[0017]In order to make the purpose and advantages of the present invention clearer, the following further describes the present invention in conjunction with the embodiments; it should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0018]The preferred embodiments of the present invention will be described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.
[0019]It should be noted that in the description of the present invention, the terms "upper", "lower", "left", "right", "inner", "outer" and other terms indicating directions or positional relationships are based on the attached drawings. The direction or position relationship shown is only for ease of description, and does not indicate or imply that the device or element must have a specific orientation, be configured and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.
[0020]In addition, it should be noted that in the description of the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it may be a fixed connection or It is a detachable connection or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For those skilled in the art, the specific meaning of the above-mentioned terms in the present invention can be understood according to specific circumstances.
[0021]Seefigure 1 As shown, the method for optimizing service quality of far-reaching sea information based on drone network provided by the embodiment of the present invention includes:
[0022]S100: Establish an inventive embodiment of a hybrid network based on maritime base stations and air base stations. The air base stations include L standby base stations, and at least one of the L standby base stations is selected and added to the maritime base station according to a greedy algorithm, Form the first hybrid network;
[0023]S200: Calculate the optimal deployment height of the aerial base station in the first hybrid network by using a heuristic algorithm;
[0024]S300: After the altitude deployment of the air base station is completed, adjust the horizontal position of the air base station according to the actual position of the sea user, and move it to the center point of the served sea user to achieve optimal coverage.
[0025]The embodiment of the present invention further deploys UAV base stations on the sea where marine base stations are deployed to improve the coverage and overall capacity of the marine information network. In order to achieve efficient joint deployment of UAV base stations, a greedy-based algorithm is designed The horizontal deployment location optimization strategy of multi-UAV base stations effectively enhances the information coverage of sparse areas of maritime base stations. On the basis of improving the regularity of base station deployment space, it maximizes the network coverage and user service quality. The drone covers the invention embodiments and the characteristics of actual ship user distribution, and further optimizes and adjusts the height and horizontal position of the drone base station.
[0026]Specifically, a hybrid network composed of M maritime base stations V={v1,v2,...,vM} and N drone base stations U={u1,u2,...,uN} is set. Such asfigure 2As shown, the transmission power of each maritime base station using an omnidirectional antenna is PB, and the transmission power of each aerial base station directional antenna is PU. According to the invention embodiment of the path loss of electromagnetic wave propagation on the sea surface, the path between the maritime base station and the ship user Loss PLB, according to the embodiment of the sea channel transmission invention, under the condition that the height of the buoy base station and the shipborne antenna are 1.7m and 9.8m, respectively, if the C-band is used as a common transmission frequency band at sea, d0=600m corresponding to PLB(d0) =91.51dB, and n represents the path loss coefficient. In the embodiment of the present invention, n=4.58, and χσ obeys a normal distribution with a mean and variance of (0,σ2), where σ=3.49. The inventive embodiment of the path loss from the air to the sea channel is similar to it, and the path loss PLU(d) is obtained. For the inventive embodiment of this loss, when the C-band is used for transmission, the transmission reference distance d0=2600m corresponds to PLB(d0)=116.4dB, path loss coefficient n=1.6, and normal distribution standard deviation σ=2.7, in addition, D(θ) is the transmission directivity correction coefficient, where θ is the difference between the antenna pointing and the actual transmission direction Included angle, for all ship users, it is assumed that their receiving equipment and service quality requirements are the same. Considering the distribution characteristics of marine ship users, in the embodiment of the present invention, a two-dimensional Poisson Point Process (PPP) is used to model the distribution of marine ship users, and the number of ship users per unit area conforms to the Poisson distribution. .
[0027]According to actual scenarios, the spatial distribution of the original maritime base stations has various situations. First, if the offshore base stations are deployed in a unified plan, their distribution is generally uniform and regular. In this case, the Delaunay triangulation is divided into regular triangular grids. However, if the offshore base stations are deployed completely randomly and the regularity of their spatial distribution is very low, the two-dimensional Poisson point process can be directly used for simulation. A more common situation is that in the deployment process of offshore base stations, there are base stations that are uniformly planned and deployed, and there are also base stations that are randomly deployed, or the base stations are positionally shifted due to factors such as sea waves after unified planning and deployment. In this case, you can use the Perturbed Triangular Lattice (PTL) method for simulation. First, a regular triangular grid is generated, and then each point in the uniform triangular grid is randomly displaced within a certain radius. Freely adjust the spatial regularity of base station distribution.
[0028]In the embodiment of the present invention, the purpose of deploying UAV base stations is to improve network service quality. Therefore, in order to evaluate the effectiveness of UAV deployment methods, the deployment methods can be compared according to the regularity of the deployment space of base stations in the network, the user’s signal-to-interference ratio and the overall capacity of the network system to measure the coordination of UAVs and maritime base stations. In order to improve the network coverage effect of the network service quality, base stations should be distributed as evenly and regularly in space as possible in the joint deployment of base stations. Therefore, the spatial regularity of the base station deployment positions can be used for evaluation. The spatial regularity of base station deployment can be statistically analyzed with the help of the Voronoi diagram of the deployment range of the base station and the Delaunay triangulation diagram of the corresponding base station location. In statistics, the coefficient of variation (CoV) of a statistic can be used, that is, the ratio of the standard deviation σ of a random quantity to the average value to measure its regularity. There are many options for specific statistics.
[0029]First, the cell area in the Voronoi diagram of the base station deployment range can be used for measurement. Secondly, the triangle side length in the Delaunay triangulation diagram corresponding to the Voronoi diagram of the base station deployment range can be used to measure. The distance between the nearest neighboring base stations is measured. For the above three spatial regularity calculation formulas, C=0 can be obtained when the position of the base station is distributed regularly and equidistantly, and when the position of the base station obeys the two-dimensional Poisson point process distribution, it can be obtained C=1, under normal circumstances its value is between 0 and 1. In actual calculations, the results of the three methods are relatively similar. Therefore, in the embodiment of the present invention, the coefficient of variation CD corresponding to the triangle side length in the Delaunay triangulation diagram is mainly selected as the spatial regularity index of the space base station deployment for simulation and testing.
[0030]In a hybrid network composed of maritime base stations and drone base stations, since the base stations use the same frequency band for communication and cause interference, they can be measured by calculating the user’s SINR. In the embodiment of the present invention, the median of all maritime users’ SINR is used. As a measure of network quality. In a hybrid network, if the communication rate of ship user i is Ci, and the total capacity of this network system is the sum of the rates of all users in the system, in the embodiment of the present invention, the network after the deployment of UAV base stations is obtained After the system capacity, the original network system capacity with only maritime base stations is used to normalize it, and the normalized network system capacity is used to measure the network service quality.
[0031]It is required to solve the joint deployment problem of UAV base stations and maximize the network service quality. It is necessary to optimize the plane position and height of UAV base stations. In view of the complexity of the problem, the original problem can be disassembled into two sub-problems to be solved separately. Optimize the horizontal deployment position of UAV base stations based on the spatial regularity of the base station coverage. After determining the horizontal deployment position, adjust the deployment height according to the actual coverage and user distribution. According to the above analysis, the level of the UAV base station The deployment needs to maximize the spatial regularity of the network. In order to avoid uneven spatial distribution, the new base station should be as far away from the existing base station as possible, so it can be achieved by maximizing the sum of the distances between all base stations, because the problem is more complicated , So it is simplified to select a part of the UAVs at a given location to get the approximate optimal solution.
[0032]First, for a given set of maritime base station locations A={a1,a2,……,aM}, and a set of alternative deployment locations of UAV base stations C={c1,c2,……,cL}, where L is The number of alternative deployment locations for UAV base stations. Therefore, the optimization problem of the horizontal deployment position of UAVs can be expressed as selecting N locations in the position set C to deploy UAVs in order to maximize the spatial regularity of the network. Let D=A∪C={a1,a2,……,aM,c1,c2,……,cL} be the union of set A and set C, and I is the index set of the set. In the constraint condition, the first One constraint indicates that the total number of base stations is M+N, and the last constraint ensures that all original maritime base stations need to appear in the solution. It is difficult to obtain an optimal solution when the number of base stations is large. Therefore, the embodiment of the present invention proposes a heuristic method based on a greedy algorithm to obtain an approximate optimal solution. The main variables include the currently selected base station location set B and the currently selected drone location set S. In the algorithm, set B is initialized by the position A of the maritime base station, and then in each iteration, a new UAV base station's horizontal position that is the farthest from all points in the solution set is added to the solution set until the solution set S reaches The number of air base stations required. Among them, the distance dist(ai,aj)=|ai-aj| represents the Euclidean distance between the positions ai and aj, and the distance dist(ai,B) represents the Euclidean distance between the position ai and the nearest position in the position set B, which is The deployment position of the base station in the air selected by the algorithm can be found that by reasonably deploying the base station in the air, the spatial regularity of the base station in the maritime network has been significantly improved, showing the effectiveness of the algorithm.
[0033]After determining the horizontal deployment position of the base station in the air, it is also necessary to optimize the deployment height of each base station in the air to achieve the best coverage and improve the quality of service for ship users. Here, H={h1,h2,……,hP} represents the air Base station deployment height. Considering that the interference to the user to the base station will change with the actual location of the user, it is difficult to accurately solve this problem. Therefore, the coverage of the air base station ui can be assumed to be a circular area, and its coverage radius Ri is equal to the base station. Half of the horizontal distance from the nearest neighbor base station. In this case, if users are randomly distributed in the coverage area, it can be considered that the optimal deployment height hi is only related to the coverage radius Ri and the optimal angle φopt between the air base station and the edge of the coverage area and the horizontal plane, where, 0
[0034]In the embodiment of the present invention, it is assumed that K=200 users are distributed in an open sea area with an area of 20km×20km. This area is equipped with M=20 maritime base stations. The average distance between the maritime base stations is 5km. For the influence of the simulation results, only the user data within the 15km×15km range of the center of the sea area is counted in the calculation. For maritime base stations and aerial drone base stations, assume that the transmit power PB=PU=10W, the path loss invention embodiments and parameters are as described above, the Gaussian white noise power density in the environment is -174dB/Hz, and the frequency band used by the communication equipment The center frequency is 5.8GHz and the bandwidth is 50kHz.
[0035]In the experiment, the change of network service quality is observed by gradually adjusting the number of drones N. In the result, β=N/M is used to represent the ratio of the number of base stations in the air to the number of base stations on the sea.
[0036]In addition, in order to verify the effectiveness of the proposed deployment scheme, two other methods are used for comparison. In the simulation results, scheme one represents the method described above, and scheme two represents the use of horizontal deployment scheme to deploy air base stations, but all air base stations are fixed They are at the same height, and the horizontal position is not re-adjusted according to the user's position. Solution 3 represents randomly deploying the horizontal positions of all air base stations and fixing them at the same height. In the latter two schemes, the height of the base station in the air is set to the average value of the height of all base stations in the air solved in scheme 1. In practical applications, the number of air base stations that need to be deployed can be estimated based on the original base station distribution and the required network service quality indicators. For example, for a maritime buoy network with a coefficient of variation CD=0.75 in the embodiment of the present invention, if the network capacity needs to be increased by 1.5 times, only the number of additional aerial drones 0.8 times the original number of maritime buoys needs to be deployed Base stations to reduce deployment costs and maximize deployment efficiency.
[0037]So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the drawings. However, those skilled in the art will readily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.
[0038]The above descriptions are only preferred embodiments of the present invention, and are not used to limit the present invention; for those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc., made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
