20 results about "Network utility" patented technology

The present invention relates to the field of vehicle networking communication technologies, and particularly to a delay-tolerant data distribution method and system based on a time-varying graph. The delay-tolerant data distribution method based on a time-varying graph includes: through a time-varying graph data transmission model with delay constraints, multiple vehicle-mounted nodes exchange initial data with each other to obtain the communication data and link data of each vehicle-mounted node; an unmanned aerial vehicle collects the communication data and link data, and obtains the channel capacity, transmission rate, and transmission delay based on the link data; the unmanned aerial vehicle calculates the network utility value according to the transmission rate and transmission delay, and uses the transmission rate and transmission delay with the maximum network utility value as the communication transmission rate and communication transmission delay; the unmanned aerial vehicle calls the k data distribution algorithm based on the channel capacity, communication transmission rate, and communication transmission delay to distribute the communication data to multiple vehicle-mounted nodes. The present invention provides real-time and reliable data transmission for delay-sensitive vehicle networking applications, and improves the communication efficiency of vehicle networking.

The invention relates to the technical field of wireless communication, in particular to a resource allocation method of a static base station, which comprises the following steps of: S1, collecting user equipment information in a coverage range of the base station once at preset intervals, and preliminarily allocating wireless resources for each user equipment in combination with a service type; s2, local fine adjustment is performed on the resource allocation condition in combination with the channel quality monitored in real time, the real-time position change condition of the user equipment is recognized in combination with a Beidou positioning system, and if it is recognized that the user equipment is located in a signal blind area or a weak coverage area, a communication link of the user equipment is adjusted; and S3, in consideration of the network transmission rate, the resource utilization condition and the user channel state, generating an optimal resource allocation scheme by taking maximization of the overall network utility as a target, and issuing the optimal resource allocation scheme to the base station and the user equipment. According to the invention, the static base station resources can be efficiently and reasonably configured, the service requirements of different users are met, and the overall performance of the network is improved.

The invention provides a routing optimization method and device for distributed network slices, and the method comprises the steps: constructing a network utility evaluation model of a physical network according to a performance demand index of a current time period provided by the physical network for each network slice under each path set, and a traffic demand index of each network slice in a next time period; constructing a routing optimization model of the physical network according to the output of the network utility evaluation model and the constraint function corresponding to each target sub-index in the performance demand indexes; determining an optimized routing action of each network slice based on a multi-agent reinforcement learning algorithm and a routing optimization model; and according to the optimized routing action, carrying out routing path optimization selection in the plurality of path sets corresponding to each network slice to obtain an optimized routing path set of each network slice, thereby ensuring the consistency of the routing paths of the multiple network slices and the optimization of global cooperation, further ensuring the balanced distribution of dynamic resources and improving the benefits of the whole network.

The present application relates to a kind of resource allocation methods of digital twin assisted end-to-end deterministic latency network slice in industrial internet of things, belong to mobile communication technical field.The method includes the following steps: S1: in the industrial internet of things scene, construct digital twin assisted end-to-end network slice architecture;S2: analysis the influence of the computing resource offset and synchronous delay of digital twin mapping process on the end-to-end delay of service;S3: introduce random network calculus SNC to characterize the upper bound of delay in service transmission;S4: with the goal of maximizing system utility while ensuring deterministic delay, build joint multi-dimensional resource allocation problem;S5: propose DT- PER-MASAC algorithm to solve optimization problem.The present application can realize the optimization of resource allocation strategy in end-to-end network slice, improve network utility while guaranteeing the certainty of service delay.

This invention relates to a two-stage joint optimization and collaborative transmission method for space-based Internet of Things (IoT), belonging to the field of satellite network transmission technology. It addresses the shortcomings of existing single-stage rate allocation strategies, which struggle to coordinate resource allocation and task scheduling, failing to balance resource consumption and service quality, leading to transmission bottlenecks or resource waste. The method employs a two-stage collaborative mechanism to achieve optimized collaborative transmission. This mechanism establishes a two-stage collaborative framework between the first and second stages, adding inter-stage collaborative constraints in the second stage's resource optimization. Specifically: In the first stage, utility-driven collaborative sensing aims to maximize network utility and path efficiency by constructing a standardized objective function; in the second stage, resource optimization maintains synergy by using the optimal service order obtained in the first stage as a fixed parameter to optimize the transmission rate allocation strategy, constructing an objective function to minimize system resource consumption. This invention is applicable to data collaborative transmission in the agricultural field and can be applied to farmland monitoring.

The invention discloses an Internet of Things routing method based on a graph neural network and reinforcement learning, and relates to the technical field of Internet of Things communication. The method comprises the following steps: firstly, constructing a network logic topology and a state model containing parameters such as bandwidth and time delay; based on the time random walk sequence, using an LSTM auto-encoder to extract node time evolution characteristics, migrating a weight initialization Node2vec model to extract static structure characteristics, and generating space-time fused node representation; then, neighbor message updating features are aggregated through a graph neural network, and global states are spliced to construct reinforcement learning input; and finally, designing a differentiated reward function for different service types sensitive to time delay or throughput, and optimizing next hop selection by using a deep reinforcement learning agent to maximize network utility as a target. According to the method, the topology dynamic change can be accurately sensed, multi-service differentiated scheduling is realized, the network throughput and robustness are remarkably improved, and the transmission delay is reduced.

The invention discloses a proportional fair scheduling method based on a block chain. The method comprises the steps that a user and an operator complete identity authentication and registration through a registration contract; a user periodically uploads channel state information and bandwidth requirements through a state reporting contract; the scheduling contract calculates the scheduling priority of each user according to a proportional fairness principle, determines a to-be-scheduled user for each operator, and completes the updating of the historical throughput of the user; and the settlement contract generates a bill according to the scheduling result and carries out chaining for evidence storage. The invention further discloses a cross-network joint scheduling performance evaluation method in the multi-operator collaborative environment, and a mathematical characterization model of the long-term average throughput of the user and the network utility is constructed. According to the method, automatic scheduling is realized by using the intelligent contract, and the scheduling operation is recorded on the chain in a tampering-free manner, so that the whole scheduling process is open, transparent, safe and credible, cross-network resource aggregation and cooperative scheduling in a multi-operator environment are realized, and the utilization efficiency of spectrum resources and the overall performance of the network are effectively improved.

This invention discloses a robust resource scheduling method and related apparatus for UAV-assisted cellular networks, comprising: 1) initializing the basic parameters of the UAV-assisted cellular network and sampling uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples; 2) constructing a polyhedral model with a high-probability region based on the uncertain channel state information samples; 3) constructing a two-stage robust optimization model, wherein the first stage is the main problem MP and the second stage is the subproblem SP; 4) linearizing the subproblem SP to obtain a mixed-integer linear programming problem SP. MILP 5) Design a solution to the mixed-integer linear programming problem SP. MILP The branch and bound algorithm; 6) Control the drone users and base station users according to the optimal transmit power of the drone users and base station users. This method and related devices can effectively improve the reliability and network utility of the drone-assisted network.

The invention discloses a satellite-ground communication network user association method based on multi-agent deep reinforcement learning. The method comprises the following steps: establishing a cache auxiliary satellite-ground fusion network system; by taking realization of network utility maximization in a cache replacement period as an optimization target, and by taking limitation of base stations or low earth orbit satellites used by users, an upper limit of the service quantity of the base stations and an upper limit of the service quantity of the low earth orbit satellites as constraint conditions, constructing an optimization problem P0 for maximizing the network utility; converting an optimization problem P0 for maximizing network utility into a partially observable Markov process, and constructing a neural network structure framework for decentralized execution and centralized training; and based on a neural network structure framework, selecting optimal user association distribution by adopting a multi-agent deep learning strategy based on a value decomposition network. According to the method, high-energy-efficiency low-delay high-quality service is taken as a core target, and a multi-agent deep reinforcement learning algorithm is designed to solve the problem of user association.

The application relates to a wireless network congestion control optimization method based on a heavy ball method, which comprises the following steps: initialization, selection of system parameters and iteration step length; emptying all queues at the initial state; initial value assignment of weights; iteration, start of calculation of the difference of the weight parameters, determination of a service rate for each link by a scheduling module to obtain a service rate vector; congestion control, determination of an integer random variable for each data flow, wherein the random variable needs to meet the constraint of queue stability; update of the queue length and update of the weight parameters by using the heavy ball method; and finally, return to the iteration start point until the algorithm converges. The application realizes the maximization of network utility under the constraint of queue stability by introducing the heavy ball method based on momentum, effectively reduces the queue delay, and achieves the effect of improving the communication quality by reliable resource allocation and routing scheduling when the devices accessing the data link network are continuously increased.

The present application discloses a RAN slice orchestration method, system, device, and storage medium. This method constructs a function by introducing elastic variables to quantify the gap between the actual transmission rate and latency of the user side and the required rate and latency for user screening, uses the Lagrangian relaxation method to solve the established function and screen out the set of users that can access the base station, and all users in this set meet the QoS requirements; a resource allocation scheme is designed by constructing a function with the goal of maximizing network utility to obtain the final resource allocation result. The user access control scheme based on access user QoS and maximizing the number of access users in the RAN slice scenario of the present application is of great significance for RAN slice user access control and improving resource utilization.

The invention discloses a zero-forcing transmitting and receiving beam forming joint optimization method, device and equipment based on an original decomposition method and a medium, and belongs to the field of wireless communication networks, and the method comprises the following steps: constructing a zero-forcing transmitting and receiving beam forming joint optimization model of all transmitters and receivers in a multi-user multiple-input multiple-output wireless communication network, the zero-forcing transmit-receive beamforming joint optimization model comprises a network utility objective function, transmit-receive beamforming power constraint and zero interference constraint; and decomposing the zero-forcing transmit-receive beamforming joint optimization model based on an original decomposition method, designing an original decomposition algorithm based on a parallel continuous convex approximation algorithm, and carrying out parallel iteration to update transmit-receive beamforming until a preset iteration termination condition is satisfied, thereby obtaining feasible transmit-receive beamforming of all links after optimization, the transmitting and receiving beamforming obtained by each iteration of the original decomposition algorithm is feasible.

The invention discloses a network utility optimization method for a multi-unmanned aerial vehicle cooperative mobile edge computing network, and belongs to the field of wireless communication. The implementation method of the invention comprises the following steps: constructing a multi-UAV collaborative MEC network architecture; respectively constructing a communication and calculation model, a network cost model and a network utility model by combining the unloading selection variable and the calculation selection variable; 2, constructing an optimization problem by taking maximization of the network utility model obtained in the step 1 as a target; the UPTSM algorithm decomposes an original optimization problem into two sub-problems, and the two sub-problems are converted into two bipartite graph matching problems; sequentially solving the two problems by adopting a Hungary algorithm to obtain an unloading selection variable and a calculation selection variable; the VC-WOA limits variables violating the task processing time limit to quickly search legal solutions, so that the network utility is improved to the greatest extent. According to the invention, by optimizing multi-UAV cooperative task scheduling and energy management, the cooperation efficiency can be improved, and fair task distribution is realized, so that the network income is maximized.

The invention provides an edge server dynamic dormancy method and system based on iterative self-organizing clustering, and the method comprises the following steps: S1, building an MEC architecture which comprises a road side unit and an MEC edge server, defining key variables, and initializing the system; s2, activating an MEC edge server; s3, calculating the load and service quality constraint of the MEC edge server; s4, performing cluster splitting; s5, merging clusters; s6, calculating the energy consumption of the MEC edge server; s7, calculating a network utility function of the MEC edge server; s8, updating the state of the MEC edge server to obtain a new MEC edge server; and S9, according to the network utility function, judging whether optimization is converged or not, repeating the step S3 until the change of the network utility function F between the MEC edge server and the new MEC edge server is smaller than a threshold value epsilon, and terminating the algorithm.

The invention discloses a space-based Internet of Things two-stage joint optimization cooperative transmission method, and belongs to the technical field of satellite network transmission. The method solves the problems that an existing single-stage rate allocation strategy is difficult to cooperate with resource allocation and task scheduling, resource consumption and service quality cannot be balanced, and transmission bottleneck or resource waste is caused. According to the method, a two-stage coordination mechanism is adopted to realize optimization of coordination transmission, the two-stage coordination mechanism is established between a first stage and a second stage, and inter-stage coordination constraints are newly added in resource optimization of the second stage. Wherein in the first stage, utility-driven cooperative sensing: a standardized objective function is constructed by taking maximization of network utility and path efficiency as an objective; in the second stage, resource optimization effectiveness keeps collaborative, the optimal service sequence obtained in the first stage is adopted as a fixed parameter, a transmission rate allocation strategy is optimized, and a target function is constructed with the purpose of minimizing system resource consumption. The method is suitable for realizing data cooperative transmission in the agricultural field, and can be applied to the field of farmland detection.

The application belongs to the technical field of general computing resource allocation, and particularly relates to a time-differentiated general computing resource allocation method, which comprises the following steps: constructing a time-differentiated ISCC system model, which comprises a base station, a mobile user and a sensing target; establishing a time-varying request model, a service cache model, a communication model, a computing model and a sensing model; taking the maximization of a network utility function as an objective, under the constraints of a time delay tolerance and a storage capacity, establishing a joint optimization problem comprising service caching, task offloading, spectrum resources, power resources and computing resources; converting the joint optimization problem into a partially observable Markov decision process, taking the base station as an agent, and defining a triple; and solving the problem by using a deep deterministic policy gradient algorithm based on LSTM; and the application uses an LSTM network to capture historical information, and the agent makes an optimal decision.

The invention discloses an application method and system of a decentralized control system in a power plant auxiliary network public system, and relates to the technical field of industrial automation control, and the method comprises the steps: building a turbo HNICS-T316 decentralized control system, dismounting hardware equipment and a system of an original PLC control system, and carrying out the reconfiguration; dividing a new control system architecture according to an existing control domain, and improving a network security policy and a clock synchronization device to carry out network evaluation; the improved control system is subjected to comprehensive testing including control logic compiling, engineering picture configuration and equipment debugging, and the DCS performance standard is met. Desulfurization, dust removal, wet removal, dust removal and other systems of the unit are uniformly incorporated into DCS centralized control, integrated control of the unit is achieved, and the requirement for centralized control of an auxiliary network is met.

The invention discloses a high-energy-efficiency self-adaptive dual-mode data acquisition method, device and equipment and a medium, and belongs to the technical field of novel power distribution system communication. The method comprises the following steps: firstly, constructing a target function and determining a constraint condition by taking maximization of network utility as a target, wherein the network utility is defined as a difference between network operation income and network operation cost; and then solving the objective function by adopting a hierarchical decoupling strategy, including solving an optimal network topology under a given hovering point set through a block coordinate descent method to minimize the network operation cost, and solving an optimal computing power resource allocation scheme through a Lagrange function to maximize the operation income. Searching an optimal hovering point set by taking the number of hovering points as an action variable based on a deep reinforcement learning framework; and finally, adaptively selecting a fixed or mobile data acquisition mode according to a solving result. According to the method, the contradiction between energy consumption and data integrity in unmanned aerial vehicle data acquisition is solved, and collaborative optimization of system energy efficiency and reliability is realized.

The application discloses a kind of multi-antenna unmanned plane communication network robust resource scheduling method and related device, comprising: the basic parameters of unmanned plane communication network are initialized, each ground user is arranged in ascending order according to the horizontal distance between ground user and unmanned plane ground projection point, according to channel gain difference, the user with unmanned plane ground projection point horizontal distance is close and the user with unmanned plane ground projection point horizontal distance is far away is paired;Determine high-probability confidence region;With the minimum unmanned plane total transmit power as target, construct the mathematical model P1 of joint optimization beam forming and power allocation;Solve the mathematical model P1, obtain unmanned plane robust beam forming and power allocation coefficient, according to unmanned plane robust beam forming and power allocation coefficient, multi-antenna unmanned plane communication network robust resource scheduling, the method and related device can effectively improve the reliability and network utility of multi-antenna unmanned plane network.

The invention discloses a network optimization method and a server, and relates to the technical field of network communication, and the method comprises the steps: obtaining a target network topological graph of a target network; wherein the target network is a content distribution network powered by green power. And then, according to the target network topological graph, performing problem positioning on the target network to obtain a network optimization problem. And then, solving a network optimization problem by using a gradient descent algorithm to obtain an optimal solution of the problem. And then, according to the optimal solution of the problem, determining a target request rate and a target cache allocation strategy. Wherein the target request rate and the target cache allocation strategy are used for maximizing the network utility of the target network. According to the embodiment of the invention, the effective consumption of green electricity can be realized while the network utility is maximized.