130 results about "Coordinate descent" patented technology

The invention discloses a large language model discrete cue word search method and device, and aims to solve the technical problem of overhigh cue word optimization calculation overhead caused by an existing discrete cue word search technology. The method comprises the steps of obtaining training prompt words and training sentences, updating an initial parameter matrix of an initial strategy model by adopting a preset parameter matrix updating function according to the training prompt words, and determining an intermediate strategy model; reasoning according to the training sentence by adopting an intermediate strategy model and a preset generative language model to generate a plurality of disturbance discrete cues and a plurality of reasoning results; based on a preset gradient calculation formula, performing iterative optimization on the intermediate parameter matrix of the intermediate strategy model by adopting an elite coordinate descent algorithm according to the plurality of disturbance discrete cues and the plurality of reasoning results, and determining a target strategy model; and generating a target discrete cue word based on the target strategy model.

The invention discloses a single-bit image recovery method and device, a terminal and a medium, and relates to the field of image processing. The method comprises the following steps: performing single-bit quantization on a to-be-recovered image by adopting a non-zero quantization threshold, and determining an initial image quantization model; replacing a sign function in the initial image quantization model with a smooth hyperbolic tangent function, performing low-rank matrix decomposition on the to-be-recovered image, and determining a target image quantization model; determining an initial optimization model according to the truncated least square function and the target image quantization model; a semi-quadratic minimization method is adopted to convert the initial optimization model, and a target optimization model is determined; solving the target optimization model by adopting a near-end block coordinate descent algorithm to obtain each low-dimension sub-matrix and a noise matrix; and determining a reconstructed image by using each low-dimensional sub-matrix. The problem that in the prior art, quantization noise cannot be effectively restrained, and therefore image restoration precision is affected is effectively solved.

The invention relates to the technical field of marketing operation based on big data analysis, and discloses a marketing operation method based on big data analysis. Preprocessing the user-commodity interaction data to construct an initial interaction matrix; constructing a co-occurrence counting matrix; automatically calculating a sparse penalty coefficient and a convergence threshold; setting an optimization objective function and a soft threshold function; initializing a projection matrix and iteratively solving a sparse projection matrix through a coordinate descent algorithm; constructing a commodity sparse co-occurrence adjacency set; and generating a personalized recommendation result in combination with user historical behaviors. The analysis quality is improved through data preprocessing and denoising, commodity potential correlation is mined through co-occurrence modeling, adaptability is enhanced through automatic parameter adjustment, a projection matrix optimization result is sparse and interpretable, the structure sensing capacity of recommendation is improved through an adjacent structure, and finally high-precision and high-correlation recommendation is achieved. The structure definition, the calculation efficiency and the model generalization ability are excellent, and an efficient and stable personalized marketing recommendation system is constructed.

The invention relates to a sum rate optimization method of an STAR-RIS auxiliary rate division multiple access system, and belongs to the technical field of wireless communication. According to the method provided by the invention, the communication performance of a downlink rate division multiple access (RSMA) system assisted by an intelligent reconfigurable surface (STAR-RIS) capable of transmitting and reflecting at the same time is improved by combining a beam forming method and a rate allocation optimization method, and an effective technical reference is provided for improving the communication performance of a wireless communication system. According to the method, an optimization problem of maximization and rate is constructed, optimization parameters are a beam forming vector, a rate allocation vector and an STAR-RIS transmission and reflection beam forming matrix, then the optimization problem is decomposed into two sub-problems by using a block coordinate descent (BCD) method, the beam forming vector and the rate allocation vector are optimized by using continuous convex approximation (SCA), and the beam forming vector and the rate allocation vector are optimized by using the STAR-RIS transmission and reflection beam forming matrix. And based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) methods, STAR-RIS transmission and reflection beam forming matrixes are optimized, and finally, sum rate maximization under the power condition is realized through alternate iteration.

The invention provides a communication auxiliary sensing system and method based on an intelligent super surface (RIS). The communication auxiliary sensing system comprises a multi-antenna base station, a multi-user terminal and an active RIS. According to the system, an uplink pilot frequency and a data signal are jointly utilized, an expectation maximization (EM) algorithm is adopted to iteratively recover equivalent channel state information (CSI), and a Cramer-Rao bound (CRB) of sensing performance is deduced. Combined optimization of user precoding and RIS reflection coefficients is innovatively proposed under the uncertainty of CSI, so as to minimize CRB and guarantee user communication rate at the same time. An original problem is converted into a processable form by establishing a traversal MIMO spectrum efficiency compact lower bound, and efficient optimization is realized by combining a WMMSE method with a block coordinate descent (BCD) frame. According to the scheme, the sensing precision and the spectrum efficiency are remarkably improved, the method is suitable for an active RIS-assisted MU-MIMO network, and the technology can also be expanded to a traditional RIS-free system.

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 invention discloses a manifold optimization transmitting directional diagram synthesis method for an analog digital hybrid array. According to the manifold optimization transmitting directional diagram synthesis method for the analog digital hybrid array, the HAD array transmitting directional diagram synthesis problem with the criterion of minimizing the matching error between an expected beam directional diagram and an actual beam directional diagram is researched. According to the method, the solution is carried out by utilizing block coordinate descent (BCD) and Riemannian manifold optimization. Firstly, a constant modulus auxiliary variable is introduced to equivalently represent that an objective function is a smooth biquadratic function; then, alternately optimizing a scaling factor, a digital weight, a simulation weight and a constant modulus auxiliary variable under a BCD framework, reexpressing the simulation weight and the auxiliary variable as a quadratic programming problem under constant modulus constraint, and solving through a Riemannian gradient descent method; the globally optimal solution of the digital weight is obtained through a Lagrange multiplier method. A numerical result shows that compared with other representative algorithms, the algorithm provided by the invention has better beam matching performance.

The present invention discloses a sparse aperture ISAR maneuvering target imaging and calibration method, comprising the following steps: establishing a maneuvering target ISAR sparse aperture signal model, characterizing the maneuverability of the target by constructing a variable scale non-uniform Fourier two-dimensional dictionary containing MTRC; constructing a parameterized sparse imaging model by maximum a posteriori estimation, expressed as a cost function of joint target parameter estimation and sparse imaging; finding the target rotation parameter by the minimum entropy method, and implementing a rough estimation of the maneuvering target rotation parameter by performing parameter iteration by the coordinate descent method; and implementing high-quality imaging with MTRC correction and accurate parameter estimation by combining sparse ISAR imaging and target rotation parameter estimation, decomposing into sparse imaging and dictionary learning, and iterating and solving in sequence. The present invention realizes the use of sparse aperture echo data to solve the problem of high-resolution imaging of ISAR maneuvering targets, greatly increasing the implementation scope and application scenarios of radar imaging.

The invention provides a single-bit digital beam forming method and device, a terminal and a medium, and relates to the technical field of radar detection and wireless communication, and the method comprises the steps: constructing a target single-bit low-rank matrix recovery model of a uniform linear array comprising a plurality of sensors, and an optimization problem of the target single-bit low-rank matrix recovery model, and converting the target single-bit low-rank matrix recovery model into a processable problem, solving and determining a low-rank matrix by using a near-end block coordinate descent algorithm and a gradient descent algorithm, calculating an estimated covariance matrix of a target and an interference signal based on the matrix, performing eigendecomposition to calculate an interference subspace, and modeling a single-bit digital beam forming problem according to a base vector of the interference subspace to obtain a single-bit digital beam forming matrix. And calculating a closed-form solution based on the principle that the optimal solution is located in the orthogonal complement space of the interference subspace. According to the method, the resolution requirement of the analog-to-digital converter can be reduced by fully utilizing the single-bit quantization characteristic, and the beamforming performance of single-bit digital beamforming is close to that of high-bit digital beamforming when the hardware complexity and the power consumption are reduced.

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 present invention relates to a fast image clustering method based on bipartite graph embedding and discriminant information. Aiming at the problems of high spatio-temporal complexity required for eigenvalue decomposition and similarity matrix construction in spectral clustering algorithms, and the independence between low-dimensional feature space embedding and label learning and the lack of discriminant information, the present invention proposes a fast image clustering method based on bipartite graph embedding and discriminant information. A bipartite graph is constructed in combination with anchor points, and the coordinate descent optimization method is used to replace eigenvalue decomposition and post-processing to reduce the difficulty of data storage and improve the data processing speed. In addition, the present invention simultaneously performs subspace learning and label learning, which can preserve the local features of data and make full use of discriminant information. Therefore, fast and efficient clustering analysis of large-scale and high-dimensional image data can be realized.

The present invention discloses a single-bit image restoration method, device, terminal, and medium, relating to the field of image processing. The method employs a non-zero quantization threshold to single-bit quantize an image to be restored, determining an initial image quantization model; employs a smooth hyperbolic tangent function to replace the sign function in the initial image quantization model, and performs low-rank matrix decomposition on the image to be restored, determining a target image quantization model; determines an initial optimization model based on a truncated least squares function and the target image quantization model; employs a semi-quadratic minimization method to transform the initial optimization model and determine a target optimization model; employs a proximal block coordinate descent algorithm to solve the target optimization model to obtain low-dimensional submatrices and a noise matrix; and utilizes the low-dimensional submatrices to determine a reconstructed image. This method effectively addresses the problem that prior art cannot effectively suppress quantization noise, thereby affecting image restoration accuracy.

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 application discloses a kind of computing unloading and resource allocation method under relay auxiliary MEC architecture, belong to wireless communication and computing resource management technical field.The application provides multiple task processing modes by introducing relay device, including local execution, relay unloading and edge computing, in order to minimize execution cost from user angle, joint optimization method of task division strategy, computing resource allocation strategy, relay selection strategy and uplink spectrum allocation strategy is proposed, can reduce network congestion while guaranteeing quality of service, improve user experience;In order to solve joint optimization problem, using block coordinate descent, reconstruction linearization technology and convex optimization method, with fast convergence speed and lower computational complexity, and relay auxiliary MEC architecture and optimization method can adapt to different types of intelligent devices and application scenarios, with wide applicability and flexibility, meet the needs of future mobile edge computing development.

An iterative statistical algorithm based on a discrete-to-discrete data model with iterative coordinate descent optimization for image reconstruction from radiation measurements obtained in emission tomography, i.e. in a Positron Emission Tomography scanner, is described in this invention. The method presented here allows for the presence of the regularization as an additive term. Furthermore, this method makes it possible to control the convergence of the algorithm using a specific parameter. These improvements are due to the signals obtained regarding the given statistics of this imaging technique.

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 invention discloses a joint service caching and task unloading method for air-ground integrated edge computing in the ocean Internet of Things. The method comprises the following steps: S1, constructing an air-ground-sea integrated network system model; s2, constructing a communication model; s3, formulating a service caching and task unloading strategy; s4, constructing a calculation model for calculating the total delay and the total energy consumption required for completing the task; formulating an optimization model for jointly optimizing a user association decision, a service cache decision and a task unloading proportion problem; s6, designing an iterative algorithm based on block coordinate descent; and S7, solving through multiple iterations to obtain an optimal user association decision result, a service cache decision result and a task unloading proportion, so that the total delay of the air-ground-sea integrated network system model is minimum. According to the invention, the communication efficiency is improved, the UAV position can be dynamically adjusted in the three-dimensional space based on the MIOTD distribution, and the communication quality is ensured.

This invention discloses a task offloading and resource allocation method under D2D-assisted MEC, belonging to the field of wireless communication and computing resource management technology. The method introduces service-oriented devices assisted by terminal direct transmission communication technology and rationally selects task processing modes, achieving efficient resource utilization and avoiding resource waste. By jointly optimizing computation offloading, D2D selection, computing resource allocation, and spectrum resource allocation, it can both guarantee service quality and reduce network congestion, thereby improving user experience. The two-stage iterative algorithm of this invention combines block coordinate descent, reconstruction linearization technology, and convex optimization methods, effectively solving non-convex optimization problems, and has a fast convergence speed and low computational complexity. Furthermore, the D2D-assisted MEC architecture and optimization method are applicable to different types of intelligent devices and diverse application scenarios, possessing broad applicability and flexibility, and can meet the needs of future mobile edge computing development.

The present invention proposes a control method, device, electronic device, and storage medium for a piezoelectric nanopositioning system based on rolling optimization, belonging to the field of precision electromechanical control technology. The method comprises: establishing a high-order transfer function model of a piezoelectric nanopositioning system and identifying the model parameters through a frequency sweep method; secondly, converting the model into a high-dimensional state space model, designing optimal control indicators, constructing a finite-time rolling optimization control problem, and improving the tracking capability of the piezoelectric nanopositioning system; finally, converting the rolling optimization control problem into a quadratic programming problem, and designing a coordinate descent method to accelerate the online calculation of the optimal control and ensure that the optimal control input satisfies the state and input constraints of the system. The method of the present invention can solve the problems of existing piezoelectric nanopositioning systems, such as the low order and limited expression capabilities of mathematical models, the narrow bandwidth of control methods, and the difficulty of digital implementation, thereby improving the ability of piezoelectric nanopositioning systems to track high-frequency reference instructions and being suitable for engineering applications.

The invention provides a mobile cell resource minimization slice resource configuration method and system, and belongs to the technical field of train communication, and the method comprises the steps: building a train mobile network model based on a relay system, and constructing a resource slice framework; establishing a communication and service quality assurance model for a passenger on-demand service and a beyond-visual-range service by considering a downlink bearing demand of a train intelligent service; establishing an optimization problem taking bandwidth allocation and terminal preemptive grouping as target variables; simplifying an original problem by using a convex optimization theory, and decomposing the original problem into a resource block allocation sub-problem and a mini time slot preemption sub-problem; and iteratively solving the two sub-problems by using a resource block allocation and preemption efficient algorithm based on block coordinate descent to obtain an optimal resource allocation strategy. Under the condition of non-ideal train-ground link transmission caused by the high-speed movement characteristic of a train, the deterministic transmission requirement of heterogeneous services is ensured, and the joint design of a preemption-based slice deployment strategy and a resource allocation algorithm is realized.

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.

The present invention discloses a method for dynamically tracking the knowledge changes of students, which relates to the field of knowledge tracking in intelligent education. The method includes the following steps: constructing a knowledge state model of students based on the independent incremental characteristics of students' knowledge growth; constructing a meta-knowledge dictionary based on the knowledge points of learning projects, and constructing a Bayesian framework for students to answer questions according to the meta-knowledge dictionary and the knowledge state model of students, so as to obtain a loss function model for students to answer questions; according to the answer data of students and the loss function model for students to answer questions, using the alternating direction multiplier method to obtain the loss function result for students to answer questions, and according to the loss function result for students to answer questions, using the coordinate descent strategy to update the meta-knowledge dictionary and the knowledge state of students, so as to dynamically track the changes of students' knowledge state. The present invention can effectively diagnose the current knowledge state of students, and does not require a large amount of offline training, and can adapt to the dynamically changing online learning environment.

The invention discloses a secure transmission method for improving the sensing performance of an ISAC system for an active RIS scene, and the method constructs an ISAC system based on the assistance of an active RIS to complete communication and sensing tasks at the same time. When the active RIS assists the system in safe communication, the active RIS can effectively overcome serious multiplicative interference caused by the four-hop radar, so that the sensing performance of the system is improved. Considering that the active RIS has inevitable thermal noise, the Cramer-Rao lower bound (CRLB) of system perception under thermal noise interference is deduced. In order to efficiently solve the constructed non-convex optimization problem, the constructed non-convex optimization problem is divided into two sub-problems to be optimized and solved respectively. Through a block coordinate descent method, a minimization-maximization algorithm (MM), a positive semidefinite relaxation method (SDR) and a Taylor approximation method, a BS emission beam forming matrix and a gain phase shift joint matrix of an active RIS are solved respectively. According to the invention, the beamforming and active RIS reflection design is combined, so that the comprehensive performance of system perception and safety is effectively improved.

The invention provides a robust task unloading method for unmanned aerial vehicle assisted mobile edge computing, which comprises the following steps of: establishing an unmanned aerial vehicle assisted mobile edge computing system scene model, and constructing a problem model for minimizing the maximum task completion time of the unmanned aerial vehicle assisted mobile edge computing system under the condition of satisfying energy consumption constraint and time slot limitation. And decomposing the problem model, approaching a non-convex constraint condition by using a continuous convex approximation method, and carrying out alternate iterative solution by combining a block coordinate descent frame. According to the method, the potential of partial unloading of the unmanned aerial vehicle can be fully excavated, and the system delay can be effectively reduced and the overall energy efficiency can be effectively improved under the condition of conforming to the actual NLOS channel condition.

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.

The invention discloses a trajectory and resource allocation optimization method and system in an unmanned aerial vehicle auxiliary cache system, and belongs to the technical field of wireless communication. The trajectory and resource allocation optimization method in the unmanned aerial vehicle auxiliary cache system comprises the steps of constructing the unmanned aerial vehicle auxiliary cache system, deriving a relationship between a user request time delay and an unmanned aerial vehicle hovering position, cache and trajectory based on unmanned aerial vehicle coverage radius and cache capacity constraints, establishing a user average request time delay minimization optimization problem, and optimizing the trajectory and resource allocation in the unmanned aerial vehicle auxiliary cache system. The optimization problem is decomposed into a user clustering sub-problem, a caching strategy sub-problem, a trajectory optimization sub-problem and a bandwidth power joint optimization sub-problem; and a neighbor clustering algorithm, a dynamic programming algorithm, an ant colony algorithm and a block coordinate descent algorithm are utilized to solve a user clustering sub-problem, a cache strategy sub-problem, a trajectory optimization sub-problem and a bandwidth power joint optimization sub-problem respectively, and finally an optimal user clustering, a cache placement strategy, an unmanned aerial vehicle flight trajectory and a bandwidth power distribution result are determined.

The present invention relates to the field of wireless power supply edge computing and task scheduling technology, and specifically to a resource allocation method for a wireless power supply edge computing network. First, heterogeneous access points, mobile terminals, time-varying channels, and heterogeneous tasks are comprehensively considered, and corresponding system models, equipment and service models, and task model calculation models are established; then, a selection algorithm based on hybrid delay tasks is designed by comprehensively considering various factors affecting computing performance; finally, considering the network model characteristics of the wireless power supply edge computing network, a coordinate descent algorithm based on one-dimensional time variable binary search is used to solve the mobile terminal's offloading decision and system time resource allocation problems. The present invention can effectively make heterogeneous access point selection, offloading decision-making, and time resource allocation schemes under time-varying channels, thereby improving the overall computing efficiency of the system.