CN114448490B - 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 path planning and spectrum resource allocation method and system of a multi-unmanned aerial vehicle, comprising 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 the unmanned aerial vehicles which can be accessed into the ground terminal, the unmanned aerial vehicles are configured for the ground terminal, and the network coverage rate and the data acquisition efficiency of the emergency communication system are improved through the network coverage of different disaster areas and the cooperative cooperation among unmanned aerial vehicles on the same layer; based on interference parameters of other unmanned aerial vehicles and channel gains between the unmanned aerial vehicles and a ground terminal, the current received data quantity of each unmanned aerial vehicle is calculated, according to the current received data quantity of each unmanned aerial vehicle, the maximum arrangement gain is obtained as a target by combining multidimensional influence parameters, a COBSO intelligent algorithm is utilized to allocate frequency resources for each unmanned aerial vehicle, the track and the deployment quantity of the unmanned aerial vehicles are optimized, and therefore 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 use intelligent terminal equipment such as mobile phones, computers and the like to communicate with other people at any time and any place, communication between people has no time and space barrier, and a network-based communication mode also becomes a main mode that people acquire external information and are connected with the world. However, when natural or artificial disasters such as earthquake, fire, tsunami, war and the like occur, the ground communication infrastructure can be destroyed greatly or even completely, so that the rescue personnel can be timely rescued to generate great barriers, and the life and property safety of trapped personnel can be greatly threatened. Owing to the increasingly mature manufacturing process and stability of unmanned aerial vehicles, unmanned aerial vehicles gradually develop from army to civilian use and are applied to aspects of people's production and life, and particularly have good application prospects in the auxiliary emergency communication field, and the advantages are concentrated in the following points:
(1) The air flight ad hoc network (Flying Ad hoc network, FANET) formed by the multiple unmanned aerial vehicles has strong adaptability and expansibility. It can carry various sensing devices, such as sensors, cameras, etc. to detect the environment; the system can be also provided with communication equipment such as a wireless signal transceiver and the like to serve as an aerial base station, and information is transmitted in a relay mode, so that effective communication among ground personnel is ensured. In addition, due to the high-altitude flight characteristic, the communication between unmanned aerial vehicles and ground users can be regarded as line-of-sight transmission. In this case, the quality of information transmission can be well ensured.
(2) The unmanned aerial vehicle has the characteristic of being used and flown, and the flexibility and the high mobility of deployment enable the unmanned aerial vehicle to face a plurality of complex situations.
(2) FANET, if one node fails, the unmanned aerial vehicle group can rapidly deploy another unmanned aerial vehicle to replace the failed unmanned aerial vehicle, so that stronger robustness of the unmanned aerial vehicle FANET is reflected. In addition, as the system is deployed at high altitude, secondary disasters such as aftershocks can be effectively avoided, and the system is influenced.
From the current state of development and research, unmanned aerial vehicle auxiliary communication systems are mainly divided into the following three directions: unmanned aerial vehicle auxiliary communication coverage, unmanned aerial vehicle auxiliary relay transmission, unmanned aerial vehicle auxiliary information propagation and data acquisition. However, the existing unmanned aerial vehicle auxiliary emergency communication system has the following defects: (1) The flight speed of the drone clusters is not considered more fully. For example, dynamic adjustments should be made based on the density of ground service terminals. Although the trajectory of the drone has been optimized, the result of the optimization is more to serve more accurate positioning and collision avoidance, possibly lacking in terms of the guarantee of the communication quality. (2) Although TDMA techniques are used to propose a novel channel access mechanism, the size of each slot is essentially fixed. In an actual application scenario, a better performance index, such as a channel utilization rate, a 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 invention is to overcome the defects that the full coverage of the disaster area network and the reasonable distribution of frequency resources cannot be realized in the prior art, thereby providing a path planning and frequency spectrum resource distribution method and system for multiple unmanned aerial vehicles.
In order to achieve the above purpose, the present invention provides the following technical solutions:
In a first aspect, an embodiment of the present invention provides a method for path planning and spectrum resource allocation of a multi-unmanned aerial vehicle, including the following steps: respectively simulating the distribution of the ground terminals after the disaster 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; 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 each unmanned aerial vehicle can access; the method comprises the steps of realizing channel access of unmanned aerial vehicles by utilizing a TDMA technology, calculating the current received data quantity of 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 current received data quantity of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement gain, distributing frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm, and optimizing the track and deployment quantity of the unmanned aerial vehicles.
In an embodiment, a process for respectively simulating distribution of ground terminals after disaster and distribution of hot spots to obtain a plurality of clusters of ground terminals centering on the hot spots by using a preset simulation method includes: the distribution of the ground terminals after the disaster is simulated by using a Thomas cluster process, the distribution of the hot spots is simulated by using a Poisson point process, and a plurality of ground terminal clusters centering on the hot spots are obtained.
In an embodiment, a process for configuring each ground terminal with an unmanned aerial vehicle according to a distance between each unmanned aerial vehicle and each cluster and a number of ground terminals each unmanned aerial vehicle can access, includes: according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access, one cluster unmanned aerial vehicle is deployed for each cluster in sequence, and the auxiliary unmanned aerial vehicle is used for covering the non-clustered ground terminals and the ground terminals which can not be effectively covered by the current cluster unmanned aerial vehicle and are provided with delay sensitive data.
In an embodiment, a process for deploying a cluster drone for a single cluster includes: judging whether the current cluster is associated with the cluster unmanned aerial vehicle or not; when the current cluster is not related to the unmanned aerial vehicle, finding all unmanned aerial vehicles currently within the coverage range of the unmanned aerial vehicle, and sorting from the near to the far according to the distance sequence; correlating the cluster unmanned aerial vehicle which is closest to the current cluster and has the number of the access ground terminals which does not reach the upper limit with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminals reaches the upper limit, distributing new cluster unmanned aerial vehicles for the current cluster.
In an embodiment, a process for implementing channel access of a drone 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 utilizing a TDMA technology.
In one embodiment, the process of calculating the current received data amount of each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicle and the ground terminal comprises the following steps: calculating the instantaneous accessibility of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle in the current time slot and each ground terminal administered and the interference parameters of other unmanned aerial vehicles on the channel in the current time slot; and calculating to obtain the current received data quantity of the unmanned aerial vehicle according to the instantaneous accessibility 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 multi-dimensional influencing parameters include: the data packet size, the time delay sensitivity degree of each ground terminal, the current unmanned aerial vehicle position and the residual spectrum resources.
In a second aspect, an embodiment of the present invention provides a path planning and spectrum resource allocation system for a multi-unmanned aerial vehicle, including: the simulation distribution module is used for respectively simulating the distribution of the ground terminals and the distribution of the hot spots after the disaster 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 unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access; the frequency spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicle by utilizing a TDMA technology, calculating the current received data volume of each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicle and a ground terminal, combining multidimensional influence parameters according to the current received data volume of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement income, and allocating frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm and optimizing the track and the deployment quantity of the unmanned aerial vehicle.
In a third aspect, an embodiment of the present invention provides a computer apparatus, including: the system comprises at least one processor and a memory in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle of the first aspect of the embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a computer to execute the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle 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 path planning and spectrum resource allocation method and a system for multiple unmanned aerial vehicles, wherein the distribution of ground terminals and the distribution of hot spots after disaster are respectively simulated by using a preset simulation method to obtain a plurality of ground terminal clusters taking hot spots as centers; according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals which each unmanned aerial vehicle can access, configuring unmanned aerial vehicles for each ground terminal, and obviously improving the network coverage rate and the data acquisition efficiency of an emergency communication system through the network coverage of different disaster areas and the cooperative cooperation among multiple unmanned aerial vehicles on the same floor; the method comprises the steps of realizing channel access of unmanned aerial vehicles by utilizing a TDMA technology, calculating the current received data quantity of 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 current received data quantity of each unmanned aerial vehicle, taking the maximum arrangement gain as a target, distributing frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm, optimizing the track and the deployment quantity of the unmanned aerial vehicles, and accordingly improving the utilization rate of limited bandwidth resources and the life cycle of a system.
2. According to the path planning and spectrum resource distribution method and system for the multiple unmanned aerial vehicles, the clustered unmanned aerial vehicles and the auxiliary unmanned aerial vehicles are arranged, reliable emergency communication and data acquisition in post-disaster areas are realized by means of mobile edge calculation and optimal unmanned aerial vehicle deployment, so that time delay of the whole communication system is reduced, loss of time delay sensitive data is reduced, positions of trapped people are accurately positioned, and effective rescue of rescue workers is assisted; by considering the data packet size and the time delay requirement of the ground terminal, the optimal unmanned aerial vehicle flight path and the reasonable distribution of limited spectrum resources are obtained through the calculation of an intelligent algorithm, the optimal scheduling of the 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 that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a specific example of a path planning and spectrum resource allocation method of a multi-unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a flowchart of another specific example of a path planning and spectrum resource allocation method of a multi-unmanned aerial vehicle according to an embodiment of the present invention;
Fig. 3 is a three-layer network architecture according to an embodiment of the present invention;
Fig. 4 is a flowchart of another specific example of a path planning and spectrum resource allocation method of a multi-unmanned aerial vehicle 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 of a multi-unmanned aerial vehicle 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 following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, or can be communicated inside the two components, or can be connected wirelessly or in a wired way. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1
The embodiment of the invention provides a path planning and spectrum resource allocation method of a multi-unmanned aerial vehicle, which is shown in fig. 1 and comprises the following steps:
Step S11: and respectively simulating the distribution of the ground terminals after the disaster 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 hot spots, including: densely populated areas, such as schools, hospitals, and the like; because the affected degree of each area after disaster is different, the density of the ground terminals between 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 communicable 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 hotspot (cluster center) independently according to the same gaussian distribution, where unlike a cellular network where the service area overlaps and is complex, embodiments of the present invention assume that the clusters are scattered and do not overlap.
Specifically, in the embodiment of the present invention, the distribution of all cluster centers is represented by PPP with independent same distribution and a distribution density of λ hs; other ground terminals surrounding the hotspot are distributed around the hotspot in TCP. Since the ground terminals modeled by TCP are more discrete with respect to MCP ((Model Core Potential)), the range of their emissions will also be greater. Furthermore, MCP requires the radius of coverage to be set in advance, which is unpredictable during actual disaster relief. In addition, in the rescue process after disaster, although the rescue process is clustered with the hot spot as the center and mainly serves the hot spot area, more trapped people are found in order to serve more areas, so that the embodiment of the invention uses TCP based on the PCP protocol to represent all the ground terminals scattered near the hot spot.
Step S12: and configuring unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access.
Specifically, the implementation process of step S12 includes deploying one cluster unmanned aerial vehicle for each cluster in turn 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, and covering the non-clustered ground terminals by using the auxiliary unmanned aerial vehicle and the ground terminals that the current cluster unmanned aerial vehicle cannot effectively cover the time delay sensitive data.
Specifically, the ground terminals in the embodiment of the invention are divided into a ground terminal which can be reached by clustered unmanned aerial vehicles in time, a ground terminal which can not reach clustered experimental sensitive data in time and a ground terminal which is not clustered, and aiming at three ground terminals, unmanned aerial vehicles are deployed for each ground terminal by using different deployment methods.
Specifically, each cluster is provided with an unmanned aerial vehicle for forwarding and data acquisition of messages in the cluster, the clusters can cooperate with each other to jointly complete network coverage and data acquisition in a responsible district, and the unmanned aerial vehicles are named as clustered unmanned aerial vehicles; unmanned aerial vehicles that provide communication coverage and data acquisition for non-clustered ground terminals are named auxiliary unmanned aerial vehicles.
Specifically, due to limited bandwidth resources and energy limitations of the unmanned aerial vehicle, the number of ground terminals that each unmanned aerial vehicle can access is limited, and when the ground terminals in a cluster are too dense, the service quality of the whole system is likely to be affected. When the cluster unmanned aerial vehicle is overloaded, the auxiliary unmanned aerial vehicle completes the access of the terminal for the auxiliary cluster unmanned aerial vehicle, provides communication coverage and data acquisition for the occurrence experiment sensitive data ground terminal where the unmanned aerial vehicle cannot reach the clustered unmanned aerial vehicle in time, and ensures the communication coverage and the data acquisition timeliness of the whole disaster area as much as possible.
Specifically, communication cooperation can be carried out among the cluster unmanned aerial vehicles, and the positions of the cluster unmanned aerial vehicles are continuously changed to adapt to different communication requirements. The information collected by the cluster unmanned aerial vehicle is connected with a 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 collection of data packets as possible, it is also necessary to reduce the number of arrangement of the unmanned aerial vehicle as little as possible, and for the case that serious spectrum resource competition will occur when too many terminals are simultaneously accessed, thereby affecting normal data collection, as shown in fig. 2, the process of deploying clustered unmanned aerial vehicles for a single cluster includes steps S21 to S23, and the procedure of executing steps S21 to S23 is shown in table 1, as follows:
Step S21: judging whether the current cluster is associated with the cluster unmanned aerial vehicle.
Step S22: when the current cluster is not associated with the cluster unmanned aerial vehicle, all the cluster unmanned aerial vehicles currently within the coverage range of the unmanned aerial vehicle are found, and the unmanned aerial vehicles are ordered from the near to the far according to the distance sequence.
Step S23: correlating the cluster unmanned aerial vehicle which is closest to the current cluster and has the number of the access ground terminals which does not reach the upper limit with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminals 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 unmanned aerial vehicles cooperated, wherein the first layer of the three-layer network architecture is distributed by hot spots and ground terminals, the second layer is a clustered unmanned aerial vehicle layer, and the third layer is an auxiliary unmanned aerial vehicle layer.
Step S13: the method comprises the steps of realizing channel access of unmanned aerial vehicles by utilizing a TDMA technology, calculating the current received data quantity of 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 current received data quantity of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement gain, distributing frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm, and optimizing the track and deployment quantity of the unmanned aerial vehicles.
Specifically, in the communication between the unmanned aerial vehicle and the ground terminal, the embodiment of the invention considers an air-to-ground channel dominated by line-of-sight (LoS) and adopts a random access mechanism in the MAC layer. When the drone achieves communication coverage, the ground terminal can only communicate with the drone when the signal-to-noise ratio (Signal to Noise Ratio, SNR) of the ground terminal receiver must be greater than a threshold.
Specifically, in a cruising period T, each ground terminal must transmit data to a corresponding unmanned aerial vehicle within a limited connection time due to movement of the unmanned aerial vehicle, so that the unmanned aerial vehicle in the embodiment of the invention also communicates with each ground terminal in a administered cluster by using a TDMA technology, the TDMA technology is adopted to divide the time T into N equal time slots, and when the channel state (meeting the signal-to-noise ratio condition) between the ground terminal and the corresponding unmanned aerial vehicle meets the communication requirement, the ground terminal can be connected with the unmanned aerial vehicle and can be allocated with corresponding spectrum resources.
Specifically, as shown in fig. 4, the process of calculating the current data amount received by each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain with the ground terminal includes steps S31 to S32, as follows:
Step S31: and calculating the instantaneous accessibility of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle and each administered ground terminal in the current time slot 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 plane and the single administered ground terminal in the current time slot is as follows:
g(r,q)＝γ0γ|r2| (1)
Where g (r, q) denotes the channel gain between the drone and the ground terminal, r denotes the distance between the drone and the ground terminal, q denotes the spatial coordinates of the drone, γ is the small-scale fading in the determined distribution, which follows the Gamma distribution, γ 0 denotes the channel power gain at a reference distance of 1 m.
Since the channel between the unmanned aerial vehicle and the ground terminal used in the line-of-sight transmission model is orthogonal to the channel between the unmanned aerial vehicle and the other ground terminal. In this way, the channel between the unmanned aerial vehicle and the ground terminal and the channel between the unmanned aerial vehicle and the other ground terminal can not be interfered, so that the problem of interference between the channels is not considered any more, only the interference of other unmanned aerial vehicles on the channel is considered, and then the calculation formula of the interference parameters of other unmanned aerial vehicles on the channel in the current time slot is as follows:
Wherein, P u' is the transmission power of the u ' th unmanned aerial vehicle, L u,u'(n)＝Pu'||du,u'||-α represents the gain of the channel between the u ' th unmanned aerial vehicle and the u ' th unmanned aerial vehicle, d u,u' is the distance between the u ' th unmanned aerial vehicle and the u ' th unmanned aerial vehicle, and n is the nth time slot.
Then, according to the formula (1) and the formula (2), the instantaneous accessibility of the unmanned aerial vehicle in the nth time slot can be calculated as follows:
Wherein b g (n) represents the spectrum resource obtained by the ground terminal g in the time slot n; p g ground terminal g; c g,u (n) shows whether the ground terminal g is connected with the unmanned aerial vehicle u in the time slot n, wherein the connection is 1, otherwise, the connection is 0; representing the noise power.
Step S32: and calculating to obtain the current received data quantity of the unmanned aerial vehicle according to the instantaneous accessibility 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 invention dynamically allocates the available spectrum resources in each time slot to be not fixed allocation, by analyzing the size and the time delay sensitivity of the data packet of each ground terminal, the current unmanned aerial vehicle position and the residual spectrum resources, and the data quantity S g currently received by each unmanned aerial vehicle is as follows:
Where, delta represents the slot size, And/>Respectively representing the generation time and expiration time of the data packet,/>And the time when the unmanned aerial vehicle starts to receive the ground terminal data is represented.
Specifically, according to the current received data volume of each unmanned aerial vehicle, the embodiment of the invention combines the multidimensional influence parameters, aims at obtaining the maximum arrangement income, and utilizes COBSO intelligent algorithm to allocate frequency resources for each unmanned aerial vehicle and optimize the track and the deployment quantity of the unmanned aerial vehicle, wherein in a specific embodiment, the multidimensional influence parameters comprise: the data packet size, the time delay sensitivity degree of each ground terminal, the current unmanned aerial vehicle position and the residual spectrum resources. The execution of COBSO intelligent algorithms is shown in table 2.
TABLE 2
The COBSO intelligent algorithm used in the embodiment of the invention has the main innovation points that:
(1) Cross-operation-based population initialization mechanism
T(m)＝floor[α1*In(α2+m)] (5)
Where α 1 and α 2 are scaling constants and m is the current number of iterations. T (m) is the current counter, and when the iteration counter is larger than the value, the cross initialization operation is performed.
(2) Self-adaptive step length updating method
Where M max is the maximum number of iterations, o is a constant, ub d and lb d are the upper and lower boundaries, respectively, of the d-th dimension variable.
Example 2
An embodiment of the present invention provides a path planning and spectrum resource allocation system for multiple unmanned aerial vehicles, as shown in fig. 5, including:
The simulation distribution module is used for respectively simulating the distribution of the ground terminals and the distribution of the hot spots after the disaster by using a preset simulation method to obtain a plurality of ground terminal clusters taking the hot spots as centers; this module performs the method described in step S11 in embodiment 1, and will not be described here.
The unmanned aerial vehicle deployment module is used for configuring unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access; this module performs the method described in step S12 in embodiment 1, and will not be described here.
The frequency spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicle by utilizing a TDMA technology, calculating the current received data volume of each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicle and a ground terminal, combining the multidimensional influence parameters according to the current received data volume of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement income, and allocating frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm; this module performs the method described in step S13 in embodiment 1, and will not be described here.
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, a memory 404, at least one communication bus 402. Wherein communication bus 402 is used to enable connected communications between these components. The communication interface 403 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may further include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Ramdom Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 404 may also optionally be at least one storage device located remotely from the aforementioned processor 401. Wherein the processor 401 may perform the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle of embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for performing the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle of embodiment 1.
The communication bus 402 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. 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 not only one bus or one type of bus.
Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk (English: HARD DISK DRIVE, abbreviation: HDD) or a solid-state disk (English: solid-STATE DRIVE, abbreviation: SSD); memory 404 may also include a combination of the above types of memory.
The processor 401 may be a central processor (english: central processing unit, abbreviated: CPU), a network processor (english: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the processor 401 may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field-programmable gate array (English: field-programmable GATE ARRAY, abbreviated: FPGA), a general-purpose array logic (English: GENERIC ARRAY logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 404 is also used for storing program instructions. The processor 401 may invoke program instructions to implement the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle according to embodiment 1 of the present application.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores computer executable instructions thereon, wherein the computer executable instructions can execute the path planning and spectrum resource allocation method of the multi-unmanned aerial vehicle of the embodiment 1. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid-state disk (Solid-state-STATE DRIVE, SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (8)
1. The path planning and spectrum resource allocation method for the multiple unmanned aerial vehicles is characterized by comprising the following steps of:
Respectively simulating the distribution of the ground terminals after the disaster 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;
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 each unmanned aerial vehicle can access;
The method comprises the steps of realizing channel access of unmanned aerial vehicles by utilizing a TDMA technology, calculating the current received data quantity of 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 current received data quantity of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement income, distributing frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm, and optimizing the track and deployment quantity of the unmanned aerial vehicles;
The process for calculating the current received data quantity of each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicle and the ground terminal comprises the following steps: calculating the instantaneous accessibility of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle in the current time slot and each ground terminal administered and the interference parameters of other unmanned aerial vehicles on the channel in the current time slot; calculating to obtain the current received data quantity of the unmanned aerial vehicle according to the instantaneous availability 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;
the calculation formula of the interference parameters of the unmanned aerial vehicle to the current channel received by other unmanned aerial vehicles in the current time slot is as follows:
wherein, P u' is the transmitting power of the (u ' th) unmanned aerial vehicle, L u,u'(n)＝Pu'||du,u'||-α represents the gain of the channel between the (u) th unmanned aerial vehicle and the (u ' th) unmanned aerial vehicle, d u,u' is the distance between the (u) th unmanned aerial vehicle and the (u ' th) unmanned aerial vehicle, and n is the (n) th time slot; alpha is an index constant;
the instantaneous reachability of the unmanned aerial vehicle in the nth time slot is:
Wherein b g (n) represents the spectrum resource obtained by the ground terminal g in the time slot n; p g ground terminal g; c g,u (n) shows whether the ground terminal g is connected with the unmanned aerial vehicle u in the time slot n, wherein the connection is 1, otherwise, the connection is 0; representing noise power;
The current received data amount S g of each unmanned aerial vehicle is:
Where, delta represents the slot size, And/>Respectively representing the generation time and expiration time of the data packet,/>The time when the unmanned aerial vehicle starts to receive the ground terminal data is represented;
The multi-dimensional influencing parameters include: the data packet size, the time delay sensitivity degree of each ground terminal, the current unmanned aerial vehicle position and the residual spectrum resources.
2. The method for path planning and spectrum resource allocation of multiple unmanned aerial vehicles according to claim 1, wherein the process of simulating the distribution of post-disaster ground terminals and the distribution of hot spots to obtain a plurality of clusters of ground terminals centered on the hot spots by using a preset simulation method comprises:
The distribution of the ground terminals after the disaster is simulated by using a Thomas cluster process, the distribution of the hot spots is simulated by using a Poisson point process, and a plurality of ground terminal clusters centering on the hot spots are obtained.
3. The method for path planning and spectrum resource allocation of multiple unmanned aerial vehicles according to claim 1, wherein the process of configuring the unmanned aerial vehicle for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of ground terminals each unmanned aerial vehicle can access comprises:
according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access, one cluster unmanned aerial vehicle is deployed for each cluster in sequence, and the auxiliary unmanned aerial vehicle is used for covering the non-clustered ground terminals and the ground terminals which can not be effectively covered by the current cluster unmanned aerial vehicle and are provided with delay sensitive data.
4. The method for path planning and spectrum resource allocation for multiple drones of claim 1, wherein deploying a clustered drone for a single cluster comprises:
judging whether the current cluster is associated with the cluster unmanned aerial vehicle or not;
when the current cluster is not related to the unmanned aerial vehicle, finding all unmanned aerial vehicles currently within the coverage range of the unmanned aerial vehicle, and sorting from the near to the far according to the distance sequence;
correlating the cluster unmanned aerial vehicle which is closest to the current cluster and has the number of the access ground terminals which does not reach the upper limit with the current cluster; and when the number of all cluster unmanned aerial vehicles accessing the ground terminals reaches the upper limit, distributing new cluster unmanned aerial vehicles for the current cluster.
5. The method for path planning and spectrum resource allocation for multiple unmanned aerial vehicles according to claim 1, wherein the process of implementing channel access for unmanned aerial vehicles by TDMA technology 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 utilizing a TDMA technology.
6. A multiple unmanned aerial vehicle path planning and spectrum resource allocation system, comprising:
the simulation distribution module is used for respectively simulating the distribution of the ground terminals and the distribution of the hot spots after the disaster 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 unmanned aerial vehicles for each ground terminal according to the distance between each unmanned aerial vehicle and each cluster and the number of the ground terminals which each unmanned aerial vehicle can access;
The frequency spectrum resource allocation module is used for realizing channel access of the unmanned aerial vehicle by utilizing a TDMA technology, calculating the current received data volume of each unmanned aerial vehicle based on interference parameters of other unmanned aerial vehicles and channel gain between the unmanned aerial vehicle and a ground terminal, combining the multidimensional influence parameters according to the current received data volume of each unmanned aerial vehicle, aiming at obtaining the maximum arrangement income, and allocating frequency resources for each unmanned aerial vehicle by utilizing COBSO intelligent algorithm and optimizing the track and the deployment quantity of the unmanned aerial vehicle;
The process for calculating the current received data quantity of each unmanned aerial vehicle based on the interference parameters of other unmanned aerial vehicles and the channel gain between the unmanned aerial vehicle and the ground terminal comprises the following steps: calculating the instantaneous accessibility of the unmanned aerial vehicle in the current time slot according to the channel gain between the unmanned aerial vehicle in the current time slot and each ground terminal administered and the interference parameters of other unmanned aerial vehicles on the channel in the current time slot; calculating to obtain the current received data quantity of the unmanned aerial vehicle according to the instantaneous availability 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;
the calculation formula of the interference parameters of the unmanned aerial vehicle to the current channel received by other unmanned aerial vehicles in the current time slot is as follows:
wherein, P u' is the transmitting power of the (u ' th) unmanned aerial vehicle, L u,u'(n)＝Pu'||du,u'||-α represents the gain of the channel between the (u) th unmanned aerial vehicle and the (u ' th) unmanned aerial vehicle, d u,u' is the distance between the (u) th unmanned aerial vehicle and the (u ' th) unmanned aerial vehicle, and n is the (n) th time slot; alpha is an index constant;
the instantaneous reachability of the unmanned aerial vehicle in the nth time slot is:
Wherein b g (n) represents the spectrum resource obtained by the ground terminal g in the time slot n; p g ground terminal g; c g,u (n) shows whether the ground terminal g is connected with the unmanned aerial vehicle u in the time slot n, wherein the connection is 1, otherwise, the connection is 0; representing noise power;
The current received data amount S g of each unmanned aerial vehicle is:
Where, delta represents the slot size, And/>Respectively representing the generation time and expiration time of the data packet,/>The time when the unmanned aerial vehicle starts to receive the ground terminal data is represented;
The multi-dimensional influencing parameters include: the data packet size, the time delay sensitivity degree of each ground terminal, the current unmanned aerial vehicle position and the residual spectrum resources.
7. 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 to cause the at least one processor to perform the path planning and spectrum resource allocation method of the multi-drone of any of claims 1-5.
8. A computer readable storage medium storing computer instructions for causing the computer to perform the path planning and spectrum resource allocation method of the multi-drone of any one of claims 1-5.
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