131 results about "Wireless mobile communications" patented technology

A system of security that prevents misuse of identity data of an identity data owner in an identity data driven transaction in a global commerce network, that has in the system, a transaction processing entity that after it receives a identity data driven transaction from a transaction initiating entity, puts on hold the processing of the transaction for a period of time and via the identity data owner's wireless mobile communication device, contacts the identity data owner for authorization of the transaction before the transaction processing is completed. The system of security for identity data may be used in the identity data driven transaction is one from a group of (i) credit card payment, (ii) bank account payment, (iii) release of credit profile, (iv) release of financial data, and (v) release of medical data.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model training modes 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 training performance can be degraded depending on UE ML condition or capability changes. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring model training capability elements.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model training in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested based on different ML conditions. Therefore, model operation (e.g., model re-training / updating) can be set up between network and UE by identifying the most applicable selection of parameter combinations.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based operation modes with ML functionality 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 operation using model activation for LCM phases can be degraded due to ML conditions. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring applicable operation modes with ML functionality.

The application describes methods of using the pre-configured AI / ML artificial intelligence / machine learning based ML clustering 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 of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring ML clustering.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model operation modes 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 of model information exchange can be heavily congested. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring mapping relation look-up table.

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 application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based multi-model combinations 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 of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by configuring multi-model combinations in association with UE RRC states.

The present disclosure describes method of advanced condition-based model signaling by of configuring a finite set of multiple model versions for mapping relation using the pre-configured AI / ML (artificial intelligence / machine learning) based model version selection in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model condition information exchange can be heavily congested. Therefore, model operation (e.g., model re-training / updating) can be set up between network and UE by configuring model version selection.

The present embodiments relate to a method for a terminal to perform a random access procedure, the method comprising the steps of: receiving random access channel (RACH) configuration information; determining one RACH occasion (RO) among a plurality of ROs configured on the basis of the RACH configuration information; and transmitting a random access preamble in the determined RO, wherein the RACH configuration information includes first RACH configuration information for configuring at least one first RO and second RACH configuration information for configuring at least one second RO for a subband full duplex (SBFD).

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.

The present embodiments provide a method by which a terminal performs beam failure recovery (BFR), the method comprising the steps of: receiving configuration information related to beam failure detection (BFD) and configuration information related to beam failure recovery; performing a beam failure detection operation on the basis of the configuration information related to the beam failure detection; and, if the beam failure recovery is triggered on the basis of the beam failure detection, performing a beam failure recovery operation on the basis of the configuration information related to the beam failure recovery, wherein, if a subband full duplex (SBFD) symbol for supporting an SBFD operation is configured by a base station, the beam failure detection operation and / or the beam failure recovery operation is performed separately with respect to each of the SBFD symbol and non-SBFD symbol.

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 present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based multi-sided model pairing 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 of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by configuring multi-sided model pairing in association with model ID set.

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 present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model density group 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, single-cell based model can be well-trained and deployed for any specific cell environment (e.g., serving cell). However, single-cell based model cannot be directly applied to any other neighboring cells as ML conditions (e.g., dataset, site, configuration, etc.) can vary across different cells. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring multi-cell based model concatenation.

The present disclosure describes a method of using a pre-configured AI / ML (artificial intelligence / machine learning)-based model list in a wireless mobile communication system that includes a base station (e.g., gNB) and a mobile station (e.g., UE). Model performance, such as reasoning and / or training, when the AI / ML model is applied in a radio access network depends on different model execution environments having different models. Thus, a model operation may be established between the network and the UE by selecting a relevant model from the model list.

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 invention provides a user positioning method and device based on a sparse sampling perception RIS architecture, and relates to the technical field of wireless mobile communication, the sparse sampling perception RIS architecture comprises an RIS array plane composed of a plurality of RIS units, the plurality of target RIS units are sparsely distributed on the RIS array plane, the target RIS units are a preset number of RIS units integrated with power sensors in the plurality of RIS units; the method comprises the following steps: preprocessing a power receiving signal reflected by a target user and received by the target RIS unit to obtain an angle domain signal; inputting the angle domain signal into a pre-trained convolutional neural network to obtain a probability distribution diagram of a direct path interference peak value; and determining the target azimuth information of the target user based on the peak position index of the probability distribution diagram of the direct path interference peak and the azimuth information of the target base station. Through the method provided by the invention, the hardware overhead and power consumption are reduced, and the user positioning precision is improved.

A method and apparatus for uplink transmission in RRC_CONNECTED state and in RRC_INACTIVE state is provided. A method and apparatus for uplink transmission in RRC_CONNECTED state and in RRC_INACTIVE state includes receiving configuration information in RRCRelease, performing a PUSCH transmission in RRC_INACTIVE based on a power offset and a RNTI indicated in the RRCRelease.

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.