57 results about "Coordinate descent" patented technology

The invention provides an unmanned aerial vehicle covert communication method and system supported by a movable antenna, and the method comprises the steps: calculating the maximum communication rate of a legal user under the support of the movable antenna according to a communication scene of the movable antenna; according to a movable antenna communication scene, calculating a minimum detection error rate under multi-illegal-guard cooperative detection; based on the maximum communication rate and the minimum detection error rate, a joint optimization problem of time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position is established, and the optimization objective is to maximize the minimum throughput of legal users under the condition of satisfying covert communication constraints; an iterative algorithm based on block coordinate descent and continuous convex approximation is adopted to obtain optimized time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position parameters, and the parameters are applied to covert communication of the unmanned aerial vehicle. According to the method provided by the invention, the concealment requirement of unmanned aerial vehicle communication can be well met, and the network security is improved.

The application discloses a sensing-integrated waveform and unmanned aerial vehicle (UAV) trajectory design method for realizing secure mobile edge computing, and aims at the case that potential eavesdroppers exist in a mobile edge computing network architecture in which multiple ground users perform uplink information transmission to a UAV, optimizes and designs a sensing-integrated waveform and a UAV trajectory with the minimum total user energy consumption as the target. First, an optimization problem conforming to the scene is constructed, then the original optimization problem is decoupled into sub-problems about the sensing-integrated waveform and the UAV flight trajectory based on a Block Coordinate Descent (BCD) method, and a convex approximation fitting is performed on each non-convex sub-problem, finally, an iterative algorithm is adopted to solve the problem, so that a global suboptimal solution of the whole optimization problem and optimal sensing-integrated waveform and UAV flight trajectory are found.

The invention discloses an implementation method of a sub-connection active STAR-RIS assisted secure communication system. A base station, a sub-connection active STAR-RIS, a legal user and an eavesdropping user are included. According to the invention, M units of STAR-RIS are grouped into L sub-arrays, and an amplifier is shared to reduce power consumption and cost; with maximization of system secrecy energy efficiency as a target, base station beam forming, STAR-RIS reflection / transmission coefficients and amplifier amplification coefficients are jointly optimized; and solving the non-convex optimization problem by adopting an efficient algorithm based on block coordinate descent. According to the invention, the hardware overhead and the system power consumption are effectively reduced through the sub-connection structure, meanwhile, the secure transmission efficiency of the system is remarkably improved through joint optimization, multiplicative fading is overcome, and an efficient and reliable solution can be provided for 6G secure communication.

The invention relates to a dynamic self-adaptive communication, sensing and calculation integrated method for an air-space-ground integrated network. The method comprises the following steps: S1, constructing an air-space-ground integrated network model fusing a low earth orbit satellite, an unmanned aerial vehicle and ground users / edge nodes; s2, establishing a cross-layer communication-perception-calculation integrated joint optimization model, and respectively forming two modes of binary unloading and partial unloading according to whether a task can be segmented or not; s3, for a binary unloading mode, realizing distributed solution and generating an unloading decision and a beam / power / bandwidth / computing resource scheme by using a dynamic adaptive prediction alternating direction multiplier (DAP-ADMM); and S4, aiming at a partial unloading mode, adopting a weighted minimum mean square error alternative optimization method, introducing a receiving equalizer, error weight and other auxiliary variables, executing block coordinate descent iteration, and outputting an unloading proportion, power distribution and a beam forming result. According to the DA-ISCC architecture and the optimization algorithm thereof provided by the invention, cross-domain calculation unloading with low time delay, high throughput and low overhead can be realized in a high-dynamic space-air-ground integrated network environment, and the DA-ISCC architecture and the optimization algorithm thereof can be widely applied to emergency communication, low-altitude logistics, ocean monitoring and other scenes.

The invention discloses a block coordinate descent and matching theory-based low-orbit satellite hopping beam calculation unloading method, and belongs to the field of low-orbit satellite communication. The method comprises the following steps: firstly, a core network counts the reported service demand of each user, considers the lower limit of time delay satisfaction, and constructs a hopping beam and resource scheduling initial model; and then, based on a matching theory, carrying out hopping beam pattern initialization, and obtaining an optimal solution of unloading-resource scheduling by using a block coordinate descent algorithm, thereby obtaining the delay satisfaction degree of each sink node corresponding to the hopping beam pattern. And then, the core network randomly selects satellites and two different time slots to execute exchange matching, updates a beam hopping pattern and unloading-resource scheduling, selects and rejects the updated beam hopping pattern according to the time delay satisfaction degree, and iteratively updates repeatedly until the number of times is reached. And sending the finally reserved hopping beam pattern and the corresponding optimal resource scheduling strategy to each user / satellite node, and carrying out beam lightening and unloading strategy execution. According to the invention, the management complexity is effectively reduced.

The application belongs to the technical field of unmanned aerial vehicle communication sensing, and specifically discloses a method for jointly scheduling communication and sensing resources of an unmanned aerial vehicle in a covert communication scenario, which comprises the following steps: establishing a communication performance model and a radar sensing performance model; combining covert communication constraints and no-fly zone avoidance constraints to build a joint optimization problem of communication and sensing resources; solving the closed-form optimal solution of sensing power and communication power, and reducing variables of the joint optimization problem; introducing a segmented hovering flight structure to discretize the continuous flight trajectory of the unmanned aerial vehicle, and combining the closed-form optimal solution to convert the joint optimization problem into a discrete optimization problem; using a block coordinate descent algorithm to alternately and iteratively solve the discrete optimization problem, and outputting an optimal user association strategy, an unmanned aerial vehicle hovering time and a continuous flight trajectory of the unmanned aerial vehicle. The application can improve the covert communication capability and comprehensive sensing accuracy of the unmanned aerial vehicle under the premise of meeting the covert communication demand and physical no-fly zone constraints.

This invention discloses an energy efficiency optimization method for a hybrid RIS-assisted UAV mobile edge computing system, comprising: establishing a system model including a ground terminal, a fixed RIS, and a UAV-mounted RIS; configuring system parameters; defining offloading decisions; determining the total offloading workload of the ground terminal and the total system energy consumption, and defining system energy efficiency; constructing an optimization problem aimed at maximizing system energy efficiency, jointly optimizing the ground terminal offloading decisions, etc., and simplifying the optimization problem using the Tinkerbach method; comparing the offloading rates of the ground terminal offloading tasks via the fixed RIS and the UAV-mounted RIS, and solving the offloading decisions; using a semidefinite relaxation method and introducing a penalty function to solve the beamforming matrix of the fixed RIS and the UAV-mounted RIS, solving the time slot allocation, and using continuous relaxation to solve the UAV trajectory; and iterating the offloading decisions continuously using the block coordinate descent method until the optimal solution and the maximum system energy efficiency are obtained.

This application discloses a block coordinate descent joint spatiotemporal calibration method and system, belonging to the field of sensor fusion and calibration technology. The method involves acquiring camera image sequences and inertial measurement unit (IMU) data; performing a first-stage coarse temporal alignment, estimating the temporal mapping parameters between the camera and IMU based on the camera image sequences and IMU data; performing a second-stage spatiotemporal joint optimization, jointly optimizing spatial extrinsic parameters, camera intrinsic parameters, and IMU data under the constraints of the temporal mapping parameters; iterating alternately between the first-stage coarse temporal alignment and the second-stage spatiotemporal joint optimization until a preset convergence condition is met; and outputting calibration results, including spatial extrinsic parameters, temporal mapping parameters, a noise model, and a calibration quality report. The method employs an alternating strategy of first performing coarse temporal alignment and then optimizing spatial extrinsic parameters, significantly improving the algorithm's convergence stability and calibration reliability.

The invention relates to a method for maximizing the average secrecy throughput in a short packet communication system assisted by an unmanned aerial vehicle (UAV). The method comprises the following steps of: firstly, selecting a short packet communication system; the method maximizes the average confidential throughput of all users by jointly optimizing user scheduling, transmit power, transport block length, and UAV trajectory. Firstly, an original problem is converted into four sub-problems of user scheduling optimization, power distribution optimization, block length selection optimization and unmanned aerial vehicle trajectory optimization. In order to solve the non-convex subproblem of unmanned aerial vehicle trajectory optimization, an improved particle swarm optimization (PSO) algorithm is adopted to search for an optimal or suboptimal UAV flight path. According to the method, under the optimization framework of overall block coordinate descent (BCD), other sub-problems are solved by means of methods such as continuous convex approximation (SCA), and the like, and therefore, the method can be used for solving the sub-problems of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD) of the block coordinate descent (BCD). According to the method provided by the invention, the communication safety performance of all users in the UAV-assisted short packet communication system can be improved, and a new design thought and optimization method are provided for the application of the UAV in a time delay sensitive wireless communication scene.

This invention discloses a resource optimization method, system, and storage medium for a STAR-RIS-assisted UAV communication system. The method includes establishing a STAR-RIS-assisted UAV communication system model; constructing an objective optimization problem, aiming to maximize the system's total transmission rate under constraints such as UAV transmit power, spatial multiplexing, user scheduling binary constraints, STAR-RIS energy conservation constraints, and phase shift orthogonality constraints, and optimizing user scheduling, active beamforming, and STAR-RIS transmission and reflection coefficients; employing an alternating optimization framework to decompose the objective optimization problem into user scheduling sub-problems and active beamforming and STAR-RIS configuration sub-problems; using a penalized successive convex approximation method to solve the user scheduling sub-problem; and using a penalized dual decomposition method combined with block coordinate descent to solve the active beamforming and STAR-RIS configuration sub-problems. The method of this invention can significantly improve the overall system transmission rate while suppressing multi-user interference, meeting the high-bandwidth service requirements of surveillance video and other applications.

The application relates to the technical field of communication, and specifically provides a reconfigurable intelligent surface (RIS) assisted ocean communication method and system based on LR-BCD, which comprises the following steps: constructing an RIS assisted ocean communication system model; constructing an ocean double-path channel model based on the ocean double-path propagation theory and in combination with a sea surface reflection coefficient; extracting low-rank characteristics of the channel by using singular value decomposition (SVD), projecting high-dimensional beamforming and phase shift optimization problems into a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem by using a weighted minimum mean square error (WMMSE) criterion; alternately updating a low-dimensional precoding matrix and a phase shift vector by using a block coordinate descent (BCD) algorithm; and finally reconstructing the optimized low-dimensional variables back to a high-dimensional space and performing discretization and quantization. The application utilizes the sparsity of the ocean channel, significantly reduces the calculation complexity of large-scale RIS optimization through dimension reduction processing, and meanwhile, maintains the system and rate performance equivalent to that of a full-rank algorithm.

The application provides a distributed pose graph optimization method and device based on multi-level graph segmentation, which is used for communication between multiple robots, and the method comprises the following steps: performing a multi-level graph segmentation operation on an initial pose graph and constructing a distributed optimization problem; updating selected variable blocks by using a Riemannian gradient coordinate descent algorithm until an optimal solution meeting a condition is searched; and projecting the optimal solution to a feasible solution of the pose graph optimization by using singular value decomposition. Through the design of the distributed pose graph optimization method based on multi-level graph segmentation, the application constructs more balanced optimization sub-problems which are easier to process, reduces the communication cost in the multi-robot network, and improves the overall optimization performance of the multi-robot system.

This invention relates to a satellite-ground collaborative edge computing method for 6G space-ground integrated networks, comprising: constructing a system model for a mobile edge computing scenario; based on the system model, constructing a computation offloading model, a low Earth orbit satellite communication time model, and a channel model; based on the computation offloading model, the low Earth orbit satellite communication time model, and the channel model, constructing an offloading optimization problem with the goal of minimizing the weighted sum of total system latency and energy consumption; and iteratively solving the offloading optimization problem using a block coordinate descent algorithm, wherein the offloading optimization problem is decomposed into two sub-problems: task offloading optimization and resource allocation, which are solved separately. This invention solves the task allocation problem between satellites and base stations for multiple users by establishing a refined satellite-ground geometric model and resource scheduling model, minimizing the total system overhead while satisfying task deadlines and satellite service window constraints.

The present application relates to a kind of resource optimization method based on RIS auxiliary wireless energy-carrying backscattering communication system, belong to low-power Internet of Things technical field, the present application is aimed at the problem that transmission performance is poor caused by obstruction obstruction and channel perturbation, consider transmission time constraint, energy collection constraint, the maximum interference power constraint allowed by main receiver, channel uncertainty constraint and intelligent metasurface phase shift constraint, construct energy efficiency maximization resource management model based on bounded channel uncertainty, utilize worst criterion method and Cauchy inequality to convert original non-convex optimization problem containing parameter uncertainty into deterministic problem;By variable substitution method, continuous convex approximation method, block coordinate descent method and semi-positive definite programming method, the deterministic problem is converted into convex problem solution, the present application can effectively improve system total energy efficiency, while reducing main user interruption probability, improve signal propagation path.

The application discloses a method for anti-clutter of airborne radar, which comprises the following steps: firstly, modeling the pulse-amplitude-phase time sequence hopping signal, establishing the airborne radar echo model, constructing the pulse-amplitude-phase agile waveform and the joint design of the anti-clutter optimization problem model of the receiving filter bank, and finally, quickly solving the pulse-amplitude-phase agile waveform and the receiving filter bank to realize the robust detection of multiple targets in the clutter background. The method can break through the technical bottleneck that the existing low PRF and medium-high PRF cannot simultaneously consider the range ambiguity and velocity ambiguity by modulating the pulse-amplitude-phase time sequence hopping signal, and based on the block coordinate descent and the efficient optimization method of quadratic transformation, the pulse-amplitude-phase agile waveform and the receiving filter bank are jointly designed to realize the robust detection of multiple targets in the clutter background. Compared with the existing pulse-amplitude-phase agile waveform technology, the method can realize the suppression of the range-velocity clutter and has the multiple target detection capability.

The application discloses a beamforming design method of an intelligent reflecting surface assisted wireless communication system, a transmitting beamforming vector and a phase shift matrix are obtained by establishing a model of the intelligent reflecting surface assisted wireless communication system; based on the transmitting beamforming vector and the phase shift matrix, an optimization objective function of maximizing the weighted sum rate of users is established, and a first auxiliary variable and a second auxiliary variable are introduced to optimize and solve the optimization objective function; the block coordinate descent method is used to iteratively update the optimization objective function, and initial optimal solutions of the first auxiliary variable and the second auxiliary variable are obtained; the initial optimal solutions are substituted into the optimization objective function to obtain a new optimization objective function; based on the new optimization objective function, the transmitting beamforming vector and the phase shift matrix are sequentially optimized until the weighted sum rate of users converges. By jointly optimizing the transmitting beamforming vector of the BS and the phase shift matrix of the RIS, the weighted sum rate of the UE is maximized.

The invention discloses a relay power and edge computing resource optimization method and system based on cloud edge collaboration, and the method comprises the steps: constructing a cloud edge collaboration resource optimization model with the purpose of minimizing the weighted sum of the total task time delay and the total task energy consumption in a relay-assisted NOMA-MEC system scene; the relay-assisted NOMA-MEC system scene comprises a cloud control center, a plurality of relays, a plurality of power areas and a plurality of edge gateways for acquiring data from the corresponding power areas; the task total time delay comprises a first time slot, a second time slot, a third time slot and a calculation total time delay of the cloud control center; the task total energy consumption comprises task transmission total energy consumption and task calculation total energy consumption; and solving the cloud edge collaborative resource optimization model based on a joint solving algorithm of block coordinate descent to obtain an optimal resource allocation scheme. According to the invention, real-time reliable transmission and processing of mass power data can be realized, and stable operation of a smart power grid is guaranteed.

This invention discloses a method for optimizing resources in a drone-assisted, self-free, large-scale MIMO system. In a drone-assisted, self-free, large-scale MIMO system scenario, drones act as aerial access points, coordinating with ground access points to provide services to ground users. The specific design of this method includes designing user scheduling rules based on the coordinated service provision of aerial and ground access points; designing a communication model in which aerial and ground access points cooperate to provide services according to the scheduling rules; designing a fair resource allocation scheme including a user scheduling scheme, a drone positioning scheme, and a power allocation scheme to users; maximizing the minimum downlink speed for users in the above communication model; and finally, verifying the feasibility of the proposed utility model by optimization methods such as block coordinate descent and continuous convex optimization techniques. This invention can propose an optimal resource allocation strategy, including drones, depending on the current distribution of users and ground resources, achieving blind spot coverage for providing services to ground users.

The invention relates to the technical field of communication, and particularly provides a reconfigurable intelligent surface-assisted ocean communication method and system based on LR-BCD, and the method comprises the steps: constructing an RIS-assisted ocean communication system model; constructing an ocean double-path channel model based on an ocean double-path propagation theory in combination with a sea surface reflection coefficient; singular value decomposition (SVD) is utilized to extract low-rank features of a channel, and high-dimensional beam forming and phase shift optimization problems are projected to a low-dimensional subspace; constructing a low-dimensional and rate maximization problem, and converting the problem into a convex subproblem through a weighted minimum mean square error (WMMSE) criterion; utilizing a block coordinate descent (BCD) algorithm to alternately iteratively update a low-dimensional precoding matrix and a phase shift vector; and finally, reconstructing the optimized low-dimensional variable back to a high-dimensional space and carrying out discretization quantization. According to the method, the sparsity of an ocean channel is utilized, the calculation complexity of large-scale RIS optimization is remarkably reduced through dimension reduction processing, and meanwhile the system and rate performance equivalent to that of a full-rank algorithm is kept.

The invention discloses a communication perception security joint beam forming method for an intelligent granary. The method comprises the following steps: constructing a completely known communication perception integrated system model considering channel state information of an eavesdropper; establishing a communication performance index and a sensing performance index of the ISAC system model; constructing an optimization problem by taking the sum of the minimum communication beam power and the artificial noise power as a target; splitting the optimization problem into three sub-problems by adopting a block coordinate descent method; and solving the sub-problem 1 by using an algorithm combining fractional programming and positive semidefinite relaxation, modeling the sub-problem 2 into positive semidefinite programming and solving, and solving the sub-problem 3 according to generalized Rayleigh entropy to obtain respective optimal solutions of a sensor beam forming vector, an artificial noise covariance matrix and a base station receiving filter vector. According to the method, the safety communication of the system is ensured under the condition that the requirements of communication quality and sensing precision are met, and a high-robustness optimization approach is provided for the intelligent granary which is a multi-node, multi-target and multi-interference-source complex system.

The invention provides an age-aware routing and unloading joint optimization method for a satellite network, and the method comprises the steps: enabling each satellite node to broadcast a lightweight HELLO beacon to a neighbor at the beginning of each decision time slot, and receiving the beacon of the neighbor; starting an independent optimization process for each observation task newly arriving at a satellite node in the time slot to determine a joint decision tuple; each satellite node enters a local execution stage, and after each time slot is finished, the queue value and the real battery energy of the virtual energy queue are updated according to the actual energy consumption and the energy collection amount of the satellite node in the time slot; according to the invention, a virtual energy queue is introduced to realize long-term energy balance constraint conversion, a block coordinate descent method is adopted to solve an optimal routing path, unloading nodes and a data compression ratio are calculated, and a self-adaptive energy sensing routing mechanism and a scheduling strategy based on an information age priority are combined to obtain an optimal routing path. And dynamic optimal balance among information freshness, data processing precision and energy sustainability is realized.

The invention discloses a bipartite graph clustering-based live pig sound anomaly detection method and device, and the method comprises the steps: carrying out the preprocessing of collected audio data, and obtaining a pig sound standardized audio sample; extracting acoustic characteristic parameters of the audio samples, and constructing a sample characteristic matrix; dividing the sample feature space into a plurality of balanced sub-clusters by adopting a balanced hierarchical K-means algorithm, and selecting a centroid as an anchor point; the sample-anchor point similarity is calculated, and a bipartite graph is constructed; inputting the bipartite graph into a preset clustering model BGCB, optimizing a sample and an anchor label matrix by adopting an alternating coordinate descent algorithm, generating a clustering result and calculating a clustering center; and identifying abnormal sounds of pigs such as cough, squeal and moan according to the clustering center difference and the spectrum energy distribution characteristics, and outputting an alarm. The method can achieve the automatic detection of the abnormal sound of the live pigs without manual marking, has the advantages of balanced clustering result, low calculation complexity and high robustness, and is suitable for the health monitoring and disease early warning of intelligent farms.

The application discloses a multi-objective optimization method for trade-off energy efficiency and spectrum efficiency of space-air-ground-thing Internet of Things, and belongs to the field of communication resource allocation.The application realizes the method as follows: constructing a space-air-ground-thing Internet of Things model, constructing a multi-objective optimization problem of maximizing system energy efficiency and system spectrum efficiency according to the space-air-ground-thing Internet of Things model; introducing an energy efficiency and spectrum efficiency trade-off coefficient, and converting the multi-objective optimization problem of trade-off energy efficiency and spectrum efficiency in the space-air-ground-thing Internet of Things network into a single-objective optimization problem by using a constraint method; dividing the complex single-objective optimization problem into three sub-problems of sub-channel selection, unmanned aerial vehicle and Internet of Things device power control and unmanned aerial vehicle position deployment, and further improving optimization efficiency; and adopting a block coordinate descent method to iteratively solve the three sub-problems in turn to obtain a solution of the multi-objective optimization problem of trade-off energy efficiency and spectrum efficiency, until a multi-objective optimization result is obtained.The application is suitable for the case that there are obstacles between unmanned aerial vehicles and Internet of Things devices, and can improve the energy efficiency and spectrum efficiency of the system.

This invention discloses a method for optimizing resources in a drone-assisted, self-free, large-scale MIMO system. In a drone-assisted, self-free, large-scale MIMO system scenario, drones act as aerial access points, coordinating with ground access points to provide services to ground users. The specific design of this method includes designing user scheduling rules based on the coordinated service provision of aerial and ground access points; designing a communication model in which aerial and ground access points cooperate to provide services according to the scheduling rules; designing a fair resource allocation scheme including a user scheduling scheme, a drone positioning scheme, and a power allocation scheme to users; maximizing the minimum downlink speed for users in the above communication model; and finally, verifying the feasibility of the proposed utility model by optimization methods such as block coordinate descent and continuous convex optimization techniques. This invention can propose an optimal resource allocation strategy, including drones, depending on the current distribution of users and ground resources, achieving blind spot coverage for providing services to ground users.

The application discloses a data storage load balancing method, device and equipment and a storage medium, and the method comprises the following steps: receiving all file data to be stored; mapping the file data to a two-dimensional coordinate system with a preset size after vectorization, and confirming the two-dimensional coordinate points corresponding to each file data and the size of each file data; based on the coordinate descent method, the X axis and the Y axis of the two-dimensional coordinate system are divided into multiple regions by using a dynamic adjustment mode, and during the dynamic adjustment process, the maximum value of the total size of the file data in all regions continuously decreases until the maximum value meets a preset requirement, and the optimal division of the X axis and the Y axis is obtained; and the file data is stored in the disk space after being divided according to the optimal division. After the file data is mapped to a two-dimensional space, the file data is divided by combining the dynamic programming and the coordinate descent idea, so that the file data is stored in the disk in a balanced manner.

The application relates to a pathological image classification ensemble method based on feature information guidance and belongs to the field of artificial intelligence and medical image processing. The method comprises the following steps: acquiring an unlabeled pathological image patch dataset; using multiple pre-trained visual language models to extract respective visual embedding features and category probability distributions; integrating the prediction results of the multiple models through an average fusion mode to obtain a robust preliminary semantic prediction; splicing the multiple model visual features to construct a joint representation, and modeling a class condition structure prior using a Gaussian mixture model; introducing a KL divergence to construct a double alignment objective function, and jointly optimizing the semantic prediction and the structure prior; and alternately optimizing the label distribution and the structure prior parameters through a block coordinate descent method to realize mutual guidance and enhancement of the two. The application fully integrates multiple model knowledge and structure information, effectively improves the classification performance and generalization ability of pathological images, and provides a more accurate and efficient solution for intelligent pathological auxiliary diagnosis.

The application relates to an unmanned aerial vehicle (UAV) integrated sensing and communication imaging system and an energy consumption optimization method thereof. The system comprises a detection UAV carrying a synthetic aperture radar (SAR), a relay UAV and a ground data processing center. In order to minimize the energy consumption of the system while ensuring the performance of the system, an energy consumption optimization method based on the system constructs a joint optimization problem, which considers the trajectory, flight speed, beamforming design of the detection UAV and the deployment of the relay UAV. In order to solve the non-convex optimization problem, a block coordinate descent algorithm based on a penalty coefficient is proposed, which solves a series of convex sub-problems in each iteration. Compared with the prior art, the application has the advantages of being capable of significantly reducing energy consumption and improving system performance.

This invention discloses a precoding method for maximizing the energy efficiency of multicast groups in a continuous aperture antenna array (CAPA) system. First, an integral-based energy efficiency maximization problem model for the multicast system is constructed, where each user group must satisfy a minimum multicast rate requirement and a total transmit power constraint. Then, the Dinkelbach method is used to transform the problem, and a quadratic transformation is used to equivalently transform the minimum multicast rate constraint. Based on this, the block coordinate descent (BCD) method is further used to alternately optimize the optimization variables. The variational method (CoV) and Lagrange duality theory are used to directly solve related subproblems, obtaining a CAPA precoder in continuous space. To further reduce complexity, a CAPA-ZF precoder is designed based on the zero-forcing (ZF) precoding principle, simplifying the original problem into a directly solvable power allocation problem. Compared to traditional spatial discrete antenna arrays (SPDA), CAPA can significantly improve the energy efficiency of multicast systems.

The application provides a movable antenna supported unmanned aerial vehicle covert communication method and system, comprising the following steps: calculating the maximum communication rate of a legal user under the support of a movable antenna according to a movable antenna communication scene; calculating the minimum detection error rate under multi-illegal guard cooperative detection according to the movable antenna communication scene; establishing a joint optimization problem of time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position based on the maximum communication rate and the minimum detection error rate, wherein the optimization target is to maximize the minimum throughput of the legal user under the condition of meeting the covert communication constraint; and obtaining the optimized time slot segmentation, beam forming, unmanned aerial vehicle trajectory and movable antenna position parameters by using an iterative algorithm based on block coordinate descent and continuous convex approximation, and applying the parameters to the covert communication of the unmanned aerial vehicle. The method provided by the application can well meet the concealment requirement of unmanned aerial vehicle communication and improve network security.