and airborne MECs, the GS comprises an RTK (real-time kinematic), a power management module and a GPRS (general packet radio service) communication module, communication equipment and computing equipment are also carried on the GS, a camera and an airborne computing platform Jetson TX2 and various sensing devices are arranged on the unmanned aerial vehicle, the unmanned aerial vehicle collects sensing data through the camera and numbers the data, the unmanned aerial vehicle transmits the numbered data to Jetson TX2 and the GS for computing so as to identify and track a target and obtain relative position information between the target and the unmanned aerial vehicle, then the unmanned aerial vehicle recovers the number of the data, the Jetson TX2 and the GS send respective computing results back to the MCU for filtering the data by the MCU, identifying and tracking the pedestrians by adopting a distributed selection sending protocol to perform edge calculation through visual sensing equipment; and the singlechip imports the filtered data into a motion estimation model, integrates the filtered data with airborne sensing calculation of the unmanned aerial vehicle, and outputs control information of the unmanned aerial vehicle.
The unmanned aerial vehicle parking apron mainly completes storage, daily management and data processing of unmanned aerial vehicles in the unmanned area. It has various equipment to maintain the health of unmanned aerial vehicle to protect and charge when unmanned aerial vehicle does not carry out the task. In addition, the apron can also replace the equipment that unmanned aerial vehicle carried by machinery, satisfies different work needs. In addition, because the air park is provided with the edge equipment with stronger data processing capability than the embedded processor carried by the unmanned aerial vehicle, the big data can be rapidly processed, and the defect of the computing capability of the airborne terminal is overcome.
The unmanned aerial vehicle often operates in areas without signals, so that it is very necessary to deploy a special communication network to cover a flight area, and ensure flight safety and smooth information reception. Note that GS communications include drone communications, satellite communications, and internet communications. Unmanned aerial vehicle communication includes image transmission and text transmission of radio frequency equipment, and satellite communication equipment includes global navigation satellite system.
As an actuator, drones typically carry different devices to perform tasks, including visible light cameras, communication relays, transmitters. When performing the task, a plurality of ground stations provide corresponding types of unmanned aerial vehicles carrying equipment. Wherein, unmanned aerial vehicle divide into terminal unmanned aerial vehicle and communication unmanned aerial vehicle according to its function.
The terminal unmanned aerial vehicle is used as a high-altitude nacelle and performs various high-altitude tasks by using various types of equipment. However, due to limited device capabilities, tasks are not always successful. Generally, the task success rate is inversely related to the distance between the task target and the drone.
The terminal drone communicates with a single or multiple ground sensors. The terminal unmanned aerial vehicle with different mission targets has different distances from the ground military system. In some cases, no one will enter an area that cannot be covered by the GS signal. For example, drones need to operate on mountains. In this case, the drone cannot maintain smooth communication. Thus, the communicating drone can stay between the terminal drone and the ground sensor, which provides wide coverage to establish a temporary, dedicated relay communication channel. The unmanned aerial vehicle terminal can ensure that the unmanned aerial vehicle terminal can receive and send information in time during working; the channel bandwidth of the communication equipment is 8MHz, the frequency band is 750MHz, the video transmission delay in the test range is 0.47 seconds, and the computing equipment comprises an Intel Xeon E5-2678V 3 processor and a 2080Ti display card.
Further, the wireless remote control device selects the unmanned aerial vehicle edge computing device, and the unmanned aerial vehicle edge computing device comprises A, B, C three modes, wherein the mode A supports onboard edge computing device transmission and network edge computing device transmission; mode B allows onboard edge computing device transmissions only; mode C only allows network edge computing device transmissions; the image refresh rate reaches 3-5fps when Jetson-TX2 tracks pedestrians. In the test, subaerial pedestrian slowly moves to ensure that aerial camera can catch the target, let unmanned aerial vehicle follow in the air, switch the A, B, C three kinds of modes of unmanned aerial vehicle edge computing equipment, at the in-process of switching edge computing equipment at random, unmanned aerial vehicle can correctly track ground target.
In conducting the test, Jetson-TX2 tracked pedestrians, the image refresh rate reached 3-5 fps. In the test, subaerial pedestrian slowly moves to ensure that aerial camera can catch the target, let unmanned aerial vehicle follow aloft. In the next process, we switch the mode of the drone edge computing device randomly. Fig. 5 illustrates the test results. From the test results, it can be seen that the unmanned aerial vehicle can correctly track the ground target in the process of randomly switching the edge computing device.
Further, the distributed selective transmission protocol comprises the following phases: data perception, edge processing and data selection; the data perception is that an unmanned aerial vehicle acquires perception data from sensing equipment, the unmanned aerial vehicle carries GNSS equipment with a timing function during outdoor operation, the error of the timing function is within 25 nanoseconds, a timestamp is added into each group of perception data to indicate the generation time of the perception data, the perception frequency of the GNSS equipment is 10-20hz, the sampling frequency of data perception is greater than the perception frequency of the GNSS equipment, the data are marked by the same timestamp and form a unique time sequence in sequence, so that the data synchronization is realized, and then the unmanned aerial vehicle distributes the data to an airborne computing platform and a ground sensor; the edge processing steps are as follows: after receiving the data, the airborne computing platform and the ground sensor respectively process the sensing data; extracting a time sequence of the perception data to obtain only required data, and solving a time sequence of the data; screening, discarding and circulating the sensing data, and putting the data into a queue according to a time sequence to ensure the consistency of the data; adding the original data number to the processed data, attaching edge computing equipment information to the data, and then sending the processed data back to the unmanned aerial vehicle micro central processing unit; the data selection is: and the UAV MCU receives data sent back by the GS and the airborne MECs, the UAV obtains the data sequence according to the data number, sequentially reads and processes the data, and the UVA discards the received data, wherein the data number is later than the number of the processed data.
By means of the distributed selection sending protocol, the optimal MEC and the optimal calculation data can be obtained under the conditions that the calculation capacity of the MEC is unknown, the cellular communication network of the unmanned aerial vehicle is unknown, and the size of the perception data is unknown.
Further, the unmanned aerial vehicle performs edge calculation on the mass data and is divided into three stages: data perception, data transmission and data processing, the data sensing stage is: at this stage, the drone is aimed at performing tasks with the sensing devices it carries; the airborne equipment classifies the data, performs local processing on the simple data, and sends the complex data to the GS through a special transmission channel; then, the GS adopts high-performance edge computing equipment to process the received data;
the data transmission stage is as follows: at this stage, the unmanned aerial vehicle establishes a plurality of communication channels, detects the communication channels, finds an idle channel and transmits data; the unmanned aerial vehicle preferentially establishes a transmission channel which can be directly connected to the ground sensor; if the drone cannot directly contact the ground sensor, it will search for nearby communicating drones and then create relay communications;
the data processing stage is as follows: at this stage, the GS processes the received perception data. Because the real-time property of data transmission is difficult to realize and time delay exists; therefore, in some cases, prediction needs to be performed on part of the data in the data processing process; and after data processing, transmitting the result back to the terminal unmanned aerial vehicle.
Furthermore, an air park is introduced into the fully autonomous unmanned aerial vehicle cellular network, the air park can work cooperatively with a Ground Station (GS) to provide service for unmanned aerial vehicles which cannot be recovered, and the unmanned aerial vehicle communication network is composed of a plurality of GSs and a plurality of unmanned aerial vehicles. GSs are placed in a non-signal area according to a certain rule and are divided into an air park and a base station, when an unmanned aerial vehicle executes tasks, the data transmission environment comprises mass data transmission, long-distance communication and serious channel interference, when the unmanned aerial vehicle transmits mass data, the unmanned aerial vehicle can carry multi-channel transmission equipment, and a deep Q-network (DQN) is used for switching communication channels; when the unmanned aerial vehicles are used for long-distance communication, the unmanned aerial vehicles work cooperatively, one unmanned aerial vehicle serves as an operating terminal for data sensing, other unmanned aerial vehicles carry communication equipment for communication, and resources in a cellular network of the unmanned aerial vehicles need to be managed when serious channel interference occurs, wherein the resources comprise user association, power supply management, subchannel allocation and the like.
As can be seen from the above description, the present invention has the following advantageous effects: the invention analyzes the situation that the unmanned aerial vehicle flies completely and autonomously in the unmanned environment, and the autonomous flying performance of the unmanned aerial vehicle is calculated by the edge in the cellular network. The specific summary is as follows:
The present invention develops an improved cellular network for unmanned aerial vehicles for communication and management in autonomous combat services.
The present invention utilizes Global Navigation Satellite System (GNSS) devices for time synchronization and then proposes a distributed selection transmission protocol to select the appropriate edge computing device.
The invention provides a method for reducing edge calculation time loss by using an unmanned aerial vehicle dynamic model by taking unmanned aerial vehicle autonomous target tracking as an example, and verification is carried out.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the technical solutions of the present invention and not for the purpose of limiting the same, and other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. Deployment of a multiple access edge computing cellular network in an unmanned environment, characterized by: the unmanned aerial vehicle MECs are divided into two types of networked MECs and airborne MECs, the GS comprises an RTK (real-time kinematic), a power management module and a GPRS (general packet radio service) communication module, the GS also carries communication equipment and computing equipment, the unmanned aerial vehicle is provided with a camera, an airborne computing platform Jetson TX2 and various types of sensing devices, the unmanned aerial vehicle acquires sensing data through the camera and numbers the data, the unmanned aerial vehicle transmits the numbered data to Jetson TX2 and the GS for computing so as to identify a tracked target and obtain relative position information between the target and the unmanned aerial vehicle, then the unmanned aerial vehicle recovers the number of the data, the Jetson TX2 and the GS send respective computing results back to the UAV MCU for filtering the data, and the visual sensing devices adopt a distributed selection sending protocol to carry out edge computing to identify and track pedestrians by adopting a distributed selection sending protocol (ii) a The single chip microcomputer imports the filtered data into a motion estimation model, and outputs unmanned aerial vehicle control information through fusion with airborne sensing calculation of the unmanned aerial vehicle.
2. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 1, wherein: the communication equipment comprises a channel bandwidth of 8MHz, a frequency band of 750MHz and a video transmission delay of 0.47 second in a test range, and the computing equipment comprises an Intel Xeon E5-2678V 3 processor and a 2080Ti display card.
3. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 1, wherein: the wireless remote control device selects an unmanned aerial vehicle edge computing device, the unmanned aerial vehicle edge computing device comprises A, B, C three modes, and the mode A supports the transmission of an onboard edge computing device and the transmission of a network edge computing device; mode B allows only onboard edge computing device transmissions; mode C allows only network edge computing devices to transmit.
4. The deployment of multiple access edge computing cellular networks in an unmanned environment of claim 3, wherein: when the Jetson-TX2 tracks pedestrians, the image refresh rate reaches 3-5fps, the pedestrians on the ground move slowly, so that the aerial camera can capture a target, the unmanned aerial vehicle can follow the target in the air, the A, B, C modes of the edge computing equipment of the unmanned aerial vehicle are switched, and the unmanned aerial vehicle can correctly track the ground target in the process of randomly switching the edge computing equipment.
5. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 1, wherein: the distributed selective transmission protocol comprises the following stages: data awareness, edge processing, and data selection.
6. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 5, wherein: the data perception is that the unmanned aerial vehicle acquires perception data from the sensing equipment, the unmanned aerial vehicle carries GNSS equipment with a timing function during outdoor operation, the error of the timing function is within 25 nanoseconds, a timestamp is added into each group of perception data to indicate the generation time of the perception data, the perception frequency of the GNSS equipment is 10-20hz, the sampling frequency of data perception is greater than the perception frequency of the GNSS equipment, the data are marked by the same timestamp, a unique time sequence is formed in sequence, and therefore data synchronization is achieved, and then the unmanned aerial vehicle distributes the data to an airborne computing platform and a ground sensor.
7. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 5, wherein: the edge processing steps are as follows: after receiving the data, the airborne computing platform and the ground sensor respectively process the sensing data; extracting the time sequence of the perception data to obtain only required data, and solving the time sequence of the data; screening, discarding and circulating the sensing data, and putting the data into a queue according to a time sequence to ensure the consistency of the data; and adding the original data number to the processed data, attaching the edge computing equipment information to the data, and then sending the processed data back to the unmanned aerial vehicle micro central processing unit.
8. The deployment of multiple access edge computing cellular networks in an unmanned environment of claim 5, wherein: the data selection is as follows: and the UAV MCU receives the data sent back by the GS and the airborne MECs, the UAV obtains the data sequence according to the data number, sequentially reads and processes the data, and the UVA discards the received data, wherein the data number is later than the number of the processed data.
9. The deployment of multiple access edge computing cellular networks in an unmanned environment as claimed in claim 1, wherein: the unmanned aerial vehicle carries out edge calculation on the mass data and is divided into three stages: data sensing, data transmission and data processing, wherein the data sensing stage comprises the following steps: at this stage, the drone is aimed at performing tasks with the sensing devices it carries; the airborne equipment classifies the data, performs local processing on the simple data, and sends the complex data to the GS through a special transmission channel; then, the GS adopts high-performance edge computing equipment to process the received data;
the data transmission stage is as follows: at this stage, the unmanned aerial vehicle establishes a plurality of communication channels, detects the communication channels, finds an idle channel and transmits data; the unmanned aerial vehicle preferentially establishes a transmission channel which can be directly connected to the ground sensor; if the drone cannot directly contact the ground sensor, it will search for nearby communicating drones and then create relay communications;
The data processing stage comprises the following steps: at this stage, the GS processes the received sensing data because the real-time performance of data transmission is difficult to implement and there is a time delay; therefore, in some cases, prediction needs to be performed on part of the data in the data processing process; and after data processing, transmitting the result back to the terminal unmanned aerial vehicle.
10. The deployment in an unmanned environment of a multiple access edge computing cellular network as claimed in claim 9, wherein: the data transmission environment comprises mass data transmission, long-distance communication and serious channel interference when the unmanned aerial vehicle executes tasks, and the unmanned aerial vehicle can carry multi-channel transmission equipment and utilize a deep Q-network to switch communication channels when mass data transmission is carried out; use a plurality of unmanned aerial vehicle collaborative work during long distance communication, one of them unmanned aerial vehicle is as data sensing's operation terminal, and other unmanned aerial vehicles carry communications facilities to communicate, need manage the resource in the unmanned aerial vehicle cellular network when serious channel interference, including user association, power management, subchannel distribution etc..
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| CN116321061A (en) * | 2022-12-09 | 2023-06-23 | 湖南精飞智能科技有限公司 | Unmanned aerial vehicle big data management and control platform based on cloud computing |
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