Method and system for estimating same-frequency dense networking scale of unmanned system based on interference separation

Abstract

The invention discloses an unmanned system same-frequency dense networking scale estimation method and system based on interference separation, the unmanned system is an unmanned aerial vehicle or an unmanned vehicle, and the method comprises the following steps: inputting a communication area dimension; inputting networking information; calculating a minimum safety distance according to the networking information; and calculating the upper bound of the communication networking scale according to the communication area parameters and the minimum safety distance, thereby obtaining a networking base station cell radius setting scheme in a dense networking scene. The estimation system comprises a dimension input module, a networking information input module, a minimum safe distance calculation module and a scheme setting module. According to the method, the minimum safety distance of the cell can be quickly calculated according to the networking information, the communication networking scale can be quickly calculated according to the communication area parameters and the minimum safety distance, and a networking base station cell radius setting scheme in a corresponding scene can be efficiently designed according to the minimum safety radius.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultra-dense networks, and in particular to a method and system for estimating the scale of unmanned system co-frequency dense networking based on interference separation. Background Art

[0002] At present, the development of unmanned systems has made great progress. In the civilian field, unmanned systems have broad application prospects and continue to promote the transformation of social production and lifestyle. For example, unmanned systems represented by drones and unmanned vehicles have excellently completed the task of coordinated search and rescue in dangerous scenes such as fires and earthquakes; in large-scale gatherings and other scenes that require temporary communication service coverage and enhancement, drones can also be deployed as air base stations to provide communication access services for ground users. Due to the advantages of low cost, timely deployment and recovery of drones and unmanned vehicles, they can be optimized through location deployment to form a flexible, large-area, high-quality and robust network, and they also have great development potential in the military field. The military can use unmanned systems to achieve long-distance communication. Whether in a distant battlefield or in a harsh environment, unmanned systems can provide real-time communication and intelligence exchange, increase combat efficiency and survivability.

[0003] Unmanned systems often use cluster networking to perform collaborative transmission, so they often face extreme scenarios of small-scale dense networking. Limited by scarce spectrum resources, when unmanned systems are densely networked, the mutual interference caused by spectrum sharing within the cluster seriously restricts its network scale and communication performance. Therefore, how to achieve dynamic, efficient, and intelligent dense networking of unmanned systems is a key issue worthy of attention, especially the analysis and estimation of the scale of dense networking of unmanned systems during the networking process, which is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a simple, efficient, accurate and easy-to-implement method and system for estimating the scale of unmanned system co-frequency dense networking based on interference separation.

[0005] The technical solution to achieve the purpose of the present invention is: a method for estimating the scale of dense networking of unmanned systems with the same frequency based on interference separation, wherein the unmanned system is a drone or an unmanned vehicle, and the method comprises the following steps:

[0006] Step 1. Enter the communication area dimension;

[0007] Step 2: Enter the networking information;

[0008] Step 3: Calculate the minimum safety distance based on the networking information;

[0009] Step 4: Calculate the upper limit of the communication network scale based on the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting plan in the dense networking scenario.

[0010] Furthermore, the communication area dimensions are input in step 1, as follows:

[0011] If the communication area is two-dimensional, enter the communication area parameters, including the length L1 meter and the width W1 meter of the two-dimensional communication area;

[0012] If the communication area is three-dimensional, input the communication area parameters, including the three-dimensional communication area length L2 meters, width W2 meters and height H meters.

[0013] Furthermore, the input networking information in step 2 includes a path fading coefficient α, a channel noise level N0, a transmission power P of an unmanned system base station, a user transmission power P0, and a two-dimensional or three-dimensional signal-to-interference-and-noise ratio detection threshold SINR1 or SINR2.

[0014] Furthermore, in step 3, the minimum safety distance is calculated according to the networking information, as follows:

[0015] If it is a two-dimensional same-frequency network, according to the two-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the circular cell min1 , output the minimum safety radius r of the circular cell min1 =d min1 / 2;

[0016] If it is a three-dimensional same-frequency network, according to the three-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the spherical cell min2 , output the minimum safety radius r of the spherical cell min2 =d min2 / 2.

[0017] Furthermore, in step 4, the upper limit of the communication network scale is calculated according to the communication area parameters and the minimum safety distance, as follows:

[0018] If it is a two-dimensional co-frequency network, the number of base stations of the unmanned system in the circular area

[0019] If it is a three-dimensional same-frequency network, the number of base stations of the unmanned system in the spherical cell

[0020] Furthermore, in step 4, the dense networking scenario is: the unmanned system is deployed flexibly to build a backbone network, and the unmanned system serves as a base station to provide communication access services for other ground users or aerial drones; the dense networking is multi-point short-distance transmission, and the base station power is less than the set value.

[0021] Furthermore, the network base station cell radius setting scheme in the dense networking scenario in step 4 is as follows:

[0022] If it is a two-dimensional co-frequency network, the maximum radius r of the circular cell is calculated according to the propagation model formula max1 , combined with the minimum safety radius r of the circular cell min1 =d min1 / 2, the radius of the circular cell is set to meet r min1 ≤r1≤r max1 ;

[0023] If it is a three-dimensional co-frequency network, the maximum radius r of the spherical cell is calculated according to the propagation model formula max2 , combined with the minimum safety radius r of the spherical cell min2 =d min2 / 2, the radius of the spherical cell is set to meet r min2 ≤r2≤r max2 .

[0024] A system for estimating the scale of densely connected unmanned systems with the same frequency based on interference separation, the system is used to implement the method for estimating the scale of densely connected unmanned systems with the same frequency based on interference separation, the estimation system comprises a dimension input module, a networking information input module, a minimum safety distance calculation module and a scheme setting module, wherein:

[0025] A dimension input module, used for inputting the communication area dimension;

[0026] A networking information input module, used to input networking information;

[0027] A minimum safety distance calculation module is used to calculate the minimum safety distance according to the networking information;

[0028] The scheme setting module is used to calculate the upper limit of the communication network scale according to the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting scheme in the dense networking scenario.

[0029] A mobile terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for estimating the scale of densely connected unmanned systems with the same frequency based on interference separation is implemented.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for estimating the scale of unmanned system co-frequency dense networking based on interference separation.

[0031] Compared with the prior art, the present invention has the following significant advantages:

[0032] (1) According to the communication area parameters, networking information, the total number of base stations N, the networking mode, and the signal-to-interference-to-noise ratio detection threshold SINR, the scale of the two-dimensional or three-dimensional co-frequency networking of the unmanned system is determined, which is simple, efficient, and easy to implement;

[0033] (2) The minimum safety radius of the cell can be combined with the maximum radius of the cell to quickly and efficiently calculate the setting range of the cell radius of the networking base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The flowchart of the method for estimating the scale of dense co-frequency networking of unmanned systems is shown in FIG.

[0035] Figure 2 This is a diagram of the system model that was built.

[0036] Figure 3 It is a schematic diagram of dense stacking of two-dimensional planar same-frequency networking involved in an embodiment of the present invention.

[0037] Figure 4 It is a schematic diagram of dense stacking of three-dimensional same-frequency networking involved in an embodiment of the present invention.

[0038] Figure 5 It is a schematic diagram of densely stacked cells in the same-frequency networking in a two-dimensional plane.

[0039] Figure 6 It is a two-dimensional plane co-frequency networking cell number relative to the detection threshold change trend chart.

[0040] Figure 7 This is a graph showing the variation trend of the normalized error of the two-dimensional co-frequency networking with respect to granularity.

[0041] Figure 8 This is a schematic diagram of densely stacked cells in the same-frequency networking in three-dimensional space.

[0042] Fig. 9 This is a trend chart of the number of cells in the same frequency network in three-dimensional space with respect to the detection threshold.

[0043] Fig.10 This is a trend chart of the change of the normalized error of the three-dimensional co-frequency networking with respect to the granularity. DETAILED DESCRIPTION

[0044] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can imagine various implementation methods of the present invention. Therefore, the following specific implementation methods and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction of the technical solution of the present invention.

[0045] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0046] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0047] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0048] The embodiment of the present invention provides a method for estimating the scale of dense co-frequency networking of unmanned systems based on interference separation. The scale of dense co-frequency networking of unmanned systems in two or three dimensions can be determined according to communication area parameters, networking information, networking mode (two-dimensional or three-dimensional), and signal-to-interference-to-noise ratio detection threshold SINR. The method is simple, efficient, and easy to implement. For any given communication area, the minimum safety distance of the cell is calculated according to parameters such as networking information, signal-to-interference-to-noise ratio detection threshold, channel noise level, transmission power of the unmanned vehicle base station, and user transmission power; then, the upper bound formula of the networking scale is calculated according to the minimum safety distance of the cell; the minimum safety radius of the cell can be combined with the maximum radius of the cell to obtain the cell radius setting scheme of the networking base station in the corresponding scenario.

[0049] Combination Figure 1 The present invention provides a method for estimating the scale of a co-frequency dense network of an unmanned system based on interference separation, wherein the unmanned system is a drone or an unmanned vehicle, and the method comprises the following steps:

[0050] Step 1. Enter the communication area dimension;

[0051] Step 2: Enter the networking information;

[0052] Step 3: Calculate the minimum safety distance based on the networking information;

[0053] Step 4: Calculate the upper limit of the communication network scale based on the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting plan in the dense networking scenario.

[0054] As a specific example, the communication area dimensions are input in step 1 as follows:

[0055] If the communication area is two-dimensional, enter the communication area parameters, including the length L1 meter and the width W1 meter of the two-dimensional communication area;

[0056] If the communication area is three-dimensional, input the communication area parameters, including the three-dimensional communication area length L2 meters, width W2 meters and height H meters.

[0057] As a specific example, the input networking information in step 2 includes path fading coefficient α, channel noise level N0, transmission power P of the unmanned system base station, user transmission power P0, and two-dimensional or three-dimensional signal to interference and noise ratio detection threshold SINR1 or SINR2.

[0058] As a specific example, in step 3, the minimum safety distance is calculated according to the networking information, as follows:

[0059] If it is a two-dimensional same-frequency network, according to the two-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the circular cell min1 , output the minimum safety radius r of the circular cell min1 =d min1 / 2;

[0060] If it is a three-dimensional same-frequency network, according to the three-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the spherical cell min2 , output the minimum safety radius r of the spherical cell min2 =d min2 / 2.

[0061] As a specific example, in step 4, the upper limit of the communication network scale is calculated according to the communication area parameters and the minimum safety distance, as follows:

[0062] If it is a two-dimensional co-frequency network, the number of base stations of the unmanned system in the circular area

[0063] If it is a three-dimensional same-frequency network, the number of base stations of the unmanned system in the spherical cell

[0064] As a specific example, in step 4, the dense networking scenario is: the unmanned system is deployed flexibly to build a backbone network, and the unmanned system acts as a base station to provide communication access services for other ground users or aerial drones; the dense networking is multi-point short-distance transmission, and the base station power is less than the set value.

[0065] As a specific example, the network base station cell radius setting scheme in the dense networking scenario in step 4 is as follows:

[0066] If it is a two-dimensional co-frequency network, the maximum radius r of the circular cell is calculated according to the propagation model formula max1 , combined with the minimum safety radius r of the circular cell min1 =d min1 / 2, the radius of the circular cell is set to meet r min1 ≤r1≤r max1 ;

[0067] If it is a three-dimensional co-frequency network, the maximum radius r of the spherical cell is calculated according to the propagation model formula max2 , combined with the minimum safety radius r of the spherical cell min2 =d min2 / 2, the radius of the spherical cell is set to meet r min2 ≤r2≤r max2 .

[0068] Furthermore, based on the upper bounds of the number of unmanned system base stations N1 and N2 determined in step 4, r min1 / min{L1,W1}<<10 -1 or min2 / min{L2,W2,H}<<10 -1 , the maximum number of unmanned vehicle base stations in actual networking tends to the upper limit. The present invention solves the upper limit of network scale based on the hexagonal close packing principle, d min1 / min{L1,W1} or d min2 / min{L2,W2,H} is the ratio of particle size to container size, which actually represents the particle size. The smaller the accuracy (the closer to 0), the denser the particles fill the container.

[0069] The present invention also provides an unmanned system co-frequency dense networking scale estimation system based on interference separation, which is used to implement the unmanned system co-frequency dense networking scale estimation method based on interference separation. The estimation system includes a dimension input module, a networking information input module, a minimum safety distance calculation module and a scheme setting module, wherein:

[0070] A dimension input module, used for inputting the communication area dimension;

[0071] A networking information input module, used to input networking information;

[0072] A minimum safety distance calculation module is used to calculate the minimum safety distance according to the networking information;

[0073] The scheme setting module is used to calculate the upper limit of the communication network scale according to the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting scheme in the dense networking scenario.

[0074] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for estimating the scale of co-frequency dense networking of unmanned systems based on interference separation is implemented.

[0075] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps in the method for estimating the scale of unmanned system co-frequency dense networking based on interference separation are implemented.

[0076] The present invention is based on the interference separation unmanned system same-frequency dense networking scale estimation method, which temporarily does not consider the far-end interference at a long distance and the terminal interference with a small impact on the base station. It can combine the minimum safety radius of the cell with the maximum radius of the cell, and quickly and efficiently calculate the setting range of the cell radius of the networking base station.

[0077] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0078] Example 1

[0079] This embodiment provides a method for estimating the scale of densely connected unmanned systems on the same frequency based on interference separation, including the following parts:

[0080] (1) Calculate the minimum safety distance of the cell based on the networking information:

[0081] In this embodiment, the two-dimensional communication area is set to be length L1 = 200 meters, width W1 = 200 meters, and the three-dimensional area is set to be length L2 = 200 meters, width W2 = 200 meters, and height H = 200 meters.

[0082] Next, according to the two-dimensional same-frequency networking formula Or three-dimensional same-frequency networking formula Numerical solution of the minimum safety distance d of a circular cell min1 , the minimum safety distance d of the spherical area min2 ;

[0083] (2) Calculate the communication network scale based on the communication area parameters and the minimum safety distance:

[0084] In this embodiment, the base station power is set to P = 10dB, the user power is set to P0 = 500mW, and the noise power is set to N0 = 1dB. If it is a three-dimensional same-frequency network, the number of base stations of the unmanned system in the spherical cell

[0085] (3) Design a network base station cell radius setting plan for the corresponding scenario based on the minimum safety radius:

[0086] If it is a two-dimensional co-frequency network, the maximum radius r of the circular cell is calculated according to the propagation model formula max1 , combined with the minimum safety radius r of the circular cell min1 =d min1 / 2, the radius of the circular cell is set to meet r min1 ≤r1≤r max1 ;

[0087] If it is a three-dimensional co-frequency network, the maximum radius r of the spherical cell is calculated according to the propagation model formula max2 , combined with the minimum safety radius r of the spherical cell min2 =d min2 / 2, the radius of the spherical cell is set to meet r min2 ≤r2≤r max2 .

[0088] (4) This embodiment is based on the same-frequency networking in any fixed area, and each drone base station just meets the detection threshold of the dense cell stacking. Figure 2 This is a diagram of the system model that was built.

[0089] In the example described in this implementation, the two-dimensional same-frequency networking process is as follows: Figure 3 As shown in the figure, the mobile drone base stations need to be densely networked in a known rectangular area. All base stations meet the detection threshold. The upper limit of the number of base stations is the scale of the same frequency network. min1 The circular area with a radius of is used as the communication cell. If the communication cells are tangent to each other, the minimum safe distance d can always be maintained between adjacent base stations. min1 At this time, the question is "When networking with the same frequency, each base station maintains a minimum safe distance d min1 , how many base stations can be distributed in a known area at most" is converted into "when the same frequency network is formed, the radius of each base station cell r min1 , how many tangent cells can be distributed in a known area at most? The above problem is a plane circle dense stacking problem. According to the study of Axel graph, hexagonal dense packing is the most dense stacking method on the plane. When the base stations are dense enough, the plane arrangement is as follows Figure 3 As shown. If base station A receives the communication signal of users in the cell, the adjacent base stations B, C, D, E, F, G will generate co-frequency interference to it, that is, B, C, D, E, F, G → A (the connection of most points in the plane distribution is similar to A, and the interference source at the boundary point may be less than 6). The minimum safe distance d between base stations min1 , using frequency f n When the same frequency network is formed, there is only same frequency interference. The base station receives the cell edge user message, that is, the distance d from the cell edge user to the base station 01 =r min1 / 2, the signal-to-interference-noise ratio threshold for correct demodulation is just met.

[0090] In the example described in this implementation, the three-dimensional same-frequency networking process is as follows: Figure 4 As shown in the figure, the mobile drone base stations need to be densely networked in the known cube area, and all base stations meet the detection threshold. The upper limit of the number of base stations is the scale of the same frequency network. min2A spherical area with a radius of is used as a communication cell. According to Gauss's theorem, when the base stations in three-dimensional space are dense enough, the dense arrangement of spherical cells is as follows: Figure 4 The right picture is the front view of the left picture (yellow layer and green layer). There is no adjacent frequency interference in the same frequency network. The base station receives the cell edge user message, that is, the distance d from the cell edge user to the base station 02 =r min2 / 2, the signal-to-interference-noise ratio threshold for correct demodulation is just met.

[0091] Example 2

[0092] Based on Example 1, this example provides a basic process of same-frequency networking, which is as follows:

[0093] It is assumed that each unmanned vehicle or drone base station can flexibly move and adjust its area;

[0094] (1) First, the minimum safety radius is calculated for a fixed area, and the minimum safety radius is used to design a network base station cell radius setting plan.

[0095] (2) Apply the cell radius setting scheme designed in step (1) to adjust the layout of each unmanned vehicle or drone base station to achieve the largest-scale co-frequency networking.

[0096] The parameter information used in the simulation is shown in Table 1:

[0097] Table 1 Parameter settings in simulation

[0098] <![CDATA[Two-dimensional and three-dimensional communication regions with lengths L1 and L2]]> 200 meters <![CDATA[Two-dimensional and three-dimensional communication regions with widths W1 and W2]]> 200 meters Three-dimensional communication area high H 200 meters Base station power P 10dB <![CDATA[User power P0]]> 500mW <![CDATA[User power N0]]> 1dB <![CDATA[Two-dimensional co-frequency networking SINR1]]> -17dB, -20dB <![CDATA[3D co-frequency networking SINR2]]> -30dB, -25dB

[0099] Figure 5 A schematic diagram of densely stacked cells in a two-dimensional co-frequency networking configuration according to this embodiment is given. Figure 6 The variation trend of the number of cells in the two-dimensional co-frequency networking in this embodiment with respect to the detection threshold is given. Figure 7 The variation trend of the normalized error of the two-dimensional co-frequency networking with respect to the granularity of this embodiment is given. Figure 8 A schematic diagram of densely stacked cells in the same frequency networking in three-dimensional space is given in this embodiment. Fig. 9 The variation trend of the number of cells in the same frequency networking in three-dimensional space with respect to the detection threshold in this embodiment is given. Fig.10 The variation trend of the normalized error of the three-dimensional co-frequency networking in this embodiment with respect to the granularity is given. When SINR1 and SINR2 are lower, the detection capability of the base station is stronger, the tolerable self-interference in the network is stronger, and therefore the minimum safety radius of the cell is larger, which is consistent with the actual situation (such as Figure 5 , Figure 8As shown in Figure 2, the simulation value is not much different from the theoretical value, which verifies the correctness of the theoretical upper bound derivation. The number of cells increases with the increase of SINR1 and SINR2, which means that the larger the detection threshold SINR1 and SINR2, the weaker the detection capability of the base station, and the relatively low self-interference in the network that can be tolerated. Therefore, the smaller the minimum safety radius of the cell (such as Figure 6 , Fig. 9 When the particle size is larger, the difference between the theoretical value and the simulation value is more obvious than when the particle size is smaller (as shown in Figure 7 , Fig.10 shown).

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0101] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for estimating the scale of densely connected unmanned systems with the same frequency based on interference separation, characterized in that: The unmanned system is a drone or an unmanned vehicle, and the method comprises the following steps: Step 1. Enter the communication area dimension; Step 2: Enter the networking information; Step 3: Calculate the minimum safety distance based on the networking information; Step 4: Calculate the upper limit of the communication network scale based on the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting plan in the dense networking scenario.

2. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 1 is characterized in that: In step 1, enter the communication area dimensions as follows: If the communication area is two-dimensional, enter the communication area parameters, including the length L1 meter and the width W1 meter of the two-dimensional communication area; If the communication area is three-dimensional, input the communication area parameters, including the three-dimensional communication area length L2 meters, width W2 meters and height H meters.

3. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 2 is characterized in that: The input networking information in step 2 includes the path fading coefficient α, the channel noise level N0, the transmission power P of the unmanned system base station, the user transmission power P0, and the two-dimensional or three-dimensional signal to interference and noise ratio detection threshold SINR1 or SINR2.

4. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 3 is characterized in that: In step 3, the minimum safety distance is calculated based on the networking information, as follows: If it is a two-dimensional same-frequency network, according to the two-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the circular area min1 , output the minimum safety radius r of the circular cell min1 =d min1 / 2; If it is a three-dimensional same-frequency network, according to the three-dimensional signal-to-interference-noise ratio detection threshold Calculate the minimum safety distance d of the spherical cell min2 , output the minimum safety radius r of the spherical cell min2 =d min2 / 2.

5. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 4 is characterized in that: In step 4, the upper limit of the communication network scale is calculated based on the communication area parameters and the minimum safety distance, as follows: If it is a two-dimensional co-frequency network, the number of base stations of the unmanned system in the circular area If it is a three-dimensional same-frequency network, the number of base stations of the unmanned system in the spherical cell 6. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 5 is characterized in that: In step 4, the dense networking scenario is: the unmanned system is deployed flexibly to build a backbone network, and the unmanned system serves as a base station to provide communication access services for other ground users or aerial drones; the dense networking is multi-point short-distance transmission, and the base station power is less than the set value.

7. The method for estimating the scale of unmanned system co-frequency dense networking based on interference separation according to claim 6 is characterized in that: The network base station cell radius setting scheme in the dense networking scenario in step 4 is as follows: If it is a two-dimensional co-frequency network, the maximum radius r of the circular cell is calculated according to the propagation model formula max1 , combined with the minimum safety radius r of the circular cell min1 =d min1 / 2, the radius of the circular cell is set to meet r min1 ≤r1≤r max1 ; If it is a three-dimensional co-frequency network, the maximum radius r of the spherical cell is calculated according to the propagation model formula max2 , combined with the minimum safety radius r of the spherical cell min2 =d min2 / 2, the radius of the spherical cell is set to meet r min2 ≤r2≤r max2 .

8. A system for estimating the scale of densely connected unmanned systems with the same frequency based on interference separation, characterized in that: The system is used to implement the method for estimating the scale of densely networked unmanned systems with the same frequency based on interference separation according to any one of claims 1 to 7, wherein the estimation system comprises a dimension input module, a networking information input module, a minimum safety distance calculation module and a scheme setting module, wherein: A dimension input module, used for inputting the communication area dimension; A networking information input module, used to input networking information; A minimum safety distance calculation module is used to calculate the minimum safety distance according to the networking information; The scheme setting module is used to calculate the upper limit of the communication network scale according to the communication area parameters and the minimum safety distance, so as to obtain the network base station cell radius setting scheme in the dense networking scenario.

9. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for estimating the scale of unmanned system co-frequency dense networking based on interference separation as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the steps in the method for estimating the scale of unmanned system co-frequency dense networking based on interference separation as described in any one of claims 1 to 7 are implemented.

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