CN116634468B - Unmanned aerial vehicle channel selection method based on accurate potential game - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle channel selection method based on accurate potential game, which relates to the field of information and communication engineering, in particular to an unmanned aerial vehicle channel selection method based on accurate potential game, comprising the following steps of firstly, establishing a channel selection game model; step two, proving that the channel selection game model is an accurate potential game model; and thirdly, designing a distributed channel selection algorithm without information interaction. According to the unmanned aerial vehicle channel selection method based on the accurate potential game, internal co-channel interference and external malicious interference are considered during channel selection, so that the unmanned aerial vehicle can be ensured to achieve the purposes of minimizing the co-channel interference and the external malicious interference inside the unmanned aerial vehicle cluster and fully utilizing channel resources through selecting channels.
Description
Technical Field
The invention relates to the technical field of information and communication engineering, in particular to an unmanned aerial vehicle channel selection method based on accurate potential game.
Background
For unmanned clusters, the quality of communication is critical, as it directly determines whether a task can be completed. However, due to the increasing problem of shortage of spectrum resources, co-channel interference inevitably exists inside the unmanned aerial vehicle cluster. In addition, due to the advantages of flexibility, maneuver and the like of the unmanned aerial vehicle, the unmanned aerial vehicle is inevitably applied to a severe environment, and therefore, the unmanned aerial vehicle cluster can be subjected to external malicious interference. Serious interference can cause problems such as communication interruption, increased energy consumption, task failure and the like.
At present, when a channel selection scheme is carried out on unmanned aerial vehicles in an unmanned aerial vehicle cluster, the design of the channel selection scheme is not comprehensively considered on internal co-channel interference and malicious interference, so that the aim of minimizing interference and fully utilizing channel resources cannot be achieved.
Disclosure of Invention
In view of the above, the invention provides a method for selecting a channel of an unmanned aerial vehicle based on accurate potential game, so that the unmanned aerial vehicle can minimize co-channel interference inside an unmanned aerial vehicle cluster and malicious interference outside the unmanned aerial vehicle cluster by selecting the channel, and the purpose of fully utilizing channel resources is achieved.
In order to achieve the above purpose, the technical scheme of the invention comprises the following steps:
step one, establishing a channel selection game model: the method comprises an interference mathematical model and a utility function aiming at the interference mathematical model, wherein the interference mathematical model is used for simultaneously considering internal co-channel interference and external malicious interference when a channel is selected.
Judging whether the channel selection game model is an accurate potential game model or not; if yes, executing step three, otherwise returning to step one to reestablish the channel selection game model.
Step three, designing a distributed channel selection algorithm without decision information interaction, and carrying out channel selection by the unmanned aerial vehicle according to the distributed channel selection algorithm.
Further, an interference mathematical model which simultaneously considers internal co-channel interference and external malicious interference during channel selection is specifically as follows:
relay unmanned aerial vehicleSelect channel->When from the same selection of the current channel +.>Co-channel interference of a relay unmanned aerial vehicle is：
wherein Representation and relay drone>A set of other relay drones using the same channel; />Representing the transmitting power of the relay unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representing relay unmanned plane->And relay unmanned plane->European distance between->Representing the path loss factor.
Relay unmanned planenSelecting a channelAnd when the co-channel interference from other source unmanned aerial vehicles is:
wherein Representation and relay drone>Relay unmanned aerial vehicle using the same channel +.>A managed source unmanned aerial vehicle collection;representing the transmitting power of the source unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representing relay unmanned plane->And source unmanned plane->A Euclidean distance between them; />Representing the path loss factor.
Unmanned aerial vehicle for relayIs subjected to malicious interference, including the malicious interference generated by a ground jammer->Malicious interference generated by air jammers>Comprehensive two-source malicious interference, relay unmanned aerial vehicle +.>Sum of malicious interference suffered->。
When a surface-to-surface jammer is present,1, otherwise 0; when an air-borne jammer is present, the air-borne jammer is present>1, otherwise 0; relay unmanned plane->Comprehensively considering co-channel interference inside an unmanned cluster and external malicious interference when selecting channels, and total interference。
Further, the utility function for the interference mathematical model is specifically:
communication quality determines whether the unmanned aerial vehicle cluster can successfully complete tasks, and is of great importance to the unmanned aerial vehicle cluster, so that the relay unmanned aerial vehicle needs to search a channel with minimum interference to the unmanned aerial vehicle, and the relay unmanned aerial vehicleThe utility function of (2) is: />；Is the total interference; for the whole unmanned aerial vehicle cluster, the utility is the negative number of the interference sum perceived by all relay unmanned aerial vehicles, namely the utility function of the whole unmanned aerial vehicle cluster is +.>，/>The total number of the relay unmanned aerial vehicles is; the unmanned aerial vehicle channel selection method based on accurate potential game aims at maximizing the utility of unmanned aerial vehicle clusters, and builds the optimization problem of multiple participants。
Further, multiple participantsIs solved by using game theoryProblems;
the game model for the relay drone select channel is expressed as, wherein ,represents a set of relay drones, +.>Representing relay unmanned plane->Is (are) mobility space (are) is (are)>Represents a relay unmanned plane +.>Utility of (2); relay unmanned plane->The utility function of (2) is expressed as: />； wherein />Representation except relay unmanned plane->Other action sets of the relay unmanned aerial vehicle; each relay drone is an independent and intelligent individual, all of which wish to maximize their utility, the gaming model is: />。
Further, the process of proving that the channel selection game model is an accurate potential game model in the step two includes: selecting a utility function in the game model according to the channel, and constructing a potential function; the potential function change caused by unilateral selection change of the unmanned aerial vehicle is proved to be consistent with the utility function change of the unmanned aerial vehicle, so that the unmanned aerial vehicle is proved to be an accurate potential game model.
Further, the distributed channel selection algorithm without decision information interaction designed in the third step comprises the following steps:
S301: the following parameters are initialized: including the number of drones, transmit power, and number of channels available; meanwhile, the unmanned plane cluster initializes a channel selection probability vector according to the number of channels.
S302: the drone selects a channel according to the channel selection probability vector.
S303: each time slot, the drone transmits data over the selected channel and estimates the utility function value when using that channel.
S304: the unmanned aerial vehicle updates the channel selection probability vector according to the following rules:
update formula (1)
Update formula (2)
wherein ,time and->When the channels selected at the moment are consistent, the updating strategy is the updating formula (1), otherwise, the updating strategy is the updating formula (2); />Respectively represent unmanned plane->At->Time and->Time selection channel +.>Probability of (2); />Represents a learning step size; />Representing unmanned plane->At->Normalized rewards harvested at time.
S305: judging whether the selection probability of the unmanned aerial vehicle on one of the channels is converged to 1, if not, returningS302, continuing to execute the loop; if yes, the termination condition is satisfied and the cycle is terminated.
The beneficial effects are that:
1. the invention provides an unmanned aerial vehicle channel selection scheme based on accurate potential game, which consists of three main parts of establishing a channel selection game model, proving that the channel selection game model is an accurate potential game model and designing a distributed channel selection algorithm without decision information interaction, so as to ensure that unmanned aerial vehicles can select channels to achieve the purposes of minimizing co-frequency interference and external malicious interference in an unmanned aerial vehicle cluster and fully utilizing channel resources.
2. The unmanned aerial vehicle channel selection scheme based on the accurate potential game provided by the invention considers two types of interference mainly existing in the unmanned aerial vehicle cluster under the condition of channel resource shortage: internal co-channel interference and external malicious interference. And considering that external interference can come from two parts, namely a malicious interference device existing on the ground and a malicious interference device existing in the air, the research blank is made up, the same-frequency interference inside the unmanned aerial vehicle cluster and the external malicious interference can be minimized theoretically, and channel resources are fully utilized.
Drawings
FIG. 1 is a schematic illustration of a drone cluster of the present invention performing tasks in an interfered environment;
fig. 2 is a flowchart of a method for selecting a channel of an unmanned aerial vehicle based on a precision potential game.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
The invention provides an unmanned aerial vehicle channel selection method based on accurate potential game, which simultaneously considers co-frequency interference and external malicious interference in an unmanned aerial vehicle cluster and constructs a corresponding utility function so as to achieve the purposes of helping an unmanned aerial vehicle to effectively avoid ground malicious interferents and air malicious interferents and fully utilizing channel resources by selecting channels.
The invention discloses an unmanned aerial vehicle channel selection method based on accurate potential game, which consists of three main parts of establishing a channel selection game model, proving that the channel selection game model is an accurate potential game model and designing a distributed channel selection algorithm without decision information interaction, wherein the specific process is shown in figure 2 and comprises the following steps:
step one, establishing a channel selection game model: the method comprises a mathematical model which simultaneously considers internal co-channel interference and external malicious interference when selecting channels and a utility function aiming at the mathematical model. The interference experienced by the drone during communication comes mainly from two aspects (as shown in figure 1). On the one hand, when unmanned aerial vehicles which do not form a communication pair select the same channel for communication, co-channel interference exists among unmanned aerial vehicles, and when a plurality of unmanned aerial vehicles work simultaneously, the interference is particularly serious, and the interference is unavoidable, but can be regarded as friendly interference; on the other hand, outside the drone cluster, there may be interference released by the jammers, which may be regarded as malicious interference.
Relay unmanned aerial vehicleSelect channel->When (I)>For the set of selectable channels +.>In other words, from the same selection of the current channel +.>The co-channel interference of the relay drone may be expressed as:
,
wherein Representation and relay drone>A set of other relay drones using the same channel; />Representing the transmitting power of the relay unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representing a path loss factor; />Representing relay unmanned plane->And relay unmanned plane->The Euclidean distance between them is expressed as follows:
wherein a three-dimensional coordinate system is constructed for the workspace of the drone, 、/>respectively is a relay unmanned plane->And relay unmanned plane->Coordinate position in three-dimensional coordinate system
After the relay unmanned aerial vehicle selects the channel, the source unmanned aerial vehicle led by the relay unmanned aerial vehicle also uses the channel to communicate, so the relay unmanned aerial vehicle also considers the interference caused by the source unmanned aerial vehicle which does not form a communication pair when selecting the channel.
Relay unmanned planeSelect channel->And when the co-channel interference from other source unmanned aerial vehicles is:
wherein Relay unmanned aerial vehicle representing use of same channel as relay unmanned aerial vehicle +.>A managed source unmanned aerial vehicle collection;
representing the transmitting power of the source unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representation relay unmanned planeAnd source unmanned plane->A Euclidean distance between them; />Representing the path loss factor.
Due to the advantages of low activity and cost of the unmanned aerial vehicle, the unmanned aerial vehicle can be inevitably applied to severe scenes, and therefore the unmanned aerial vehicle can be subjected to malicious interference. The malicious interference may come from both aspects and both ground and air may be subject to the presence of a malicious jammer. Considering first that there is a malicious disrupter on the ground, the malicious disrupter released by it can be expressed as：
wherein Representation and relay drone>A set of terrestrial interferers using the same channel; />Representing the transmit power of the ground jammer; />Path loss under line-of-sight and non-line-of-sight conditions, respectively; />Is the space-to-ground channel gain,/->Representing relay unmanned plane->And the Euclidean distance between the ground jammers; />The line-of-sight transmission probability can be expressed as:
wherein ,/>Is an environmental parameter->Is the elevation angle of the air-ground link. Non-line-of-sight transmission probability->。
In addition, there may be unmanned aerial vehicle jammers in the air area where the unmanned aerial vehicle performs its tasks, which can be expressed as:
wherein Representation and relay drone>A set of unmanned aerial vehicle disruptors using the same channel; />Representing the transmit power of the air-borne jammer; />Channel gain, which is the space-to-space channel; />Relay unmanned plane->And Euclidean distance of the air jammer;。
comprehensive two-source malicious interference and relay unmanned aerial vehicleThe sum of the malicious interference is:
。
when a surface-to-surface jammer is present,1, otherwise 0; when an air-borne jammer is present, the air-borne jammer is present>1, otherwise 0.
Relay unmanned planeRequiring synthesis when selecting channelsConsidering both friendly interference inside the unmanned cluster and malicious interference outside, the total interference can be expressed as:
communication quality determines whether the unmanned aerial vehicle cluster can successfully complete tasks, and is of great importance to the unmanned aerial vehicle cluster, so that the relay unmanned aerial vehicle needs to search a channel with minimum interference to the unmanned aerial vehicle, and the relay unmanned aerial vehicle is provided with the unmanned aerial vehicleIn terms of its utility function is:；/>is the total interference.
For the whole unmanned aerial vehicle cluster, the utility is the negative number of the interference sum perceived by all relay unmanned aerial vehicles, namely the utility function of the whole unmanned aerial vehicle cluster is that，/>The total number of the relay unmanned aerial vehicles.
The unmanned aerial vehicle channel selection method based on accurate potential game aims at maximizing the utility of unmanned aerial vehicle clusters, and builds the optimization problem of multiple participants。
Is an optimization problem of multiple participants, and due to the limited scene, the numerous participants and the like, the use of the centralized solution brings about non-negligible delay and high calculation amount and complexity. Thus, these problems are more suitable to be solved in a distributed way. Much work in the past has demonstrated that game theory is a powerful tool for solving problems in a distributed manner. In the present invention the approach using game theory is solved +.>Problems.
The game model for the relay drone select channel is expressed as, wherein ,represents a set of relay drones, +.>Represents a relay unmanned plane +.>Is (are) mobility space (are) is (are)>Represents a relay unmanned plane +.>Is effective in the present invention.
Relay unmanned planeThe utility function of (2) is expressed as:
wherein Representation except relay unmanned plane->And other action sets of the relay unmanned aerial vehicle. Each relay drone is an independent and intelligent individual, all of which wish to maximize their utility, the gaming model is:。
and secondly, proving that the channel selection game model is an accurate potential game model. The utility function change caused by the unilateral action change of each relay unmanned aerial vehicle is as follows:
representing relay unmanned plane->And (5) selecting a changed channel.
The potential function can directly represent the change condition of the utility function of the game participants, so that the utility of any participant is consistent with the global target; nash equalization by constructing the potential functionNashEquilibrium,NE) The presence of (c) is more easily demonstrated. Thus, the present invention constructs the potential function as follows:
the potential function change caused by the unilateral action change of each unmanned aerial vehicle is as follows:
；
then, it provesNamely, the utility function change caused by the unilateral action change of each relay unmanned aerial vehicle is the same as the potential function change. According to the definition of the accurate potential game, the unmanned plane channel selection game model provided by the invention is known to be the accurate potential game, so that the game model provided by the invention is ensured to have at least one Nash equilibrium state of pure strategies.
And thirdly, designing a distributed channel selection algorithm without decision information interaction. The method mainly comprises the following steps:
(1) The following parameters are initialized: parameters such as the number of unmanned aerial vehicles, the transmitting power, the number of available channels and the like are included; meanwhile, the unmanned plane cluster initializes a channel selection probability vector according to the number of channels:
(2) The drone selects a channel according to the channel selection probability vector.
(3) Each time slot, the drone transmits data over the selected channel and estimates the utility function value when using that channel.
(4) The unmanned aerial vehicle updates the channel selection probability vector according to the following rules:
update formula (1)
Update formula (2)
wherein ,time and->When the channels selected at the moment are consistent, the updating strategy is the updating formula (1), otherwise, the updating strategy is the updating formula (2); />Respectively represent unmanned plane->At->Time and->Time selection channel +.>Probability of (2); />Represents a learning step size; />Representing unmanned plane->At->Normalized rewards for time of day harvest:； wherein
wherein ,representing unmanned plane->Transmit power of>Representing unmanned plane->And receiving end->The euclidean distance between the two,representing background noise. />Unmanned plane without interference>The maximum rate achievable. When the distance between the unmanned aerial vehicle and the receiving end is kept unchanged, the unmanned aerial vehicle is provided with the left/right parts and the right parts>Is a fixed value.
(5) And judging whether the selection probability of the unmanned aerial vehicle on one channel is converged to 1. If not, returning to the step (2), and continuing to execute the cycle; if yes, the termination condition is satisfied and the cycle is terminated.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The unmanned aerial vehicle channel selection method based on the accurate potential game is characterized by comprising the following steps of:
step one, establishing a channel selection game model: the method comprises an interference mathematical model and a utility function aiming at the interference mathematical model, wherein the interference mathematical model is used for simultaneously considering internal co-channel interference and external malicious interference when a channel is selected;
judging whether the channel selection game model is an accurate potential game model or not; if yes, executing a third step, otherwise, returning to the first step to reestablish the channel selection game model;
step three, designing a distributed channel selection algorithm without decision information interaction, and carrying out channel selection by the unmanned aerial vehicle according to the distributed channel selection algorithm;
the interference mathematical model which simultaneously considers internal co-channel interference and external malicious interference during channel selection is specifically as follows:
relay unmanned aerial vehicleSelect channel->When from the same selection of the current channel +.>Co-channel interference of the relay unmanned aerial vehicle is as follows:
wherein Representation and relay drone>A set of other relay drones using the same channel; />Representing the transmitting power of the relay unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representing relay unmanned plane->And relay unmanned plane->European distance between->Representing a path loss factor;
relay unmanned planeSelect channel->And when the co-channel interference from other source unmanned aerial vehicles is:
wherein Representation and relay drone>Relay unmanned aerial vehicle using the same channel +.>A managed source unmanned aerial vehicle collection;
representing the transmitting power of the source unmanned aerial vehicle; />Channel gain, which is the space-to-space channel; />Representing relay unmanned plane->And source unmanned plane->A Euclidean distance between them; />Representing a path loss factor;
unmanned aerial vehicle for relayIs subjected to malicious interference, including the malicious interference generated by a ground jammer->Malicious interference generated by air jammers>Comprehensive two-source malicious interference, relay unmanned aerial vehicle +.>Sum of malicious interference received；
When a surface-to-surface jammer is present,1, otherwise 0; when an air-to-air jammer is present,/>1, otherwise 0;
relay unmanned planeComprehensively considering co-channel interference inside an unmanned cluster and external malicious interference when selecting channels, and total interference>。
2. The unmanned aerial vehicle channel selection method based on the accurate potential game according to claim 1, wherein the utility function for the interference mathematical model is specifically:
communication quality determines whether the unmanned aerial vehicle cluster can successfully complete tasks, and is of great importance to the unmanned aerial vehicle cluster, so that the relay unmanned aerial vehicle needs to search a channel with minimum interference to the unmanned aerial vehicle, and the relay unmanned aerial vehicleThe utility function of (2) is: /> ；/>Is the total interference; for the whole unmanned aerial vehicle cluster, the utility is the negative number of the interference sum perceived by all relay unmanned aerial vehicles, namely the utility function of the whole unmanned aerial vehicle cluster is +.>，/>The total number of the relay unmanned aerial vehicles is; unmanned aerial vehicle channel selection method based on accurate potential game aims at maximizing utility of unmanned aerial vehicle cluster, and multiple participants are builtOptimization problem of (a)。
3. The unmanned aerial vehicle channel selection method based on precision potential gaming according to claim 2, wherein the optimization problem of multiple participants is solved by using a method of game theoryProblems;
the game model for the relay drone select channel is expressed as, wherein ,/>Represents a set of relay drones, +.>Representing relay unmanned plane->Is (are) mobility space (are) is (are)>Represents a relay unmanned plane +.>Utility of (2);
relay unmanned planeThe utility function of (2) is expressed as: />； wherein />Representation removes relay unmanned aerial vehicleOther action sets of the relay unmanned aerial vehicle;
each relay drone is an independent and intelligent individual, all of which wish to maximize their utility, the gaming model is:。
4. the method for selecting a channel of an unmanned aerial vehicle based on a precision potential game according to claim 1, wherein the step of proving that the channel selection game model is a precision potential game model comprises: selecting a utility function in the game model according to the channel, and constructing a potential function; the potential function change caused by unilateral selection change of the unmanned aerial vehicle is proved to be consistent with the utility function change of the unmanned aerial vehicle, so that the unmanned aerial vehicle is proved to be an accurate potential game model.
5. The unmanned aerial vehicle channel selection method based on precision potential gaming according to claim 1, wherein the method comprises the following steps: the distributed channel selection algorithm without decision information interaction designed in the third step comprises the following steps:
S301: the following parameters are initialized: including the number of drones, transmit power, and number of channels available; meanwhile, initializing a channel selection probability vector by the unmanned aerial vehicle cluster according to the number of channels;
S302: the unmanned aerial vehicle selects a channel according to the channel selection probability vector;
S303: each time slot, the unmanned plane sends data through the selected channel, and estimates the utility function value when the channel is used;
S304: the unmanned aerial vehicle updates the channel selection probability vector according to the following rules:
update formula (1)
Update formula (2)
wherein ,time and->When the channels selected at the moment are consistent, the updating strategy is the updating formula (1), otherwise, the updating strategy is the updating formula (2); />Respectively represent unmanned plane->At->Time and->Time of day selection channelProbability of (2); />Represents a learning step size; />Representing unmanned plane->At->Normalized rewards for time of day harvest:
S305: judging whether the selection probability of the unmanned aerial vehicle on one of the channels is converged to 1, if not, returningS302, continuing to execute the loop; if yes, the termination condition is satisfied and the cycle is terminated.
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