CN112188515B - Deep and distant sea information service quality optimization method based on unmanned aerial vehicle network - Google Patents
Deep and distant sea information service quality optimization method based on unmanned aerial vehicle network Download PDFInfo
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Abstract
The invention relates to a deep ocean information service quality optimization method based on an unmanned aerial vehicle network, which comprises the following steps: the method comprises the steps of establishing a hybrid network based on an offshore base station and an aerial base station, wherein the aerial base station comprises L standby position base stations, and selecting at least one standby position base station from the L standby position base stations according to a greedy algorithm to be added into the offshore base station to form a first hybrid network; calculating the optimal deployment height of the aerial base station in the first hybrid network by using a heuristic algorithm; and after the height deployment of the aerial base station is finished, adjusting the horizontal position of the aerial base station according to the actual position of the offshore user, and moving the aerial base station to the central point of the served offshore user. By further deploying the unmanned aerial vehicle base station on the sea surface where the offshore base station is deployed, the coverage range and the whole capacity of the offshore information network are improved, and the information coverage of the offshore base station sparse area is effectively enhanced.
Description
Technical Field
The invention relates to the technical field of network communication, in particular to a deep ocean information service quality optimization method based on an unmanned aerial vehicle network.
Background
In recent years, the development of the marine industry has been receiving increasing attention from countries around the world. In order to meet the major development requirements of various marine industries, a perfect marine information network must be built so as to realize seamless, efficient and reliable information coverage in a wide sea area. However, compared with the more developed land-based information network, the marine information network has great differences in electromagnetic environment, user distribution, node performance, etc., and communication and network resources such as energy, bandwidth, storage, computation, etc. are relatively in short supply, so that these factors severely limit the service efficiency and capability, i.e., network performance, of the marine information network.
In the ocean information network, in order to connect ocean and land-based communication, a large number of deployed communication nodes including a maritime communication base station, an AIS navigation aid node, a sensor buoy and the like float on the sea surface, and the nodes can provide information services such as positioning navigation, information broadcasting, broadband internet surfing and the like for ocean vessel users. However, in open sea areas, the offshore infrastructure is still scarce, and the global sea seamless coverage is difficult to realize by a small number of offshore base stations. In addition, due to marine development activities or emergency rescue and emergencies, ship users sometimes gather in a specific sea area, resulting in overload of network capacity based on a marine base station. Therefore, in order to offload the load of the offshore base station or provide seamless information coverage in the area with incomplete coverage of the marine network, the air may be deployed as an on-demand air access point to establish a temporary air network, and the air network and the offshore base station jointly form a heterogeneous network to carry the suddenly increased network requirements. However, how to deploy an air network in an original offshore base station network to solve the problem of network capacity overload is still continuously explored.
Disclosure of Invention
Therefore, the invention provides a deep ocean information service quality optimization method based on an unmanned aerial vehicle network, and the optimization of the network service quality is realized.
In order to achieve the above object, the present invention provides a method for optimizing the quality of service of information in deep ocean based on an unmanned aerial vehicle network, comprising: establishing a hybrid network based on an offshore base station and an aerial base station, wherein the aerial base station comprises L standby position base stations, and selecting at least one standby position base station from the L standby position base stations according to a greedy algorithm to join the standby position base stations into the offshore base station to form a first hybrid network; calculating the optimal deployment height of the aerial base station in the first hybrid network by utilizing a heuristic algorithm; and after the height deployment of the aerial base station is finished, adjusting the horizontal position of the aerial base station according to the actual position of the offshore user, and moving the aerial base station to the central point of the served offshore user to realize optimal coverage.
Further, the marine base stations are drone base stations, the marine base stations include M marine base stations, a set of positions of the marine base stations a is { a1, a2, … …, aM }, and a set of alternative deployment positions of the drone base stations C is { C1, C2, … …, cL }, where L is the number of the alternative deployment positions of the drone base stations, N drone deployment positions are selected from the set of positions C, and the first hybrid network includes M marine base stations and N drone base stations.
Further, a currently selected position set B of the offshore base stations and a currently selected position set S of the unmanned aerial vehicles are obtained, the position set B in the greedy algorithm is initialized by the position A of the offshore base stations, and then in each iteration, the horizontal positions of the unmanned aerial vehicle base stations which are farthest away from all points in the solution set are added to the solution set until the solution set S reaches the required number of the aerial base stations.
Further, in the offshore base station, 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, that is, the distance between the position ai and the nearest position in the position set B
Further, H ═ H1, H2, … …, hP denotes the air base station deployment height, the coverage area of the air base station ui is set to a circular area, the coverage radius Ri of which is equal to half of the horizontal distance between the base station and the nearest neighbor base station, if the users are randomly distributed in the coverage area, the optimal deployment height hi can be considered to be only related to the coverage radius Ri and the optimal included angle Φ opt between the edge connecting line of the air base station and the coverage area and the horizontal plane, that is:
hi ═ α × Ri tan (Φ opt), where 0< α ≦ 1 represents a normalization coefficient for the air base station height.
Further, the change of the network service quality is observed by gradually adjusting the number N of the unmanned aerial vehicle base stations, the ratio of the number of the aerial unmanned aerial vehicle base stations to the number of the original offshore base stations is represented by using the value of beta-N/M, and the coverage of the marine network is improved by the joint deployment position obtained when the number N of the aerial unmanned aerial vehicle base stations is 20, namely the value of beta-1.
Further, the network service quality includes the spatial-based regularity of the network, the user SINR and the overall network capacity.
Further, the distribution of marine vessel users is modeled by using a two-dimensional poisson point process, and the number of the vessel users in unit area accords with poisson distribution.
Compared with the prior art, the unmanned aerial vehicle base station horizontal deployment position optimization method has the advantages that the unmanned aerial vehicle base station is further deployed on the sea surface where the offshore base station is deployed to improve the coverage range and the overall capacity of an ocean information network, a multi-unmanned aerial vehicle base station horizontal deployment position optimization strategy based on a greedy algorithm is designed for achieving efficient combined deployment of the unmanned aerial vehicle base stations, information coverage of sparse areas of the offshore base stations is effectively enhanced, network coverage and user service quality are improved to the maximum extent on the basis of improvement of base station deployment space regularity, and the height and the horizontal position of the unmanned aerial vehicle base station are further optimized and adjusted on the basis of the embodiment of the invention of offshore unmanned aerial vehicle coverage and the characteristics of actual ship user distribution.
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Fig. 1 is a schematic flow chart of a deep ocean information service quality optimization method based on an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 2 is a schematic network structure diagram of a deep ocean information service quality optimization method based on an unmanned aerial vehicle network according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described in conjunction with the following examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a method for optimizing the quality of service of information in deep ocean based on an unmanned aerial vehicle network according to an embodiment of the present invention includes:
s100, establishing a hybrid network based on an offshore base station and an aerial base station, wherein the aerial base station comprises L standby position base stations, and selecting at least one standby position base station from the L standby position base stations according to a greedy algorithm to join the standby position base stations into the offshore base station to form a first hybrid network;
s200, calculating the optimal deployment height of the aerial base station in the first hybrid network by using a heuristic algorithm;
s300, after the height deployment of the aerial base station is completed, the horizontal position of the aerial base station is adjusted according to the actual position of the offshore user, and the aerial base station is moved to the central point of the served offshore user to realize the optimal coverage.
The embodiment of the invention further deploys the unmanned aerial vehicle base station on the sea surface where the offshore base station is deployed to improve the coverage area and the overall capacity of the ocean information network, designs a multi-unmanned aerial vehicle base station horizontal deployment position optimization strategy based on a greedy algorithm for realizing efficient combined deployment of the unmanned aerial vehicle base stations, effectively enhances the information coverage of sparse areas of the offshore base stations, furthest improves the coverage and the user service quality of the network on the basis of improving the regularity of the deployment space of the base stations, and further optimizes and adjusts the height and the horizontal position of the unmanned aerial vehicle base station on the basis of the embodiment of the invention for the coverage of the offshore unmanned aerial vehicle and the characteristics of actual ship user distribution.
Specifically, a hybrid network is set, which is composed of M maritime base stations V ═ { V1, V2, … …, vM } and N drone base stations U ═ { U1, U2, … …, uN }. As shown in fig. 2, where the transmission power of each offshore base station using an omnidirectional antenna is PB, the transmission power of each aerial base station directional antenna is PU, according to an embodiment of the invention of path loss of sea surface electromagnetic wave propagation, the path loss PLB between an offshore base station and a ship user, according to an embodiment of the invention of sea surface channel transmission, under the condition that the heights of a buoy base station and a ship-borne antenna are 1.7m and 9.8m, respectively, if a C-band is used as an offshore common transmission band, d0 is 91.51dB for 600m (d0), and n represents a path loss coefficient, in the embodiment of the invention, n is 4.58, and σ is subject to a normal distribution with a mean value and a variance of (0, σ 2), where σ is 3.49. Similarly, the embodiment of the invention of the path loss from air to offshore channel obtains the path loss plu (D), and for this embodiment of the invention, when the C-band is used for transmission, the reference distance D0 of transmission is 2600m corresponding to PLB (D0) is 116.4dB, the path loss coefficient n is 1.6, and the standard deviation σ of normal distribution is 2.7, and furthermore, D (θ) is a transmission directivity correction coefficient, where θ is the angle between the antenna pointing direction and the actual transmission direction, and for all ship users, it is assumed that their receiving devices and service quality requirements are the same. In consideration of the distribution characteristics of marine vessel users, in the embodiment of the invention, a two-dimensional Poisson Point Process (PPP) is used for modeling the marine vessel user distribution, and the number of the vessel users in a unit area accords with the Poisson distribution.
According to the actual scene, the spatial distribution of the original offshore base station has various conditions. First, if the offshore base stations are uniformly planned and distributed, the distribution of the offshore base stations is generally uniform and regular, and in this case, the Delaunay triangulation is divided into regular triangular meshes. If the offshore base stations are completely randomly distributed and the regularity of spatial distribution is low, the two-dimensional poisson point process can be directly used for simulation. More commonly, in the deployment process of the offshore base station, both the base stations which are planned and arranged in a unified way and the base stations which are arranged randomly exist, or the base stations are subjected to position deviation due to factors such as sea waves and the like after being planned and arranged in a unified way. For this situation, a method of a disturbed Triangular mesh (PTL) may be used to perform simulation, and a regular Triangular mesh is first generated, and then each point in the uniform Triangular mesh is randomly displaced within a certain radius to freely adjust the spatial regularity of the base station distribution.
In the embodiment of the invention, the purpose of deploying the unmanned aerial vehicle base station is to improve the network service quality. Therefore, in order to evaluate the effectiveness of the unmanned aerial vehicle deployment method, the deployment method can be compared according to the deployment spatial regularity of the base stations in the network, the signal-to-interference ratio of users and the overall capacity of the network system, so as to measure the service quality of the cooperative network of the unmanned aerial vehicle and the offshore base stations, and in the joint deployment of the base stations, the base stations should be distributed as uniformly and regularly as possible in the space in order to improve the coverage effect of the network, so that the spatial regularity of the deployment positions of the base stations can be used for evaluation. The spatial regularity of the base station deployment can be statistically analyzed by means of a Voronoi diagram of the base station deployment range and a Delaunay triangulation diagram of the base station position corresponding to the Voronoi diagram. In statistics, the regularity of a statistic can be measured by the Coefficient of Variation (CoV), i.e., the ratio of the standard deviation σ of the random quantity to the mean, and there are many choices for the specific statistic.
Firstly, the area of a cell in a Voronoi diagram of a base station deployment range can be used for measurement, secondly, the side length of a triangle in a Delaunay triangular subdivision diagram corresponding to the Voronoi diagram of the base station deployment range can be used for measurement, and finally, the distance between each base station and the nearest base station can be used for measurement, for the three spatial regularity calculation formulas, C is 0 when the base station positions are regularly and equidistantly distributed, and C is 1 when the base station positions are distributed according to a two-dimensional cedar point process, and the value is between 0 and 1 under a general condition. In actual calculation, the results of the three methods are similar, so that the variation coefficient CD corresponding to the side length of the triangle in the Delaunay triangulation graph is mainly selected as the spatial regularity index for spatial base station deployment in the embodiment of the invention for simulation and test.
In a hybrid network formed by the offshore base station and the unmanned aerial vehicle base station, the base station uses the same frequency band for communication and causes interference, so the SINR of users can be calculated for measurement. In the embodiment of the invention, after the capacity of a network system after an unmanned aerial vehicle base station is deployed is obtained, the original capacity of the network system only having a marine base station is used for normalizing the capacity, and the normalized network system capacity is used for measuring the network service quality.
In order to solve the problem of unmanned aerial vehicle base station joint deployment, maximize network service quality, optimize the plane position and height of an unmanned aerial vehicle base station, and solve the original problem into two sub-problems respectively in view of complexity of the problem, firstly optimize the horizontal deployment position of the unmanned aerial vehicle base station based on the spatial regularity of the coverage area of the base station, after determining the horizontal deployment position, adjust the deployment height according to the actual coverage area and the user distribution condition, according to the analysis, the horizontal deployment of the unmanned aerial vehicle base station needs to maximize the spatial regularity of the network, and in order to avoid the nonuniformity of spatial distribution, the newly added base station should be far away from the existing base station as possible, so that the problem can be realized by maximizing the sum of distances between all base stations, and the problem is complicated, so that a part is selected to deploy the unmanned aerial vehicle in a set position point in a centralized manner, to obtain an approximately optimal solution.
First, for a given set of marine base station locations a ═ a1, a2, … …, aM, and set of alternative deployment locations C of drone base stations ═ C1, C2, … …, cL, where L is the number of drone base station alternative deployment locations. So the optimization problem of the horizontal deployment position of the unmanned plane canThe expression is to select N positions in the position set C to deploy the drone, so as 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, I be the index set of the set, in the constraint condition, the first constraint indicates that the total number of base stations is M + N, and the last constraint ensures that all the original base stations at sea need to appear in the solution. When the number of the base stations is large, the optimal solution is difficult to obtain, so that the embodiment of the invention provides a heuristic method based on a greedy algorithm to obtain the approximate optimal solution. The main variables comprise a currently selected base station position set B and a currently selected unmanned aerial vehicle position set S. In the algorithm, a set B is initialized by the position A of the offshore base station, and then in each iteration, the horizontal position of a new unmanned aerial vehicle base station which is farthest away from all the points in the solution set is added to the solution set until the solution set S reaches the required number of airborne base stations. Where distance dist (ai, aj) ═ ai-aj represents the Euclidean distance between locations ai and aj, and distance dist (ai, B) represents the Euclidean distance between location ai and the nearest location in location set B, i.e., the distance between location ai and the nearest location in location set B
The deployment position of the aerial base station selected by the algorithm can show that the spatial regularity of the base station in the offshore network is obviously improved by reasonably deploying the aerial base station, and the effectiveness of the algorithm is displayed.
After the horizontal deployment position of the air base station is determined, the deployment height of each air base station needs to be optimized to achieve the best coverage and improve the service quality of ship users, wherein H ═ { H1, H2, … …, hP } is used for representing the deployment height of the air base station. Considering that the interference to the base station from the user varies with the actual location of the user, it is difficult to solve the problem accurately, and therefore the coverage area of the air base station ui may be assumed to be a circular area with a coverage radius Ri equal to half of the horizontal distance between the base station and the nearest neighbor base station. Under the condition, if users are randomly distributed in a coverage area, the optimal deployment height hi is only related to a coverage radius Ri and an optimal included angle phi opt between an edge connecting line of an air base station and the coverage area and a horizontal plane, wherein alpha is more than 0 and less than or equal to 1 and represents a normalized coefficient of the height of the air base station, the interference of the air base station on the users in other surrounding cells is weakened by properly reducing the actual deployment height of the air base station, and the parameter and the optimal included angle phi opt can be subjected to simulation according to actual environmental parameters and base station density and solved by a heuristic method. After the height deployment is finished, the horizontal position of the air base station can be further adjusted according to the actual position of the user, and the air base station is moved to the central points of all the users to be served, so that the optimal coverage is realized.
In the embodiment of the invention, it is assumed that K is 200 users distributed on an open sea area with an area of 20km × 20km, the area is provided with M is 20 offshore base stations, the average distance between the offshore base stations is 5km, and in order to eliminate the influence of a boundary area on a simulation result, only user data in a range of 15km × 15km at the center of the sea area is counted during calculation. For the offshore base station and the aerial unmanned aerial vehicle base station, assuming that the transmission power PB is 10W, the path loss embodiments and parameters are as described above, the gaussian white noise power density in the environment is-174 dB/Hz, the center frequency of the frequency band used by the communication device is 5.8GHz, and the bandwidth is 50 kHz.
In the experiment, the change of the network service quality is observed by gradually adjusting the number N of the drones, and in the result, the ratio of the number of the aerial base stations to the number of the original offshore base stations is represented by using β ═ N/M.
In addition, in order to verify the effectiveness of the proposed deployment scheme, another two methods are used for comparison, and in the simulation result, the scheme one represents the method described above, the scheme two represents that the air base stations are deployed by using the horizontal deployment scheme, but all the air base stations are fixed at the same height, and the horizontal positions are not readjusted according to the user positions, and the scheme three represents that the horizontal positions of all the air base stations are randomly deployed and fixed at the same height. In the latter two schemes, the height of the airborne base station is set to be the average of all the airborne base station heights solved for scheme one. In practical application, the number of aerial base stations to be deployed can be estimated according to the distribution of the original base stations and the required network service quality index. For example, for an offshore buoy network with a coefficient of variation CD of 0.75 in the embodiment of the present invention, if the network capacity needs to be increased to 1.5 times of the original network capacity, only aerial drone base stations with the number 0.8 times of the original number of offshore buoys need to be additionally deployed, so as to reduce the deployment cost and maximize the deployment efficiency.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A deep ocean information service quality optimization method based on an unmanned aerial vehicle network is characterized by comprising the following steps:
establishing a hybrid network based on an offshore base station and an aerial base station, wherein the aerial base station comprises L standby position base stations, selecting at least one of the L standby position base stations as the aerial base station according to a greedy algorithm, and adding the aerial base station into the offshore base station to form a first hybrid network;
calculating the optimal deployment height of the aerial base station in the first hybrid network by utilizing a heuristic algorithm;
after the height deployment of the aerial base station is completed, the horizontal position of the aerial base station is adjusted according to the actual position of the offshore user, and the aerial base station is moved to the central point of the served offshore user to realize the optimal coverage;
the aerial base stations are unmanned aerial vehicle base stations, the offshore base stations comprise M offshore base stations, the position set A of the offshore base stations is { a1, a2, … …, aM }, and the alternative deployment position set C of the unmanned aerial vehicle base stations is { C1, C2, … …, cL }, N unmanned aerial vehicle base stations are selected from the position set C and deployed, and the first hybrid network comprises M offshore base stations and N unmanned aerial vehicle base stations;
the method comprises the steps that a currently selected offshore base station position set B and a currently selected unmanned aerial vehicle base station position set S are adopted, the set B in the greedy algorithm is initialized by the position A of the offshore base station, and then in each iteration, the horizontal positions of the unmanned aerial vehicle base stations which are farthest away from all points in the position set are added into the position set until the position set S reaches the required number of aerial base stations;
in the offshore base station, 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, that is, dist (ai, B) | min { dist (ai, aj):
h ═ H1, H2, … …, hP } represents the aerial base station deployment height, the coverage area of any aerial base station ui is set as a circular area, the coverage radius Ri of the aerial base station ui is equal to half of the horizontal distance between the base station and the nearest neighbor base station, if the users are randomly distributed in the coverage area, the optimal deployment height hi is only related to the coverage radius Ri and the optimal included angle phiopt between the aerial base station and the edge connecting line of the coverage area and the horizontal plane, the optimal included angle phiopt is simulated according to the actual environmental parameters and the base station density, and the calculation is carried out by using a heuristic method, namely:
hi ═ α × Ri tan (Φ opt), where 0< α ≦ 1 represents a normalization coefficient for the air base station height.
2. The method of claim 1, wherein the change of the network service quality is observed by gradually adjusting the number N of the drone base stations, wherein β N/M represents the ratio of the number of the drone base stations in the air to the number of the original base stations at sea, and the joint deployment position obtained when the number N of the drone base stations in the air is 20, that is, β 1 improves the coverage of the sea network.
3. The method of claim 2, wherein the network quality of service comprises spatial regularity of the network, user SINR, and overall network capacity.
4. The unmanned aerial vehicle network-based deep and offshore information service quality optimization method according to claim 1, wherein a marine vessel user distribution is modeled using a two-dimensional poisson point process, and the number of vessel users per unit area conforms to the poisson distribution.
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