CN116073924B - Anti-interference channel allocation method and system based on Stackelberg game - Google Patents

Anti-interference channel allocation method and system based on Stackelberg game Download PDF

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CN116073924B
CN116073924B CN202310206515.6A CN202310206515A CN116073924B CN 116073924 B CN116073924 B CN 116073924B CN 202310206515 A CN202310206515 A CN 202310206515A CN 116073924 B CN116073924 B CN 116073924B
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user
interference
jammer
channel
channel access
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CN116073924A (en
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谢智东
袁昕旺
王鹏
秦姗
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National Defense Technology Innovation Institute PLA Academy of Military Science
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/345—Interference values
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an anti-interference channel allocation method and system based on a Stackelberg game, which comprehensively considers the mutual interference existing in the system and the malicious interference from the outside, builds a multi-layer Stackelberg game model, and updates a strategy according to a random automatic learning algorithm by taking a user as a follower; the jammer is the leader and updates the strategy according to the Q-learning algorithm. The anti-interference performance of the method is superior to that of a random selection algorithm, and the anti-interference performance of the network channel of the unmanned aerial vehicle is obviously improved.

Description

Anti-interference channel allocation method and system based on Stackelberg game
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an anti-interference channel allocation method and system based on a Stackelberg game.
Background
By distributing Computing nodes at the Edge of a network, edge Computing (EC) can enable mobile equipment at a terminal to acquire services such as communication, computing, storage, perception and the like in a short distance, and compared with cloud Computing, the EC can better meet the application requirements of low time delay and high density. However, in the face of special scenarios such as natural disasters, equipment failures, complex terrains, etc., conventional methods are difficult to deploy computing nodes and cannot continuously guarantee normal service. Unmanned aerial vehicles have been attracting attention in recent years due to their high maneuverability, which is light and flexible. In the edge computing network, computing nodes can be deployed more flexibly by equipping the unmanned aerial vehicle, and the emergency service capability of the network is improved. With the increasing number of devices and task demands, the service resources (such as operation memory, network bandwidth, communication channels, etc.) of the system are consumed sharply, and it is significant to formulate efficient task allocation and resource management schemes.
In response to spectrum shortage and interference attack, the prior art uses a partially overlapped channel technology, so that the channel utilization rate is improved while the interference is reduced. The hypergraph theory in the prior art can accurately capture the interference condition existing in the system. However, these conventional approaches consider jammer strategies that are either predetermined or fixed and are insufficient to deal with dynamic jammer attacks. In the prior art, intelligent interference is avoided by changing the motion trail of the ground users, but the same-frequency interference among the users is also considered. In the prior art, by setting up a honey pot to actively attract an intelligent jammer, all researches assume that channel state information is completely observable, and future communication networks tend to be complex, and the completely observable channel information becomes unrealistic, so that an anti-interference strategy under incomplete information also needs to be considered. By adopting game theory and reinforcement learning, the problems of incomplete observation information and dynamic interference can be well solved. In addition, the deployment scale of unmanned aerial vehicles will gradually become larger in the future, the problem of resource shortage will be aggravated by high mobility of unmanned aerial vehicles, the problem of channel resource management added into unmanned aerial vehicle networks is considered in the prior art, but the application scene combined with edge calculation is still few.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims at providing an anti-interference channel allocation method based on a Stackelberg game, which comprehensively considers the mutual interference existing in a system and the malicious interference from the outside, and builds a multi-layer Stackelberg game model to improve the anti-interference performance. It is another object of the present invention to provide a tamper resistant channel allocation system based on the Stackelberg game which implements the above method.
In order to achieve the above purpose, the present invention provides an anti-interference channel allocation method based on a Stackelberg game, including:
step 1), constructing a multi-layer Stackelberg model according to different requirements of a user and an jammer, wherein the jammer is a leader, and the user is a follower; setting up
Figure SMS_1
A user consisting of an unload-accept pair of computing tasks, set->
Figure SMS_2
Available channels, set->
Figure SMS_3
A jammer;
step 2) setting a game model of a follower layer:
user' s
Figure SMS_4
The received interference includes internal co-channel interference and external malicious interference, expressed as:
Figure SMS_5
(1)
wherein ,
Figure SMS_7
indicating co-channel interference between user n and other users,/->
Figure SMS_9
Representing the malicious interference received by the user,
Figure SMS_12
for the transmission power of user m, +.>
Figure SMS_8
For the transmission power of jammer j, +.>
Figure SMS_10
For the instantaneous interference of user m to user n,
Figure SMS_11
instantaneous interference of the jammer j to the user n; />
Figure SMS_13
Interference channel access strategy for jammer j, +.>
Figure SMS_6
A channel access strategy for minimizing the total interference suffered by the user n and the user m respectively;
the sub-game of the follower layer can be expressed as:
Figure SMS_14
(5)
wherein ,
Figure SMS_15
a utility function for user n;
step 3) setting a game model of a leader layer:
Figure SMS_16
(8)
wherein ,
Figure SMS_17
for interference policy->
Figure SMS_18
Utility functions of (2);
step 4) setting a multi-layer Stackelberg game, which is expressed as:
Figure SMS_19
(9)
and 5) carrying out strategy iteration, and finally, achieving the balance of the Stackelberg, wherein all users can not obtain higher utility values by changing own strategies.
Further, the instantaneous interference of user m to user n
Figure SMS_20
Instantaneous interference of jammer j to user n>
Figure SMS_21
； wherein ,/>
Figure SMS_22
For the planar distance between user m and user n, < >>
Figure SMS_23
For the planar distance between jammer j and user n, < >>
Figure SMS_24
Is a path loss factor, +.>
Figure SMS_25
As a coefficient of instantaneous fading of the channel,
Figure SMS_26
is a Kronecker function.
Further, the utility function for user n is expressed as:
Figure SMS_27
(3)
wherein
Figure SMS_28
Is->
Figure SMS_29
Is>
Figure SMS_30
The transmission power of user n and user m, respectively. />
Further, jammer j's interference strategy
Figure SMS_31
The utility function of (2) can be expressed as:
Figure SMS_32
(6)
wherein
Figure SMS_33
For the planar distance between user n and jammer j, +.>
Figure SMS_34
Is a path loss factor, +.>
Figure SMS_35
For user->
Figure SMS_36
And the instantaneous fading coefficient of the channel between jammers j.
Further, the step 5) may be expressed as:
Figure SMS_37
(10)
wherein ,
Figure SMS_38
a channel access policy is obtained for the optimization objective of user n to minimize the interference experienced.
Further, the optimization objective of user n is to obtain a channel access policy that minimizes the interference experienced, expressed as:
Figure SMS_39
(4)。
further, the optimization objective of the jammer layer is to maximize the disruption caused by the jammers, expressed as:
Figure SMS_40
(7)。
further, in the policy iteration, each jammer of the leader layer is at
Figure SMS_41
The Q-learning algorithm is used for updating the channel access strategy in each round, and the method specifically comprises the following steps:
jammer j based on channel access probability at the beginning of each training round
Figure SMS_42
Select interference strategy->
Figure SMS_43
Calculate its utility value->
Figure SMS_44
The Q-table is then updated, namely:
Figure SMS_45
(11)
Figure SMS_46
for learning step length, satisfy->
Figure SMS_47
Updating the channel access probability on the basis of this +.>
Figure SMS_48
：
Figure SMS_49
(12)
wherein
Figure SMS_50
Is the learning rate.
Further, in the policy iteration, each user of the follower layer divides in each round
Figure SMS_51
The channel access strategy is updated by using a random learning algorithm in each time slot, specifically:
at the position of
Figure SMS_52
Updating the channel access strategy by a random automatic learning algorithm within each time slot so that the interference is +.>
Figure SMS_53
Minimizing; />
At the beginning of each time slot t, according to the channel access probability
Figure SMS_54
Channel for selecting access->
Figure SMS_55
Then calculate its utility->
Figure SMS_56
Updating the channel access probability of the next time slot on the basis of this +.>
Figure SMS_57
Specifically, the method can be expressed as:
Figure SMS_58
(13)
wherein ,
Figure SMS_59
for learning step length, satisfy->
Figure SMS_60
。
An anti-interference channel distribution system based on a Stackelberg game is used for implementing the anti-interference channel distribution method based on the Stackelberg game.
According to the invention, the mutual interference existing in the system and the malicious interference from the outside are comprehensively considered, and a multi-layer Stackelberg game model is constructed to improve the anti-interference performance, and the anti-interference performance is superior to that of a random selection algorithm.
Drawings
Fig. 1 is a schematic diagram of a problem existing in channel resource allocation;
FIG. 2 is a schematic diagram of an interference-free channel allocation system model;
FIG. 3 is an iterative graph of channel access probabilities for user 1 within a single epoch;
fig. 4 is an iterative chart of channel access probabilities of the jammer 1;
FIG. 5 is a graph showing performance comparisons in different user number scenarios;
fig. 6 is a schematic diagram showing performance comparison under a scene of different numbers of jammers.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific 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 explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Specific embodiments of the present invention are described in detail below with reference to fig. 1-6. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
According to the anti-interference channel allocation method and system based on the Stackelberg game, on one hand, as the number of legal channels is limited as communication equipment in a network is increased, different equipment can be connected to the same channel, and co-channel interference can occur to influence normal communication of the equipment. On the other hand, due to the exposure of the communication channel, a malicious jammer can eavesdrop on the communication process or apply an interfering signal, which presents challenges for the secure communication of the system. Thus, as shown in fig. 1, allocation management of channel resources needs to consider internal mutual interference and external malicious interference.
In the invention, in unmanned plane-assisted edge calculation, the resource allocation of a channel part is researched on the basis of an unloading allocation mechanism of a calculation task, a user comprehensively considers the mutual interference existing in a system and the malicious interference from the outside for unloading-receiving pairs of the calculation task, a multi-layer Stackelberg game model is constructed, and the user updates a strategy according to a random automatic learning algorithm for a follower; the jammer is the leader and updates the strategy according to the Q-learning algorithm.
The invention discloses an anti-interference channel allocation method based on a Stackelberg game, which comprises the following steps:
1. constructing a double-layer Stackelberg game model;
as shown in fig. 2, in the application scenario, there are
Figure SMS_61
A user composed of an unload-accept pair of computing tasks, available channels are +.>
Figure SMS_62
And each. By introducing an edge computing application scenario, consider +.>
Figure SMS_63
The intelligent jammer, jammer j is added according to the channel access interference strategy of the user>
Figure SMS_64
Selecting the interference channel with the greatest disruption, on the basis of which user n updates the channel access policy which minimizes the total interference suffered>
Figure SMS_65
。
According to different requirements of users and jammers, a multi-layer Stackelberg model is constructed, wherein the jammers are leaders, the users are followers, and utility expressions of players of each layer are described below.
(1) The following: game model of user layer
User' s
Figure SMS_66
The received interference mainly comes from internal co-channel interference and external malicious interference, and can be expressed as follows:
Figure SMS_67
(1)
wherein ,
Figure SMS_68
indicating co-channel interference between user n and other users,/->
Figure SMS_69
Representing the malicious interference received by the user,
Figure SMS_70
the transmission power of the user m and the interference unit j are respectively; />
Figure SMS_71
And (3) a channel access strategy for minimizing the total interference suffered by the user n and the user m respectively.
Figure SMS_72
Respectively representing the instantaneous interference of the user m and the jammer j to the user n; />
Figure SMS_73
For the planar distance between user m and user n, < >>
Figure SMS_74
Is the planar distance between jammer j and user n; />
Figure SMS_75
Is a path loss factor, +.>
Figure SMS_76
For the instantaneous fading coefficient of the channel->
Figure SMS_77
Is a Kronecker function, namely:
Figure SMS_78
(2)。
thus, the utility function for user n can be expressed as:
Figure SMS_79
(3)/>
wherein ,
Figure SMS_80
is->
Figure SMS_81
Is>
Figure SMS_82
The transmission power of user n and user m respectively,
Figure SMS_83
the method comprises the steps of carrying out a first treatment on the surface of the The optimization objective of user n is to obtain a channel access scheme (policy function) that minimizes the interference that is expressed as:
Figure SMS_84
(4)
the sub-game of the follower layer can be expressed as:
Figure SMS_85
(5)。
(2) The leader: game model of jammer layer
For jammer j, its interference strategy
Figure SMS_86
The utility function of (2) can be expressed as:
Figure SMS_87
(6)
wherein ,
Figure SMS_88
，/>
Figure SMS_89
for the planar distance between user n and jammer j, +.>
Figure SMS_90
Is a path loss factor, +.>
Figure SMS_91
Is the instantaneous fading coefficient of the channel between user n and jammer j.
The optimization goal is to maximize the disruption caused by the interference, namely:
Figure SMS_92
(7)
the sub-game of the leader layer can be described as:
Figure SMS_93
(8)
in summary, the multi-layer Stackelberg game can be expressed as:
Figure SMS_94
(9)。
through the strategy iteration of the intelligent learning algorithm, the system finally reaches the balance of the Stackelberg, namely, all users can not obtain higher utility values by changing own strategies any more, and the method can be specifically expressed as follows:
Figure SMS_95
(10)。
2. strategy iterative algorithm
After the parameter initialization is completed, each jammer in the leader layer is in
Figure SMS_96
Updating channel access policies using Q-learning algorithm within a round of time, each user of the follower layer is inThe interior of each round is>
Figure SMS_97
The channel access strategy is updated by using a random learning algorithm in each time slot, so that malicious interference of an external jammer is dealt with. And finally outputting the channel access strategy after the players at each layer converge.
The parameter update expressions for each layer of game iterations are as follows:
(1) Leader layer: q-learning
As a leader, jammer j starts training each round according to the channel access probability
Figure SMS_98
Select interference strategy->
Figure SMS_99
Calculate its utility value->
Figure SMS_100
The Q-table is then updated, namely:
Figure SMS_101
(11)
wherein ,
Figure SMS_102
for learning step length, satisfy->
Figure SMS_103
Updating the channel access probability on the basis of this>
Figure SMS_104
：
Figure SMS_105
(12)
wherein
Figure SMS_106
Is the learning rate.
The double-layer Stackelberg game anti-interference channel allocation algorithm is as follows:
Figure SMS_107
(2) Follower layer: random automatic learning
User n as follower, in
Figure SMS_108
Updating the channel access strategy by a random automatic learning algorithm within each time slot so that the interference is +.>
Figure SMS_109
Minimizing.
At the beginning of each time slot t, according to the channel access probability
Figure SMS_110
Channel for selecting access->
Figure SMS_111
Then calculate its utility->
Figure SMS_112
Updating the channel access probability of the next time slot on the basis of this +.>
Figure SMS_113
Specifically, the method can be expressed as:
Figure SMS_114
(13)
wherein ,
Figure SMS_115
for learning step length, satisfy->
Figure SMS_116
。
The invention also provides an anti-interference channel distribution system based on the Stackelberg game, which is used for implementing the anti-interference channel distribution method based on the Stackelberg game.
In one embodiment of the invention, the simulation scenario is: as shown in FIG. 2, there are 2 base stations, 6 unmanned aerial vehicles and 8 terminal devices in the simulation scene, all distributed in
Figure SMS_117
Within a range of (2). In addition, there are 3 jammers with coordinates (1200,0), (1200 ) and (0,1200), respectively. Each user is an off-load-receive pair of a computing task, number of users
Figure SMS_118
The number of available channels +.>
Figure SMS_119
The channel selection probability of the user and the jammer in the initial state is uniform, i.e
Figure SMS_120
Training round +.>
Figure SMS_121
In each training round, the user has 100 time slots.
Taking the scenario shown in fig. 2 as an example, a specific embodiment is as follows:
1. coordinates of each end user: terminal 1 (633,958); terminal 2 (98,486); terminal 3 (859,801); terminal 4 (547,142); a terminal 5 (576,650); terminal 6 (60,732); terminal 7 (235,648); terminal 8 (354,451).
2. Coordinates of each unmanned aerial vehicle: a drone 1 (250 ); unmanned plane 2 (250, 750); an unmanned plane 3 (500, 250); an unmanned plane 4 (500, 750); unmanned plane 5 (750,250); unmanned aerial vehicle 6 (750).
3. Coordinates of each ground base station: base station 1 (250, 500), base station 2 (750,500).
4. According to the equipment conditions of connection establishment, obtaining equipment matching relations of a user 1 (terminal 6-unmanned aerial vehicle 2), a user 2 (terminal 7-unmanned aerial vehicle 2), a user 3 (terminal 2-base station 1), a user 4 (terminal 8-unmanned aerial vehicle 1), a user 5 (terminal 4-unmanned aerial vehicle 3), a user 6 (terminal 3-unmanned aerial vehicle 6), a user 7 (terminal 5-unmanned aerial vehicle 4-base station 2) and a user 8 (terminal 1-unmanned aerial vehicle 4).
5. The coordinates of each user take the geometric center coordinates of the matching equipment, and then the coordinates of each user are as follows: user 1 (566.5,854); user 2 (174,493); user 3 (804.5,775.5); user 4 (523.5,196); user 5 (608.7,633.3); user 6 (155,741); user 7 (242.5,699); user 8 (302,350.5).
6. The coordinates of each external intelligent jammer are 0,1200; (1200,0); (1200).
7. Training rounds
Figure SMS_124
Number of slots per round +.>
Figure SMS_125
User transmission power +.>
Figure SMS_128
Jammer transmit power->
Figure SMS_123
Path loss factor->
Figure SMS_126
The number of available channels>
Figure SMS_127
Learning rate of jammer->
Figure SMS_129
Learning step size ∈of user>
Figure SMS_122
。
Output value of final result:
1. channel interference policy set of jammer
Figure SMS_130
Channel access policy set for users
Figure SMS_131
。/>
2. The total interference condition of the system is 0.0020 units, if a user adopts a random channel access scheme, the average value of the total interference of the system subjected to 50 repeated tests is 0.0022 units, and under the condition of no external jammer, the total interference condition of the system is 0.0010 units (mainly internal co-channel interference).
The simulation effect is as follows: fig. 3 is an iterative change chart of the selection probability of each channel in a certain training round of the user 1, and it can be found that, after 72 time slots, the probability of selecting the channel 2 by the user gradually converges to 1, and the selection probabilities of other channels gradually converges to 0, so that the user 1 finally exercises to select the channel 2 in the round. Fig. 4 is a probabilistic iteration diagram of jammer 1 accessing all channels in all training rounds. It can be seen that the probability of accessing channel 4 after 16 training gradually converges to 1, so in this scenario, the jammer 1 will eventually select the interfering channel 4.
Performance comparative analysis: fig. 5 shows the performance of the proposed algorithm in a scenario of 5 different numbers of users (2, 4, 5,6, 8, 10, respectively) compared with the random channel selection scheme and the case without jammers. First, the proposed algorithm can be found to suffer less interference than the random selection strategy, and the performance gap becomes more and more apparent as the number of users increases. In the interference-free machine scene, the interference suffered by the user is only the same-frequency interference. Further, it can be found that co-channel interference is 0 when the number of users is 2, because the number of available channels is sufficient, and co-channel interference does not exist. Second, it can be found that the interference experienced by users increases with the number of users, as the resources within the system become more and more competitive.
Fig. 6 shows a comparison of performance under different numbers of jammers, and it can be found that the total interference suffered by the system gradually increases as the number of jammers increases, and the anti-interference performance of the algorithm provided by the invention is better than that of the random selection algorithm under the three conditions.

Claims (7)

1. An anti-interference channel allocation method based on a Stackelberg game is characterized by comprising the following steps:
step 1), constructing a multi-layer Stackelberg model according to different requirements of a user and an jammer, wherein the jammer is a leader, and the user is a follower; setting up
Figure QLYQS_1
A user consisting of an unload-accept pair of computing tasks, set->
Figure QLYQS_2
Available channels, set->
Figure QLYQS_3
A jammer;
step 2) setting a game model of a follower layer:
user' s
Figure QLYQS_4
The received interference includes internal co-channel interference and external malicious interference, expressed as:
Figure QLYQS_5
(1)
wherein ,
Figure QLYQS_7
indicating co-channel interference between user n and other users,/->
Figure QLYQS_9
Indicating the malicious interference received by the user, < >>
Figure QLYQS_11
For the transmission power of user m, +.>
Figure QLYQS_8
For the transmission power of jammer j, +.>
Figure QLYQS_10
For usersm instantaneous interference to user n, +.>
Figure QLYQS_12
Instantaneous interference of the jammer j to the user n; />
Figure QLYQS_13
Interference channel access strategy for jammer j, +.>
Figure QLYQS_6
A channel access strategy for minimizing the total interference suffered by the user n and the user m respectively;
the sub-game of the follower layer can be expressed as:
Figure QLYQS_14
(5)
wherein ,
Figure QLYQS_15
a utility function for user n;
step 3) setting a game model of a leader layer:
Figure QLYQS_16
(8)
wherein ,
Figure QLYQS_17
for interference policy->
Figure QLYQS_18
Utility functions of (2);
step 4) setting a multi-layer Stackelberg game, which is expressed as:
Figure QLYQS_19
(9)
step 5) carrying out strategy iteration, and finally achieving the balance of the Stackelberg, wherein all users can not obtain higher utility values by changing own strategies;
the utility function for user n is expressed as:
Figure QLYQS_20
(3)
wherein ,
Figure QLYQS_21
is->
Figure QLYQS_22
Is>
Figure QLYQS_23
The transmission power of user n and user m respectively,
Figure QLYQS_24
；
interference strategy for jammer j
Figure QLYQS_25
The utility function of (2) can be expressed as:
Figure QLYQS_26
(6)/>
wherein ,
Figure QLYQS_27
，/>
Figure QLYQS_28
for the planar distance between user n and jammer j, +.>
Figure QLYQS_29
Is a path loss factor, +.>
Figure QLYQS_30
Instantaneous fading coefficients for the channel between user n and jammer j;
wherein ,
Figure QLYQS_31
is a Kronecker function, namely: />
Figure QLYQS_32
(2)。
2. The anti-interference channel allocation method based on the Stackelberg game according to claim 1, wherein the instantaneous interference of user m to user n
Figure QLYQS_33
Instantaneous interference of jammer j to user n
Figure QLYQS_34
； wherein ,/>
Figure QLYQS_35
For the planar distance between user m and user n, < >>
Figure QLYQS_36
For the planar distance between jammer j and user n, < >>
Figure QLYQS_37
Is a path loss factor, +.>
Figure QLYQS_38
Is the instantaneous fading coefficient of the channel.
3. The method for assigning anti-interference channels based on a jackberg game according to claim 1, wherein said step 5) is expressed as:
Figure QLYQS_39
(10)
wherein ,
Figure QLYQS_40
a channel access policy is obtained for the optimization objective of user n to minimize the interference experienced.
4. The method for anti-interference channel allocation based on the jackberg game according to claim 3, wherein the optimization objective of the user n is obtained as a channel access strategy for minimizing the interference, expressed as:
Figure QLYQS_41
(4)。
5. the method for assigning anti-interference channels based on the jackberg game of claim 4, wherein the optimization objective of the jammer layer is to maximize the interference-induced disruption expressed as:
Figure QLYQS_42
(7)。
6. the method for anti-interference channel allocation based on a Stackelberg game according to claim 1, wherein each jammer of a leader layer is in a strategy iteration
Figure QLYQS_43
The Q-learning algorithm is used for updating the channel access strategy in each round, and the method specifically comprises the following steps:
jammer j based on channel access probability at the beginning of each training round
Figure QLYQS_44
Select interference strategy->
Figure QLYQS_45
Calculate its utility value->
Figure QLYQS_46
And then furtherThe new Q-table, namely:
Figure QLYQS_47
(11)
Figure QLYQS_48
for learning step length, satisfy->
Figure QLYQS_49
Updating channel access probability on the basis of the channel access probability
Figure QLYQS_50
：/>
Figure QLYQS_51
(12)
wherein
Figure QLYQS_52
Is the learning rate.
7. The method for anti-interference channel allocation based on a Stackelberg game according to claim 1, wherein each user of a follower layer is distributed in each round in the policy iteration
Figure QLYQS_53
The channel access strategy is updated by using a random learning algorithm in each time slot, specifically:
at the position of
Figure QLYQS_54
Updating the channel access strategy by a random automatic learning algorithm within each time slot so that the interference is +.>
Figure QLYQS_55
Minimizing;
at the beginning of each time slot t, according to the channel access probability
Figure QLYQS_56
Channel for selecting access->
Figure QLYQS_57
Then calculate its utility->
Figure QLYQS_58
Updating the channel access probability of the next time slot on the basis of this +.>
Figure QLYQS_59
Specifically, the method can be expressed as:
Figure QLYQS_60
(13)
wherein ,
Figure QLYQS_61
for learning step length, satisfy->
Figure QLYQS_62
。/>
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