CN112040440A - Unmanned aerial vehicle channel resource allocation method supporting different QoS - Google Patents

Unmanned aerial vehicle channel resource allocation method supporting different QoS Download PDF

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CN112040440A
CN112040440A CN202010735235.0A CN202010735235A CN112040440A CN 112040440 A CN112040440 A CN 112040440A CN 202010735235 A CN202010735235 A CN 202010735235A CN 112040440 A CN112040440 A CN 112040440A
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unmanned aerial
aerial vehicle
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drone
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王军
毛毅
安永旺
陈晶
王海
李媛丽
汪晓婧
孟祥豪
潘建军
王红军
段永胜
王昊
张坤峰
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National University of Defense Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
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    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0446—Resources in time domain, e.g. slots or frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
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Abstract

The invention discloses an unmanned aerial vehicle communication channel resource allocation method supporting different QoS (quality of service). The method comprises the steps of calculating the number of unmanned aerial vehicles, predicting newly arrived unmanned aerial vehicles, predicting departing unmanned aerial vehicles according to movement, optimizing time frames and the like. The channel allocation method allocates channel resources according to the traffic flow demand of the unmanned aerial vehicle, guarantees the bandwidth of the video service, and comprises the steps of mobile prediction, traffic test, allocation of channel resources for the video service, allocation of channel resources for other services and the like. The scheme disclosed by the invention avoids link interruption caused by movement of the unmanned aerial vehicle, thereby improving the reliability of safety grouping and ensuring the bandwidth of video service.

Description

Unmanned aerial vehicle channel resource allocation method supporting different QoS
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle networking communication, and particularly relates to an MAC protocol for unmanned aerial vehicle networking.
Background
In the unmanned aerial vehicle centralized network, a central unmanned aerial vehicle can coordinate the communication of all unmanned aerial vehicles in the network, and the networking mode widely exists in the application of multiple unmanned aerial vehicles. For example, in the drone sensor network, the Sink drone collects traffic packets including video, images, or other sensory data to other drones in a unicast manner. In addition, in order to guarantee the flight safety of the unmanned aerial vehicles, each unmanned aerial vehicle broadcasts safety groups to adjacent unmanned aerial vehicles periodically, wherein the safety groups contain safety information such as positions, speeds and obstacles. The safety grouping has higher requirements on time delay and reliability, and the service grouping needs higher throughput and has certain tolerance capability on time delay and reliability.
Mac (media access control) is a key factor for securing communication performance. Unlike general mobile ad hoc networking, drone networking has unique challenges. Because directional antenna communication distance is far away, transmission rate advantage such as high, it is a trend of unmanned aerial vehicle network to equip directional antenna on unmanned aerial vehicle. However, the moving speed of the drone is high and the coverage angle of the directional antenna is limited, and the drone is prone to link interruption during transmission, thereby causing serious channel resource loss. In addition, drones often need to transmit video packets while performing reconnaissance and surveillance tasks. In order to improve the effectiveness and real-time performance of video applications, the bandwidth of video packets should be preferentially guaranteed. Therefore, how to overcome the link interruption caused by the high mobility of the drone to provide different qos (quality of service) guarantees is an important challenge for the drone network.
At present, although most of the MAC protocols for networking the unmanned aerial vehicle adopt the directional antenna to improve the network performance, they all have the following disadvantages: (1) the high mobility of the unmanned aerial vehicle is not overcome, and the problem of link interruption cannot be solved; (2) most MAC protocols are based on competition, and serious channel resource loss is caused by competition conflict under the condition that the number of nodes is large; (3) without differentiated services, different QoS requirements of different groups cannot be guaranteed.
Disclosure of Invention
In order to solve the technical problem, the invention provides an unmanned aerial vehicle channel resource allocation method supporting different QoS, wherein the unmanned aerial vehicle comprises a central unmanned aerial vehicle and a plurality of other unmanned aerial vehicles which wirelessly communicate with the central unmanned aerial vehicle;
the communication channel is divided in time into time frames at equal intervals, each time frame comprising: control interval CI time frame and service interval SI: a Service Interval time frame for providing services of different QoS;
the CI time frame controls the unmanned aerial vehicle to broadcast the security packet, and the SI time frame controls the unmanned aerial vehicle to transmit the service packet point to point.
According to the method of the present invention, preferably, the CI time frame includes: a safety notification phase, a TP transmission phase and a CP competition phase;
the transmission TP phase and the competition CP phase comprise: the number of the time slots is indefinite, the time slots are the same at intervals, the unmanned aerial vehicle which has accessed the network is allocated with the channel time slot in the TP transmission stage, and the unmanned aerial vehicle which newly joins the network is allocated with the idle channel time slot in the CP competition stage.
According to the method of the present invention, preferably, during the security announcement phase, the central drone broadcasts an announcement containing the following information: the number and allocation of channel time slots in the TP transmission stage, the number of idle time slots in the CP competition stage, and the safety grouping of the self;
after receiving the notification information, the unmanned aerial vehicle broadcasts the safety packet in the TP phase time slot allocated by the central unmanned aerial vehicle according to the time slot allocation result of the central unmanned aerial vehicle, and the newly added unmanned aerial vehicle randomly occupies the idle time slot in the CP phase and broadcasts the safety packet.
According to the method of the present invention, preferably, the SI time frame includes: a channel allocation stage and a service transmission stage;
after all the unmanned aerial vehicles broadcast the security grouping, the central unmanned aerial vehicle allocates channel resources of SI time frames for the unmanned aerial vehicles which need to send the service grouping according to the queue length and the type of the service grouping;
in the channel allocation stage, the central unmanned aerial vehicle sends an announcement again, wherein the announcement comprises a channel allocation result of an SI time frame;
and in the service transmission stage, each unmanned aerial vehicle switches the antenna to the directional mode according to the distribution result and transmits service packets to the central unmanned aerial vehicle.
In order to solve the technical problem, the invention provides an unmanned aerial vehicle communication method based on movement prediction, which adopts the method to allocate communication channel resources to an unmanned aerial vehicle, and comprises the following steps:
step 1, calculating the number of unmanned aerial vehicles in the nth time frame unmanned aerial vehicle network:
step 2, predicting the number of newly arrived unmanned aerial vehicles in a new nth time frame according to historical data of the unmanned aerial vehicles;
step 3, predicting the number of unmanned aerial vehicles leaving the communication range of the central unmanned aerial vehicle at the nth time frame;
and 4, optimizing the channel resources of the unmanned aerial vehicle centralized network of the unmanned aerial vehicle in the (n +1) th time frame again.
According to the method of the present invention, preferably, in step 1, the number of drones in the nth time frame drone network is calculated according to the following steps:
firstly, the number of unmanned aerial vehicle nodes in the network is calculated:
N(n)＝Ntp(n)+Nn(n-1) (1)
wherein N (N) is the number of the unmanned aerial vehicles in the nth time frame, Ntp(N) is the number of slots in the TP phase, i.e. the number of drones that have accessed the drone network, Nn(N-1) represents the number of newly arrived unmanned aerial vehicles in the N-1 th time frame, and N can be obtained by calculation through the following formula according to the conflict proportion of the CP time slot of the N-1 th time framen(n-1)：
Figure BDA0002604672730000031
Wherein N iscp-c(N) and Ncp(n) respectively indicates the number of colliding slots and the total number of slots in the n-th time frame CP phase, and is predetermined.
According to the method of the present invention, preferably, the step 2 specifically includes:
Figure BDA0002604672730000032
where m is the number of historical data, representing the number of drones that arrived at the nth-i time frame.
According to the method of the present invention, preferably, the step 3 specifically includes:
Figure BDA0002604672730000041
wherein N isleaveIndicating the number of drones leaving the communication range of the central drone at the nth time frame, PcAnd PiRespectively showing the positions of the now central drone and drone i,
Figure BDA0002604672730000042
and
Figure BDA0002604672730000043
respectively representing the speed, t, of the current central unmanned aerial vehicle and unmanned aerial vehicle iSIIndicating the length, R, of the current time frame SIoIs the communication distance, x, of the omnidirectional antennaiFor indicating the communication range of a drone at the central drone omni-directional antenna, if xiEqual to 0, it means that drone i will leave the communication range of the central drone omni-directional antenna.
According to the method of the present invention, preferably, the step 4 specifically includes:
due to the fact that the unmanned aerial vehicles move at a high speed, the topology of the unmanned aerial vehicle centralized network changes frequently, time slots do not need to be allocated for competition of the unmanned aerial vehicles leaving the network in the next time frame, and the number of idle time slots in the CP needs to be adjusted to enable new unmanned aerial vehicles to access the network. So in n +1 frames, the allocation of the number of slots is calculated as follows:
Figure BDA0002604672730000044
Ntp(n+1)＝Ntp(n)+Ncp-s(n)-Nleave(n), (6)
Ncp(n+1)＝Nn(n-1)-Ncp-s(n)+Nn(n), (7)
tSI＝T-tCI
(8)
wherein N isCI(N +1) is the number of CI slots in the (N +1) th time frame, Ncp-s(n) is the number of time slots successfully contended in the CP stage of the nth time frame, i.e. the number of new unmanned planes successfully accessing the network, the length of the time slots is fixed, and the length t of the SI time frame is fixedSIWith the length t of the CI time frameCIAnd, varying, T is the total length of the time frame;
the central drone obtains the queue length and type of all drone traffic packets and then allocates channel resources to them based on this information.
In order to solve the above technical problem, the present invention provides a channel resource allocation method for video packet transmission, which performs communication channel resource allocation for an unmanned plane by using the foregoing method, and the method includes the following steps:
step 1, a central unmanned aerial vehicle judges which unmanned aerial vehicles can trigger link interruption in the transmission process according to movement prediction, and cancels the allocation of channel resources for the unmanned aerial vehicles;
step 2, the central unmanned aerial vehicle predicts the arrival rate of each unmanned aerial vehicle service packet;
step 3, channel resources are allocated to each unmanned aerial vehicle for transmitting video packets;
and 4, distributing channel resources for other unmanned planes.
The invention utilizes the central unmanned aerial vehicle to schedule the communication among the unmanned aerial vehicles, can ensure different QoS services, overcomes the problem of link interruption caused by high mobility of the unmanned aerial vehicle, and reduces the channel resource loss; the high reliability of the safety grouping and the high throughput of the service grouping are improved; the bandwidth of video packets is guaranteed, and the communication quality of video application is improved.
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Fig. 1 is a diagram of an unmanned aerial vehicle communication architecture of the present invention;
FIG. 2 is a time frame structure of the present invention;
FIG. 3 is a schematic diagram of CI time frame composition according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
Referring to fig. 1, the present invention provides a communication device for guaranteeing different QoS, which includes a central drone and other drone nodes communicating with the central drone. The communication channel between the unmanned aerial vehicle and the central unmanned aerial vehicle is divided into time frames with the same Interval in time, and then each time frame is further divided into a Control Interval (CI) and a Service Interval (SI) so as to provide services with different QoS. Fig. 2 is a schematic diagram of the composition structure of the time frame. CI controls the unmanned plane node to broadcast the safety grouping, and SI controls the unmanned plane to transmit the service grouping point to point. The method mainly comprises the following steps:
(1) and CI interval, wherein the antenna is set to be in an omnidirectional mode, and all unmanned aerial vehicle nodes occupy CI time slots to broadcast security packets. As shown in FIG. 3, to differentiate services, the CI is further divided into a security advertisement Phase, a Transmission Phase (TP), and a Contention Phase (CP). The TP and the CP are formed by time slots with the same quantity and interval, the TP is the time slot allocated by the unmanned plane node which has accessed the network, and the CP is the idle time slot provided for the unmanned plane node which newly joins the network. In the security announcement phase, the central drone broadcasts an announcement containing the following information: (1) the number and allocation condition of the time slots forming the TP; (2) the number of time slots constituting a CP; (3) own security groups. The number of slots of CI (i.e., total number of TP and CP slots) is dynamically changed according to the number of drones. After receiving the notification information, according to the result of the time slot allocation of the central unmanned aerial vehicle, other unmanned aerial vehicles broadcast the security packet in the TP time slot allocated to the central unmanned aerial vehicle, and the newly added unmanned aerial vehicle randomly occupies the idle time slot in the CP and broadcasts the security packet (a data packet containing security information).
(2) The method comprises the following steps that in the SI interval, an antenna is set to be in a directional mode, and the SI is further divided into a channel allocation stage and a service transmission stage. When all the nodes of the unmanned aerial vehicle broadcast the security packet (i.e. the end time of the CP), the central unmanned aerial vehicle allocates the channel resource (i.e. transmission time) of the SI to the unmanned aerial vehicle that is to send the service packet according to the queue length and type of the service packet. In the channel allocation phase, the central drone sends again an announcement, which contains the channel allocation result of the SI. And in the service transmission stage, each unmanned aerial vehicle switches the antenna to the directional mode according to the distribution result and transmits service packets to the central unmanned aerial vehicle.
The time frame interval is fixed to 100ms to control the latency of the security packets.
A method of communication based on motion prediction, characterized by: at the ending moment of the CI, the central unmanned aerial vehicle calculates the number of the unmanned aerial vehicles through movement prediction; and then, in the safety notification stage of the next time frame CI, the central unmanned aerial vehicle adjusts the time slot allocation and the number in the CI according to the number of the unmanned aerial vehicles. The method mainly comprises the following steps:
and step 1, calculating the number of the unmanned aerial vehicles. Assuming that the current time frame is the nth time frame, the number of time slots of the future time frame, i.e., the time slot of the (n +1) th time frame, needs to be calculated. Firstly, the number of unmanned aerial vehicle nodes in the network is calculated:
N(n)＝Ntp(n)+Nn(n-1), (1)
where N (N) is the number of drones that have accessed the network in the nth time frame, N being a time slot for each dronetp(n) is the number of slots in the TP, i.e. the number of drones that have access to the network. N is a radical ofn(n-1) represents the number of drones that newly arrive at the access network at the time frame n-1. Since a newly arriving drone must receive the announcement first before it can know the CP's slot, it will access the network the next time frame after it arrives. According to the collision ratio of the N-th time frame CP time slot, N can be obtained by the following formulan(n-1)
Figure BDA0002604672730000071
Wherein N iscp-c(N) and Ncp(n) denotes the number of CP colliding slots and the total number of slots, respectively, at the nth time frame. N is a radical ofcp-c(N) and Ncp(N) are known values, and by substituting them into the equations (4-2) and (4-1), N can be obtainedn(n-1) and N (n).
And 2, predicting the number of newly arrived unmanned aerial vehicles. The central drone predicts the number of drones newly arriving at the network at the nth time frame, i.e. Nn(n) it may beEstimating according to the mean value of historical data:
Figure BDA0002604672730000072
where m is the number of historical data.
And 3, predicting the unmanned aerial vehicle which leaves according to the movement. Predicting the number of unmanned aerial vehicles leaving the communication range of the central unmanned aerial vehicle at the nth time frame by using NleaveAnd (4) showing. Since the moving track of the unmanned aerial vehicle is smooth, the following formula is used for calculating:
Figure BDA0002604672730000073
wherein P iscAnd PiRespectively showing the positions of the now central drone and drone i,
Figure BDA0002604672730000074
and
Figure BDA0002604672730000075
the speed of the now central drone and drone i, respectively. t is tSIIndicating the length, R, of the current time frame SIoIs the communication distance of the omni-directional antenna. x is the number ofiIndicating whether drone i is within communication range of the central drone omni-directional antenna, if xiEqual to 0, it means that drone i will leave the communication range of the central drone omni-directional antenna.
And 4, optimizing the time frame. Due to the fact that the unmanned aerial vehicles move at a high speed, the topology of the unmanned aerial vehicle centralized network changes frequently, time slots do not need to be allocated for competition of the unmanned aerial vehicles leaving the network in the next time frame, and the number of idle time slots in the CP needs to be adjusted to enable new unmanned aerial vehicles to access the network. So in n +1 frames, the allocation of the number of slots is calculated as follows:
NCI(n+1)＝Ntp(n+1)+Ncp(n+1) (5)
＝N(n)-Nleave(n)+Nn(n),
Ntp(n+1)＝Ntp(n)+Ncp-s(n)-Nleave(n), (6)
Ncp(n+1)＝Nn(n-1)-Ncp-s(n)+Nn(n), (7)
wherein N isCI(N +1) is the number of slots of the (N +1) th time frame CI, Ntp(n+1)，Ncp(n +1) indicates the number of slots in the (n +1) th time frame, i.e., the next time frame TP, CP, respectively. N is a radical ofcp-sAnd (n) is the number of time slots successfully contended in the nth time frame CP, i.e. the number of new drones successfully accessing the network. Equations (1), (3) and (4) are substituted into equations (5), (6) and (7). The central drone can calculate the number of slots and the allocation of the next time frame, i.e. the n +1 time frame CI. Since the length of the whole time frame is fixed, the length of the time frame SI varies with the length of the time frame CI, and is specifically calculated as follows:
tSI(n+1)＝T-tCI(n+1) (8)
t is the total length of the time frame.
The central drone obtains the queue length and type of all drone traffic packets and then allocates channel resources to them based on this information.
The invention also discloses a channel allocation method for guaranteeing the transmission of video packets, which is characterized by comprising the following steps: at the end of the CI, the central drone preferentially allocates reasonable channel resources to the video packets (in the channel allocation phase of the SI) by the movement prediction of the drone and the prediction of the traffic flow, as follows.
Step 1, movement prediction of the unmanned aerial vehicle. In order to guarantee sufficient transmission bandwidth of video packets and prevent link interruption, a central unmanned aerial vehicle firstly judges which unmanned aerial vehicles can trigger link interruption in the transmission process according to movement prediction, and cancels allocation of channel resources for the unmanned aerial vehicles. The prediction formula is as follows:
Figure BDA0002604672730000091
xi＝1,otherwise,
wherein R isdAnd thetaIndicating the coverage distance and angle of the directional antenna. Unlike equation (4), equation (9) modifies the communication distance and adds the constraint of angle. If xiEqual to 0, drone i cannot maintain communication with the central drone in future SIs even if the antenna is in directional mode.
And 2, predicting the service flow. And the central unmanned aerial vehicle predicts the arrival rate of each unmanned aerial vehicle service packet to formulate a channel allocation scheme. Suppose there are N drones in the network, with Ri(n) represents the queue length of the ith unmanned plane in the service packet of the nth time frame, and the queue length of the previous m time frames is Ri(n-1)，Ri(n-2)，…，Ri(n-m). The arrival rate of each time frame traffic packet can be calculated based on the queue length. For example, the arrival rate of drone i at time frame n (denoted as r) is calculatedi(n))：
ri(n)＝Ri(n)-[Ri(n-1)-Ti(n-1)], (10)
Wherein T isi(n-1) represents the packet size that drone i has transmitted in the (n-1) th time frame. Similarly, the packet arrival rate for historical time frames, r, can be calculatedi(n-1)，ri(n-2)，…，ri(n-m). Supposing that the packet arrival rate of the unmanned aerial vehicle has autocorrelation in time, predicting the arrival rate r of the next time frame according to a historical value by a flow prediction theoryi(n+1)。
And 3, distributing channel resources for the video service. And when the current channel resources of the SI cannot meet the service requirements of all unmanned aerial vehicles, the bandwidth of the video packets is preferentially ensured. Assuming that k sets of drones are to transmit video packets in SI, their packet queue length is defined as Rv1(n)，Rv2(n)，…，Rvj(n)，…，Rvk(n) of (a). In order to guarantee minimum bandwidth B of video packetsminThe minimum transmission time of each drone can be calculated as:
Figure BDA0002604672730000101
whereintvjmin(n) represents the minimum transmission time required to transmit the jth drone in the video packet, rtIndicating the channel capacity, i.e. the maximum transmission rate, xvjAnd indicating the boolean constant of the jth video service transmitting drone. The calculation is only an initial value, and the actual distribution value needs to be adjusted according to the congestion condition of the channel of the next time frame. If the current channel resource is relatively short and the channel is relatively idle in the future, the allocation value can be reduced appropriately to make more channel resources for other services. If the current channel is more idle and the future channel is more crowded, more channel resources need to be allocated to the video packets to relieve the contention of the congested channel for the video traffic in the future. The channel resources ultimately allocated to each drone transmitting the video are therefore:
Figure BDA0002604672730000102
wherein t isvj(n) denotes the channel resources allocated for the jth drone transmitting the video, α is the adjustment factor, Ri(n) represents the queue length of the ith drone in the nth time frame traffic packet. In equation (12), two factors are used to compare the congestion condition of the next time frame. The first factor is the comparison of the arrival rates of the traffic of the current time frame and the next time frame, and the other factor is the length ratio of the two time frames SI. Since the length of each time frame is fixed, the number of slots of the CI is dynamically adjusted according to claim 2, and therefore the length of the SI changes accordingly. Furthermore, the mechanism incorporates movement prediction if drone i generates a link outage xiEqual to 0, otherwise equal to 1. When the current channel resources cannot meet the requirements of all video users, that is
Figure BDA0002604672730000103
A greedy algorithm is applied to discard the need of one or more least influential users.
And 3, distributing channel resources for other services. And after the unmanned aerial vehicle for transmitting the video is distributed with the channel, distributing channel resources for other unmanned aerial vehicles. If the channel resources are insufficient, allocating the channel resources for the queue according to the queue length in proportion, namely:
Figure BDA0002604672730000111
vj∈C,j∈(S-C)
tiindicating the channel resources allocated to the ith non-video service. C denotes the set of all users transmitting video packets and S denotes the set of all users.
It will be evident to those skilled in the art that the embodiments of the present invention are not limited to the details of the foregoing illustrative embodiments, and that the embodiments of the present invention are capable of being embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the embodiments being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Several units, modules or means recited in the system, apparatus or terminal claims may also be implemented by one and the same unit, module or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the embodiments of the present invention and not for limiting, and although the embodiments of the present invention are described in detail with reference to the above preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the embodiments of the present invention without departing from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A channel resource allocation method for unmanned aerial vehicles supporting different QoS is characterized in that the unmanned aerial vehicles comprise a central unmanned aerial vehicle and a plurality of other unmanned aerial vehicles which wirelessly communicate with the central unmanned aerial vehicle;
the communication channel is divided in time into time frames at equal intervals, each time frame comprising: control interval CI time frame and service interval SI: a Service Interval time frame for providing services of different QoS;
the CI time frame controls the unmanned aerial vehicle to broadcast the security packet, and the SI time frame controls the unmanned aerial vehicle to transmit the service packet point to point.
2. The method of claim 1, wherein the CI time frame comprises: a safety notification phase, a TP transmission phase and a CP competition phase;
the transmission TP phase and the competition CP phase comprise: the number of the time slots is indefinite, the time slots are the same at intervals, the unmanned aerial vehicle which has accessed the network is allocated with the channel time slot in the TP transmission stage, and the unmanned aerial vehicle which newly joins the network is allocated with the idle channel time slot in the CP competition stage.
3. The method of claim 2, wherein during the security advertisement phase, the central drone broadcasts an advertisement containing the following information: the number and allocation of channel time slots in the TP transmission stage, the number of idle time slots in the CP competition stage, and the safety grouping of the self;
after receiving the notification information, the unmanned aerial vehicle broadcasts the safety packet in the TP phase time slot allocated by the central unmanned aerial vehicle according to the time slot allocation result of the central unmanned aerial vehicle, and the newly added unmanned aerial vehicle randomly occupies the idle time slot in the CP phase and broadcasts the safety packet.
4. The method of claim 1, wherein the SI time frame comprises: a channel allocation stage and a service transmission stage;
after all the unmanned aerial vehicles broadcast the security grouping, the central unmanned aerial vehicle allocates channel resources of SI time frames for the unmanned aerial vehicles which need to send the service grouping according to the queue length and the type of the service grouping;
in the channel allocation stage, the central unmanned aerial vehicle sends an announcement again, wherein the announcement comprises a channel allocation result of an SI time frame;
and in the service transmission stage, each unmanned aerial vehicle switches the antenna to the directional mode according to the distribution result and transmits service packets to the central unmanned aerial vehicle.
5. A method for communication of drones based on movement prediction, which uses the method according to any one of claims 1 to 4 for communication channel resource allocation to drones, characterized in that it comprises the following steps:
step 1, calculating the number of unmanned aerial vehicles in the nth time frame unmanned aerial vehicle network:
step 2, predicting the number of newly arrived unmanned aerial vehicles in a new nth time frame according to historical data of the unmanned aerial vehicles;
step 3, predicting the number of unmanned aerial vehicles leaving the communication range of the central unmanned aerial vehicle at the nth time frame;
and 4, optimizing the channel resources of the unmanned aerial vehicle centralized network of the (n +1) th time frame again.
6. The method of claim 5, wherein in step 1, the number of drones in the nth time frame drone network is calculated according to the following steps:
firstly, the number of unmanned aerial vehicle nodes in the network is calculated:
N(n)＝Ntp(n)+Nn(n-1) (1)
wherein N (N) is the number of the unmanned aerial vehicles in the nth time frame, Ntp(N) is the number of slots in the TP phase, i.e. the number of drones that have accessed the drone network, Nn(N-1) represents the number of unmanned aerial vehicles which newly arrive at the nth-1 time frame and access to the network, and N can be obtained by calculation through the following formula according to the conflict proportion of the CP time slot of the nth time framen(n-1)：
Figure FDA0002604672720000021
Wherein N iscp-c(N) and Ncp(n) respectively indicates the number of colliding slots and the total number of slots in the n-th time frame CP phase, and is predetermined.
7. The method according to claim 5, characterized in that said step 2 comprises in particular:
Figure FDA0002604672720000022
where m is the number of historical data, representing the number of drones that arrived at the nth-i time frame.
8. The method according to claim 5, characterized in that said step 3 comprises in particular:
Figure FDA0002604672720000031
wherein N isleaveIndicating the number of drones leaving the communication range of the central drone at the nth time frame, PcAnd PiRespectively showing the positions of the now central drone and drone i,
Figure FDA0002604672720000032
and
Figure FDA0002604672720000033
respectively representing the speed, t, of the current central unmanned aerial vehicle and unmanned aerial vehicle iSIIndicating the length, R, of the current time frame SIoIs the communication distance, x, of the omnidirectional antennaiFor indicating the communication range of a drone at the central drone omni-directional antenna, if xiEqual to 0, it means that drone i will leave the communication range of the central drone omni-directional antenna.
9. The method according to claim 8, wherein the step 4 specifically comprises:
in the n +1 frame, the allocation of the number of slots is calculated as follows:
Figure FDA0002604672720000034
Ntp(n+1)＝Ntp(n)+Ncp-s(n)-Nleave(n), (6)
Ncp(n+1)＝Nn(n-1)-Ncp-s(n)+Nn(n), (7)
tSI(n+1)＝T-tCI(n+1) (8)
wherein N isCI(N +1) is the number of CI slots in the (N +1) th time frame, Ncp-s(n) is the number of time slots successfully contended in the CP stage of the nth time frame, i.e. the number of new unmanned planes successfully accessing the network, because the length of the whole time frame is fixed, the length t of the SI time frame isSI(n +1) length t of time frame with CICI(n +1), T being the total length of the time frame;
and the central unmanned aerial vehicle distributes channel resources according to the queue length and the type of all unmanned aerial vehicle service groups.
10. A channel resource allocation method for video packet transmission, the method performing communication channel resource allocation for drones using the method according to any of claims 1 to 4, characterized in that the method comprises the following steps:
step 1, a central unmanned aerial vehicle judges which unmanned aerial vehicles can trigger link interruption in the transmission process according to movement prediction, and cancels the allocation of channel resources for the unmanned aerial vehicles;
step 2, the central unmanned aerial vehicle predicts the arrival rate of each unmanned aerial vehicle service packet;
step 3, channel resources are allocated to each unmanned aerial vehicle for transmitting video packets;
and 4, distributing channel resources for other unmanned planes.
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