CN115226068A - Drone-assisted cellular mobile base station downlink content distribution system and method - Google Patents
Drone-assisted cellular mobile base station downlink content distribution system and method Download PDFInfo
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Abstract
The application relates to the technical field of unmanned aerial vehicle base station scheduling and wireless downlink combination, and provides an unmanned aerial vehicle-assisted cellular mobile base station downlink content distribution system and method, wherein the system comprises: a macro base station, an unmanned aerial vehicle base station and a ground service interest point; the method comprises the following steps: acquiring basic information in a content distribution system area; an unmanned aerial vehicle group starting algorithm in a three-dimensional space is formulated according to basic information, and fairness of coverage of an unmanned aerial vehicle base station to ground service interest points is guaranteed; the connectivity of an unmanned aerial vehicle base station and a ground service interest point is ensured by adopting a k-means clustering algorithm introducing parameters, and a D2U link is formed after the unmanned aerial vehicle base station is associated with the ground service interest point; and a non-orthogonal multiple access mode is used in the link transmission process of the D2U link, and the required content is distributed through the D2U link. The method and the device can effectively control the whole transmission power of the system and improve the service quality of the network while ensuring the path loss.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to a system and a method for distributing content of a cellular mobile base station downlink assisted by an unmanned aerial vehicle.
Background
Due to the high flexibility of the unmanned aerial vehicle, the unmanned aerial vehicle can be actively close to a ground user, and a highly reliable network link is established at a lower transmitting power, and the advantages can provide great help for improving the reliability of a cellular mobile network. However, in order to effectively use drones for the above scenario, several technical challenges, such as optimal deployment, mobility and energy-saving usage of drones, must be solved at the level of physical handling of drone base stations and communication link control.
In the wireless communication aspect, a new generation of cellular mobile network has a new frequency spectrum band, higher frequency spectrum efficiency and higher peak throughput. Taking a 5G network as an example, the main service scenario is high-speed, low-delay, large-scale network communication service, and for these demands, the radio access network needs to make corresponding changes to adapt to the new generation of wireless communication environment. And, in the prior art, the problem of optimal deployment and mobility of a single drone is only solved in the context of downlink wireless communication of the drone. The case of multiple drones is not considered.
As can be seen from the above, although previous research relates to various aspects of power control of a drone communication system, none of the previous research addresses the problem of jointly optimizing drone deployment and mobility, mobile caching, and downlink power control to achieve downlink reliable and energy efficient communication.
Disclosure of Invention
The invention aims to solve the problems of jointly optimizing the deployment and mobility of the unmanned aerial vehicle, mobile caching and downlink power control, ensures the path loss of a wireless downlink on the basis of meeting the content request of a ground user, and optimizes the power distribution of the unmanned aerial vehicle base station so as to maximally ensure the integral service quality of the system. One aspect of the present application provides an unmanned aerial vehicle-assisted cellular mobile base station downlink content distribution system, which is characterized in that the system comprises: a macro base station, an unmanned aerial vehicle base station and a ground service interest point;
the macro base station is configured to receive the demand of the ground service interest point, forward the demand to the unmanned aerial vehicle base station through a public or private network, receive a request sent by the unmanned aerial vehicle base station, acquire demand content corresponding to the request from a core network, and send the demand content to the unmanned aerial vehicle base station;
the unmanned aerial vehicle base station is configured to receive the requirement of the ground service interest point, send a request for acquiring core network content to the macro base station according to the requirement, and send the acquired requirement content to the ground service interest point through a D2U link;
the ground service interest point is configured to send the demand to the macro base station through a public or private network and receive the demand content sent back by the unmanned aerial vehicle base station.
The second aspect of the present application provides a cellular mobile base station downlink content distribution method assisted by an unmanned aerial vehicle, which is characterized by comprising the following steps:
acquiring basic information in a content distribution system area, wherein the basic information comprises: the method comprises the following steps that a macro base station position, the fixed height of an unmanned aerial vehicle base station, the grid side length of an available flight area of the unmanned aerial vehicle base station and a D2U communication carrier frequency band are used as original point coordinates calculated by a method;
acquiring a horizontal distance value from any unmanned aerial vehicle base station to a macro base station and a horizontal distance value from any ground service interest point to the macro base station in the content distribution system area;
calculating a first path loss from the unmanned aerial vehicle base station to the macro base station and a second path loss from the ground service interest point to the macro base station in a single time slot according to a horizontal distance value from the unmanned aerial vehicle base station to the macro base station and a horizontal distance value from the ground service interest point to the macro base station, wherein the time slot is a unit time for the unmanned aerial vehicle base station to provide service for any one ground service interest point;
planning an unmanned aerial vehicle starting algorithm according to the first path loss and the second path loss;
planning a dispatching scheme of the unmanned aerial vehicle cluster by using a k-means clustering algorithm introducing parameters;
associating the unmanned aerial vehicle base station with the ground service interest point according to the planned unmanned aerial vehicle starting algorithm and the planned unmanned aerial vehicle group scheduling scheme, wherein a D2U link is formed after the unmanned aerial vehicle base station is associated with the ground service interest point;
and a non-orthogonal multiple access mode is used in the link transmission process of the D2U link, and the required content is distributed through the D2U link.
In one possible implementation, the planning drone launching algorithm includes: planning unmanned aerial vehicle base station orbit, formulating D2U communication scheduling scheme.
In one possible implementation, the drone base station trajectory is configured to: each drone base station needs to return to its initial position at the end of each cycle, i.e. the trajectory of each drone base station is a closed curve in the plane space.
In one possible implementation, the drone base station trajectory is configured to: in any time slot, the horizontal displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum horizontal distance, and the vertical displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum height difference.
In one possible implementation, the drone base station trajectory is configured to: for any timeslot, the straight-line distance between any two drone base stations is greater than or equal to a predefined guard distance.
In one possible implementation, the D2U communication scheduling scheme is further configured to: in any time slot, one unmanned aerial vehicle base station only serves one ground service interest point; in all time slots, one ground service point of interest is associated with a unique drone base station.
In one possible implementation, the D2U communication scheduling scheme is further configured to: for any one drone base station, all time slots within a given period are allocated equally to each of the ground service points of interest given the period and the number of ground service points of interest associated with the drone base station service.
In one possible implementation, the D2U communication scheduling scheme is further configured to: the sum of the time slots scheduled for each ground service interest point is greater than or equal to a predefined threshold value Smin, where Smin is the service time provided by the lowest drone base station for any ground service interest point.
In one possible implementation, the D2U communication scheduling scheme is further configured to: all time slots scheduled to the same said terrestrial service point of interest within a given period are consecutive.
Compared with the prior art, the method and the device have the advantages that the high maneuverability and the sight distance transmission characteristic of the unmanned aerial vehicle are utilized, so that the wireless connection is more stable and reliable; the clustering algorithm is utilized to optimize the connection between the base station of the unmanned aerial vehicle and the ground user, so that the transmission is more efficient; the method proves that the ground user request in the cache of the mobile base station can be stably and efficiently distributed according to simulation results.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the embodiments of the invention and, together with the description, serve to explain the principles of the embodiments of the invention. It is obvious that the drawings in the following description are only some of the embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic diagram of a drone assisted cellular mobile base station downlink content distribution system of the present application;
fig. 2 is a schematic flow diagram of a drone-assisted cellular mobile base station downlink content distribution method of the present application;
fig. 3 is a diagram of the trajectory planning results for three drone base stations serving multiple ground service points of interest in accordance with the present application;
fig. 4 is a diagram of a trajectory planning result of the unmanned aerial vehicle base station affected by low-level speed in the present application;
fig. 5 is a diagram of a trajectory planning result of the unmanned aerial vehicle base station affected by high-level speed in the present application;
fig. 6 is an average D2U path loss plot of different drone base station horizontal velocities and drone base station numbers in the present application;
FIG. 7 is a comparison of the initial trajectory of the drone swarm in the present application;
fig. 8 is a graph comparing the average D2U path loss for trajectory planning and static deployment for multiple drone base stations in the present application;
fig. 9 is a diagram of transmission power versus system power savings in the present application.
Detailed Description
The invention aims to solve the problems of jointly optimizing the deployment and mobility of the unmanned aerial vehicle, mobile caching and downlink power control, ensures the path loss of a wireless downlink on the basis of meeting the content request of a ground user, and optimizes the power distribution of the unmanned aerial vehicle base station so as to maximally ensure the integral service quality of the system.
As shown in fig. 1, fig. 1 is a schematic diagram of a drone-assisted cellular mobile base station downlink content distribution system according to the present application, and in one aspect, the present application provides a drone-assisted cellular mobile base station downlink content distribution system, including: a macro base station, an unmanned aerial vehicle base station and a ground service interest point;
the macro base station is configured to receive the demand of the ground service interest point, forward the demand to the unmanned aerial vehicle base station through a public or private network, receive a request sent by the unmanned aerial vehicle base station, acquire demand content corresponding to the request from a core network, and send the demand content to the unmanned aerial vehicle base station; a macro base station is a public mobile communication base station, is an interface device for a mobile device to access the internet, and is a form of a radio station, and refers to a radio transceiver station for information transmission between a mobile telephone terminal and a mobile communication switching center in a certain radio coverage area. The macro base station is a large base station, needs to be erected on an iron tower, generally has three sectors, is omni-directional covered, has high power and wide coverage, and needs to be equipped with a special machine room for storing equipment. The macro base station is connected with an antenna above the iron tower through a feeder line.
The unmanned aerial vehicle base station is configured to receive the requirement of the ground service interest point, send a request for acquiring core network content to the macro base station according to the requirement, and send the acquired requirement content to the ground service interest point through a D2U link; the unmanned aerial vehicle basic station is not restricted by ground infrastructure communication facility, can be quick provide reliable communication on a large scale, and the flight that the unmanned aerial vehicle basic station can last in the air, and unmanned aerial vehicle is as communication terminal, because cellular network is in the accessibility of global scope for ground personnel can remote control unmanned aerial vehicle basic station, makes it provide continuous stable communication.
The ground service interest point is configured to send the demand to the macro base station through a public or private network and receive the demand content sent back by the unmanned aerial vehicle base station. The ground service interest point is terminal intelligent equipment adopted by a ground user, and the ground service interest point is communicated with the unmanned aerial vehicle base station and the macro base station through the terminal intelligent equipment according to the use requirement, and feeds back and receives the content of the self requirement.
As shown in fig. 2, fig. 2 is a schematic flow chart of the drone-assisted cellular mobile base station downlink content distribution method according to the present application, and a second aspect of the present application provides a drone-assisted cellular mobile base station downlink content distribution method, including the following steps:
s001: acquiring basic information in a content distribution system area, wherein the basic information comprises: the method comprises the steps of using a macro base station position, the fixed height of an unmanned aerial vehicle base station, the grid side length of an available flight area of the unmanned aerial vehicle base station, and the carrier frequency band of D2U communication, and using the macro base station position as an origin point coordinate calculated by the method.
As shown in fig. 1, in this embodiment, assuming that the Drone Base Station is working at a ground service Interest point and hovering at a position right above the center of the Drone Base Station, the Drone Base Station (DBS) and the ground service Interest point (Area of Interest, aoI) have the same horizontal distance to the macro Base Station. The unmanned aerial vehicle base station forms an angle theta with the macro base station due to the height of the unmanned aerial vehicle base station, covers a plurality of ground users through unmanned aerial vehicle-User (DBS to User, D2U for short) links, and provides cellular network service for the users.
S002: and acquiring a horizontal distance value from any unmanned aerial vehicle base station to the macro base station and a horizontal distance value from any ground service interest point to the macro base station in the content distribution system area.
S003: according to the horizontal distance value from the unmanned aerial vehicle base station to the macro base station and the horizontal distance value from the ground service interest point to the macro base station, calculating to obtain the first path loss from the unmanned aerial vehicle base station to the macro base station and the second path loss from the ground service interest point to the macro base station in a single time slot, wherein the time slot is the unit time for providing service for any one ground service interest point by the unmanned aerial vehicle base station.
The path loss refers to the loss caused by the propagation of radio waves in space, and is caused by the radiation spread of the transmission power and the propagation characteristics of the channel, and reflects the change of the mean value of the received signal power in a macroscopic range. Theoretically, it is considered that the path loss is the same for the same transmission/reception distance. However, in practice, it is often found that the received power at different receiving points with the same transceiving distance has a large variation, and even the received power at the same receiving point fluctuates greatly at different time points.
The present invention is based on the above system embodiment to formulate the trajectory planning problem of multiple DBSs. Defining one drone base station as DBSd and one terrestrial service interest point as AoIu, in this system, the 3D distance from DBSd to AoIu in a single timeslot can be expressed as:
wherein,
is AoiuThe plane coordinates of (a) are calculated,
is a two-dimensional projection of the DBSd onto the X-Y plane.
Indicating the horizontal distance between DBSd and AoIu,
indicating the height distance between DBSd and AoIu. The macro base station is arranged at the origin of the coordinate system,
referring to the D2U distance, the D2U path loss between DBSd and AoIu in a single timeslot can be calculated according to the D2U distance:
since all users and DBS have the same antenna specifications, with fixed transmission power and transmission bandwidth in each period, the achievable D2U data transmission rate between DBSd and AoIu is inversely related to the D2U path loss. Therefore, the goal of the multiple DBS trajectory planning and scheduling problem is to minimize the average D2U path loss of the network over one period T.
S004: planning an unmanned aerial vehicle starting algorithm according to the first path loss and the second path loss;
in one possible implementation, the planning drone launching algorithm includes: planning unmanned aerial vehicle base station orbit, formulating D2U communication scheduling scheme.
In the present application, for the problem of planning and scheduling multiple DBS trajectories, the decision variable set is divided into four blocks, multiple subproblems are proposed, all the blocks or their subblocks are optimized respectively, and the DBS trajectory variable W is further divided into two independent blocks, i.e., a horizontal DBS trajectory L and a DBS flying height H.
Given the constants K, L and H, which represent predefined trajectories for multiple DBS, the AoI associated sub-problem can be written as a problem:
since the exact K can only be determined by the number AoI of a given DBSd association, one initial D2U communication schedule K0 is defined for the first AoI association optimization, where Kd, U [ n ] =1, ∀ D, U, n. Various solver-supported branch-and-bound methods can be used to effectively solve the problem.
Assuming that all the trajectories are closed curves in 3D space, the start position of each trajectory can be randomly generated on the cabinet. Since it is assumed that all tracks have the same period length N, different starting positions may result in different distances between DBSs in all subsequent time slots for any track. Thereafter, the time at which each DBS starts to operate is given to prevent violation of the guard distance constraint in the subsequent time slot.
Wherein, the protection distance constraint means that the planned trajectory of the unmanned aerial vehicle base station is to satisfy a plurality of restrictions. In one possible implementation, the drone base station trajectory is configured to: each unmanned aerial vehicle base station needs to return to the initial position of the unmanned aerial vehicle base station when each period is finished, namely the track of each unmanned aerial vehicle base station is a closed curve in a plane space; this constraint ensures that the unmanned aerial vehicle base station can stably circulate its own track, and maintain communication of the same frequency on the planned track route. As shown in fig. 3, fig. 3 is a diagram of a trajectory planning result of three drone base stations serving multiple ground service interest points according to the present application, and specifically shows a scenario of 15 AoI serving with Vmax = 90m/slot by optimizing trajectories of three DBSs. The closed curves of the different marked points in fig. 3 represent different DBS tracks; the squares on the X-Y plane represent AoI. AoI are associated with corresponding DBSs having the same color. As shown in fig. 3, for all DBSs, the optimized trajectory can fly over all its associations AoI and form a closed curve in planar space. In actual simulated DBS flight, DBS tends to select to hover over the corresponding AoI for a certain period of time, and the minimum average D2U path loss can be achieved by the trajectory of most of the time slots hovering over AoI.
In one possible implementation, the drone base station trajectory is configured to: in any time slot, the horizontal displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum horizontal distance, and the vertical displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum height difference; as shown in fig. 4 and 5, fig. 4 and 5 respectively show two sets of trajectory planning results, where fig. 4 is a diagram of a trajectory planning result of the drone base station affected by a low horizontal velocity, and fig. 5 is a diagram of a trajectory planning result of the drone base station affected by a high horizontal velocity, and specifically, the horizontal velocities are Vmax =30 m/slot and Vmax = 110m/slot, respectively. As shown in fig. 4 and 5, the AoI associations at different Vmax are the same. The trace in FIG. 4 cannot fly over each associated AoI because the maximum horizontal velocity is too small to ensure that DBS approaches AoI in one cycle. Whereas in fig. 5 Vmax is high enough, DBS can hover for even a few slots over each associated AoI because the flight interval between two hover positions requires fewer slots.
The present application further studies the average path loss performance for different Vmax and DBS numbers. The average D2U path loss is calculated in particular by using data collected when Vmax =30,50,70,90, 110m/slot. As shown in fig. 6, fig. 6 is an average D2U path loss diagram for different horizontal velocities of the drone base stations and the number of drone base stations, and specifically, given the same number of DBSs, the average path loss level decreases slightly as Vmax increases, it can be concluded that increasing the horizontal velocity and increasing the number of available DBSs can both improve the average D2U path loss performance of DBS trajectory planning, while increasing the number of available DBSs proves to be more effective than increasing the horizontal flight velocity.
There are two reasons why the present application addresses the guard distance constraint in the process of starting the scheduling of time slots:
first, feasible constraint set equations are non-convex with respect to trajectory-related variables, which optimization in a horizontal trajectory or height optimization problem significantly increases the complexity of the problem. In addition, by determining the protection distance constraint from the beginning, better average D2U path loss can be obtained for the unmanned aerial vehicle base station trajectory planning result compared with modifying those optimized trajectories. As the initial time slot scheduling is excluded from the optimization problem, the drone can take off randomly as long as constraints are ensured.
In one possible implementation, the drone base station trajectory is further configured to: for any timeslot, the straight-line distance between any two drone base stations is greater than or equal to a predefined guard distance.
A start-up algorithm for the drone, including a start-up time problem; for the start-up time problem, a greedy search algorithm is applied to iteratively schedule the start slots of all DBS di, as shown by the algorithm in the following calculation procedure, in each iteration di sets its start slot to nj in turn, and calculates the distance between di and the previously scheduled dk at each slot. If any start slot nj ensures a distance constraint that protects each slot, then the start slot of di is temporarily scheduled to nj and interrupted to di +1 iteration. If N on the track of di of all nj cannot guarantee the guard distance constraint, the algorithm discards the current and all previously scheduled DBSs and re-runs the first iteration of d1 with updated start slot N1= N1+1 until all di are scheduled to a feasible start position algorithm 1 stops. The calculation program of the starting algorithm is as follows:
1 generating initial set of timeslots for DBS
2:for
do
3:for 
do
4, setting the start time slot nj of di.
Calculating the distance between di and dk in all slots (d
) And di starts at nj.
And 6, if all the distances are greater than Zmin, interrupting.
7:end for
8:if
All distances of (2) are greater than Zmin
9:Set i = 1,n1 = n1+ 1.
10:end if
11:end for
Wherein Zmin is a predefined guard distance.
In this embodiment of the present application, corresponding constraint conditions are also set for the D2U communication scheduling scheme, so as to implement stability and continuity of communication, where the D2U communication scheduling scheme is configured to: in any time slot, one unmanned aerial vehicle base station only serves one ground service interest point; in all time slots, one ground service point of interest is associated with a unique drone base station.
In one possible implementation, the D2U communication scheduling scheme is further configured to: for any one drone base station, all time slots within a given period are equally allocated to each said ground service point of interest given the period and the number of ground service points of interest associated with said drone base station service. In a given period, ensuring that all the ground service interest points have the same service time so as to ensure the fairness among all the related ground service interest points;
in one possible implementation, the D2U communication scheduling scheme is further configured to: the sum of the time slots scheduled for each ground service interest point is greater than or equal to a predefined threshold value Smin, where Smin is the service time provided by the lowest drone base station for any ground service interest point. According to the set minimum service time and the given period, the number of the ground service interest points associated with each unmanned aerial vehicle base station is limited, and the problem that the service quality is influenced due to the fact that the service time of each ground service interest point is too short due to the fact that the number of the associated ground service interest points is too large is solved.
In one possible implementation, the D2U communication scheduling scheme is further configured to: all time slots scheduled to the same terrestrial service point of interest in a given period are contiguous. Under the condition that the time slots are ensured to be continuous, the ground service interest points can receive the demand content with smooth content display, and the stability of the distribution process is ensured by the continuity of the time slots while the content is distributed, so that the conditions of sudden interruption or content distribution delay and the like are avoided.
S005: and planning the unmanned aerial vehicle cluster scheduling scheme by using a k-means clustering algorithm introduced with parameters.
In the coverage range of the macro base station, a plurality of ground service interest points simultaneously request files from the macro base station, and the unmanned aerial vehicle base station clusters the ground service interest points according to the distribution of the ground service interest points. When an emergency occurs, the drone base station may serve a large number of aggregated ground service points of interest according to the algorithm proposed by the present invention to reduce the load on the core network and reduce user delay. In addition, because the line-of-sight channel of the unmanned aerial vehicle base station is flexible to deploy and high in probability, when the ground service interest point is not in the coverage range of the macro base station or the ground service interest point channel condition is poor, the unmanned aerial vehicle base station can serve as edge equipment to provide services, so that the service quality and the user experience are improved. The K-means method is a classical clustering algorithm and is used for clustering users and deploying the unmanned aerial vehicle according to the result, and the purpose is to minimize the distance from the ground service interest point to the unmanned aerial vehicle base station. In addition, according to the channel gain from the unmanned aerial vehicle base station to the ground service interest points, each unmanned aerial vehicle groups the ground service interest points, and non-orthogonal multiple access type ground service interest point matching is achieved. However, the K-means approach minimizes the distance between the drone base station and the ground service point of interest without considering the drone base station to macro base station distance. In order to consider the distances between the unmanned aerial vehicle base station and the ground service interest points and the macro base station together, the invention provides a K-means algorithm introducing distance parameters,
in the formula, rho is a distance factor, rho is more than or equal to 0 and less than or equal to 1, the distance from the unmanned aerial vehicle base station to the macro base station is reduced along with the increase of rho, and the distance from the unmanned aerial vehicle base station to the ground service interest point is increased. In the formula, (ii) (
，
) Is the coordinates of the drone base station in the system.
In an embodiment of the present application, a cellular mobile communication application scenario in which a macro Base Station (BS) fixed on the ground, a Base Station with multiple unmanned planes (DBS), and multiple ground service points of interest are located in a single cell is simulated through a simulation environment. The communication range of the ground base station radio covers the communication areas of all the unmanned aerial vehicle base stations. BS is located at the origin coordinate of the coordinate system (0,0,0), the fixed height h0 of DBS in the area is set to be 80m, and the side length of the grid is set to be 20m. In order to reduce interference to the ground service interest point, the 2.4GHz unlicensed frequency band is used as a carrier wave of D2U communication. And evaluating the system performance in the aspects of D2U path loss performance, energy efficiency and power consumption, and representing the system performance of the content distribution mechanism by using the file interruption probability. Based on a power allocation strategy in a non-orthogonal multiple access mode, when the data rate of the unmanned aerial vehicle decoding file is less than the requirement of the service quality of the decoding file, the transmission of the decoding file is regarded as interruption. File outage probability is defined as the average outage probability of each file across all drones. In content distribution, the power saving percentage of the unmanned aerial vehicle base station is applied to measure the performance of the power distribution strategy in the non-orthogonal multiple access ground service interest points. Within the unmanned aerial vehicle base station transmitting power range set by the invention, the ground service interest points can be successfully transmitted all the time. Therefore, the aim of the service quality of the parameter-introduced K-means algorithm is to shorten the distance between the unmanned aerial vehicle base station and the macro base station while ensuring the successful transmission of the ground service interest point.
The influence of different initial trajectories on the realized average D2U path loss performance is further analyzed in the embodiment of the application. As shown in fig. 7, fig. 7 is a comparison diagram of initial trajectories of the unmanned aerial vehicle fleet in the present application, specifically, the present application compares four initial trajectories in total, that is:
1) Initial trajectory of the center position determined by k-means of the incoming parameters.
2) The trajectory converges to a hover point, the point location being determined by the k-means of the imported parameters.
3) Circular initial trajectories evenly distributed with a central position.
4) And (5) point initial tracks, wherein the point positions are uniformly distributed.
Fig. 7 shows the comparison results. It can be seen from the results that the initial trajectory generated based on the introduced parameter k-means used in the proposed algorithm achieves the minimum average D2U path loss.
S006: and associating the unmanned aerial vehicle base station with the ground service interest point according to the planned unmanned aerial vehicle starting algorithm and the planned unmanned aerial vehicle group scheduling scheme, wherein a D2U link is formed after the association between the unmanned aerial vehicle base station and the ground service interest point.
S007: and a non-orthogonal multiple access mode is used in the link transmission process of the D2U link, and the required content is distributed through the D2U link.
Conventional Access technologies can only allocate a single radio resource to one user, and a Non-Orthogonal Multiple Access (NOMA) method can allocate a resource to Multiple users. NOMA adopts non-orthogonal transmission at a transmitting end, actively introduces Interference information, realizes correct demodulation and multi-user detection at a receiving end by Serial Interference Cancellation (SIC) technology, and can obtain higher spectrum efficiency. SIC eliminates multiple access interference at a receiving end, and needs to judge users in a received signal to discharge the sequence of users eliminating the interference, and the judgment basis is the signal power of the users. The base station distributes different signal powers to different users at the transmitting end to obtain the maximum performance gain of the system, and simultaneously, the purpose of distinguishing the users is achieved, namely, the power multiplexing technology. In the research background of the invention, compared with the traditional method, the non-orthogonal access multiple access mode adopting power multiplexing has obvious performance advantage.
The trajectory planning algorithm provided by the application is compared with the average D2U path loss performance realized by the static DBS deployment scheme. As shown in fig. 8, fig. 8 is a graph comparing the average D2U path loss for trajectory planning and static deployment for multiple drone base stations; the average D2U path loss for both algorithms decreases as the number of available DBSs increases. In comparison of the number of the unmanned aerial vehicles, the D2U path loss performance generated by the trajectory planning algorithm provided by the invention is more efficient than that of a static algorithm.
As shown in fig. 9, fig. 9 is a graph of transmission power versus energy consumption of the system, and it can be seen from fig. 9 that the energy saving performance of the system gradually increases with the increase of the transmission power. The increase of the interest points of the ground service can reduce the energy saving performance to a certain extent, and when the number of users increases from 20 to 30, the energy consumption of the unmanned aerial vehicle base station increases. According to the experimental result obtained according to the specific data in the embodiment, the content distribution method has the advantages of low path loss, high energy saving performance and better channel gain, so that the content distribution process is more stable and efficient.
It can be seen from the foregoing embodiments that the present application provides a cellular mobile base station downlink content distribution system assisted by a drone, including: a macro base station, an unmanned aerial vehicle base station and a ground service interest point; and a drone-assisted cellular mobile base station downlink content distribution method, comprising the steps of: acquiring basic information in a content distribution system area; acquiring a horizontal distance value from any unmanned aerial vehicle base station to a macro base station and a horizontal distance value from any ground service interest point to the macro base station in the content distribution system area; calculating to obtain a first path loss from the unmanned aerial vehicle base station to the macro base station and a second path loss from the ground service interest point to the macro base station in a single time slot according to the horizontal distance value from the unmanned aerial vehicle base station to the macro base station and the horizontal distance value from the ground service interest point to the macro base station; planning an unmanned aerial vehicle starting algorithm according to the first path loss and the second path loss; planning a dispatching scheme of the unmanned aerial vehicle cluster by using a k-means clustering algorithm introducing parameters; associating the unmanned aerial vehicle base station with the ground service interest points according to the planned unmanned aerial vehicle starting algorithm and the planned unmanned aerial vehicle group scheduling scheme, and forming a D2U link after the association between the unmanned aerial vehicle base station and the ground service interest points; and a non-orthogonal multiple access mode is used in the link transmission process of the D2U link, and the required content is distributed through the D2U link. Compared with the prior art, the method and the device have the advantages that the high maneuverability and the sight distance transmission characteristic of the unmanned aerial vehicle are utilized, so that the wireless connection is more stable and reliable; the clustering algorithm is utilized to optimize the connection between the base station of the unmanned aerial vehicle and the ground user, so that the transmission is more efficient; the method proves that the ground user request in the cache of the mobile base station can be stably and efficiently distributed through simulation results.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A drone-assisted cellular mobile base station downlink content distribution system, comprising: a macro base station, an unmanned aerial vehicle base station and a ground service interest point;
the macro base station is configured to receive the demand of the ground service interest point, forward the demand to the unmanned aerial vehicle base station through a public or private network, receive a request sent by the unmanned aerial vehicle base station, acquire demand content corresponding to the request from a core network, and send the demand content to the unmanned aerial vehicle base station;
the unmanned aerial vehicle base station is configured to receive the requirement of the ground service interest point, send a request for acquiring core network content to the macro base station according to the requirement, and send the acquired requirement content to the ground service interest point through a D2U link;
the ground service interest point is configured to send the demand to the macro base station through a public or private network and receive the demand content sent back by the unmanned aerial vehicle base station.
2. An unmanned aerial vehicle-assisted cellular mobile base station downlink content distribution method, characterized by comprising the following steps:
acquiring basic information in a content distribution system area, wherein the basic information comprises: the method comprises the following steps that a macro base station position, the fixed height of an unmanned aerial vehicle base station, the available flight area of the unmanned aerial vehicle base station, the D2U communication carrier frequency band and the origin point coordinate calculated by taking the macro base station position as a method are obtained;
acquiring a horizontal distance value from any unmanned aerial vehicle base station to a macro base station and a horizontal distance value from any ground service interest point to the macro base station in the content distribution system area;
calculating a first path loss from the unmanned aerial vehicle base station to the macro base station and a second path loss from the ground service interest point to the macro base station in a single time slot according to a horizontal distance value from the unmanned aerial vehicle base station to the macro base station and a horizontal distance value from the ground service interest point to the macro base station, wherein the time slot is a unit time for the unmanned aerial vehicle base station to provide service for any one ground service interest point;
planning an unmanned aerial vehicle starting algorithm according to the first path loss and the second path loss;
planning a dispatching scheme of the unmanned aerial vehicle cluster by using a k-means clustering algorithm introducing parameters;
associating the unmanned aerial vehicle base station with the ground service interest point according to the planned unmanned aerial vehicle starting algorithm and the planned unmanned aerial vehicle group scheduling scheme, wherein a D2U link is formed after the unmanned aerial vehicle base station is associated with the ground service interest point;
and a non-orthogonal multiple access mode is used in the link transmission process of the D2U link, and the required content is distributed through the D2U link.
3. The drone-assisted cellular mobile base station downlink content distribution method of claim 2,
the planning drone start algorithm includes: planning the unmanned aerial vehicle base station track and formulating a D2U communication scheduling scheme.
4. The drone-assisted cellular mobile base station downlink content distribution method of claim 3, wherein the drone base station trajectory is configured to:
each drone base station needs to return to its initial position at the end of each cycle, i.e. the trajectory of each drone base station is a closed curve in the plane space.
5. The drone-assisted cellular mobile base station downlink content distribution method of claim 3, wherein the drone base station trajectory is configured to:
in any time slot, the horizontal displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum horizontal distance, and the vertical displacement of the unmanned aerial vehicle base station is smaller than or equal to a preset maximum height difference.
6. The drone-assisted cellular mobile base station downlink content distribution method of claim 3, wherein the drone base station trajectory is configured to:
for any timeslot, the straight-line distance between any two drone base stations is greater than or equal to a predefined guard distance.
7. The drone-assisted cellular mobile base station downlink content distribution method according to claim 3, wherein the D2U communication scheduling scheme is further configured to:
in any time slot, one unmanned aerial vehicle base station only serves one ground service interest point; in all time slots, one ground service point of interest is associated with a unique drone base station.
8. The drone-assisted cellular mobile base station downlink content distribution method according to claim 3, wherein the D2U communication scheduling scheme is further configured to:
for any one drone base station, all time slots within a given period are allocated equally to each of the ground service points of interest given the period and the number of ground service points of interest associated with the drone base station service.
9. The drone-assisted cellular mobile base station downlink content distribution method according to claim 3, wherein the D2U communication scheduling scheme is further configured to:
the sum of the time slots scheduled for each ground service interest point is greater than or equal to a predefined threshold value Smin, where Smin is the service time provided by the lowest drone base station for any ground service interest point.
10. The drone-assisted cellular mobile base station downlink content distribution method according to claim 3, wherein the D2U communication scheduling scheme is further configured to:
all time slots scheduled to the same terrestrial service point of interest in a given period are contiguous.
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