Abstract:
The present invention discloses a sequence generation method and a base station. The method includes: determining, by a terminal, at least one first sequence according to at least one candidate sequence, where a length of the candidate sequence is less than a sequence length corresponding to a maximum available system bandwidth; and connecting, by the terminal, the at least one first sequence to generate a second sequence or directly using the at least one first sequence as a second sequence, and receiving a signal according to the second sequence, where the second sequence is at least one of a reference signal sequence or a scrambling code sequence.
Abstract:
Embodiments of the disclosure provide a power control method, a base station, and user equipment. The method includes: determining, by the base station, a transmit power control parameter, where the power control parameter is used to determine maximum transmit power of the user equipment; sending, by the base station, the transmit power control parameter to the user equipment; and instructing, by the base station, the user equipment to use the maximum transmit power of the user equipment to send a device-to-device (D2D) signal, so that the user equipment can quickly acquire the maximum transmit power when performing D2D communication in a mobile telecommunication system (such as an LTE system).
Abstract:
This application provides a model training method and apparatus. The method includes: A first processing node obtains at least one first model; the first processing node processes the at least one first model to generate a first common model; and the first processing node determines a second processing node, where the second processing node is a processing node for a next round of model processing, and the first common model is obtained by the second processing node before the next round of model processing. In technical solutions provided in this application, before the next round of model processing, a processing node for the next round of model processing may be determined based on an actual requirement, to adapt to a change of an application scenario.
Abstract:
This application relates to an address allocation method. The method includes: receiving a first message sent by a node that applies for an address, where the first message includes a first type identifier, and the first type identifier indicates a first address type; sending a second message to the node, where the second message includes a first network address allocated to the first node or a service on the node, and an address type of the first network address is a first address type. This application can be used to implement allocation of a network address of a specified address type, and improve network address allocation efficiency.
Abstract:
This application relates to the field of communication technologies, and provides an AI service information obtaining method, an apparatus, and a system. In the method, a first AI node sends measurement configuration information to the terminal device, where the measurement configuration information indicates to measure an AI service of a to-be-measured AI node; and the first AI node receives a measurement report from the terminal device, where the measurement report includes candidate AI service information, the candidate AI service information is AI service information that meets a first condition and that is in one or more pieces of AI service information obtained by measuring the to-be-measured AI node based on the measurement configuration information, and the first condition is carried in the measurement configuration information.
Abstract:
Embodiments of this application belong to the field of communication technologies. In the method, a first node may send first measurement configuration information to a second node, where the first measurement configuration information indicates to measure synchronization information of a third node on a first channel. Further, the second node may obtain the synchronization information, and the first node receives the synchronization information from the second node, where the synchronization information may indicate one or more of the following information of the third node: time synchronization information, frequency synchronization information, synchronization communication domain set information, RSRP, RSRQ, SINR, or RSSI. In this way, time-frequency synchronization between different nodes in a communication system can be implemented, interference between the different nodes can be effectively reduced, and system communication performance can be improved.
Abstract:
An information transmission method and apparatus are provided, to reduce feedback overheads of a master node to a slave node. The information transmission method and apparatus are applied to intelligent driving or assisted driving. The method in embodiments of this application includes: each of a plurality of second nodes sends a first message to a first node. Each second node may send at least one first message, and there are N first messages in total. After receiving the N first messages, the first node may send, in multicast mode on a first resource, a second message that includes N pieces of first feedback information that are in a one-to-one correspondence with the N first messages. The method in this application may be applied to the internet of vehicles, for example, vehicle-to-everything V2X, long term evolution-vehicle LTE-V, or vehicle-to-vehicle V2V.
Abstract:
A first machine learning model is deployed in a first communication apparatus, and a second machine learning model is deployed in a second communication apparatus. First information is obtained that carries indication information of both a first transmission resource and a second transmission resource, wherein the first transmission resource is for the first communication apparatus to transmit a first output of the first machine learning model to the second communication apparatus, and wherein the second transmission resource is for the first communication apparatus to receive first feedback data that is from the second communication apparatus. The first feedback data includes a first gradient, wherein the first gradient is for updating the first machine learning model. The first communication apparatus transmits the first output to the second communication apparatus on the first transmission resource.
Abstract:
A communication method and apparatus are provided. A modulation and coding scheme is determined based on a first norm of a first communication apparatus, and the first communication apparatus uploads information about a first learning model based on the modulation and coding scheme. The first norm of the first communication apparatus represents an importance degree of a local dataset of the first communication apparatus. The modulation and coding scheme used in a process in which a local training result is uploaded is determined based on the first norm, so that efficiency of federated learning can be improved.
Abstract:
Example data transmission methods and devices are described. In one example method, data and attribute information of the data are received at a transmitting end from at least one data source. The original data is segmented based on the attribute information to obtain a plurality of data sections. A respective check code for each of the plurality of data sections is calculated based on each of the plurality of data sections. A plurality of coded blocks are sent. The plurality of coded blocks are received at a receiving end. A check is performed on data sections included in the plurality of coded blocks by using check codes included in the plurality of coded blocks. At least a data section that is successful in the check is provided for a user.