Method and device for estimating pilot frequency dense networking scale of unmanned system

Abstract

The invention discloses a method and a device for estimating pilot frequency dense networking scale of an unmanned system. The method comprises the following steps: step 1, inputting communication area parameters; step 2, inputting networking information; step 3, calculating a minimum safety distance according to the networking information; step 4, detecting whether the total number N of unmanned systems is greater than the number M of frequency points; if so, executing step 5; otherwise, any operation does not need to be executed; and step 5, calculating the upper bound of the networking scale in the dense networking scene according to the minimum safety distance obtained in the step 3. The device comprises a first input module, a second input module, a safety distance calculation module, a judgment module and a scale upper bound calculation module. According to the method, the two-dimensional pilot frequency dense networking scale of the unmanned system can be judged according to the communication area parameters, the networking information, the total number of the base stations and the signal to interference plus noise ratio detection threshold, and the method has the advantages of being simple, efficient and easy to implement.

Description

Technical Field

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

[0002] As an emerging technology, ultra-dense networks have greatly improved network capacity by deploying more dense low-power access points. With the large-scale deployment of access points in the network architecture, the complexity of the network has increased exponentially. In order to improve the network capacity, the cell radius of the two-dimensional plane base station in the grid has gradually decreased. Usually, the maximum cell radius is automatically calculated according to the formula of the typical propagation model, such as the maximum cell radius of the parch channel. However, as the cell density continues to increase, due to the limited spectrum resources, the interference between access points in the network will be very serious, which means that the distance between access points cannot be reduced indefinitely. However, research on ultra-dense networks often focuses on network performance analysis, resource allocation, interference management and other contents.

[0003] Unmanned systems represented by drones and unmanned vehicles have the ability of self-organization and self-repair and the advantages of efficient and fast networking, and can also be used in the construction of ultra-dense networks. In the civilian field, unmanned system networking has broad application prospects. For example, in scenarios such as large-scale gatherings that require temporary communication service coverage and enhancement, drones can be deployed as air base stations to provide communication access services for ground users. Unmanned system networking also has 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 system networking can provide real-time communication and intelligence exchange, increase combat efficiency and survivability. Research on unmanned system networking also focuses on technologies such as power control, relay selection, and trajectory optimization, which improve security and reliability by resisting malicious interference attacks. Unmanned system networking often conducts cooperative transmission, so there are often extreme scenarios of small-scale networking, so it also has the dilemma of self-interference in ultra-dense networking, that is, the mutual interference problem caused by spectrum sharing within the fleet seriously restricts its network scale and communication performance.

[0004] At present, there is no relevant theoretical research on the analysis of the upper bound of network scale caused by dense network self-interference, and it is urgent to solve the scale of ultra-dense networks under internal self-interference, that is, co-frequency and adjacent-frequency interference. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for estimating the scale of heterogeneous dense networking of unmanned systems which is simple, efficient, accurate in results and easy to implement.

[0006] The technical solution to achieve the purpose of the present invention is: a method for estimating the scale of an unmanned system with different frequencies and dense networking, wherein the unmanned system is an unmanned vehicle, comprising the following steps:

[0007] Step 1. Enter the communication area parameters;

[0008] Step 2: Enter the networking information;

[0009] Step 3: Calculate the minimum safety distance based on the networking information;

[0010] Step 4: Check whether the total number of unmanned systems N is greater than the number of frequency points M: if so, execute step 5; otherwise, no operation is required;

[0011] Step 5: Calculate the upper limit of the network scale in the dense networking scenario based on the minimum safety distance obtained in step 3.

[0012] A device for estimating the scale of an unmanned system with different frequencies and dense networking, the device is used to implement the method for estimating the scale of an unmanned system with different frequencies and dense networking, the device comprises a first input module, a second input module, a safety distance calculation module, a judgment module and a scale upper limit calculation module, wherein:

[0013] A first input module, used for inputting communication area parameters;

[0014] The second input module is used to input networking information;

[0015] A safety distance calculation module is used to calculate the minimum safety distance based on networking information;

[0016] The judgment module is used to detect whether the total number of unmanned systems N is greater than the number of frequency points M: if so, the scale upper limit calculation module is executed; otherwise, no operation is required;

[0017] The upper limit calculation module calculates the upper limit of the network scale in the dense networking scenario according to the minimum safety distance obtained by the safety distance calculation module.

[0018] 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 heterogeneous and dense networking of an unmanned system is implemented.

[0019] 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 heterogeneous dense networking of unmanned systems.

[0020] Compared with the prior art, the present invention has the following significant advantages:

[0021] (1) It can quickly calculate the minimum safety distance of the cell based on the networking information, quickly calculate the communication network scale based on the communication area parameters and the minimum safety distance, and efficiently design the cell radius setting plan of the inter-frequency networking base station based on the minimum safety radius;

[0022] (2) The scale of the two-dimensional heterogeneous frequency dense networking of the unmanned system can be determined based on the communication area parameters, networking information, the total number of base stations, and the signal-to-interference-noise ratio detection threshold. The method is simple, efficient, and easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The flowchart of the method for estimating the scale of dense heterogeneous networking of unmanned systems is shown in FIG.

[0024] Figure 2 This is a diagram of the system simulation model that was built.

[0025] Figure 3 A schematic diagram of dense stacking of two-dimensional planar heterogeneous frequency networking designed for an embodiment of the present invention.

[0026] Figure 4 Schematic diagram of the normalized power spectral density function after zero-forcing technology.

[0027] Figure 5 This is a trend chart of the number of cells in two-dimensional inter-frequency networking with respect to the detection threshold.

[0028] Figure 6 This is a graph showing the variation trend of the normalized error of two-dimensional heterogeneous frequency networking with respect to granularity. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] The present invention relates to an estimation method for the scale of hetero-frequency dense networking of an unmanned system. For a given arbitrary two-dimensional communication area, the minimum safety distance of a cell is calculated according to parameters such as networking information, signal-to-interference-noise ratio detection threshold, channel noise level, transmission power of an 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 a setting scheme for the cell radius of a networking base station in a corresponding scenario.

[0034] Combination Figure 1 The present invention provides a method for estimating the scale of an unmanned system with different frequencies and dense networking, wherein the unmanned system is an unmanned vehicle, and comprises the following steps:

[0035] Step 1. Enter the communication area parameters;

[0036] Step 2: Enter the networking information;

[0037] Step 3: Calculate the minimum safety distance based on the networking information;

[0038] Step 4: Check whether the total number of unmanned systems N is greater than the number of frequency points M: if so, execute step 5; otherwise, no operation is required;

[0039] Step 5: Calculate the upper limit of the network scale in the dense networking scenario based on the minimum safety distance obtained in step 3.

[0040] As a specific example, the communication area parameters in step 1 include a communication area length of L meters and a width of W meters.

[0041] As a specific example, the networking information in step 2 includes:

[0042] The total number of base stations N, the signal to interference and noise ratio detection threshold SINR, the path fading coefficient α, the channel noise level N0, the unmanned system base station transmission power P, and the user transmission power P0. Furthermore, the path fading coefficient α is set to 2.

[0043] As a specific example, in step 3, due to the unsatisfactory receiver performance and spectrum leakage, adjacent frequency interference between adjacent frequency points should also be considered. The minimum safe distance is related to the adjacent frequency interference between adjacent frequency points, which is related to the communication modulation method. Different modulation methods have different normalized power spectrum densities, and the main lobe width of each frequency carrier is also different, which will lead to different adjacent frequency interference. Therefore, it is necessary to first obtain the normalized power spectrum density and the first sidelobe bandwidth of each modulation method.

[0044] Furthermore, in order to improve spectrum utilization and reduce the impact of adjacent frequency interference, it is necessary to keep the minimum safe frequency interval Δf between adjacent frequency points. min = inf{Δf i},i=1,…,M-1,Δf i It is the main lobe width of each frequency carrier. It should be noted that the minimum safe frequency intervals of different transmission functions after zero forcing technology are different, so the present invention is a heterogeneous frequency networking under a fixed transmission function and fixed zero forcing technology.

[0045] Furthermore, when the transmission function and the zero forcing technology are fixed, the power of the other side lobes is smaller than that of the first side lobe, so the present invention only considers the adjacent frequency interference of the first side lobe. It should be noted that when the adjacent frequency intervals of the multi-frequency point network are taken as the main lobe width, the first side lobe width is within the main lobe width, so the first side lobe falls entirely into the adjacent frequency main lobe.

[0046] Therefore, in step 3, the minimum safety distance is calculated based on the networking information, as follows:

[0047] Input frequency points M and available frequency set F = [f1, f2, ..., f M ];

[0048] Enter the network modulation mode and calculate the adjacent frequency interference Where psd(f) is the normalized power spectral density function after zero forcing technology under the current modulation mode, W1 is the frequency range of the first sidelobe, f is the frequency, d min The minimum safety distance of the community;

[0049] according to Calculate the minimum safety distance d of the circular cell min , determine the minimum safety radius r of the circular cell min =d min / 2.

[0050] The present invention also provides an unmanned system heterogeneous frequency dense networking scale estimation device, which is used to implement the unmanned system heterogeneous frequency dense networking scale estimation method, and the device includes a first input module, a second input module, a safety distance calculation module, a judgment module and a scale upper limit calculation module, wherein:

[0051] A first input module, used for inputting communication area parameters;

[0052] The second input module is used to input networking information;

[0053] A safety distance calculation module is used to calculate the minimum safety distance based on networking information;

[0054] The judgment module is used to detect whether the total number of unmanned systems N is greater than the number of frequency points M: if so, the scale upper limit calculation module is executed; otherwise, no operation is required;

[0055] The upper limit calculation module calculates the upper limit of the network scale in the dense networking scenario according to the minimum safety distance obtained by the safety distance calculation module.

[0056] 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 heterogeneous dense networking of an unmanned system is implemented.

[0057] 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 heterogeneous and dense networking of unmanned systems are implemented.

[0058] 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.

[0059] Example

[0060] This embodiment provides a method for estimating the scale of an unmanned system inter-frequency dense networking, including the following parts:

[0061] (1) Calculate the minimum safety distance of the cell based on the networking information:

[0062] In this implementation, the two-dimensional area is set to be 200 meters long and 200 meters wide. Numerical solution of the minimum safety distance d of the cell min ;

[0063] (2) Calculate the communication network scale based on the communication area parameters and the minimum safety distance:

[0064] 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. Next, according to the upper bound formula of the two-dimensional heterogeneous frequency network scale, the number of unmanned vehicle base stations

[0065] (3) Design a network base station cell radius setting plan for the corresponding scenario based on the minimum safety radius:

[0066] Calculate the maximum radius r of the cell according to the typical propagation model formula max , combined with the minimum safety radius r of the cell min =d min / 2, the cell radius is set to meet r min ≤r≤r max .

[0067] (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 simulation model that was built.

[0068] In this embodiment, the two-dimensional inter-frequency networking process is as follows: Figure 3 As shown, the mobile drone base stations need to be densely networked in a known rectangular 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. The two-dimensional plane of the base station distribution is combined with the frequency domain to obtain the three-dimensional spectrum of the heterogeneous frequency network - the communication network area. When heterogeneous frequency networking, sufficient space intervals need to be maintained between base stations to reduce the impact of co-frequency interference; a certain frequency interval needs to be maintained between frequency points to reduce the impact of adjacent frequency interference. If the minimum safe frequency interval Δf between adjacent frequency points min =inf{Δf i},i=1,…,M-1,Δf i is the main lobe width of each adjacent frequency carrier. The minimum safe distance determines the frequency range of adjacent frequency interference that falls into the main lobe of the adjacent frequency. If the available frequency band range is fixed, in order to improve the spectrum utilization of heterogeneous frequency networking, the number of frequencies can be increased, and the frequency interval is reduced; in order to reduce the impact of adjacent frequency interference, the minimum safe frequency interval can be increased. When the modulation mode of each base station is the same, the main lobe width Δf can be selected as the minimum safe frequency interval, such as Figure 4 As shown. Each base station is the center of the cylinder, and the bottom surface is d min / 2 is the radius and Δf / 2 is the height of the cylinder area as the generalized "abstract" communication cell concept. If the communication cells at the same frequency are tangent, then the minimum safe distance d can always be maintained between adjacent base stations. min At this time, the question is "When networking with different frequencies, each base station maintains a minimum safe distance d min , the maximum number of base stations that can be distributed in a known area is converted into "When multiple frequency points are used, the long semi-axis of each base station cell is d min / 2, the height is Δf / 2, and it is known that the expanded dimension area can be distributed with at most tangent cylindrical cells", that is, the three-dimensional cylindrical dense stacking problem. The base station of this cell will be subject to both co-channel interference and adjacent-channel interference, but the base station of this cell still needs to meet the correct demodulation threshold when receiving messages transmitted by users.

[0069] The basic process of heterodyne networking is as follows:

[0070] It is assumed that each unmanned vehicle base station can be flexibly moved to adjust its area;

[0071] (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.

[0072] (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 heterogeneous frequency networking.

[0073] The parameter information used in the simulation is shown in Table 1:

[0074] Table 1 Parameter settings in simulation

[0075] Length of two-dimensional communication area L 200 meters The width of the two-dimensional communication area is W 200 meters Base station power P 10dB <![CDATA[User power P0]]> 500mW <![CDATA[User power N0]]> 1dB

[0076] Figure 5 The variation trend of the number of cells in the two-dimensional inter-frequency networking in this embodiment with respect to the detection threshold is given. Figure 6 The variation trend of the normalized error of the two-dimensional plane heterogeneous frequency networking in this embodiment with respect to the granularity is given. 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 SINR, which means that the larger the detection threshold SINR, the weaker the detection capability of the base station, and the tolerable self-interference in the network needs to be relatively low, so the minimum safety radius of the cell is smaller (such as Figure 5 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 6 shown).

[0077] 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.

[0078] 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 dense networking of unmanned systems with different frequencies, characterized in that: The unmanned system is an unmanned vehicle, comprising the following steps: Step 1. Enter the communication area parameters; Step 2: Enter the networking information; Step 3: Calculate the minimum safety distance based on the networking information; Step 4: Check whether the total number of unmanned systems N is greater than the number of frequency points M: if so, execute step 5; otherwise, no operation is required; Step 5: Calculate the upper limit of the network scale in the dense networking scenario based on the minimum safety distance obtained in step 3.

2. The method for estimating the scale of the unmanned system heterogeneous dense networking according to claim 1 is characterized in that: The communication area parameters in step 1 include a communication area length of L meters and a width of W meters.

3. The method for estimating the scale of the unmanned system heterogeneous dense networking according to claim 2, characterized in that: The networking information in step 2 includes: The total number of base stations N, the signal-to-interference-and-noise ratio detection threshold SINR, the path fading coefficient α, the channel noise level N0, the unmanned system base station transmission power P, and the user transmission power P0.

4. The method for estimating the scale of the unmanned system heterogeneous frequency dense networking according to claim 3 is characterized in that: In step 2, the path fading coefficient α is set to 2.

5. The method for estimating the scale of the unmanned system heterogeneous frequency dense networking according to claim 4 is characterized in that: In step 3, the minimum safety distance is calculated based on the networking information, as follows: Input frequency points M and available frequency set F = [f1, f2, ..., f M ]; Enter the network modulation mode and calculate the adjacent frequency interference Where psd(f) is the normalized power spectral density function after zero forcing technology under the current modulation mode, W1 is the frequency range of the first sidelobe, f is the frequency, d min The minimum safety distance of the community; according to Calculate the minimum safety distance d of the circular cell min , determine the minimum safety radius r of the circular cell min =d min / 2.

6. The method for estimating the scale of the unmanned system heterogeneous dense networking according to claim 5, characterized in that: In step 5, the upper limit of the network size is calculated as follows: According to the minimum safety distance d obtained in step 3 min , the upper limit of the number of unmanned vehicle base stations N is calculated as 7. The method for estimating the scale of the unmanned system heterogeneous dense networking according to claim 6, characterized in that: In step 5, 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.

8. A device for estimating the scale of an unmanned system's densely packed network with different frequencies, characterized in that: The device is used to implement the method for estimating the scale of the unmanned system hetero-frequency dense networking according to any one of claims 1 to 7, and the device comprises a first input module, a second input module, a safety distance calculation module, a judgment module and a scale upper limit calculation module, wherein: A first input module, used for inputting communication area parameters; The second input module is used to input networking information; A safety distance calculation module is used to calculate the minimum safety distance based on networking information; The judgment module is used to detect whether the total number of unmanned systems N is greater than the number of frequency points M: if so, the scale upper limit calculation module is executed; otherwise, no operation is required; The upper limit calculation module calculates the upper limit of the network scale in the dense networking scenario according to the minimum safety distance obtained by the safety distance calculation module.

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 heterogeneous dense networking of an unmanned system 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 heterogeneous dense networking of unmanned systems as described in any one of claims 1 to 7 are implemented.

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