38 results about "Wireless resource allocation" patented technology

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 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.

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.

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.

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.

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.

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.

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.

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.

The invention provides a vehicle and road cloud integrated network architecture system based on an O-RAN. According to the system, a radio access network intelligent controller (RIC) is deployed in cloud equipment, and network state data acquired and processed by vehicle-side and roadside equipment are received by the RIC; the RIC makes a radio resource allocation decision based on the data and issues the decision to a central unit (CU) and / or a distributed unit (DU) in the network for execution. The core of the application lies in that a closed-loop control system integrating sensing, decision making and execution is constructed, and dynamic and adaptive management of network resources is realized. The open and intelligent characteristics of the O-RAN are introduced into the vehicle-road cloud system, so that the problem that a traditional architecture is rigid in the aspects of mobility management and resource scheduling is effectively solved, and the communication stability and the resource utilization efficiency of the whole network in a dynamic traffic scene are remarkably improved.

According to an example aspect of the present invention, there is provided an apparatus comprising at least one processing core and at least one memory storing instructions that, when executed by the at least one processing core, cause the apparatus at least to transmit in uplink or sidelink, or receive in downlink, using orthogonal frequency-division multiplexing, via a physical shared channel in a system comprising resource block groups, each resource block group comprising two or more resource blocks, each resource block comprising plural subcarriers which are consecutive to each other in frequency, and process an allocation of resources for the physical shared channel, the allocation received from a network node, the allocation defining that the apparatus may use a part of, but not all, subcarriers of each one of one or more resource blocks for communication via the physical shared channel.

The application discloses a wireless resource allocation optimization device and method, the device comprises: a data acquisition module for collecting channel data uploaded by a UE and realizing positioning of the UE; a data prediction module for realizing training of a neural network model and prediction of collected data; and a strategy optimization module for realizing allocation of subcarrier resources and selection of transmission rates. In the wireless resource allocation optimization device and method, inter-cell interference is introduced, different schemes are used to calculate channel capacity for edge UEs and center UEs, and the schemes are more in line with real systems; prediction technology is used for interference information and channel information, more accurate channel and interference information can be obtained, the problem that channel states in feedback are different from channel states at data transmission time is solved, subcarrier allocation and transmission rate selection are realized, and greater throughput can be obtained in the whole transmission time period.

The application discloses a wireless resource allocation method and device, equipment and storage medium, relates to the technical field of communication, and is used for improving the rationality of wireless resource allocation. Including: obtaining a first parameter corresponding to a target integrated access and backhaul IAB system, the target IAB system comprising a macro base station, an IAB device and a plurality of terminal devices, determining a signal-to-noise ratio SNR corresponding to each link in the plurality of links included in the target IAB system based on the first parameter, determining a throughput corresponding to each link in the plurality of links based on the Shannon formula according to the SNR corresponding to each link, determining a second parameter corresponding to the target IAB system according to the throughput corresponding to each link, and allocating resources for the plurality of terminal devices based on the macro base station according to the second parameter, the second parameter comprising a target time allocation factor and a target power optimization factor, and the application is applied to the scene of allocating wireless resources to the IAB system.

This application discloses a LoRa wireless resource allocation method, apparatus, and storage medium. The method includes: acquiring node information and channel information forwarded by a gateway; obtaining a corresponding adjustment strategy through a neural network based on the node information and the channel information; setting node parameter values ​​according to the adjustment strategy and sending them to the gateway; generating a corresponding reward value and a next state based on a preset reward function; placing the reward value and the next state into an experience pool; and training and updating the neural network based on the data in the experience pool. This reduces the collision rate of data in the LoRa channel, reduces transmission latency, and increases the number of IoT devices that can be connected to a single gateway.

A wireless power transfer system includes a wireless terminal apparatus, a wireless power transmitter, and a wireless base station (a wide-area base station and a narrow-area base station). The wireless base station creates schedule information containing allocation of wireless resources regarding the wireless data communication and the wireless power transfer on the basis of grasp of a request of the wireless data communication and a request of the wireless power transfer from the wireless terminal apparatus, and transmits the schedule information to the wireless power transmitter and the wireless terminal apparatus. The wireless power transmitter executes the wireless power transfer to the wireless terminal apparatus in accordance with the schedule information. The wireless terminal apparatus receives the wireless power transfer from the wireless power transmitter in accordance with the schedule information, and executes the wireless data communication via the wireless base station.

The invention discloses a wireless ad hoc network link resource management method, which comprises the following steps of: in an ad hoc network formed by a plurality of nodes, performing a first round of broadcast of each node according to a node access sequence, and determining whether a communication link exists between a broadcast node and an interception node; each node performs a second round of broadcasting, and all link information in the ad hoc network is summarized through the last node; generating a node connection matrix according to the summarized link information of the ad hoc network, calculating the total number of links according to the node connection matrix, and generating a link correlation matrix; the degree of each link is calculated according to the link correlation matrix, and the links are arranged in a descending order according to the degrees; and carrying out resource allocation on all links according to the correlation of the links in the sequence. According to the method, a link correlation matrix and node degree calculation mode is adopted, a resource allocation result can be automatically generated, channel delay caused by carrier sensing is avoided, and the problem of wireless resource allocation under an ad hoc network is solved; the distribution scheme is low in calculation complexity and does not need extra hardware support.

This invention provides a method for optimizing wireless network communication efficiency in federated learning. It models the wireless network communication efficiency optimization problem in federated learning using the amount of training data per unit of wireless resources. The problem is simplified to a wireless resource allocation subproblem using alternating iterations. The optimal solution is then obtained through transformation and the Lagrange multiplier method. The spectral bandwidth of nodes used to ensure the optimal solution satisfies the constraints of the wireless network communication efficiency optimization problem is calculated, resulting in a surrogate selection subproblem. Parameters for determining whether a node participates in training are obtained. Based on the optimization results, the transmit power, spectral bandwidth, and nodes participating in federated learning are set. This method achieves an optimal trade-off between training accuracy and communication resource consumption, effectively improving network communication efficiency. It has low complexity, significantly increases solution speed, and has strong engineering applicability.

The invention relates to the field of communication resource allocation, and particularly discloses a power wireless resource allocation system based on a digital twinning technology, which does not take wireless resource allocation and edge computing scheduling as two independent processes any more, but tightly couples the wireless resource allocation and the edge computing scheduling through a unified joint optimization decision mechanism. Specifically, according to the scheme, the terminal reports the task portrait, the base station enhances the wireless state, the MEC senses the own load, and key information of an end-to-end full link is gathered. Based on the global view, joint optimization solution is carried out, and a collaborative allocation strategy simultaneously covering wireless authorization and calculation reservation is generated. According to the scheduling mode integrating communication and calculation, resources can be foreseeably inclined for short plate links with poor channels or heavy tasks, the wooden barrel effect caused by information islands is fundamentally solved, global minimization of end-to-end time delay of electric power collaborative services is achieved, and accurate alignment of resource allocation and real service targets is ensured.

The application provides a distributed multi-agent wireless resource allocation method. The wireless resource allocation method comprises a subcarrier allocation method and a power allocation method. Firstly, a sum rate maximization model of the subcarrier allocation and the power allocation is established. Then, according to an agent load minimum standard, all agents and users are allocated. Further, the sum rate maximization model is decomposed into a subcarrier allocation optimization model and a power allocation optimization model. The subcarrier allocation optimization model and the power allocation optimization model are solved alternately by using a matching theory method and a dual subgradient iteration method respectively. Thus, the distributed multi-agent wireless resource allocation is realized. By using the method, compared with an intensive learning mode wireless resource allocation method, the complexity is low, the iteration number is small, the required calculation resource is small, and the maximum system capacity is obtained.

Embodiments include systems, methods, and devices for wireless resource allocation to support multicast services in networks such as Fifth Generation (5G) New Radio (NR) (5G NR). In some embodiments, the methods can be performed by a processor of a base station. In some embodiments, the methods can be performed by a processor of a wireless device. Various embodiments can include configuring two or more downlink (DL) frequency resources for multicast communications within a carrier bandwidth. Various embodiments can include selecting a DL frequency resource for multicast communications from the two or more DL frequency resources for multicast communications to monitor for multicast data transmissions from a base station based at least in part on an active DL bandwidth part (BWP) of a wireless device.

The application is suitable for the technical field of wireless communication resource allocation, and provides a wireless resource allocation method and system based on multi-agent deep reinforcement learning, which comprises the following steps: a heterogeneous multi-agent system is established, and a resource allocation problem in a wireless communication scenario is constructed; a neural network parameterization function, an energy-based strategy and an SGD optimization method are introduced, and a target function is built; an upper bound function of strategy update error is established, and the coupling relationship between the strategy network radius and the hyperparameter is qualitatively analyzed; a hyperparameter adaptive updating mechanism is designed, a multi-agent deep reinforcement learning is used to solve the resource allocation problem, and a final resource allocation result is output. The application solves the blindness problem of heuristic parameter adjustment, and is oriented to a heterogeneous multi-agent system, solves the problem that the learning ability of an agent is limited under fixed unified hyperparameters, effectively improves the learning ability of the agent, and thus improves key communication indicators such as reward, energy efficiency, throughput and signal-to-noise ratio of the whole system.

The application provides a 5G and MEC coupling method and device for low-latency video services, which comprises the following steps: determining a next frame size prediction result of a target image frame according to application layer context information; performing real-time link dynamicity estimation on 5G access network state information to determine a wireless link quality estimation result of the 5G access network; fusing the next frame size prediction result and the wireless link quality estimation result into a wireless resource demand rule; and controlling real-time data interaction between the 5G access network and the edge server according to the wireless resource demand rule based on an incentive and acceleration mechanism to adaptively adjust the wireless resource allocation for the video service. The application can accurately determine the wireless resource demand of the video service, adaptively adjust the wireless resource for the video service, and guarantee the reliability of resource allocation, thereby ensuring the latency sensitivity of the video service such as video analysis and video processing.

Aspects of the disclosure relate to transmission configuration indicator (TCI) state mapping for multiple transport block (TB) transmission with downlink control information (DCI). In one example, a scheduled entity may receive a scheduling grant from a scheduling entity. The scheduling grant may include scheduling assignments configured to assign wireless resources for corresponding enabled communications. The scheduling grant may further include non-scheduling assignments without assigning wireless resources. The scheduling grant may further include a codepoint including TCI state indicators mapping the scheduling and non-scheduling assignments to corresponding TCI states. The scheduled entity may further receive a control message to activate the TCI states mapped to the corresponding indicators. Other aspects, embodiments, and features are also claimed and described.