Unmanned aerial vehicle communication method based on low-orbit satellite communication and unmanned aerial vehicle

Abstract

This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and discloses a UAV communication method and a UAV based on low-Earth orbit (LEO) satellite communication. The method includes: establishing a communication subnet to share satellite measurement information, comprehensively evaluating multiple indicators with weights, and selecting the optimal primary serving satellite; calculating phase compensation based on a virtual phase center to achieve in-phase signal superposition, employing multi-user MIMO technology to transmit composite signals, and combining a cooperative interference cancellation algorithm to separate target data; when satellite visibility is predicted to be unavailable, forwarding unacknowledged data packets through newly connected satellite nodes or the node with the longest visibility time within the subnet to avoid data loss due to handover; discretizing spectrum units and randomly testing and evaluating their quality, marking high-quality spectrum units, and then dynamically adjusting the operating frequency band using a probabilistic migration method. This invention can effectively solve problems such as link instability, low spectrum utilization, and communication interruption caused by high dynamism in LEO satellite scenarios.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to UAV communication methods and UAVs based on low-Earth orbit (LEO) satellite communication. Background Technology

[0002] Low-Earth orbit (LEO) satellite-based UAV communication technology offers a solution for beyond-line-of-sight (BLOS) control and data transmission in remote areas, oceans, or complex terrains. However, this technology also faces other challenges. For instance, the connection between the UAV and the LEO satellite needs to be switched frequently within minutes to ensure link continuity. If the switching is not timely, it can lead to link termination. Existing switching mechanisms lack subnet collaborative forwarding capabilities, resulting in a high packet loss rate when the primary serving satellite switches over. The relative motion between the UAV and the satellite causes significant Doppler frequency offset, and traditional single-node modulation lacks a phase compensation mechanism, leading to severe superimposed interference. Static spectrum allocation is difficult to adapt to dynamic changes in traffic volume, resulting in both peak-hour spectrum congestion and idle-hour resource waste.

[0003] Therefore, the present invention provides a UAV communication method and a UAV based on low-orbit satellite communication. Summary of the Invention

[0004] This application provides a UAV communication method and a UAV based on low-Earth orbit satellite communication, which can improve the communication, stability and reliability of UAV swarms.

[0005] In a first aspect, this application provides a UAV communication method based on low-Earth orbit satellite communication, the method comprising:

[0006] Step S1: The UAV establishes a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected. Based on the primary service satellite, a primary UAV node is selected from the UAV subnet. The primary UAV node represents the entire UAV communication subnet and initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet.

[0007] Step S2: When the UAV transmits signals, the phase compensation amount of each UAV is calculated in real time. Each UAV adds the corresponding phase compensation amount in advance when modulating the signal before transmitting. When the main service satellite transmits signals, it transmits composite signals. After receiving the signals, each UAV exchanges data through the UAV communication subnet and uses the cooperative interference cancellation algorithm to separate the target data.

[0008] Step S3: When it is predicted that the primary service satellite is out of sight, each UAV shares the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new primary service satellite or the original primary service satellite.

[0009] In conjunction with the first aspect, in the first implementation of the first aspect of this application, selecting the primary serving satellite based on measurement information includes:

[0010] Step S11: Measurement information includes signal strength, Doppler frequency offset, and visible time window. The signal strength received by all UAVs in the UAV communication subnet is normalized, and the link stability index between each satellite and UAV is calculated based on the Doppler frequency offset.

[0011] Step S12: For each visible satellite, the average of its normalized signal strength and that of all drones is taken as the first evaluation value, the average of its link stability index and that of all drones is taken as the second evaluation value, and the average of its visible time window and that of all drones is taken as the third evaluation value.

[0012] Step S13: Add up the bandwidth requirements of each UAV in the UAV communication subnet to get the overall bandwidth requirement, divide the overall bandwidth requirement by the available bandwidth of each visible satellite to get the satellite capacity matching degree, and use the satellite capacity matching degree as the fourth evaluation value of the corresponding visible satellite.

[0013] Step S14: Set different weight values ​​for all evaluation values, calculate the weighted sum of all evaluation values, use the weighted sum as the comprehensive evaluation value of each visible satellite, and use the visible satellite with the highest comprehensive evaluation value as the primary service satellite.

[0014] In conjunction with the first aspect, in the second implementation of the first aspect of this application, the phase compensation amount of each UAV is calculated in real time, including:

[0015] The main UAV calculates the virtual phase center of the UAV communication subnet, calculates the first propagation distance from the virtual phase center to the main service satellite, and calculates the second propagation distance from each UAV to the main service satellite in real time based on high-precision positioning information and satellite ephemeris data. Based on the first propagation distance and each second propagation distance, the distance difference between each UAV and the main service satellite relative to the virtual phase center is calculated. The corresponding phase compensation amount is obtained by multiplying the distance difference of each UAV by 2π and dividing by the carrier wavelength.

[0016] In conjunction with the first aspect, in the third implementation of the first aspect of this application, calculating the virtual phase center of the UAV communication subnet includes:

[0017] Obtain the position coordinates of each drone, add the position coordinates of each drone together and divide by the number of drones to get the corresponding average coordinates, and use the position corresponding to the average coordinates as the virtual phase center.

[0018] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, sharing the data packets to be sent through the UAV communication subnet includes:

[0019] Step S31: When any UAV in the UAV communication subnet predicts that it will be out of sight of the current primary service satellite in the future, it determines whether there is already a first UAV communicating with the new primary service satellite in the UAV communication subnet. If there is, proceed to step S32; otherwise, proceed to step S33.

[0020] Step S32: Send the data packets to be sent to the corresponding first UAV. After receiving the data packets sent by other UAVs, the first UAV forwards the corresponding data packets to the new main service satellite.

[0021] Step S33: Obtain the second UAV with the longest visible time window within the UAV communication subnet and the main service satellite, and send the data packets to be sent to the second UAV. After receiving the data packets sent by other UAVs, the second UAV forwards them to the original main service satellite.

[0022] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, before the UAV communicates with the main serving satellite, the following is also performed:

[0023] A dynamic spectrum sharing protocol is established with the main service satellite. When the traffic volume is low, the basic bandwidth is occupied. When the traffic volume is high, idle spectrum segments are temporarily applied for and activated based on real-time spectrum sensing results, and released immediately after use.

[0024] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, dynamic spectrum sharing is optimized based on the following method:

[0025] The spectrum resources are discretized into multiple spectrum units. The main UAV periodically conducts communication tests based on any spectrum unit and evaluates the communication quality to obtain a quality assessment value. If the quality assessment value of a spectrum unit is found to be higher than a preset quality threshold and the communication quality is stable, the corresponding spectrum unit is marked as a high-quality spectrum unit. The spectrum information of the high-quality spectrum unit and the corresponding quality assessment value are sent to all UAVs in the UAV communication subnet. UAVs in the UAV communication subnet adjust their operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner based on the received spectrum quality assessment value.

[0026] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, the evaluation of communication quality to obtain a quality evaluation value includes:

[0027] The signal strength of the corresponding spectrum unit is detected, the false alarm rate is calculated, the maximum signal strength obtained in history is obtained, the current signal strength is divided by the maximum signal strength to obtain the first ratio, the ratio and the false alarm rate are added together and then divided by two to obtain the value as the communication quality assessment value.

[0028] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, adjusting the operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner includes:

[0029] After each UAV receives a high-quality spectrum unit, it presets a migration coefficient and a maximum communication quality threshold, obtains the quality assessment value of the high-quality spectrum unit, multiplies the quality assessment value by the migration coefficient and divides it by the maximum communication quality threshold to obtain the migration probability value, generates a random number, and compares the random number with the migration probability value. If the random number is less than the migration probability value, it switches to the corresponding high-quality spectrum unit; otherwise, it maintains communication on the original frequency band.

[0030] Secondly, this application provides a drone, including the following modules:

[0031] The subnet construction module enables the UAV to establish a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected, and a primary UAV node is selected from the UAV subnet based on the primary service satellite. The primary UAV node represents the entire UAV communication subnet and initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet.

[0032] The collaborative communication module calculates the phase compensation amount of each UAV in real time when the UAV sends signals. Each UAV adds the corresponding phase compensation amount in advance when modulating the signal before sending it. When the main service satellite sends signals, it sends composite signals. After receiving the signals, each UAV exchanges data through the UAV communication subnet and uses a collaborative interference cancellation algorithm to separate the target data.

[0033] When the main service satellite is predicted to be out of sight, the switching forwarding module allows each UAV to share the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new main service satellite or the original main service satellite.

[0034] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0035] The technical solution provided in this application establishes a dynamically self-organizing UAV communication subnet, intelligently selecting the primary serving satellite based on multiple dimensions such as signal strength, link stability, and bandwidth requirements. This effectively solves the problem of frequent link switching caused by traditional single-node decision-making. Combined with a satellite switching prediction mechanism, multi-hop forwarding within the subnet enables redundant data packet transmission, ensuring continuous data transmission even when the satellite is not visible, avoiding data loss due to link interruption, and significantly improving communication reliability. Based on a virtual phase center, the phase compensation amount of each UAV is calculated, enabling the signals transmitted by multiple UAVs to be superimposed in phase at the satellite receiver, greatly enhancing signal strength. A cooperative interference cancellation algorithm is adopted, achieving effective separation of multi-user interference through data sharing within the subnet, reducing the bit error rate. Through discretized spectrum units and a random testing mechanism, the primary UAV periodically evaluates spectrum quality and marks high-quality spectrum units, dynamically adjusting the operating frequency band using a probabilistic migration strategy. This avoids resource waste caused by traditional fixed allocation and prevents local congestion caused by multiple UAVs simultaneously occupying the same frequency band, enabling on-demand allocation of spectrum resources in scenarios with fluctuating traffic, and improving spectrum resource utilization. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the steps of the UAV communication method based on low-orbit satellite communication in the embodiments of this application;

[0038] Figure 2 This is a structural diagram of the drone in the embodiments of this application. Detailed Implementation

[0039] This application provides a UAV communication method and a UAV based on low-Earth orbit satellite communication. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0040] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the UAV communication method based on low-Earth orbit satellite communication in this application includes:

[0041] Step S1: The UAV establishes a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected. Based on the primary service satellite, a primary UAV node is selected from the UAV subnet. The primary UAV node, representing the entire UAV communication subnet, initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet.

[0042] Specifically, when a drone enters an unfamiliar mission area, it is initially isolated and lacks a local communication network. At the same time, the satellite signal measurement results of a single drone are limited, making it impossible to fully assess the satellite coverage quality. This may also lead to blind access decisions in the future. Therefore, it is necessary to first establish a drone communication subnet with other drones within the communication range to ensure that drones within the drone communication subnet can communicate with each other and share measurement information from different low-orbit satellites. Subsequently, a primary access satellite is selected to provide a data foundation to avoid problems such as poor access satellite quality and unstable links due to missing information.

[0043] Because low-Earth orbit satellites move rapidly and their coverage is dynamically changing, and drone swarms are widely distributed, the measurement results of drones in different locations on the same satellite can vary greatly. If each drone selects a satellite independently, it may lead to inconsistent access satellites, wasted resources, frequent link switching, and other problems. Therefore, satellite quality is comprehensively evaluated based on measurement information to ensure that all drones in the drone communication subnet select the optimal primary serving satellite.

[0044] When initiating an access request, if each UAV initiates an access request to the satellite individually, it will lead to signaling redundancy, channel contention, and reduced access efficiency. At the same time, the satellite cannot quickly obtain overall information about the subnet, making it difficult to optimize resource allocation. Therefore, a master UAV node is selected from the UAV subnet based on the master serving satellite. The master UAV node agrees to initiate an aggregated access request. The access request includes the identity identifiers, service requirements, and collaborative capabilities of all UAVs in the subnet.

[0045] Selecting a master drone node from the drone subnet based on the master serving satellite means specifically choosing the drone with the strongest signal strength to the master serving satellite as the master drone node.

[0046] Step S2: When the UAV transmits signals, the phase compensation amount of each UAV is calculated in real time. Each UAV adds the corresponding phase compensation amount in advance when modulating the signal before transmitting. When the main service satellite transmits signals, it transmits composite signals. After receiving the signals, each UAV exchanges data through the UAV communication subnet and uses a cooperative interference cancellation algorithm to separate the target data.

[0047] Specifically, when multiple drones transmit signals simultaneously, if the phases are not aligned, the signals may cause destructive interference in the satellite segment due to the phase difference. For example, if two signals have a phase difference of 180 degrees, they will cancel each other out after superposition. To solve the above problem, when a drone transmits a signal, the phase compensation amount of each drone is calculated in real time. Each drone adds the corresponding phase compensation amount in advance when modulating the signal before transmitting it. This not only avoids signal cancellation caused by phase inconsistency, but also improves the signal strength.

[0048] To improve throughput, the main service satellite uses multi-user MIMO technology to send composite signals to UAVs. The target data of different UAVs overlaps in the frequency domain or spatial domain. If the UAV decodes independently, the bit error rate will increase due to multi-user interference. A single UAV can only use its own received signal to cancel interference. Due to the lack of global information, it is difficult to completely eliminate interference. After receiving the signal, each UAV exchanges the received data through the UAV communication subnet to obtain a copy of the signal from other UAVs. Then, a cooperative interference cancellation algorithm (such as serial interference cancellation) is used to separate the target data and eliminate interference.

[0049] Suppose a satellite wants to send data x1 to drone A and x2 to drone B, and the composite signal sent is X = x1 + x2. Drone A receives the signal y. a =h a1 x1+h a2 +n a , where h a1 h a2 n is the channel coefficient. a As noise, the composite signal received by UAV B is y b =h b1 x1+h b2 +n b , where h b1 h b2 n is the channel coefficient. b Due to noise, the received signal of UAV A contains x2, while the signal of UAV B contains x1, leading to an increased bit error rate. In traditional methods, the UAV only utilizes its own received signal y. a or y bIndependent decoding typically cannot completely separate x1 and x2. Therefore, data is received through the UAV communication subnet, obtaining signal copies from other UAVs. Then, a cooperative interference cancellation algorithm (such as serial interference cancellation algorithm or minimum mean square error method) is used to separate the target data, and y is exchanged through the UAV communication subnet. a y b The algorithm solves for x1 and x2 together to eliminate the interference between them, thus achieving a better goal of eliminating interference. The specific cooperative interference elimination algorithm is an existing technology and will not be explained in detail here.

[0050] The above methods can solve the problems of increased bit error rate and limited throughput caused by interference from multiple users.

[0051] Step S3: When it is predicted that the primary service satellite is out of sight, each UAV shares the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new primary service satellite or the original primary service satellite.

[0052] Specifically, when the primary access satellite is about to become invisible, directly switching to a new primary access satellite may result in data loss due to link switching delays or unacknowledged data packets. Traditional methods require retransmission of unacknowledged data packets, but the high-speed movement of low-Earth orbit satellites leads to frequent switching and low retransmission efficiency. To solve this problem, when it is predicted that the primary service satellite will become invisible, that is, when the satellite is about to move out of the UAV communication subnet's field of view, each UAV shares the data packets to be sent through the UAV communication subnet. Due to their different locations, each UAV in the UAV communication subnet establishes a connection with the new primary service satellite at different times and disconnects from the original primary service satellite. Assuming that the UAVs that have disconnected from the original primary service satellite have unacknowledged data packets, these data packets need to be retransmitted. To avoid retransmission, after each UAV in the UAV communication subnet receives data packets sent by other UAVs, other UAVs in the UAV communication subnet that are still connected to the original primary service satellite or have already connected to the new primary service satellite forward the data packets to the new primary service satellite or the original primary service satellite. Through the above method, data loss or retransmission delays caused by the interruption of a single link are effectively avoided.

[0053] In one specific embodiment, selecting a primary serving satellite based on measurement information includes the following steps:

[0054] The measurement information includes signal strength, Doppler frequency offset, and visible time window. The signal strength received by all UAVs in the UAV communication subnet is normalized, and the link stability index between each satellite and UAV is calculated based on the Doppler frequency offset.

[0055] For each visible satellite, the average of its normalized signal strength and that of all drones is used as the first evaluation value, the average of its link stability index and that of all drones is used as the second evaluation value, and the average of its visible time window and that of all drones is used as the third evaluation value.

[0056] The bandwidth requirements of each UAV in the UAV communication subnet are added together to obtain the overall bandwidth requirement. The overall bandwidth requirement is divided by the available bandwidth of each visible satellite to obtain the satellite capacity matching degree. The satellite capacity matching degree is used as the fourth evaluation value of the corresponding visible satellite.

[0057] Different weight values ​​are assigned to all evaluation values. The weighted sum of all evaluation values ​​is calculated, and the weighted sum is used as the comprehensive evaluation value of each visible satellite. The visible satellite with the highest comprehensive evaluation value is selected as the primary service satellite.

[0058] Specifically, signal strength refers to the signal strength received by the UAV from a low-Earth orbit (LEO) satellite. It is used to assess the quality of the communication link between the UAV and the satellite. By sharing the signal strength measurements of different satellites, it is possible to determine which LEO satellite has the strongest signal at the current geographical location and time. For each visible satellite, the average normalized signal strength of that satellite and all UAVs is calculated, and this average signal strength is used as the primary evaluation value for that visible satellite. Visible satellites refer to all LEO satellites that can be scanned by all UAVs within the UAV communication subnet.

[0059] Doppler frequency offset is caused by the extremely high relative radial velocity between the satellite and the drone. According to the Doppler effect, the frequency of the signal transmitted by the satellite will change at the drone receiver. The difference between the received frequency and the transmitted frequency is the Doppler frequency offset. The absolute value of the Doppler frequency offset indicates the stability of the link. The larger the frequency offset, the more unstable the link. Therefore, the link stability index is calculated based on the Doppler frequency offset. Assuming that the Doppler frequency offset is represented by f and the link stability index is represented by S, then S = 1 / (1+f). For each visible satellite, the average value of its link stability index and that of all drones is calculated. The average value of the link stability index is used as the second evaluation value.

[0060] Visual satellites refer to the set of all low-orbit satellites within the drone communication subnet that drones can detect satellite signals from.

[0061] The visible time window refers to the remaining time a satellite is within the field of view of a drone. The visible time window can be calculated by pre-shared satellite ephemeris and drone position prediction. For each visible satellite, the average of its and all drones' visible time windows is used as the third evaluation value.

[0062] The bandwidth requirements of each UAV within the UAV communication subnet are summed to obtain the overall bandwidth requirement. The overall bandwidth requirement is then divided by the available bandwidth of each satellite to obtain the satellite capacity matching degree. The satellite capacity matching degree is used as the fourth evaluation value for the corresponding visible satellite. The satellite capacity matching degree can be used to evaluate whether the satellite meets the bandwidth requirements of the UAV communication subnet. The larger the satellite capacity matching degree, the better it can meet the bandwidth requirements of the UAV communication subnet.

[0063] By assigning a corresponding weight value to each evaluation value and then summing the evaluation values ​​in a weighted manner, a comprehensive evaluation value is obtained. The higher the comprehensive evaluation value, the more suitable the satellite is as a primary service satellite.

[0064] Through the above steps, the UAVs in the UAV communication subnet quantify the signal strength, link quality, visibility time window, and capacity matching degree of each visible satellite based on shared measurement information. The comprehensive evaluation value is determined by weighted averaging, and the visible satellite with the highest comprehensive evaluation value is selected as the primary access satellite to provide the optimal primary service satellite for subsequent communications.

[0065] In one specific embodiment, the phase compensation amount of each UAV is calculated in real time, which specifically includes the following steps:

[0066] The main UAV calculates the virtual phase center of the UAV communication subnet, calculates the first propagation distance from the virtual phase center to the main service satellite, and calculates the second propagation distance from each UAV to the main service satellite in real time based on high-precision positioning information and satellite ephemeris data. Based on the first propagation distance and each second propagation distance, the distance difference between each UAV and the main service satellite relative to the virtual phase center is calculated. The corresponding phase compensation amount is obtained by multiplying the distance difference of each UAV by 2π and dividing by the carrier wavelength.

[0067] Specifically, to calculate the phase compensation, the master UAV first calculates the virtual phase center of the UAV communication subnet. The virtual phase center is an artificially defined reference point in space, serving to provide a common phase reference for all UAVs within the communication subnet. Calculations ensure that the phase of all UAV signals in the satellite segment matches the phase of the signal emitted from this virtual phase center, guaranteeing signal coherence and significantly enhancing the signal strength. The detailed steps for calculating the virtual phase center will be explained later. The first propagation distance from the virtual phase center to the master service satellite is then calculated, based on the high-precision positioning information of each UAV. Based on information and satellite ephemeris data, the second propagation distance from each UAV to the main service satellite is calculated in real time. The absolute value of the difference between the first and second propagation distances is taken as the distance difference between each UAV and the main service satellite relative to the virtual phase center. For every wavelength traveled, the phase of an electromagnetic wave changes by 2π radians. Therefore, to compensate for the distance difference, the UAV signal is advanced or delayed by a corresponding phase compensation amount. This is obtained by multiplying the distance difference of each UAV by 2π and dividing by the carrier wavelength. The carrier wavelength is the distance an electromagnetic wave travels in one complete oscillation cycle and is inversely proportional to the carrier frequency: carrier wavelength = speed of light / carrier frequency. After this phase compensation, the phase of all signals transmitted by the UAVs at the main service satellite receiver will be completely consistent with the phase of the signal emitted from the virtual phase center that has traveled the first propagation distance. These signals can then be superimposed in phase, avoiding signal cancellation caused by phase inconsistency.

[0068] In one specific embodiment, calculating the virtual phase center of the UAV communication subnet includes the following steps:

[0069] Obtain the position coordinates of each drone, add the position coordinates of each drone together and divide by the number of drones to get the corresponding average coordinates, and use the position corresponding to the average coordinates as the virtual phase center.

[0070] Specifically, assuming there are a total of 3 drones, the position coordinates of the 3 drones (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) are obtained, and the position of the average coordinates ((x1+x2+x3) / 3, (y1+y2+y3) / 3, (z1+z2+z3) / 3) is used as the virtual phase center.

[0071] In one specific embodiment, sharing the data packets to be sent through the UAV communication subnet includes the following steps:

[0072] When any UAV in the UAV communication subnet predicts that it will be out of sight of the current primary service satellite in the first moment of the future, it determines whether there is already a first UAV in the UAV communication subnet that is communicating with the new primary service satellite.

[0073] In some cases, subsequent data packets are sent to the corresponding first UAV. After receiving data packets from other UAVs, the first UAV forwards the corresponding data packets to the new primary service satellite.

[0074] If none is available, the second drone with the longest visible time window within the drone communication subnet and the main service satellite will send the subsequent data packets to the second drone. After receiving the data packets sent by other drones, the second drone will forward them to the original main service satellite.

[0075] Specifically, when a drone is about to switch its primary service satellite, it may lose connection with the primary service satellite after sending data packets without receiving the corresponding confirmation message. To ensure that the drone's data packets are not lost due to the switch, if the drone predicts that it will be out of sight of the current primary service satellite in the near future, and if drone A in the drone communication subnet predicts that it will lose connection with the current primary service satellite in 1 second, then sending data packets at this time may fail or succeed but no confirmation message will be received. Therefore, the drone first checks whether there is a first drone in the drone communication subnet that has already established a communication connection with the new primary service satellite. If so, the subsequent data packets are sent to the first drone. Since the first drone has already established a connection with the new primary service satellite, it will definitely not lose connection in a short time. Therefore, the data packets are sent to the corresponding first drone, which then forwards the data packets to the new primary service satellite until the drone switches to the new primary service satellite, at which point the sending of data packets to the first drone stops.

[0076] If no connection is established, it means that no drone in the current drone communication subnet has established a communication connection with the new primary service satellite. Therefore, the second drone with the longest visible time window between itself and the current primary service satellite is selected. Since the second drone has the longest visible time window with the primary service satellite, it is the most reliable drone to help forward data packets. Therefore, subsequent data packets are sent to the second drone, which then forwards the data packets to the primary service satellite until the current drone switches to the new primary service satellite, at which point the sending of data packets to the second drone stops.

[0077] In one specific embodiment, the following steps are also performed before the drone communicates with the main serving satellite:

[0078] A dynamic spectrum sharing protocol is established with the main service satellite. When the traffic volume is low, the basic bandwidth is occupied. When the traffic volume is high, idle spectrum segments are temporarily applied for and activated based on real-time spectrum sensing results, and released immediately after use.

[0079] Specifically, traditional satellite communication allocates fixed frequency bands to drones. However, when traffic is low (meaning the amount of data to be transmitted per unit time is less than or equal to a preset transmission threshold, such as when drones only need to transmit control information), a large amount of spectrum resources are idle. Conversely, when traffic is high (e.g., when transmitting video), the fixed frequency bands are insufficient, leading to congestion. To address this issue, a dynamic spectrum sharing protocol is established with the primary serving satellite. When traffic is low, a basic bandwidth, such as 2MHz, is used. When traffic is high, based on real-time spectrum sensing results (e.g., detecting idle frequency bands), idle spectrum segments are temporarily requested and activated, and released immediately after use. This method coordinates the use of spectrum resources by multiple drones through pre-negotiation and real-time sensing results. This not only avoids conflicts caused by simultaneous occupation of the same frequency band but also solves the problems of low efficiency in static spectrum resource allocation and mismatch between dynamic and static demands.

[0080] In one specific embodiment, dynamic spectrum sharing is optimized based on the following method:

[0081] The spectrum resources are discretized into multiple spectrum units. The main UAV periodically conducts communication tests based on any spectrum unit and evaluates the communication quality to obtain a quality assessment value. If the quality assessment value of a spectrum unit is found to be higher than a preset quality threshold and the communication quality is stable, the corresponding spectrum unit is marked as a high-quality spectrum unit. The spectrum information of the high-quality spectrum unit and the corresponding quality assessment value are sent to all UAVs in the UAV communication subnet. UAVs in the UAV communication subnet adjust their operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner based on the received spectrum quality assessment value.

[0082] Specifically, to support dynamic spectrum sharing, continuous spectrum resources are divided into independently manageable discrete units. For example, the 20MHz bandwidth of the 5GHz band is divided into four 5MHz spectrum units. Each unit can be independently allocated to different drones or service types. The master drone periodically and randomly selects any spectrum unit for communication testing to evaluate the communication quality and obtain a quality assessment value. If the quality assessment value of a spectrum unit is found to be higher than a preset quality threshold and the quality is stable, it indicates that the spectrum unit is suitable for communication. Therefore, the corresponding spectrum unit is marked as a high-quality spectrum unit, and the spectrum information and corresponding quality assessment value of the high-quality spectrum unit are sent to all drones in the drone communication subnet. This can notify other drones to prioritize accessing the high-quality spectrum unit. If all drones switch to the same high-quality spectrum unit at the same time, it may cause congestion. Therefore, drones in the drone communication subnet adjust their operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner based on the received spectrum quality assessment value to avoid local congestion.

[0083] In one specific embodiment, evaluating communication quality to obtain a quality assessment value specifically includes the following steps:

[0084] The signal strength of the corresponding spectrum unit is detected, the false alarm rate is calculated, the maximum signal strength obtained in history is obtained, the current signal strength is divided by the maximum signal strength to obtain the first ratio, the ratio and the false alarm rate are added together and then divided by two to obtain the value as the communication quality assessment value.

[0085] Specifically, since the measurement is of the communication quality of a spectrum unit, only two simple dimensions, signal strength and false alarm rate, are used to quickly assess the quality. The above method can be used to quickly obtain the communication quality assessment value, making it easy to quickly determine whether a spectrum unit is of high quality.

[0086] In one specific embodiment, adjusting the operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner specifically includes the following steps:

[0087] After each UAV receives a high-quality spectrum unit, it presets a migration coefficient and a maximum communication quality threshold, obtains the quality assessment value of the high-quality spectrum unit, multiplies the quality assessment value by the migration coefficient and divides it by the maximum communication quality threshold to obtain the migration probability value, generates a random number, and compares the random number with the migration probability value. If the random number is less than the migration probability value, it switches to the corresponding high-quality spectrum unit; otherwise, it maintains communication on the original frequency band.

[0088] Specifically, to avoid all drones migrating to the same high-quality spectrum unit, which could lead to local congestion, each drone, upon receiving a high-quality spectrum unit, presets a migration coefficient and a maximum communication quality threshold. To avoid full-probability migration, the migration coefficient is usually set to a decimal less than 1, such as 0.8. The preset maximum communication quality threshold is set to, for example, 1. The quality assessment value is multiplied by the migration coefficient and then divided by the maximum communication quality threshold to obtain the migration probability value. The migration probability value serves as the data basis for whether to migrate to the high-quality spectrum unit. The drone generates a random number greater than 0 and less than 1, and compares the random number with the migration probability value. If the random number is less than the migration probability value, it switches to the corresponding high-quality spectrum unit; otherwise, it maintains communication on the original frequency band.

[0089] Furthermore, to avoid long-term reliance on outdated information, the migration coefficient is dynamically modified based on the number of drones in a high-quality spectrum unit. For example, if the number of users in a high-quality spectrum unit exceeds a certain threshold, it means that there are not many drones in the current high-quality spectrum unit. In order to reduce the probability of other drones migrating to this high-quality spectrum unit, the migration coefficient is reduced, thereby reducing the migration probability value.

[0090] According to another aspect of the embodiments of the present invention, reference is made to... Figure 2As shown, a drone is also provided, including a drone communication method based on low-Earth orbit satellite communication as described above. The specific functions of each module are as follows:

[0091] The subnet construction module enables the UAV to establish a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected, and a primary UAV node is selected from the UAV subnet based on the primary service satellite. The primary UAV node represents the entire UAV communication subnet and initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet.

[0092] The collaborative communication module calculates the phase compensation amount of each UAV in real time when the UAV sends signals. Each UAV adds the corresponding phase compensation amount in advance when modulating the signal before sending it. When the main service satellite sends signals, it sends composite signals. After receiving the signals, each UAV exchanges data through the UAV communication subnet and uses a collaborative interference cancellation algorithm to separate the target data.

[0093] When the main service satellite is predicted to be out of sight, the switching forwarding module allows each UAV to share the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new main service satellite or the original main service satellite.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A UAV communication method based on low-Earth orbit satellite communication, characterized in that, The method includes: Step S1: The UAV establishes a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected. Based on the primary service satellite, a primary UAV node is selected from the UAV subnet. The primary UAV node represents the entire UAV communication subnet and initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet. Step S2: When the UAV transmits signals, the phase compensation amount of each UAV is calculated in real time, including: the main UAV calculates the virtual phase center of the UAV communication subnet, calculates the first propagation distance from the virtual phase center to the main service satellite, calculates the second propagation distance from each UAV to the main service satellite in real time based on high-precision positioning information and satellite ephemeris data, calculates the distance difference between each UAV and the main service satellite relative to the virtual phase center based on the first propagation distance and each second propagation distance, multiplies the distance difference of each UAV by 2π and divides by the carrier wavelength to obtain the corresponding phase compensation amount, each UAV adds the corresponding phase compensation amount in advance when modulating the signal before transmitting, the main service satellite transmits a composite signal when transmitting the signal, and each UAV exchanges data through the UAV communication subnet after receiving the signal and uses a cooperative interference cancellation algorithm to separate the target data. Step S3: When it is predicted that the primary service satellite is out of sight, each UAV shares the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new primary service satellite or the original primary service satellite.

2. The method according to claim 1, characterized in that, The primary service satellite is selected based on measurement information, including: Step S11: Measurement information includes signal strength, Doppler frequency offset, and visible time window. The signal strength received by all UAVs in the UAV communication subnet is normalized, and the link stability index between each satellite and UAV is calculated based on the Doppler frequency offset. Step S12: For each visible satellite, the average of its normalized signal strength and that of all drones is taken as the first evaluation value, the average of its link stability index and that of all drones is taken as the second evaluation value, and the average of its visible time window and that of all drones is taken as the third evaluation value. Step S13: Add up the bandwidth requirements of each UAV in the UAV communication subnet to get the overall bandwidth requirement, divide the overall bandwidth requirement by the available bandwidth of each visible satellite to get the satellite capacity matching degree, and use the satellite capacity matching degree as the fourth evaluation value of the corresponding visible satellite. Step S14: Set different weight values ​​for all evaluation values, calculate the weighted sum of all evaluation values, use the weighted sum as the comprehensive evaluation value of each visible satellite, and use the visible satellite with the highest comprehensive evaluation value as the primary service satellite.

3. The method according to claim 1, characterized in that, Calculating the virtual phase center of the UAV communication subnet includes: Obtain the position coordinates of each drone, add the position coordinates of each drone together and divide by the number of drones to get the corresponding average coordinates, and use the position corresponding to the average coordinates as the virtual phase center.

4. The method according to claim 1, characterized in that, Data packets to be sent are shared through the drone communication subnet, including: Step S31: When any UAV in the UAV communication subnet predicts that it will be out of sight of the current primary service satellite in the future, it determines whether there is already a first UAV communicating with the new primary service satellite in the UAV communication subnet. If there is, proceed to step S32; otherwise, proceed to step S33. Step S32: Send the data packets to be sent to the corresponding first UAV. After receiving the data packets sent by other UAVs, the first UAV forwards the corresponding data packets to the new main service satellite. Step S33: Obtain the second UAV with the longest visible time window within the UAV communication subnet and the main service satellite, and send the data packets to be sent to the second UAV. After receiving the data packets sent by other UAVs, the second UAV forwards them to the original main service satellite.

5. The method according to claim 1, characterized in that, Before the drone communicates with the main service satellite, the following also applies: A dynamic spectrum sharing protocol is established with the main service satellite. When the traffic volume is low, the basic bandwidth is occupied. When the traffic volume is high, idle spectrum segments are temporarily applied for and activated based on real-time spectrum sensing results, and released immediately after use.

6. The method according to claim 5, characterized in that, Optimize dynamic spectrum sharing based on the following method: The spectrum resources are discretized into multiple spectrum units. The main UAV periodically conducts communication tests based on any spectrum unit and evaluates the communication quality to obtain a quality assessment value. If the quality assessment value of a spectrum unit is found to be higher than a preset quality threshold and the communication quality is stable, the corresponding spectrum unit is marked as a high-quality spectrum unit. The spectrum information of the high-quality spectrum unit and the corresponding quality assessment value are sent to all UAVs in the UAV communication subnet. UAVs in the UAV communication subnet adjust their operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner based on the received spectrum quality assessment value.

7. The method according to claim 6, characterized in that, The quality assessment of communication results in a quality evaluation value, including: The signal strength of the corresponding spectrum unit is detected, the false alarm rate is calculated, the maximum signal strength obtained in history is obtained, the current signal strength is divided by the maximum signal strength to obtain the first ratio, the ratio and the false alarm rate are added together and then divided by two to obtain the value as the communication quality assessment value.

8. The method according to claim 6, characterized in that, Adjusting the operating frequency band to the corresponding high-quality spectrum unit in a probabilistic manner includes: After each UAV receives a high-quality spectrum unit, it presets a migration coefficient and a maximum communication quality threshold, obtains the quality assessment value of the high-quality spectrum unit, multiplies the quality assessment value by the migration coefficient and divides it by the maximum communication quality threshold to obtain the migration probability value, generates a random number, and compares the random number with the migration probability value. If the random number is less than the migration probability value, it switches to the corresponding high-quality spectrum unit; otherwise, it maintains communication on the original frequency band.

9. A drone for implementing the drone communication method based on low-Earth orbit satellite communication as described in any one of claims 1-8, characterized in that, Includes the following modules: The subnet construction module enables the UAV to establish a UAV communication subnet with other UAVs within its communication range. Within the UAV communication subnet, each UAV shares measurement information from different low-orbit satellites. Based on the measurement information, a primary service satellite is selected, and a primary UAV node is selected from the UAV subnet based on the primary service satellite. The primary UAV node represents the entire UAV communication subnet and initiates an aggregation access request to the primary service satellite. The request includes the identity identifiers of each UAV within the UAV communication subnet. The collaborative communication module calculates the phase compensation amount of each UAV in real time when the UAV transmits signals. This includes: the main UAV calculates the virtual phase center of the UAV communication subnet, calculates the first propagation distance from the virtual phase center to the main service satellite, calculates the second propagation distance from each UAV to the main service satellite in real time based on high-precision positioning information and satellite ephemeris data, calculates the distance difference between each UAV and the main service satellite relative to the virtual phase center based on the first propagation distance and each second propagation distance, multiplies the distance difference of each UAV by 2π and divides by the carrier wavelength to obtain the corresponding phase compensation amount, pre-adds the corresponding phase compensation amount when each UAV modulates the signal before transmitting, transmits a composite signal when the main service satellite transmits the signal, and exchanges data through the UAV communication subnet after receiving the signal and uses a collaborative interference cancellation algorithm to separate the target data. When the main service satellite is predicted to be out of sight, the switching forwarding module allows each UAV to share the data packets to be sent through the UAV communication subnet. After receiving the data packets sent by other UAVs, each UAV in the UAV communication subnet forwards them to the new main service satellite or the original main service satellite.

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