154 results about "Coordinate descent" patented technology

A resource allocation method for an unmanned aerial vehicle-assisted edge computing network based on intelligent reflecting surface assistance, comprising: constructing an unmanned aerial vehicle-assisted edge computing network system model based on intelligent reflecting surface assistance; constructing a communication model, a time delay model and an energy consumption model; according to the communication model, the time delay model and the energy consumption model, constructing a maximum average throughput function; optimizing the maximum average throughput function by using successive convex approximation and block coordinate descent algorithms, and according to the optimized maximum average throughput function, calculating an optimal resource allocation result. The present application uses an intelligent reflecting surface to assist in an unmanned aerial vehicle-assisted network system, thereby increasing the channel gain of links between automobiles and unmanned aerial vehicles.

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 multi-target CRB minimization method based on near-field flux and inductance integration of rate splitting, and relates to the technical field of wireless communication and novel multiple access. The method comprises the following four steps: S1, establishing a near-field communication channel and a near-field sensing channel according to a spherical wave propagation model; s2, the base station encodes information required by a communication user into public and private information streams by using a rate splitting multiple access technology and sends the public and private information streams to the user side; s3, according to the array response vector of the near-field sensing channel, deriving CRBs for simultaneously estimating angles and distances of a plurality of sensing targets; and S4, establishing an optimization problem by satisfying the communication rate requirement and minimizing the CRB of multiple targets, and jointly optimizing the hybrid beam forming matrix and the common rate allocation variable by using a penalty dual decomposition and block coordinate descent method. The method can achieve the simultaneous estimation of the distances and angles of a plurality of sensing targets, and effectively reduces the estimation error.

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 invention discloses a task unloading and resource allocation method under D2D-assisted MEC, and belongs to the technical field of wireless communication and computing resource management. According to the method, service equipment assisted by a terminal direct transmission communication technology is introduced, and a task processing mode is reasonably selected, so that efficient utilization of resources is realized, and resource waste is avoided; through joint optimization of calculation unloading, D2D selection, calculation resource allocation and spectrum resource allocation, the service quality can be ensured, and network congestion can be reduced, so that the user experience is improved; the two-stage iterative algorithm combines the block coordinate descent, the reconstruction linearization technology and the convex optimization method, the non-convex optimization problem is effectively solved, and the fast convergence speed and the low calculation complexity are achieved. Besides, the D2D-assisted MEC architecture and the optimization method are suitable for different types of intelligent devices and diversified application scenes, have wide applicability and flexibility, and can meet the requirements of future mobile edge computing development.

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 belongs to the technical field of unmanned aerial vehicles, discloses a lightweight slice resource allocation method for an unmanned aerial vehicle auxiliary network, and aims to maximize the minimum user rate by jointly optimizing channel allocation, power allocation and unmanned aerial vehicle deployment. The UAWN slicing problem is modeled as non-convex mixed integer nonlinear programming. Considering that the computing power of the unmanned aerial vehicle is limited, a low-complexity decomposition framework is provided, and the problem is solved by combining problem decomposition, integer relaxation and a batch coordinate descent method. In addition, an optimal rounding algorithm is designed to obtain a relaxation solution, and the feasibility and optimality of the solution after rounding are ensured. In order to verify the effectiveness of the scheme, a large number of numerical experiments are performed. A simulation result shows that the convergence speed and the performance of the scheme are superior to those of some existing algorithms.

The invention discloses a robust semi-supervised multi-view face recognition system (RS2 Mv), and belongs to the field of face recognition. The method comprises the following steps: firstly, representing a collected face image through various types of features, and describing the face image from different feature levels; secondly, decomposing a feature matrix corresponding to each type of features into a pure data matrix and a noise matrix; then, learning self-representation subspaces corresponding to view angle features based on pure data obtained through decomposition; and finally, respectively optimizing each parameter through a coordinate descent method in an iterative optimization process until the system converges, and spreading label information of a labeled face image to a non-labeled face image through an image fused by the self-representation subspaces of multiple visual angles to complete face recognition. According to the method, the recognition precision of the face recognition system on the face image doped with noise data can be effectively improved, and accurate recognition of the non-label face image can be completed under the condition that only a small number of labeled face images exist.

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 invention belongs to the technical field of wireless communication, and particularly relates to an IRS-assisted multi-unmanned aerial vehicle NOMA communication system rate optimization method, which comprises the following steps: step 1, constructing an IRS-assisted multi-unmanned aerial vehicle NOMA communication system rate optimization model; 2, initializing an unmanned aerial vehicle position Q, an SIC decoding sequence A, an IRS reflection matrix theta, an unmanned aerial vehicle transmitting power P and a judgment threshold value epsilon; according to the method, under the constraint conditions of the SIC decoding sequence, the two-dimensional position of the UAV, the IRS reflection matrix and the UAV transmitting power, the four variables are jointly optimized, and the sum rate of the IRS-assisted UAV communication system is optimized; binary variables are introduced to participate in channel state sorting, different from a traditional channel condition sorting method, the method adopts a block coordinate descent method, a continuous convex approximation (SCA) method and a penalty-based method to approach a globally optimal solution, and compared with other resource allocation schemes of a network without IRS assistance, the method provided by the invention has the advantages that the system rate is increased, and the system performance is improved. And the method has better practicability and feasibility.

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 RSMA-based IRS-assisted cellular removal large-scale MIMO resource allocation method. The method comprises the following steps: constructing an RSMA-based IRS-assisted cellular removal large-scale MIMO system model and an RSMA framework; constructing an SR maximization problem of the IRS-assisted cellular removal large-scale MIMO system based on the RSMA; the fraction is converted into a convex function through Lagrange dual transformation and secondary transformation, and the SR maximization problem is converted into an active beam forming and rate allocation problem and a passive beam forming problem through block coordinate descent (BCD); solving an active beamforming and rate allocation problem and a passive beamforming problem; and deducing an optimal user and rate based on a joint beam forming and rate allocation algorithm. According to the method, the problem of SR maximization in an IRS-assisted CF-mMIMO system based on RSMA is solved by designing a joint beam forming and rate allocation algorithm.

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 proposes a transmission scheme design method and system for a RIS-assisted sparse federated learning network. The present invention first models the local gradient information sparsity rate and constructs a transmission scheme design optimization problem for maximizing federated learning performance. By finding the second-order derivative of the optimization problem with respect to the transmission code length, it is proved that the optimization problem is a convex problem with respect to the transmission code length, and then the KKT condition is used to derive the closed-form solution of the optimal code length, and based on this, the dimension reduction of the optimization problem is achieved. Then, the optimization problem after dimension reduction is solved based on the block coordinate descent algorithm, and an efficient design scheme for coding rate, transmission power and RIS phase matrix is ​​obtained. The present invention significantly improves the performance of RIS-assisted sparse federated learning networks, can be widely used in many practical application scenarios, and has extremely high scalability and economic benefits.

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 discloses a DMA (Direct Memory Access) and RIS (Remote Information System) combined assisted MIMO (Multiple Input Multiple Output) secure communication method and system, and belongs to the field of wireless communication. The method comprises the following steps: constructing a DMA model, an RIS model, a channel model and a communication model based on a transmission system model comprising a base station, a user and an eavesdropper; constructing an optimization problem for improving the secrecy capacity and establishing an optimization model and constraint conditions under the maximum transmitting power constraint, the phase shift unit mode constraint and the phase shift Lorentz constraint by jointly optimizing a beam forming vector, an RIS phase shift matrix and a DMA weight matrix; dividing the optimization problem into sub-problems of beam forming vector optimization, RIS phase shift matrix optimization and DMA weight matrix optimization by using a block coordinate descent method; in each iteration, solving sub-problems of optimization of the beam forming vector, the RIS phase shift matrix and the DMA weight matrix according to any sequence; and iteration is carried out until convergence.

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.