185 results about "Non convex optimization" patented technology

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 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.

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 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 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.

The invention relates to the technical field of intelligent sensors, in particular to an unmanned aerial vehicle track intelligent tracking method based on a millimeter-wave radar, which comprises the following steps: receiving echo signals collected by the front end of the millimeter-wave radar, carrying out constant false alarm rate detection and Doppler frequency shift extraction on the echo signals, generating an unmanned aerial vehicle target space coordinate set, and carrying out target tracking on the unmanned aerial vehicle. And mapping the target space coordinate set of the unmanned aerial vehicle to a three-dimensional Euclidean space. According to the method, a non-convex optimization objective function containing beam switching time cost and precision constraint is constructed, and mathematical optimal solution of radar beam resources is realized. The generated adaptive scheduling instruction is used for adjusting the pulse repetition frequency and the beam residence time, it is ensured that radar front-end parameters dynamically evolve along with the target state, overall centroid tracking is emphasized for rigid body formation so as to save resources, independent high-frequency refreshing is emphasized for discrete targets so as to ensure the precision, and the accuracy is improved. And the continuity and robustness of trajectory tracking of the unmanned aerial vehicle in a complex electromagnetic environment are improved.

The invention provides an IRS-assisted cellular-free large-scale URLLC network optimization method based on sum rate maximization. The IRS-assisted cellular-free large-scale URLLC network optimization method comprises the following steps: establishing an IRS-assisted cellular-free large-scale URLLC network model under Rician channel fading; performing channel estimation by using a linear minimum mean square error method; analyzing a downlink reachable rate when a zero-forcing precoding scheme is used, constructing a non-convex optimization problem by taking maximization of a total rate as a target, and decomposing the non-convex optimization problem into a power control sub-problem and a phase optimization sub-problem; and designing an alternating optimization algorithm, and solving two sub-problems of power control and phase optimization to obtain a power control coefficient and intelligent metasurface phase shift. According to the alternating optimization algorithm designed by the invention, the interference suppression effect is remarkably improved, so that the reliability of the system is greatly enhanced. The technology is specially designed for a 6G scene, supports multi-service dynamic scheduling, can realize low-cost IRS hardware deployment, and provides an extensible and low-delay solution for innovation and performance breakthrough of a next-generation communication network.

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

The invention discloses a multi-user single-eavesdropper resource management and optimization method based on a Stackelberg game, and in a network scene with eavesdropping equipment, although each user can improve the data transmission rate by improving the respective transmitting power, the data eavesdropping rate of the eavesdropping equipment is also increased at the same time. How to carry out modeling of effective resource allocation for data sending power of users is a key problem to be solved in the invention. Based on the above problem, the invention provides a multi-user single-eavesdropper resource management and optimization method based on the Stackelberg game, the multi-user power distribution and eavesdropper eavesdropping rate limitation problem jointly form a game model, and Taylor expansion is adopted to convert the original non-convex optimization problem into a convex optimization problem for modeling.

This invention proposes an integrated beamforming method and system for communication and sensing based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The aim is to design suitable beamforming to maximize communication and data rate while ensuring adequate radar sensing performance. The method includes: first, constructing an ISAC system containing J RIS elements, a base station (BS), and M single-antenna mobile users (MUs), and completing the modeling of baseband signals and user received signals; second, constructing user communication and data rate models and radar target detection signal-to-noise ratio models, establishing unit mode constraints on RIS reflection coefficients and transmit power constraints; third, employing an improved algorithm combining alternating optimization and fractional programming to decompose the non-convex optimization problem into sub-problems for iterative solution, ultimately maximizing communication and data rate and radar sensing performance, while also meeting the dual functional requirements of communication and sensing. This invention is applicable to providing target detection and communication functions for vehicles.

An energy efficiency optimization method for multi-cell communication systems based on intelligent reflector-assisted rate division multiple access (RSMA) combines RSMA with intelligent reflector RIS. RIS intelligently regulates signal transmission links to enhance signal transmission quality, thereby improving signal quality for users at cell edges. RSMA effectively suppresses co-channel interference between users by flexibly controlling rate division strategies and beamforming. To this end, an optimization problem is constructed with the goal of maximizing system energy efficiency, jointly optimizing the beamforming vector, rate division multiple access matrix, and phase shift matrix on the base station side and the RIS side. To solve this non-convex optimization problem, an iterative solution is proposed for the beamforming, rate division, and phase shift optimization subproblems. Continuous convex optimization is used for the former, and semidefinite programming and penalty function methods are used for the latter. Compared with traditional space division multiple access and non-orthogonal frequency division multiple access, this method can achieve energy efficiency improvements of 28.5% and 10.2% for multi-cell communication systems, respectively.

The application discloses an ISAC system security transmission method for an illegal RIS scene, and the method constructs an ISAC system based on RIS assistance to simultaneously complete communication and sensing tasks. The sensing signal can effectively interfere with the IRIS-assisted eavesdropping link while completing the sensing task. In order to more effectively cope with the system security performance deterioration challenge brought by IRIS, a beamforming matrix of the radar signal is designed. Considering the uncertainty of the accurate position of the eavesdropper (Eve), the ergodic security rate is used to represent the security performance of the system, and an approximate ergodic eavesdropping rate is derived. In order to efficiently solve the two non-convex optimization problems, the ergodic objective function is appropriately converted, and then an efficient alternating optimization algorithm is proposed to solve each optimization variable. It is verified that the joint beamforming and RIS reflection design of the application is more effective in coping with the eavesdropping process of IRIS, and it is revealed that the radar beamforming design plays a key role in alleviating the influence of IRIS.

The invention discloses a symbiotic radio system design and optimization method based on multiple active intelligent reflection surfaces. According to the method, high-efficiency backscatter communication is realized while the performance of the main transmission system is enhanced by using the intelligent reflection surface. A plurality of active intelligent reflecting surfaces are configured in the system, the main transmission performance is enhanced, and meanwhile, the active intelligent reflecting surfaces serve as backscattering equipment in turn, and transmission of secondary information is achieved by conducting scattering modulation on incident main signals. The method aims at maximizing the main transmission rate of the system, and jointly optimizes the base station sending power, the active beam forming vector and the reflection coefficient of the intelligent reflection surface. An original non-convex optimization problem is decomposed into a plurality of sub-problems, semi-definite relaxation, continuous convex approximation and other methods are adopted for conversion, and finally an optimal solution is obtained through iterative solution. Compared with a scheme based on a passive intelligent reflection surface and an active intelligent reflection surface scheme based on phase random, a higher main transmission rate can be realized under the condition of ensuring the same secondary symbol signal to interference plus noise ratio.

The invention provides a high resource efficiency allocation method in an intelligent reflecting surface assisted cellular-free large-scale MIMO (Multiple Input Multiple Output) system. The method comprises the following steps: establishing an intelligent reflecting surface assisted cellular-free large-scale MIMO system model under a Rician fading channel; based on the system model, performing channel estimation by using a linear minimum mean square error method; based on the system model, constructing a resource efficiency closed expression when the zero-forcing receiving scheme is used; according to the expression, constructing a non-convex optimization problem by taking maximization of system resource efficiency as a target, and decomposing the non-convex optimization problem into a power control sub-problem and a phase optimization sub-problem; and designing an alternative optimization algorithm according to the optimized sub-problems, and solving the power control sub-problem and the phase optimization sub-problem to obtain a power control coefficient and intelligent reflector shift. When a ZF receiving scheme is used, the resource efficiency optimization algorithm has excellent signal processing capacity, the system resource efficiency is remarkably improved, operation steps are convenient to implement, and the dual requirements of a future wireless communication system for high data rate and low energy consumption are met.

The invention provides an on-line resource management system and method oriented to a requirement of integration of communication, inductance and calculation, and the method comprises the steps: carrying out the correlation scheduling constraint between a sensing target and edge intelligent equipment under the constraint of transmitting power resources, the constraint of calculation resources of a base station end, the constraint of long-term queue backlog and the constraint of average power; constructing a non-convex optimization problem of the common inductance calculation integrated system under a long-term optimization framework; and solving the non-convex optimization problem to obtain an optimal perception scheduling decision, an optimal transmitting beam forming vector of perception and communication, an optimal receiving beam forming variable of perception echoes and calculation resource allocation of a base station end. The online resource management strategy provided by the invention aims to maximize the long-term weighted average rate, meet the requirements of queue stability, average power constraint and quality of service (QoS) at the same time, and explore the cooperative gain of the communication, inductance and calculation integrated system from a long-term perspective.

The present application relates to a kind of facing polynomial fuzzy control system cost control stability judging method, comprising the following steps: establishing polynomial fuzzy model;According to the first SOS condition based on Lyapunov function, try to solve the first polynomial matrix and the second polynomial matrix, omit non-convex term in solving process, judge whether it is successful, if yes, based on the first polynomial matrix and the second polynomial matrix, obtain feedback gain;Based on feedback gain, according to the second SOS condition, try to solve positive definite matrix, judge whether it is successful, if yes, based on positive definite matrix, construct polynomial Lyapunov function, realize cost control analysis based on polynomial Lyapunov function.Compared with prior art, the present application solves the non-convex optimization problem in cost control in two steps, avoids the influence of the constraint existing in input matrix itself on the flexibility of fuzzy control system design, and improves the performance of control system by obtaining the numerical value of cost J.