40 results about "Macro base stations" patented technology

The invention relates to the technical field of coal mine telecommunication, and discloses a coal mine underground integrated private network coverage system based on a 5G communication and AR double-transmitting selective-receiving technology, an efficient transmission and baseband processing network is constructed by taking a macro base station as a core on the ground and matching with PTN equipment and a BBU, a 5G micro base station and an RRU are deployed under the ground for a complex environment, and low-loss signal coverage is realized through optical fibers. The AR double-transmitting selective-receiving module ensures that underground data are not lost and have no error through redundant coding and double-link transmission; the edge computing module sinks data processing to the underground, reduces transmission delay and optimizes resource allocation; and the system monitoring and interference suppression unit monitors the operation state in real time, performs multi-stage alarm and resists multipath effect and electromagnetic interference, and finally realizes integration of communication above and under the well, reliability of data transmission, high efficiency of resource utilization and stability of system operation, thereby providing solid communication support for safety production, equipment management and control and personnel scheduling under the coal mine.

The invention discloses a heterogeneous cellular network task unloading method oriented to location privacy protection, and belongs to the technical field of communication. The invention provides a heterogeneous cellular network task unloading method oriented to location privacy protection, aiming at solving the problem that location privacy leakage is possibly caused when a mobile user unloads a task to a micro base station edge server to which the mobile user belongs in heterogeneous cellular network edge calculation. According to the method, the privacy leakage risk of task unloading is evaluated according to user position distribution, the number of unit tasks to be unloaded and the resource state of each edge server, the position correlation between users and target servers is weakened by using a relay forwarding mechanism of a macro base station, and a deep reinforcement learning method is adopted, so that the task unloading efficiency is improved. And the position privacy risk and the task unloading strategy of the user are dynamically learned and optimized, so that the position privacy protection of the user is effectively realized and the average unit task energy consumption is minimized under the constraint of average unit task tolerance time delay.

At a high level, the technology disclosed herein relates to methods, systems, media, etc., for a network coverage optimization engine. In embodiments, network coverage can be optimized by applying particular updates, changes, etc., to a network configuration for one or more coverage areas provided by one or more base stations (e.g., a macro base station, another type of outdoor base station, an indoor cell, a distributed antenna system). For example, the network configuration can be determined based on one or more particular radio frequency (RF) performance metrics for a coverage area being below a threshold. In embodiments, one or more machine learning models may be implemented to predict signal coverage changes upon applying the determined network configuration based on the collected data for the coverage area (e.g., network data, user device network feedback, user device location data, environmental profiles for the coverage area, etc.).

The application discloses a kind of unmanned aerial vehicle base station resource allocation method, device and equipment based on hierarchical game, storage medium, it is related to data communication technical field, including: obtaining the access link transmission information and backhaul link transmission information when macro base station, unmanned aerial vehicle base station and user end carry out wireless information backhaul operation, and the flight position information of unmanned aerial vehicle base station, and the unmanned aerial vehicle base station resource allocation problem is split into the associated relationship optimization problem corresponding to user level and the transmission resource optimization problem corresponding to resource level;The receiving rate of user end and the transmission resource of target base station are modeled respectively to obtain corresponding first game model and second game model;Determine the corresponding target personal utility and target resource backhaul utility of two models in game equilibrium state to obtain the target resource allocation strategy of unmanned aerial vehicle base station for wireless information backhaul operation.This way, the user association state can be accurately analyzed, and the optimal allocation of unmanned aerial vehicle base station resources is realized.

The present disclosure relates to a model learning method, a model learning device and a storage medium. The model learning method is applied to a macro base station and includes: in response to receiving a model training request sent by an operation maintenance management (OAM) entity, sending the model training request to a first number of micro base stations; wherein the communication coverage range of the first number of micro base stations is within the communication coverage range of the macro base station. Through the present disclosure, the interaction between the macro base station and the micro base station is realized, the task allocation of the trained model is realized, the signal quality number is fast, the data transmission rate is improved, and the utilization rate of the wireless access network is improved.

Embodiments of the present application provide a distributed virtual cell deployment method, network device and mobile communication network, wherein the present application proposes to deploy one or more virtual base stations by grouping small base stations and setting the first network device between the small base station group and the macro base station in the mobile communication network, and control the number of virtual cells provided by each virtual base station, and respectively provide virtual cell feature information for each virtual base station to configure the virtual cells, so that the multiple small base stations contained in the same virtual cell use the same physical cell identifier and frequency point, and on this basis, record the above information in the neighbor cell relation table. Since the number of virtual base stations is much smaller than the number of small base stations in mutual neighbor cell relation with the macro base station, thus, the purpose of simplifying the neighbor cell relation configuration and reducing the maintenance and operation cost can be achieved. Further, using the neighbor cell relation table for service cell switching management is also more simple, fast and efficient.

The present disclosure relates to a terminal detection and cell allocation method using image sensing data acquired from a plurality of small base stations (SBSs). A method performed by a macro base station (MBS) of the present disclosure may comprise the steps of: receiving at least one piece of image sensing data from at least one SBS; generating voxel data on the basis of the at least one piece of image sensing data; identifying, on the basis of the voxel data, at least one SBS having a line of sight (LoS) path to at least one terminal; and transmitting, to the identified at least one SBS, information about the at least one terminal having the LoS path.

The embodiment of the present disclosure relates to an optimization method for content placement and transmission based on scalable video coding. The method comprises: constructing a wireless cache network; in the content placement stage, a macro base station transmits cached files to edge devices; in the content transmission stage, a user sends request information to an edge device in a cell; if the edge device has cached the requested content, the edge device directly transmits the requested content to the user; if the edge device does not store the requested content, the macro base station directly transmits the requested content to the user; in the content placement stage, an optimization problem for minimizing user transmission delay is constructed and solved to obtain an optimal cache strategy; in the content layer transmission stage, an optimization problem for maximizing user sum rate is established and solved to obtain an optimal transmission strategy. The embodiment of the present disclosure effectively improves the efficiency of file transmission, reduces transmission delay, and improves the service quality of users.

This invention discloses a method for determining the weather boundary based on 5G service migration characteristics, comprising: acquiring historical rainfall data covering 5G cells, extracting service migration characteristics and corresponding weather conditions from the data for regression analysis, and establishing a weather boundary determination model based on 5G service migration characteristics; monitoring and extracting service migration characteristics one from real-time service data of 5G cells, and inputting it into the weather boundary determination model to obtain weather condition one; extracting cells with weather boundaries in weather condition one, and determining the weather boundary between the cells based on macro base station information within the cells. The weather boundary determination method disclosed in this invention can achieve more refined weather monitoring. By incorporating dense 5G data, it can effectively improve the resolution of weather boundaries and determine them more accurately; simultaneously, based on existing 5G equipment, it does not incur additional costs.

The application discloses a home base station switching method, device, equipment and medium. The method is applied to a macro base station and comprises the following steps: obtaining home base station information in capability information of an access user equipment; when receiving a switching measurement report of the user equipment, issuing a measurement instruction to the user equipment, and switching the user equipment into a target home base station corresponding to the home base station information according to the home base station information and the measurement report fed back by the user equipment. The home base station information is written into the capability information of the user equipment, so that the information of the target home base station can be obtained from the capability information of the user equipment when the user equipment is switched from a macro base station cell to a home base station, and the switching process is completed. Therefore, the switching can be completed without configuring adjacent area and adjacent station information in the macro station, that is, without establishing an SCTP connection, so that the problem that the adjacent area of the macro base station is limited is avoided. The automatic switching of the home base station is realized, and the experience of users is improved.

This application relates to the field of communication network technology, specifically to a reinforcement learning-based method for enhancing user association in dynamic air-to-ground heterogeneous networks. This method can solve the problem of user-base station association in dynamic scenarios of integrated air-to-ground heterogeneous networks. The method includes: constructing an air-to-ground heterogeneous network and initializing its parameters, wherein the air-to-ground heterogeneous network includes ground macro base stations, ground small base stations, airborne base stations, and ground users; modeling the system channels and channel capacity based on the air-to-ground heterogeneous network; determining the optimization problem and constraints for maximizing network capacity; and solving the optimization problem using a Q-learning reinforcement algorithm to obtain the optimal user association solution.

The invention is applicable to the technical field of network deployment, and particularly relates to a data communication network deployment method and system, and the method comprises the steps: delimiting a coverage area of a data communication service, defining edge access points which at least comprise curbside commercial tenants and public facilities, constructing a registration platform of the edge access points, and carrying out the registration of the edge access points, selecting an available unit, obtaining the access authority of the Wi-Fi network of the available unit, receiving a data communication task uploaded by a user, and segmenting the data communication task into a social task and a content task; and drawing a plane distribution diagram of the coverage area, and marking a personnel aggregation place. According to the invention, the deployment position of the macro base station is determined according to the edge access point and the micro base station, so that a backbone communication network can be constructed, wide-area coverage can be carried out, a multi-level cooperative processing network can be constructed in a coverage area, deep coverage can be carried out on the coverage area, and the processing efficiency of a data communication task and the use experience of a user can be greatly improved.

The invention discloses a user-small base station-macro base station combined three-layer MEC network resource allocation method and system. The method comprises the following specific steps: step 1, acquiring basic configuration information of a network model; step 2, establishing an optimization model P1 by taking the time delay of the minimum network model as a target and taking the task allocation proportion of user tasks on the local user, the small base station and the macro base station, the user-small base station matching result and the computing resources of the small base station as optimization variables, and setting constraint conditions; 3, decomposing the optimization model in the step 2, and dividing the optimization model into a matching sub-problem and a resource allocation sub-problem according to different constraint conditions; step 4, solving the matching sub-problem in the step 3; and step 5, solving the resource sub-allocation problem in the step 3. According to the joint optimization scheme, the problems of task matching and resource allocation under the three-layer MEC network architecture are effectively solved, and the time delay performance of the system is improved.

The application discloses a multi-user task offloading calculation method, system and device and a storage medium, belongs to the wireless communication technical field, and comprises the following steps: calculating the channel transmission rate of user task offloading to a small base station and the channel transmission rate of user task being forwarded to a macro base station through the small base station; calculating the transmission delay of user task offloading to the small base station and the transmission delay of user task being forwarded to the macro base station through the small base station according to the two channel transmission rates; calculating the processing delay of user task calculation at the small base station and the macro base station; inputting the two transmission delays and the two processing delays into a task offloading and calculation resource joint allocation model based on delay minimization which is constructed, and obtaining the optimal allocation scheme of MEC calculation resources in the small base station and the macro base station by solving; and based on the optimal allocation scheme, user task is offloaded to the small base station and the macro base station for calculation; the application reduces the total delay of user task processing through joint optimization of task offloading and calculation resource allocation.

The application provides a 5G base station virtual power plant feasible region aggregation method based on coupling degree clustering, belongs to the flexible resource aggregation field, and comprises the following steps: a super-dense heterogeneous 5G base station network model containing macro base stations and micro base stations is constructed, and base station positions, user distribution, channel parameters and service quality constraints are obtained; the base station coupling degree concept is proposed; the coupling degree is used as the similarity measurement, the greedy clustering idea is adopted, the k-means++ optimization initial value is used as the initial clustering center, the remaining base stations are sequentially grouped into the cluster with the maximum coupling degree, the clustering is iterated, and the dynamic grouping is realized according to the flow prediction change; for each base station cluster, the base station communication energy consumption and energy storage resources that can be reduced are combined, the multi-objective optimization is used to solve the adjustable power internal distribution feasibility, and the cluster-level feasible region is obtained. The application significantly improves the accuracy and efficiency of the 5G base station virtual power plant feasible region aggregation, and provides key technical support for the large-scale participation of 5G base stations in the power system auxiliary service market.

The application discloses a RIS-assisted RSMA super-dense network and a coverage performance analysis method. The RIS-assisted RSMA super-dense network comprises: a plurality of micro base stations arranged in a hotspot area with dense users; the spatial positions of the micro base stations are modeled by using a Poisson cluster process, and the micro base stations are independently and identically distributed around a cluster center; a plurality of macro base stations, the spatial positions of which are modeled by using a Poisson point process, and the spatial positions of the macro base stations are independent and uniformly distributed; wherein each micro base station and macro base station is respectively equipped with RIS. The RIS-assisted RSMA super-dense network provided by the application can improve the coverage rate of the system and the regional spectrum efficiency of the system.

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.

The application discloses a minimum system total time delay resource allocation method based on RAA, and comprises the following steps: S1, a macro base station and micro base stations and users exchange messages through HELLO packets to obtain information and hand over the information to the macro base station for summarization; S2, through a communication model and a task offloading model, taking the minimum system total transmission time delay as an objective function, taking the total computing resource quantity of the micro base station MEC and the indivisibility of the task offloading and the residual energy of the user equipment as constraints, the problem is modeled; S3, according to the model obtained in step three, the problem is divided into two sub-problems for solving; S4, by gradually solving the two sub-problems in step four, an effective and feasible solution of the total problem is obtained, and the total time delay of the minimum system is minimized. In the case that the user equipment battery power is limited, the computing resource of the micro base station edge server is reasonably allocated, and the total time delay of the minimum system transmission is minimized.

This invention provides a millimeter-wave spectrum resource sharing method, resource pool reflector, device, and medium, relating to the field of communication technology. The method includes: acquiring the shareable spectrum resources available from each millimeter-wave base station; upon receiving a resource request from a macro base station, acquiring the location grid of the user equipment (UE) and its associated target base station from the resource request; if the target base station has available spectrum resources, generating resource allocation information based on the available spectrum resources and sending it to the macro base station; and the macro base station instructing the UE to establish a communication connection with the target base station based on the resource allocation information, thereby achieving spectrum resource sharing. By sharing the idle resources of millimeter-wave base stations with base stations requiring additional wireless resources, interference between different devices accessing broadband spectrum heterogeneously in millimeter-wave mobile communication networks can be resolved, reducing problems such as terminals being unable to use millimeter-wave broadband networks in weak signal areas and communication interruptions caused by differences in the hardware performance of millimeter-wave transceivers from different terminals.

A data driven spectrum sharing approach is implemented amongst users of a shared band. A spectrum access controller (SAC) estimates interference and allocates spectrum in one or more bands to a plurality of spectrum user devices, e.g., macro base stations, small cell base stations, access points, etc. Different spectrum user devices in the plurality of spectrum user devices may correspond to different service providers, may use different technologies, may use different communications protocols, and / or may have different transmission power levels. The spectrum user devices send data feedback reports to the SAC including, e.g., received signals strength information, signal quality information, interference information, etc. The SAC uses the received data feedback reports to adjust, e.g., fine tune, interference limits and / or propagation model parameters. The SAC uses the adjusted, e.g., refined, interference limits and adjusted, e.g., refined propagation models, to make more accurate decisions regarding spectrum reuse.

This invention discloses a dynamic resource allocation optimization method based on reinforcement learning in heterogeneous networks. Step 1: Establish a heterogeneous network model, clearly defining energy efficiency as the maximum optimization objective; treat the macro base station as an agent, establish a Markov decision process, and determine the state space, action space, and reward function; divide the coverage area of ​​the macro base station and the bandwidth of the heterogeneous network; train the heterogeneous network model using a D3QN network, set the maximum transmit power of the macro base station and small base stations, calculate the signal-to-interference-plus-noise ratio (SNR) from the macro base station to the user, and minimize the loss function; introduce a double DQN network into the dueling DQN network to obtain the value function and dominance function, determine the optimal heterogeneous network hyperparameters K and β, maximize the energy efficiency of the heterogeneous network, and achieve dynamic resource allocation optimization. This invention proposes a heterogeneous network model with a centralized reinforcement learning framework to achieve global optimization of dynamic resource allocation, and proposes using a D3QN network to optimize load balancing and interference coordination, maximizing the energy efficiency of the heterogeneous network and achieving dynamic resource allocation optimization.

The invention belongs to the technical field of wireless communication, and discloses a multiple access multiplexing transmission method. Comprising the following steps: a macro base station and a micro base station send pilot frequencies by adopting random opportunity precoding; the user calculates and feeds back a signal to interference plus noise ratio; the base station sorts and selects users according to the sequence; splitting a selected user message into a public part and a private part, combining the public part into a public stream, respectively pre-coding, distributing power, and transmitting; the user side firstly decodes and eliminates the public stream by adopting a serial interference elimination technology, and then decodes the private stream of the user side; updating the user sequence and circularly executing the selection and transmission process; a mathematical model for joint optimization of power distribution, rate splitting and user pairing is established with maximization of system and spectrum efficiency as a target, and the mathematical model is decomposed into three sub-problems to be solved through a joint iterative algorithm. The method does not depend on perfect channel state information, multi-domain multiplexing is achieved through opportunity selection and rate splitting, and the system spectrum efficiency is improved.

A cell-free massive MIMO system according to the present disclosure comprises a plurality of APs, a plurality of RISs, and a CPU-equipped macro base station connected to the APs and the RISs. A CPU: detects a plurality of UEs by using radio waves from a macro base station; determines, for each UE, among the plurality of APs, one or a plurality of related APs to be respectively related with the plurality of UEs; gives user information pertaining to the detected UE to the one or the plurality of related APs; and determines, for each related AP, among the plurality of RISs, one or a plurality of related RISs to be related with the one or the plurality of related APs on the basis of mapping information between the plurality of APs and the plurality of RISs. The CPU: determines allocation of resources of each of the one or the plurality of related RISs to the plurality of UEs such that a minimum required quality of service is obtained in each of the plurality of UEs; gives RIS information pertaining to the allocation to the one or the plurality of related APs; and controls the one or the plurality of related RISs according to the allocation.

The invention discloses a base station construction evaluation method, electronic equipment, a storage medium and a program product, and relates to the field of wireless. The base station construction evaluation method comprises the following steps: attributing a plurality of 4G indoor distribution base stations to the same property management point based on 4G indoor distribution base station information; obtaining the coverage degree of a 5G macro station to a 4G indoor distribution base station in the property management point; and on the basis of the coverage degree and the 4G indoor distribution base station information, the construction of the 5G indoor distribution base station is evaluated. The method and the device are used for solving the problem that 4G service volume evaluation based on a base station cannot evaluate the 4G service volume condition of a building or a property management point and the necessity of 5G station establishment in related technologies.

The application discloses a time domain resource allocation method and device, electronic equipment and storage medium, the method comprises the following steps: receiving the base station information and user information sent by all macro base stations and small base stations deployed in the same frequency; dividing the users accessing the same small base station into a center user group and multiple edge user groups, and dividing the users accessing the same macro base station into a macro base station user group; constructing an edge user group interference diagram; allocating virtual resources for all macro base station user groups, center user groups and edge user groups of the small base station; and determining the time domain resource segments contained in the virtual resources of each user group. The application allocates time domain resources on demand in units of user groups, can coordinate the interference of the macro base station on the edge users of the small base station, and the interference between the small base stations, and improves the resource utilization rate and the performance of the edge users of the small base station.

This invention provides a method, apparatus, storage medium, and computer device for locating cells with out-of-coverage coverage. The method includes: constructing a data matrix based on acquired sampling point identifiers, primary serving cell identifiers, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information; generating a TA distribution curve based on the data matrix; calculating the TA distribution curve using a differential vector method to generate an out-of-coverage distance interval; selecting sampling points within the out-of-coverage distance interval based on the data matrix; calculating the sampling points using a DBSCAN clustering algorithm to generate an out-of-coverage area; and determining the out-of-coverage cell based on acquired macro base station information and the out-of-coverage area. The technical solution provided by this invention can determine out-of-coverage cells based on the TA distribution curve, improving the accuracy of locating out-of-coverage cells.

The invention relates to the technical field of mobile communication, in particular to a macro-micro base station cooperative sensing system and method.The macro-micro base station cooperative sensing system comprises a macro base station cluster, a micro base station cluster and a cooperative control and processing unit, and the cooperative control and processing unit comprises a dynamic resource allocation module and a sensing security module; compared with the prior art in which a resource allocation scheme driven by a fixed or simple rule is adopted, which is difficult to deal with a dynamically changing network environment and diversified perception task requirements, so that the resource utilization efficiency is low and the system performance is limited, the scheme adopts an intelligent dynamic resource allocation mechanism based on deep reinforcement learning; the network state and task demand change can be autonomously perceived, the global configuration of power, frequency spectrum, wave beam and time resources is optimized in real time, and the resource utilization efficiency and the system self-adaptive capability are remarkably improved.

The application discloses a vehicle networking security offloading and cache optimization method based on a graph neural network, relates to the fields of intelligent transportation and edge computing, and aims to solve the problems of low resource scheduling efficiency, insufficient privacy protection and task and cache processing fragmentation in vehicle networking. The method comprises the following steps: S1, a vehicle networking system model is constructed, including a vehicle-roadside unit-macro base station architecture, a communication model of an orthogonal frequency division multiple access and a dedicated short-range communication protocol, and a calculation model including local processing and partial remote offloading; S2, Gaussian noise is introduced to realize differential privacy protection, and an asynchronous federated learning is combined with a long short-term memory network to predict task popularity; and S3, a dynamic heterogeneous graph is constructed, time and space features are extracted by using a graph neural network, an approximate policy optimization algorithm is input, and offloading targets, proportions and cache strategies are jointly optimized. The application realizes the collaborative improvement of privacy security, low system total time delay, high cache hit rate and resource utilization rate, and is suitable for the dynamic topology and complex task demand of vehicle networking.

This disclosure relates to a wireless edge caching method based on full-duplex relay in a heterogeneous wireless network. The method includes: constructing a heterogeneous wireless network; wherein the heterogeneous wireless network includes a core network, macro base stations, relay stations, and multiple users; employing a random caching strategy based on Scalable Video Coding (SVC) technology, whereby a video content server located in the core network pre-places some files requiring caching in the local cache of the relay stations; users sending request information to the relay stations; and analyzing the transmission rate and successful transmission probability in the heterogeneous wireless network. This disclosure employs a random caching scheme based on SVC under full-duplex relay to improve data transmission rate, achieving better caching efficiency, improving the user's content experience quality, and effectively alleviating network congestion problems.

The application provides a dense heterogeneous network secure communication resource allocation method, device and equipment. The method comprises the following steps: constructing a secrecy rate model based on user link communication rate and eavesdropping link communication rate; modeling the secrecy rate model as a multi-agent Markov decision process, and taking each micro base station as an agent in the multi-agent Markov decision process; updating the policy parameters corresponding to the agent in the multi-agent Markov decision process based on a policy gradient method, to obtain the local policy model parameters corresponding to each micro base station; performing collaborative optimization processing on the local policy model parameters based on a federal learning framework and a macro base station, until a convergence condition is reached; and determining a transmission power allocation strategy according to the local policy model parameters corresponding to the convergence condition. The application realizes efficient, stable and scalable secure communication resource allocation in a dense heterogeneous network by deeply combining distributed learning, federal collaboration and security targets.