71 results about "Wireless resource allocation" patented technology

The invention discloses an Internet of Vehicles information age optimization method and system based on graph reinforcement learning, and the method comprises the steps: firstly constructing a batch modeling and limited buffering queue structure of a vehicle state, carrying out the modeling of perception data into a multi-data-packet batch, and carrying out the queue management; modeling V2V link topology by using a graph neural network, and extracting large-scale channel embedding representation reflecting a topological structure; a multi-agent reinforcement learning system based on a centralized training distributed execution framework is constructed, each agent makes a decision according to a local state containing graph embedding features, a mixed action space is output, and an AoI opposite number of a receiving end is used as a reward; a graph embedding supervision mechanism based on a dominant function is introduced, so that topological features are aligned with a long-term optimization target; and network parameters are updated through multiple rounds of training, and finally, autonomous optimization control of each agent on packet loss and power is realized. According to the invention, data packet queue management and wireless resource allocation can be effectively coordinated, and efficient and low-overhead AoI minimization is realized in a complex dynamic topology environment.

A method and a device for allocating radio resources in in-band full duplex communication are disclosed. The method of a terminal comprises the steps of: receiving slot format information from a base station; confirming a DL section, an FL section, and an UL section on the basis of the slot format information; receiving UL subband information from the base station; and regarding a DL resource area as an UL resource area if an UL subband indicated by the UL subband information overlaps the DL resource area in the DL section.

The invention discloses a dynamic beam hopping and resource allocation method for NGSO satellite security communication, and belongs to the field of sixth-generation mobile communication security communication and wireless resource allocation. According to the method, significant non-uniformity of distribution of ground flow requirements in a time domain and a space domain is considered, and a satellite-ground security communication network dynamic beam hopping and resource allocation problem model is constructed by considering ground user flow requirements and composition elements and channel characteristics of an NGSO multi-beam satellite network. Through joint optimization of satellite hopping beam scheduling, power resource allocation and auxiliary interference unmanned aerial vehicle deployment strategies, service requirements of different users and ground eavesdropping environments are dynamically adapted, and the safety throughput and queue delay fairness of the system are improved. According to the method, mixed integer linear programming modeling is adopted, and a low-complexity approximation algorithm is combined, so that the method not only has optimality guarantee, but also can be deployed and operated in an actual satellite communication system, and thus unification of secure communication and efficient resource scheduling is realized.

The invention relates to resource allocation in a wireless communication network. The present disclosure proposes a network device for efficient multi-target radio resource allocation for user equipment. The network device includes a preference module and a policy module. The preference module is configured to determine a global preference vector, where the global preference vector describes an overall weight of each network performance metric in a set of network performance metrics for one or more user devices, and provide the global preference vector to the policy module. And the strategy module is used for acquiring the global preference vector from the preference module and making a resource allocation decision based on the global preference vector.

The application discloses a wireless resource allocation parameter determination method and device and electronic equipment. The method comprises the following steps: obtaining an initial topology structure corresponding to a wireless network; determining a plurality of resource allocation relationships corresponding to a plurality of links according to the initial topology structure; determining a target topology structure corresponding to the wireless network according to the plurality of links and the plurality of resource allocation relationships corresponding to the plurality of links, wherein the target topology structure comprises points obtained according to the plurality of links and edges obtained according to the corresponding resource allocation relationships; and determining a resource allocation parameter corresponding to the wireless network according to the target topology structure, wherein the resource allocation parameter represents a resource parameter allocated by an access end to a corresponding device end. The application solves the technical problem of unreasonable resource allocation caused by unreasonable resource allocation parameter determination in the prior art.

The invention discloses a multi-user wireless resource collaborative optimization distribution method, device, equipment and program product, and the method comprises the steps: obtaining multi-source network environment data of a plurality of target access equipment in a target region, and uploading the multi-source network environment data to a cloud; determining a signal interference matrix of the target area according to the peripheral AP list and the equipment position code, and generating a resource conflict thermodynamic diagram of the target area according to the signal interference matrix; predicting a network load of each target access device in a preset future time period according to the traffic time sequence data; and determining a target channel and target signal power of each target access device in a preset future time period according to the resource conflict thermodynamic diagram and the network load, obtaining a wireless resource allocation strategy, and issuing the wireless resource allocation strategy to each target access device. According to the invention, co-channel interference between adjacent users is avoided, the efficiency of wireless resource allocation and the accuracy of power control are improved, and the method can be widely applied to the technical field of wireless communication.

This application provides a wireless resource allocation method, apparatus, computer device, and readable storage medium. The method includes: acquiring channel state data between multiple target base stations and multiple target terminals; inputting the channel state data into a model to obtain a corresponding wireless resource allocation result; a first model is obtained by adjusting a pre-trained second model according to target model parameters; the target model parameters are the values ​​of model parameter variables that minimize the total loss value obtained by the second model after allocating wireless resources for multiple sample channel state data, determined in the sample null space by combining an objective function; the objective function includes a parameter optimization function that includes the functional relationship between the total loss variable and the model parameter variables while minimizing the total loss value as the optimization objective, and a constraint function used to limit the equality relationship between the first loss and the second loss; thereby improving the real-time performance and accuracy of wireless resource allocation.

Aspects of the present disclosure provide methods for wireless communication at a wireless node, generally including detecting, while on a first primary channel, an event related to a second primary channel and performing one or more actions on at least one of the first primary channel or the second primary channel after detecting the event, the performance being based on at least one prioritization rule being satisfied.

The application discloses a multi-task semantic communication system resource allocation method based on deep reinforcement learning, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a multi-task semantic communication network model assisted by semantic relays; based on the multi-task semantic communication network model, a multi-task resource allocation optimization model is established with the target of maximizing multi-task user experience quality; wherein the multi-task resource allocation optimization model is used for adjusting power allocation, sub-channel allocation and transmission semantic symbol number allocation of each task to maximize multi-task user experience quality; a hybrid deep reinforcement learning model is constructed and trained to obtain a strategy network capable of realizing optimal resource allocation, and wireless resource allocation optimization is performed on the basis of the multi-task resource allocation optimization model. The application can meet user equipment restrictions while reducing semantic network deployment overhead and realizing efficient utilization of spectrum bandwidth resources.

A lead UE (200) communicates a wireless guide identifier assigned to it via a base station (300). The lead UE (200) sends a request message containing the communicated wireless guide identifier to the base station. The base station receives the request message from the subscriber UE, determines a wireless guide resource assigned to the lead terminal based on the wireless guide identifier contained in the request message from the subscriber terminal, assigns a wireless resource whose timing is close to that of the wireless guide resource to the subscriber terminal as a wireless subscriber resource, and sends a response message indicating the wireless subscriber resource to the subscriber UE.

The application discloses a wireless resource allocation method for a LEO satellite-ground fusion uplink communication system, which comprises user base station selection, power and carrier allocation and power and carrier allocation methods for base station to satellite link, first establishes a system energy efficiency maximization model for subcarrier and power allocation; then quotes a slack variable to convert the model into a target function lower bound model; uses a symbolic function to couple power allocation variables and carrier allocation variables; and further decomposes the target function lower bound model into a base station access and carrier allocation optimization model and a power allocation optimization model; linear approximation method and successive convex approximation method are respectively used for solving, so that the wireless resource allocation of the uplink communication system is realized. By using the method, 0-1 integer optimization variables are removed, the calculation complexity is low, and higher uplink system energy efficiency is obtained.

A method comprising configuring a first radio resource allocation pattern, identifying that a quality of service violation may occur during a measurement gap of a terminal device when the first radio resource allocation pattern is utilized by the terminal device, determining at least one second radio resource allocation pattern that may be utilized by the terminal device during the measurement gap, and transmitting the at least one second radio resource allocation pattern to the terminal device.

A method (100) of allocating discrete radio resources of an access node of a telecommunication network to wireless devices The method is performed by a scheduler node of the telecommunication network. The method comprises, sequentially, for each radio resource solving (S101) a lower-bound optimization problem, where is the number of wireless devices. The lower-bound optimization problem is dependent on a respective mean reward and a respective value, for each wireless device, to obtain an estimated optimal resource allocation. The respective parameters are updated, for each wireless device, by sampling from a probability function defined as a convex combination of the estimated optimal resource allocation and a fairness constraint. The method further comprises initiating (S103) allocation of the radio resource to the wireless device having the largest mean reward.

This information processing device is provided with a transmission unit that, when one or more imaging devices among two or more imaging devices is designated as an imaging device related to prescribed processing, transmits control information, which is for preferentially allocating wireless resources to the imaging device related to the designation or a wireless communication terminal connected to the imaging device related to the designation, to a network device that performs processing relating to the allocation of the wireless resources. The transmission unit transmits multiple pieces of control information to the network device on a control-flow-by-control-flow basis.

This invention belongs to the field of 5G communication technology, specifically a wireless resource allocation method based on the deep reinforcement learning (DQN) algorithm under the 5G standard. The invention includes: combining wireless resources with dual-layer coding technology to model the panoramic video experience quality of individual users; fully considering user experience quality requirements and the heterogeneity of user channel states to determine the order of user resource allocation; modeling state information and user information; designing a neural network architecture and combining it with the deep reinforcement learning (DQN) algorithm to allocate appropriate optional parameter sets and minimum time slots to each user, thereby maximizing the overall panoramic video experience quality while meeting the basic experience quality requirements of all users. This invention can provide higher scalability and practicality for wireless resource allocation at the physical layer level, improve the utilization rate of limited communication resources, and has broad application prospects.

The invention relates to a method for computer-implemented optimized controlling of a communication channel (2) of a wireless communication system (1) in an industrial environment, the wireless communication system (1) comprising a sending communication device (10), a receiving communication device (20) and a wireless communication network (30) having at least one antenna (311, 312), a radio access network (320) configured to be controlled by a RAN intelligent controller, RIC, and a core network (330). The communication channel (2) is established between the sending communication device (10) and the receiving communication device (20) via the wireless communication network (30). The following steps are performed: a) obtaining channel quality data (CQD) of the communication channel (2), the channel quality data (CQD) being current data obtained from the radio access network (320); b) determining future channel quality data (FCQD) of the communication channel (2) by processing the obtained channel quality data (CQD) by a trained data driven model (MO), where the obtained channel quality data (CQD) are fed as digital input to the trained data driven model (MO) and the trained data driven model (MO) provides the future channel quality data (FCQD) as a digital output; c) processing the future channel quality data (FCQD) in order to determine radio resource allocation parameters (RSAP); and d) reconfigure the at least one antenna (311, 312) using the determined radio resource allocation parameters (RSAP).

A method for performing in-band spectrum sharing in a mixed TN-NTN system is provided. The method is performed by a user equipment (UE). The UE receives reference signal (RS) configuration information from a satellite or TN base station and obtains measurement results indicative of coupling loss or interference based on the RS configuration. The UE transmits a measurement report to the satellite or an uplink RS to the TN base station for resource allocation decisions. The UE operates according to resource allocation results indicating a partition of shared radio resources across frequency, time, or polarization domains to mitigate interference. Finally, the UE communicates using the allocated resource portion of the shared radio resources.

A method, performed by a base station, of allocating a wireless resource includes: obtaining a cycle parameter indicating a cycle which the base station uses to transmit data to and receive data from at least one User Equipment (UE); obtaining downlink data indicating an amount of data in a downlink buffer that is to be transmitted to the at least one UE; generating, based on the obtained cycle parameter and the obtained downlink data, an active UE set including a UE that needs to be allocated to the wireless resource; and allocating the wireless resource to at least one active UE included in the generated active UE set.

This invention belongs to the field of wireless resource allocation technology and discloses a wireless resource allocation method and related apparatus based on multi-agent reinforcement learning and dynamic graph learning. The wireless resource allocation method includes: at each wireless resource allocation time step, constructing a heterogeneous temporal graph using channel states and communication relationships in the vehicular network environment; performing dynamic graph representation learning on the heterogeneous temporal graph using a heterogeneous temporal graph neural network, obtaining the embedding features of each node through intra-relationship aggregation, inter-relationship aggregation, and cross-time aggregation; and making decisions based on the embedding features of each node using a multi-agent deep deterministic policy gradient reinforcement learning model to obtain a wireless resource allocation scheme. This invention can extract heterogeneous and temporal information beneficial to current decision-making from historical channel state information to support resource allocation decisions, thereby improving the reliability, stability, and overall service quality of vehicular network communication.

This document relates to full duplex (FD)-related radio resource allocation and random access of a UE. To this end, a random access method performed by a UE comprises: when there is second-type random access opportunity (RO) information for FD-related random access in addition to first-type RO information for general random access among pre-secured pieces of RO-related information, performing random access to a network on the basis of the second-type RO information; and transmitting / receiving a signal to / from the network through FD-supporting radio resources. Here, the UE is a full duplex-aware UE, and indicates that the UE is the full duplex-aware UE to the network through the random access based on the second-type RO information.

Embodiments provide resource-efficient techniques to help UEs perform QoS satisfaction projection, such as for access stratum (AS) layer optimization (e.g., scheduling optimization for service cell, radio resource allocation optimization for serving cell, and hand-off optimization). For example, a UE may utilize one or more ML models to make QoS satisfaction projections for various target base stations. In such examples, assistance information associated with a target base station may be provided to the UE. The UE may input the assistance information to an ML model, which may be specific to the target base station, to generate the QoS satisfaction projection. The UE may then send the QoS satisfaction projection to the network for AS layer optimizations.

The application discloses a kind of vehicle networking information age optimization method and system based on graph reinforcement learning, first constructs the batch modeling of vehicle state and limited buffer queue structure, and the perception data is modeled as multiple data packet batches and is managed in queue;Graph neural network is used to model V2V link topology, and large-scale channel embedding representation reflecting topological structure is extracted;A multi-agent reinforcement learning system based on centralized training and distributed execution framework is constructed, each agent makes decisions according to the local state containing graph embedding features, outputs a hybrid action space, and receives the inverse number of end AoI as a reward;Introduce the graph embedding supervision mechanism based on advantage function, align the topological features with the long-term optimization goal;Through multiple rounds of training to update network parameters, finally realize the autonomous optimization control of each agent to packet loss and power.The application can effectively coordinate packet queue management and wireless resource allocation, and realize efficient and low-overhead AoI minimization in complex dynamic topology environment.

The application discloses a kind of MU-MIMO system wireless resource allocation method and system based on condition diffusion strategy enhancement, for MU-MIMO system downlink transmission, method includes: with the total capacity of the MU-MIMO wireless communication system as target, jointly optimize resource block allocation and transmission power, construct optimization problem;Condition diffusion model is trained based on expert dataset;Multiple candidate solutions are generated using the trained model, and the optimal solution of the optimization problem is obtained by further optimizing the multiple candidate solutions through policy enhancement;By constructing a condition diffusion model, imitation learning is performed on the expert dataset;And introduce resampling mechanism, select the optimal solution from multiple candidate solutions, while ensuring to meet system constraints, significantly improve system throughput and reduce computational complexity.

Aspects of the present disclosure provide methods for wireless communication at a wireless node, generally including detecting, while on a first primary channel, an event related to a second primary channel and performing one or more actions on at least one of the first primary channel or the second primary channel after detecting the event, the performance being based on at least one prioritization rule being satisfied.

The invention provides a time-frequency three-dimensional wireless resource allocation method for a directional ad hoc network, the ad hoc network and a medium. The time-frequency three-dimensional wireless resource allocation method comprises the following steps: a wireless resource demand node sends a wireless resource application message; after receiving the wireless resource application message, all neighbor nodes perform conflict check on the wireless resources to obtain conflicting wireless resources, and feed back the conflicting wireless resources to the wireless resource demand node; and the wireless resource demand node enables conflict-free wireless resources to take effect according to the resource feedback message. According to the method, the space-time-frequency three-dimensional wireless resource allocation under the application scene of the directional ad hoc network is carried out by utilizing the advantage of small directional signal interference among different nodes of the directional ad hoc network of the space domain dimension and combining time domain and frequency domain dimension resources, so that the utilization efficiency of wireless spectrum resources under the application scene of the directional ad hoc network is effectively improved, and the utilization rate of the wireless spectrum resources under the application scene of the directional ad hoc network is improved. Meanwhile, deployment is easy, and use is convenient.

The application discloses a kind of based on reinforcement learning's multi-agent's air interface spectral efficiency promotion method, it is related to high-flux communication system technical field, utilize the attention mechanism in Transformer structure to solve the problem of data dimension explosion, low sample efficiency in the wireless resource allocation scheme based on deep reinforcement learning existing.The application, by using deep reinforcement learning technology, and in combination with the attention mechanism in Transformer structure, the correlation of user location distribution in multi-user cellular network and the allocation relationship between each resource can be mined and analyzed, to a certain extent, the generation of co-channel interference is avoided, not only the improvement of system spectral efficiency is realized, but also the problem of data dimension explosion, low sample efficiency in the resource allocation scheme based on deep reinforcement learning exists.

This application relates to a wireless resource allocation method, apparatus, computer device, and computer-readable storage medium. The method includes: obtaining the utilization rate of a first resource block of the uplink shared channel corresponding to each BWP in a target cell; when the utilization rate of any first resource block is lower than a first preset threshold, obtaining the terminal capability characteristic distribution and the BWP performance characteristic distribution of the target cell; inputting the terminal capability characteristic distribution and the BWP performance characteristic distribution into a pre-constructed uplink control channel resource allocation model, and obtaining the optimal uplink control channel resource allocation scheme for the target cell through the uplink control channel resource allocation model; and configuring the uplink control channel resources of the target cell according to the optimal uplink control channel resource allocation scheme. This method can improve the accuracy of wireless resource allocation while avoiding fragmentation of uplink shared channel resources.

This invention discloses a wireless resource allocation method, apparatus, and system that supports the coexistence of periodic and aperiodic machine users. The wireless resource allocation method includes classifying machine users into periodic and aperiodic types; allocating resources to periodic machine users based on transmission delay duration constraints; and allocating resources to aperiodic users based on delay statistical performance constraints. In this invention, since resource allocation is fixed, there is no need for dynamic requests to the base station, and idle wireless resources can be fully utilized, thereby improving resource utilization efficiency and reducing signaling complexity.