19 results about "Cross-layer optimization" patented technology

The invention provides a CKKS three-way decision dynamic hierarchical encryption neural network training method and system. The method comprises the following steps of performing data coding on original input data, performing CKKS encryption into a polynomial, and adding a noise item to generate a ciphertext; performing homomorphic calculation on the ciphertext through alternate row and column encryption matrix multiplication, processing the intermediate feature by combining an EncryptedPolyReLU activation function and a three-way decision to obtain an encryption feature, and finally outputting an encryption prediction result; the gradient is calculated through an encryption residual sum of squares and a loss function, gradient updating is accelerated by adopting a Nesterov momentum method so as to process an encryption prediction result, and encryption model parameters are obtained by combining a layered noise perception guide mechanism and optimizing encryption model parameters. According to the method, efficient privacy protection training is realized through dynamic coding and row-column alternate encryption, and a safe and efficient deep learning solution is provided for high-sensitivity data in combination with three-way decision screening and cross-layer optimization.

The invention relates to the technical field of wireless communication, in particular to a vehicle intra-cluster resource scheduling and multi-hop forwarding cross-layer optimization method and system, and the method comprises the steps that cluster members periodically sense wireless channel resources and report resource occupation information to a cluster head; in the plurality of vehicle cluster networks, each cluster comprises a cluster head meeting a preset similarity condition and a plurality of members; the cluster head calculates and evaluates resource availability and dynamically allocates transmission resources through distance weighted average based on a reported result; and in combination with the resource allocation state and topological characteristics, intra-cluster or inter-cluster forwarding nodes are selected through a multi-dimensional decision model to realize multi-hop message forwarding. According to the method, the resource conflict rate of a wireless channel is remarkably reduced through intra-cluster collaborative resource perception and a dynamic distance weighted evaluation mechanism, and the multi-hop transmission efficiency and the communication reliability are synchronously improved through optimizing forwarding node selection by combining a multi-dimensional decision model of topological characteristics, so that the low-delay requirement of security services is ensured.

The application discloses a federated continual learning cross-layer optimization method for a UAV relay network, and aims at the problems of easy occurrence of catastrophic forgetting of a model and high system time delay in a streaming task training scene.The application constructs a system architecture comprising a base station, a UAV and a plurality of ground clients; a task stability index is obtained by calculating the gradient similarity between a current task and a historical task of a client, and a system efficiency index is obtained by combining the client computing time delay and the communication time delay, so as to jointly select a target client participating in aggregation; in order to anchor historical knowledge, local compensation updating is performed at the client side based on the historical task gradient, and post-aggregation compensation correction is performed at the base station side based on the historical global gradient; and a cross-layer optimization model about the client computing frequency, bandwidth allocation, UAV trajectory and relay strategy is further established to minimize the maximum completion time delay of the system. The application can improve the model learning precision, reduce catastrophic forgetting and reduce the training time delay.

The invention discloses a multi-layer unmanned aerial vehicle cooperative edge calculation method and system, which are applied to an air cooperative network consisting of an HAP and an unmanned aerial vehicle (UAV) and are used for access and unloading of ground PU and SU tasks. A ground user accesses the UAV in a CR-RSMA mode, the UAV establishes a communication link with the HAP in an OMA mode, and the HAP is selected for cooperative processing according to task delay and computing power. The AGT2 algorithm is adopted to realize double-layer stable matching of the PU, the SU and the UAV, the AGT2-HBS algorithm is combined to perform hierarchical joint optimization on power, bandwidth, task unloading proportion and calculation frequency, and the purpose is to minimize the total energy consumption of the system. The system is composed of a ground access part, a communication connection part, a cooperative calculation part, a matching optimization module, a resource allocation module and a control output module, and cross-layer optimization of multi-layer unmanned aerial vehicle cooperation can be realized.

The invention discloses a distributed data link ad hoc network routing construction method based on DRL and cross-layer optimization, and the method comprises the steps: abstracting a to-be-established end-to-end data stream into a partially observable Markov hop-by-hop decision process when a data stream communication request from a source node to a destination node is received; wherein in each hop, the current node only carries out joint decision-making of routing and transmitting power based on local observation information. In the decision of each hop, irregular topology and interference relation between adjacent nodes are flexibly described by using a graph; designing a GNN-based model architecture as a strategy of an agent node, and processing input / output fluctuation dimensions by using a graph relationship; in the optimization process, the information exchange between the nodes strictly follows the actual limitation of the sensing ability, and accords with the distributed configuration of the data link self-organizing network; the learned strategy can quickly establish a high-quality routing path between a source node and a target node, and shows strong generalization ability for random network topology.

The application provides a CKKS three-branch decision dynamic hierarchical encryption neural network training method and system. The method comprises the following steps: data encoding is performed on original input data, CKKS encryption is performed to generate ciphertext by adding a noise term to a polynomial; homomorphic calculation is performed on the ciphertext by alternating row-column encryption matrix multiplication, the intermediate features are processed in combination with an EncryptedPolyReLU activation function and a three-branch decision, encrypted features are obtained, and encrypted prediction results are finally output; the gradient is calculated by using an encrypted residual sum of squares loss function, and the Nesterov momentum method is used to accelerate the gradient update to process the encrypted prediction results, in combination with a hierarchical noise perception guide mechanism, so that the encrypted model parameters are optimized to obtain the encrypted model parameters. The application realizes efficient privacy protection training through dynamic encoding and row-column alternating encryption, and provides a safe and efficient deep learning solution for high-sensitive data in combination with three-branch decision screening and cross-layer optimization.

The invention relates to the technical field of data processing, in particular to a high-speed mobile terminal routing addressing method and a communication device, and the method comprises the steps: collecting three-dimensional space coordinates, channel state indication information and adjacent node connection states, and constructing a network state data set with a timestamp; on the basis of node positions and connection states, a routing algorithm is combined with a topology prediction model to pre-judge a motion track, an optimal path containing a time effectiveness factor is calculated, and convergence delay is reduced through an increment updating mechanism; based on the path quality and the channel state, path stability and channel conditions are analyzed through a deep neural network, and a coding scheme is dynamically selected from a forward error correction coding library in combination with parallel simulation verification; evaluating energy efficiency of a resource allocation algorithm and a task scheduling algorithm in a digital twin simulation environment according to routing and coding requirements, and selecting a resource allocation strategy; through cross-layer optimization of an interface collaborative application path, a coding scheme and a resource strategy, a transmission index is monitored in real time and parameters are dynamically adjusted.

This invention provides a method, apparatus, system, terminal, and storage medium for 3D scene reconstruction. The method includes: responding to a rendering task execution command, acquiring the current field of view of several user terminals, and evaluating the rendering task volume within the current field of view of each user terminal; based on the rendering task volume, modeling a cross-layer optimization problem based on end-to-end latency and rendering quality constraints with the goal of minimizing system energy consumption; solving the cross-layer optimization problem to obtain optimization variables, including a target rendering model selected from a set of candidate rendering models; and based on the optimization variables, distributing the rendering task to several distributed nodes so that the distributed nodes can perform collaborative rendering to reconstruct the 3D scene. This application, by considering the differences in field of view, constructs a cross-layer optimization problem and ensures that end-to-end latency and rendering quality constraints are met, reduces the energy consumption of the rendering task and solves the resource optimization problem of distributed 3D reconstruction.

The invention discloses an intelligent storage and accurate scheduling method for a multi-energy complementary power system. The method comprises the following steps: collecting and preprocessing wind and light output, energy storage state, load and meteorological data in real time; predicting a load demand based on a hybrid neural network of LSTM and an attention mechanism; the weight coefficients of the cost and the energy abandoning rate are dynamically adjusted through a fuzzy inference system; constructing a power generation, energy storage and power grid intelligent agent collaborative decision by adopting an improved depth deterministic strategy gradient algorithm, and generating a scheduling instruction in combination with a Pareto frontier storage pool and an attention mechanism; controlling the energy storage system to charge and discharge according to the instruction; and online updating of model parameters and dynamic correction of a scheduling strategy are realized through closed-loop feedback and cross-layer optimization. According to the method, the dynamic balance problem in multi-objective optimization is effectively solved, the energy utilization efficiency is improved, the operation cost and the energy abandoning rate are reduced, the system stability is enhanced, and the energy storage life is prolonged.

Managing Low Latency, Low Loss, Scalable Throughput (L4S) traffic may be provided. Managing L4S traffic can include receiving network data comprising one or more Physical (PHY) layer metrics, one or more Media Access Control (MAC) layer metrics, and one or more network layer metrics. Network conditions are determined based on the network data. One or more L4S characteristics are then set based on the network conditions.

The invention discloses a cross-layer optimization routing protocol method and system for a high-dynamic ad hoc network scene. The method specifically comprises the following steps: firstly, establishing a cross-layer optimization model for a high-dynamic ad hoc network; then, physical layer link quality is calculated, and link quality evaluation is carried out in a windowed exponentially weighted moving average smoothing mode; counting the load condition of the MAC layer based on Kalman filtering, and carrying out updating interaction through a network maintenance message; and finally, constructing a comprehensive evaluation model, and realizing MPR selection based on a greedy algorithm for joint optimization of cross-layer resources. The system comprises a cross-layer optimization model construction module, a link quality evaluation module, an update interaction module and an MPR selection module. According to the invention, the problem of low network stability in a multi-node dynamic topology environment is solved, the end-to-end delay is reduced, the packet delivery success rate is improved, the routing overhead is reduced, and the adaptability to the dynamic environment is enhanced.

Managing Low Latency, Low Loss, Scalable Throughput (L4S) traffic may be provided. Managing L4S traffic can include receiving network data comprising one or more Physical (PHY) layer metrics, one or more Media Access Control (MAC) layer metrics, and one or more network layer metrics. Network conditions are determined based on the network data. One or more L4S characteristics are then set based on the network conditions.

The invention relates to the technical field of wireless communication and multimedia communication, and discloses a cross-protocol-layer packing optimization method for lightweight image transmission of the Internet of Things and related equipment. According to the method, based on an application layer model, an access layer model and a physical layer model, a cross-layer optimization objective function based on a weighted product form is constructed for three sub-indexes of image quality, time delay and energy consumption; constructing a cross-protocol layer model, and solving by optimizing a cross-layer optimization objective function to obtain an optimal package size and optimal transmitting power; calculating a corresponding optimal quantization step size according to the optimal package size; and the Internet of Things node encodes the acquired lightweight image according to the optimal quantization step size, and sends a generated data packet to a receiver at the optimal transmitting power. The method can be applied to the transmission of the lightweight images of the Internet of Things through the random access network, and achieves the flexible balance and collaborative optimization of the image quality, the time delay and the energy consumption.

The present application relates to the technical field of unmanned aerial vehicle detection and identification, and particularly relates to a method and system for detecting unmanned aerial vehicles based on visible light polarization imaging. The method constructs a time-aligned channel state sequence by acquiring multi-source environment perception data, dynamically calculates cross-layer optimization parameters and decomposes them into calculated and transmitted characteristic quantities, schedules edge nodes to cooperatively generate resource scheduling instructions, matches physical resources to construct a virtual instance topology, generates control instructions through a double-channel graph neural network training model for online inference, and finally feeds back to the polarization sensor to realize link calibration. The system integrates polarization imaging and cross-layer optimization technology, significantly improves the accuracy and real-time performance of unmanned aerial vehicle detection in complex environments, reduces the consumption of computing resources, and realizes efficient spectrum reuse and power adaptive adjustment in dynamic environments.

The invention discloses an implementation method of an intelligent cross-layer MAC (Media Access Control) protocol based on multiple priorities. The method is based on the idea of cross-layer optimization, and comprises the following steps: one is a multi-priority queue scheduling mechanism oriented to an OLSR protocol, the multi-priority queue mechanism is introduced into an MAC layer, and the types of different sending nodes and the types of messages in the OLSR protocol are subjected to priority differentiation, so that the transmission efficiency of service messages can be improved, and the transmission efficiency of the service messages is improved; meanwhile, the problem of starvation of messages with low priorities can be relieved through a priority improvement mechanism; and the other one is a hierarchical backoff algorithm based on deep Q learning, the neighbor change rate, the total length of a cache queue and the size of a contention window at the current moment are taken as state input, and a decision is guided by using a deep neural network, so that the node can perform intelligent backoff according to the priority of the message to be sent, and the network performance is further improved. According to the invention, the packet delivery rate performance of the CMA-OLSR protocol in a scene with a relatively high load is improved.

The application discloses an edge collaborative intelligent agent management method based on a mechanism framework and AI, belongs to the technical field of industrial internet, and comprises the following steps: acquiring the packet return states of all edge nodes on a production line to construct a node connection matrix, and combining the hardware parameters of online nodes to establish a resource perception topology; running tests are performed on all types of tasks on the nodes, and the hardware functions of the nodes are normalized to construct a resource matching degree matrix; an interlayer optimization mechanism framework is constructed by deploying an INT plug-in, and the running state of the resource perception topology and the resource matching degree matrix are fused based on the interlayer optimization mechanism framework to generate a comprehensive cost matrix; based on the comprehensive cost matrix, the resource utilization rates of all nodes are combined to define a task action matrix; corresponding intelligent agents are allocated to each type of task, intelligent agent actions are determined based on the task action matrix, an intelligent agent strategy network for allocating node task actions is generated, and the real-time performance of task execution and the resource utilization rate of nodes are improved.

The invention relates to the technical field of wireless communication, discloses a WiFi (Wireless Fidelity) multi-link dynamic management system and method based on AI (Artificial Intelligence) prediction and DPI (Deep Packet Inspection) technologies, and solves the problems that the existing WiFi multi-link operation technology is lack of application perception capability, link strategy static solidification, no predictive management capability and cross-layer optimization. The problems that different application differentiation requirements cannot be met, the resource allocation efficiency is low and the network performance fluctuates are solved. According to the scheme, firstly, a DPI technology is adopted to analyze a data packet in real time, the type of an application program is identified, and the bandwidth requirement, the delay sensitivity and the packet loss tolerance of the application are extracted; secondly, predicting application network requirements and future network quality changes; and then according to an application type identification result, an application network demand and a future network quality prediction result, dynamically adjusting a multi-link operation strategy, simulating an execution effect of the multi-link operation strategy through a digital twin technology, and if a simulation result meets a requirement, applying the multi-link operation strategy to an actual network.

The application provides a communication method of a photovoltaic interface adapter and related equipment, and the communication method comprises the following steps: acquiring a signal quality index of the photovoltaic interface adapter and analyzing the signal quality index to obtain an analysis result; generating a channel quality optimization instruction according to the analysis result; obtaining real-time communication correction parameters according to the channel quality optimization instruction; predicting the real-time communication correction parameters to obtain a prediction result and generate a real-time adjustment signal; monitoring the utilization rate of communication resources of the photovoltaic interface adapter according to the real-time adjustment signal and generating a load scheduling instruction; performing cross-layer optimization on the real-time communication correction parameters and the load scheduling instruction; and performing real-time updating on the communication protocol and network configuration of the photovoltaic interface adapter to obtain an optimal communication link. The communication work of the photovoltaic interface adapter is performed through the optimal communication link, the overall efficiency and reliability of the communication of the photovoltaic interface adapter are improved, and the problems of poor communication link stability and low resource utilization efficiency are solved.

A network communicates an extended reality (XR) traffic with a user equipment (UE) with frame error concealment to limit a duration of temporal error propagation. The network also transmits a report of failure or a request for retransmission to the UE in an event of a network transmission failure.