CN114448490A - Path planning and spectrum resource allocation method and system for multiple unmanned aerial vehicles - Google Patents
Path planning and spectrum resource allocation method and system for multiple unmanned aerial vehicles Download PDFInfo
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Abstract
The invention discloses a method and a system for path planning and spectrum resource allocation of multiple unmanned aerial vehicles, wherein the method comprises the following steps: simulating ground terminal distribution and hot spot distribution to obtain a plurality of clusters; according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle, the unmanned aerial vehicles are configured for the ground terminals, and the network coverage rate and the data acquisition efficiency of the emergency communication system are improved by performing cooperative cooperation on network coverage of different disaster areas and the unmanned aerial vehicles on the same layer; based on the interference parameters of other unmanned aerial vehicles and the channel gain between the ground terminals, the data volume currently received by each unmanned aerial vehicle is calculated, and according to the data volume currently received by each unmanned aerial vehicle, the multi-dimensional influence parameters are combined to obtain the maximum arrangement income as a target, the COBSO intelligent algorithm is utilized to distribute frequency resources for each unmanned aerial vehicle, the track of the unmanned aerial vehicle is optimized, the deployment quantity of the unmanned aerial vehicle is optimized, and the utilization rate of limited bandwidth resources and the life cycle of the system are improved.
Description
Technical Field
The invention relates to the fields of unmanned aerial vehicle application, emergency communication and mobile edge calculation, in particular to a method and a system for path planning and spectrum resource allocation of multiple unmanned aerial vehicles.
Background
With the development of science and technology, people can communicate with other people at any time and any place by using intelligent terminal equipment such as mobile phones and computers, the communication among people has no obstacle of time and space, and a network-based communication mode also becomes a main mode for people to acquire external information and connect with the world. However, when natural or man-made disasters such as earthquake, fire, tsunami, war and the like occur, the communication infrastructure on the ground is greatly damaged or even completely destroyed, so that great obstacles are generated for timely rescue of rescuers, and the life and property safety of trapped people is greatly threatened. Benefiting from the increasingly mature manufacturing process and stability of the unmanned aerial vehicle, the unmanned aerial vehicle gradually develops from military use to civil use and is applied to the aspects of production and life of people, and particularly has good application prospect in the field of auxiliary emergency communication, and the advantages of the unmanned aerial vehicle are reflected in the following points in a centralized manner:
(1) an air flight Ad hoc network (FANET) formed by multiple unmanned aerial vehicles has strong adaptability and expansibility. The device can carry various sensing devices, such as a sensor, a camera and the like, to detect the environment; the communication equipment such as a wireless signal transceiver can be also equipped to be used as an aerial base station, information is transmitted in a relay mode, and effective communication between ground personnel is guaranteed. In addition, due to the characteristic of high-altitude flight, the communication between the unmanned aerial vehicles and the ground users can be regarded as line-of-sight transmission. In this case, the quality of information transmission can be well ensured.
(2) The ready-to-fly nature of drones, the flexibility of deployment and high mobility make drones feel a lot of complications.
(2) In the FANET, if one node fails, the unmanned aerial vehicle cluster can rapidly deploy another unmanned aerial vehicle to replace the failed unmanned aerial vehicle, so that the strong robustness of the FANET of the unmanned aerial vehicle is reflected. In addition, as the system is deployed at high altitude, the influence of secondary disasters, such as aftershocks, on the system can be effectively avoided.
From the present development and research situation, the auxiliary communication system of the unmanned aerial vehicle mainly comprises the following three directions: unmanned aerial vehicle auxiliary communication covers, unmanned aerial vehicle-assisted relay transmission, unmanned aerial vehicle auxiliary information's propagation and the collection of data. However, the current unmanned aerial vehicle auxiliary emergency communication system has the following defects: (1) the flight speed of the drone cluster is not considered more fully. For example, the dynamic adjustment should be made according to the density of the ground service terminals. Although the trajectory of the drone has been optimized, the result of the optimization is more to serve more precise positioning and collision avoidance, and may be deficient in terms of guarantee of communication quality. (2) Although TDMA techniques are used to propose novel channel access mechanisms, the size of each time slot is substantially fixed. In an actual application scenario, better performance indexes, such as channel utilization rate, communication delay, and the like, can be obtained by dynamically adjusting the size of the time slot according to an actual task request.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects that the full coverage of the disaster area network and the reasonable allocation of frequency resources cannot be realized in the prior art, thereby providing a method and a system for path planning and spectrum resource allocation of multiple unmanned aerial vehicles.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for path planning and spectrum resource allocation for multiple drones, including the following steps: respectively simulating the distribution of post-disaster ground terminals and the distribution of hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers; configuring unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle; the method comprises the steps of utilizing a TDMA technology to achieve channel access of the unmanned aerial vehicles, calculating the data volume currently received by each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the data volume currently received by each unmanned aerial vehicle to obtain the maximum arrangement income, and utilizing a COBSO intelligent algorithm to allocate frequency resources, optimize unmanned aerial vehicle tracks and deploy quantity for each unmanned aerial vehicle.
In one embodiment, a preset simulation method is used to respectively simulate the distribution of post-disaster ground terminals and the distribution of hot spots, so as to obtain a process of clustering a plurality of ground terminals with the hot spots as the center, including: and simulating the distribution of the post-disaster ground terminals by utilizing a Thomas cluster process, and simulating the distribution of hot spots by utilizing a Poisson point process to obtain a plurality of ground terminal clusters taking the hot spots as centers.
In one embodiment, the process of configuring each ground terminal with drones according to the distance of each drone from each cluster and the number of ground terminals each drone can access includes: according to the distance between each unmanned aerial vehicle and each cluster and the number of the unmanned aerial vehicles capable of accessing the ground terminals, one cluster unmanned aerial vehicle is deployed for each cluster in sequence, and the auxiliary unmanned aerial vehicles are used for covering the ground terminals which are not clustered and the ground terminals which cannot effectively cover the unmanned aerial vehicles clustered at present and have time delay sensitive data.
In an embodiment, a process for deploying cluster drones for a single cluster includes: judging whether the current cluster is associated with a cluster unmanned aerial vehicle; when the cluster is not associated with the cluster unmanned aerial vehicles, all the unmanned aerial vehicles currently in the coverage range of the unmanned aerial vehicles are found and are sorted from near to far according to the distance sequence; associating cluster unmanned aerial vehicles which are closest to the current cluster and have access to ground terminals, wherein the number of the access ground terminals does not reach the upper limit, with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminal reaches the upper limit, distributing new cluster unmanned aerial vehicles for the current cluster.
In one embodiment, the process of implementing channel access of the drone by using TDMA technology includes: when the channel state between the ground terminal and the unmanned aerial vehicle meets the signal-to-noise ratio condition, the ground terminal establishes communication with the unmanned aerial vehicle, and the unmanned aerial vehicle communicates with each ground terminal in the administered cluster by using a TDMA technology.
In an embodiment, the process of calculating the data amount currently received by each drone based on the interference parameters of other drones and the channel gain between the drone and the ground terminal includes: calculating the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle and each managed ground terminal and the interference parameters of the unmanned aerial vehicle on the channel by other unmanned aerial vehicles in the current time slot; and calculating to obtain the data volume currently received by the unmanned aerial vehicle according to the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot, the time slot size, the data packet generation time of the ground terminal and the data packet expiration time.
In one embodiment, the multidimensional influence parameters include: the size of the data packet of each ground terminal, the sensitivity degree of time delay, the current position of the unmanned aerial vehicle and the residual frequency spectrum resources.
In a second aspect, an embodiment of the present invention provides a path planning and spectrum resource allocation system for multiple drones, including: the simulation distribution module is used for respectively simulating the distribution of the post-disaster ground terminals and the distribution of the hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers; the unmanned aerial vehicle deployment module is used for configuring the unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle; the spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicles by utilizing a TDMA technology, calculating the data volume currently received by each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the data volume currently received by each unmanned aerial vehicle to obtain the maximum arrangement income as a target, and allocating frequency resources, optimizing the tracks of the unmanned aerial vehicles and deploying the quantity for each unmanned aerial vehicle by utilizing a COBSO intelligent algorithm.
In a third aspect, an embodiment of the present invention provides a computer device, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to execute the method for path planning and spectrum resource allocation for multiple drones according to the first aspect of the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the method for path planning and spectrum resource allocation for multiple drones according to the first aspect of the embodiment of the present invention.
The technical scheme of the invention has the following advantages:
1. the invention provides a method and a system for path planning and spectrum resource allocation of multiple unmanned aerial vehicles, which are characterized in that a preset simulation method is used for respectively simulating the distribution of post-disaster ground terminals and the distribution of hot spots, so that a plurality of ground terminal clusters taking the hot spots as centers are obtained; according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle, the unmanned aerial vehicles are configured for each ground terminal, and the network coverage rate and the data acquisition efficiency of the emergency communication system are obviously improved through network coverage of different disaster areas and cooperative cooperation among multiple unmanned aerial vehicles on the same layer; utilize TDMA technique to realize unmanned aerial vehicle's channel access, based on the interference parameter that receives other unmanned aerial vehicles, and the channel gain between the ground terminal, calculate the data bulk that every unmanned aerial vehicle received at present, according to the data bulk that every unmanned aerial vehicle received at present, combine the multidimension influence parameter, with the biggest income of arranging of acquireing is the target, utilize COBSO intelligent algorithm for every unmanned aerial vehicle distribution frequency resource, optimize unmanned aerial vehicle orbit and deployment quantity, thereby promote the life cycle of the utilization ratio of limited bandwidth resource and system.
2. According to the multi-unmanned aerial vehicle path planning and frequency spectrum resource allocation method and system, the cluster unmanned aerial vehicle and the auxiliary unmanned aerial vehicle are arranged, and reliable emergency communication and data acquisition in a post-disaster area are realized by utilizing mobile edge calculation and optimal unmanned aerial vehicle deployment, so that the time delay of the whole communication system is reduced, the loss of time delay sensitive data is reduced, the position of trapped personnel is accurately positioned, and the effective rescue of rescue personnel is assisted; by considering the size of a data packet of the ground terminal and the time delay requirement, the optimal flight path of the unmanned aerial vehicle and the reasonable distribution of limited frequency spectrum resources are obtained through calculation of an intelligent algorithm, the optimal scheduling of the frequency spectrum resources is realized, the resource utilization rate is improved, and the operation efficiency and the life cycle of the whole emergency communication system are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a method for path planning and spectrum resource allocation for multiple drones according to an embodiment of the present invention;
fig. 2 is a flowchart of another specific example of a method for path planning and spectrum resource allocation for multiple drones according to an embodiment of the present invention;
fig. 3 is a three-layer network architecture provided by an embodiment of the present invention;
fig. 4 is a flowchart of another specific example of a method for path planning and spectrum resource allocation for multiple drones according to an embodiment of the present invention;
fig. 5 is a composition diagram of another specific example of a path planning and spectrum resource allocation system for multiple drones according to an embodiment of the present invention;
fig. 6 is a composition diagram of a specific example of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment of the invention provides a path planning and spectrum resource allocation method for multiple unmanned aerial vehicles, which comprises the following steps as shown in fig. 1:
step S11: and respectively simulating the distribution of the post-disaster ground terminals and the distribution of the hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers.
Specifically, the present invention uses a Poisson Point Process (PPP) to simulate the location distribution of the hot spots, where the hot spots include: densely populated areas, such as schools, hospitals, etc.; because the affected degree of each area after the disaster is different, the ground terminal density between the disaster areas is correspondingly different, the position distribution of the ground terminals is described by using a Thomas Clustering Process (TCP), the ground terminals are communication devices carried by trapped people, and each cluster is clustered by taking a hot spot as a cluster center. In TCP, all ground terminals will be distributed around a hot spot (cluster center) independently according to the same gaussian distribution, where the clusters are scattered and do not overlap, unlike cellular networks where there is overlap and complexity of service area.
Specifically, in the embodiment of the present invention, the distribution of all the cluster centers uses independent same distribution and the distribution density is λhsPPP of (e); other ground terminals surrounding the hot spot are distributed around the hot spot in TCP. Due to TCP modellingThe ground terminal is more discrete and has a larger radiation range than MCP (Model Core Potential). Moreover, the MCP needs to set the radius of coverage in advance, which cannot be predicted in the actual disaster relief process. In addition, in the post-disaster rescue process, although the cluster is centered on the hot spot and mainly serves the hot spot area, more trapped persons are found in order to serve more areas, and therefore, the embodiment of the invention uses the TCP based on the PCP protocol to represent all the ground terminals scattered around the hot spot.
Step S12: and configuring the unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle.
Specifically, the step S12 includes deploying a cluster of unmanned aerial vehicles for each cluster in sequence according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals that each unmanned aerial vehicle can access to, and covering the ground terminals that are not clustered and the ground terminals that cannot effectively cover the delay-sensitive data of the current cluster of unmanned aerial vehicles by using the auxiliary unmanned aerial vehicle.
Specifically, the ground terminals of the embodiment of the invention are divided into ground terminals which can be reached by clustered unmanned aerial vehicles in time, ground terminals which cannot reach clustered experiment sensitive data in time, and ground terminals which are not clustered, and the unmanned aerial vehicles are deployed for each ground terminal by using different deployment methods aiming at the three ground terminals.
Specifically, each cluster is provided with an unmanned aerial vehicle for forwarding messages and acquiring data in the cluster, the cluster-unmanned aerial vehicles can cooperate with each other to jointly complete network coverage and data acquisition in responsible regions, and the unmanned aerial vehicle is named as a cluster unmanned aerial vehicle; unmanned aerial vehicles providing communication coverage and data acquisition for non-clustered ground terminals are named as auxiliary unmanned aerial vehicles.
Specifically, due to the limited bandwidth resources and energy limitations of the drones, the number of ground terminals that each drone can access is limited, and when the ground terminals in a cluster are too dense, the service quality of the whole system is inevitably affected. When the unmanned aerial vehicle that clusters transships, supplementary unmanned aerial vehicle will assist the unmanned aerial vehicle that clusters to accomplish the access at terminal, for unmanned aerial vehicle can not in time reach the sensitive data ground terminal of appearing of clustering and provide communication cover and data acquisition, guarantee the communication cover of whole disaster area and the promptness of data acquisition as far as.
Specifically, communication cooperation can be carried out between cluster unmanned aerial vehicles, and its position is also constantly changing in order to adapt to different communication demands. The information collected by the cluster unmanned aerial vehicle is connected with the server carried by the emergency communication vehicle in a multi-strip or direct connection mode through other cluster unmanned aerial vehicles or auxiliary unmanned aerial vehicles.
Specifically, in order to achieve as many successful data packet collection as possible, it is also required to reduce the number of unmanned aerial vehicles arranged as possible, and for the case that when too many terminals access simultaneously, serious spectrum resource competition occurs, thereby affecting normal data collection, as shown in fig. 2, the process of deploying cluster unmanned aerial vehicles for a single cluster includes steps S21 to S23, and the procedures of executing steps S21 to S23 are as shown in table 1, as follows:
step S21: and judging whether the current cluster is associated with the cluster unmanned aerial vehicle.
Step S22: when the cluster unmanned aerial vehicles are not associated with the cluster, all cluster unmanned aerial vehicles in the coverage range of the unmanned aerial vehicles are found and sorted from near to far according to the distance sequence.
Step S23: associating cluster unmanned aerial vehicles which are closest to the current cluster and have access to ground terminals, wherein the number of the access ground terminals does not reach the upper limit, with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminal reaches the upper limit, distributing new cluster unmanned aerial vehicles for the current cluster.
TABLE 1
Specifically, based on the above method, as shown in fig. 3, the technical solution of the embodiment of the present invention is a novel three-layer emergency communication network architecture with multiple coordinated drones, where a first layer of the three-layer network architecture is a hotspot and ground terminal distribution, a second layer is a cluster drone layer, and a third layer is an auxiliary drone layer, and network coverage and data acquisition efficiency of the emergency communication system are significantly improved through network coverage in different disaster areas and collaborative cooperation among the drones on the same layer.
Step S13: the method comprises the steps of utilizing a TDMA technology to achieve channel access of the unmanned aerial vehicles, calculating the data volume currently received by each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the data volume currently received by each unmanned aerial vehicle to obtain the maximum arrangement income, and utilizing a COBSO intelligent algorithm to allocate frequency resources, optimize unmanned aerial vehicle tracks and deploy quantity for each unmanned aerial vehicle.
Specifically, in communication between the drone and the ground terminal, the embodiment of the present invention considers an air-to-ground channel dominated by line-of-sight (LoS) transmission and employs a random access mechanism in the MAC layer. When the unmanned aerial vehicle realizes communication coverage, the ground terminal can communicate with the unmanned aerial vehicle only when the Signal-to-Noise Ratio (SNR) of the ground terminal receiver is larger than a threshold value.
Specifically, in a cruise period T, each ground terminal must transmit data to the corresponding unmanned aerial vehicle within a limited connection time due to movement of the unmanned aerial vehicle, so the unmanned aerial vehicle of the embodiment of the present invention further communicates with each ground terminal in the administered cluster by using a TDMA technique, the TDMA technique is adopted to divide the time T into N equal time slots, and when a channel state (satisfying a signal-to-noise ratio condition) between the ground terminal and the corresponding unmanned aerial vehicle meets a communication requirement, the ground terminal can be connected with the unmanned aerial vehicle and can be allocated with a corresponding spectrum resource.
Specifically, as shown in fig. 4, the process of calculating the data volume currently received by each drone based on the interference parameters of other drones and the channel gain between the drone and the ground terminal includes steps S31 to S32, as follows:
step S31: and calculating the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle and each managed ground terminal and the interference parameters of other unmanned aerial vehicles on the channel in the current time slot.
The channel gain calculation formula between the unmanned aerial vehicle and the single administered ground terminal in the current time slot is as follows:
g(r,q)＝γ0γ|r2| (1)
wherein g (r, q) represents the channel gain between the drone and the ground terminal, r represents the distance between the drone and the ground terminal, q represents the spatial coordinates of the drone, γ is a small scale fading in determining the distribution, the distribution obeying a Gamma distribution, γ0Representing the channel power gain at a reference distance of 1 m.
Since the channels of the drone and the ground terminal, and the channel of the drone and the other ground terminal used in the line-of-sight transmission model are orthogonal. Through this kind of mode, unmanned aerial vehicle and ground terminal's channel, with unmanned aerial vehicle and another ground terminal's channel also can not have the interference between, consequently no longer consider the interference problem between this channel, only consider to receive the interference of other unmanned aerial vehicles to this channel, then unmanned aerial vehicle receives the interference parameter computational formula of other unmanned aerial vehicles to this channel in the current time slot and is:
in the formula, Pu'Is the transmit power of the u' th drone, Lu,u'(n)＝Pu'||du,u'||-αRepresents the gain of the channel between the u-th drone and the u' -th drone, du，u'The distance between the u-th drone and the u' -th drone is n, which is the nth time slot.
Then, according to equations (1) and (2), the instantaneous reachable rate of the drone in the nth time slot can be calculated as:
in the formula, bg(n) represents the spectrum obtained by the ground terminal g during time slot nA resource; p is a radical ofgThe transmitting power of a ground terminal g; cg,u(n) indicates whether the ground terminal g is connected with the unmanned aerial vehicle u in the time slot n, the connection is 1, otherwise, the connection is 0; sigma2Representing the noise power.
Step S32: and calculating to obtain the data volume currently received by the unmanned aerial vehicle according to the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot, the time slot size, the data packet generation time of the ground terminal and the data packet expiration time.
Specifically, the embodiment of the present invention dynamically allocates, instead of fixedly allocating, the spectrum resources available to the ground terminals in each timeslot by analyzing the size of the data packet, the delay sensitivity, the current position of the unmanned aerial vehicle, and the remaining spectrum resources of each ground terminal based on a spectrum resource allocation strategy of time division multiplexing, and the data size S currently received by each unmanned aerial vehiclegComprises the following steps:
where, δ represents the slot size,
and
respectively representing the generation time and expiration time of the data packet,
indicating the time at which the drone begins to receive ground terminal data.
Specifically, according to the data volume currently received by each unmanned aerial vehicle, the embodiment of the present invention combines multidimensional influence parameters to obtain the maximum arrangement benefit, and allocates frequency resources to each unmanned aerial vehicle, optimizes the trajectory of the unmanned aerial vehicle, and optimizes the deployment number by using a COBSO intelligent algorithm, wherein in a specific embodiment, the multidimensional influence parameters include: the size of a data packet of each ground terminal, the time delay sensitivity degree, the current position of the unmanned aerial vehicle and the residual frequency spectrum resources. The execution program of the COBSO intelligent algorithm is shown in table 2.
The COBSO intelligent algorithm used in the embodiment of the invention has the main innovation points that:
(1) group initialization mechanism based on cross operation
T(m)＝floor[α1*In(α2+m)] (5)
In the formula, alpha1And alpha2Is the scaling constant and m is the current iteration number. T (m) is the current counter, and when the iteration counter is larger than the value, the cross initialization operation is carried out.
(2) Self-adaptive step size updating method
In the formula, MmaxIs the maximum number of iterations, o is a constant, ubdAnd lbdRespectively, an upper boundary and a lower boundary of the d-th dimension variable.
Example 2
An embodiment of the present invention provides a system for path planning and spectrum resource allocation for multiple unmanned aerial vehicles, as shown in fig. 5, including:
the simulation distribution module is used for respectively simulating the distribution of the post-disaster ground terminals and the distribution of the hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers; this module executes the method described in step S11 in embodiment 1, and is not described herein again.
The unmanned aerial vehicle deployment module is used for configuring the unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle; this module executes the method described in step S12 in embodiment 1, and is not described herein again.
The spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicles by using a TDMA (time division multiple access) technology, calculating the currently received data volume of each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the currently received data volume of each unmanned aerial vehicle, and allocating frequency resources to each unmanned aerial vehicle by using a COBSO (chip on Board SO) intelligent algorithm with the aim of obtaining the maximum arrangement benefit; this module executes the method described in step S13 in embodiment 1, and is not described herein again.
Example 3
An embodiment of the present invention provides a computer device, as shown in fig. 6, including: at least one processor 401, such as a CPU (Central Processing Unit), at least one communication interface 403, memory 404, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The communication interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a standard wireless interface. The Memory 404 may be a RAM (random Access Memory) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The memory 404 may optionally be at least one memory device located remotely from the processor 401. The processor 401 may execute the method for path planning and spectrum resource allocation of multiple drones according to embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 invokes the program codes stored in the memory 404 for executing the path planning and spectrum resource allocation method of the multiple drones of embodiment 1.
The communication bus 402 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in FIG. 6, but it is not intended that there be only one bus or one type of bus.
The memory 404 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated: HDD) or a solid-state drive (english: SSD); the memory 404 may also comprise a combination of memories of the kind described above.
The processor 401 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor 401 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 404 is also used to store program instructions. The processor 401 may call a program instruction to implement the method for path planning and spectrum resource allocation of multiple drones in embodiment 1 executed in this application.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer-executable instruction is stored on the computer-readable storage medium, and the computer-executable instruction can execute the method for path planning and spectrum resource allocation for multiple drones in embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A path planning and spectrum resource allocation method for multiple unmanned aerial vehicles is characterized by comprising the following steps:
respectively simulating the distribution of post-disaster ground terminals and the distribution of hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers;
configuring unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle;
the method comprises the steps of utilizing a TDMA technology to achieve channel access of the unmanned aerial vehicles, calculating the data volume currently received by each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the data volume currently received by each unmanned aerial vehicle to obtain the maximum arrangement income, and utilizing a COBSO intelligent algorithm to allocate frequency resources, optimize unmanned aerial vehicle tracks and deploy quantity for each unmanned aerial vehicle.
2. The method for path planning and spectrum resource allocation of multiple unmanned aerial vehicles according to claim 1, wherein the process of obtaining multiple ground terminal clusters centered on hot spots by respectively simulating distribution of post-disaster ground terminals and distribution of hot spots by using a preset simulation method comprises:
and simulating the distribution of the post-disaster ground terminals by utilizing a Thomas cluster process, and simulating the distribution of hot spots by utilizing a Poisson point process to obtain a plurality of ground terminal clusters taking the hot spots as centers.
3. The method of claim 1, wherein the process of configuring each ground terminal with drones according to the distance between each drone and each cluster and the number of ground terminals accessible to each drone includes:
according to the distance between each unmanned aerial vehicle and each cluster and the number of the unmanned aerial vehicles capable of accessing the ground terminals, one cluster unmanned aerial vehicle is deployed for each cluster in sequence, and the auxiliary unmanned aerial vehicles are used for covering the ground terminals which are not clustered and the ground terminals which cannot effectively cover the unmanned aerial vehicles clustered at present and have time delay sensitive data.
4. The method of claim 1, wherein the process of deploying cluster drones for a single cluster comprises:
judging whether the current cluster is associated with a cluster unmanned aerial vehicle;
when the cluster is not associated with the cluster unmanned aerial vehicles, all the unmanned aerial vehicles currently in the coverage range of the unmanned aerial vehicles are found and are sorted from near to far according to the distance sequence;
associating cluster unmanned aerial vehicles which are closest to the current cluster and have access to ground terminals, wherein the number of the access ground terminals does not reach the upper limit, with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminal reaches the upper limit, distributing new cluster unmanned aerial vehicles for the current cluster.
5. The method for path planning and spectrum resource allocation of multiple drones according to claim 1, wherein the process of accessing the channel of the drones by using TDMA technique comprises:
when the channel state between the ground terminal and the unmanned aerial vehicle meets the signal-to-noise ratio condition, the ground terminal establishes communication with the unmanned aerial vehicle, and the unmanned aerial vehicle communicates with each ground terminal in the administered cluster by using a TDMA technology.
6. The method of claim 1, wherein the calculating the amount of data currently received by each drone based on the interference parameters of other drones and the channel gain between the drone and the ground terminal comprises:
calculating the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle and each managed ground terminal and the interference parameters of the unmanned aerial vehicle on the channel by other unmanned aerial vehicles in the current time slot;
and calculating to obtain the data volume currently received by the unmanned aerial vehicle according to the instantaneous reachable rate of the unmanned aerial vehicle in the current time slot, the time slot size, the data packet generation time of the ground terminal and the data packet expiration time.
7. The method of claim 1, wherein the multidimensional impact parameters comprise:
the size of a data packet of each ground terminal, the time delay sensitivity degree, the current position of the unmanned aerial vehicle and the residual frequency spectrum resources.
8. A path planning and spectrum resource allocation system for multiple unmanned aerial vehicles is characterized by comprising:
the simulation distribution module is used for respectively simulating the distribution of the post-disaster ground terminals and the distribution of the hot spots by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers;
the unmanned aerial vehicle deployment module is used for configuring the unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which can be accessed by each unmanned aerial vehicle;
the spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicles by utilizing a TDMA technology, calculating the data volume currently received by each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicles and a ground terminal, combining multidimensional influence parameters according to the data volume currently received by each unmanned aerial vehicle to obtain the maximum arrangement income as a target, and allocating frequency resources, optimizing the tracks of the unmanned aerial vehicles and deploying the quantity for each unmanned aerial vehicle by utilizing a COBSO intelligent algorithm.
9. A computer device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the at least one processor to perform the method for path planning and spectrum resource allocation for multiple drones as recited in any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method for path planning and spectrum resource allocation for multiple drones of any of claims 1-7.
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