13 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 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 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.

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 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 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 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.