A UAV-based power emergency communication and resource collaborative scheduling system

Abstract

This invention provides a UAV power emergency communication and resource collaborative scheduling system, relating to the field of wireless communication technology. Through system initialization, data acquisition and hierarchical coding, satellite-to-ground link establishment, NOMA access and scheduling modeling, resource collaborative configuration model establishment, joint optimization solution, online collaborative execution, and anomaly response, it achieves dynamic resource scheduling and optimization, offering advantages such as efficient collaborative resource scheduling, improved spectrum utilization, optimized energy use, guaranteed access to key nodes, and enhanced system reliability and response speed.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a UAV power emergency communication and resource collaborative scheduling system. Background Technology

[0002] With the continuous expansion of urban scale and the significant increase in the complexity of power infrastructure, severe challenges are posed to the reliability, coverage, and response speed of emergency communication systems. In critical scenarios such as power restoration, post-disaster reconstruction, and operations in remote mountainous areas, traditional terrestrial communication networks often fail due to infrastructure damage, necessitating rapidly deployable temporary communication methods to ensure fault location, resource scheduling, and personnel coordination. A space-ground integrated network architecture combined with UAV relay technology, with its flexibility, mobility, and wide-area coverage, has emerged as a potential solution to these problems, suitable for temporary access and relay services in areas with power line interruptions. However, existing technologies generally employ UAVs with fixed hovering modes or pre-set circular flight paths, relying on terrestrial cellular backhaul or simple satellite links, exhibiting significant limitations in dynamic and ever-changing emergency environments.

[0003] A deeper analysis reveals three core flaws in current practices: First, communication schemes based on orthogonal multiple access (OMA) employ static power configuration, which, while simple to implement, suffers from low spectrum utilization efficiency and cannot provide stable access guarantees for edge power distribution terminals with severe signal attenuation. Second, trajectory or power adjustment strategies that prioritize system throughput as the sole optimization objective, while capable of short-term data transmission rate improvements, neglect the energy constraints of the UAV platform and the fairness of service among users, resulting in critical fault nodes with poor channel conditions being in a state of communication "starvation" for extended periods. Third, resource scheduling, flight control, and other modules employ fragmented optimization methods, failing to comprehensively consider the strong coupling relationship between flight energy consumption, wireless link quality, and satellite backhaul bandwidth, making it difficult to achieve optimal overall performance. Particularly noteworthy is that UAVs, limited by finite battery capacity, consume significant energy through radio frequency transmission and flight maneuvers. Without an energy efficiency awareness mechanism, they are highly susceptible to a vicious cycle of expanding coverage and drastically reducing endurance, failing to meet the fundamental requirements of "full coverage of critical nodes, uninterrupted service, and persistent system operation" in power emergency scenarios. Furthermore, existing non-orthogonal multiple access relay schemes mostly employ fixed user pairing and constant power ratio strategies. While these can achieve high instantaneous rates under ideal channel conditions, they struggle to adapt to rapid fluctuations in channel conditions during dynamic multi-user environments caused by power outages. This results in the inability to guarantee the minimum rate requirements of critical power distribution terminals. Simultaneously, fixed flight trajectories or unconstrained maneuvers lead to significant unnecessary displacement, further exacerbating energy waste. These shortcomings make it difficult for existing technologies to achieve a coordinated balance between communication coverage, energy efficiency optimization, and user equity in complex emergency situations.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide an unmanned aerial vehicle (UAV) power emergency communication and resource collaborative scheduling system, which has the advantages of efficient collaborative scheduling of resources, improved spectrum utilization, optimized energy use, guaranteed access of key nodes, and enhanced system reliability and response speed.

[0006] Firstly, the UAV power emergency communication and resource collaborative scheduling system provided in this application adopts the following technical solution:

[0007] A UAV power emergency communication and resource collaborative scheduling system includes a ground power distribution terminal, a UAV equipped with a phased array antenna, a low-orbit satellite, and a command and dispatch center. The system performs the following collaborative scheduling method:

[0008] S1: Perform a one-time initialization of satellite link parameters, UAV flight control parameters, NOMA communication module parameters, phased array antenna beam pointing parameters, and ground power distribution terminal access parameters;

[0009] S2: Ground distribution terminals collect data on the operating status, fault alarms, and geographical location of power equipment, and classify and encode them according to the importance of nodes;

[0010] S3: The UAV establishes an uplink LoS channel link with the low-orbit satellite, receives satellite signals through phased array antenna beamforming, and maximizes the signal-to-noise ratio of satellite signal reception using a detection vector optimization algorithm;

[0011] S4: The UAV downlink adopts NOMA mode. Under the constraints of serving at most 2 users per time slot and having a unique decoding order, user service time slots and decoding positions are allocated based on channel state information to establish the instantaneous channel capacity model of the power distribution terminal.

[0012] S5: Define the power distribution terminal energy efficiency as the ratio of data rate to total UAV energy consumption, and construct the maximum and minimum energy efficiency optimization targets;

[0013] S6: The Dinkelbach method is used to reconstruct the fractional optimization problem into a parameterized difference maximization problem. The problem is decomposed into three sub-problems through alternating optimization: trajectory optimization, user scheduling, and power allocation. The solution is iteratively solved under the premise of satisfying kinematic constraints, SIC feasibility constraints, and power budget constraints until convergence.

[0014] S7: The optimized trajectory and power allocation strategy are sent to the UAV. The UAV flies according to the predetermined trajectory and allocates power signals to the ground power distribution terminal through NOMA broadcast. Key power distribution nodes are given priority access. At the same time, the communication power and flight trajectory are dynamically adjusted according to the energy margin.

[0015] S8: Real-time detection of link status and drone energy status. When a new fault, link anomaly, or power margin below the threshold is detected, the drone is coordinated to replan its flight trajectory and communication parameters.

[0016] Optionally, the phased array antenna in step S3 is an m×m two-dimensional uniform planar array antenna with the beam pointing towards the zenith direction z. The LoS channel model is expressed as:

[0017]

[0018] in, This represents the total number of antennas. For complex line-of-sight gain; For the angle of elevation Azimuth and time-related A 1×1 dimensional matrix; the signal received by the UAV at time t is , This represents the channel coefficient from the drone to the ground user. For beamforming vector, To transmit signals from the satellite, It is additive white Gaussian noise.

[0019] Optionally, the decoding order constraint in step S4 satisfies:

[0020]

[0021] in, Let be the decoding order variable for terminal g at time t, and let i represent its position in the serial interference cancellation sequence. The maximum number of users that can be served by a drone at the same time, and each terminal occupies a unique decoding order position at any given moment.

[0022] Optionally, the energy efficiency of the power distribution terminal is defined as the ratio of data rate to the total energy consumption of the UAV, and a maximum and minimum energy efficiency optimization target is constructed, including:

[0023] Establish a resource collaborative allocation model for power emergency scenarios, where the energy efficiency of distribution terminal g at time t is:

[0024]

[0025] In the formula: Let be the flight energy consumption of the UAV at time t; For the data rate of the power distribution terminal g; Let be the power used for communication by the UAV at time t;

[0026]

[0027] In the formula: A set of two-dimensional coordinates for the UAV; Let be the set of power allocation variables, representing the transmit power vector allocated by the UAV to the power distribution terminal node at time t; Let G be the set of decoding order variables, representing the position of each user in the SIC decoding sequence of NOMA at time t, and let G represent the set of distribution terminals.

[0028] Optionally, the Dinkelbach method described in step S6 includes:

[0029] In each iteration, the energy efficiency ratio is fixed, and the fractional objective is transformed into a linear weighted sum, letting... For the parameter that minimizes energy efficiency, the Dinkelbach method transforms fractional maximization into a parameterized difference maximization problem:

[0030]

[0031] Iterative updates using the following formula Until convergence:

[0032]

[0033] The convergence condition is: , This is the convergence threshold.

[0034] Optionally, the kinematic constraints of the trajectory optimization subproblem in step S6 include maximum velocity constraints and maximum acceleration constraints, and the trajectory optimization objective is to move closer to users with weak channels to improve communication quality and reduce energy consumption.

[0035] Optionally, the user scheduling subproblem described in step S6 adopts a round-robin scheduling strategy, periodically rotating user pairings and service time slots to ensure fair service opportunities for users with weak channels.

[0036] Optionally, the power allocation sub-problem described in step S6 follows the principle of maximizing the minimum rate within the group. Under the power budget constraint, the minimum rate requirement of key nodes in weak channels is guaranteed first, and then the data rate of nodes in strong channels is optimized.

[0037] Optionally, the energy management mechanism in step S7 includes: when the drone's power margin is lower than a threshold, automatically triggering trajectory correction, prioritizing flight to the nearest recharge point or returning to the base station.

[0038] In summary, this application achieves dynamic resource scheduling and optimization through system initialization, data acquisition and hierarchical coding, satellite-to-ground link establishment, NOMA access and scheduling modeling, resource collaborative configuration model establishment, joint optimization solution, online collaborative execution and anomaly response. It has the advantages of efficient collaborative resource scheduling, improved spectrum utilization, optimized energy use, guaranteed access to key nodes, and enhanced system reliability and response speed. Attached Figure Description

[0039] Figure 1 This is a structural block diagram of the first embodiment of the UAV power emergency communication and resource collaborative scheduling system of this application;

[0040] Figure 2 This is a flowchart illustrating the first embodiment of the UAV power emergency communication and resource collaborative scheduling system of this application;

[0041] Figure 3 This is a comparison diagram of the energy efficiency of the UAV power emergency communication and resource collaborative scheduling system in this application;

[0042] Figure 4 This is a comparison chart of the total throughput of the UAV power emergency communication and resource collaborative scheduling system in this application as a function of the cycle. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] Traditional space-ground integrated networks and UAV relay solutions suffer from insufficient adaptability in complex, multi-objective, and multi-constraint emergency environments. Specifically, this manifests in the following ways: OMA-based access spectrum utilization is low, offering limited support for users with weak coverage; throughput-oriented optimization neglects energy efficiency and user equity, easily leading to "starvation" of weak users; independent optimization of modules struggles to balance flight energy consumption, link quality, and backhaul constraints, resulting in limited overall performance; the lack of energy efficiency constraints or energy awareness strategies leads to insufficient UAV endurance; and existing NOMA relay methods struggle to guarantee scheduling fairness and minimum user rate in dynamic multi-user emergency situations, with fixed trajectories or unconstrained maneuvers easily causing energy waste.

[0045] In response, this application proposes a UAV-based power emergency communication and resource collaborative scheduling system. For example... Figure 1 As shown, the system includes a ground power distribution terminal 10, a UAV 20 equipped with a phased array antenna, a low-orbit satellite 30, and a command and dispatch center 40, and performs tasks such as... Figure 2 The cooperative scheduling method shown:

[0046] S1: Perform a one-time initialization of satellite link parameters, UAV flight control parameters, NOMA communication module parameters, phased array antenna beam pointing parameters, and ground power distribution terminal access parameters;

[0047] S2: Ground distribution terminals collect data on the operating status, fault alarms, and geographical location of power equipment, and classify and encode them according to the importance of nodes;

[0048] S3: The UAV establishes an uplink LoS channel link with the low-orbit satellite, receives satellite signals through phased array antenna beamforming, and maximizes the signal-to-noise ratio of satellite signal reception using a detection vector optimization algorithm;

[0049] S4: The UAV downlink adopts NOMA mode. Under the constraints of serving at most 2 users per time slot and having a unique decoding order, user service time slots and decoding positions are allocated based on channel state information to establish the instantaneous channel capacity model of the power distribution terminal.

[0050] S5: Define the power distribution terminal energy efficiency as the ratio of data rate to total UAV energy consumption, and construct the maximum and minimum energy efficiency optimization targets;

[0051] S6: The Dinkelbach method is used to reconstruct the fractional optimization problem into a parameterized difference maximization problem. The problem is decomposed into three sub-problems through alternating optimization: trajectory optimization, user scheduling, and power allocation. The solution is iteratively solved under the premise of satisfying kinematic constraints, SIC feasibility constraints, and power budget constraints until convergence.

[0052] S7: The optimized trajectory and power allocation strategy are sent to the UAV. The UAV flies according to the predetermined trajectory and allocates power signals to the ground power distribution terminal through NOMA broadcast. Key power distribution nodes are given priority access. At the same time, the communication power and flight trajectory are dynamically adjusted according to the energy margin.

[0053] S8: Real-time detection of link status and drone energy status. When a new fault, link anomaly, or power margin below the threshold is detected, the drone is coordinated to replan its flight trajectory and communication parameters.

[0054] For ease of understanding, the following explains some key terms in this embodiment:

[0055] A satellite-terrestrial converged network refers to a communication architecture that integrates satellite and terrestrial communication networks to achieve wide-area coverage, high reliability, and flexible deployment. In power emergency scenarios, this network can provide rapid communication recovery services to damaged areas.

[0056] The Unmanned Aerial Vehicle (UAV) Power Emergency Communication and Resource Coordination Dispatch System is a comprehensive solution that utilizes UAVs as an aerial communication platform, combined with satellite backhaul links, to provide emergency communication services to ground power equipment and to collaboratively optimize communication resources. This system aims to improve the efficiency and reliability of emergency communications.

[0057] Ground distribution terminals are intelligent devices deployed in power infrastructure that are responsible for collecting real-time data on the operating status, fault alarms, and geographical location of power equipment, and for connecting to the UAV relay network as communication nodes.

[0058] A phased array antenna is an antenna that electronically controls the phase of each element in an antenna array, thereby achieving rapid beam pattern scanning and precise pointing. Equipping UAVs with phased array antennas enables directional reception and transmission of satellite signals, optimizing link performance.

[0059] A NOMA communication module refers to a communication unit that uses Non-Orthogonal Multiple Access (NOMA) technology. NOMA technology allows multiple users to serve on the same time-frequency resources, and improves spectrum efficiency by separating user signals at the receiving end through Serial Interference Cancellation (SIC) technology.

[0060] A LoS channel link refers to a line-of-sight channel link. In this channel, there is a direct propagation path between the transmitter and receiver, resulting in relatively small signal attenuation and high transmission quality.

[0061] The Dinkelbach method is an iterative algorithm for solving fractional programming problems. This method simplifies the optimization process by transforming the fractional objective function into a parameterized difference maximization problem, and thus converges to the global optimum.

[0062] Alternating optimization is an optimization strategy that decomposes a complex optimization problem into multiple subproblems and iteratively optimizes each subproblem until the overall problem converges. This method is often used to handle non-convex optimization problems with multiple coupled variables.

[0063] It is understandable that the technical problems to be solved in this embodiment include: for access sets composed of DTU / FTU terminals, it is difficult to simultaneously guarantee the minimum terminal rate and system energy efficiency under conditions of energy constraints and backhaul constraints. A joint optimization method of maximum-minimum energy efficiency (Max–MinEE) is proposed without changing the original objectives and constraints. This method coordinates the scheduling, power allocation, and trajectory control of the UAV. Under a complete energy consumption model of flight power consumption and RF circuit power consumption, it achieves a unified improvement in fairness and energy efficiency for DTU / FTU through Dinkelbach equivalent reconstruction and alternating optimization (AO).

[0064] In practice:

[0065] Step S1 is system initialization, which includes the initialization process of satellite link parameters, UAV flight control parameters, NOMA communication module, phased array antenna beam pointing parameters, and ground power distribution terminal access parameters. One-time initialization ensures that the satellite-UAV-ground terminal link is available after startup, avoiding the response delay caused by multiple manual configurations required in the prior art.

[0066] Step S2: The ground distribution terminal (DTU / FTU) collects real-time data on the operating status of power equipment, fault alarms, and geographical location, and encodes the data according to the importance of nodes (such as hospitals, substations, important loads, etc.). This hierarchical encoding facilitates the subsequent scheduling algorithm to prioritize key nodes when resources are limited. It is an important improvement over traditional communication schemes that only transmit data in a fixed polling order.

[0067] Step S3: The UAV establishes an uplink with the low-Earth orbit (LEO) satellite before executing the mission. Assume an m×m two-dimensional uniform planar array antenna is installed above the UAV, pointing towards the zenith (z), and beamforming reception is performed using a phased array antenna. When the UAV hovers at an altitude relatively higher than ground scattering objects, it can be assumed that there is nothing in the line-of-sight (LoS) path from the satellite to the UAV. Furthermore, ground reflections can be ignored because beamforming technology is used to ensure that the UAV only receives signals from the satellite direction. In summary, the channel model established between the LEO satellite and the UAV is a LoS channel model, specifically expressed as:

[0068]

[0069] In the formula: This represents the total number of antennas. For complex line-of-sight gain; For the angle of elevation Azimuth and time-related A 1×1 dimensional matrix is ​​specifically represented as:

[0070]

[0071] In the formula: and These are the two dimensions of a uniform planar array antenna; The normalized antenna spacing.

[0072] In summary, the signal received by the UAV at time t can be expressed as:

[0073]

[0074] In the formula: The beamforming vector is composed of a weighted matrix of phased array antennas on the UAV or receiver antennas, and is used to enhance the directivity of the received signal and improve the signal-to-noise ratio. This represents the channel coefficient from the drone to the ground user. To transmit signals to the satellite; It is additive white Gaussian noise.

[0075] The instantaneous channel capacity of a UAV can be expressed as:

[0076]

[0077] In the formula: This refers to the satellite's launch power. This represents the noise power of the drone.

[0078] Furthermore, the detection vector optimization algorithm maximizes the signal-to-noise ratio (SNR) of satellite signal reception. This optimization can improve link capacity under low SNR conditions and effectively solve the problem of unstable relay quality caused by weak satellite signals in disaster areas in existing technologies.

[0079] The above technical solution clarifies the physical structure of the phased array antenna as an m×m two-dimensional uniform planar array antenna and provides a precise LoS channel model, thus providing a solid technical foundation for establishing a stable and efficient communication link between UAVs and low-Earth orbit satellites. This specific antenna configuration enables UAVs to achieve flexible and high-precision beamforming, effectively addressing the rapid movement characteristics of low-Earth orbit satellites. Simultaneously, the detailed LoS channel model, particularly the introduction of an array response matrix related to elevation, azimuth, and time, allows the system to more accurately predict and compensate for signal changes during transmission, providing precise input parameters for the detection vector optimization algorithm. This not only significantly improves the received signal-to-noise ratio of satellite signals, ensuring the stability and reliability of the satellite-to-ground link, but also lays a data foundation for subsequent communication resource scheduling and energy efficiency optimization, enabling the entire collaborative scheduling system to operate more efficiently, especially in power emergency communication scenarios, ensuring the timely transmission of critical data.

[0080] Step S4: Modeling NOMA Access and Terminal Scheduling in the UAV Downlink. The UAV uses NOMA mode in the downlink to simultaneously allocate the same time-frequency resource block to multiple ground power distribution terminals, thereby improving link utilization. Let the decoding order variable of terminal g at time t be... Where i represents the position in the continuous interference cancellation sequence, the decoding order of the terminal is expressed as:

[0081]

[0082] In the formula: This refers to the maximum number of users that a drone can serve simultaneously.

[0083] To ensure that each terminal occupies only one unique decoding sequence position at any given time, this constraint can be further expressed as:

[0084]

[0085] Decoding order and power allocation are performed based on Channel State Information (CSI), prioritizing the decoding of terminals with poor channel conditions and then decoding terminals with better channel conditions, in order to eliminate interference from the former during the decoding process. The expression is as follows:

[0086]

[0087] In the formula: Let be the channel gain of terminal u and UAV at time t.

[0088] The channel gain between the UAV and the power distribution terminal is modeled using the LoS model, specifically:

[0089]

[0090] In the formula: Let g be the channel gain of terminal g at time t, used to characterize the strength of the link quality between the UAV and the power distribution terminal; is the reference channel gain, corresponding to the free space channel gain at the reference distance (1 meter), used to normalize the channel strength at different distances; H is the UAV altitude, which is assumed to be a constant as the UAV flies at a fixed altitude. The coordinates of the drone's horizontal position; These are the horizontal coordinates of the power distribution terminal.

[0091] Assuming that channel state information is fully accessible to the UAV and that the Doppler effect caused by the UAV's motion is well compensated, an instantaneous channel capacity model for a power distribution terminal g with decoding order i is established as follows:

[0092]

[0093]

[0094] In the formula: B is the bandwidth allocated to the power distribution terminal, which is considered a constant within the period T; This is a constant representing the radio frequency power of the drone; The noise power for all receivers; Assign 0-1 variables to the power of the distribution terminal with decoding order i.

[0095] However, since the selection relationship between the UAV and the power distribution terminal and the decoding order is not fixed, a scheduling function is established to determine the channel capacity of the power distribution terminal g, as follows:

[0096]

[0097] In the formula: The data rate of the power distribution terminal g.

[0098] Through the above technical solution, the system provides clear and strict mathematical constraints for the decoding process of the NOMA downlink. This is achieved by defining decoding order variables. By defining (t) and its position i in the serial interference cancellation sequence, and limiting the maximum number of users the UAV can serve simultaneously and the unique decoding order position occupied by each terminal at any given time, the system can ensure the determinism and feasibility of the serial interference cancellation process in NOMA communication. This effectively avoids interference cancellation failures caused by ambiguous decoding order, thereby significantly improving the reliability and data transmission efficiency of communication between the UAV and the ground power distribution terminal. Precise decoding order management enables the NOMA system to utilize power domain resources more effectively, thereby optimizing overall spectral efficiency and energy efficiency, and providing a solid communication foundation for resource collaborative scheduling in power emergency communication scenarios.

[0099] Step S5: Establish a resource collaborative configuration model for power emergency scenarios. Considering that the system needs to simultaneously ensure priority communication of key distribution nodes and improve overall energy efficiency in disaster scenarios, this application adopts a maximum-minimum energy efficiency optimization objective to ensure that the worst energy efficiency among all nodes is maximized. The energy efficiency of distribution terminal g at time t is defined as:

[0100]

[0101] In the formula: Let be the flight energy consumption of the UAV at time t; For the data rate of the power distribution terminal g; Let be the power used for communication by the UAV at time t;

[0102]

[0103] In the formula: A set of two-dimensional coordinates for the UAV; Let be the set of power allocation variables, representing the transmit power vector allocated by the UAV to the power distribution terminal node at time t; Let G be the set of decoding order variables, representing the position of each user in the SIC decoding sequence of NOMA at time t, and let G represent the set of distribution terminals.

[0104] This model not only explicitly defines the energy efficiency calculation method for power distribution terminals, but more importantly, it explicitly incorporates key decision variables such as the UAV's flight trajectory D(t), power allocation P(t), and NOMA decoding order S(t) into the optimization objective. This comprehensive modeling approach enables the system to accurately quantify and evaluate the impact of different resource allocation strategies on energy efficiency, thus providing a solid mathematical foundation for subsequent joint optimization solutions. Specifically, by combining flight energy consumption and communication energy consumption with data rate, the system can more accurately measure the trade-off between the UAV's total energy consumption and communication benefits, thereby achieving the maximum and minimum energy efficiency optimization objective within a limited UAV energy budget. This ensures that even power distribution terminals with poor channel conditions can obtain reliable communication services in dynamically changing power emergency environments, while effectively extending the UAV's operating time and significantly improving the robustness and sustainability of the entire emergency communication system.

[0105] Step S6: Iteratively solve the problem using the Dinkelbach method. This involves fixing the energy efficiency ratio in each iteration, transforming the fractional objective into a linear weighted sum, thus significantly reducing the problem's complexity. Specifically:

[0106] make For the parameter representing "minimum energy efficiency", the Dinkelbach method transforms fractional maximization into a parameterized difference maximization problem:

[0107]

[0108] Iterative updates using the following formula Until convergence ( ):

[0109]

[0110] After transformation, the original problem can be further decomposed into three sub-problems:

[0111] 1) UAV trajectory optimization subproblem: Optimize the UAV's three-dimensional position so that its flight path, while satisfying maximum speed and maximum acceleration constraints, is as close as possible to users with weak channel conditions to improve communication quality and reduce energy consumption. The trajectory and kinematic constraints are expressed as follows:

[0112]

[0113] 2) User Scheduling Subproblem: Under the constraints of ensuring the uniqueness of the decoding order and the feasibility of SIC (System-In-Channel Interchange), allocate service time slots and decoding positions for users. The NOMA scheduling / decoding order constraints are expressed as follows:

[0114]

[0115] 3) Power allocation sub-problem: Allocate transmit power to different users under power budget constraints, prioritize key nodes in weak channels, and then optimize the data rate of nodes in strong channels to balance fairness and efficiency.

[0116] The power budget and nonnegativity representation are as follows:

[0117]

[0118] Rate / SINR reachability is represented as follows:

[0119]

[0120] Where: v represents the power distribution terminal that performs decoding after the power distribution terminal g. This represents the set of distribution terminals that are decoded after distribution terminal g.

[0121] Update parameters after each iteration. When the convergence condition is met The algorithm terminates when the time is reached. Through the above loop, this step achieves joint optimization of the UAV trajectory, power, and decoding order, obtaining the optimal energy efficiency configuration of the system under multi-dimensional constraints.

[0122] By employing the Dinkelbach method, the original non-convex fractional programming problem is equivalently transformed into a series of easily solvable parameterized difference maximization problems. This transformation avoids the complexity of directly dealing with the fractional objective function, enabling the optimization process to converge more effectively to the global optimum or close to the global optimum. Fixing the energy efficiency ratio and solving a linear weighted sum in each iteration significantly reduces computational complexity and improves the efficiency and stability of the optimization algorithm. Ultimately, this ensures that the UAV power emergency communication and resource collaborative scheduling system can achieve the optimization objective of maximizing and minimizing energy efficiency. This allows for more efficient and reliable communication services to ground power distribution terminals within a limited UAV energy budget, especially in power emergency scenarios, where it can more effectively guarantee the communication needs of critical nodes.

[0123] It should be noted that the kinematic constraints of the trajectory optimization subproblem include maximum velocity constraints and maximum acceleration constraints, and the trajectory optimization objective is to move closer to users with weak channels to improve communication quality and reduce energy consumption.

[0124] Specifically, the maximum speed constraint means that the magnitude of the instantaneous velocity of the UAV during flight cannot exceed the preset maximum permissible speed. This constraint is set based on the UAV's own dynamic performance, structural strength, and flight safety requirements. During trajectory optimization, by incorporating this constraint into the optimization model, it is ensured that the velocity of the generated trajectory at any given time is within the UAV's physical tolerance range. For example, inequality constraints can be introduced into the optimization algorithm to limit the UAV's displacement within each time step, thereby indirectly or directly limiting its velocity. This ensures the feasibility and safety of the UAV's flight trajectory. Simultaneously, the maximum acceleration constraint means that the magnitude of the instantaneous acceleration of the UAV during flight cannot exceed the preset maximum permissible acceleration. This constraint is also based on the UAV's dynamic performance, control stability, and energy consumption efficiency. Excessive acceleration not only increases the structural stress of the UAV but may also lead to flight attitude instability and significantly increase energy consumption. In the trajectory optimization model, by limiting the velocity change of the UAV between consecutive time steps, the maximum acceleration constraint can be effectively applied. This helps generate smoother, more controllable, and more energy-efficient flight trajectories.

[0125] Furthermore, the trajectory optimization objective aims to guide the UAV to prioritize moving towards ground distribution terminals with poor channel conditions when planning its flight path. Weak channel users typically refer to those with low received signal strength and poor signal-to-noise ratio (SNR) due to factors such as distance, severe obstruction, or interference. By bringing the UAV closer to these users, the communication distance can be significantly shortened, path loss reduced, and the received signal strength and SNR of these users directly improved. In the optimization model, this can be achieved by introducing a term related to the distance from the UAV to the user into the objective function, or by setting higher weights or minimum guarantee requirements for the communication quality (such as data rate or SNR) of weak channel users. The direct effect of bringing the UAV closer to weak channel users is to improve the channel conditions of these users. Improved channel conditions mean higher signal received power and SNR, thereby increasing data transmission rate, reducing bit error rate, and enhancing the reliability and stability of the communication link. This is crucial for power emergency communication scenarios, ensuring the timely and accurate transmission of critical data (such as fault alarms and equipment status). This energy consumption reduction is mainly reflected in two aspects: firstly, the reduction in communication energy consumption. When the drone approaches the user, the improved channel conditions significantly reduce the transmission power required to transmit the same amount of data, thus saving energy consumption of the communication module. Secondly, by optimizing the trajectory, unnecessary long-distance flights or high-intensity maneuvers are avoided, thereby reducing flight energy consumption. The optimization algorithm finds the flight path with the lowest overall energy consumption while meeting communication requirements, balancing flight energy consumption and communication energy consumption.

[0126] Through the above technical solutions, the system can ensure that the planned UAV flight trajectory is physically feasible when solving the trajectory optimization sub-problem in the joint optimization process. The introduction of maximum speed and maximum acceleration constraints ensures that the UAV strictly follows the preset path during actual flight, avoiding flight instability, abnormal energy consumption increases, or safety risks caused by exceeding its performance limits. Simultaneously, targeting users with weak channels in the trajectory optimization allows the UAV to proactively adjust its flight path, prioritizing coverage and service to ground power distribution terminals with poor communication conditions. This strategy effectively improves the communication quality for users with weak channels, ensuring the reliability of data transmission for all critical power distribution nodes in power emergency scenarios. Furthermore, improving channel conditions by moving closer to users reduces the required transmission power of the UAV, thereby effectively reducing its communication energy consumption while maintaining communication quality. Combined with the optimized flight path, the overall total energy consumption of the UAV is reduced, extending its endurance and improving the system's emergency response capability and continuous operation efficiency.

[0127] Step S7: After the algorithm is solved offline, this step realizes the online collaborative communication process between the UAV, satellite, and ground nodes.

[0128] The system first sends the optimized trajectory and power allocation strategy to the UAV mission control module. The UAV flies along the predetermined trajectory and establishes an uplink with the satellite in real time to obtain control commands and network synchronization information for the disaster area. During the downlink process, the UAV operates according to the optimized scheduling matrix. Multi-node NOMA broadcasting is conducted to allocate corresponding power signals to ground power distribution terminals. Critical power distribution nodes are given priority access, while ordinary nodes communicate later according to the decoding order. Simultaneously, the system incorporates an energy management mechanism: the communication power of the UAV is controlled. and flight energy consumption The system dynamically adjusts according to the optimal energy efficiency results in step five to achieve optimal energy allocation between the communication and propulsion systems. If the power margin is detected to drop to a threshold, the system automatically triggers trajectory correction, prioritizing flight to the nearest refueling point or returning to the base station.

[0129] Step S8: Real-time detection of link status and UAV energy status. When a new fault, link anomaly, or power margin below the threshold is detected, the UAV is coordinated to replan its flight trajectory and communication parameters.

[0130] However, in actual long-duration or high-intensity missions, the drone's own energy consumption is a factor that cannot be ignored. If the drone fails to effectively manage its energy state while performing communication missions, it may experience service interruption due to power depletion midway through the mission, thereby affecting the continuity and reliability of emergency communications.

[0131] To address the aforementioned issues, this embodiment proposes an energy management mechanism, which includes: when the drone's power margin is below a threshold, automatically triggering trajectory correction, prioritizing flight to the nearest refueling point or returning to the base station.

[0132] Specifically, this energy management mechanism aims to ensure the continuous and stable operation of drones during emergency communication missions, preventing service interruptions due to energy depletion. It is a comprehensive strategy for monitoring, assessing, and responding to the drone's own energy status to guarantee mission continuity. The drone's power reserve refers to its remaining available electrical or fuel charge, monitored in real-time by internal sensors and compared to a preset threshold. This threshold can be dynamically or statically set based on mission type, flight distance, communication load, environmental conditions, and the drone's performance parameters (such as battery capacity and flight time). When the monitored power reserve falls below this threshold, it indicates that the drone needs to take measures to replenish energy or return to base.

[0133] When the drone's power margin falls below a preset threshold, the system will automatically initiate a trajectory correction procedure without manual intervention. This means the drone will recalculate and adjust its current flight path according to preset strategies and algorithms. This automatic triggering mechanism ensures a rapid response in emergencies, avoiding mission interruptions due to delays. During trajectory correction, the system intelligently selects the nearest and most accessible refueling point as the new flight target based on the drone's current location, refueling point location information, and energy consumption estimates for the flight path. Prioritizing the nearest refueling point aims to minimize the drone's energy consumption on its journey to refueling and restore its mission capabilities as quickly as possible. In certain situations, such as when there are no available refueling points nearby, or when the drone's power margin is extremely low and insufficient to support travel to any refueling point, the system will instruct the drone to return to its base station. Returning to the base station is the final guarantee for ensuring the safe recovery of the drone and enabling comprehensive maintenance.

[0134] The aforementioned energy management mechanism effectively addresses the potential energy depletion issue faced by drones during prolonged or high-intensity emergency communication missions. When a drone's power reserve falls below a preset threshold, the system automatically triggers trajectory correction, guiding the drone to prioritize flying to the nearest refueling point or returning to the base station. This ensures that drones can obtain timely energy replenishment, preventing communication interruptions due to power depletion and significantly improving the continuity and reliability of emergency communication services. This mechanism, in conjunction with the system's existing trajectory optimization and power allocation strategies, enables drones to manage their energy more intelligently, guaranteeing the continuous communication capabilities of the space-ground integrated network in power emergency scenarios.

[0135] It should be noted that, in order to verify the performance of this embodiment in the power emergency communication scenario, system integration and testing were performed through simulation.

[0136] To verify the effectiveness of the proposed EE-NOMA joint optimization method in integrated space-air-ground emergency communication, Dinkelbach equivalent reconstruction and alternating optimization (AO) are used for numerical solution without changing the original objective function and constraints. Scheduling is performed within the original constraints of "maximum of 2 users per time slot and unique decoding order," employing a feasible round-robin strategy to ensure coverage fairness. The power within the two-user NOMA group is approximated efficiently using monotonic binary search based on the principle of "maximizing the minimum rate within the group." The trajectory subproblem is updated using a gradient-based method with cumulative rate weighting under kinematic constraints such as velocity, acceleration, and periodic closure. To avoid numerical divergence, geofencing and smoothing regularization are introduced, with their thresholds set to inactive to prevent shrinking the theoretically feasible region. Unless otherwise specified, propulsion power consumption is calculated according to the established model, and RF / circuit power consumption is included as a constant in the total power consumption to provide a more engineering-meaning energy efficiency benchmark. All the above implementation details pertain to the solution and parameter instantiation within the model's feasible region and do not change the original model definition or the validity of the conclusions.

[0137] This embodiment uses the following simulation configuration. The power distribution terminals are uniformly and randomly distributed within a 400m × 400m area, and the number of users... =6; The drone (UAV) cruises at a fixed altitude of 50m, with a maximum speed of Maximum acceleration Time period Each cycle is discrete into 60 time slots, with a step size of [missing information]. The channel adopts a line-of-sight (LoS) model, where =1; System bandwidth =1MHz; noise power is =-50dBm; NOMA serves a maximum of 2 users simultaneously per time slot, with a unique decoding order that satisfies serial interference cancellation (SIC) constraints; transmit power is approximated by continuous transmission, and average RF power consumption is... =3.1W. Propulsion power consumption is based on a rotorcraft model:

[0138]

[0139] in: v is the horizontal velocity norm of the UAV; the initial trajectory is a circular trajectory centered at the center of the field region, with a radius equal to one-sixth of the side length of the field region. The Dinkelbach outer convergence threshold is set to... In each outer iteration, Alternating Optimization (AO) is used to update the schedule, power, and trajectory. To avoid numerical divergence, kinematic constraints such as velocity / acceleration / period closure are applied during trajectory updates, and a weakly regularized smoothing term and geofencing are used (the threshold is set to inactive to avoid shrinking the theoretically feasible region).

[0140] The superiority of the proposed method is compared by contrasting three typical schemes.

[0141] 1) OMA fixed circular trajectory;

[0142] 2) NOMA fixed circular trajectory;

[0143] 3) The EE-NOMA method proposed in this embodiment.

[0144] Specific comparison results are as follows: Figure 3 As shown, this embodiment demonstrates significant energy efficiency advantages. When the time T is within the range of 30S to 90S, the energy efficiency of the proposed method is improved from 150 bits / J to 1300 bits / J, which is significantly higher than the 120 bits / J to 870 bits / J of the OMA method; the fixed trajectory NOMA suffers from insufficient service to weak users, resulting in an EE that is always close to 0.

[0145] Total throughput changes with period T as follows Figure 4 As shown, throughput remains at a high level and the ordering is reasonable. The total throughput of fixed NOMA is the highest (≈10.5Mb / s), the proposed method is slightly lower (≈9.4-9.5Mb / s), and OMA is the lowest (≈8.8Mb / s). This indicates that the proposed method does not sacrifice too much throughput to improve energy efficiency, maintaining a high spectral efficiency matching the NOMA system; at the same time, compared with fixed NOMA, the proposed method distributes the benefits more evenly among all users, thereby significantly improving the minimum quality of service and energy efficiency of the system.

[0146] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0147] In addition, for technical details not described in detail in this embodiment, please refer to the method for emergency power communication and resource collaborative scheduling of UAVs for space-ground integrated networks provided in any embodiment of this application, which will not be repeated here.

[0148] Furthermore, it should be noted that in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0149] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A UAV power emergency communication and resource collaborative scheduling system, characterized in that, The system includes ground power distribution terminals, UAVs equipped with phased array antennas, low-orbit satellites, and a command and dispatch center. The system executes the following collaborative scheduling method: S1: Perform a one-time initialization of satellite link parameters, UAV flight control parameters, NOMA communication module parameters, phased array antenna beam pointing parameters, and ground power distribution terminal access parameters; S2: Ground distribution terminals collect data on the operating status, fault alarms, and geographical location of power equipment, and classify and encode them according to the importance of nodes; S3: The UAV establishes an uplink LoS channel link with the low-orbit satellite, receives satellite signals through phased array antenna beamforming, and maximizes the signal-to-noise ratio of satellite signal reception using a detection vector optimization algorithm; S4: The UAV downlink adopts NOMA mode. Under the constraints of serving at most 2 users per time slot and having a unique decoding order, user service time slots and decoding positions are allocated based on channel state information to establish the instantaneous channel capacity model of the power distribution terminal. S5: Define the power distribution terminal energy efficiency as the ratio of data rate to total UAV energy consumption, and construct the maximum and minimum energy efficiency optimization targets; S6: The Dinkelbach method is used to reconstruct the fractional optimization problem into a parameterized difference maximization problem. The problem is decomposed into three sub-problems through alternating optimization: trajectory optimization, user scheduling, and power allocation. The solution is iteratively solved under the premise of satisfying kinematic constraints, SIC feasibility constraints, and power budget constraints until convergence. S7: The optimized trajectory and power allocation strategy are sent to the UAV. The UAV flies according to the predetermined trajectory and allocates power signals to the ground power distribution terminal through NOMA broadcast. Key power distribution nodes are given priority access. At the same time, the communication power and flight trajectory are dynamically adjusted according to the energy margin. S8: Real-time detection of link status and drone energy status. When a new fault, link abnormality or power margin is detected below the threshold, the drone is linked to replan its flight trajectory and communication parameters. In step S3, the phased array antenna is an m×m two-dimensional uniform planar array antenna with the beam pointing towards the zenith direction z. The LoS channel model is expressed as: in, This represents the total number of antennas. For complex line-of-sight gain; For the angle of elevation Azimuth and time-related A 1×1 dimensional matrix; the signal received by the UAV at time t is , This represents the channel coefficient from the drone to the ground user. For beamforming vector, To transmit signals from the satellite, It is additive white Gaussian noise; In step S4, the decoding order constraint satisfies: in, Let be the decoding order variable for terminal g at time t, and let i represent its position in the serial interference cancellation sequence. The maximum number of users that can be served by a drone at the same time, and each terminal occupies a unique decoding order position at any given moment; The energy efficiency of the power distribution terminal is defined as the ratio of data rate to the total energy consumption of the UAV. The maximum and minimum energy efficiency optimization targets are constructed, including: Establish a resource collaborative allocation model for power emergency scenarios, where the energy efficiency of distribution terminal g at time t is: In the formula: Let be the flight energy consumption of the UAV at time t; For the data rate of the power distribution terminal g; Let be the power used for communication by the UAV at time t; In the formula: A set of two-dimensional coordinates for the UAV; Let be the set of power allocation variables, representing the transmit power vector allocated by the UAV to the power distribution terminal node at time t; Let G be the set of decoding order variables, representing the position of each user in the SIC decoding sequence of NOMA at time t, and let G represent the set of distribution terminals.

2. The system according to claim 1, characterized in that, The Dinkelbach method described in step S6 includes: In each iteration, the energy efficiency ratio is fixed, and the fractional objective is transformed into a linear weighted sum, letting... For the parameter that minimizes energy efficiency, the Dinkelbach method transforms fractional maximization into a parameterized difference maximization problem: Iterative updates using the following formula Until convergence: The convergence condition is: , This is the convergence threshold.

3. The system according to claim 1, characterized in that, The kinematic constraints of the trajectory optimization subproblem in step S6 include maximum velocity constraints and maximum acceleration constraints. The trajectory optimization objective is to move closer to users with weak channels to improve communication quality and reduce energy consumption.

4. The system according to claim 1, characterized in that, The user scheduling subproblem described in step S6 adopts a round-robin scheduling strategy, periodically rotating user pairings and service time slots to ensure fair service opportunities for users with weak channels.

5. The system according to claim 1, characterized in that, The power allocation subproblem described in step S6 follows the principle of maximizing the minimum rate within the group. Under the power budget constraint, the minimum rate requirement of key nodes in weak channels is guaranteed first, and then the data rate of nodes in strong channels is optimized.

6. The system according to claim 1, characterized in that, The energy management mechanism in step S7 includes: when the drone's power margin is lower than the threshold, automatically triggering trajectory correction, prioritizing flight to the nearest recharge point or returning to the base station.

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