CN115278905B - Multi-node communication opportunity determination method for unmanned aerial vehicle network transmission - Google Patents

Multi-node communication opportunity determination method for unmanned aerial vehicle network transmission Download PDF

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CN115278905B
CN115278905B CN202211196341.1A CN202211196341A CN115278905B CN 115278905 B CN115278905 B CN 115278905B CN 202211196341 A CN202211196341 A CN 202211196341A CN 115278905 B CN115278905 B CN 115278905B
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unmanned aerial
transmission
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CN115278905A (en
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陈俊挺
李博文
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Chinese University of Hong Kong Shenzhen
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    • 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
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • 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
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract

The invention discloses a multi-node communication opportunity determination method for network transmission of an unmanned aerial vehicle, which comprises the following steps: s1, giving a transmission task and defining a transmission time; s2. At
Figure 100004_DEST_PATH_IMAGE001
At all times, each node
Figure 736675DEST_PATH_IMAGE002
Acquiring the motion track of the user in the future
Figure DEST_PATH_IMAGE003
And transmitted to the adjacent nodes; s3, each unmanned aerial vehicle node deduces a future channel state between each unmanned aerial vehicle node and an adjacent node based on the received adjacent node track information and the track information of the unmanned aerial vehicle node; and S4, based on the motion tracks of the unmanned aerial vehicle and the adjacent nodes and the transmission opportunity of the adjacent nodes, updating the transmission opportunity of the unmanned aerial vehicle nodes in a distributed iterative manner. The invention realizes the optimization of distributed communication energy consumption by utilizing the received track information between adjacent nodes and the track information of the nodes, wherein the proposed transmission opportunity negotiation strategy can effectively utilize channels between the nodes, and the reduction of the whole communication energy is realized.

Description

Multi-node communication opportunity determination method for network transmission of unmanned aerial vehicle
Technical Field
The invention relates to the field of communication, in particular to a multi-node communication opportunity determination method for network transmission of unmanned aerial vehicles.
Background
The unmanned aerial vehicle is widely applied to the fields of monitoring and reconnaissance, communication relay, cargo transportation, emergency search and the like. Drones deployed at various tasks are being widely and densely distributed over cities. Drones are typically self-contained with wireless communication capabilities. Existing drone applications typically deploy a special drone or a cluster of drones individually for different application scenarios, such as using drone-assisted logistics, drone-assisted monitoring, and so on. In these applications, there is no consideration of how to reuse deployed drones to establish an auxiliary communication network as a secondary task for the drones. In fact, the deployed unmanned aerial vehicle network enjoys a good transmission opportunity of an aerial line-of-sight link, and has the potential of constructing a large-capacity wireless communication network on the premise of not influencing primary tasks (such as logistics, monitoring and the like) of an unmanned aerial vehicle cluster, assists ground communication, and improves the communication performance of the ground network. However, an effective technical solution is still lacked, on the premise that a specific task of the drone cluster is not affected, how to develop the network communication performance of the drone cluster to the maximum extent.
The key technical problem of building the wireless communication network of the unmanned aerial vehicle is how to reduce the communication energy consumption of the unmanned aerial vehicle, and the influence on the main task of the unmanned aerial vehicle is reduced to the maximum extent. The existing scheme is that a communication network is constructed by unmanned aerial vehicle clusters which execute non-communication tasks in a multiplexing mode without consideration, the communication energy consumption is reduced by using the track information of the unmanned aerial vehicles aiming at a low-delay sensitive data transmission scene, and a multi-unmanned aerial vehicle cooperative communication network is not established from a distributed angle under a limited information interaction scene.
Specifically, in the scene of the internet of things, a sensor collects a large amount of data, wherein a lot of data belong to low-delay sensitive data, for example, the communication delay tolerance can reach more than 1 minute. The existing communication network is usually designed in a "best-effort" manner, and the delay index is usually below the second level, so that it is not the most effective scheme to transmit massive and ultra-low delay sensitive data by using the design architecture of the existing communication network. For example, the design of existing drone communication networks usually aims at optimizing transmission capacity, such as: effective coverage of the unmanned aerial vehicle network is guaranteed by optimizing the distance between the unmanned aerial vehicle and the user, but the unmanned aerial vehicle communication network is not optimized for the ultra-low delay sensitivity characteristic of data transmission. In addition, most of the existing unmanned aerial vehicle communication network designs focus on deployment or track design of unmanned aerial vehicles or unmanned aerial vehicle clusters, but no consideration is given to multiplexing deployed unmanned aerial vehicles to establish a communication network, so that the track information of the unmanned aerial vehicles is not utilized to optimize the unmanned aerial vehicle network. In addition, most of the existing multi-unmanned aerial vehicle cooperative network architectures adopt a centralized control cooperative mode or a distributed cooperative mode based on global information. However, both centralized control collaboration and distributed collaboration based on global information have high computational complexity, and the intensive information exchange also brings a huge communication burden.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multi-node communication opportunity determination method for network transmission of unmanned aerial vehicles.
The purpose of the invention is realized by the following technical scheme: a multi-node communication opportunity determination method for network transmission of unmanned aerial vehicles comprises the following steps:
s1, giving a transmission task and defining a transmission opportunity:
given transmission tasks at a delay tolerance threshold
Figure DEST_PATH_IMAGE001
Passing in seconds
Figure 717104DEST_PATH_IMAGE002
Erect unmanned aerial vehicle with source end production
Figure 59224DEST_PATH_IMAGE003
Transmitting the bit data to a destination;
setting source end as node
Figure 314756DEST_PATH_IMAGE004
，
Figure 33313DEST_PATH_IMAGE002
Erecting unmanned aerial vehicle as slave node
Figure DEST_PATH_IMAGE005
To a node
Figure 159532DEST_PATH_IMAGE002
The destination is a node
Figure 938132DEST_PATH_IMAGE006
In a
Figure 212119DEST_PATH_IMAGE001
Defining the transmission time under the requirement of second transmission delay
Figure 734367DEST_PATH_IMAGE007
Satisfy the requirement of
Figure 777409DEST_PATH_IMAGE008
Wherein in
Figure 726911DEST_PATH_IMAGE009
Node point
Figure 482334DEST_PATH_IMAGE010
Need to be in the transmission interval
Figure 542694DEST_PATH_IMAGE011
Integrally transmitting data to nodes
Figure DEST_PATH_IMAGE012
；
S2, in
Figure 909084DEST_PATH_IMAGE013
At all times, each node
Figure 29487DEST_PATH_IMAGE010
Obtaining the motion track of the user in the future
Figure 278066DEST_PATH_IMAGE014
And transmitted to the adjacent nodes;
s3, based on the received track information of the adjacent nodes and the track information of the unmanned aerial vehicle, each unmanned aerial vehicle node deduces the future channel state between each unmanned aerial vehicle node and the adjacent node;
and S4, updating the transmission opportunity of the unmanned aerial vehicle node in a distributed iterative manner based on the motion tracks of the unmanned aerial vehicle node and the adjacent nodes and the transmission opportunity of the adjacent nodes.
In the step S2, any node is processed
Figure 876538DEST_PATH_IMAGE010
The neighboring nodes refer to nodes communicating with themselves:
when the temperature is higher than the set temperature
Figure 159751DEST_PATH_IMAGE015
Time, i.e. node
Figure 185476DEST_PATH_IMAGE010
When the source node is a node, the adjacent nodes are nodes
Figure 921351DEST_PATH_IMAGE012
；
When in use
Figure 323514DEST_PATH_IMAGE016
Time, i.e. node
Figure 461234DEST_PATH_IMAGE010
When the unmanned aerial vehicle node is used, the adjacent nodes comprise nodes
Figure 657860DEST_PATH_IMAGE017
And node
Figure 881031DEST_PATH_IMAGE012
；
When the temperature is higher than the set temperature
Figure 86884DEST_PATH_IMAGE018
When being a node
Figure 813532DEST_PATH_IMAGE010
When the destination node is a node, its neighboring nodes are nodes
Figure 712218DEST_PATH_IMAGE002
。
The step S3 includes:
unmanned aerial vehicle node
Figure 157106DEST_PATH_IMAGE010
According to the received motion track
Figure 901071DEST_PATH_IMAGE019
And
Figure 747804DEST_PATH_IMAGE020
inference and node
Figure 551812DEST_PATH_IMAGE017
And node
Figure 483996DEST_PATH_IMAGE012
In future channel states, namely:
unmanned aerial vehicle node
Figure 31652DEST_PATH_IMAGE010
By passing
Figure 998471DEST_PATH_IMAGE019
And of itself
Figure 973380DEST_PATH_IMAGE014
Inferring AND nodes
Figure 392860DEST_PATH_IMAGE017
Channel state of
Figure 496206DEST_PATH_IMAGE021
；
Unmanned aerial vehicle node
Figure 317531DEST_PATH_IMAGE010
By passing
Figure 463342DEST_PATH_IMAGE022
And of itself
Figure 104539DEST_PATH_IMAGE023
Inferring AND nodes
Figure 993997DEST_PATH_IMAGE012
Channel state of (2)
Figure 935409DEST_PATH_IMAGE024
。
Unmanned aerial vehicle node
Figure 252120DEST_PATH_IMAGE010
By passing
Figure 646193DEST_PATH_IMAGE022
And of itself
Figure 73763DEST_PATH_IMAGE023
Inferring AND nodes
Figure 604101DEST_PATH_IMAGE012
Channel state of (2)
Figure 91715DEST_PATH_IMAGE024
The specific mode of (1) includes any one of the following:
first, communication model-based inference: obtaining unmanned aerial vehicle nodes according to position information of unmanned aerial vehicle
Figure 973083DEST_PATH_IMAGE010
Distance from neighboring nodes as a function of time is
Figure 469923DEST_PATH_IMAGE025
Inferring unmanned aerial vehicles using communication models
Figure 854768DEST_PATH_IMAGE010
And neighboring node
Figure 247703DEST_PATH_IMAGE012
Is channel information of
Figure 616368DEST_PATH_IMAGE026
Wherein,
Figure 385741DEST_PATH_IMAGE027
is the power gain factor due to amplifier and antenna gain,
Figure 890671DEST_PATH_IMAGE028
in order to be a path loss index,
Figure DEST_PATH_IMAGE029
in order to be a noise spectral density,
Figure 188929DEST_PATH_IMAGE030
is the signal bandwidth;
second, radio map based inference: given a radiomap function
Figure 779310DEST_PATH_IMAGE031
To unmanned aerial vehicle
Figure 352374DEST_PATH_IMAGE010
And neighboring nodes, acquiring channel information according to the radio map, and expressing as
Figure DEST_PATH_IMAGE032
Third, data-based inference: for any two locations, there is a historical channel data set between them
Figure 915073DEST_PATH_IMAGE033
And is recorded as:
Figure 915390DEST_PATH_IMAGE034
；
wherein,
Figure 721629DEST_PATH_IMAGE035
represent
Figure 363963DEST_PATH_IMAGE036
To
Figure 577907DEST_PATH_IMAGE037
The number of the data is one,
Figure 749125DEST_PATH_IMAGE038
，
Figure DEST_PATH_IMAGE039
for historical channel data sets
Figure 517361DEST_PATH_IMAGE033
The number of data contained in;
Figure 432227DEST_PATH_IMAGE040
is a first
Figure 766257DEST_PATH_IMAGE037
Data of a person
Figure 108376DEST_PATH_IMAGE041
The channel information of (a);
Figure 895067DEST_PATH_IMAGE042
is shown as
Figure 613624DEST_PATH_IMAGE037
Data of a person
Figure 802160DEST_PATH_IMAGE041
Two pieces of position information contained in (1);
for unmanned plane
Figure 580760DEST_PATH_IMAGE043
And screening out historical data meeting the position relation from the adjacent nodes, and recording the historical data as:
Figure 854747DEST_PATH_IMAGE044
the process is as follows:
(1) For historical channel data set
Figure 376995DEST_PATH_IMAGE033
Any one of the data in (1)
Figure 420037DEST_PATH_IMAGE041
Determine whether or not to satisfy
Figure 369539DEST_PATH_IMAGE045
If so, will
Figure 865242DEST_PATH_IMAGE046
Join to collections
Figure 925602DEST_PATH_IMAGE047
The preparation method comprises the following steps of (1) performing;
(2) For each one
Figure 88730DEST_PATH_IMAGE038
Repeatedly executing (1);
when the temperature is higher than the set temperature
Figure 943554DEST_PATH_IMAGE038
After traversing, regression algorithm is utilized
Figure 457712DEST_PATH_IMAGE048
The channel information between the drones is presumed as:
Figure 56183DEST_PATH_IMAGE049
unmanned aerial vehicle node
Figure 808238DEST_PATH_IMAGE043
By passing
Figure 93683DEST_PATH_IMAGE019
And of itself
Figure 563979DEST_PATH_IMAGE014
Inferring AND nodes
Figure 966141DEST_PATH_IMAGE017
Channel state of
Figure 572703DEST_PATH_IMAGE021
When is in use, and
Figure 34908DEST_PATH_IMAGE043
get
Figure 992500DEST_PATH_IMAGE017
Any one of the first to third methods is adopted to obtain the required result.
The step S4 includes the following substeps:
s401. At
Figure 932774DEST_PATH_IMAGE050
Time, default node
Figure 659422DEST_PATH_IMAGE043
The initial transmission opportunity is a uniform transmission opportunity
Figure 433474DEST_PATH_IMAGE051
Node of
Figure 878362DEST_PATH_IMAGE043
In a period of time
Figure 622327DEST_PATH_IMAGE052
Will be provided with
Figure 469060DEST_PATH_IMAGE003
Transmission of bit data to a node
Figure 7489DEST_PATH_IMAGE012
；
S402, at
Figure 205252DEST_PATH_IMAGE053
At all times, each node
Figure 487329DEST_PATH_IMAGE043
Transmit self timeTransmitting to the neighbor node;
s403, setting a node updating triggering strategy, and updating the unmanned aerial vehicle node according to the node triggering strategy;
s404, the updating process in the step S403 is repeated until the transmission opportunity change of the neighbor node is not received any more, namely
Figure 454148DEST_PATH_IMAGE054
And
Figure DEST_PATH_IMAGE055
no longer changed.
The node updating triggering strategy comprises a field cooperative response strategy:
node point
Figure 366740DEST_PATH_IMAGE043
Finish the first
Figure 51799DEST_PATH_IMAGE056
After the round of updating, the tuple is used
Figure 137567DEST_PATH_IMAGE057
Recording the transmission opportunity result and transmitting the transmission opportunity result to the neighbor node;
each odd node is updated at the neighbor node
Figure 958893DEST_PATH_IMAGE056
Behind-the-wheel for self
Figure 833264DEST_PATH_IMAGE058
Updating the wheel;
each even node is updated in the neighbor node
Figure 943303DEST_PATH_IMAGE058
To go on itself after the turn
Figure 239286DEST_PATH_IMAGE058
And updating the wheel.
Step S403, the node k updates the transmission opportunity
Figure 587222DEST_PATH_IMAGE059
The method comprises the following steps:
a. for any unmanned aerial vehicle node
Figure 903934DEST_PATH_IMAGE043
Given transmission opportunity
Figure 32427DEST_PATH_IMAGE059
The solving problem of (2):
Figure 459997DEST_PATH_IMAGE060
wherein
Figure 255915DEST_PATH_IMAGE061
Is a node
Figure 743528DEST_PATH_IMAGE043
The utility function of (a) is determined,
Figure 562579DEST_PATH_IMAGE062
for the transmission opportunity decided by the neighboring node,
Figure DEST_PATH_IMAGE063
the minimum power for point-to-point transmission for a given transmission opportunity is specifically defined as follows:
Figure 997103DEST_PATH_IMAGE064
Figure 381948DEST_PATH_IMAGE065
wherein
Figure DEST_PATH_IMAGE066
Is a node
Figure 509304DEST_PATH_IMAGE043
In that
Figure 877968DEST_PATH_IMAGE067
Transmission power, parameter of time of day
Figure 647341DEST_PATH_IMAGE030
Representing bandwidth, parameter of signal
Figure 886693DEST_PATH_IMAGE068
Representing losses due to small scale fading, digital modulation and coding;
b. determining transmission timing
Figure 999266DEST_PATH_IMAGE059
Whether the solving problem has a solution or not is determined, and then the transmission opportunity transmitted to the neighbor node is selected according to the determination result:
b1, judging whether the problem (1) has a solution:
set A as being at time
Figure 589647DEST_PATH_IMAGE069
Inner node
Figure DEST_PATH_IMAGE070
To node
Figure 365973DEST_PATH_IMAGE043
B is time
Figure 990990DEST_PATH_IMAGE071
Inner node
Figure DEST_PATH_IMAGE072
To node
Figure 460148DEST_PATH_IMAGE073
Wherein the effective communication time refers to
Figure 272246DEST_PATH_IMAGE074
Time of；
Building operators
Figure 649001DEST_PATH_IMAGE075
If, if
Figure DEST_PATH_IMAGE076
The problem (1) has a solution; otherwise, the problem (1) is not solved;
b2, if the problem (1) has a solution, obtaining the updated transmission opportunity by solving the problem (2) firstly and then solving the sequence of the problem (1);
b3, according to the steps b1 and b2, transmitting the updated transmission opportunity to the neighbor node:
if step b1 judges that the problem (1) has a solution, the node
Figure 331786DEST_PATH_IMAGE043
Transmitting updated transmission opportunities
Figure 503005DEST_PATH_IMAGE059
Give to unmanned aerial vehicle
Figure 67978DEST_PATH_IMAGE077
And backward unmanned aerial vehicle
Figure DEST_PATH_IMAGE078
；
If step b1 determines that the problem (1) is not solved, the node
Figure 920528DEST_PATH_IMAGE043
Transmission is from preceding unmanned aerial vehicle
Figure 520136DEST_PATH_IMAGE077
Received transmission opportunity
Figure 862256DEST_PATH_IMAGE079
Give to unmanned aerial vehicle and transmit from unmanned aerial vehicle backward
Figure 383367DEST_PATH_IMAGE078
Received transmission opportunity
Figure 39608DEST_PATH_IMAGE055
Give to unmanned aerial vehicle backward
Figure 634668DEST_PATH_IMAGE078
。
The step b2 of solving the order of the problem (1) to obtain the updated transmission opportunity includes:
b21. solving problem (2) to obtain
Figure 876250DEST_PATH_IMAGE063
And obtaining the minimum energy consumption of point-to-point transmission at the transmission opportunity, wherein the solving mode comprises any one of the following modes:
1. and (3) a water injection algorithm: according to the KKT algorithm, the optimum transmission power is
Figure 150237DEST_PATH_IMAGE080
And satisfy
Figure 672485DEST_PATH_IMAGE081
Wherein
Figure 715527DEST_PATH_IMAGE082
Is a Lagrange parameter; the point-to-point minimum energy consumption is thus
Figure DEST_PATH_IMAGE083
；
2. Constant power timely transmission: when the unmanned aerial vehicle receives the transmission task, the transmission task is directly transmitted to the target node at a certain constant power, and the strategy can use preset power or can search for the optimal power exhaustively
Figure 868291DEST_PATH_IMAGE084
In a period of time
Figure 363995DEST_PATH_IMAGE085
Carry out data transmission internally, makeTo obtain
Figure 689934DEST_PATH_IMAGE086
In which
Figure 587483DEST_PATH_IMAGE087
(ii) a Thus point-to-point with a minimum energy consumption of
Figure DEST_PATH_IMAGE088
。
b22. Updating transmission opportunities
Figure 645568DEST_PATH_IMAGE059
Either of the following two methods is employed:
1. the better reaction is as follows: i.e. selecting a better transmission opportunity than the current one:
Figure 159726DEST_PATH_IMAGE089
select one
Figure 758198DEST_PATH_IMAGE090
So that the objective function
Figure 775833DEST_PATH_IMAGE091
Target function before updating
Figure 67137DEST_PATH_IMAGE092
Smaller to enable updates; selecting an optimal reaction time by using a gradient descent method, comprising the following steps of:
(1) Computing
Figure 537432DEST_PATH_IMAGE093
、
Figure 674016DEST_PATH_IMAGE094
Thereby obtaining
Figure 811736DEST_PATH_IMAGE095
Wherein
Figure 273941DEST_PATH_IMAGE096
Calculating a constant for a preset gradient approximation; computing by the same principle
Figure DEST_PATH_IMAGE097
；
(2) Calculating an objective function
Figure 434795DEST_PATH_IMAGE098
At the point of
Figure 640649DEST_PATH_IMAGE059
Approximate gradient of (c)
Figure DEST_PATH_IMAGE099
(3) Updating with gradients
Figure 836138DEST_PATH_IMAGE059
Figure 197806DEST_PATH_IMAGE100
Wherein
Figure 908273DEST_PATH_IMAGE101
For the search step size, update
Figure DEST_PATH_IMAGE102
Up to
Figure 855500DEST_PATH_IMAGE103
Is provided with
Figure 702233DEST_PATH_IMAGE104
。
(4) Repeating (1) to (3) until the
Figure 506241DEST_PATH_IMAGE105
Output thisOf
Figure 172846DEST_PATH_IMAGE059
As a result of the update;
2. optimal reaction: i.e. directly selecting the best transmission time
Figure 986081DEST_PATH_IMAGE106
Select one
Figure 687321DEST_PATH_IMAGE107
The objective function is minimized to realize updating and find the best reaction, and a one-dimensional poor search method is utilized:
with accuracy
Figure 662230DEST_PATH_IMAGE108
Sampling interval
Figure 816131DEST_PATH_IMAGE109
Get a set
Figure 167478DEST_PATH_IMAGE110
For each time
Figure 457645DEST_PATH_IMAGE111
Computing
Figure 337876DEST_PATH_IMAGE112
、
Figure 713494DEST_PATH_IMAGE113
Thereby obtaining
Figure 602953DEST_PATH_IMAGE114
Comparison of
Figure 13205DEST_PATH_IMAGE114
Is selected such that
Figure 329917DEST_PATH_IMAGE114
Minimum value
Figure 458410DEST_PATH_IMAGE115
As updated
Figure 885981DEST_PATH_IMAGE059
。
The beneficial effects of the invention are: the invention predicts the future channel quality in the dynamic unmanned aerial vehicle network topology, iteratively determines the communication time among the unmanned aerial vehicles by the effective information exchange of the adjacent unmanned aerial vehicle nodes aiming at the transmission of time delay insensitive data, and realizes the optimization of distributed communication energy consumption based on the game theory by utilizing the track information of the unmanned aerial vehicles and the ultra-low time delay sensitive characteristic of data transmission, wherein the proposed transmission time negotiation strategy can effectively utilize the channels among the nodes, and the reduction of the whole communication energy is realized.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic diagram of a multi-drone relay transmission scenario;
FIG. 3 is a schematic diagram of a cache-transfer protocol;
FIG. 4 is an operator
Figure DEST_PATH_IMAGE116
A schematic diagram;
FIG. 5 is a graph of total energy consumption for different delay tolerances;
FIG. 6 is a schematic diagram of energy consumption for different source-target distances;
fig. 7 is a diagram of total energy consumption for different response rounds.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following descriptions.
The invention hopes that the unmanned aerial vehicle establishes an energy-efficient communication link in a self-organizing manner under the condition of limited information interaction. The core technical route is to construct the association between an energy optimization problem and a potential game (potential game). The invention aims to complete data transmission from a source end to a destination end through the relay of an unmanned aerial vehicle cluster with energy consumption as low as possible by distributing transmission time and transmission power among unmanned aerial vehicles under the requirement of time delay sensitivity. However, under the condition of limited information interaction (including channel state information and queue state information), the energy optimization problem cannot be directly solved. On the other hand, in the potential game, by mapping the revenue function of each node into a global "potential function", since the potential function has a consistent trend with the revenue function of each node, the optimization of the global potential function can be researched by using the nash balance of the game.
The key point is how to establish the relation between an objective function in the energy optimization problem and a potential function in the potential game, and the energy optimization problem is solved by searching for Nash balance of the game problem. The method constructs a local utility function to enable a game model to be a potential game and the potential function is equal to a target function in an energy optimization problem; designing a trigger strategy of a local user game to enable an asynchronous reaction to reach Nash equilibrium; and designing an update algorithm and a parameter transmission strategy of the nodes by using the track information of the unmanned aerial vehicle. Therefore, the core of the method can be summarized into two points, namely designing a local utility function, designing a transmission opportunity updating algorithm and a parameter transfer scheme according to the track information of the local unmanned aerial vehicle, and designing a trigger strategy of local user game.
As shown in fig. 1, a multi-node communication opportunity determining method for network transmission of an unmanned aerial vehicle includes the following steps:
s1, a transmission task is given and a transmission opportunity is defined:
as shown in fig. 2, a schematic diagram of data transmission by using a drone is shown. Given a transmission task at a delay tolerance threshold
Figure 885161DEST_PATH_IMAGE117
Passing in seconds
Figure 366914DEST_PATH_IMAGE002
Unmanned aerial vehicle is erected and source end is produced
Figure 717124DEST_PATH_IMAGE003
Transmitting the bit data to a destination;
as shown in fig. 3, the transmission mechanism adopted in the present invention is buffer-transfer transmission, that is, the whole data packet is sequentially transmitted from one drone to another drone when the channel condition is good; transmission timing in fig. 3 from
Figure 213965DEST_PATH_IMAGE059
Become in (b)
Figure 598810DEST_PATH_IMAGE118
Is a node
Figure 522903DEST_PATH_IMAGE017
And node
Figure 625989DEST_PATH_IMAGE043
Provide better conditions for transmission between nodes without affecting the nodes
Figure 395361DEST_PATH_IMAGE043
And node
Figure 900292DEST_PATH_IMAGE012
The data transmission of (a) and thus (b) achieves better channel utilization than (a), and consumes less energy.
The source end is assumed to be node 0,
Figure 729708DEST_PATH_IMAGE002
erecting unmanned aerial vehicle from node 1 to node
Figure 320089DEST_PATH_IMAGE002
The destination is a node
Figure 893153DEST_PATH_IMAGE006
In a
Figure 252590DEST_PATH_IMAGE117
Defining the transmission time under the requirement of second transmission delay
Figure 252907DEST_PATH_IMAGE007
Satisfy the requirement of
Figure 330585DEST_PATH_IMAGE008
Wherein the node
Figure 441760DEST_PATH_IMAGE043
Need to be in the transmission interval
Figure 593387DEST_PATH_IMAGE119
Internally transmitting data to a node in its entirety
Figure 764605DEST_PATH_IMAGE012
，
Figure 63999DEST_PATH_IMAGE050
;
S2. At
Figure 244445DEST_PATH_IMAGE013
At all times, each node
Figure 844054DEST_PATH_IMAGE043
Acquiring the motion track of the user in the future
Figure 186173DEST_PATH_IMAGE023
And transmitted to the adjacent nodes;
in the step S2, for any node
Figure 972864DEST_PATH_IMAGE043
The neighboring nodes refer to nodes communicating with themselves:
when the temperature is higher than the set temperature
Figure 691421DEST_PATH_IMAGE015
When being a node
Figure 163114DEST_PATH_IMAGE043
When the source node is a node, the adjacent nodes are nodes
Figure 941714DEST_PATH_IMAGE012
；
When k =
Figure DEST_PATH_IMAGE120
Time, i.e. node
Figure 153384DEST_PATH_IMAGE043
When the unmanned aerial vehicle node is used, the adjacent nodes comprise nodes
Figure 675632DEST_PATH_IMAGE017
And node
Figure 718675DEST_PATH_IMAGE012
；
When the temperature is higher than the set temperature
Figure 402597DEST_PATH_IMAGE018
When being a node
Figure 429459DEST_PATH_IMAGE043
When the destination node is a node, its neighboring nodes are nodes
Figure 224239DEST_PATH_IMAGE002
。
S3, based on the received track information of the adjacent nodes and the track information of the unmanned aerial vehicle, each unmanned aerial vehicle node deduces the future channel state between each unmanned aerial vehicle node and the adjacent node;
the step S3 includes:
the unmanned aerial vehicle node k receives the motion trail
Figure 387367DEST_PATH_IMAGE019
And
Figure 507770DEST_PATH_IMAGE121
inference and node
Figure 490770DEST_PATH_IMAGE017
And node
Figure 89241DEST_PATH_IMAGE012
In future channel states, namely:
unmanned aerial vehicle node
Figure 372455DEST_PATH_IMAGE043
By passing
Figure 398180DEST_PATH_IMAGE019
And of itself
Figure 868475DEST_PATH_IMAGE014
Inferring AND nodes
Figure 270638DEST_PATH_IMAGE017
Channel state of (2)
Figure 142779DEST_PATH_IMAGE021
；
Unmanned aerial vehicle node
Figure 604984DEST_PATH_IMAGE043
By passing
Figure 562576DEST_PATH_IMAGE020
And of itself
Figure 768430DEST_PATH_IMAGE014
Inferring AND nodes
Figure 495077DEST_PATH_IMAGE012
Channel state of
Figure 862604DEST_PATH_IMAGE122
。
The unmanned aerial vehicle node k passes through
Figure 301633DEST_PATH_IMAGE020
And of itself
Figure 311177DEST_PATH_IMAGE014
Inferring AND nodes
Figure 892331DEST_PATH_IMAGE012
Channel state of
Figure 696339DEST_PATH_IMAGE122
The specific mode of (1) includes any one of the following:
first, communication model-based inference: obtaining unmanned aerial vehicle nodes according to position information of unmanned aerial vehicle
Figure 628523DEST_PATH_IMAGE043
Distance from neighboring nodes as a function of time is
Figure 176179DEST_PATH_IMAGE123
Inferring unmanned aerial vehicles using a communication model
Figure 611840DEST_PATH_IMAGE043
And neighboring node
Figure 6229DEST_PATH_IMAGE012
Is channel information of
Figure 357576DEST_PATH_IMAGE124
Wherein,
Figure 913322DEST_PATH_IMAGE125
is the power gain factor due to amplifier and antenna gain,
Figure 59133DEST_PATH_IMAGE126
in order to be a path loss index,
Figure 965909DEST_PATH_IMAGE127
in order to be able to determine the spectral density of the noise,
Figure 855367DEST_PATH_IMAGE030
is the signal bandwidth;
second, radio map based inference: given a radiomap function
Figure 265620DEST_PATH_IMAGE128
To unmanned aerial vehicle
Figure 582332DEST_PATH_IMAGE043
And neighboring nodes, acquiring channel information according to the radio map, and expressing as
Figure 976404DEST_PATH_IMAGE129
Third, data-based inference: for any two locations, there is a set of historical channel data between them
Figure 403975DEST_PATH_IMAGE130
And is recorded as:
Figure 199892DEST_PATH_IMAGE131
；
wherein,
Figure 421926DEST_PATH_IMAGE035
to represent
Figure DEST_PATH_IMAGE132
To
Figure 506557DEST_PATH_IMAGE037
The number of the data is one,
Figure 737818DEST_PATH_IMAGE038
，
Figure 122663DEST_PATH_IMAGE039
for historical channel data sets
Figure 46757DEST_PATH_IMAGE033
The number of data contained in;
Figure 878403DEST_PATH_IMAGE040
is a first
Figure 647776DEST_PATH_IMAGE037
Data of a person
Figure 418286DEST_PATH_IMAGE041
The channel information of (a);
Figure 247702DEST_PATH_IMAGE042
is shown as
Figure 572504DEST_PATH_IMAGE037
Data of a person
Figure 411147DEST_PATH_IMAGE041
Two pieces of position information contained in (1);
for unmanned plane
Figure 770584DEST_PATH_IMAGE043
And screening out historical data meeting the position relation from the adjacent nodes, and recording the historical data as:
Figure 770901DEST_PATH_IMAGE133
the process is as follows:
(1) For historical channel data set
Figure 848579DEST_PATH_IMAGE033
Any one of the data in
Figure 959754DEST_PATH_IMAGE041
Determine whether or not to satisfy
Figure 439277DEST_PATH_IMAGE045
If so, will
Figure 610495DEST_PATH_IMAGE046
Join to collections
Figure 175469DEST_PATH_IMAGE047
Performing the following steps;
(2) For each one
Figure 90335DEST_PATH_IMAGE038
Repeatedly executing (1);
when in use
Figure 158785DEST_PATH_IMAGE038
After traversing, regression algorithm is utilized
Figure 766484DEST_PATH_IMAGE048
The channel information between the drones is presumed as:
Figure 553175DEST_PATH_IMAGE134
unmanned aerial vehicle node
Figure 271732DEST_PATH_IMAGE043
By passing
Figure 194688DEST_PATH_IMAGE019
And of itself
Figure 973289DEST_PATH_IMAGE014
Inferring AND nodes
Figure 247275DEST_PATH_IMAGE017
Channel state of
Figure 503944DEST_PATH_IMAGE021
When it is not used, and
Figure 546987DEST_PATH_IMAGE043
get the
Figure 959470DEST_PATH_IMAGE017
Any one of the first to third methods is adopted to obtain the required result.
And S4, based on the motion tracks of the unmanned aerial vehicle and the adjacent nodes and the transmission opportunity of the adjacent nodes, updating the transmission opportunity of the unmanned aerial vehicle nodes in a distributed iterative manner.
The step S4 includes the following substeps:
s401, in
Figure 720753DEST_PATH_IMAGE050
Then, the default node
Figure 515533DEST_PATH_IMAGE043
The initial transmission time is a uniform transmission time
Figure 413082DEST_PATH_IMAGE135
Node of
Figure 533485DEST_PATH_IMAGE043
In a period of time
Figure 516485DEST_PATH_IMAGE136
Will be provided with
Figure 849377DEST_PATH_IMAGE003
Transmission of bit data to a node
Figure 132591DEST_PATH_IMAGE012
；
S402, at
Figure 158316DEST_PATH_IMAGE053
While each node
Figure 628611DEST_PATH_IMAGE043
Transmitting the self transmission opportunity to the neighbor node;
s403, setting a node updating triggering strategy, and updating the unmanned aerial vehicle node according to the node triggering strategy;
s404, the updating process in the step S403 is repeated until the transmission opportunity change of the neighbor node is not received any more, namely
Figure 296353DEST_PATH_IMAGE054
And
Figure 168494DEST_PATH_IMAGE055
no longer changed.
The node updating triggering strategy comprises a field cooperative response strategy:
node point
Figure 630699DEST_PATH_IMAGE043
Finish the first
Figure 588291DEST_PATH_IMAGE137
After the round of updating, the tuple is used
Figure 794144DEST_PATH_IMAGE057
Recording the transmission opportunity result and transmitting the transmission opportunity result to the neighbor node;
each odd node is updated at the neighbor node
Figure 520792DEST_PATH_IMAGE138
Behind-the-wheel for self
Figure 153899DEST_PATH_IMAGE058
Updating the wheel;
each even node is updated in the neighbor node
Figure 598786DEST_PATH_IMAGE058
To go on itself after the turn
Figure 77172DEST_PATH_IMAGE058
And (4) updating in turn.
In other embodiments of the present application, a simultaneous response policy can also be used: and all the nodes are updated simultaneously until all the unmanned planes do not update the transmission opportunity any more. Wherein to make the updated transmission timing meet the constraint
Figure 189485DEST_PATH_IMAGE139
The actual update of each drone requires the implementation of smoothing techniques, namely:
Figure 727913DEST_PATH_IMAGE140
wherein
Figure 660097DEST_PATH_IMAGE141
A better or optimal strategy for the current round of practice,
Figure 207753DEST_PATH_IMAGE142
Is as follows
Figure 926571DEST_PATH_IMAGE138
The wheel strategy,
Figure 901481DEST_PATH_IMAGE143
Is as follows
Figure 320961DEST_PATH_IMAGE058
The actual strategy of the wheel,
Figure DEST_PATH_IMAGE144
。
Step S403, the node k updates the transmission opportunity
Figure 609991DEST_PATH_IMAGE059
The method comprises the following steps:
a. for any unmanned aerial vehicle node
Figure 431316DEST_PATH_IMAGE043
Given transmission opportunity
Figure 577127DEST_PATH_IMAGE059
The solving problem of (2):
Figure 483903DEST_PATH_IMAGE145
wherein
Figure 107782DEST_PATH_IMAGE061
Is a node
Figure 49193DEST_PATH_IMAGE043
The utility function of (a) is determined,
Figure 365905DEST_PATH_IMAGE062
for a transmission opportunity determined by a neighboring node,
Figure 494398DEST_PATH_IMAGE063
the minimum power for point-to-point transmission for a given transmission opportunity is specifically defined as follows:
Figure 187548DEST_PATH_IMAGE146
Figure DEST_PATH_IMAGE147
wherein
Figure 655569DEST_PATH_IMAGE148
Is a node
Figure 408762DEST_PATH_IMAGE043
In that
Figure DEST_PATH_IMAGE149
Transmission power of time of day, parameter
Figure 227813DEST_PATH_IMAGE030
Representing bandwidth, parameter of signal
Figure 724653DEST_PATH_IMAGE150
Represents the loss due to small-scale fading, digital modulation, and coding;
b. determining transmission timing
Figure 109498DEST_PATH_IMAGE059
Whether the solution problem has a solution or not is determined, and then the transmission opportunity transmitted to the neighbor node is selected according to the determination result:
b1, judging whether the problem (1) has a solution:
set A as at time
Figure 502434DEST_PATH_IMAGE071
Inner node
Figure 605519DEST_PATH_IMAGE077
To the node
Figure 843733DEST_PATH_IMAGE043
B is time
Figure 77225DEST_PATH_IMAGE071
Inner node
Figure 906641DEST_PATH_IMAGE043
To node
Figure 497022DEST_PATH_IMAGE078
Wherein the effective communication time refers to
Figure 70086DEST_PATH_IMAGE151
The time of (d);
as shown in fig. 4, the operator is constructed
Figure 429523DEST_PATH_IMAGE152
If, if
Figure 429840DEST_PATH_IMAGE153
The problem (1) has a solution; otherwise, the problem (1) is not solved;
b2, if the problem (1) has a solution, obtaining the updated transmission opportunity by solving the problem (2) firstly and then solving the sequence of the problem (1);
b3, according to the steps b1 and b2, transmitting the updated transmission opportunity to the neighbor node:
if step b1 determines that the problem (1) has a solution, the node
Figure 507518DEST_PATH_IMAGE043
Transmitting updated transmission opportunities
Figure 884273DEST_PATH_IMAGE059
Give preceding unmanned aerial vehicle
Figure 98216DEST_PATH_IMAGE077
And backward unmanned aerial vehicle
Figure 535014DEST_PATH_IMAGE078
；
If step b1 determines that the problem (1) is not solved, the node
Figure 834408DEST_PATH_IMAGE043
Transmission is from preceding unmanned aerial vehicle
Figure 14854DEST_PATH_IMAGE077
Received transmission opportunity
Figure 348883DEST_PATH_IMAGE079
Give to unmanned aerial vehicle and transmit from unmanned aerial vehicle backward
Figure 691003DEST_PATH_IMAGE078
Received transmission opportunity
Figure 477693DEST_PATH_IMAGE055
Give to unmanned aerial vehicle
Figure 196250DEST_PATH_IMAGE078
。
The step b2 of solving the order of the problem (1) to obtain the updated transmission opportunity includes:
b21. solve problem (2) to obtain
Figure 384786DEST_PATH_IMAGE154
And obtaining the minimum energy consumption of point-to-point transmission at the transmission opportunity, wherein the solving mode comprises any one of the following modes:
1. and (3) water injection algorithm: according to the KKT algorithm, the optimum transmission power is
Figure 897807DEST_PATH_IMAGE155
And satisfy
Figure 437373DEST_PATH_IMAGE156
Wherein
Figure 694042DEST_PATH_IMAGE157
Is a Lagrange parameter; the point-to-point minimum energy consumption is thus
Figure 2664DEST_PATH_IMAGE158
；
2. Constant power timely transmission: when the unmanned aerial vehicle receives the transmission task, the transmission task is directly transmitted to the target node at a certain constant power, and the strategy can use preset power or can search for the optimal power exhaustively
Figure 686586DEST_PATH_IMAGE084
In a period of time
Figure 447868DEST_PATH_IMAGE159
Carry out data transmission internally, so that
Figure 508228DEST_PATH_IMAGE160
Wherein
Figure 399918DEST_PATH_IMAGE161
(ii) a The point-to-point minimum energy consumption is thus
Figure DEST_PATH_IMAGE162
。
b22. Updating transmission opportunities
Figure 458004DEST_PATH_IMAGE059
Either of the following two methods is employed:
1. the better reaction is as follows: i.e. selecting a better transmission opportunity than the current one:
Figure 706582DEST_PATH_IMAGE089
select one
Figure 570633DEST_PATH_IMAGE090
So that the objective function
Figure 322689DEST_PATH_IMAGE163
Target function before update
Figure DEST_PATH_IMAGE164
Smaller to enable updates; selecting an optimal reaction time by using a gradient descent method, comprising the following steps of:
(1) Computing
Figure 82834DEST_PATH_IMAGE093
、
Figure 553130DEST_PATH_IMAGE094
Thereby obtaining
Figure 955292DEST_PATH_IMAGE165
Wherein
Figure 827433DEST_PATH_IMAGE096
Calculating a constant for a preset gradient approximation; computing by the same principle
Figure 289639DEST_PATH_IMAGE097
；
(2) Calculating an objective function
Figure 512810DEST_PATH_IMAGE098
At the point of
Figure 453084DEST_PATH_IMAGE059
Approximate gradient of (c)
Figure 445311DEST_PATH_IMAGE166
(3) Updating with gradients
Figure 78417DEST_PATH_IMAGE059
Figure 523305DEST_PATH_IMAGE167
Wherein
Figure 267270DEST_PATH_IMAGE101
For the search step size, update
Figure 379583DEST_PATH_IMAGE102
Up to
Figure 918011DEST_PATH_IMAGE168
Is provided with
Figure 850195DEST_PATH_IMAGE104
。
(4) Repeating (1) to (3) until the
Figure 397851DEST_PATH_IMAGE169
Output here
Figure 99091DEST_PATH_IMAGE059
As a result of the update;
2. optimal reaction: i.e. directly selecting the best transmission time
Figure 808421DEST_PATH_IMAGE170
Select one
Figure 222042DEST_PATH_IMAGE090
The objective function is minimized to realize updating and find the best reaction, and a one-dimensional finite search method is utilized:
with accuracy
Figure 307809DEST_PATH_IMAGE171
Sampling interval
Figure DEST_PATH_IMAGE172
Get a set
Figure 801239DEST_PATH_IMAGE173
For each time
Figure 947049DEST_PATH_IMAGE174
Computing
Figure 57088DEST_PATH_IMAGE175
、
Figure 149809DEST_PATH_IMAGE176
Thereby obtaining
Figure 825641DEST_PATH_IMAGE177
Comparison of
Figure 814456DEST_PATH_IMAGE178
Is selected such that
Figure 942949DEST_PATH_IMAGE177
Minimum value of value
Figure 370520DEST_PATH_IMAGE179
As updated
Figure 900858DEST_PATH_IMAGE059
。
The invention utilizes the track information of the unmanned aerial vehicle and the ultra-low time delay sensitivity characteristic of data transmission, realizes the optimization of distributed communication energy consumption based on the game theory, wherein the proposed transmission opportunity negotiation strategy can effectively utilize channels between nodes, and realizes the reduction of the whole communication energy, as shown in fig. 5 and 6, fig. 5 is a comparison of optimization time interval strategy, equal time interval transmission strategy and real-time relay transmission strategy on energy consumption aiming at different delay sensitivities, 5 seconds (upper graph) and 15 seconds (lower graph). It can be seen that the proposed transmission opportunity optimization strategy significantly reduces energy consumption performance, which indicates that the proposed transmission opportunity negotiation strategy can effectively utilize channels between nodes, and realizes reduction of overall communication energy. In addition, compared to the result with delay tolerance of 15 seconds, the superiority of the proposed algorithm is more significant at the tolerance of 5 seconds, which indicates that the present invention can be effectively applied to the environment with high delay sensitivity.
Fig. 6 compares the performance of the optimization time interval strategy, the equal time interval transmission strategy and the real-time relay transmission strategy as a function of the distance between the source node and the target node. It can be seen that the superiority of the proposed algorithm increases with increasing source-target distance. This demonstrates that the proposed algorithm works significantly in the context of long-distance data transmission.
In addition, compared with the sequential response strategy and the simultaneous response strategy, the proposed domain collaborative response strategy has faster convergence speed and better convergence result, and as shown in fig. 7, the convergence performance of three distributed update response strategies is compared. It can be seen that on the one hand the domain co-response strategy is able to converge to the nash equilibrium point at a faster rate and the convergence results better.
The foregoing is a preferred embodiment of the present invention, and it is to be understood that the invention is not limited to the form disclosed herein, but is not intended to be foreclosed in other embodiments and may be used in other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A multi-node communication opportunity determination method for unmanned aerial vehicle network transmission is characterized by comprising the following steps: the method comprises the following steps:
s1, giving a transmission task and defining a transmission opportunity:
given a transmission task at a delay tolerance threshold
Figure 608070DEST_PATH_IMAGE001
Passing in seconds
Figure 99095DEST_PATH_IMAGE002
Unmanned aerial vehicle is erected and source end is produced
Figure 16235DEST_PATH_IMAGE003
Transmitting the bit data to a destination;
setting source end as node
Figure 858289DEST_PATH_IMAGE004
，
Figure 784657DEST_PATH_IMAGE002
Support unmanned aerial vehicle as follow node
Figure 130187DEST_PATH_IMAGE005
To a node
Figure 749388DEST_PATH_IMAGE002
The destination is a node
Figure 813159DEST_PATH_IMAGE006
In a
Figure 277638DEST_PATH_IMAGE007
Defining the transmission time under the requirement of second transmission delay
Figure 743254DEST_PATH_IMAGE008
Satisfy the requirement of
Figure 533356DEST_PATH_IMAGE009
Wherein is at
Figure 818844DEST_PATH_IMAGE010
Node point
Figure 352593DEST_PATH_IMAGE011
Need to be in the transmission interval
Figure 407137DEST_PATH_IMAGE012
Integrally transmitting data to nodes
Figure 102560DEST_PATH_IMAGE013
；
S2. At
Figure 140924DEST_PATH_IMAGE014
At all times, each node
Figure 478364DEST_PATH_IMAGE011
Obtaining the motion track of the user in the future
Figure 387414DEST_PATH_IMAGE015
And transmitted to the adjacent nodes;
s3, each unmanned aerial vehicle node deduces a future channel state between each unmanned aerial vehicle node and an adjacent node based on the received adjacent node track information and the track information of the unmanned aerial vehicle node;
the step S3 comprises the following steps:
unmanned aerial vehicle node
Figure 519318DEST_PATH_IMAGE011
According to the received motion track
Figure 513819DEST_PATH_IMAGE016
And
Figure 654950DEST_PATH_IMAGE017
inference and node
Figure 418507DEST_PATH_IMAGE018
And node
Figure 721312DEST_PATH_IMAGE013
In future channel states, namely:
unmanned aerial vehicle node
Figure 468689DEST_PATH_IMAGE011
By passing
Figure 882352DEST_PATH_IMAGE016
And of itself
Figure 765995DEST_PATH_IMAGE015
Inferring AND nodes
Figure 974122DEST_PATH_IMAGE018
Channel state of (2)
Figure 943215DEST_PATH_IMAGE019
；
Unmanned aerial vehicle node
Figure 160570DEST_PATH_IMAGE011
By passing
Figure 633140DEST_PATH_IMAGE017
And of itself
Figure 277748DEST_PATH_IMAGE015
Inferring AND nodes
Figure 734137DEST_PATH_IMAGE013
Channel state of (2)
Figure 489603DEST_PATH_IMAGE020
；
Unmanned aerial vehicle node
Figure 347838DEST_PATH_IMAGE011
By passing
Figure 163347DEST_PATH_IMAGE017
And of itself
Figure 575874DEST_PATH_IMAGE015
Inferring AND nodes
Figure 135031DEST_PATH_IMAGE013
Channel state of
Figure 847772DEST_PATH_IMAGE020
The concrete mode of (2) includes any one of the following:
first, communication model-based inference: obtaining unmanned aerial vehicle nodes according to position information of unmanned aerial vehicle
Figure 834183DEST_PATH_IMAGE011
Distance from neighboring nodes as a function of time is
Figure 999585DEST_PATH_IMAGE021
Inferring unmanned aerial vehicles using communication models
Figure 362433DEST_PATH_IMAGE011
And neighboring node
Figure 929681DEST_PATH_IMAGE013
The channel information of
Figure 821413DEST_PATH_IMAGE022
Wherein,
Figure 739691DEST_PATH_IMAGE023
is the power gain factor due to amplifier and antenna gain,
Figure 906230DEST_PATH_IMAGE024
in order to be a path loss index,
Figure 593563DEST_PATH_IMAGE025
in order to be a noise spectral density,
Figure 921776DEST_PATH_IMAGE026
is the signal bandwidth;
second, radio map based inference: given radiomap function
Figure 796191DEST_PATH_IMAGE027
To unmanned aerial vehicle
Figure 235263DEST_PATH_IMAGE011
And neighboring nodes, acquiring channel information according to the radio map, and expressing as
Figure 777103DEST_PATH_IMAGE028
Third, data-based inference: for any two locations, there is a set of historical channel data between them
Figure 276217DEST_PATH_IMAGE029
And is recorded as:
Figure 903508DEST_PATH_IMAGE030
；
wherein,
Figure 880691DEST_PATH_IMAGE031
to represent
Figure 366835DEST_PATH_IMAGE032
To
Figure 36851DEST_PATH_IMAGE033
The number of the data is one,
Figure 885858DEST_PATH_IMAGE034
，
Figure 932312DEST_PATH_IMAGE035
for a historical channel data set
Figure 448744DEST_PATH_IMAGE029
The number of data contained in;
Figure 289661DEST_PATH_IMAGE036
is a first
Figure 625964DEST_PATH_IMAGE033
Data of a person
Figure 476108DEST_PATH_IMAGE031
The channel information of (a);
Figure 847047DEST_PATH_IMAGE037
denotes the first
Figure 124444DEST_PATH_IMAGE033
Data of a person
Figure 213623DEST_PATH_IMAGE031
Two pieces of position information contained in (1);
for unmanned plane
Figure 601879DEST_PATH_IMAGE011
And screening out historical data meeting the position relation from the adjacent nodes, and recording the historical data as:
Figure 827324DEST_PATH_IMAGE038
the process is as follows:
(1) For historical channel data set
Figure 10044DEST_PATH_IMAGE039
Any one of the data in (1)
Figure 320939DEST_PATH_IMAGE031
Determine whether or not to satisfy
Figure 247307DEST_PATH_IMAGE040
If so, will
Figure 592838DEST_PATH_IMAGE041
Join to collections
Figure 946459DEST_PATH_IMAGE042
The preparation method comprises the following steps of (1) performing;
(2) For each one
Figure 744650DEST_PATH_IMAGE034
Repeatedly executing (1);
when the temperature is higher than the set temperature
Figure 474709DEST_PATH_IMAGE034
After traversing, regression algorithm is utilized
Figure 674746DEST_PATH_IMAGE043
The channel information between drones is presumed to be:
Figure 464848DEST_PATH_IMAGE044
unmanned aerial vehicle node
Figure 750336DEST_PATH_IMAGE011
By passing
Figure 815243DEST_PATH_IMAGE016
And of itself
Figure 869787DEST_PATH_IMAGE015
Inferring AND nodes
Figure 361948DEST_PATH_IMAGE018
Channel state of
Figure 400312DEST_PATH_IMAGE019
When is in use, and
Figure 737752DEST_PATH_IMAGE011
get the
Figure 381223DEST_PATH_IMAGE018
Any one of the first to third modes is adopted to obtain a required result;
s4, based on the motion tracks of the unmanned aerial vehicle and the adjacent nodes and the transmission opportunity of the adjacent nodes, updating the transmission opportunity of the unmanned aerial vehicle nodes in a distributed iterative manner;
the step S4 includes the following substeps:
s401. At
Figure 513127DEST_PATH_IMAGE045
Time, default node
Figure 507628DEST_PATH_IMAGE011
The initial transmission time is a uniform transmission time
Figure 383180DEST_PATH_IMAGE046
Node of
Figure 881157DEST_PATH_IMAGE011
In a period of time
Figure 183963DEST_PATH_IMAGE012
Will be provided with
Figure 931339DEST_PATH_IMAGE003
Transmission of bit data to a node
Figure 79423DEST_PATH_IMAGE013
；
S402, in
Figure 963066DEST_PATH_IMAGE047
While each node
Figure 436772DEST_PATH_IMAGE011
Transmitting the self transmission opportunity to the neighbor node;
s403, setting a node updating triggering strategy, and updating the unmanned aerial vehicle node according to the node triggering strategy;
node point
Figure 405865DEST_PATH_IMAGE011
Updating transmission timing
Figure 623220DEST_PATH_IMAGE048
The method comprises the following steps:
a. for any unmanned aerial vehicle node
Figure 361369DEST_PATH_IMAGE011
Given transmission opportunity
Figure 5977DEST_PATH_IMAGE048
The solving problem of (2):
Figure 462366DEST_PATH_IMAGE049
wherein
Figure 217833DEST_PATH_IMAGE050
Is a node
Figure 76067DEST_PATH_IMAGE011
The utility function of (a) is determined,
Figure 625997DEST_PATH_IMAGE051
for the transmission opportunity decided by the neighboring node,
Figure 835262DEST_PATH_IMAGE052
the minimum power for point-to-point transmission for a given transmission opportunity is specifically defined as follows:
Figure 394419DEST_PATH_IMAGE053
Figure 107160DEST_PATH_IMAGE054
wherein
Figure 359150DEST_PATH_IMAGE055
Is a node
Figure 524552DEST_PATH_IMAGE011
In that
Figure 887400DEST_PATH_IMAGE056
Transmission power, parameter of time of day
Figure 720227DEST_PATH_IMAGE026
Representing bandwidth, parameter of signal
Figure 877539DEST_PATH_IMAGE057
Represents the loss due to small-scale fading, digital modulation, and coding;
b. determining transmission timing
Figure 264658DEST_PATH_IMAGE048
Whether the solving problem has a solution or not is determined, and then the transmission opportunity transmitted to the neighbor node is selected according to the determination result:
b1, judging whether the problem (1) has a solution:
is provided with
Figure 165618DEST_PATH_IMAGE058
To be at time
Figure 852951DEST_PATH_IMAGE059
Inner node
Figure 181164DEST_PATH_IMAGE060
To the node
Figure 321158DEST_PATH_IMAGE011
The effective channel time of (a) is,
Figure 506370DEST_PATH_IMAGE026
is time of day
Figure 48209DEST_PATH_IMAGE059
Inner node
Figure 547324DEST_PATH_IMAGE011
To the node
Figure 174614DEST_PATH_IMAGE061
Wherein the effective communication time refers to
Figure 214114DEST_PATH_IMAGE062
The time of (d);
constructing operators
Figure 610461DEST_PATH_IMAGE063
If at all
Figure 280476DEST_PATH_IMAGE064
The problem (1) has a solution; otherwise, the problem (1) is not solved;
b2, if the problem (1) has a solution, solving the problem (2) first, and then solving the sequence of the problem (1) to obtain the updated transmission opportunity;
b3, according to the steps b1 and b2, transmitting the updated transmission opportunity to the neighbor node:
if step b1 determines that the problem (1) has a solution, the node
Figure 395063DEST_PATH_IMAGE011
Transmitting updated transmission opportunities
Figure 707096DEST_PATH_IMAGE048
Give to unmanned aerial vehicle
Figure 489107DEST_PATH_IMAGE060
And backward unmanned aerial vehicle
Figure 330024DEST_PATH_IMAGE061
；
If step b1 determines that the problem (1) is not solved, the node
Figure 931907DEST_PATH_IMAGE011
Transmission is from preceding unmanned aerial vehicle
Figure 250892DEST_PATH_IMAGE060
Received transmission opportunity
Figure 621831DEST_PATH_IMAGE065
Give to unmanned aerial vehicle and transmit from unmanned aerial vehicle backward
Figure 899228DEST_PATH_IMAGE061
Received transmission opportunity
Figure 988407DEST_PATH_IMAGE066
Give to unmanned aerial vehicle backward
Figure 111084DEST_PATH_IMAGE061
；
S404, the updating process in the step S403 is repeated until the transmission opportunity change of the neighbor node is not received any more, namely
Figure 336529DEST_PATH_IMAGE065
And
Figure 519249DEST_PATH_IMAGE066
no longer changed.
2. The multi-node communication opportunity determination method for network transmission of drones according to claim 1, characterized in that: in the step S2, any node is subjected to
Figure 830144DEST_PATH_IMAGE011
The neighboring nodes refer to nodes communicating with themselves:
when the temperature is higher than the set temperature
Figure 22091DEST_PATH_IMAGE067
Time, i.e. node
Figure 633201DEST_PATH_IMAGE011
When the source node is a node, the adjacent nodes are nodes
Figure 986822DEST_PATH_IMAGE013
；
When the temperature is higher than the set temperature
Figure 785014DEST_PATH_IMAGE068
When being a node
Figure 515072DEST_PATH_IMAGE011
When being unmanned aerial vehicle node, the adjacent nodes comprise nodes
Figure 980689DEST_PATH_IMAGE018
And node
Figure 505211DEST_PATH_IMAGE013
；
When in use
Figure 56278DEST_PATH_IMAGE069
When being a node
Figure 590028DEST_PATH_IMAGE011
When the destination node is a node, its neighboring nodes are nodes
Figure 644571DEST_PATH_IMAGE002
。
3. A multi-node communication occasion determination method for network transmission of unmanned aerial vehicles according to claim 1, characterized in that: the node updating triggering strategy comprises a field cooperative response strategy:
node point
Figure 871153DEST_PATH_IMAGE011
Finish the first
Figure 643937DEST_PATH_IMAGE070
After the round of updating, the tuple is used
Figure 450219DEST_PATH_IMAGE071
Recording the transmission opportunity result and transmitting the transmission opportunity result to the neighbor node;
each odd node is updated in the neighbor node
Figure 624849DEST_PATH_IMAGE070
Behind-the-wheel for self
Figure 491173DEST_PATH_IMAGE072
Updating the wheel;
each even node is updated in the neighbor node
Figure 16833DEST_PATH_IMAGE072
To go on itself after the turn
Figure 892385DEST_PATH_IMAGE072
And (4) updating in turn.
4. A multi-node communication occasion determination method for network transmission of unmanned aerial vehicles according to claim 1, characterized in that: the process of solving the order of the problem (1) in the step b2 to obtain the updated transmission opportunity includes:
b21. solve problem (2) to obtain
Figure 921521DEST_PATH_IMAGE052
And obtaining the minimum energy consumption of point-to-point transmission at the transmission opportunity, wherein the solving mode comprises any one of the following modes:
1. and (3) a water injection algorithm: according to the KKT algorithm, the optimum transmission power is
Figure 224326DEST_PATH_IMAGE073
And satisfy
Figure 440544DEST_PATH_IMAGE074
Wherein
Figure 854208DEST_PATH_IMAGE075
Is a Lagrangian parameter; the point-to-point minimum energy consumption is thus
Figure 472271DEST_PATH_IMAGE076
；
2. Constant power timely transmission: when receiving the transmission task, the unmanned aerial vehicle directly transmits to the target node at a certain constant power: searching for optimum power using preset power or exhaustive search
Figure 945977DEST_PATH_IMAGE077
In a period of time
Figure 915070DEST_PATH_IMAGE078
Carry out data transmission internally, so that
Figure 132425DEST_PATH_IMAGE079
Wherein
Figure 401732DEST_PATH_IMAGE080
(ii) a Thus point-to-point with a minimum energy consumption of
Figure 311920DEST_PATH_IMAGE081
；
b22. Updating transmission opportunities
Figure 502729DEST_PATH_IMAGE048
Either of the following two methods is employed:
1. the better reaction is as follows: i.e. selecting a better transmission opportunity than the current one:
Figure 523775DEST_PATH_IMAGE082
select one
Figure 116430DEST_PATH_IMAGE083
So that the objective function
Figure 931940DEST_PATH_IMAGE084
Target function before update
Figure 875625DEST_PATH_IMAGE085
Smaller to enable updates; the method for selecting the optimal reaction time by using gradient descent comprises the following steps:
(1) Computing
Figure 434782DEST_PATH_IMAGE086
、
Figure 147523DEST_PATH_IMAGE087
Thereby obtaining
Figure 868355DEST_PATH_IMAGE088
In which
Figure 299336DEST_PATH_IMAGE089
Calculating a constant for a preset gradient approximation; computing by analogy
Figure 396605DEST_PATH_IMAGE090
；
(2) Calculating an objective function
Figure 241151DEST_PATH_IMAGE091
At the point of
Figure 132883DEST_PATH_IMAGE048
Approximate gradient of (c)
Figure 785581DEST_PATH_IMAGE092
(3) Updating with gradients
Figure 686541DEST_PATH_IMAGE048
Figure 108295DEST_PATH_IMAGE093
Wherein
Figure 498825DEST_PATH_IMAGE094
For the search step size, update
Figure 638820DEST_PATH_IMAGE095
Up to
Figure 343471DEST_PATH_IMAGE096
Is provided with
Figure 885310DEST_PATH_IMAGE097
；
(4) Repeating (1) to (3) until the
Figure 384425DEST_PATH_IMAGE098
Output here
Figure 11715DEST_PATH_IMAGE048
As a result of the update;
2. optimal reaction: i.e. directly selecting the best transmission time
Figure 254478DEST_PATH_IMAGE099
Select one
Figure 650824DEST_PATH_IMAGE083
The objective function is minimized to realize updating and find the best reaction, and a one-dimensional poor search method is utilized:
with accuracy of
Figure 320840DEST_PATH_IMAGE100
Sampling interval
Figure 435426DEST_PATH_IMAGE101
Get a set
Figure 216300DEST_PATH_IMAGE102
For each time
Figure 732732DEST_PATH_IMAGE103
Computing
Figure 573650DEST_PATH_IMAGE104
、
Figure 175532DEST_PATH_IMAGE105
Thereby obtaining
Figure 760097DEST_PATH_IMAGE106
Comparison of
Figure 131036DEST_PATH_IMAGE106
Is selected such that
Figure 142854DEST_PATH_IMAGE106
Minimum value
Figure 232033DEST_PATH_IMAGE107
As updated
Figure 354710DEST_PATH_IMAGE048
。
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