CN112615913B - Information returning method for cooperation of unmanned aerial vehicle and unmanned ship for marine environment monitoring - Google Patents

Information returning method for cooperation of unmanned aerial vehicle and unmanned ship for marine environment monitoring Download PDF

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CN112615913B
CN112615913B CN202011449832.3A CN202011449832A CN112615913B CN 112615913 B CN112615913 B CN 112615913B CN 202011449832 A CN202011449832 A CN 202011449832A CN 112615913 B CN112615913 B CN 112615913B
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吕玲
林彬
戴燕鹏
初振航
韩晓玲
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Dalian Maritime University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
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    • H—ELECTRICITY
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
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    • 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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Abstract

The invention provides an information feedback method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring, which comprises the steps of uniformly expressing the influence of channel conditions in wireless transmission and communication distance between the unmanned aerial vehicle and the unmanned ship on information feedback as the preference of the unmanned ship to a wireless channel distributed by the unmanned aerial vehicle, and constructing a preference ranking table of the unmanned ship to the unmanned aerial vehicle; analyzing the transmission rate of information when the unmanned ship and the unmanned ship collaboratively transmit information back, and maximizing the total throughput of the system through the joint optimization of the association relation between the unmanned ship and the transmission power of signals; a heuristic algorithm based on a matching theory is designed, the matching relation between the unmanned aerial vehicle and the unmanned ship is determined, and the generation power of the unmanned ship and the unmanned aerial vehicle is further determined based on a convex optimization theory. The method can effectively reduce the time complexity and the calculation time of the algorithm, simultaneously solve the problem of weak connectivity of a communication link when the unmanned aerial vehicle is used as a mobile communication relay, and effectively monitor the marine environment.

Description

Information returning method for cooperation of unmanned aerial vehicle and unmanned ship for marine environment monitoring
Technical Field
The invention relates to the technical field of wireless communication, in particular to an information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring.
Background
With the increasing business requirements of offshore wide area internet of things, higher requirements are put on wireless data transmission capability. At present, the main work of the marine internet of things comprises hydrological meteorological monitoring, marine emergency communication system requirements, marine natural disaster early warning, hydrological survey and the like, the marine industrial application is also wider and wider, for example, sewage treatment, fuel oil leakage, excessive harmful metal discharge and the like are important, if information transmission is not timely and accurate, serious loss can be caused to the marine environment, and even the life and property safety of human beings can be threatened. For example, the acquisition of marine hydrological information, in the past, the acquired hydrological information is directly transmitted to an information processing center on the shore through a buoy, a navigation mark and the like, the network architecture has longer construction time and poorer flexibility and cannot adapt to a complex and changeable marine environment, and in the process of transmitting information, because the transmission distance is longer, the distance from the shore is farther, the path loss is larger, the required transmitting power is higher, the interference among signals is also larger, the finally received information has higher packet loss rate, the time delay is long, the consumed energy is also faster, and the working time is limited.
Because wireless communication is easily interfered by environmental changes, in the process of information return, the marine environmental condition changes greatly, the channel condition changes greatly, and the channel condition directly influences the information transmission rate, so that the information can be transmitted to the unmanned aerial vehicle timely and reliably only by selecting the channel with the best channel condition when selecting the channel, and the unmanned aerial vehicle can complete information processing and forwarding within the specified time. If the information is not transmitted in time, the information of the next step is estimated according to the prior information according to the existing technology, but the information has low effectiveness and can not solve the problem fundamentally.
In the current algorithm research, many optimization algorithms for solving the matching relation between the unmanned aerial vehicle and the unmanned ship exist, but all have own disadvantages, for example, a branch-and-bound method can obtain an optimal solution after operations such as branching, branch reduction and bounding, a value space is divided for a non-integer independent variable solution, the divided solution is changed into an integer, the optimal solution at the moment is used as an upper bound, then the solution is continuously branched to find an objective function solution and an integer variable solution in the solution space, if the branched solution is not in the solution space, pruning is carried out, and finally the integer optimal value of the objective function in the solution space is obtained. One of the key problems when the branch-and-bound algorithm solves the subcarrier allocation optimization model is parametric processing. However, the parameter matrix of the model must conform to the parameter form of the Linprog function, and the requirement on the mathematical model is high. And the algorithm complexity varies with the network model, and in the worst case, the calculation complexity is exponentially increased. The many-to-one matching method is a matching relation problem among objects with unequal numbers, and carries out preferential selection through a preference ranking table, wherein one with a smaller number is used as a matching reference, one with a larger number is used as an individual to be matched, the one with the smaller number is completely matched, the other one with the larger number still has the remaining individual, and then the remaining individuals are matched until the algorithm is completed after all matching. The selection result focuses more on the stability of matching, but the final optimization target is not ideal compared with other optimization algorithms.
Disclosure of Invention
According to the technical problems, an information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring is provided. The problem is modeled by a Shannon formula and optimized by an algorithm, so that the unmanned ship is related to the unmanned ship which is most suitable for the unmanned ship, the unmanned ship is matched with a channel with the best channel condition, the optimal solution of the problem is found, and the original problem is decomposed into two sub-problems.
The technical means adopted by the invention are as follows:
an information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring comprises the following steps:
s1, uniformly expressing the influence of the channel condition in wireless transmission and the communication distance between the unmanned aerial vehicle and the unmanned ship on information return as the preference of the unmanned ship to the wireless channel allocated by the unmanned aerial vehicle, and constructing a preference ranking table of the unmanned ship to the unmanned aerial vehicle;
s2, analyzing the transmission rate of information when the unmanned ship and the unmanned ship collaboratively return information, and maximizing the total throughput of the system through the joint optimization of the association relationship between the unmanned ship and the transmission power of signals;
s3, designing a heuristic algorithm based on a matching theory, determining the matching relation between the unmanned aerial vehicle and the unmanned ship, and further determining the transmitting power of the unmanned ship and the unmanned aerial vehicle based on a convex optimization theory.
Further, the step S1 specifically includes:
s11, acquiring the environmental information data packet sensed by each unmanned ship, and accurately transmitting the environmental information data packet to a shore-based communication system;
s12, calculating the receiving power of the mth drone from the nth drone, wherein the calculation formula is as follows:
Figure GDA0003679388990000031
wherein q is n Representing the transmit power of the nth drone,
Figure GDA0003679388990000032
representing the channel gain; d n,m Represents the distance, alpha, from the nth drone to the mth drone f Which represents the index of the path loss,
Figure GDA0003679388990000033
representing small-scale channel fading between the nth unmanned ship and the mth unmanned plane, wherein the small-scale channel fading is distribution with a mean value and a unit variance of zero;
s13, assuming that the random channel is an independent and uniformly distributed random variable, the mean value is zero, the variance is N0, and calculating the channel gain between the mth drone and the remote shore base, that is, the remote shore base receives the channel gain of the signals sent to the mth drone by the nth drone, and the calculation formula is as follows:
Figure GDA0003679388990000034
wherein, d m,0 Represents the distance, alpha, between the mth drone and the distant shore base station s Which represents the index of the path loss,
Figure GDA0003679388990000035
represents the small-scale channel fading with mean 0 and unit variance 0;
s14, further calculating the receiving power of signals sent to the m unmanned aerial vehicle by the nth unmanned ship received from the shore base at a remote location based on the calculated channel gain, wherein the calculation formula is as follows:
Figure GDA0003679388990000036
s15, setting that an unmanned ship can only be associated with an unmanned plane, which is represented as:
Figure GDA0003679388990000037
in the above formula, if the variable is binary n,m 1, it means that the nth unmanned ship is associated with the mth unmanned plane;
s16, due to the limitation of the transmitting power, the mth drone needs to consider the following constraints when forwarding the received sensor data of the nth drone to the remote shore-based communication system:
Figure GDA0003679388990000041
s17, calculating the interruption probability in the information transmission process, wherein the calculation formula is as follows:
Figure GDA0003679388990000042
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003679388990000043
representing the probability of successful decoding of the nth drone signal, Γ, of the mth drone th Represents a decoding threshold;
s18, calculating an end-to-end transmission rate of the auxiliary communication of the unmanned aerial vehicle, wherein the calculation formula is as follows:
Figure GDA0003679388990000044
wherein the content of the first and second substances,
Figure GDA0003679388990000045
representing the information transfer rate from drone to drone,
Figure GDA0003679388990000046
representing the information transfer rate from the drone to the shore.
Further, the step S2 specifically includes:
s21, under the constraint of interruption probability, the problem of maximizing the total information transmission rate is represented as:
Figure GDA0003679388990000047
wherein, C1 represents that the interruption probability does not exceed the rated threshold value in the process of collecting the surrounding hydrological information by the unmanned ship
Figure GDA0003679388990000048
C2 indicates that each drone can only be associated with one drone at most; c3 and C4 represent the transmit power limits of the drone, not exceeding the upper limit of the drone transmit power; c5 indicates the transmit power limit of the drone, which cannot be exceeded; c6 indicates that the relationship between the unmanned ship and the unmanned plane is indicated by an integer variable of 0-1;
s22, determining the binary association relation variable: assume that a given transmit power satisfies the constraints C3, C4, C5, and
Figure GDA0003679388990000049
then there are:
Figure GDA0003679388990000051
further, the step S3 specifically includes:
s31, calculating the distance between each unmanned ship and each unmanned aerial vehicle, introducing a random wireless channel condition function, obtaining a final channel gain matrix according to the distance between the unmanned ships and the unmanned aerial vehicles and the random wireless channel condition, and obtaining a preference ranking table of the unmanned ships to the unmanned aerial vehicles;
s32, determining the upper limit of unmanned aerial vehicles associated with unmanned aerial vehicles according to the number of the unmanned aerial vehicles and the unmanned ships based on the severe environment of the offshore wireless channel and the weak connectivity of the communication link of the unmanned aerial vehicles when the unmanned aerial vehicles are used as mobile relays, setting that each unmanned aerial vehicle is associated with L unmanned ships at most, and selecting L unmanned ships with preference degrees before ranking for association;
s33, if the unmanned ship is associated with a plurality of unmanned aerial vehicles, matching the unmanned ship with a first name in the preference ranking list of the unmanned ship, and removing the matching relation with other unmanned aerial vehicles;
s34, if the number of unmanned aerial vehicles matched with the unmanned aerial vehicle exceeds L, removing the matching relation with the unmanned aerial vehicle from the unmanned aerial vehicle with the rank of L +1, selecting the unmanned aerial vehicle with the first preference rank from other unmanned aerial vehicles for correlation, and repeatedly executing the step S34 until all unmanned aerial vehicles are not overloaded;
s35, under the condition that the incidence relation between the unmanned aerial vehicle and the unmanned ship is given, calculating the transmitting power of the unmanned aerial vehicle and the unmanned ship:
Figure GDA0003679388990000052
s36, in case of given association, each drone can perform power control on its associated drone, maximizing the data transmission rate of uplink:
Figure GDA0003679388990000061
s37, if
Figure GDA0003679388990000062
Then the transmission rate that indicates that the drone transmits marine environmental information to the drone is a limiting condition, and therefore, the transmission rate of the drone needs to be optimized:
Figure GDA0003679388990000063
s38, when satisfied
Figure GDA0003679388990000064
In time, the transmit power of the drone is adjusted to minimize power consumption:
Figure GDA0003679388990000065
s39, if
Figure GDA0003679388990000066
This means that when the drone forwards data to a remote shore base, the transmission rate of the information is a limiting condition, and therefore, the transmit power of the drone needs to be optimized:
Figure GDA0003679388990000067
s40, when satisfied
Figure GDA0003679388990000068
The transmit power of the drone is adjusted to minimize power consumption:
Figure GDA0003679388990000069
compared with the prior art, the invention has the following advantages:
1. the invention provides an information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship facing marine environment monitoring, which is mainly used for modeling the problem through a Shannon formula aiming at the requirement of a system on the total transmission rate in the data transmission process, enabling the unmanned ship to be associated with the unmanned aerial vehicle which is most suitable for the unmanned ship to be matched with a channel with the best channel condition through algorithm optimization, finding the optimal solution of the problem, and decomposing the original problem into two sub-problems.
2. The heuristic algorithm designed by the invention keeps the original 0 and 1 backpack dynamic change arrays by improving the backpack algorithm, introduces the idea of a preference ranking table in a many-to-one matching algorithm, determines the upper limit of the unmanned aerial vehicle associated with the unmanned ship based on the network scale of the unmanned aerial vehicle and the unmanned ship, and establishes a new selection mechanism based on the unmanned aerial vehicle load upper limit.
3. The unmanned aerial vehicle and unmanned ship cooperative operation network model is established, the unmanned ship is adopted to collect water temperature environment information, the unmanned aerial vehicle is used as an aerial base station, the collected water temperature environment information is forwarded to a remote shore base station, the communication distance can be obviously reduced, unnecessary path loss and multipath effect are reduced, and high-reliability and low-delay transmission of information is completed on the premise of ensuring the maximization of the information transmission rate. Meanwhile, the unmanned ship and the unmanned aerial vehicle also have the characteristic of flexibility and maneuverability, the unmanned ship and the unmanned aerial vehicle can be quickly deployed to a required place, the unmanned ship and the unmanned aerial vehicle have strong capability of cooperative operation, the self environment adaptation capability is strong, the unmanned ship and the unmanned aerial vehicle adapt to the ocean environment with severe conditions, and along with the fact that the unmanned aerial vehicle and the unmanned ship are developed more and more quickly at present, the investment of the society on the unmanned aerial vehicle and the unmanned ship is more and more large, the prices of the unmanned aerial vehicle and the unmanned ship are more and more cheap, and the unmanned aerial vehicle and the unmanned ship can be recycled for multiple times, so that the application prospect is very wide.
For the above reasons, the present invention can be widely applied to the fields of wireless communication and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow chart of an information feedback technology for cooperation between an unmanned aerial vehicle and an unmanned ship for marine environment monitoring according to an embodiment of the present invention.
Fig. 2 is a network scenario diagram for use in an embodiment of the present invention.
Fig. 3 is a flow chart of a heuristic algorithm based on a matching theory according to an embodiment of the present invention.
Fig. 4 is a graph showing the total throughput of the system varying with the number of drones according to the embodiment of the present invention.
Fig. 5 is a graph of the total system throughput as a function of the number of unmanned vessels in accordance with an embodiment of the present invention.
Fig. 6 is a diagram illustrating the total throughput of the system varying with the transmission power according to an embodiment of the present invention.
FIG. 7 is a diagram of algorithm convergence according to an embodiment of the present invention.
FIG. 8 is a comparison graph of algorithm runtime for an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, the invention provides an information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring, which comprises the following steps:
s1, uniformly expressing the influence of the channel condition in wireless transmission and the communication distance between the unmanned aerial vehicle and the unmanned ship on information return as the preference of the unmanned ship to the wireless channel allocated by the unmanned aerial vehicle, and constructing a preference ranking table of the unmanned ship to the unmanned aerial vehicle;
s2, analyzing the transmission rate of information when the unmanned ship and the unmanned ship collaboratively return information, and maximizing the total throughput of the system through the joint optimization of the association relationship between the unmanned ship and the transmission power of signals;
s3, designing a heuristic algorithm based on a matching theory, determining the matching relation between the unmanned aerial vehicle and the unmanned ship, and further determining the transmitting power of the unmanned ship and the unmanned aerial vehicle based on a convex optimization theory.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, a network scenario to which the present invention is applied is that monitoring information of uplink is transmitted back to the network. In a specific application scenario of marine internet of things, details of unmanned-plane-assisted cooperative transmission communication are shown in fig. 2, in which multiple unmanned ships are deployed on the sea level, information such as hydrology and weather of marine environment is collected in a cooperative manner, collected sensor data is transmitted to a shore-based communication system through a wireless channel, and sensor performance is analyzed to monitor the marine environment. If the shore-based communication system cannot successfully receive the sensor data within a prescribed time, the current marine environmental situation is presumed from the previous sensor data, but this may lead to a decrease in monitoring performance. Therefore, the reliability and timeliness of sensor data transmission seriously affect the detection performance of the marine environment. In order to accurately monitor the dynamic marine environment, each unmanned ship accurately transmits the environmental information data packet sensed by the unmanned ship to a shore-based communication system with the help of unmanned plane cooperative communication.
The ocean communication system consists of a remote shore base station, M half-duplex unmanned aerial vehicles and N unmanned ships, and the unmanned aerial vehicles hover above the unmanned ships. Unmanned ship and unmanned planeAre denoted as N and M, respectively. In addition, the data packet length of the marine environment information generated by the nth unmanned ship is l n The time of the first hop and the second hop of data transmission is t f And t s . Due to the limitation of transmission power, the maximum transmission power of the unmanned ship and the unmanned plane is q respectively max And p max The available bandwidth is evenly distributed to all drones, and the bandwidth allocated to each drone may be denoted as B.
In a specific implementation, as a preferred embodiment of the present invention, the step S1 specifically includes:
s11, acquiring the environmental information data packet sensed by each unmanned ship, and accurately transmitting the environmental information data packet to a shore-based communication system;
s12, the channel gain is kept constant in the resource block of one duration, but the channel gain may vary from resource block to resource block. Therefore, the reception power of the mth drone from the nth drone is calculated by the following formula:
Figure GDA0003679388990000101
wherein q is n Representing the transmit power of the nth drone,
Figure GDA0003679388990000102
representing the channel gain; d n,m Represents the distance, alpha, from the nth drone to the mth drone f Which represents the index of the path loss,
Figure GDA0003679388990000103
representing small-scale channel fading between the nth unmanned ship and the mth unmanned plane, wherein the small-scale channel fading is distribution with a mean value and a unit variance of zero;
s13, assuming that the random channel is an independent and uniformly distributed random variable, the mean value is zero, the variance is N0, and calculating the channel gain between the mth drone and the remote shore base, that is, the remote shore base receives the channel gain of the signals sent to the mth drone by the nth drone, and the calculation formula is as follows:
Figure GDA0003679388990000104
wherein d is m,0 Represents the distance, alpha, between the mth drone and the distant shore base station s Which is indicative of the path loss exponent,
Figure GDA0003679388990000105
represents the small-scale channel fading with mean 0 and unit variance 0;
s14, further calculating the receiving power of signals sent to the m unmanned aerial vehicle by the nth unmanned ship received from the shore base at a remote location based on the calculated channel gain, wherein the calculation formula is as follows:
Figure GDA0003679388990000106
s15, in order to avoid the situation that a certain unmanned aerial vehicle has an excessive load, redundant information in information transmission between the unmanned aerial vehicle and the unmanned ship is reduced, and the information transmitted by the unmanned ship is prevented from being repeated, so that one unmanned ship can only be associated with one unmanned aerial vehicle, and the method is represented as follows:
Figure GDA0003679388990000107
in the above formula, if the variable is binary n,m 1, it means that the nth unmanned ship is associated with the mth unmanned plane;
s16, due to the limitation of the transmitting power, the mth drone needs to consider the following constraints when forwarding the received sensor data of the nth drone to the remote shore-based communication system:
Figure GDA0003679388990000108
s17, because the unmanned aerial vehicle decodes the information received from the unmanned ship at a distance, and then sends the decoded information to the shore-based communication system at a distance, in the process of information decoding and sending, the signal-to-noise ratio of the unmanned ship for sending the information to the unmanned aerial vehicle can be expressed as
Figure GDA0003679388990000109
The signal-to-noise ratio at which the drone forwards information to the shore-based communication system may be expressed as
Figure GDA00036793889900001010
In this case, the signal-to-noise ratio of the unmanned ship's transmissions to the shore-based communication system can be expressed as
Figure GDA0003679388990000111
Therefore, the probability of interruption in the information transmission process needs to be calculated, and the calculation formula is as follows:
Figure GDA0003679388990000112
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003679388990000113
representing the probability of successful decoding of the nth drone signal, Γ, by the mth drone th Represents a decoding threshold;
s18, calculating an end-to-end transmission rate of the auxiliary communication of the unmanned aerial vehicle, wherein the calculation formula is as follows:
Figure GDA0003679388990000114
wherein the content of the first and second substances,
Figure GDA0003679388990000115
representing the information transfer rate from drone to drone,
Figure GDA0003679388990000116
indicating unmanned to shore baseThe information transmission rate of (2). In fact, the formula
Figure GDA0003679388990000117
Equivalent to B theta n,m log(1+Γ n,m )。
In a specific implementation, as a preferred embodiment of the present invention, the step S2 specifically includes:
s21, in order to improve the monitoring effect of the communication system in the severe marine environment, under the constraint condition of interruption probability, the problem of maximizing the total information transmission rate is expressed as follows:
Figure GDA0003679388990000118
wherein, C1 represents that the interruption probability does not exceed the rated threshold value in the process of collecting the surrounding hydrological information by the unmanned ship
Figure GDA0003679388990000119
C2 indicates that each drone can only be associated with one drone at most; c3 and C4 represent the transmit power limits of the drone, not exceeding the upper limit of the drone transmit power; c5 indicates the transmit power limit of the drone, which cannot be exceeded; c6 indicates that the relationship between the unmanned ship and the unmanned plane is indicated by an integer variable of 0-1;
due to P 0 As a mixed integer programming problem, in which the solution of binary association variables and continuous power distribution variables is involved, and P 0 Is non-linear, the representation of the decision variables is not very well defined, so that P is found directly with a suitable computational complexity 0 Is very difficult. The present invention decomposes the original problem into two sub-problems and focuses on finding an iterative method to obtain an approximately optimal solution. The total data transmission rate obtained by the updated association relation and the updated transmission rate may be not less than the previous rate, and the obtained total data rate is limited by the limited transmission power, so that it may be ensured that the entire iterative process is converged.
S22, determining the binary association relation variable: assume that a given transmit power satisfies the constraints C3, C4, C5, and
Figure GDA0003679388990000121
then there are:
Figure GDA0003679388990000122
the above problem is a 0-1 integer linear programming problem, and the optimal solution can be obtained by branch-and-bound (BNB) algorithm. However, in the worst case, the computational complexity of the BNB algorithm is exponential. In fact, SP 1 In fact a backpack problem. To solve this problem, a heuristic algorithm was designed to find the near-optimal solution, as shown in fig. 1.
In a specific implementation, as a preferred embodiment of the present invention, the step S3 specifically includes:
s31, calculating the distance between each unmanned ship and each unmanned aerial vehicle, introducing a random wireless channel condition function, obtaining a final channel gain matrix according to the distance between the unmanned ships and the unmanned aerial vehicles and the random wireless channel condition, and obtaining a preference ranking table of the unmanned ships to the unmanned aerial vehicles;
s32, determining the upper limit of unmanned aerial vehicles associated with unmanned aerial vehicles according to the number of the unmanned aerial vehicles and the unmanned ships based on the severe environment of the offshore wireless channel and the weak connectivity of the communication link of the unmanned aerial vehicles when the unmanned aerial vehicles are used as mobile relays, setting that each unmanned aerial vehicle is associated with L unmanned ships at most, and selecting L unmanned ships with preference degrees before ranking for association;
s33, if the unmanned ship is associated with a plurality of unmanned aerial vehicles, matching the unmanned ship with a first name in the preference ranking list of the unmanned ship, and removing the matching relation with other unmanned aerial vehicles;
s34, if the number of unmanned aerial vehicles matched with the unmanned aerial vehicle exceeds L, removing the matching relation with the unmanned aerial vehicle from the unmanned aerial vehicle with the rank of L +1, selecting the unmanned aerial vehicle with the first preference rank from other unmanned aerial vehicles for correlation, and repeatedly executing the step S34 until all unmanned aerial vehicles are not overloaded;
s35, under the condition that the incidence relation between the unmanned aerial vehicle and the unmanned ship is given, calculating the transmitting power of the unmanned aerial vehicle and the unmanned ship:
Figure GDA0003679388990000131
s36, in case of given association, each drone can perform power control on its associated drone, maximizing the data transmission rate of uplink:
Figure GDA0003679388990000132
s37, if
Figure GDA0003679388990000133
Then the transmission rate that indicates that the drone transmits marine environmental information to the drone is a limiting condition, and therefore, the transmission rate of the drone needs to be optimized:
Figure GDA0003679388990000134
s38, when satisfied
Figure GDA0003679388990000135
In time, the transmit power of the drone is adjusted to minimize power consumption:
Figure GDA0003679388990000136
s39, if
Figure GDA0003679388990000137
This means that when the drone forwards data to a remote shore base, the transmission rate of the information is a limiting condition, and therefore, the transmit power of the drone needs to be optimized:
Figure GDA0003679388990000141
s40, when satisfied
Figure GDA0003679388990000142
The transmit power of the drone is adjusted to minimize power consumption:
Figure GDA0003679388990000143
the following describes the application effect of the present invention in detail with reference to simulation.
First, simulation condition
In the simulation scenario, the monitoring range of the marine environment is [0, 10 ]] km ×[0，10] km As shown in fig. 3, 10 drones and 30 drones are randomly deployed within this sensing range. The path loss parameter of the radio channel is 2.31, the mean of the rayleigh distribution is 0 and the variance is 1, and the mean of the rice distribution is 0 and the variance is 1. The reference distance is 1m, the path loss at the reference distance is 56.7dB, the Gaussian white noise power spectral density is-87 dbm/Hz, the bandwidth is 10MHz, the length of a sensing period is 100ms, the maximum transmitting power of the unmanned ship is 500mw, and the maximum transmitting power of the unmanned plane is 1 w.
Second, simulation content and result analysis
The performance of the proposed algorithm is demonstrated by comparison with other transmission methods.
Comparison algorithm 1 — branch and bound method: one of the key problems in solving the channel allocation optimization model is parametric processing. However, the parameter matrix of the model must conform to the parameter form of the Linprog function, and the requirement on the mathematical model is high. Moreover, the algorithm complexity varies with the network model, and in the worst case, the computational complexity increases exponentially. And the algorithm does not consider the problem of weak connectivity communication link when the unmanned aerial vehicle is used as a mobile communication relay.
Comparison algorithm 2-many-to-one matching algorithm: the many-to-one matching method is a matching relation problem among objects with unequal numbers, and carries out preferential selection through a preference ranking table, wherein one with a smaller number is used as a matching reference, one with a larger number is used as an individual to be matched, the one with the smaller number is completely matched, the other one with the larger number still has the remaining individual, and then the remaining individuals are matched until the algorithm is completed after all matching. The selection result focuses more on the stability of matching, but the final optimization target is not ideal compared with other optimization algorithms.
Simulation 1: and analyzing the influence of the number of unmanned ships and unmanned planes and the transmission power of the unmanned planes on the total throughput of the communication system.
It can be seen from fig. 4-6 that, as the number of unmanned ships and unmanned planes increases and the transmission power increases, the total throughput of the system increases continuously, because the network scale increases and the data amount required to be transmitted also increases continuously, the network architecture and the heuristic algorithm designed by the present invention can meet the requirement of the increase of the network model on the data transmission rate, and fig. 4-6 can be regarded as that the total throughput of the communication system obtained by comparing the algorithm 1 with the heuristic algorithm designed by the present invention is almost the same, but compared with the global optimization of the comparison algorithm 1, the speed of the heuristic algorithm designed by the present invention when searching for a matching object is faster, and the calculation time of the comparison algorithm 1 is 1000 times of that of the heuristic algorithm 1. Compared with the comparison algorithm 2, the heuristic algorithm has better algorithm performance, and as can be seen from fig. 5, as the number of unmanned ships increases, the total throughput required to be transmitted by the system increases, but the throughput obtained by calculation of the comparison algorithm 2 increases more slowly, and the heuristic algorithm can meet the requirement of the network throughput which increases rapidly along with the increase of the network model, so that the information transmission rate of the whole communication system is ensured. As can be seen from fig. 4, as the number of drones increases, the number of drones which can be selected and associated by the drones also increases, so that the number of information transmission links which can be selected by the drones also increases, the selectable channel condition is better, the information transmission environment is guaranteed, and the overall throughput growth rate of the system is faster than that of fig. 5. As can be seen from fig. 6, the rate of increase of the total system throughput of the heuristic algorithm is exponential due to the superiority of the water-filling algorithm, but is relatively slow compared to algorithm 2. It can be seen that the computation time of the heuristic algorithm is less than that of comparison algorithm 2, and the total throughput of the resulting system is much higher than that of comparison algorithm 2.
Simulation 2: and analyzing the influence of the calculation time and the convergence speed of the algorithm on the performance of the algorithm.
As can be seen from fig. 7-8, under the condition that the convergence rates of the heuristic algorithm and the comparison algorithm 1 are not much, the calculation speed of the heuristic algorithm is 1000 times faster than that of the comparison algorithm 1, so that higher matching efficiency is ensured, lower time delay is also ensured, and the capability of the unmanned aerial vehicle for processing information as a mobile communication relay is improved. But also the heuristic algorithm is more advantageous in terms of computation time than the comparison algorithm 2.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (1)

1. An information returning method for cooperation of an unmanned aerial vehicle and an unmanned ship for marine environment monitoring is characterized by comprising the following steps:
s1, uniformly expressing the influence of the channel condition in wireless transmission and the communication distance between the unmanned aerial vehicle and the unmanned ship on information return as the preference of the unmanned ship to the wireless channel allocated by the unmanned aerial vehicle, and constructing a preference ranking table of the unmanned ship to the unmanned aerial vehicle;
the step S1 specifically includes:
s11, acquiring the environmental information data packet sensed by each unmanned ship, and accurately transmitting the environmental information data packet to a shore-based communication system;
s12, calculating the receiving power of the mth drone from the nth drone, wherein the calculation formula is as follows:
Figure FDA0003679388980000011
wherein q is n Representing the transmit power of the nth drone,
Figure FDA0003679388980000012
representing the channel gain; d n,m Represents the distance, alpha, from the nth drone to the mth drone f Which represents the index of the path loss,
Figure FDA0003679388980000013
representing small-scale channel fading between the nth unmanned ship and the mth unmanned plane, wherein the small-scale channel fading is distribution with a mean value and a unit variance of zero; m represents a set of unmanned vessels;
s13, assuming that the random channel is an independent and uniformly distributed random variable, the mean value is zero, the variance is N0, and calculating the channel gain between the mth drone and the remote shore base, that is, the remote shore base receives the channel gain of the signals sent to the mth drone by the nth drone, and the calculation formula is as follows:
Figure FDA0003679388980000014
wherein d is m,0 Represents the distance, alpha, between the mth drone and the distant shore base station s Which represents the index of the path loss,
Figure FDA0003679388980000015
represents the small-scale channel fading with mean 0 and unit variance 0;
s14, further calculating the receiving power of signals sent to the m unmanned aerial vehicle by the nth unmanned ship received from the shore base at a remote location based on the calculated channel gain, wherein the calculation formula is as follows:
Figure FDA0003679388980000016
s15, setting that an unmanned ship can only be associated with an unmanned plane, which is represented as:
Figure FDA0003679388980000017
in the above formula, if the variable θ is binary n,m 1, it means that the nth unmanned ship is associated with the mth unmanned plane;
s16, due to the limitation of the transmitting power, the mth drone needs to consider the following constraints when forwarding the received sensor data of the nth drone to the remote shore-based communication system:
Figure FDA0003679388980000021
s17, calculating the interruption probability in the information transmission process, wherein the calculation formula is as follows:
Figure FDA0003679388980000022
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003679388980000023
representing the probability of successful decoding of the nth drone signal, Γ, by the mth drone th Represents a decoding threshold;
Figure FDA0003679388980000024
a signal-to-noise ratio representing the information transmitted by the drone to the drone;
Figure FDA0003679388980000025
signal-to-noise ratio representing information forwarded by the drone to the shore-based communication system;
s18, calculating an end-to-end transmission rate of the auxiliary communication of the unmanned aerial vehicle, wherein the calculation formula is as follows:
Figure FDA0003679388980000026
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003679388980000027
representing the information transfer rate from drone to drone,
Figure FDA0003679388980000028
representing the information transmission rate from the unmanned aerial vehicle to the shore base;
s2, analyzing the transmission rate of information when the unmanned ship and the unmanned ship collaboratively return information, and maximizing the total throughput of the system through the joint optimization of the association relationship between the unmanned ship and the transmission power of signals;
the step S2 specifically includes:
s21, under the constraint of the outage probability, the problem of maximizing the total information transmission rate is expressed as:
Figure FDA0003679388980000029
Figure FDA00036793889800000210
Figure FDA00036793889800000211
Figure FDA00036793889800000212
Figure FDA00036793889800000213
Figure FDA00036793889800000214
Figure FDA00036793889800000215
wherein, C1 represents that the interruption probability does not exceed the rated threshold value in the process of collecting the surrounding hydrological information by the unmanned ship
Figure FDA00036793889800000216
C2 indicates that each drone can only be associated with one drone at most; c3 and C4 represent the transmit power limits of the drone, not exceeding the upper limit of the drone transmit power; c5 indicates the transmit power limit of the drone, which cannot be exceeded; c6 indicates that the relationship between the unmanned ship and the unmanned plane is indicated by an integer variable of 0-1; n represents a set of drones; p is a radical of max Represents the maximum transmit power of the drone; q. q.s max Represents the maximum transmit power of the unmanned ship;
s22, determining the binary association relation variable: assume that a given transmit power satisfies the constraints C3, C4, C5, and
Figure FDA0003679388980000031
then there are:
Figure FDA0003679388980000032
Figure FDA0003679388980000033
Figure FDA0003679388980000034
Figure FDA0003679388980000035
wherein B represents the bandwidth allocated by each unmanned ship;
s3, designing a heuristic algorithm based on a matching theory, determining the matching relation between the unmanned aerial vehicle and the unmanned ship, and further determining the transmitting power of the unmanned ship and the unmanned aerial vehicle based on a convex optimization theory;
the step S3 specifically includes:
s31, calculating the distance between each unmanned ship and each unmanned aerial vehicle, introducing a random wireless channel condition function, obtaining a final channel gain matrix according to the distance between the unmanned ships and the unmanned aerial vehicles and the random wireless channel condition, and obtaining a preference ranking table of the unmanned ships to the unmanned aerial vehicles;
s32, determining the upper limit of unmanned aerial vehicles associated with unmanned aerial vehicles according to the number of the unmanned aerial vehicles and the unmanned ships based on the severe environment of the offshore wireless channel and the weak connectivity of the communication link of the unmanned aerial vehicles when the unmanned aerial vehicles are used as mobile relays, setting that each unmanned aerial vehicle is associated with L unmanned ships at most, and selecting L unmanned ships with preference degrees before ranking for association;
s33, if the unmanned ship is associated with a plurality of unmanned aerial vehicles, matching the unmanned ship with the first name in the preference ranking table of the unmanned ship, and removing the matching relation with other unmanned aerial vehicles;
s34, if the number of unmanned aerial vehicles matched with the unmanned aerial vehicle exceeds L, removing the matching relation with the unmanned aerial vehicle from the unmanned aerial vehicle with the rank of L +1, selecting the unmanned aerial vehicle with the first preference rank from other unmanned aerial vehicles for correlation, and repeatedly executing the step S34 until all unmanned aerial vehicles are not overloaded;
s35, under the condition that the incidence relation between the unmanned aerial vehicle and the unmanned ship is given, calculating the transmitting power of the unmanned aerial vehicle and the unmanned ship:
Figure FDA0003679388980000041
Figure FDA0003679388980000042
Figure FDA0003679388980000043
Figure FDA0003679388980000044
Figure FDA0003679388980000045
s36, in case of a given association, each drone may perform power control on its associated drone, maximizing the data transmission rate of the uplink:
Figure FDA0003679388980000046
Figure FDA0003679388980000047
Figure FDA0003679388980000048
Figure FDA0003679388980000049
Figure FDA00036793889800000410
s37, if
Figure FDA00036793889800000411
Then the transmission rate that indicates that the drone transmits marine environmental information to the drone is a limiting condition, and therefore, the transmission rate of the drone needs to be optimized:
Figure FDA00036793889800000412
Figure FDA00036793889800000413
Figure FDA00036793889800000414
s38, when satisfied
Figure FDA00036793889800000415
In time, the transmit power of the drone is adjusted to minimize power consumption:
Figure FDA00036793889800000416
Figure FDA00036793889800000417
Figure FDA00036793889800000418
Figure FDA00036793889800000419
s39, if
Figure FDA00036793889800000420
This means that when the drone forwards data to a remote shore base, the transmission rate of the information is a limiting condition, and therefore, the transmit power of the drone needs to be optimized:
Figure FDA0003679388980000051
Figure FDA0003679388980000052
Figure FDA0003679388980000053
Figure FDA0003679388980000054
s40, when satisfied
Figure FDA0003679388980000055
The transmit power of the drone is adjusted to minimize power consumption:
Figure FDA0003679388980000056
Figure FDA0003679388980000057
Figure FDA0003679388980000058
wherein, gamma is n,m Representing the signal-to-noise ratio of the unmanned ship transmitted to the shore-based communication system.
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