CN114501551B - Multi-user distributed heterogeneous network selection strategy method based on ordered potential game - Google Patents

Multi-user distributed heterogeneous network selection strategy method based on ordered potential game Download PDF

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CN114501551B
CN114501551B CN202210345625.6A CN202210345625A CN114501551B CN 114501551 B CN114501551 B CN 114501551B CN 202210345625 A CN202210345625 A CN 202210345625A CN 114501551 B CN114501551 B CN 114501551B
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CN114501551A (en
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谢智东
贺超
郑建超
韩素丹
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National Defense Technology Innovation Institute PLA Academy of Military Science
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0925—Management thereof using policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/80—Responding to QoS
    • 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]

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Abstract

The invention discloses a multi-user distributed heterogeneous network selection strategy method based on ordered potential game, which is applied to the selection control of a plurality of video users of a unmanned aerial vehicle cluster to access a network and comprises the following steps: s1, determining a network selection model based on an ordered potential game; s2, determining a utility function in the game process; and S3, adopting a multi-video user heterogeneous network selection distributed algorithm to solve the game model. The method realizes the optimal decision aiming at the scene that a plurality of users share a plurality of heterogeneous networks for video transmission, effectively solves the problem of multi-node access network selection, and ensures that the overall video experience quality of the multi-video users is the best.

Description

Multi-user distributed heterogeneous network selection strategy method based on ordered potential game
Technical Field
The invention relates to the field of unmanned aerial vehicle cluster transmission control, in particular to a multi-user distributed heterogeneous network selection strategy method based on ordered potential game.
Background
For an unmanned aerial vehicle cluster formed by multiple unmanned aerial vehicles, the information carrying capacity of a single network is limited, and video transmission has certain bandwidth requirements, so that the selection of an access network cannot be arbitrary, and the decision results of multiple nodes are inevitably influenced mutually. Since the network status information is dynamic, secondly, the network selection behavior of the user may further cause dynamic changes of the network status information. For example, when the number of users in a network increases, the probability of congestion increases. This information, i.e. how many users have selected a certain same network for transmitting video, may not be known to the sending node itself. On one hand, due to the limited resources, the occupation of the network resources by each user inevitably forms a competitive relationship in the group. An individual user needs to select a "premium" wireless network that is advantageous to him/herself, e.g., a network with sufficient channel bandwidth, a low packet loss rate, and a low charging standard. On the other hand, the choices affect each other, especially in the problem of packet loss caused by congestion. For an operator of the whole unmanned aerial vehicle cluster, the cluster is a whole, and the return quality of the video needs to be measured by integrating the overall effect of all transmissions. Therefore, for a scene that a plurality of users share a plurality of networks for video transmission, a network selection algorithm needs to be found to ensure that each wireless node can compete with each other and partially cooperate with each other, so that the user experience quality brought by the video transmission of the whole system can be optimal globally, and the problem of multi-node access network selection is effectively solved. The Potential Game (PG) method is used as a branch of Game theory, and can represent the motivation of all unmanned aerial vehicles for changing the strategy as a global function, thereby providing an idea for solving the network selection problem.
Disclosure of Invention
The invention mainly aims to provide a multi-user distributed heterogeneous network selection strategy method, which aims at a scene that a plurality of users share a plurality of heterogeneous networks for video transmission, realizes an optimal decision, effectively solves the problem of multi-node access network selection and enables the overall video experience quality of the multi-video users to be the best.
Based on the above purpose, the invention provides a multi-user distributed heterogeneous network selection strategy method based on ordered potential game, which is characterized in that the method is applied to the selection control of multiple video users accessing to the network in an unmanned aerial vehicle cluster, and the method comprises the following steps:
s1, determining a network selection model based on an ordered potential game;
s2, determining a utility function in the game process;
s3, adopting a multi-video user heterogeneous network to select a distributed algorithm to solve a game model;
further, the ordering based potentialsThe network selection model of the game can be expressed as
Figure 981262DEST_PATH_IMAGE001
Wherein, in the step (A),
Figure 433103DEST_PATH_IMAGE002
for unmanned aerial vehicle aggregation, i.e.
Figure 625050DEST_PATH_IMAGE003
An unmanned aerial vehicle video communication node needing video transmission;
Figure 580368DEST_PATH_IMAGE004
represents the first
Figure 402830DEST_PATH_IMAGE005
A selection policy set for individual drones, wherein
Figure 201022DEST_PATH_IMAGE006
，
Figure 806447DEST_PATH_IMAGE007
Is a binary vector which represents that the unmanned aerial vehicle user is in the access network set
Figure 209746DEST_PATH_IMAGE008
The network selection made in (1); wherein, unmanned aerial vehicle
Figure 999848DEST_PATH_IMAGE005
Can decide whether to select a network
Figure 895122DEST_PATH_IMAGE009
Carry out video transmission, as shown in
Figure 694451DEST_PATH_IMAGE010
（1）
Figure 612642DEST_PATH_IMAGE011
Is the corresponding utility set;
Figure 308066DEST_PATH_IMAGE012
indicating in addition to the user
Figure 956216DEST_PATH_IMAGE005
Selection strategy for all other drones than that in which
Figure 231339DEST_PATH_IMAGE013
Represents a cartesian product;
Figure 140390DEST_PATH_IMAGE007
and
Figure 147660DEST_PATH_IMAGE014
in combination describe all
Figure 407740DEST_PATH_IMAGE003
The behavior strategy of each unmanned aerial vehicle node is as follows
Figure 893079DEST_PATH_IMAGE015
（2）
Further, unmanned aerial vehicle
Figure 922215DEST_PATH_IMAGE005
The information that can be obtained is that other drones select a policy of
Figure 100387DEST_PATH_IMAGE014
By observing the congestion status of each network
Figure 51025DEST_PATH_IMAGE016
（3）
When UAV j selects network k to transmit video, each
Figure 464689DEST_PATH_IMAGE017
The congestion degree of the network k faced by the unmanned aerial vehicle is reflected; network congestion may be represented by the bandwidth occupied by the network, i.e.
Figure 489277DEST_PATH_IMAGE018
（4）
Wherein Z is a three-dimensional matrix,
Figure 697404DEST_PATH_IMAGE019
is that the kth network corresponds to a size in Z of
Figure 869759DEST_PATH_IMAGE020
The elements on the main diagonal of the two-dimensional matrix of (1) are all 0, and the rest values are all 1;
Figure 228060DEST_PATH_IMAGE021
video code rate vectors respectively transmitted by X unmanned aerial vehicles;
Figure 966208DEST_PATH_IMAGE022
is the total bandwidth of the network k,
Figure 814079DEST_PATH_IMAGE023
representing the congestion state of network k, then
Figure 145834DEST_PATH_IMAGE024
（5）。
Further, in step S2, the difference between the transmission quality and the transmission cost of the video is used as a utility function, that is
Figure 166880DEST_PATH_IMAGE025
Wherein, in the step (A),
Figure 900480DEST_PATH_IMAGE026
utility vectors representing QoE corresponding to video transmissions of different drones,
Figure 715990DEST_PATH_IMAGE027
representing the cost vector after each node has selected the corresponding access network,
Figure 269462DEST_PATH_IMAGE028
is a constant coefficient with a total utility function vector of
Figure 31882DEST_PATH_IMAGE029
。
Further, to unmanned aerial vehicle
Figure 744623DEST_PATH_IMAGE005
In particular, when the access network selected is
Figure 871979DEST_PATH_IMAGE030
And the transmission rate of the video is
Figure 302960DEST_PATH_IMAGE031
When the utility function related to the video quality can be expressed as the utility function related to the network state
Figure 10016DEST_PATH_IMAGE032
Function of correlation
Figure 577264DEST_PATH_IMAGE033
Figure 875521DEST_PATH_IMAGE034
Figure 528219DEST_PATH_IMAGE035
（6）
In the formula (6), the reaction mixture is,
Figure 304545DEST_PATH_IMAGE036
the video content representing the current time slot is,
Figure 195141DEST_PATH_IMAGE037
is a constant number of times, and is,
Figure 788933DEST_PATH_IMAGE038
in the form of a function of a logarithm,
Figure 804294DEST_PATH_IMAGE039
in the form of an exponential function of the signal,
Figure 446628DEST_PATH_IMAGE040
is a constant. To unmanned aerial vehicle
Figure 254047DEST_PATH_IMAGE005
In other words, the frame rate of the video
Figure 628527DEST_PATH_IMAGE041
And transmission rate
Figure 459080DEST_PATH_IMAGE031
Are all constant values;
Figure 967422DEST_PATH_IMAGE042
is about
Figure 504713DEST_PATH_IMAGE032
Monotonically increasing.
Further, the cost of a user accessing the network is related to the transmission rate of the video, i.e.
Figure 377992DEST_PATH_IMAGE043
（7）
Wherein the content of the first and second substances,
Figure 226999DEST_PATH_IMAGE044
is as follows
Figure 148818DEST_PATH_IMAGE045
The total cost factor associated with each network.
Further, unmanned aerial vehicle
Figure 930830DEST_PATH_IMAGE005
The video transmission utility function can be expressedIs composed of
Figure 647113DEST_PATH_IMAGE046
（8）
The network selection strategy problem based on the ordered potential game model can be expressed as
Figure 983416DEST_PATH_IMAGE047
Figure 443348DEST_PATH_IMAGE048
Figure 17549DEST_PATH_IMAGE049
Figure 294946DEST_PATH_IMAGE050
（9）
Wherein, the first and the second end of the pipe are connected with each other,
Figure 259491DEST_PATH_IMAGE051
a constraint condition is expressed in terms of the number of the elements,
Figure 585430DEST_PATH_IMAGE052
indicating the best selection strategy.
Further, in step S3, the game model is solved by using a regret matching algorithm, which has the general idea that: the probability of a certain unmanned plane changing its strategy is proportional to the regret degree of the unmanned plane not selecting other strategies at the past moment.
Further, the specific implementation steps of the algorithm include:
s31, initializing, at first
Figure 76454DEST_PATH_IMAGE053
Each drone is in the policy space
Figure 400119DEST_PATH_IMAGE054
Randomly selecting one from the group;
and S32, an iterative updating process, wherein the iterative updating process comprises two substeps of strategy updating and strategy judgment.
Further, in the policy updating step, when
Figure 914277DEST_PATH_IMAGE055
Then, each node calculates the current policy separately
Figure 840645DEST_PATH_IMAGE056
And selecting another policy
Figure 327121DEST_PATH_IMAGE057
The utility of time, and calculate the average difference between these two utilities:
Figure 884004DEST_PATH_IMAGE058
（10）
wherein the content of the first and second substances,
Figure 682196DEST_PATH_IMAGE059
represents time and
Figure 553200DEST_PATH_IMAGE060
. Then, get
Figure 956500DEST_PATH_IMAGE061
The value is the average regret factor;
further, in the policy decision step, in the time slot
Figure 746601DEST_PATH_IMAGE062
Of 1 at
Figure 235351DEST_PATH_IMAGE005
Strategy of individual unmanned aerial vehicle
Figure 644467DEST_PATH_IMAGE056
Then is at
Figure 964590DEST_PATH_IMAGE063
The slot, the drone will reconsider the policy and its basis for selecting the policy will obey the following probability distribution:
Figure 535380DEST_PATH_IMAGE064
（11）
wherein the content of the first and second substances,
Figure 511426DEST_PATH_IMAGE065
. According to the distribution rule, the strategy space can be divided into
Figure 848866DEST_PATH_IMAGE054
In be unmanned aerial vehicle
Figure 961179DEST_PATH_IMAGE005
The strategy is selected probabilistically. After solving the equations (10) and (11) through multiple iterations, the calculation and selection results are not changed any more, and the algorithm is converged; and each user follows the distributed algorithm updating strategy, and the whole network selection potential game is finally converged to a balanced state.
According to the method, a multi-video user distributed access network selection model based on the ordered potential game is established aiming at the scene that a plurality of users share a plurality of heterogeneous networks for video transmission, so that the wireless nodes can compete with each other and have partial cooperation, the problem of multi-node access network selection is effectively solved, the overall video experience quality of the multi-video users is the best, the problem of network congestion is solved, reasonable selection is carried out among the networks, and the load of each network is kept balanced basically.
Drawings
Fig. 1 is a diagram of a selection result of a user corresponding to 7 segments of videos on 3 networks by using a multi-user distributed heterogeneous network selection strategy method based on ordered potential gaming in the embodiment of the present invention;
FIG. 2 is a diagram illustrating congestion levels of various networks according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the loading of various networks in an embodiment of the present invention;
FIG. 4 is a total usage graph of all video users in an embodiment of the present invention;
fig. 5 is a diagram of a network selection result of each user when transmission cost is considered in the embodiment of the present invention;
FIG. 6 is a diagram of network congestion level when transmission cost is taken into account in an embodiment of the present invention;
FIG. 7 is a diagram of network load considering transmission cost in an embodiment of the present invention;
FIG. 8 is a total utility diagram of the system in consideration of transmission cost according to an embodiment of the present invention;
fig. 9 is a diagram illustrating network load conditions at different transmission costs according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Potential games can be further classified into Exact Potential Games (EPG), Weighted Potential Games (WPG), and Ordered Potential Games (OPG). In recent years, there has been an increasing number of potential gaming methods applied to study wireless channel access and network selection issues. Under the condition that joint channel interference is symmetrical and channel effectiveness is additive, a selection problem in a heterogeneous wireless network is modeled into a selection game of a resource block, the game is the sum of ordered potential games, and an ordered potential function is closely related to total congestion of the network. A distributed channel selection mechanism based on potential game is needed to solve the problem of current interruption in the dense wireless local area network. The problem of unmanned aerial vehicle allocation and channel access is solved by adopting a dynamic layered game model, wherein the channel access problem is modeled as a strict potential game.
Compared with other two potential game algorithms, the ordered potential game is more general, and only the individual income change and the change of the overall potential function have the same trend, so that more space is provided for the design of the utility function, and the setting of the utility function mainly based on the video transmission quality can be considered from more layers when the selection of the access network of a plurality of nodes is decided. In addition, in the ordered potential game, the process of searching local optimum by the participant individuals is consistent with the process of searching global optimum by the whole game, so that the realization of distributed access network selection is possible.
The application provides a multi-user distributed heterogeneous network selection strategy method based on an ordered potential game aiming at a scene that multiple users select access networks in a heterogeneous network, and optimizes a network selection strategy from the perspective of improving video transmission quality. According to the multi-user distributed heterogeneous network selection strategy method based on the ordered potential game, the unmanned aerial vehicle carrying the video shooting and wireless communication module is taken as a main video transmission user, and multiple wireless network selection problems in the communication process of aerial shooting and real-time video return of multiple unmanned aerial vehicles are researched.
The multi-user distributed heterogeneous network selection strategy method is based on the existence
Figure 968449DEST_PATH_IMAGE066
Unmanned aerial vehicle cluster formed by unmanned aerial vehicles
Figure 228529DEST_PATH_IMAGE067
Fly in the air at
Figure 307344DEST_PATH_IMAGE068
Network set composed of heterogeneous networks
Figure 946267DEST_PATH_IMAGE069
Within the common coverage area W of the first and second,
Figure 514651DEST_PATH_IMAGE066
unmanned aerial vehicle sharing
Figure 199710DEST_PATH_IMAGE068
Each wireless video node needs to select a proper network to transmit the acquired video back to a certain receiving node, and the total video transmission quality of the system is the highest.
Figure 754320DEST_PATH_IMAGE066
Video code rate vectors transmitted by the unmanned aerial vehicle are respectively fixed as
Figure 637962DEST_PATH_IMAGE070
。
Figure 314931DEST_PATH_IMAGE066
The network selection vector of the unmanned aerial vehicle can be expressed as
Figure 424969DEST_PATH_IMAGE071
Corresponding utility function of
Figure 642324DEST_PATH_IMAGE072
. In this scenario, the total bandwidth that each heterogeneous network can provide remains the same, i.e.
Figure 583735DEST_PATH_IMAGE068
The bandwidth information vector of the heterogeneous network can be expressed as
Figure 369289DEST_PATH_IMAGE073
。
Video data packets are transmitted in a network, and two reasons for loss generally exist, one is that a channel of a wireless network has a fading phenomenon, which easily causes error codes and packet loss; the other is that the packet forwarding delay caused by network congestion exceeds the threshold value which can be tolerated by the system, and the data packet is lost. Wherein the packet loss caused by the error code is related to the transmission characteristic of the wireless channel. The latter, i.e. packet loss due to network congestion, is mainly considered in the scenarios discussed in the present application. Since the user can freely select any one of the networks, the selected network results in different degrees of congestion of each network, and the congestion degree of each network results in different packet loss rates, which adversely affects the user selection.
In the first place
Figure 28940DEST_PATH_IMAGE045
In the network, the network is divided into a plurality of networks,
Figure 784407DEST_PATH_IMAGE030
the total time delay of the coded video from the sending node to the receiving node in the transmission process is
Figure 783587DEST_PATH_IMAGE074
The transmission delay threshold of the video data packet is
Figure 536779DEST_PATH_IMAGE075
. The packet loss rate vector of each network can be expressed as
Figure 746044DEST_PATH_IMAGE076
. The end-to-end transmission delay can be approximated as following an exponential distribution, then at the second
Figure 192286DEST_PATH_IMAGE045
The probability of a packet being lost in a network can be expressed as
Figure 249235DEST_PATH_IMAGE077
. Wherein the content of the first and second substances,
Figure 235645DEST_PATH_IMAGE078
represents a mathematical expectation of the transmission delay of video data packets,
Figure 276414DEST_PATH_IMAGE079
is a smoothing parameter derived from historical observations,
Figure 373682DEST_PATH_IMAGE080
for the remaining bandwidth of the network,
Figure 816296DEST_PATH_IMAGE081
is a network
Figure 973608DEST_PATH_IMAGE045
The total bandwidth of the network (c) is,
Figure 767252DEST_PATH_IMAGE082
the representative selects a network
Figure 668212DEST_PATH_IMAGE045
The sum of the transmission code rates of the videos of the unmanned aerial vehicles. Thus, the network is lostThe packet rate can be expressed as:
Figure 230911DEST_PATH_IMAGE083
（1）
thus, the packet loss rate of a network is related to both the bandwidth capacity of the network and the coding rate of the video transmitted by the nodes accessing the network. By using
Figure 293545DEST_PATH_IMAGE084
Representative network
Figure 574485DEST_PATH_IMAGE045
In a congested state of
Figure 13556DEST_PATH_IMAGE085
（2）
In summary, each wireless network can provide different total bandwidths to users, and the packet loss rates of the wireless networks are different, so that when a plurality of users select to access a network, the users all benefit themselves, and hope that a better video transmission effect can be obtained, and meanwhile, the selection of each user affects the network state. How to optimize the network selection of each user to maximize the total utility of the system is a key technology studied by the application.
Due to the heterogeneity of wireless networks, the available transmission bandwidths provided by each network to users are different, and with the change of the number of users accessed, QoS attributes such as the available bandwidth of a channel, packet loss rate, transmission delay and the like also change, which in turn affects the selection of video users. In the application, a multi-video user distributed access network selection model based on the ordered potential game is established aiming at a scene that a plurality of users share a plurality of heterogeneous networks for video transmission.
The method comprises the following steps:
s1, determining a network selection model based on the ordered potential game. The network selection model based on the ordered potential game can be expressed as
Figure 165183DEST_PATH_IMAGE086
Wherein, in the step (A),
Figure 8505DEST_PATH_IMAGE067
for a set of participants, i.e.
Figure 635796DEST_PATH_IMAGE066
And the unmanned aerial vehicle video communication node is required to transmit videos.
Figure 753925DEST_PATH_IMAGE087
Represents the first
Figure 150271DEST_PATH_IMAGE005
A set of selection policies of individual participants, wherein
Figure 695653DEST_PATH_IMAGE088
，
Figure 810239DEST_PATH_IMAGE054
Is a binary vector which represents that the unmanned aerial vehicle user is in the access network set
Figure 997638DEST_PATH_IMAGE069
The network selection made in (1); wherein the participants
Figure 248491DEST_PATH_IMAGE005
Can decide whether to select a network
Figure 964774DEST_PATH_IMAGE045
Carry out video transmission, as shown in
Figure 566657DEST_PATH_IMAGE089
（3）
Figure 26588DEST_PATH_IMAGE090
For the corresponding utility set, it is related to the video QoE, which will be described in detail in the following section.
Figure 600789DEST_PATH_IMAGE091
Indicating in addition to the user
Figure 612607DEST_PATH_IMAGE005
Selection strategy of all participants except, wherein
Figure 577152DEST_PATH_IMAGE092
Representing the cartesian product.
Figure 699829DEST_PATH_IMAGE054
And
Figure 800640DEST_PATH_IMAGE093
in combination describe all
Figure 514518DEST_PATH_IMAGE066
The behavior strategy of each unmanned aerial vehicle node is as follows
Figure 700780DEST_PATH_IMAGE015
（4）
In the process that the unmanned aerial vehicle selects to access the network, each participant does not know the behaviors of other participants, and therefore the unmanned aerial vehicle is an incomplete information game. First, the
Figure 627148DEST_PATH_IMAGE005
The information that can be obtained by each participant is that other unmanned aerial vehicles select the strategy as
Figure 848045DEST_PATH_IMAGE093
By observing the congestion status of each network
Figure 201666DEST_PATH_IMAGE016
（5）
When participant j selects network k to transmit video, each
Figure 875224DEST_PATH_IMAGE017
Reflects the degree of congestion of the network k faced by the participant, which depends only on the transmission of other nodes occupying the network channel, which occupation is also an interference, as understood from the communication point of view. Network congestion may be represented by the bandwidth occupied by the network, i.e.
Figure 605282DEST_PATH_IMAGE018
（6）
Wherein Z is a three-dimensional matrix.
Figure 680686DEST_PATH_IMAGE019
Is that the kth network corresponds to a size in Z of
Figure 205208DEST_PATH_IMAGE020
The elements on the main diagonal of the two-dimensional matrix of (2) are all 0, and the remaining values are all 1.
Figure 366062DEST_PATH_IMAGE021
Video code rate vectors respectively transmitted by X unmanned aerial vehicles;
Figure 634232DEST_PATH_IMAGE022
is the total bandwidth of the network k and,
Figure 564142DEST_PATH_IMAGE023
representing the congestion state of network k, then
Figure 525145DEST_PATH_IMAGE024
（7）
And S2, determining a utility function in the game process. In order to reasonably determine the access network selection strategy according to the network parameters and accurately evaluate the performance of the network selection strategy, a utility function in the game process needs to be determined. In a distributed network selection strategy of a plurality of wireless nodes, the determination of the utility function needs to fully consider the QoE of the video. In distributed network selection, the QoE utility is calculated not through the centralized calculation of a base station, but each unmanned aerial vehicle node performs independent calculation based on a QoE mathematical model according to observed channel state information in the game process, and the QoE utility is a prediction on video transmission quality.
The present application considers using the difference between the transmission quality and the transmission cost of the video as a utility function, i.e.
Figure 173295DEST_PATH_IMAGE025
Wherein, in the process,
Figure 979577DEST_PATH_IMAGE026
utility vectors representing QoE corresponding to video transmissions of different drones,
Figure 29573DEST_PATH_IMAGE027
representing the cost vector after each node has selected the corresponding access network,
Figure 99160DEST_PATH_IMAGE028
is a constant coefficient with a total utility function vector of
Figure 359240DEST_PATH_IMAGE029
。
First, a video classification method is used, i.e. the video is classified into three categories of slow SM, medium GW and fast RM according to the content characteristics of the video itself, and the video is still used
Figure 844579DEST_PATH_IMAGE036
The video content representing the current time slot can be classified into one of the three types described above. To the first
Figure 545819DEST_PATH_IMAGE005
For an UAV, when the selected access network is
Figure 51886DEST_PATH_IMAGE030
And the transmission rate of the video is
Figure 533683DEST_PATH_IMAGE031
When the utility function related to the video quality can be expressed as the utility function related to the network state
Figure 822713DEST_PATH_IMAGE032
Function of correlation
Figure 706356DEST_PATH_IMAGE094
In the formula (8), the reaction mixture is,
Figure 321008DEST_PATH_IMAGE036
the video content representing the current time slot is,
Figure 290101DEST_PATH_IMAGE037
is a constant number of times, and is,
Figure 117243DEST_PATH_IMAGE038
in the form of a function of a logarithm,
Figure 120971DEST_PATH_IMAGE039
in order to be an exponential function of the,
Figure 640945DEST_PATH_IMAGE095
is a constant. To unmanned aerial vehicle
Figure 97334DEST_PATH_IMAGE005
In other words, the frame rate of the video
Figure 716448DEST_PATH_IMAGE041
And transmission rate
Figure 309103DEST_PATH_IMAGE031
All are constant values. As can be seen,
Figure 999979DEST_PATH_IMAGE042
is about
Figure 943664DEST_PATH_IMAGE032
Monotonically increasing.
In the present application, the cost of the user accessing the network mainly considers two aspects: one aspect is the cost of leasing the channel from the network service provider, since
Figure 378187DEST_PATH_IMAGE068
The individual networks may belong to different network service providers, which often have different charging standards, setting cost factors during transmission of the individual networks
Figure 825349DEST_PATH_IMAGE096
. For non-commercial systems, the cost factor is 0. On the other hand, the energy consumption during transmission is related to the specific network environment and channel type, for example, the energy consumption between the satellite network and the ground mobile network is greatly different, and the energy consumption factor is set as
Figure 421547DEST_PATH_IMAGE097
. Both costs of cost and energy consumption are related to the transmission rate of the video, i.e.
Figure 118107DEST_PATH_IMAGE098
（9）
Wherein the content of the first and second substances,
Figure 90743DEST_PATH_IMAGE044
is as follows
Figure 923569DEST_PATH_IMAGE045
The total cost factor associated with each network. Then, unmanned plane
Figure 690668DEST_PATH_IMAGE005
The video transmission utility function can be expressed as
Figure 608946DEST_PATH_IMAGE046
（10）
The network selection strategy problem based on the ordered potential game model can be expressed as
Figure 385272DEST_PATH_IMAGE047
Figure 807026DEST_PATH_IMAGE048
Figure 10605DEST_PATH_IMAGE049
Figure 150600DEST_PATH_IMAGE050
(11)
Wherein, the first and the second end of the pipe are connected with each other,
Figure 58513DEST_PATH_IMAGE051
representing the constraint.
And S3, adopting a multi-video user heterogeneous network selection distributed algorithm to solve the game model. The process of solving Nash equilibrium in the game is a process of finding the optimal solution through continuous iteration. The game model is solved by utilizing a Regret Matching algorithm, and a multi-video user heterogeneous network selection distributed algorithm based on the ordered potential game is designed. The general idea of the algorithm is as follows: the probability of a certain unmanned plane changing its strategy is proportional to the regret degree of the unmanned plane not selecting other strategies at the past moment. The specific implementation steps comprise initialization and iterative update processes, wherein:
s31, initialization
At first, the method
Figure 272456DEST_PATH_IMAGE053
When each participant is in the policy space
Figure 974833DEST_PATH_IMAGE054
One is randomly selected. In fact, the initial policy may be any value within the scope of the policy space.
And S32, an iterative updating process. The iterative update process further comprises two sub-steps of policy update and policy decision, wherein:
s321. strategy update
When in use
Figure 477490DEST_PATH_IMAGE055
Then, each node calculates the current policy separately
Figure 720252DEST_PATH_IMAGE056
And selecting another policy
Figure 257544DEST_PATH_IMAGE057
The utility of time, and calculate the average difference between these two utilities:
Figure 661981DEST_PATH_IMAGE058
（12）
wherein the content of the first and second substances,
Figure 651933DEST_PATH_IMAGE059
represents time and
Figure 698387DEST_PATH_IMAGE060
. Then, get
Figure 90185DEST_PATH_IMAGE061
I.e. the average regret factor.
S322. strategy judgment
Suppose in a time slot
Figure 931102DEST_PATH_IMAGE062
Of 1 at
Figure 408351DEST_PATH_IMAGE005
Policy of individual participants
Figure 992916DEST_PATH_IMAGE056
Then is at
Figure 239221DEST_PATH_IMAGE063
The participant will reconsider the policy and the basis of his choice of policy will obey the following probability distribution:
Figure 454301DEST_PATH_IMAGE064
（13）
wherein the content of the first and second substances,
Figure 950005DEST_PATH_IMAGE065
is sufficiently large. According to the distribution rule, the strategy space can be divided into
Figure 541523DEST_PATH_IMAGE054
The middle is a participant
Figure 766968DEST_PATH_IMAGE005
The strategy with the higher probability is selected.
After solving the equations (12) and (13) through multiple iterations, the calculation and selection results are not changed any more, and the algorithm converges. If each user follows the above distributed algorithm updating strategy, the whole network election potential game will finally converge to an equilibrium state.
In one embodiment, assuming that 7 drones are performing the filming task in the public coverage area of 3 heterogeneous networks, they independently shoot videos and send 7 different videos back to the same central user through the wireless network. Typical transmission rates for a particular video are further described in the present application, as shown in table 1 below. Generally, the slower the content of a video picture moves, the less transmission resources are occupied by the video, e.g., Akiyo. In addition, the size of the video transmission rate is related to the complexity of the scene, which also occupies more bandwidth, such as Coastguard. Here, the 7 segments of video are from several different scenes, and in practical cases, the number of videos to be transmitted may be more, the scenes are more complex, but the basic principle and the flow of the algorithm are consistent.
TABLE 1 parameters and characteristics of different videos
Figure 825054DEST_PATH_IMAGE099
The 3 kinds of heterogeneous networks covering the flight area of the unmanned aerial vehicle are not particularly limited, and may be a ground mobile communication network, a satellite communication network, and the like, which are respectively referred to as network 1, network 2, and network 3, and may be used for simulation and verification of an algorithm by adjusting parameters such as bandwidth and cost factor of the network to characterize the heterogeneity of the network. Assuming that the total transmission bandwidth of the three networks is matched with the total bandwidth of video transmission, i.e. 5.2 Mbps, 4.8M bps and 4.4 Mbps, the total rate obtained by summing up the 7 video transmission rates in table 1 is about 5.485 Mbps, and the total bandwidth of any single network cannot meet the requirement of all video transmission. Therefore, it is necessary to make a reasonable choice between the networks so that the load of each network is basically balanced.
Two situations are considered, one is a user insensitive to cost, and the transmission cost of each network is not considered, and in this case, according to the utility function, the user selects the network only related to the packet loss rate of the network, that is, related to the congestion degree of the network. And the other type of the method needs to consider the difference of the heterogeneous network packet loss rate and the cost factor at the same time.
Firstly, the selection conditions of different video users for 3 heterogeneous networks are analyzed without considering the transmission cost of each network, namely in the case that the cost factor is equal to 0. Fig. 1 shows a certain selection result of 7 video users for 3 heterogeneous networks in simulation, where the ordinate is a user who selects a network and the abscissa is a network. It can be seen that two users, namely, the phone and the Football user, select the network 1, four users, namely, the Akiyo user, the Coastguad user, the Mobile user and the Table user, select the network 2, and the Forman user selects the network 3, which indicates that each user can select a corresponding network after the user nodes adopt the distributed network selection strategy provided by the application under the condition that no information interaction exists among the user nodes.
Fig. 2 shows the change of the congestion degrees of three heterogeneous networks with time, and the ordinate is the normalized network congestion degree, and it can be seen that after about 20 iterations, the congestion conditions of each network basically remain stable, which indicates that the user selection reaches a balanced state and remains stable. It can also be seen that the ideal result should be that the congestion level of each network is substantially the same, i.e. 1/3, but because the number of users in the example is small, the transmission rate difference between users is large, and the congestion of the network 1 is slightly higher due to the step effect. Fig. 3 shows the average load of the three networks, and it can be seen that the loads of the three networks are sequentially reduced, which is proportional to the total bandwidth that they can provide, and more users select the network with more total bandwidth, while the number of users selecting the network with smaller bandwidth is less.
Fig. 4 shows the total utility of all videos as a function of time, in the figure, the abscissa represents time, and the ordinate represents the total utility of the videos, and it can be seen that after about 20 time slots, the total utility tends to be stable, which indicates that each user makes a reasonable selection and keeps stable, and the multi-user heterogeneous network selection game reaches relevant balance. Meanwhile, for performance comparison, the total utility function when the user randomly selects the network is also given in the figure, as shown by the dotted line in the figure, it can be seen that the method provided by the application has obvious performance improvement.
In the simulation process of the application, the influence of the network congestion degree is considered, and the influence of the transmission cost of the network is considered, for example, in practical application, the energy consumption of a satellite network is generally larger than that of a ground mobile network. In order to make the simulation more specific, without loss of generality in the present application, cost factors of three heterogeneous networks are assumed to be 1, 2, and 3, respectively. The bandwidth and cost of the network 1 are maximum and the cost of the network 3 is minimum and the cost is maximum, and the number of the networks is 2.
Fig. 5 shows the network selection result of each user in consideration of the transmission cost, with the ordinate of the user selecting the network and the abscissa of the network. In fig. 5, the network 1 has the largest bandwidth and the lowest cost, and the network 1 is selected by the videos Akiyo, phone, Coastguard, Mobile, and Table; football selects network 2 and Foreman selects network 3.
Fig. 6 shows the congestion degree of each network in consideration of the transmission cost. In fig. 6, the ordinate is normalized network congestion degree, and since the cost of the network 1 is minimum and the bandwidth is maximum, more video users select the network 1, which results in obviously higher congestion degree of the network 1; the network 3 selects fewer users due to smaller bandwidth and higher cost, so that the congestion degree is obviously lower; the congestion level of the network 2 is in between.
Fig. 7 shows the network load of each network in consideration of the transmission cost. Similar to the case of network congestion, the user makes a selection based on the network bandwidth and cost. In fig. 7, network 1 is loaded most and network 3 is loaded least, with the rule being consistent with the network bandwidth. At the same time, the gap between the network loads is further increased compared to fig. 3, because of the effect of the cost factor. The network load is further increased because the cost of the network 1 is minimal.
Fig. 8 shows the total system utility when the transmission cost is considered, and the ordinate is the total system utility, it can be seen that the total system utility can converge to a stable value, and is significantly better than the performance of the user randomly selecting the network.
Finally, the load simulation results of three networks are shown in fig. 9, wherein the abscissa represents 1 to take the cost factor [ 321 ], the abscissa represents 2 to take the cost factor [ 222 ], and the abscissa represents 3 to take the cost factor [ 123 ]. It can be seen that for each network, as the cost factor gradually increases, the network load gradually decreases, and when the cost is the same, the ratio of the network load to the total bandwidth is the same, which indicates that the algorithm can adapt to various situations with different bandwidths and costs. According to the utility function, the cost factors, besides the factors such as energy consumption and the like which cannot be easily changed, can also be considered, and the cost factors and the like indicate that each network can automatically adjust the access condition of the user by adjusting the cost factors, so that the network load is ensured.
By adopting the distributed network selection algorithm, users can only perceive the utility no matter how the network parameters change, and the users do not need to interact with each other network selection information, and the optimal network selection can be realized by the game among multiple users to achieve balance.
In the description herein, references to the description of "an embodiment," "an example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, various embodiments or examples described in this specification and features thereof may be combined or combined by those skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described, it will be understood that the embodiments are illustrative and not restrictive, and that modifications, changes, substitutions and variations may be made by those skilled in the art without departing from the scope of the present invention.

Claims (8)

1. A multi-user distributed heterogeneous network selection strategy method based on ordered potential game is characterized in that the method is applied to selection control of multiple video users accessing to a network in an unmanned aerial vehicle cluster, and comprises the following steps:
s1, determining a network selection model based on an ordered potential game;
s2, determining a utility function in the game process;
s3, adopting a multi-video user heterogeneous network to select a distributed algorithm to solve a game model;
the network selection model based on the ordered potential game can be expressed as
Figure 924432DEST_PATH_IMAGE001
Wherein, in the step (A),
Figure 501038DEST_PATH_IMAGE002
for unmanned aerial vehicle sets, i.e.
Figure 272685DEST_PATH_IMAGE003
An unmanned aerial vehicle video communication node needing video transmission;
Figure 20061DEST_PATH_IMAGE004
represent unmanned aerial vehicle
Figure 984125DEST_PATH_IMAGE005
In which the selection policy set is
Figure 867767DEST_PATH_IMAGE006
，
Figure 341474DEST_PATH_IMAGE007
Is a binary vector which represents that the unmanned aerial vehicle user is in the access network set
Figure 795720DEST_PATH_IMAGE008
The network selection made in (1); wherein, unmanned aerial vehicle
Figure 544233DEST_PATH_IMAGE005
Can decide whether to select a network
Figure 547961DEST_PATH_IMAGE009
Carry out video transmission, as shown by
Figure 412143DEST_PATH_IMAGE010
Figure 665270DEST_PATH_IMAGE011
Is the corresponding utility set;
Figure 702627DEST_PATH_IMAGE012
indicating in addition to the user
Figure 560862DEST_PATH_IMAGE005
Selection strategy for all other drones than that in which
Figure 376371DEST_PATH_IMAGE013
Represents a cartesian product;
Figure 320056DEST_PATH_IMAGE007
and
Figure 95858DEST_PATH_IMAGE014
in combination describe all
Figure 339757DEST_PATH_IMAGE003
The behavior strategy of individual UAV users, therefore, has
Figure 873638DEST_PATH_IMAGE015
Unmanned plane
Figure 39040DEST_PATH_IMAGE005
The information that can be obtained is that other drones select a policy of
Figure 136309DEST_PATH_IMAGE014
By observing the congestion status of each network
Figure 703557DEST_PATH_IMAGE016
When unmanned aerial vehicle
Figure 142759DEST_PATH_IMAGE017
Selecting a network
Figure 857775DEST_PATH_IMAGE018
When transmitting video, each
Figure 775046DEST_PATH_IMAGE019
All reflect the network faced by the unmanned aerial vehicle
Figure 462379DEST_PATH_IMAGE018
The congestion level of; network congestion may be represented by the bandwidth occupied by the network, i.e.
Figure 525013DEST_PATH_IMAGE020
Wherein the content of the first and second substances,
Figure 39626DEST_PATH_IMAGE021
is a three-dimensional matrix of which the matrix is,
Figure 275435DEST_PATH_IMAGE022
is the first
Figure 302428DEST_PATH_IMAGE018
A network is in
Figure 129439DEST_PATH_IMAGE023
One size corresponding to in
Figure 507462DEST_PATH_IMAGE024
The elements on the main diagonal of the two-dimensional matrix of (1) are all 0, and the rest values are all 1;
Figure 484645DEST_PATH_IMAGE025
is composed of
Figure 677729DEST_PATH_IMAGE026
Video code rate vectors respectively transmitted by the unmanned aerial vehicles;
Figure 832898DEST_PATH_IMAGE027
is a network
Figure 744222DEST_PATH_IMAGE018
The total bandwidth of the network (c) is,
Figure 525096DEST_PATH_IMAGE028
representative network
Figure 523752DEST_PATH_IMAGE018
In a congested state of
Figure 161407DEST_PATH_IMAGE029
。
2. The ordered potential game-based multi-user distributed heterogeneous network selection strategy method according to claim 1, wherein: in step S2, the difference between the transmission quality and the transmission cost of the video is used as a utility function, that is
Figure 763289DEST_PATH_IMAGE030
Wherein, in the step (A),
Figure 629745DEST_PATH_IMAGE031
utility vectors representing QoE corresponding to video transmissions of different drones,
Figure 735105DEST_PATH_IMAGE032
representing the cost vector after each node has selected the corresponding access network,
Figure 294393DEST_PATH_IMAGE033
is a constant coefficient with a total utility function vector of
Figure 117992DEST_PATH_IMAGE034
。
3. The multi-user distributed heterogeneous network selection strategy method based on ordered potential game as claimed in claim 2, wherein: to unmanned aerial vehicle
Figure 240669DEST_PATH_IMAGE005
In particular, when the access network selected is
Figure 13584DEST_PATH_IMAGE035
And the transmission rate of the video is
Figure 727462DEST_PATH_IMAGE036
When the utility function related to the video quality can be expressed as the utility function related to the network state
Figure 303937DEST_PATH_IMAGE037
Function of correlation
Figure 718388DEST_PATH_IMAGE038
In the formula (6), the reaction mixture is,
Figure 595077DEST_PATH_IMAGE039
the video content representing the current time slot is,
Figure 214277DEST_PATH_IMAGE040
is a constant number of times, and is,
Figure 497622DEST_PATH_IMAGE041
in the form of a function of a logarithm,
Figure 493260DEST_PATH_IMAGE042
in order to be an exponential function of the,
Figure 240767DEST_PATH_IMAGE043
as a constant to the unmanned plane
Figure 30869DEST_PATH_IMAGE005
In other words, the frame rate of the video
Figure 50777DEST_PATH_IMAGE044
And transmission rate
Figure 318948DEST_PATH_IMAGE036
Are all constant values;
Figure 920961DEST_PATH_IMAGE045
is about
Figure 350806DEST_PATH_IMAGE037
Monotonically increasing.
4. The ordered potential game-based multi-user distributed heterogeneous network selection strategy method according to claim 3, wherein:
the cost of a user accessing the network is related to the transmission rate of the video, i.e.
Figure 402551DEST_PATH_IMAGE046
（7）
Wherein, the first and the second end of the pipe are connected with each other,
Figure 474412DEST_PATH_IMAGE047
is as follows
Figure 383462DEST_PATH_IMAGE048
Total cost factor associated with individual network, unmanned aerial vehicle
Figure 62836DEST_PATH_IMAGE005
The video transmission utility function can be expressed as
Figure 57337DEST_PATH_IMAGE049
The network selection strategy problem based on the ordered potential game model can be expressed as
Figure 464048DEST_PATH_IMAGE050
Wherein the content of the first and second substances,
Figure 978337DEST_PATH_IMAGE051
a constraint condition is expressed in terms of the number of the elements,
Figure 812301DEST_PATH_IMAGE052
indicating the best selection strategy.
5. The multi-user distributed heterogeneous network selection strategy method based on ordered potential game according to any one of claims 1, 3 and 4, characterized in that in step S3, the game model is solved by using a regret matching algorithm, whose overall idea is: the probability that a certain unmanned aerial vehicle user changes the strategy is in direct proportion to the regret degree of the unmanned aerial vehicle user who does not select other strategies at the past moment.
6. The ordered potential game-based multi-user distributed heterogeneous network selection strategy method according to claim 5, wherein the specific implementation steps of the algorithm comprise:
s31, initializing, at first
Figure 559677DEST_PATH_IMAGE053
Each drone is in the policy space
Figure 724073DEST_PATH_IMAGE054
Randomly selecting one from the group;
and S32, an iterative updating process, wherein the iterative updating process comprises two substeps of strategy updating and strategy judgment.
7. The ordered potential game-based multi-user distributed heterogeneous network selection strategy method according to claim 6, wherein in the strategy updating step, when the strategy is updated, the selection strategy is executed
Figure 342136DEST_PATH_IMAGE055
Then, each node calculates the current policy separately
Figure 81422DEST_PATH_IMAGE056
And selecting another policy
Figure 589196DEST_PATH_IMAGE057
The utility of time, and calculate the average difference between these two utilities:
Figure 540972DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 279120DEST_PATH_IMAGE059
represents time and
Figure 674461DEST_PATH_IMAGE060
(ii) a Then, taking
Figure 927588DEST_PATH_IMAGE061
I.e. the average regret factor.
8. The ordered potential game-based multi-user distributed heterogeneous network selection strategy method according to claim 7, wherein in the strategy judgment step, in the time slot
Figure 699366DEST_PATH_IMAGE062
Strategy for drone j
Figure 557600DEST_PATH_IMAGE056
Then is at
Figure 169847DEST_PATH_IMAGE063
Time slots, the strategy will be reconsidered and its basis for selecting the strategy will obey the following probability distribution:
Figure 598686DEST_PATH_IMAGE064
wherein, the first and the second end of the pipe are connected with each other,
Figure 892264DEST_PATH_IMAGE065
according to the distribution rule, the strategy space can be divided into
Figure 401742DEST_PATH_IMAGE054
In be unmanned aerial vehicle
Figure 667114DEST_PATH_IMAGE005
Selecting a strategy according to the probability;
after solving the formula (10) and the formula (11) through multiple iterations, the calculation and selection results are not changed any more, and the algorithm is converged; and each user follows the distributed algorithm updating strategy, and the whole network selection potential game is finally converged to a balanced state.
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