Abstract: This disclosure generally relates to wireless communication networks and is particularly directed to cell coverage and capacity optimization (CCO) in a radio access network in the context of Self-Optimization Network (SON) based on artificial intelligence predictions. For example, radio access network nodes may be configured to assist one another in performing predictive cell CCO for a future time. For cell coverage optimization, such predictive cell optimization may be performed at one radio access network node based on predicted coverage modification information items provided by another radio access network node. The predicted coverage modification information items may be obtain based on network coverage prediction based on artificial intelligence using current and historical network condition, status, and measurement data of current and neighboring cells. As an example, such cell coverage modification may include predictive beam reconfigurations.

Abstract: A wireless communication method includes obtaining input information, including at least one of: slice measurement result information, one or more first pieces of information, one or more second pieces of information, or inference feedback information and using the input information to perform at least one of model training of a slice radio resource management model or generating an inference output with the slice radio resource management model.

Abstract: Provided are a non-public network measurement method and apparatus, a device, and a storage medium. The method includes: sending, through an air interface, a radio resource control (RRC) message including a non-public network (NPN) measurement configuration to at least one selected user equipment (UE), to instruct the UE to perform corresponding NPN measurement; and receiving a measurement report of the NPN measurement from the UE through an air interface. The measurement report includes an NPN measurement result of a corresponding UE. In the embodiment of the present disclosure, the NPN measurement configuration is sent through the RRC message, and thus NPN network information is detected, thereby facilitating NPN network capacity expansion and NPN network optimization.

Abstract: An LDMOS transistor can include: an intrinsic region and a termination region distributed along, where the intrinsic region includes a semiconductor region of a first doped type, a body region of a second doped type extending from the upper surface of the semiconductor region into the interior of the semiconductor region, a drift region located in the semiconductor region, the drift region including at least one first implant region of the first doped type and at least one second implant region of the second doped type, where the at least one second implant region is separated from the body region, a source region of the first doped type located in the body region, and a drain region of the first doped type located in the semiconductor region, where the termination region includes a third implant region of the second doped type being in contact with the second implant region.

Abstract: A hardware encryption module, a chip and an encryption method. The hardware encryption module comprising a control unit and a storage unit; the control unit is connected to the storage unit, receives external instructions, determines an encryption status based on an encryption indicator in an encryption bit address of the storage unit, generates a first control timing sequence for controlling erase of the storage unit based on an external erase instruction, and generates a second control timing sequence for controlling reading/writing of the storage unit based on the encryption status and an external read/write instruction; when in an encrypted mode, only the program area is encrypted against reading, and writing operations to the encryption bit address are prohibited. All encryption behaviors of the present disclosure are effective only for the program area and are not effective for the data area.

Abstract: This disclosure is generally directed to wireless communication systems and methods and relates particularly to a mechanism for implementing an artificial intelligence framework for adaptively configure the over-the-air communication interfaces of the wireless communication systems. AI network configuration functions may be provided as services for AI-assisted network configuration (AI configuration services, or AICS). Such AICS may be requested and configured via various messaging and signaling mechanisms. The AI model life cycle management including training, delivery, activation, inference, deactivation, switching, performance evaluation, and the like may be configured, triggered, and otherwise provisioned via control and data messages and signaling communicated between the various network elements in the wireless communication system.

Abstract: Presented are systems and methods for predicting quality of experience of user equipment (UEs) using an artificial intelligence (AI) model. A first network node of a radio access network (RAN) may receive first assistance information for use with a first quality of experience (QoE) information to perform a first function of a neural network model from a second network node of the RAN. The first network node of the RAN may perform the first function using the first QoE information and the first assistance information.

Abstract: The present application provides a network optimization method, including: sending a session setup request message to a client device to request the client device to perform measurement configuration on a designated network side device and perform measurement configuration on a terminal device connected to the designated network side device; receiving a measurement report message of the designated network side device and a measurement report message of the terminal device; determining, according to pre-acquired machine learning description information, whether to perform collaborative training with the client device; and performing designated model training processing for network optimization based on whether to perform collaborative training with the client device and measurement data in the received measurement report messages, and sending a model training processing result to the client device to instruct the client device to obtain a network optimization action according to the model training processing r

Abstract: A method for supplementary doping of monocrystalline silicon by lowering a device comprising a containing member provided with first and second through-holes are provided. The device includes a hoisting member, a connecting member, and a containing member which are arranged in sequence from top to bottom in a vertical direction, wherein the hoisting member is connected with one end of the connecting member, and another end of the connecting member is movably connected with the containing member, so that the containing member is rotatable with respect to the connecting member. The containing member is provided with a first through-hole and a second through-hole, and the connecting member and the containing member are connected at a joint. A distance between the first through-hole and the joint is less than a distance between the second through-hole and the joint in the vertical direction.

Abstract: Provided are a location prediction method and apparatus, a node and a storage medium. The method includes: a server obtaining a measurement report message sent by a base station, where the measurement report message includes historical location measurement information reported by at least one UE and/or historical location measurement information of the at least one UE measured by the base station; and the server determining prediction location information of the at least one UE at a first time point or in a first time period according to the measurement report message, and sending notification information to the base station according to the prediction location information. By introducing measurement information in multiple dimensions, the accuracy of predicting the location of UE can be effectively improved.

Abstract: This disclosure describes methods and systems for exchanging AI computing information for load prediction model between network elements of a wireless communication network. The methods include: sending, by a first network element of a wireless communication network, a first message for load prediction to a second network element of the wireless communication network, wherein the first message comprises at least one of an input to a machine learning model for load prediction of the first network element or model configuration information of the machine learning model.

Abstract: An electronic cigarette structure that can be quickly disassembled and assembled is provided, which includes a cigarette holder module, a battery cell module, and a host module. The cigarette holder module is provided with the battery cell module and the host module; the battery cell module includes a battery cell upper cover, a stainless-steel shell, a soft pack battery cell, a charging board, a second Type-C female terminal, and a battery cell lower cover. The host module includes a bracket, a magnet, a conductive pin, a first Type-C male terminal, a scheme board, a first Type-C female terminal, a base, and a button. The electronic cigarette achieves rapid disassembly and assembly through a magnetic suction structure and Type-C interface insertion and extraction structure. On the basis of one-time use, it realizes functions of recycling and disassembly, extends the service life of the product, and reduces environmental pollution.

Abstract: This document generally relates to wireless communication that includes a location management function (LMF) and at least one radio access network (RAN) node that perform an interaction associated with at least one sidelink positioning reference signal (SL-PRS) configuration. The LMF or the at least one RAN node transmits a plurality of non-overlapping SL-PRS configurations to a plurality of user devices based on the interaction.

Abstract: Systems, methods, and apparatus for wireless communications are described. A wireless communication method includes transmitting, by a network node, a request for input data. The method further includes receiving, by the network node and in response to the request, input data. The described techniques may be adopted by a network device or by a wireless device.

Abstract: An effect video generating method, an effect video generating apparatus, an electronic device, and a storage medium are provided by embodiments of the present disclosure. The method includes: obtaining a target uploaded image in response to an effect trigger operation; monitoring a contact motion track of a target contact on a display interface, and processing the target uploaded image based on the contact motion track to determine an effect superimposed image; and taking the effect superimposed image as a foreground image and taking a collected original image as a background image, so as to determine a target effect video.

Abstract: Disclosed are a network optimization method and apparatus, an electronic device, and a storage medium. The network optimization method may include: sending data analytics request information to a core network; receiving data analytics response information fed back by the core network and determining a model processing result according to an artificial intelligence learning model; and performing a network optimization operation according to the model processing result.

Abstract: An effect video determining method, an effect video determining apparatus, an electronic device, and a storage medium are provided by embodiments of the present disclosure. The method includes: obtaining an uploaded image in response to an effect trigger operation; determining, according to position information of a capturing device, a target perspective image from a 3D image surrounding scenario corresponding to the uploaded image; and generating and displaying an effect video frame based on the target perspective image and a target object, until receiving an operation to stop capturing an effect video.