33 results about "Throughput maximization" patented technology

The invention provides a low-altitude networking of intelligence multipath data transmission system based on MPQUIC, and belongs to the technical field of novel network protocols, and the system comprises a network state analysis module which is used for obtaining the network state indexes of all communication paths of an unmanned aerial vehicle under different position coordinates; the flow scheduling module is used for predicting the track of the unmanned aerial vehicle, scoring the communication paths by combining the network state indexes of the communication paths, and obtaining the communication paths corresponding to the data flows with different priorities of the unmanned aerial vehicle according to the priorities of the data flows with the scores of the communication paths; and the path selection optimization module is used for dynamically adjusting weight parameters in communication path scores based on deep reinforcement learning by taking network throughput maximization and time delay minimization as targets to obtain optimal communication paths corresponding to data streams with different priorities. According to the invention, the problem that the prior art is difficult to have high reliability and low time delay due to the dynamic change of the network environment caused by the position change of the unmanned aerial vehicle in the low-altitude Internet of Things is solved.

The invention relates to an internet of vehicles edge computing task unloading and resource scheduling optimization method, which comprises the following steps: constructing an internet of vehicles edge computing system model fusing a task jump mechanism and dual-priority scheduling, the system model comprising a road test unit deployment model, a communication model, a computing model and a task jump model; establishing a joint optimization function aiming at minimizing the total time delay and the total energy consumption of the system; decomposing a joint optimization problem into three sub-problems of unloading decision, computing resource allocation and communication strategy selection; a deep reinforcement learning algorithm is adopted to intelligently sense a road environment state, an optimal communication priority strategy combination is dynamically selected, and a task unloading decision and computing resource allocation are collaboratively optimized; and in each scheduling time slot, the edge server allocates communication bandwidth and computing resources to the vehicle tasks according to the selected strategy, and triggers a task preemption and cross-server jump mechanism, thereby realizing maximization of system throughput and minimization of task processing time delay.

The invention discloses a cross-domain collaborative multi-task load balancing method and system which are used for a multi-domain edge core fusion network, and the method comprises the steps: constructing a communication model and a calculation model, respectively depicting the transmission rate and time delay of a task on a main / standby path and the resource distribution between the task and a calculation node, automatically switching to an alternative path through a fault recovery model by utilizing a node / link availability variable when the main path fails; on this basis, a multi-constraint optimization model is established with throughput maximization, load balancing optimization and fault recovery cost minimization as targets, and a joint optimization framework based on a graph neural network and a non-dominated sorting genetic algorithm is proposed; and a non-dominated sorting genetic algorithm is guided to perform population initialization and directed evolution, and a Pareto optimal scheduling strategy is output in combination with a constraint repair mechanism, so that the performance of cross-domain task scheduling, the resource utilization balance and the fault recovery robustness are improved.

The invention provides a wireless power supply network throughput optimization method and system. The method comprises the following steps: constructing a wireless power supply communication system model with energy constraint and time delay constraint; constructing a throughput maximization problem P0; s3, on the basis of the throughput maximization problem P0, constructing a charging energy maximization sub-problem P1 under the constraints of total energy and energy collection duration, and solving the sub-problem P1 under the conditions that the total available energy of the wireless power supply base station is less and the total available energy of the wireless power supply base station is more; based on the throughput maximization problem P0, constructing a throughput maximization sub-problem P2 with wireless communication duration as a variable under the condition that an energy collection user gives an energy collection strategy, and solving the throughput maximization sub-problem P2; based on the optimal wireless energy transmission strategy and the optimal wireless communication duration strategy characteristics, the throughput maximization problem P0 is converted into a single-variable optimization problem P3, and the single-variable optimization problem P3 is solved. According to the method, the actual throughput performance of the wireless energy supply communication network is improved, and the efficient communication requirement in a complex dynamic network environment is met.

Systems, methods, and software for load management among a plurality of cells that overlap a sector within a Radio Access Network (RAN). In one embodiment, a system performs sector throughput optimization over multiple optimization iterations of determining a total number of active users in the sector, determining a target number of users per cell that maximizes an aggregated user throughput of the sector where a sum of the target number of users per cell is equal to the total number of active users in the sector, determining recommended load distribution parameters for the sector based on the target number of users per cell that maximizes the aggregated user throughput of the sector, and applying the recommended load distribution parameters in the sector to distribute users among the cells.

The application belongs to the field of low-power Internet of Things, and particularly relates to a resource allocation method for a full-duplex wireless power supply backscattering communication system; the method comprises the following steps: constructing a full-duplex wireless power supply backscattering communication system model; considering throughput constraints, transmission power constraints, energy collection constraints, transmission energy consumption constraints, reflection coefficient constraints and time allocation factor constraints, constructing a total throughput maximization resource allocation model with the optimization target of maximizing the total throughput; converting the throughput maximization resource allocation model into four optimization sub-problems; solving the four optimization sub-problems to obtain a resource allocation scheme; and allocating resources according to the resource allocation scheme; the application considers the minimum throughput requirement of each reflection node, meets the quality of service requirement of the reflection node, and improves the system throughput.

This invention discloses a communication energy efficiency optimization method assisted by a smart metasurface and a movable antenna. This method is applicable to a downlink wireless communication system consisting of a base station, a smart metasurface, and a user terminal equipped with a movable antenna. To improve the energy efficiency of this communication system, this invention proposes a joint optimization method for base station beamforming, RIS reflection coefficient, and MA spatial location. Specifically, considering the energy consumption of base station transmission signals and MA movement, the proposed method constructs a communication system energy efficiency maximization problem under constraints of user communication service quality and MA movement range, and designs an alternating optimization algorithm based on continuous convex approximation. Simulation results show that, compared with traditional fixed antenna schemes and system throughput maximization schemes, the proposed scheme can effectively improve the system's energy efficiency.

According to the method, unmanned aerial vehicle relay communication is combined with an ISCPT technology, the problem of maximizing the total throughput of system communication by jointly optimizing a communication sending power distribution parameter, an unmanned aerial vehicle flight path parameter, a time distribution parameter and an unmanned aerial vehicle task scheduling parameter is proposed, and as the optimization problem is a non-convex problem, how to give an optimal solution is a difficult problem. According to the invention, slack variables are introduced under the condition that the maximum transmitting power constraint, the unmanned aerial vehicle position constraint, the unmanned aerial vehicle sensing precision constraint, the ground IoT node energy constraint and the unmanned aerial vehicle task scheduling constraint of the base station, the unmanned aerial vehicle and the ground IoT node are satisfied; the objective function and the constraint are converted into a convex optimization problem by adopting methods such as block coordinate descent, continuous convex approximation, alternate optimization and first-order Taylor expansion, and the maximum total throughput of the system is solved. Under the condition that the system sensing performance and the energy consumption of the ground IoT node are considered successfully, the communication throughput is maximized successfully, and the method has good practicability and feasibility.

An adaptive transmit power control process enables the uplink transmit power of a user terminal to be adjusted based on a scan angle of the antenna for the user terminal. The antenna periodically reports the scan angle to the user terminal, and a current maximum transmit power for the user terminal is determined based on the scan angle. The current maximum transmit power is then used as the basis for determining an allocation of resources to the user terminal. The transmit power control process enables resources to be assigned as needed across multiple carriers and enables a power spectral density of a user terminal to be adjusted based on nominal and offset transmit power parameters.

The invention relates to the technical field of throughput of an intelligent power grid, a large number of Internet of Things nodes are integrated along with each link of a power grid system from power generation to a terminal user, and rapid increase of the number of the nodes and data acquisition of exponential increase of the nodes bring a great challenge to guarantee of reliable and low-delay communication access under scarce spectrum resources; the invention provides a throughput improving method based on attention-driven reinforcement learning in a smart power grid, and the method comprises the steps: setting an energy residual variable for secondary equipment which employs cognitive and non-orthogonal multiple access and has an energy harvesting function (EH-CR-NOMA); a long-term throughput maximization problem of secondary equipment in a smart power grid is converted into selection of transmission power and time allocation factors of the secondary equipment in each time slot and energy residual maximization of each time slot, and an attention layer is introduced; the diversity of agent action selection of an optimization object in a deep reinforcement learning algorithm is improved, and the channel state information value is extracted when the environment is changed.

This invention proposes a method and system for optimizing the throughput of wireless power supply networks, comprising: constructing a wireless power supply communication system model with energy and delay constraints; constructing a throughput maximization problem P0; S3, based on the throughput maximization problem P0, constructing a charging energy maximization sub-problem P1 under constraints of total energy and energy collection duration, and solving it under two cases: low and high total available energy of the wireless power supply base station; based on the throughput maximization problem P0, constructing and solving a throughput maximization sub-problem P2 for energy collection users with a given energy collection strategy and wireless communication duration as the variable; and transforming the throughput maximization problem P0 into a single-variable optimization problem P3 based on the characteristics of the optimal wireless energy transmission strategy and the optimal wireless communication duration strategy, and solving it. This invention improves the actual throughput performance of wireless power supply communication networks and meets the high-efficiency communication requirements in complex dynamic network environments.

The invention belongs to the technical field of short-distance wireless communication of the Internet of Vehicles, and relates to a spectrum allocation optimization method for coexistence of satellite flash and Wi-Fi in the Internet of Vehicles, which comprises the following steps: setting a coexistence scene of satellite flash and Wi-Fi in the Internet of Vehicles; the satellite flash master node periodically measures and collects observable statistic information of an MAC layer so as to establish a hidden node model; a sub-channel time allocation proportion, a hidden node correction factor, a Wi-Fi influence penalty term and a satellite flash user priority are considered, and an optimization problem of maximizing the total effective throughput of the satellite flash user is established; an original non-convex DC problem is converted into a convex problem through variable replacement and variable relaxation; decomposing the joint problem into two sub-problems of sub-channel time allocation and power control by adopting a centralized alternating iterative algorithm, and solving the two sub-problems to obtain a spectrum allocation optimization scheme; according to the invention, the interference problem during coexistence of the satellite flash and the Wi-Fi in the Internet of Vehicles can be reduced, and the total effective throughput of the satellite flash user as a high priority is improved at the same time.

This invention discloses a UAV backscatter communication method and related device based on the integration of sensing, communication, and energy. The invention uses a UAV equipped with a uniform planar array antenna as a dual-function access point in the air, transmitting communication and sensing multiplexed signals to ground-based IoT devices and potential sensing targets. Ground-based IoT devices can choose to collect energy from the signal or modulate their own information onto the transmitted signal and reflect it back to the UAV, thereby completing energy transmission and information collection tasks respectively. By using an alternating optimization algorithm to optimize the UAV's flexible mobility, beamforming spatial control capabilities, and device scheduling capabilities, the average total throughput of the device backscatter return link can be maximized while meeting the sensing performance and energy collection requirements of IoT devices. This achieves deep synergy between sensing, communication, and energy supply, thereby realizing resource reuse and transmission performance balance across multiple tasks.

The invention discloses an electric power communication resource allocation method and system based on reinforcement learning. The method comprises the following steps: acquiring node data corresponding to each sending node in a power line communication network; determining the transmission priority of each activated node according to the transmission rate data; constructing a Markov decision process model corresponding to the power line communication networking according to the node data and a preset action space; acquiring a transmission rate of the activated node on each subcarrier, and constructing an optimization objective function with task data total throughput maximization as an objective according to the transmission rate and transmission rate data; and solving a Markov decision process model according to the transmission priority, the optimization objective function and a preset dual-depth reinforcement learning algorithm to obtain subcarriers and transmitting powers corresponding to the activation nodes, so as to improve the data throughput of power line communication networking.

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

The present application relates to a vehicle networking edge computing task unloading and resource scheduling optimization method, the method comprising: constructing a vehicle networking edge computing system model integrating task jump mechanism and double priority scheduling, the system model comprising a road test unit deployment model, a communication model, a computing model and a task jump model; establishing a joint optimization function with the goal of minimizing the total system delay and total energy consumption; decomposing the joint optimization problem into three sub-problems: unloading decision, computing resource allocation and communication strategy selection; using a deep reinforcement learning algorithm, intelligently sensing the road environment state, and dynamically selecting the optimal communication priority strategy combination, while optimizing task unloading decision and computing resource allocation in coordination; in each scheduling time slot, the edge server allocates communication bandwidth and computing resources for vehicle tasks according to the selected strategy, and triggers task preemption and cross-server jump mechanism, thereby maximizing system throughput and minimizing task processing delay.

The invention belongs to the technical field of wireless communication, and particularly relates to a backscatter communication total throughput maximization method based on Stacked Intelligent Metasurface (SIM) energization, which specifically comprises the following steps of: constructing a backscatter communication system model based on SIM assistance; establishing a non-convex, multivariable coupling and multi-constraint resource allocation problem taking maximization of the total throughput of the system as a target; the original problem is converted into a transmitting end power distribution optimization sub-problem, a time slot distribution optimization sub-problem and an SIM phase shift optimization sub-problem; and converting the non-convex optimization sub-problem into a convex optimization sub-problem by using a successive convex approximation method, a penalty method and a first-order Taylor expansion, and alternately optimizing to obtain an overall resource allocation scheme. The signal beam forming operation can be realized in the electromagnetic wave domain by introducing the SIM at the transmitting end, compared with the traditional scheme, the total throughput performance of the system is remarkably improved, meanwhile, the dependence of the transmitting end on a complex radio frequency link can be effectively reduced, and good economic benefits are achieved.

The present application belongs to the field of unmanned aerial vehicle communication technology, considering that when wireless sensors communicate with unmanned aerial vehicles, a mobile malicious node on the ground interferes with the communication of unmanned aerial vehicles, assuming that each sensor and unmanned aerial vehicle adopts frequency division multiplexing for communication, so there is no mutual interference between each sensor, and thus a communication model under malicious interference is obtained. Under this model, in order to maximize the throughput of unmanned aerial vehicles under malicious interference, an optimization problem is obtained. Since the optimization problem is a non-convex problem and has a large variable space, it is difficult to obtain the optimal solution. In order to solve this problem, the path discretization method is used to transform the original problem into a discrete equivalent problem, then the successive convex approximation and block coordinate descent techniques are applied to obtain a local optimal solution, and it is proved by the convex optimization principle that the local optimal solution can be approximately replaced by the global optimal solution, and finally the convergence and effectiveness of the algorithm are verified and shown by simulation results.

The invention discloses a heterogeneous node charging scheduling method and system based on multi-agent deep reinforcement learning, and relates to the field of power grid inspection scheduling, and the method comprises the steps: obtaining an energy change result of each unmanned plane at any time slot, and a transmission parameter of a ground device for carrying out the communication service with each unmanned plane or unmanned vehicle; calculating a first parameter and a second parameter according to the energy change result of each unmanned aerial vehicle in any time slot; calculating a third parameter and a fourth parameter according to the transmission parameters; establishing an objective function for maximizing the minimum cumulative throughput of the communication service between the unmanned aerial vehicle or the unmanned vehicle and the ground equipment, and determining constraint conditions of the objective function by combining the first parameter, the second parameter, the third parameter and the fourth parameter; and under the constraint condition, an MADDPG algorithm is adopted to solve the target function, and an optimal strategy of cooperative charging scheduling of the unmanned aerial vehicle and the unmanned vehicle is obtained.

The invention discloses a PLS-oriented IRS-assisted NOMA-ISAC system throughput maximization method, and belongs to the technical field of communication perception integrated system resource allocation. According to the method, under the constraint conditions of base station transmitting power, system sensing rate, system security, user minimum rate requirement and the like, the system throughput is maximized through collaborative design of beamforming and IRS phase shift at a base station; as an optimization problem relates to coupling of multiple variables and causes non-convexity of the problem, firstly, an original problem is decoupled and divided into two sub-problems; secondly, optimizing two sub-problems of beamforming and IRS phase shift at the base station by adopting fractional programming, positive semidefinite relaxation and successive convex approximation methods; and finally, obtaining an optimal solution of the problem by utilizing an alternating optimization method. The method provided by the invention is helpful to better understand the performance of IRS and NOMA technologies in a communication perception integrated scene, and provides a new thought and method for coping with challenges brought by physical layer security.

The invention discloses a switch parameter automatic configuration method suitable for an intelligent power distribution terminal, and relates to the technical field of intelligent power distribution automation. According to the method, a double-model collaborative architecture is adopted, during starting, a first model encodes a terminal model and scene information into a two-dimensional feature matrix through a pre-trained CNN model, exclusive standardized configuration is generated and issued, and an interface of a second model is reserved and then closed; the second model outputs dynamic configuration parameters by taking throughput maximization and time delay minimization as double targets based on working condition data acquired in real time through a Transform model improved by an attention mechanism, and online adaptive optimization of the model is realized by utilizing a hierarchical updating strategy; when the terminal is closed, the second model feeds back common working condition characteristics, and when the terminal is restarted, the first model CNN model database is updated. According to the valve, adaptability and superiority under multiple working condition scenes are achieved through a double-model collaborative architecture, and wide application value is achieved.

This invention discloses a joint optimization method for access control and degree distribution for MEO satellite internet. The method includes optimizing the access prohibition probability based on a Markov decision process to obtain optimized action decisions; optimizing the degree distribution based on a population optimization algorithm to obtain the optimal degree distribution, which is then fed back to the prohibition probability optimization process until the optimal prohibition probability is determined; subsequently, a generalized degree distribution strategy is generated based on the optimal prohibition probability and deployed to the satellite; satellite broadcasting and random access by ground user terminals complete the joint optimization. This scheme deeply integrates access control technology with the IRSA random access protocol to achieve adaptive response to dynamic environments and maximize throughput. It utilizes reinforcement learning algorithms to optimize access control parameters and differential evolution algorithms to optimize degree distribution parameters, achieving optimal strategy.

The invention belongs to the technical field of optical communication, and discloses an atmospheric optical communication error control method and system. The method comprises the following steps: acquiring channel prior information, and estimating an error bit number caused by turbulence; determining an error correction code parameter by combining the estimated bit error number and the error correction performance of the error correction code; determining design parameters of the error correction interleaving cache module by combining the set continuous bit error number capable of being corrected by the error correction interleaving cache module, the error correction performance of the error correction code and the error correction code parameters; according to the channel prior information and the error correction performance of an error correction interleaving cache module, threshold information used for channel state classification is determined; and judging the state classification corresponding to the current channel according to the channel information and the received light information which are acquired in real time and the threshold information, and executing an information sending scheme corresponding to the state classification of the current channel. According to the invention, throughput maximization can be realized under the condition of ensuring reliable communication.

The present application relates to wireless communication technology and wireless energy transfer technology field, and disclose a kind of radio frequency energy reflection surface and the throughput optimization method of wireless relay system, comprising the following steps: establishing wireless relay system network model diagram;Determine relay and IRS battery energy state;Establish system throughput model;Formulate relay and IRS energy collection and consumption, relay forwarding information transmission power, mode selection constraint condition;According to all channels obtained by central controller, with system throughput maximization as optimization goal, it has solved that existing research has considered the IRS using fixed power supply mode and wireless relay assisted communication system, but is limited by battery capacity, in the situation that battery cannot be replaced or the cost of replacing battery is high, the working life of IRS and wireless relay is limited, using radio frequency energy supply IRS and wireless relay, can make system working life not be limited by the battery capacity used by wireless relay and IRS, while improving the throughput performance of system.

The invention relates to the technical field of network communication, and specifically discloses an NFV-oriented switch virtualization resource scheduling system. The system comprises a resource state sensing module, a multi-target decision engine, a dynamic frequency adjustment module and a resource allocation execution module. According to the method, by constructing the multi-objective decision engine, the maximization of the total throughput of the system and the minimization of the total power consumption of the system are determined as parallel optimization objectives, and a traditional single-performance-oriented scheduling normal form is changed fundamentally. The engine generates a Pareto optimal resource allocation strategy by solving an energy efficiency and performance balance model, so that a system can intelligently balance performance and energy consumption in a high-concurrency service scene, blind soaring of processor frequency and invalid dissipation of energy are effectively avoided, and the system performance and energy consumption are improved. The energy use efficiency of the data center is obviously improved; and the operation cost is reduced.

The application discloses a kind of high-efficiency scheduling methods of blockchain payment channel network transaction, comprising: defining payment channel network transaction scheduling problem, under the condition that transaction both sides node selects execution transaction scheduling strategy, execution node sets multiple constraint conditions with network throughput maximization as target, and the transaction scheduling optimization problem is modeled for the transaction scheduling problem in the buffer zone of both sides, to obtain transaction scheduling optimization problem;For the problem, the execution node utilizes the encoder model to extract the static features and dynamic features of each transaction in the current input sequence, to obtain the encoder output vector of each transaction, and the encoder output sequence is formed by merging;The execution node utilizes the decoder model based on attention mechanism and mask mechanism to make transaction sequence decision in a self-iterative manner to the current obtained encoder output sequence, and outputs the transaction sequence after continuous iteration to schedule both sides to complete transaction.The application can reasonably schedule the transactions in the buffer zone, improve the throughput and reduce the average waiting time of transactions.

The application discloses a kind of joint optimization methods for multi-modal network, belong to computer network technical field.The application includes: by joint optimization the routing selection of traffic in multi-modal network and the allocation strategy of multi-modal network element to controller, realize control plane load balancing and the promotion of system throughput;On the basis of considering controller resource limit, link bandwidth constraint and multi-modal network element characteristics, adopt the optimization strategy of "two steps walk ": first, based on historical traffic data, determine the matching relationship between controller and multi-modal network element, optimize the load distribution of controller;Second, combined with the result of first step, design the online traffic routing algorithm of original-dual form, realize system throughput maximization under resource constraint.The method of the application can significantly reduce controller load and improve overall system throughput performance under the condition of maintaining resource constraint.

This invention relates to an adaptive deployment method and system for edge intelligent networks in discrete manufacturing. The method aims to maximize network throughput, minimize transmission latency, and maximize network reliability. It establishes a QoS-driven edge intelligent network deployment optimization model to ensure that the network transmission requirements of all devices in the factory, especially critical equipment, are met. It employs improved K-means and particle swarm optimization algorithms, combined with dynamic clustering algorithms and adaptive weight adjustment mechanisms, to effectively improve the data rate of intelligent devices and network reliability, while reducing transmission latency and system interference. This invention can adapt to factories of different sizes and complex environments, optimize network performance, significantly improve the efficiency of edge intelligent deployment, and provide a stable and reliable communication solution for discrete manufacturing smart factories and intelligent manufacturing in the Industry 5.0 era.

The application discloses a PCRB analysis and power optimization method and system for OTFS multi-satellite channel estimation, comprising the following steps: constructing a pilot and data superposition transmission model based on an OTFS signal; constructing a pilot observation covariance matrix based on multi-satellite LoS path correlation and data interference; constructing an observation Fisher information matrix based on PCRB; using a spread pilot derivative subspace to scalarize an inverse matrix of the pilot observation covariance matrix in the observation Fisher information matrix based on PCRB; based on the scalarization processing result, constructing an equivalent observation Fisher information matrix for PCRB calculation and power optimization; calculating PCRB according to the equivalent observation Fisher information matrix; constructing a power optimization problem for throughput maximization based on PCRB; solving the power optimization problem for throughput maximization to obtain an optimal power optimization scheme. The application can achieve a better balance between channel estimation accuracy and data transmission rate, and avoid the problem that simply increasing data power leads to increased channel estimation error and decreased actual throughput.

The present application belongs to the field of Internet of Things, and particularly relates to a robust resource allocation method for a backscatter communication system for eavesdropping users, comprising: establishing a backscatter communication system for eavesdropping users; deploying an information receiver, a backscatter device and an eavesdropping user in the backscatter communication system, wherein each backscatter device has a backscatter information function, and the backscatter device is equipped with an energy collection rectifier circuit; constructing a robust resource allocation model based on channel uncertainty and maximum rate; converting the resource optimization model into a convex optimization problem based on a block coordinate descent method; solving the optimization problem by using a convex optimization toolbox to obtain a base station beamforming vector, an artificial noise vector and a backscatter device time for backscattering, i.e. obtaining a resource allocation scheme; the method can effectively curb the influence of eavesdropping users on the system while ensuring the maximization of system and throughput, and meets the service quality requirements of users.