222 results about "Non convex optimization" patented technology

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle mobile edge computing resource allocation method based on deep reinforcement learning, which constructs a mixed integer non-convex optimization problem, and the objective of the optimization problem is to collaboratively optimize the flight path of each unmanned aerial vehicle, a mobile user scheduling strategy and a task unloading proportion. Therefore, the time delay-energy consumption minimization is realized; and solving the optimization problem by adopting a multi-agent double-delay depth deterministic strategy gradient algorithm: converting the optimization problem into a Markov game model, and realizing the solution of the optimization problem through an experience playback pool and a double-evaluation network updating strategy. According to the method provided by the invention, the flight path and the unloading proportion of the unmanned aerial vehicle can be adaptively adjusted, so that calculation resource allocation for ground users is efficiently completed. Compared with a global unloading scheme, an average unloading scheme and other deep reinforcement learning methods, the method provided by the invention has excellent performance of lower energy consumption and shorter time delay while completing tasks.

The invention discloses an air federated learning realization method for collaborative optimization of client scheduling and model compression. The invention realizes the air federated learning realization method for collaborative optimization of client scheduling and model compression. Along with rapid development of federated learning in a wireless network, air computing based on a multiple-input-multiple-output technology is widely concerned due to high communication efficiency of the air computing. However, in a resource-limited large-scale device scene, channel interference, device heterogeneity and limited spectrum resources significantly restrict the convergence rate and model performance of federated learning. In order to deal with the challenges, the invention provides an air federated learning framework combining compressed sensing and client scheduling, and by collaborative design of model parameter compression, multi-antenna beam forming and dynamic equipment selection strategies, the total communication overhead of each round of training is minimized, and meanwhile, the convergence of a global model is guaranteed. In order to balance training cost and model quality, a joint optimization problem based on calculation-communication cost and model precision loss is provided. In order to solve the non-convex optimization problem, an original problem is decoupled into two sub-problems. Firstly, an optimization problem of a pre-coding and post-processing matrix is designed for a given user scheduling result to minimize a gradient aggregation error. Then, a novel user scheduling algorithm based on channels and data is provided to obtain an air aggregation result.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

A method for the joint active and passive beamforming and received signal optimization in an ISAC system assisted by dual IRSs is provided. The method jointly optimizes active beamforming at Base Station (BS), reception of sensing signals at the Base Station (BS), and passive beamforming at IRSs, so as to maximize communication sum-rate of users while ensuring that SNR of sensing signals meets a minimum requirement. To address the complex non-convex optimization problem, the method first applies fractional programming to decouple problem, then adopts successive convex approximation algorithm and alternating direction method of multipliers to transform intractable non-convex problem into multiple tractable subproblems, and finally employs an alternating optimization method to efficiently acquire the high-quality suboptimal solutions. The simulation results demonstrate that disclosed scheme exhibits satisfactory convergence and effectiveness, and can significantly improve the performance of IRS-assisted ISAC systems.

The invention discloses a joint optimization method for task unloading and power control in a satellite edge computing network, and provides a time delay-energy consumption balance optimization framework oriented to dynamic task arrival aiming at the long-term random optimization problem in satellite edge computing task unloading. Through fusion of Lyapunov optimization and a virtual queue mechanism, the long-term delay constraint is successfully converted into a time slot level optimization target which can be executed online, and a self-adaptive decision process is realized. And aiming at the generated non-convex optimization problem, constructing a robust solution algorithm under a PDD framework, and integrating secondary transformation and an SCA technology to realize efficient solution. Simulation experiments show that the method can obviously reduce the energy consumption in a random task arrival scene, and strictly meets the delay constraint requirement at the same time.

The invention discloses a dynamic confrontation sample generation method and device based on non-convex optimization, and relates to the technical field of artificial intelligence safety and confrontation machine learning, and the method comprises the steps: training to obtain a pre-training model; applying noise to the original sample image to generate an initial confrontation sample, inputting the initial confrontation sample into the pre-training model to obtain model prediction output, and calculating a loss function value; applying symmetric differential disturbance in the neighborhood of the adversarial sample along a random orthogonal direction, estimating an interpolation gradient norm according to a loss function value, and optimizing disturbance and updating the adversarial sample along a gradient symbol direction to obtain a current adversarial sample; projecting the current adversarial sample to a legal disturbance space; judging whether the current disturbance update is valid or not based on a line search condition, and if yes, accepting the current disturbance update and entering the next round of iteration; if not, the dynamic projection range is dynamically adjusted until the iteration stop condition is met, the final adversarial sample is generated, and the efficiency and stability of adversarial sample generation are improved.

The invention provides a sequence correction second-order cone global optimization method suitable for a radial power distribution network, and the method comprises the steps: introducing an auxiliary variable to decouple an original non-convex optimization problem in a power system, and enabling the original non-convex optimization problem to be equivalently converted into a bilevel programming problem; adopting a second-order cone relaxation technology to convexly solve an upper-layer planning problem and then carrying out centralized solution; decomposing the lower-layer planning problem into a plurality of quadratic planning sub-problems containing single non-convex quadratic constraint; the quadratic programming sub-problem is converted into a semi-definite programming sub-problem through Scher complement, and a strong dual gap is established through a standard S-process; and performing iterative calculation and sequence correction on the optimization result of the decoupled upper and lower layer planning problem according to an alternating direction multiplier method to ensure that the non-convex constraint is strictly established. According to the method, errors generated by convex relaxation are avoided, the reliability and global optimality of the result can be effectively ensured, and an accurate and reliable mathematical model is provided for the power distribution network with the high proportion of renewable energy sources.

The invention relates to an electric power communication system optimization technology, in particular to a physical layer security optimization method and equipment for a full-duplex multicarrier power line communication system, and the method combines a full-duplex technology and a time domain artificial noise auxiliary security strategy to construct a multicarrier power line communication system model with a relay; generating a safety rate maximization problem, designing a set of convex approximation method, and performing conversion and simplification of a non-convex optimization problem; designing an iterative solution algorithm, and jointly optimizing the confidential data of the source node, the confidential data of the relay node and the artificial noise power distribution matrix; and optimizing the physical layer safety performance of the full-duplex multi-carrier power line communication system based on the solved power distribution matrix. According to the communication scheme and the solved power distribution vector, the safety performance of the power line communication system is optimized, the limitation of the number of power transmission cables in the power line communication system is avoided, the safety rate maximization of the total transmitting power domain can be realized, and the technical problem that data in an existing multi-hop power line communication system is prone to leakage is solved.

A DFRC (radar-communication integration) system solves the problem of spectrum congestion caused by increase of wireless application requirements, but in an actual environment, a wireless transmission environment is very complex, functions of radar and communication can be affected, and the DFRC system can be applied to a wireless communication system, so that the DFRC system can be applied to a wireless communication system, and the DFRC system can be applied to the wireless communication system. The invention researches a joint design method for forming a DFRC transmitted waveform and an RIS reflected wave beam by taking a Cramer-Rao bound for minimizing radar parameter estimation as a criterion and taking a signal to interference plus noise ratio demand of a communication user under a symbol-level precoding system as a constraint. According to the invention, the proposed joint design problem is described as a non-convex optimization problem with the non-convex objective function and the constant modulus constraint. According to the method, multiple variables are decoupled under an alternating optimization framework, and a transmitted waveform optimization sub-problem and an RIS reflection coefficient optimization sub-problem are solved respectively. According to the method, an auxiliary variable is introduced, a non-convex problem is converted into a convex problem on the basis of Scherr relaxation, and then effective solving is conducted through an existing convex optimization method; according to the method, a fractional part in a target function is processed through an auxiliary variable and Scherr complementary processing, the fractional part is converted into a differential convex function form, and then iterative solution is carried out through a successive convex approximation algorithm in combination with a penalty method.The numerical experiment result shows that the radar detection parameter estimation performance is improved while the communication performance is guaranteed through the algorithm.

The invention discloses a hybrid energy power supply optimization method and device based on cooperation of energy storage vehicles and a large power grid, and the method comprises the steps: constructing a hybrid energy power supply system which uses the large power grid and a plurality of energy storage vehicles to provide power for users in the coverage range of the large power grid, and deploying the plurality of energy storage vehicles to provide power for users beyond the coverage range of the large power grid; a power distribution model is constructed, the model aims at maximizing the energy efficiency of the energy storage vehicle, and the power requirements of all users are met through optimization of the position of the energy storage vehicle, power distribution of a large power grid, association scheduling between the users and the large power grid and association scheduling between the users and energy storage workshops; and converting the power distribution model into a mixed integer non-convex optimization problem, and solving the power distribution model step by step by adopting a block coordinate descent method and a successive convex approximation method to obtain a suboptimal solution. By optimizing cooperative work of the large power grid and the energy storage vehicle, efficient power transmission is achieved, and it is ensured that a user can obtain stable power supply in various complex scenes.

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 invention discloses a communication method based on a reconfigurable intelligent surface, a communication device based on the reconfigurable intelligent surface and a communication system based on the reconfigurable intelligent surface, and belongs to the technical field of wireless communication. Jointly optimizing a current beam forming vector and a current RIS reflection parameter of the sender according to a channel estimation result obtained in real time through a depth deterministic strategy gradient algorithm; a joint optimization model of beam forming and RIS parameters of a sender is established, a non-convex optimization problem is converted into a Markov decision process, efficient solution is realized through a depth deterministic policy gradient algorithm, and the transmission capability of communication can be improved. Furthermore, a reconfigurable intelligent surface technology is introduced, a reflection unit with independent amplitude-phase regulation and control capability is designed, the amplitude fixed limitation of a traditional passive RIS is broken through, double-fading path loss can be effectively overcome, and then the anti-interference performance of a communication system in complex electromagnetic environments such as high spectral density and high interference power can be improved.

The embodiment of the invention discloses a power configuration method and device, a storage medium and terminal equipment, and is applied to the technical field of power distribution in a power system. Obtaining power distribution scene data, loading a convex neural network model, constructing an objective function based on the power distribution scene data, and constructing constraint conditions of power configuration, including physical operation constraints of power resources in a power distribution scene and output range constraints of the convex neural network model, and calculating an optimal power distribution parameter value of the power distribution equipment so as to carry out power configuration. Therefore, the most complex non-convex security constraint in power configuration is converted into a convex constraint represented by a data-driven and input convex neural network model, and the conversion enables a non-convex optimization problem which is difficult to solve originally to be changed into a convex optimization problem which can be efficiently solved, so that on the premise of ensuring the calculation efficiency, the calculation efficiency is improved, and the calculation cost is reduced. And meanwhile, the economical efficiency, the safety and the global optimality of scheduling are considered.

The invention discloses a low-altitude communication and sensing integrated resource collaborative optimization method, which comprises the following steps: firstly, carrying out three-dimensional space modeling on a base station, a cooperative unmanned aerial vehicle and a non-cooperative unmanned aerial vehicle in a system, defining communication and sensing dual-function attributes of the base station, and establishing a communication channel model and a sensing echo model; establishing a communication and perception echo model between the base station and the unmanned aerial vehicle, defining an association relationship and a power distribution optimization variable, constructing a joint performance objective function, and introducing power constraint, communication rate and positioning precision system constraint to form a mixed integer non-convex optimization problem; and finally, decomposing a mixed integer non-convex optimization problem into two sub-problems, and realizing double improvement of system communication efficiency and sensing precision through joint optimization. According to the method, high-precision positioning of the non-cooperative unmanned aerial vehicle can be realized while the communication quality of the cooperative unmanned aerial vehicle is guaranteed, so that the communication resource utilization efficiency and the airspace management safety are effectively improved, and the method is suitable for the intelligent deployment requirement of a future complex urban air network.

The invention relates to the technical field of wireless communication, and discloses a design method of an interference-minimized communication-sensing integrated waveform, which comprises the following steps of: determining parameter data of an MIMO communication-sensing integrated system so as to obtain a communication channel matrix at a transmitting end and construct a radar sensing model; on the basis of constraint conditions of a radar perception model, a problem of minimizing interference power among multiple users is constructed; adding similarity constraint and constant modulus constraint to construct an optimization problem of minimizing interference power among multiple users; then, adding a weighting factor to obtain a compromise optimization problem; and determining a concave-convex penalty algorithm to enable a constant modulus constraint in the non-convex optimization problem to be equivalent to a convex constraint and a non-convex constraint, and converting the non-convex constraint into the convex constraint by using a successive convex approximation method to obtain a globally optimal solution. Compared with a strict radar constraint scheme, the method provided by the invention can provide a higher average reachable rate and a lower bit error rate, and greatly improves the communication performance while ensuring the sensing performance.

The invention provides a k-plane clustering roof segmentation method based on multiple geometric consistency constraints. The k-plane clustering roof segmentation method comprises the steps of performing initialization preprocessing on input original building roof point cloud data; based on each point in the original building roof point cloud data and a candidate fitting plane cluster, establishing a clustering label optimization objective function fused with triple geometric constraints; iteratively updating the clustering label and the candidate fitting plane parameter of each point by adopting an alternating minimization optimization strategy, and solving a mixed integer non-convex optimization problem until the clustering label optimization objective function is converged; and based on the converged clustering tag optimization objective function, outputting a building roof point cloud instance segmentation result, the building roof point cloud instance segmentation result comprising the clustering tag of each point and the corresponding candidate fitting plane geometric parameters. According to the method, the segmentation precision and the calculation efficiency of the building roof point cloud instance are improved.

The invention relates to the technical field of power system stability constraint optimization and computer algorithms, and provides an energy storage fault ride-through optimization method considering optical storage access weak power grid stability constraint, which comprises the following steps: collecting power grid operation data to generate a dynamic enhancement data set; constructing and correcting a digital twinborn body on line; generating a guide vector field and initializing an optimization framework; executing hybrid iteration to generate a candidate strategy; an optimization process is fed back through simulation evaluation; verifying a decision output control instruction after convergence; and deploying an instruction and realizing online updating. According to the method, the characteristics of the power grid are accurately modeled by using the digital twin, optimization search is guided in combination with the guide vector field, the non-convex optimization problem caused by stability constraints is effectively processed through a hybrid iteration strategy, and the voltage and frequency recovery capability in the fault ride-through process is improved.

The invention relates to the field of energy management, and discloses a set generating capacity improvement and power grid optimization operation device based on intelligent scheduling, and the device comprises a data sensing module which is used for collecting the power grid state data of a set and carrying out the preprocessing of the data, and obtaining the processing data; the scale modeling module is used for constructing a unified mathematical model containing a fast variable and a slow variable by utilizing the processed data, describing a coupling relationship between the fast variable and the slow variable by adopting layered Hamiltonian, embedding power balance equality constraints to enable the power of the whole network to be constant, and introducing a Warisstein distance relaxation unit start-stop integer constraint to process a non-convex optimization problem, so as to obtain a scale model; and outputting the multi-objective optimization mathematical model containing the energy function and the constraint condition. Dynamic characteristics such as a rotor angle / angular velocity of a synchronous generator and scheduling decision variables such as unit start-stop, energy storage charge-discharge and the like are brought into the same energy framework by utilizing layered Hamiltonian, energy conservation of the whole network is ensured through power balance equality constraints, and meanwhile, Warisstein distance is introduced to relax unit start-stop integer constraints.

The invention discloses a finite block length secure transmission method and system based on communication perception integration, and relates to the technical field of communication. Comprising the following steps: S1, constructing a system model, determining an optimization problem, analyzing a safe transmission rate capable of reaching a limited block length, and constructing a safe rate maximization problem; s2, designing an optimization scheme: decomposing the non-convex optimization problem in the S1 into three sub-problems, namely uplink transmission power distribution of IoE equipment, sensing signal design and joint sensing and communication receiving beam forming optimization, converting each sub-problem into a solvable convex optimization problem, and respectively solving the solvable convex optimization problem, and obtaining the solution of the original problem through an alternate iteration method until the algorithm converges or reaches the maximum iteration times. According to the method, a potential threat source is identified more accurately by utilizing a sensing data auxiliary system, and the technologies of beam forming, artificial noise and the like are utilized, so that the secure transmission between legal users is ensured, the quality of a received signal of an eavesdropper is reduced, and the secure fusion of a communication function and a sensing function is realized.

The invention discloses a multi-dimensional parameter joint estimation method and system for unmanned aerial vehicle (UAV) target detection, and the method comprises the steps: a receiver receives echo signals reflected by a multi-path UAV through an antenna array, processes the echo signals and extracts signal parameters, and the signal parameters comprise an angle of arrival, time delay and Doppler frequency shift; constructing a receiving signal model according to the attributes and the signal parameters of the antenna array, estimating the state of the UAV by using the receiving signal model, and obtaining a non-convex optimization target according to a set condition; the characteristic that low-altitude millimeter wave channel parameters are sparsely distributed is utilized, an optimization target of an SOCP parameter estimation method based on sparse recovery and convex optimization is designed, and meanwhile, the global search capability is improved by utilizing a complete matrix dimensionality reduction method based on a grey wolf optimization algorithm and heavy tail distribution. According to the invention, for the key parameter estimation problem of UAV detection, sparse signal processing and convex optimization technologies are combined, and the precision and robustness of angle-time delay-Doppler joint estimation are improved.

The invention provides a tunnel activity measurement method with a bidirectional loading demand, and belongs to the technical field of tunnel activity measurement, and the method comprises the steps: collecting a multi-dimensional force signal and a displacement signal, carrying out the digital processing, building a mechanical coupling tensor matrix, and obtaining a preliminary decoupling force component through a condition number adaptive inversion strategy; tensor network high-order correlation modeling and density matrix reforming swarm optimization are utilized for optimization to obtain an accurate decoupling force component, a simulated annealing particle swarm optimization algorithm is combined for solving a non-convex optimization objective function to obtain real mechanical response parameters, defects are identified through ultrasonic detection, and an influence coefficient matrix is established; a Bayesian inversion algorithm is adopted to remove defect influence to obtain corrected tunnel activity parameters, tunnel safety monitoring is finally realized through time sequence analysis and stress redistribution evaluation, and the technical problem of insufficient measurement precision caused by multi-dimensional force signal coupling interference is solved.

The invention relates to the technical field of wireless communication, and particularly discloses a beamforming optimization method of an active-RIS (Radio Information System) assisted integrated sensing communication and energy transfer system, which aims at maximizing radar sensing SINR (Signal to Interference plus Noise Ratio) weighted sum in an active-RIS assisted multi-integrated sensing communication and energy transfer system. Constructing a non-convex optimization problem by taking an SINR demand of a communication user, an energy acquisition demand of an energy receiving user and power constraints of a base station and an Active RIS as constraint conditions; after the constructed non-convex optimization problem is decomposed into sub-problems of a receiving filter coefficient, MFBS transmitting beam forming and an active-RIS reflection coefficient, alternate iterative optimization is carried out, so that the transmitting power of the MFBS can be reasonably distributed to communication and radar beams, and the active-RIS is controlled to select an optimal reflection phase and an amplification coefficient. Therefore, under the condition that the SINR requirement of the communication user, the EH requirement of the energy receiving user and the power constraint are met, the multi-target sensing performance of the radar is effectively improved, and meanwhile the communication quality and the energy transmission efficiency are guaranteed.

The invention provides a wireless power supply mobile edge computing (WPT)-MEC (wireless power supply mobile edge computing) optimization algorithm assisted by a user cooperation (UC) and a non-orthogonal multiple access technology (NOMA) in a non-linear energy harvesting (EH) mode, the transmission power, the user unloading time and the user unloading power among different energy beams of the HAP are jointly optimized, the energy and the computing resources are fully utilized, the energy utilization rate of the HAP is improved, and the energy utilization rate of the HAP is improved. According to the method, the system weight sum calculation rate (WSCR) is maximized, and the system weight sum calculation rate (WSCR) is maximized. The model is non-convex, so that a formulation problem is difficult to solve. In order to solve the problem, an iterative optimization algorithm based on successive convex approximation (SCA) is provided, firstly, a variable substitution method is utilized to re-express a non-convex optimization problem, the non-convex optimization problem is converted into a form which is easier to process, and then the SCA is utilized to solve the non-convex optimization problem. A numerical result shows that compared with a reference scheme, the scheme provided by the invention can generate better system performance.

The embodiment of the invention discloses a power grid optimal power flow calculation method based on a convex relaxation technology and related equipment, first active power flow, first reactive power flow, second active power flow and second reactive power flow can simulate operation of alternating current and direct current power grids including high-voltage direct current power transmission, and through the convex relaxation technology, the optimal power flow of the power grid can be calculated. And a non-convex optimization problem in the power flow is converted into a convex optimization problem which is easy to solve, so that the accuracy and the calculation efficiency of the optimal power flow are ensured by calculating the optimal power flow by using the branch power flow model optimized by the convex relaxation technology, and the analysis requirement of an alternating current and direct current hybrid power grid is met.

The invention discloses a secure communication method combining unmanned aerial vehicle deployment, power distribution and coalition game, and belongs to the technical field of communication. The implementation method comprises the following steps: setting the number of subcarrier channels of the unmanned aerial vehicle as the number of alliances which can be formed, forming non-overlapping alliances by ground nodes, averagely distributing communication time slots to the nodes of the same alliance, and communicating with the unmanned aerial vehicle in a TDMA (Time Division Multiple Access) mode. A communication node in the multi-node alliance selects a node other than the communication node in the alliance as a cooperative interference person and sends the cooperative interference person to a ground eavesdropper, so that the alliance confidentiality and rate during communication are improved. And under the constraint of the cruising ability of the unmanned aerial vehicle, a double-layer optimization problem for improving the overall confidentiality and rate is constructed. In an upper layer optimization problem, a stable alliance formation is obtained based on a TCFG algorithm, and a non-convex optimization problem of a bottom layer is solved under the stable alliance formation, so that an unmanned aerial vehicle deployment result and a ground node power distribution result which enable the overall confidentiality and rate to be maximized are obtained, and the overall confidentiality and rate to be maximized are realized.

The invention discloses a precoding matrix optimization method based on a multi-user MIMO system, and the method comprises the steps: obtaining a channel matrix through a base station channel estimation module, and defining a non-convex optimization problem of multi-user precoding; converting a non-convex optimization problem into a quantum annealing solvable secondary unconstrained binary optimization model, and dynamically reflecting a channel interference relationship through quantum bit coupling strength; designing a post-processing algorithm, compensating quantum hardware noise by using gradient descent and a nonlinear function of a power amplifier, and optimizing an output pre-coding matrix; the method overcomes the limitation that a traditional method is prone to falling into local optimum in non-convex optimization, can adapt to complex interference scenes in real time, and is suitable for a current 5G environment and a future wireless communication system.

The invention discloses an RIS-assisted MIMO radar transmitted beam forming method based on beam pattern matching, and the method comprises the steps: taking the minimization of a beam pattern matching error as a criterion, and carrying out the combined optimization of a transmitted waveform and an RIS reflection coefficient; applying constant modulus constraint to the transmitted waveform; applying per-unit mode constraint to the RIS reflection coefficient; constructing a non-convex optimization problem model for a target based on beam pattern matching error minimization; decomposing an optimization problem into two sub-problems of transmitted waveform optimization and RIS reflection coefficient optimization by adopting an alternating minimization framework; converting the non-convex objective function into a quadratic convex function by using a maximum and minimum method; adopting a gradient projection algorithm to quickly solve a quadratic programming problem under the constant modulus constraint and the per-unit modulus constraint; alternately optimizing a transmitted waveform and an RIS reflection coefficient; according to the method, the optimized transmitted waveform and the RIS reflection coefficient are obtained, and the optimal matching of the actual transmitted beam pattern and the ideal beam pattern is realized.

The invention relates to the technical field of wireless communication, and provides a high-energy-efficiency wireless transmission method of a cellular communication system based on an intelligent reflecting surface, which comprises the following steps: constructing a downlink transmission system of an intelligent reflecting surface assisted multi-user NOMA (Non-Orthogonal Multiple Access); constructing an energy efficiency optimization model; moving the fixed intelligent reflecting surface, solving the maximum system energy efficiency according to the signal to interference plus noise ratio constraint of the user and the maximum transmitting power constraint of the base station, and obtaining the optimal transmitting beam of the base station; the base station is fixed to transmit wave beams, the total power consumption of the system is a fixed value, the maximum system sum rate is solved according to the signal to interference plus noise ratio constraint, the intelligent reflector shift constraint and the SIC decoding sequence constraint of the user, and the optimal intelligent reflector shift is obtained; and alternately solving the optimal base station transmitting beam and the optimal intelligent reflecting surface shift to obtain the maximum energy efficiency of the system. According to the method, an original non-convex optimization problem is converted into a convex optimization problem, the solving complexity is reduced, the increasing user requirements are met, and better communication experience is provided.

The invention discloses a networked vehicle queue system model prediction control method based on data driving. The method comprises the steps that motion data sets of different vehicle types in a vehicle queue system are acquired; carrying out data driving modeling on the longitudinal dynamics of the vehicles in the vehicle queue by combining deep learning and a Koopman operator theory to obtain a data driving model; determining a communication topological structure and a system control target; based on the data driving model, a distributed model prediction control algorithm is designed, controller design is achieved, and finally the purpose that the vehicle queue system keeps stable driving of an expected target is achieved; according to the method, non-convex optimization problem solving caused by nonlinear model constraint is avoided.

The invention relates to a Lamb wave frequency dispersion characteristic sparse reconstruction method based on non-convex optimization, and belongs to the technical field of high-end equipment perception and test.The method comprises the steps that excitation is conducted on a U-shaped suspension arm, and time domain sampling signals at the junction of a U-shaped suspension arm structure are obtained; the time domain sampling signals are preprocessed; constructing a non-convex sparse frequency dispersion curve estimation model based on the preprocessed signal; and converting the non-convex sparse frequency dispersion curve estimation model into an optimization problem, searching a convex preserving condition of a target function, selecting a forward and backward splitting algorithm for solving, obtaining frequency-wave number distribution, and further extracting a frequency dispersion curve. The method solves the problems that a traditional convex regularization method is limited in induction sparse capability and underestimates signal amplitude. Through a non-convex optimization method, the purpose of accurate estimation of the frequency dispersion curve based on fewer measurement points is achieved.