CN116634598A - Method for adjusting cluster broadcasting business competition window of unmanned aerial vehicle based on potential game - Google Patents
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Abstract
The invention discloses a method for adjusting a cluster broadcast service competition window of an unmanned aerial vehicle based on potential game, which relates to the technical field of unmanned aerial vehicle self-organizing networks, and comprises the following steps of firstly, establishing a channel access game model, and proving that the channel access game model is an accurate potential game model; and secondly, designing an algorithm capable of adaptively adjusting the size of the contention window in a distributed mode. According to the method for adjusting the size of the contention window of the periodical broadcasting service of the unmanned aerial vehicle cluster based on the accurate potential game, the change of the node density of the unmanned aerial vehicle is considered in a dynamic flight scene, so that the unmanned aerial vehicle can be ensured to achieve the purposes of maximizing the success rate of channel access and maintaining the stability of the unmanned aerial vehicle cluster structure and the connectivity of communication by adaptively adjusting the initial value of the contention window.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle self-organizing networks, in particular to a method for adjusting a cluster broadcast service competition window of an unmanned aerial vehicle based on potential game.
Background
When the unmanned aerial vehicle cluster performs tasks, in order to maintain formation stability and communication connectivity, the unmanned aerial vehicle needs to periodically send data packets containing information of position, speed, flight state and the like to neighbor nodes in a communication range in a broadcast mode. A widely used access technology in unmanned aerial vehicle ad hoc networks today is the carrier sense multiple access (Carrier Sense Multiple Access with Collision Avoidance, CSMA/CA) protocol with collision avoidance. However, in the traditional CSMA/CA protocol, the initial value of the contention window is fixed, which cannot adapt to the characteristic that the node density of the unmanned aerial vehicle in the dynamic flight scene is continuously changed. When the node density of the unmanned aerial vehicle is increased, the fixed initial value of the competition window can cause the problems of high competition and serious collision; when the confidentiality of the unmanned aerial vehicle is reduced, the fixed initial value of the contention window can reduce the contention among the unmanned aerial vehicles, and the communication resources cannot be fully utilized, so that the performance of the unmanned aerial vehicle cluster is reduced.
At present, few researches are conducted on a distributed adjustment scheme for initial values of periodic broadcast service competition windows of unmanned aerial vehicle clusters. However, with the continuous expansion and penetration of the application scenes of unmanned aerial vehicles, the requirements and technical requirements of unmanned aerial vehicle cluster communication are higher and higher, so that the research in the field is particularly necessary.
Disclosure of Invention
In view of this, the invention provides a method for adjusting the contention window of the cluster broadcast service of the unmanned aerial vehicle based on potential game, and the unmanned aerial vehicle can adaptively adjust the initial value of the contention window according to the node density, so as to achieve the purposes of maximizing the success rate of channel access and maintaining the stability of the cluster structure of the unmanned aerial vehicle and the connectivity of communication.
In order to achieve the above purpose, the technical scheme of the invention comprises the following steps:
step one: establishing a channel access game model, wherein in the channel access game model, the number change of neighbor nodes in a communication range is considered when an unmanned aerial vehicle node is accessed to a channel, the initial value of a contention window is self-adaptively adjusted, the channel access rate is changed, and the channel access success rate is used as a utility function; and the channel access gaming model is proved to be an exact potential gaming model.
Step two: based on the channel access game model, distributed self-adaptive adjustment is carried out on the initial value of the periodic broadcast service competition window of the unmanned aerial vehicle cluster.
Further, in the second step, the channel access game model is proved to be an accurate potential game model, specifically adopting the following modes: constructing a potential function according to the utility function of the channel access game model; and if the potential function change caused by the unilateral selection change of the unmanned aerial vehicle is consistent with the utility function change of the unmanned aerial vehicle, proving that the channel access game model is an accurate potential game model.
Further, step two: based on the channel access game model, performing distributed self-adaptive adjustment on the initial value of the periodic broadcast service competition window of the unmanned aerial vehicle cluster, specifically:
step (1) initializes parameters including the number of unmanned aerial vehicles, the initial value of the contention window, the adjustment range of the initial value of the contention window, the transmission period, the communication range, and the like.
Step (2) the unmanned aerial vehicle uses the initialized initial value of the contention window to communicate, judges the number of neighbor nodes according to the number of received periodical data packets, and estimates the channel condition when the current initial value of the contention window is used.
Step (3) the unmanned aerial vehicle initializes the contention window initial value selection probability vector according to the contention window initial value adjustment interval, and updates the contention window initial value selection probability vector according to the following rules:
update formula (1)
Update formula (2)
Wherein the method comprises the steps of、/>Respectively represent unmanned plane->At->Time of day,/->Time selection contention window initial value is +.>Probability of->Represents learning step size->Is unmanned plane->At->Rewards harvested at the moment,/->And->Coefficients representing the channel idle rate and the channel access success rate, respectively,/->Is unmanned plane->At->The channel idle rate calculated at the moment,probability of success for channel access; />Is thattUnmanned aerial vehicle at momentnSelected actions, i.e.tThe contention window initial value for the time instant.
Expressed as:
wherein the method comprises the steps ofIs unmanned plane->First->The access probability of each neighbor node; />Is the number of neighbor nodes of one hop.
The update formula (1) and the update formula (2) are probability update strategies, in which,time and->When the initial values of the contention windows selected at the moment are consistent, the updating strategy is to update the formula (1), otherwise, the updating strategy is to update the formula (2).
Step (4) judging whether the selection probability of the unmanned aerial vehicle on the initial value of one competition window is converged to 1, if not, updating the unmanned aerial vehicleAt->Rewarding of time of day>Returning to Step (3); if 1, step (5) is performed.
Step (5) use ofNumber of neighbors of a time instant->And->Number of neighbors of a time instant->And (3) comparing, namely, adjusting the initial value of the competition window again, wherein the update coefficient is as follows:
wherein the method comprises the steps ofThe importance degree coefficient of the unmanned aerial vehicle to the actual feedback is obtained; />To update coefficients, use ∈>Multiplying the initial value of the contention window by the initial value of the original contention window to make the adjusted initial value of the contention window.
Step (6) judges whether the adjusted initial value of the contention window output in Step (5) is in a preset interval, and if the initial value of the contention window is smaller than the lower limit value of the preset interval, the initial value of the contention window is output as the lower limit value of the preset interval; if the initial value of the contention window is larger than the upper limit value of the preset interval, outputting the initial value of the contention window as the upper limit value of the preset interval.
Step (7) is carried out repeatedly in each period from Step (3) to Step (6) in the task time until the task time is over.
The beneficial effects are that:
according to the method for adjusting the initial value of the contention window of the periodic broadcast service of the unmanned aerial vehicle cluster based on the accurate potential game, the unmanned aerial vehicle can adaptively adjust the initial value of the contention window according to the node density in a dynamic flight scene, the channel access probability is changed, and the purposes of maximizing the channel access success rate and maintaining the stability of the structure of the unmanned aerial vehicle cluster and the connectivity of communication are achieved.
Drawings
Fig. 1 is a flowchart of a method for adjusting a cluster broadcast service contention window of an unmanned aerial vehicle based on potential game;
FIG. 2 is a topology diagram of an unmanned aerial vehicle ad hoc network at a simulation time of 0s;
FIG. 3 is a topology diagram of an unmanned aerial vehicle ad hoc network at a simulation time of 20 s;
FIG. 4 is a topology diagram of an unmanned aerial vehicle ad hoc network at a simulation time of 40 s;
FIG. 5 is a topology diagram of an unmanned aerial vehicle ad hoc network at a simulation time of 60 s;
FIG. 6 is a topology diagram of an unmanned aerial vehicle ad hoc network at a simulation time of 80s;
fig. 7 is a diagram of the variation situation of different node densities of the unmanned aerial vehicle ad hoc network in the simulation time;
fig. 8 is a graph of initial value changes of contention windows of the unmanned aerial vehicle ad hoc network in different simulation stages;
fig. 9 is a graph of unmanned aerial vehicle ad hoc network channel access success rate.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
The invention provides a method for adjusting the initial value of a contention window of a periodic broadcast service of an unmanned aerial vehicle cluster based on a precision potential game, which can enable the unmanned aerial vehicle to adaptively adjust the initial value of the contention window according to the node density, change the channel access rate, and construct a utility function according to the channel access success rate so as to achieve the purposes of maximizing the channel access success rate and maintaining the stability of the unmanned aerial vehicle cluster structure and the connectivity of communication.
The invention relates to a method for adjusting the initial value of a periodical broadcasting service competition window of an unmanned aerial vehicle cluster based on accurate potential game, which comprises the steps of establishing a channel access game model; the channel access game model is proved to be an accurate potential game model and a method capable of adaptively adjusting initial values of competition windows in a distributed mode, and the specific process is as follows:
step one, establishing a channel access game model: the unmanned aerial vehicle node considers the neighbor node quantity change in the communication range when the channel is accessed, adjusts the initial value of the contention window in a self-adaptive way, changes the channel access rate, and takes the channel access success rate as a utility function;
when the unmanned aerial vehicle uses the CSMA/CA protocol to broadcast, before sending the periodic data packet, first need to monitor the channel, after perceiving the idle DIFS time, enter the back-off stage, choose the competition window size from the initial value of the competition window randomly, when the channel is idle, the competition window is reduced by 1, when the channel is busy, the competition window is frozen. And when the competition window is 0, the unmanned aerial vehicle sends a data packet. If other unmanned aerial vehicles in the communication range of the unmanned aerial vehicle transmit data packets at the moment, collision can occur, and the data packets are transmitted in a failure mode. It is noted that in broadcast mode, the receiver does not send an ACK to the sender after receiving the data packet from the neighbor.
In order to measure the influence of a contention window on the performance of a CSMA/CA protocol, the invention uses Markov models in documents QiaH J F, ho IW H, chi K T, et al A methodology for studying 802.11 p VANET broadcasting performance with practical vehicle distribution[J ]. IEEE transactions on vehicular technology, 2014, 64 (10): 4756-4769, and the relation between the channel access probability and the initial value of the contention window of the unmanned aerial vehicle is shown as follows:
where-1 represents a state in which the channel is idle,representing the probability that the channel is busy, +.>For the initial value of the contention window, for the unmanned plane +.>The access probability is +.>，/>Expressed by the following formula:
wherein the method comprises the steps ofRepresenting unmanned plane->The number of unmanned aerial vehicles in communication range, namely the number of neighbor nodes.
Is easy to be derived from the formulaSee->And->There is a one-to-one correspondence between +.>The size of (2) determines how long the unmanned aerial vehicle needs to wait for sending data packets, and each unmanned aerial vehicle needs to reasonably select +.>Size of the product.
Unmanned planeThe probability of successful transmission of a data packet can be regarded as the probability of successful access to the channel, expressed as:
for unmanned aerial vehicleBy adjusting the access probability +.>Can realize the maximization of the success rate of channel access. Then unmanned plane->Utility function of->Can be expressed as:
the utility of the entire drone cluster network may be expressed as:
the final objective of the invention is to maximize the utility of the entire unmanned aerial vehicle cluster network.
In practical application, the unmanned aerial vehicle regards the number of received data packets as the number of neighbors in a communication range, but when the node density of the unmanned aerial vehicle is suddenly increased, serious collision of channels can be caused, and at the moment, the unmanned aerial vehicle judges the number of neighbors according to the received data packets by mistakeThe difference is large. In order to more accurately enable the unmanned aerial vehicle to adjust the initial value of the competition window according to the node density, the unmanned aerial vehicle needs to consider feedback from reality, and the unmanned aerial vehicle willTime and->And comparing the quantity of the data packets received by the unmanned aerial vehicle at the moment, wherein the quantity is as follows:
wherein the method comprises the steps ofThe importance degree coefficient of the unmanned aerial vehicle to the actual feedback is obtained; />To update coefficients, use ∈>Multiplying the initial value of the original contention window to make an adjusted initial value of the contention window;
the channel access gaming model is proved to be an accurate potential gaming model. Observing utility function changes caused by unilateral action changes of each unmanned aerial vehicle as follows:
wherein the method comprises the steps ofRepresenting unmanned plane->Channel access probability after change, +.>Representation except unmanned plane->Channel access probability of all other unmanned aerial vehicles except the unmanned aerial vehicle; />Is unmanned plane->The access probability of the change channel is +.>And utility functions when the channel access probability of other unmanned aerial vehicles remains unchanged; />Is unmanned plane->The probability of selecting channel access is +.>Utility function at that time.
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; in addition, by constructing the potential function, the existence of Nash Equilibrium (NE) is more easily demonstrated. Thus, the present invention constructs potential functions：
Wherein the parameters are as defined above.
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 unmanned aerial vehicle is the same as the potential function change.
Step two, designing an algorithm capable of adaptively adjusting the initial value of the competition window in a distributed mode in the advancing process of the unmanned aerial vehicle. The method mainly comprises the following steps:
step (1) initializing parameters including the number of unmanned aerial vehicles, a contention window initial value change range, a transmission period, a communication range and the like;
step (2) the unmanned aerial vehicle uses the initialized initial value of the contention window to communicate, judges the number of neighbor nodes according to the number of received periodic data packets, and estimates the channel collision rate when the current initial value of the contention window is used;
step (3) the unmanned aerial vehicle initializes the contention window initial value selection probability vector according to the contention window initial value change interval, and updates the contention window initial value selection probability vector according to the following rules:
update formula (1)
Update formula (2)
Wherein the method comprises the steps of、/>Respectively represent unmanned plane->At->Time of day,/->Time selection contention window initial value is +.>Probability of->Represents learning step size->Is unmanned plane->At->Rewards harvested at the moment,/->And->Coefficients representing the channel idle rate and the channel access success rate, respectively,/->Is unmanned plane->At->The channel idle rate calculated at the moment,probability of success for channel access; />Is thattUnmanned aerial vehicle at momentnSelected actions, i.e.tThe contention window initial value for the time instant.
Expressed as:
wherein the method comprises the steps ofIs->The access probability of each neighbor node; />Is the number of neighbor nodes of one hop.
The update formula (1) and the update formula (2) are probability update strategies, in which,time and->When the initial values of the competition windows selected at the moment are consistent, the updating strategy is to update the formula (1), otherwise, the updating strategy is to update the formula (2); i.e. when m equals +.>When the updating strategy is the updating formula (1), otherwise, the updating strategy is the updating formula (2).
Step (4) judging whether the selection probability of the unmanned aerial vehicle on the initial value of one competition window is converged to 1, if not, updating the unmanned aerial vehicleAt->Rewarding of time of day>Returning to Step (3); if 1, executing Step (5);
step (5) use ofNumber of neighbors of a time instant->And->Number of neighbors of a time instant->And (3) comparing, namely, adjusting the initial value of the competition window again, wherein the update coefficient is as follows:
wherein the method comprises the steps ofThe importance degree coefficient of the unmanned aerial vehicle to the actual feedback is obtained; />To update coefficients, use ∈>Multiplying the initial value of the original contention window to make an adjusted initial value of the contention window;
step (6) judges whether the adjusted initial value of the contention window output in Step (5) is in a preset interval, and if the initial value of the contention window is smaller than the lower limit value of the preset interval, the initial value of the contention window is output as the lower limit value of the preset interval; if the initial value of the contention window is greater than the upper limit value of the preset interval, outputting the initial value of the contention window as the upper limit value of the preset interval.
Step (7) is carried out repeatedly in each period from Step (3) to Step (6) in the task time until the task time is over.
And (3) analyzing numerical simulation results:
the invention uses moving scenes in the literature Huang X, liu A, zhou H, et al, FMAC: A self-adaptive MAC protocol for flocking of flying ad hoc network [ J ]. IEEE Internet of Things Journal, 2020, 8 (1): 610-625 and literature Olfati-Saber R. Flocking for multi-agent dynamic systems: algorithms and theory [ J ]. IEEE Transactions on automatic control, 2006, 51 (3): 401-420, in which there are 150 unmanned aerial vehicles, 4 obstacles, which fly at a speed of 6m/s following a virtual pilot. Specific parameters of the flight control algorithm are consistent with those of the literature Huang X, liu A, zhou H, et al FMAC: A self-adaptive MAC protocol for flocking of flying ad hoc network [ J ]. IEEE Internet of Things Journal, 2020, 8 (1): 610-625, other simulation-related parameters being set as follows:
the number of unmanned aerial vehicle nodes is 150; the simulation time is set to 80s; DIFS was set to 28 μs; the cycle time is set to 0.1s; the packet size is set to 64 bytes; the channel rate is set to 500kb/s; the initial contention window is set to 32; the minimum contention window initial value is set to 32; the maximum contention window initial value is set to 128; b is set to 0.3; c is set to 0.9;and->Set to 0.8 and 0.3, respectively.
Fig. 2-6 show topology diagrams of the unmanned aerial vehicle ad hoc network within 0-80s of simulation time.
According to the invention, the number of unmanned aerial vehicles in a communication range is regarded as node density, and as can be seen from fig. 7, the node density of the unmanned aerial vehicles gradually increases along with the aggregation of the unmanned aerial vehicles before the unmanned aerial vehicle clusters pass through the obstacle within 0-15s, but is about 15-30s, and when the unmanned aerial vehicles pass through the obstacle, the unmanned aerial vehicles need to pass through a narrow gap and are limited by a physical environment, so that the node density of the unmanned aerial vehicles is reduced; most unmanned aerial vehicles pass through the obstacle within 30-40s, so that the node density of the unmanned aerial vehicles is increased; within 40-80s, most drones have traversed the obstacle, so the drone node density begins to drop.
In order to demonstrate the performance of the proposed algorithm, the present invention compares it with the conventional CSMA/CA protocol. In the conventional CSMA/CA protocol, the contention window initial value remains fixed all the time. Firstly, the invention compares the average competition window initial values of different protocols in different simulation stages. As can be seen from fig. 8, the algorithm of the present invention can change the initial value of the contention window with the change of the node density.
The invention regards the successful transmission probability of the periodic data packet as the ratio of the number of packets successfully received by all unmanned aerial vehicles to the number of packets sent, counts the successful transmission probability and the transmission collision probability of the periodic data packet every 5 seconds, and regards the successful probability of the data packet as the success rate of channel access.
As can be seen from fig. 9, although the channel access success rate decreases with the increase of the node density of the unmanned aerial vehicle by using the scheme provided by the invention, the performance of the algorithm provided by the invention is obviously better than that of the CSMA/CA protocol.
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 (3)
1. The method for adjusting the cluster broadcasting business competition window of the unmanned aerial vehicle based on the potential game is characterized by comprising the following steps:
step one: establishing a channel access game model, wherein in the channel access game model, the number change of neighbor nodes in a communication range is considered when an unmanned aerial vehicle node is accessed to a channel, the initial value of a contention window is self-adaptively adjusted, the channel access rate is changed, and the channel access success rate is used as a utility function; and proving that the channel access game model is an accurate potential game model;
step two: based on the channel access game model, distributed self-adaptive adjustment is carried out on the initial value of the periodic broadcast service competition window of the unmanned aerial vehicle cluster.
2. The method for adjusting the cluster broadcast service contention window of the unmanned aerial vehicle based on the potential game according to claim 1, wherein the method comprises the following steps: in the second step, the channel access game model is proved to be an accurate potential game model, and the following method is specifically adopted: constructing a potential function according to the utility function of the channel access game model; and if the potential function change caused by the unilateral selection change of the unmanned aerial vehicle is consistent with the utility function change of the unmanned aerial vehicle, proving that the channel access game model is an accurate potential game model.
3. The method for adjusting the cluster broadcast service contention window of the unmanned aerial vehicle based on the potential game according to claim 1 or 2, wherein the method comprises the following steps: the second step is as follows: based on the channel access game model, performing distributed self-adaptive adjustment on the initial value of the periodic broadcast service competition window of the unmanned aerial vehicle cluster, specifically:
step (1) initializing parameters including the number of unmanned aerial vehicles, a contention window initial value adjustment range, a transmission period and a communication range;
step (2) the unmanned aerial vehicle uses the initialized initial value of the contention window to communicate, judges the number of neighbor nodes according to the number of received periodic data packets, and estimates the channel condition when the current initial value of the contention window is used;
step (3) the unmanned aerial vehicle initializes the contention window initial value selection probability vector according to the contention window initial value adjustment interval, and updates the contention window initial value selection probability vector according to the following rules:
update formula (1)
Update formula (2)
Wherein the method comprises the steps of、/>Respectively represent unmanned plane->At->Time of day,/->Time selection contention window initial value is +.>Probability of->Represents learning step size->Is unmanned plane->At->Rewards harvested at the moment,/->And->Coefficients representing the channel idle rate and the channel access success rate, respectively,/->Is unmanned plane->At->Channel idle rate calculated at time,/->Probability of success for channel access; />Is thattUnmanned aerial vehicle at momentnSelected actions, i.e.tA contention window initial value for a time;
expressed as: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein->Is unmanned plane->First->The access probability of each neighbor node;the number of the neighbor nodes is one hop;
the update formula (1) and the update formula (2) are probability update strategies, in which,time and->When the initial values of the competition windows selected at the moment are consistent, the updating strategy is to update the formula (1), otherwise, the updating strategy is to update the formula (2);
step (4) judging whether the selection probability of the unmanned aerial vehicle on the initial value of one competition window is converged to 1, if not, updating the unmanned aerial vehicleAt->Rewarding of time of day>Returning to Step (3); if 1, executing Step (5);
step (5) use ofNumber of neighbors of a time instant->And->Number of neighbors of a time instant->And (3) comparing, namely, adjusting the initial value of the competition window again, wherein the update coefficient is as follows:
wherein the method comprises the steps ofThe importance degree coefficient of the unmanned aerial vehicle to the actual feedback is obtained; />To update coefficients, use ∈>Multiplying the initial value of the original contention window to make an adjusted initial value of the contention window;
step (6) judges whether the adjusted initial value of the contention window output in Step (5) is in a preset interval, and if the initial value of the contention window is smaller than the lower limit value of the preset interval, the initial value of the contention window is output as the lower limit value of the preset interval; if the initial value of the contention window is larger than the upper limit value of the preset interval, outputting the initial value of the contention window as the upper limit value of the preset interval;
step (7) is carried out repeatedly in each period from Step (3) to Step (6) in the task time until the task time is over.
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