64 results about "Wireless mobile communications" patented technology

The invention discloses a real-time wireless personnel identity recognition system constructed based on an array antenna and beam forming, and belongs to the technical field of big data processing. The real-time wireless personnel identity recognition system constructed based on the array antenna and the beam forming comprises the array antenna, a wireless signal adsorption device, a wireless signal processing transmission system and an application system which are electrically connected in sequence, and the array antenna can realize the beam forming technology. The wireless signal adsorption device strongly adsorbs wireless mobile phone communication signals within a certain range through a high-power or preset high-priority networking mode. The technical scheme provides a system which is irrelevant to media, wireless, integrated with multiple communication frequency bands, integrated with software and hardware and capable of identifying personal and crowd identities in real time.

The present disclosure describes a novel method of using the pre-configured AI / ML (artificial intelligence / machine learning) with grouping-based ML element data sharing in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for frequent signaling exchanges with varying ML element information (e.g., model, dataset, ML configuration parameter, etc.). Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring groups of ML element data sharing for ML operation.

The present disclosure describes methods for multi-resolution model identification signaling by configuring multiple subsets consisting of models in a wireless communication system by using the pre-configured AI / ML (artificial intelligence / machine learning) based multiple subsets of models in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for assessment / monitoring of all identified models at UE side. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by using multi-resolution model identification signaling for ML operation.

The present disclosure describes methods of using sensing mode based AI / ML (artificial intelligence / machine learning) model operation in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, model performance can be significantly impacted due to the collected sensing data as input to model operation. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be adaptively set up between network and UE by using sensing modes.

A method of preconfiguring AI / ML operation when multiple connection links are enabled in a wireless mobile communication system including a base station (e.g., gNB) and a mobile station (e.g., UE). When the AI / ML model is applied to a radio access network, model performance, such as reasoning and / or training, depends on different model execution environments with different configuration parameters. Thus, by reconfiguring any model in operation using multiple connection links, signaling overhead due to various LCM model operations can be reduced, and model performance can be enhanced.

A method and apparatus for configuring various gaps for a terminal is provided. The method includes receiving from a base station a SystemInformationBlock1, receiving a RRCReconfiguration, configuring by the terminal at least a gap, the gap is configured based on the first gap configuration information. If the gap starts at a first subframe, the first subframe is determined based on the first gap configuration information and if the gap is configured based on the second gap configuration information, the gap starts at a first slot, the first slot is determined based on the tdd-UL-downlink-ConfigurationCommon, the first slot is the first static uplink slot from a second subframe, the second subframe is determined based on the second gap configuration information.

1. Name of the designed product: mobile communication device (MYXU3CHA series). 2. Use of the designed product: the designed product is used for wireless mobile communication device. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

This invention discloses a method and system for reducing the power consumption of communication equipment, relating to the field of wireless mobile communication technology. Addressing the problem of energy waste in current inter-node communication processes, the proposed solution includes: a transmitting node predicting the transmission characteristics of upper-layer data and sending the prediction result as valid data carrier information to a receiving node; after obtaining the carrier information, the receiving node, based on the prediction result in the information, enables or disables the detection and processing of signals in its receiving circuit, thereby reducing the power consumption of the receiving node during time slots without data transmission. This invention reduces the power consumption of wireless communication equipment, which not only extends the equipment's operating time but also reduces system heat dissipation, decreases the size of power supply equipment, and improves the user experience.

The invention discloses a low-dielectric low-loss novel spinel microwave dielectric material and a preparation method thereof, and belongs to the technical field of new materials, the chemical formula of the material is Mg3Al2Ti1-xSnxO8, 0 < = x < = 1, and the preparation method comprises the steps of material weighing, primary ball milling, pre-sintering, secondary ball milling, granulation and sample preparation, segmented heating and sintering and the like. According to the invention, Al, Ti and other cheap elements are used for replacing Ga, Ge and other expensive elements, so that the preparation cost is obviously reduced; the low-dielectric low-loss Mg3Al2Ti (1-x) SnxO8 microwave ceramic is prepared, the Q * f value of the low-dielectric low-loss Mg3Al2Ti (1-x) SnxO8 microwave ceramic can reach 170400 Ghz, epsilon r ranges from 8.1 to 10.6, and tauf ranges from-60 ppm / DEG C to-50 ppm / DEG C; the large Q * f value can reduce the signal transmission loss and reduce the signal crosstalk risk, and can be widely applied to the field of new-generation wireless mobile communication.

The invention belongs to the technical field of electronic ceramic components and materials for wireless mobile communication and radio frequency electronic circuit systems, and particularly relates to a microwave dielectric ceramic material and a preparation method and application thereof. The prepared microwave dielectric ceramic material is excellent in comprehensive dielectric property, compact and uniform in structure, wide in preparation process temperature window and good in stability, an antenna prepared from the microwave dielectric ceramic material is excellent in performance in a target frequency band, the raw materials are easy to obtain, the cost is controllable, and the microwave dielectric ceramic material has good application prospects.

The invention provides a reference signal parameter standardization configuration method and a network equipment mutual detection method, and relates to the technical field of wireless mobile communication, in particular to the technical field of atmospheric waveguide interference and detection. The method comprises the following steps: firstly, based on acquired setting parameters of remote interference management reference signals (RIM-RS) of different manufacturers and network equipment mutual inspection requirements among the different manufacturers, determining to-be-standardized parameters of the RIM-RS and protocol versions and protocol constraints used for standardization configuration, and then, based on the mutual inspection requirements, the protocol versions and the protocol constraints, determining the to-be-standardized parameters of the RIM-RS and the protocol versions and the protocol constraints used for standardization configuration. And generating configuration parameters corresponding to the to-be-standardized parameters, and finally sending the configuration parameters to network devices of different manufacturers. Therefore, the parameters of the RIM-RS are subjected to standardized and unified configuration through mutual detection requirements of network equipment of different manufacturers and protocol versions and protocol constraints, so that the network equipment of different manufacturers can be ensured to accurately identify and detect mutual signals, and conditions are provided for realizing accurate and reliable detection and identification among the network equipment of cross manufacturers.

The application discloses a service flow transmission method, system and device, relates to the field of wireless mobile communication, and is applied to a first device. The method comprises the following steps: receiving service attribute information of a QoS flow from a third device; sending wireless air interface mapping configuration information to a second device based on the service attribute information of the QoS flow; and sending a marked data packet to the second device. Through the method, the QoS is subjected to differential transmission, so that the transmission efficiency and rationality are improved.

The present disclosure describes a novel method of using the pre-configured AI / ML (artificial intelligence / machine learning) with model type classification in wireless mobile communication system including base station e.g., gNB, TRP, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for frequent signaling exchanges with varying ML element information e.g., model, dataset, ML configuration parameter, etc. Therefore, model operation e.g., model training, inferencing, monitoring, updating, etc. can be set up between network and UE by configuring multiple model identification types for ML operation.

The application relates to the technical field of wireless mobile communication networks, and discloses a wireless communication network fault diagnosis method based on a generative adversarial network, which comprises the following steps: inputting collected network fault real data; performing data processing and extracting key performance indicators (KPI) of equipment; designing an NS-DCGAN, inputting the KPI of the equipment into the NS-DCGAN, and generating virtual fault data under different network states; supplementing external environment characteristic data to the virtual fault data by using a clustering algorithm; combining the virtual data set and the real data set, and constructing a wireless communication network fault sample data set D; dividing the data set D into an internal equipment indicator part D1 and an external environment characteristic part D2; designing an EAFD-GhostNet, inputting D1 and D2 into the EAFD-GhostNet; performing wireless communication network fault classification diagnosis, and outputting a diagnosis result. The application can solve the problems of insufficient fault samples and uneven data distribution, can realize short-time and high-efficiency multi-dimensional diagnosis and analysis of faults, and can effectively improve the accuracy of fault classification diagnosis.

A method of pre-configuring AI / ML operations when enabling multi-connectivity links in a wireless mobile communication system including base stations (e.g., gNBs) and mobile stations (e.g., UEs) is presented. If AI / ML models are applied to the radio access network, model performance such as inference and / or training depends on different model execution environments between the network side and the UE side. Therefore, by offloading model operations from the network side to a relay UE, potential performance impact due to out-of-coverage locations can be reduced with enhanced model performance.

The present disclosure describes a novel method of using the pre-configured transmission data types with semantic and non-semantic data in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). Existing systems often lack the flexibility to handle different types of data (semantic and non-semantic) efficiently. With semantic data transmission applied to radio access network, it is necessary to adapt its (re-)transmission strategies based on the actual needs of the receiving application. Therefore, semantic data operation can be set up between network and UE by providing a unified approach that accommodates both semantic and non-semantic data types.

A method and apparatus for paging is provided. Method of terminal for paging includes receiving SystemInformationBlock1, determining the paging frame based at least in part on the first T. determining the index of paging occasion based at least in part on the second T and monitoring paging occasion per DRX cycle.

This disclosure describes methods and apparatus for UE to perform flexible model update based on cluster based RRM prediction for wireless mobile communication system, comprising the steps of performing beam sweeping and measuring raw beam quality; constructing AI model input and label at training stage; performing cluster based RRM prediction with the trained model at prediction stage; performing measurement report procedures based on predicted results; updating model when UE moving out of range of cluster.

This invention discloses a method for optimizing user latency fairness in a transverse diagonal RIS and edge computing system, relating to the field of wireless mobile communication technology. The method includes: establishing an objective function based on the maximum latency of the UE device and building a network model based on a Markov decision process; after initialization, using a sliding window algorithm to establish a state sequence containing L time slot states; after obtaining the state features of the hidden states in the state sequence, combining a learning strategy and exploring random noise sequences to generate an action vector for the current time slot; after executing the action vector, generating an immediate reward and obtaining the state sequence of the next time slot; combining the current state sequence, action vector, immediate reward, and next state sequence into a training sample and storing it; sampling the training sample and injecting it into the network model for iterative training until a preset number of iterations is reached or the iteration converges. This invention can effectively handle high-dimensional non-convex constraints under dual dynamic channels and achieve multi-device latency fairness optimization.

The present disclosure describes a method for model adaptation / handover for an AI / ML-based model in a wireless mobile communication system including a base station (e.g., a gNB) and a mobile station (e.g., a UE). Threshold-based mapping relationships are configured to enable model adaptation using trigger information in accordance with dynamic changes in the device-side model operating environment.

The present disclosure describes method of model data format configuration by configuring a list of multiple ML identifier data format types for ML operation related signaling of using the pre-configured AI / ML (artificial intelligence / machine learning) based model identification with ML identifiers in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for varying types of associated IDs along with model identification. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by using a list of multiple ML identifier format types for ML operation.

Methods and devices are provided in a wireless communication system. A first radio resource control (RRC) connection release message is received that includes a resume identity. A random access preamble is transmitted. A random access response including timing alignment information and an uplink grant is received. A medium access control (MAC) protocol data unit (PDU) including the uplink data and an RRC connection resume request message is transmitted. The RRC connection resume request includes the resume identity. An RRC message is received. The random access preamble is selected from a preamble group for transmitting the uplink data associated with a data radio bearer (DRB).

The invention relates to the technical field of internet search service and wireless mobile communication short message service, and discloses a wide-area cooperative interaction control method based on a short message bearing channel, which comprises the following steps of: identifying an aggregation keyword and an environment reference variable in a cooperative interaction demand, sending a short message search request to obtain a function feature matrix and a gradient operator coefficient; the controlled terminal analyzes the short message load extraction matrix and the coefficient, and calculates a local execution gain and a time correction factor in combination with the local feature parameter, the environment variable quantity and the timestamp deviation; according to the method, through parameterization matrix distribution, a single search response spontaneously evolves into a differentiated coordination action sequence on the terminal side, the positive correlation between system scale increase and gateway bearing pressure is eliminated, and the information bearing density in a limited channel is improved.

The application discloses a broadband multi-mode planar directive antenna design method and belongs to the field of antennas and microwave technologies. The antenna is composed of a main vibrator, a reflector, tuning branches, a feeding cable and a balance branch. The main vibrator is in a fan shape structure, the length of the circular arc of which is about one wavelength corresponding to a center frequency, and a pair of tuning branches are arranged on the outer periphery of the main vibrator; the reflector is in a ring shape structure, the length of the outer circular arc of which is about 1.5 times of the wavelength corresponding to the center frequency, and the available mode of the reflector can be determined through an eigen equation. The main vibrator and the reflector are attached to the same plane of a dielectric substrate, the feeding cable and the analog cable are attached to the main vibrator and the reflector, and the balance branch connects the outer conductors of the feeding cable and the analog cable to suppress the unbalanced current on the cable. The application has the characteristics of wide bandwidth, simple structure and coplanar easy integration, and has a wide application prospect in the fields of phased array, radar system, vehicle-mounted system and other wireless mobile communication fields.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model transfer type information in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for model transfer / delivery. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by using the pre-configured type of model transfer for ML operation.

The present application describes a method for data-driven AI / ML model signaling for pairable model search signaling based on AI / ML, wherein techniques are disclosed for pre-configuration and signaling with respect to model pairing searches in machine learning model operations in wireless mobile communication systems, including base stations (e.g., gNBs) and mobile stations (e.g., UEs). A minimum set of model information is provided for matching by searching for pairable models through a model pairing search process.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) model identification with temporary / dedicated model ID in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, ML model operation can be impacted with model performance degradation in association with the matched model ID information. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by using adaptive assignment of model ID.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) combination mode with the sensing-enabled model operation in wireless mobile communication system including base station e.g., gNB, TRP, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, model performance is significantly impacted due to sensing-based data collection. Therefore, model operation e.g., model training / inferencing / monitoring / updating is adaptively set up between network and UE by using combination mode of sensing and model operation.

A method for in-device coexistence information reporting is provided. The method includes receiving from a base station a first Radio Resource Control (RRC) message, transmitting a second RRC message, the second RRC message includes an in-device coexistence information, receiving a third RRC message, and applying a configuration based on the third RRC message. A first in-device coexistence information is included in the second RRC message if the first RRC message includes a first information, the first in-device coexistence information includes information related to frequency domain, the first information configures the terminal to report in-device coexistence information related to frequency domain. A second in-device coexistence information is included in the second RRC message if the first RRC message includes a second information, the second in-device coexistence information includes information related to time pattern, the second information configures the terminal to report in-device coexistence information related to time pattern.