157 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 utility model discloses an adjustable connecting piece for installation of a wireless mobile communication base station, and relates to the field of wireless mobile communication base stations. The adjustable connecting piece for wireless mobile communication base station installation comprises a wireless communication base station body, a body frame is installed outside the wireless communication base station body, a connecting mechanism is arranged on the outer surface of the body frame, the connecting mechanism comprises an installation plate arranged outside the body frame, and a supporting piece is installed on the back face of the wireless communication base station body. The supporting piece is movably clamped in the mounting plate, and the abutting plate is slidably connected to the outer portion of the main body frame and arranged above the supporting piece. According to the adjustable connecting piece for installing the wireless mobile communication base station, the position of the wireless communication base station body is adjusted by moving the position of the clamping piece, then the position of the clamping piece is fixed through the abutting plate and the installing plate, the using complexity of a connecting structure is reduced, meanwhile, the installing position and number of the wireless communication base station body can be changed at will, and the connecting piece is convenient to use. And the use flexibility of the connecting structure is improved.

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.

A method executed by a terminal, according to an embodiment of the present disclosure, comprises: a step in which a terminal receives RRCReconfiguration from a base station, wherein the RRCReconfiguration includes a first information element or second gap configuration information, wherein the first information element includes first gap configuration information, wherein the first gap configuration information includes first information and second information, and the second gap configuration information includes third information, wherein the first information includes a first gap length, and the second information includes a value indicating a UE gap and one of an FR1 gap and an FR2 gap, and the third information includes a second gap length; and a step in which if the RRCReconfiguration includes the first gap configuration information in the first information element, the terminal sets a first gap, and if the RRCReconfiguration includes the second gap configuration information, the terminal sets a second gap.

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.

A method and apparatus for random access in repetition mode is provided. Method for random access in repetition mode includes receiving first RACH configuration, second RACH configuration and PDCCH configuration, selecting an uplink on which to perform a random access procedure based on the first RSRP threshold in the first RACH configuration, transmitting a preamble in the next available PRACH occasion based on PRACH configuration index, monitoring PDCCH in search space indicated by random access search space during while random access window is running, receiving a random access response message including a preamble identifier related to the preamble transmission and transmitting Msg3 based on the time domain resource allocation field in the uplink grant in the random access response.

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.

The present invention discloses a downlink physical layer channel feedback transmission method based on a wireless map, which relates to the field of wireless mobile communication technology. It includes: S1. Obtain the user location information of user k when moving in the spatial domain, and upload the user location information to the edge cloud; S2. The edge cloud inputs the user location information into the complex precoding network based on the wireless map; S3. The complex precoding network generates the precoding decision w of user k k , and transmits the precoding decision w k to the base station; S4. The base station executes the precoding decision w k for realizing physical layer information transmission. By proposing a downlink transmission mechanism for physical layer channel feedback and designing a complex precoding network based on the wireless map, the present invention effectively improves the transmission efficiency of physical layer information in wireless communication.

The invention discloses a wireless mobile communication network resource scheduling method and system based on a DGQN, and relates to the technical field of communication network resource scheduling, and the method comprises the steps: firstly, mapping a resource scheduling process of a wireless mobile communication network into a Markov decision process; a concavity measurement index and an additive measurement index in the resource scheduling process are processed in a double-path architecture mode, then weighted summation is carried out on q values of the two types of indexes obtained through processing, and the q values of the two types of indexes are synthesized for representation to obtain a comprehensive model q value; and finally, firstly determining a final strategy, and then determining a final routing path of the service flow, thereby realizing resource scheduling of the wireless mobile communication network. Aiming at a wireless mobile communication network with high-frequency change of a topological structure, the invention provides the improved scheme by considering real-time dynamic change of a network environment and differentiated requirements of various service flows in the network on different performance indexes.

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.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model information sharing 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 transfer / delivery can be heavily congested based on multiple models execution with varying applications. Therefore, model operation e.g., model transfer / retraining / updating can be set up between network and UE by reducing the amount of multi-model signaling overhead.

A Method and Apparatus for performing small data transmission is provided. The method includes receiving a RRCRelease, the RRCRelease includes a first information for SDT, the first information for SDT includes a SRB2 (Signaling Radio Bearer2) indicator and a DRB (Data Radio Bearer) list, performing cell selection, receiving a SystemInformationBlock1 in a selected cell, the SystemInformationBlock1 includes a second information for SDT (Small Data Transmission), initiating a SDT based on the first information and the second information, transmitting a Medium Access Control (MAC)) Protocol Data Unit (PDU) including Common Control Channel (CCCH) Service Data Unit (SDU) for initial Physical Uplink Shared Channel (PUSCH) transmission, starting a first timer, monitoring PDCCH for a first identifier based on a first search space or a second search space, and stopping the first timer when a downlink assignment for the first identifier is received via Physical Downlink Control Channel (PDCCH).

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.