Channel knowledge map assisted airborne 5G active base station beam design method

Abstract

The invention provides a channel knowledge map assisted airborne 5G active base station beam design method, and belongs to the technical field of unmanned aerial vehicle communication. The technical problem that the channel estimation overhead is too large in airborne 5G active base station beam design is solved. The technical scheme is as follows; according to the method, geographic position information and channel characteristics are combined, a localized channel knowledge map is constructed, an airborne 5G active base station is assisted to quickly acquire local channel state information, the system channel estimation overhead is reduced while accurate antenna beam design is realized, and the frequency band transmission efficiency and the system capacity are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV communication, and particularly to a beam design method for an airborne 5G mobile base station assisted by a channel knowledge map. Background Art

[0002] Different from traditional terrestrial fixed base stations, 5G mobile base stations can provide a more flexible coverage method to meet personalized networking requirements. With the rapid development of the low-altitude economy, the mounting of unmanned aerial vehicles (UAVs) is gradually becoming an important way for the deployment of 5G mobile base stations and has received extensive attention. Since the airborne 5G mobile base station has strong environmental adaptability, fast networking speed, wide coverage range, long communication distance, and can provide flexible and efficient communication service quality, it is playing an increasingly important role in the field of emergency communication.

[0003] In order to enable the low-altitude coverage network to provide a larger system capacity and better communication quality, a large-scale transceiver antenna array is usually deployed on the airborne 5G mobile base station side to achieve better spatial domain focusing performance by using multi-antenna beamforming technology. At the same time, with the rapid growth of the communication requirements of ground users, the number of antennas on the airborne 5G mobile base station side will also increase accordingly. The beam design of the antenna array requires accurate channel state information (CSI) as support. However, with the rapid increase in the number of antennas, the channel estimation overhead for obtaining channel state information will also increase accordingly, thereby causing serious system overhead problems.

[0004] To address the above technical problems, the channel knowledge map (CKM) provides the possibility for the airborne 5G mobile base station to obtain channel state information with low overhead. The channel knowledge map, as a database that can store channel state characteristics in combination with location information, can quickly retrieve and obtain channel state information, avoiding repeated measurement using sounding reference signals, thereby simplifying the channel estimation process. Considering that the airborne 5G mobile base station is usually used for emergency communication or hotspot coverage in a specified area, there is a natural connection between the coverage area and the spatial map. Therefore, the coverage area can be mapped to the channel knowledge map, and the channel state information can be quickly obtained by retrieving the channel knowledge map, reducing the channel estimation overhead while assisting in antenna beam design, thereby quickly building an efficient beam coverage network for the airborne 5G mobile base station. Therefore, the problem of excessive beam design overhead for the airborne 5G mobile base station can be solved by constructing and utilizing the channel knowledge map. Summary of the Invention

[0005] To solve the technical problem of excessive channel estimation overhead faced by the beam design of airborne 5G mobile base stations, the present invention proposes a method for beam design of airborne 5G mobile base stations assisted by a channel knowledge map; this method combines geographical location information and channel characteristics, and by constructing a localized channel knowledge map, it assists the airborne 5G mobile base station to quickly obtain local channel state information, while achieving accurate antenna beam design, reducing the system channel estimation overhead, and improving the frequency band transmission efficiency and system capacity.

[0006] To achieve the above-mentioned invention purpose, the technical solution adopted by the present invention is specifically as follows: A method for beam design of airborne 5G mobile base stations assisted by a channel knowledge map, comprising the following steps:

[0007] Step 1: Channel knowledge map construction

[0008] To construct the channel knowledge map mapped by the coverage area of the airborne 5G mobile base station, it is first necessary to measure the channel characteristics of the coverage area in advance, and then construct the channel knowledge map in combination with the spatial position. The former can be regarded as the process of channel characteristic acquisition, while the latter can be regarded as the process of constructing the channel knowledge map.

[0009] For the channel characteristic acquisition process, first, select some sampling points in the coverage area, and send pilot signals at the user terminal side based on the sampling point positions to measure the channel characteristics; secondly, receive the pilot signals at the airborne 5G mobile base station side, and parse the channel state information of each sampling point from them. During the receiving process, the signal received at the base station side can be expressed as

[0010] Y = HX + Z

[0011] where X is the known pilot signal, H is the channel response, and z represents the superimposed noise on the pilot channel. During the signal parsing process, the minimum mean square error (MMSE) algorithm can be used to solve the channel state information:

[0012]

[0013] where represents the channel estimation, W represents the weighting matrix, represents the mean square error of the channel estimation. The goal of MMSE is to make achieve minimization by selecting an appropriate W.

[0014] The channel state information obtained from channel estimation includes, but is not limited to, parameters such as channel gain, propagation angle, delay spread, multipath effect, signal-to-noise ratio (SNR), etc. Using these parameters, a multi-dimensional channel knowledge map can be constructed. For different channel state information, it can be weighted and stored according to its influence degree on beam design, and different priorities can be used for data compression and storage, thereby improving the utilization efficiency of data and reducing unnecessary storage and retrieval overhead. Among these parameters, the angles of arrival and departure are most closely related to the beam design of the airborne 5G mobile base station, and these parameters directly affect the optimization strategy of the beam design of the airborne 5G mobile base station.

[0015] There may be multi-scale differences in the channel state information. For example, there are different types of geographical environments such as cities and mountains at a large scale, while at a small scale, it may involve local environments such as buildings and streets. Therefore, the channel knowledge map should support multi-scale data processing and storage. For example, coarse-grained channel feature data is used at a large scale; while fine-grained channel feature data is used at a small scale.

[0016] For the process of constructing the channel knowledge map, first, the sampled channel state information is combined with the corresponding geographical location information; second, a spatial interpolation algorithm is used to predict the channel state information at the remaining positions outside the sampled points; then, the information of the sampled points and the predicted points is combined to jointly construct a database containing the channel characteristics of all positions within the coverage area. This database can be a two-dimensional or three-dimensional map, with efficient data storage and retrieval capabilities, and can provide retrieval services for channel characteristics at different positions. During the interpolation prediction process, the inverse distance weighted interpolation method can be used as the spatial interpolation algorithm, and its formula is:

[0017]

[0018] where, represents the interpolation result, x represents the value of the known sampled point, and d represents the distance between the interpolation point and the sampled point. The database jointly constructed by the sampled points and the predicted points, that is, the channel knowledge map, can be expressed as:

[0019] where, represents the set of channel state information of the sampled points, represents the set of channel state information of the predicted points.

[0020] In addition, with the continuous development of Deep Learning (DL) technology, methods such as Deep Neural Network (DNN) have shown good application effects in spatial interpolation and extrapolation. Therefore, the channel state information of the sampling points can also be used as input, and a deep learning model can be used to automatically learn the characteristics of complex channels, so as to quickly generate the channel state information at other positions outside the sampling points.

[0021] Step 2: Acquisition of channel state information

[0022] In the beam design process of the airborne 5G mobile base station, the base station can obtain the channel state information of the specified position in the coverage area by retrieving the channel knowledge map. First, the measured target position information is sent to the base station side, then the position index is determined at the base station side according to the position information, and then the channel state information of this position is retrieved by using the position index to retrieve the channel knowledge map. In the process of determining the position index, limited by the resolution of the channel knowledge map, the position index of the nearest ground image pixel point can be selected as the target position index, which is a quantization process of position information. In the process of retrieving the channel knowledge map, the channel state information with the position index of i0 can be expressed as:

[0023]

[0024] where represents the retrieved channel state information.

[0025] Through rapid retrieval, the system can quickly obtain the channel characteristics in the current environment, thus avoiding the time overhead caused by a large number of measurements and calculations in the traditional channel estimation method based on pilot signal measurement.

[0026] In order to achieve efficient retrieval of the channel knowledge map, multi-dimensional indexing technology can be used to quickly realize the matching of geographical location and channel characteristics. Through this indexing technology, the airborne 5G mobile base station can quickly retrieve the channel state information of the corresponding position in the coverage area without repeated calculation of the channel state information.

[0027] Step 3: Antenna beam design

[0028] First, determine the transmission codebook according to the number of antenna ports and the number of transmission layers of the airborne 5G mobile base station system; then, multiply the obtained channel state information by all the codewords in the codebook and take the norm, and select the codeword with the largest product norm as the optimal codeword; then, use this codeword for precoding processing to achieve the beamforming effect. Among them, the selection of the optimal codeword can be realized by the following formula:

[0029]

[0030] Among them, g j represents the candidate codeword, and J represents the number of codewords in the codebook. The pre-coded signal can be expressed as:

[0031] S = gX

[0032] By adopting the multi-antenna beamforming technology, the beam direction can be optimized and the signal energy can be focused, thereby improving the system capacity and expanding the effective coverage range. The goal of beam design is to focus on the signal propagation direction and maximize the signal reception intensity, so as to ensure efficient and stable communication effects. Beam design usually involves multiple aspects such as the beam direction, width, and intensity, among which the most important is the beam direction.

[0033] According to the distribution of users and communication requirements in the coverage area, the beam design needs to be adaptive. By obtaining the user location and traffic demand data in real time, the system can dynamically adjust the beam direction, width, and intensity to achieve user demand-driven beamforming. Adaptive beamforming can not only improve the system capacity but also reduce interference and improve the communication quality.

[0034] Step Four: Feedback and Map Update

[0035] With the slow change of the communication environment, the channel state information may change dynamically. The airborne 5G active base station can continuously optimize the beam design by updating the channel knowledge map in a timely manner to ensure the stable operation of the communication system in a dynamic coverage environment. The updated channel knowledge map can be expressed as:

[0036]

[0037] Among them, represents the updated channel state information.

[0038] Specifically, after achieving beam coverage, first, the user terminal evaluates the quality of the received data and sends CSI feedback to the airborne 5G active base station in a timely manner; then, the base station updates the channel knowledge map according to the feedback result and optimizes the beam design.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. By constructing and utilizing the channel knowledge map, the present invention obtains the channel state information and uses it for multi-antenna beam design, thereby meeting the requirements of the airborne 5G active base station for quickly building an efficient coverage network.

[0041] 2. The key point of the present invention is to map the channel propagation characteristics of the enhanced coverage area of the airborne 5G mobile base station into a channel knowledge map, quickly obtain channel state information by retrieving the channel map, and assist in the multi-antenna beam design on the base station side. This solution can not only reduce the channel estimation overhead and improve the system transmission efficiency, but also facilitate the rapid formation of an efficient area coverage network.

[0042] 3. Considering that the airborne 5G mobile base station usually deploys a large-scale antenna array, the present invention uses the channel state information provided by the channel knowledge map, combines with multi-antenna technology for beam design, realizes signal focusing in the spatial domain, and supports the dynamic adjustment and optimization of the beam, thus significantly improving the beam directivity and system capacity.

[0043] 4. By combining the channel knowledge map with the multi-antenna beam design, the present invention provides a fast and efficient beam design method for the airborne 5G mobile base station. This method can not only greatly reduce the channel estimation overhead, but also improve the beam pointing accuracy, so it can play an important role in various application scenarios such as emergency communication and personalized networking, providing strong support for the transmission application of the unmanned aerial vehicle (UAV)-borne 5G communication system.

[0044] 5. By retrieving the channel knowledge map to obtain the channel state information, the present invention can avoid the overhead of repeated large-scale measurements and calculations in traditional channel estimation. On the one hand, it effectively reduces the computational complexity of the system; on the other hand, the channel knowledge map can quickly provide channel characteristic information in a large range, accelerating the beam design and deployment process of the airborne 5G mobile base station and meeting the personalized and low-cost networking requirements.

[0045] 6. By using the accurate channel state information, the present invention can optimize beamforming, improve the beam design accuracy, enhance the communication quality and signal coverage range of the system, and thus improve the overall system capacity. In addition, it can also adapt to different geographical environments and communication requirements, support the dynamic adjustment and optimization of the beam design, and enhance the adaptability and communication ability of the airborne 5G mobile base station in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention.

[0047] Figure 1 It is the overall flowchart of the channel knowledge map-assisted beam design for the airborne 5G mobile base station of the present invention.

[0048] Figure 2 It is the schematic diagram of the key process of the channel knowledge map-assisted beam design for the airborne 5G mobile base station of the present invention.

[0049] Figure 3 Schematic diagram of beam coverage of the airborne 5G mobile base station of the present invention.

[0050] Figure 4 Schematic diagram for exemplifying the channel knowledge map of the angle of arrival in the embodiment of the present invention.

[0051] Figure 5 Curve graph showing the variation of the transmission efficiency of the present invention with the connection number density.

[0052] Figure 6 Curve graph showing the variation of the system capacity of the present invention with the SNR. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] Embodiment 1

[0055] Refer to Figure 1 and Figure 6 , the present invention provides its technical solution as a beam design method for an airborne 5G mobile base station assisted by a channel knowledge map. The present invention relates to the overall process of beam design for an airborne 5G mobile base station assisted by a channel knowledge map, as Figure 1 shown. In order to improve the ability of the airborne 5G mobile base station to quickly and efficiently cover and form a network, and at the same time reduce the channel estimation overhead faced by beam design, this technical solution introduces the channel knowledge map into the process of obtaining channel state information. By constructing a channel knowledge map, the channel estimation overhead is reduced, fast and accurate beam design is realized, and the system network coverage ability is enhanced; the overall process of channel knowledge map assisted beam design for an airborne 5G mobile base station is as Figure 1 shown. First, map the communication coverage area of the airborne 5G mobile base station to a map; secondly, select some sampling points from the map to measure their channel state information; thirdly, based on the measurement data of the sampling points, use a spatial interpolation algorithm to predict the channel state information of the remaining points in the map, and by combining the channel state information of the sampling points and the predicted points, jointly construct a complete channel knowledge map; then, according to the position information of the target object, retrieve the channel knowledge map to obtain its channel state information; then, use the channel state information to design the antenna beam to enhance the aerial coverage effect; finally, in order to meet the communication coverage requirements in a dynamic environment, it is also necessary to update the channel knowledge map in a timely manner and feedback it to the airborne 5G mobile base station.

[0056] The key process of channel knowledge map assisted beam design for an airborne 5G mobile base station includes four functional modules, as Figure 2As shown, they are channel knowledge map construction, channel state information acquisition, antenna beam design, and feedback and map update respectively. First, measure the channel characteristics in the coverage area and construct a channel knowledge map by combining location information; second, retrieve the channel state information of the target location in the coverage area from the channel knowledge map, especially the angle information; then, determine the antenna beam direction according to the channel state information such as the angle, design or select a beam precoding matrix according to the beam direction, and perform precoding transmission using the designed or selected precoding matrix to achieve regional coverage of multiple beams; finally, update the channel knowledge map in a timely manner according to the feedback result of the channel state information after the user side receives data.

[0057] The beam design method of the channel knowledge map-assisted UAV-mounted 5G mobile base station described in the present invention, by combining the channel knowledge map and location information, not only greatly reduces the computational overhead of channel estimation, but also can improve the accuracy of beam design and communication quality, and has broad application prospects in the actual deployment process, especially in scenarios such as post-disaster emergency communication and urban hotspot coverage.

[0058] In the disaster emergency communication scenario, in the face of the complex environment of network paralysis after the disaster, the UAV-mounted 5G mobile base station can be quickly deployed and networked, and precise beam design can be carried out according to the channel knowledge map to provide efficient and reliable communication services to ensure post-disaster rescue and reconstruction work.

[0059] Embodiment 2

[0060] In the urban hotspot coverage scenario, in the face of a hotspot area with high-density user access, the UAV-mounted 5G mobile base station can generate beams according to user needs to enhance coverage, improve system capacity and avoid communication congestion. Figure 3 Shows a general scenario of beam coverage of a UAV-mounted 5G mobile base station assisted by a channel knowledge map. The UAV-mounted 5G mobile base station covers the target area in a multi-beam manner and accesses the ground core network through a relay satellite to achieve data backhaul. The beam coverage area is mapped to generate a channel knowledge map through sampling measurement and interpolation prediction. The channel knowledge map provides guidance for beam design.

[0061] Taking the implementation of enhanced coverage by a UAV-mounted 5G mobile base station in a certain city as an example, the system obtains the channel state information in the coverage area through sampling measurement and spatial interpolation prediction, and establishes a channel knowledge map by combining location information. By retrieving the data in the channel knowledge map, the airborne 5G mobile base station can quickly and accurately obtain the channel state, flexibly generate an accurate beam design scheme, thereby effectively improving the signal quality and system capacity.

[0062] The simulation test results in the urban hotspot coverage scenario show that the channel knowledge map can effectively assist the beam design of airborne 5G active base stations, and can quickly and efficiently establish an enhanced coverage network in the hotspot area. Compared with the traditional beam design based on pilot signal detection, the method based on the channel knowledge map provided by this solution has higher system transmission efficiency.

[0063] Example 3

[0064] The generated angular domain channel knowledge map is as Figure 4 shown. The black part in the figure represents the buildings in the coverage area, and the other colors reflect the size of the received signal angle. It can be seen from the figure that the angular characteristics of the received signal are distributed in a banded shape, and the spatial clustering phenomenon is obvious. Using the angular information in the channel knowledge map can assist the beam design, so that different angular regions correspond to different beams, and simplify the pre-coding matrix selection process in the beam design.

[0065] Example 4

[0066] In the scenario where the drone is covered by the 5G active base station, beam designs are carried out based on two schemes: the traditional pilot signal measurement and the channel knowledge map proposed in the present invention. The change curves of the transmission efficiency η (unit: bps / Hz) achieved by the two schemes under different access number densities λ (unit: number / km²) are as Figure 5 shown. In this case, the method of the present invention and the traditional pilot signal measurement method are compared under different access number densities, and the key performance index of wireless communication, the transmission efficiency, is used to evaluate the performance of the method of the present invention:

[0067] Table 1 Comparison table of transmission efficiency between the method of the present invention and the traditional method

[0068]

[0069] Experiments show that the method for assisting the beam design of airborne active base stations with a channel knowledge map proposed in this embodiment can achieve higher transmission efficiency than the traditional method based on pilot signal measurement at different access number densities in the same coverage area, and this advantage becomes more obvious as the access density increases.

[0070] Specifically, the method of this embodiment maps the communication coverage area of the airborne 5G active base station into a map, and at the same time selects some sampling points from the map to measure their channel state information; then, based on the measurement data of the sampling points, a spatial interpolation algorithm is used to predict the channel state information of the remaining points in the map, and by combining the channel state information of the sampling points and the predicted points, a complete channel knowledge map is jointly constructed. According to the access number density with different configurations in the coverage area, a corresponding number of target terminals are randomly generated, and the channel state information is obtained by retrieving the channel knowledge map using the terminal positions, and then the antenna beam is designed based on the channel state information, which can reduce the channel estimation overhead and improve the transmission efficiency. In terms of transmission efficiency, the performance of the method proposed in this embodiment can exceed the traditional method based on pilot signal measurement; among them, when λ = 2000, the method of this embodiment can at least relatively improve the transmission efficiency by 6.45%; when λ = 5000, the method of this embodiment can at least relatively improve the transmission efficiency by 23.07%; when λ = 8000, the method of this embodiment can at least relatively improve the transmission efficiency by 47.61%; when λ = 10000, the method of this embodiment can at least relatively improve the transmission efficiency by 100.00%; these results demonstrate the competitiveness of the method proposed in this embodiment.

[0071] Embodiment 5

[0072] In the scenario where the drone is covered by the 5G active base station, beam design is carried out based on two schemes: the traditional pilot signal measurement and the channel knowledge map proposed in the present invention. The curves of the system capacity C (unit: Mbps) achieved by the two schemes varying with the SNR (unit: dB) are as Figure 6 shown. In this case, the method of the present invention and the traditional pilot signal measurement method are compared under different SNRs, and the key performance indicator of wireless communication, the system capacity, is used to evaluate the performance of the method of the present invention:

[0073] Table 2 Comparison table of system capacity between the method of the present invention and the traditional method

[0074]

[0075] Experiments show that a method for airborne active base station beam design assisted by a channel knowledge map proposed in this embodiment can achieve a higher system capacity at different SNRs in the same coverage area compared with the traditional method based on pilot signal measurement, especially when the SNR is small.

[0076] Specifically, the method of this embodiment maps the communication coverage area of the airborne 5G mobile base station into a map, and at the same time selects some sampling points from the map to measure their channel state information under different SNR configurations; then, based on the measurement data of the sampling points, a spatial interpolation algorithm is used to predict the channel state information of the remaining points in the map. By combining the channel state information of the sampling points and the predicted points, a complete channel knowledge map is jointly constructed. 2500 target terminals are randomly generated within the coverage area, and the channel state information is obtained by retrieving the channel knowledge map using the terminal positions. Then, the antenna beam is designed based on the channel state information, which can reduce the channel estimation overhead and improve the system capacity. In terms of system capacity, the performance of the method proposed in this embodiment can exceed the traditional method based on pilot signal measurement; among them, when SNR = -10, the method of this embodiment can at least relatively increase the system capacity by 80.0%; when SNR = -5, the method of this embodiment can at least relatively increase the system capacity by 51.3%; when SNR = 0, the method of this embodiment can at least relatively increase the system capacity by 18.3%; when SNR = 5, the method of this embodiment can at least relatively increase the system capacity by 8.8%; when SNR = 10, the method of this embodiment can at least relatively increase the system capacity by 5.7%; when SNR = 15, the method of this embodiment can at least relatively increase the system capacity by 3.1%; when SNR = 20, the method of this embodiment can at least relatively increase the system capacity by 2.0%; these results demonstrate the competitiveness of the method proposed in this embodiment.

[0077] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for beam design of an airborne 5G active base station assisted by a channel knowledge map, characterized in that, It includes the following steps: S1. Channel knowledge map construction To construct the channel knowledge map mapped by the coverage area of the airborne 5G active base station, it is first necessary to measure the channel characteristics of the coverage area in advance, and then construct the channel knowledge map in combination with the spatial positions. The former is regarded as the process of channel characteristic acquisition, while the latter is regarded as the process of channel knowledge map construction; For the channel characteristic acquisition process, first select some sampling points in the coverage area, and measure the channel characteristics by transmitting pilot signals based on the sampling point positions on the user terminal side; secondly, receive the pilot signals on the airborne 5G active base station side and parse the channel state information of each sampling point from them. During the receiving process, the signal received on the base station side is expressed as: Y = HX + Z where X is the known pilot signal, H is the channel response, and Z represents the superimposed noise on the pilot channel. During the signal parsing process, the minimum mean square error (MMSE) algorithm is used to solve the channel state information: Among them, represents channel estimation, W represents the weighting matrix, represents the mean square error of channel estimation. The goal of MMSE is to minimize by selecting an appropriate W; S2. Channel state information acquisition During the beam design process of the airborne 5G active base station, the base station retrieves the channel knowledge map to obtain the channel state information of the specified position in the coverage area; S3. Antenna beam design Determine the transmission codebook according to the number of antenna ports and the number of transmission layers of the airborne 5G active base station system; multiply the obtained channel state information by all the codewords in the codebook respectively and take the norm, and select the codeword with the largest product norm as the optimal codeword; then, use this codeword for precoding processing to achieve the beamforming effect; among them, the selection of the optimal codeword is achieved through the following formula: Among them, g j represents the candidate codeword, J represents the number of codewords in the codebook, and the signal after precoding processing is expressed as: S = gX; S4. Feedback and map update The channel state information will change dynamically. The airborne 5G active base station continuously optimizes the beam design by updating the channel knowledge map in a timely manner to ensure the stable operation of the communication system in a dynamic coverage environment. The updated channel knowledge map is expressed as: Among them, CKM represents the channel knowledge map before update, represents the updated channel state information.

2. The method for beam design of an airborne 5G active base station assisted by a channel knowledge map according to claim 1, wherein In step S1, for the channel knowledge map construction process, it includes the following steps: S11. First, combine the sampled channel state information with the corresponding geographical location information; S12. Use the spatial interpolation algorithm to predict the channel state information of the positions other than the sampling points; S13. Then, jointly construct a database containing the channel characteristics of all positions in the coverage area by combining the information of the sampling points and the predicted points. This database is a two-dimensional or three-dimensional map. During the interpolation prediction process, the inverse distance weighted interpolation method is used as the spatial interpolation algorithm, and its formula is: Among them, represents the interpolation result, x represents the value of the known sampling point, d represents the distance between the interpolation point and the sampling point, and the database jointly constructed by the sampling point and the prediction point is the channel knowledge map, which is expressed as: Among them, represents a set of channel state information of sampling points, represents a set of channel state information of prediction points.

3. The method for designing the beam of an airborne 5G active base station assisted by a channel knowledge map according to claim 1, wherein In step S2, the channel state information acquisition includes the following steps: S21. First, send the measured target position information to the base station side; S22. Then, determine the position index on the base station side according to the position information, and then use the position index to retrieve the channel knowledge map to obtain the channel state information of this position. During the process of determining the position index, limited by the resolution of the channel knowledge map, select the position index of the nearest ground pixel point as the target position index. During the process of retrieving the channel knowledge map, the channel state information with the position index of i0 is expressed as: Among them represents the retrieved channel state information; S23. Through fast retrieval, the system can quickly obtain the channel characteristics in the current environment, thus avoiding the time overhead caused by a large number of measurements and calculations in the traditional channel estimation method based on pilot signal measurement; S24. Use the multi-dimensional indexing technology to quickly match the geographical location with the channel characteristics. Through this indexing technology, the airborne 5G active base station can quickly retrieve the channel state information at the corresponding location within the coverage area without performing repeated channel state information calculation.

4. The method for designing the beam of an airborne 5G active base station assisted by a channel knowledge map according to claim 1, wherein The step S3 specifically includes the following steps: S31. After obtaining the channel state information, the active base station side first determines the number of transmission layers, and then selects a transmission codebook in combination with the number of transmission antenna ports; S32. Then, use the channel state information and the transmission codebook to obtain the optimal codeword, thereby determining the precoding matrix, and sending the precoding matrix to the user terminal side in the form of indication information; S33. The user terminal side first selects the precoding matrix using the received precoding matrix indication information, then processes the data to be transmitted using the precoding matrix to implement the codebook-based beam design, and finally sends the data based on the designed beam.

5. The method for beam design of an airborne 5G active base station assisted by a channel knowledge map according to claim 1, characterized in that In the step S4, after achieving beam coverage, first, the user terminal evaluates the quality of the received data and timely sends CSI feedback to the airborne 5G active base station; Then, the base station updates the channel knowledge map in a timely manner according to the feedback result and optimizes the beam design.

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