Abstract:
Embodiments of the present invention relate to the field of communications, and provide a network coding method, a relay apparatus and a selection apparatus, which can avoid a case that a network coding system matrix is not full rank, and improve correctness of decoding. The network coding method includes: obtaining network coding information, where the network coding information includes information of a candidate network coding vector set and a candidate transmission rate set, and transmission rates in the candidate transmission rate set are in one-to-one correspondence with network coding vectors in the candidate network coding vector set; selecting a full rank network matrix according to the network coding information; and coding received source node information according to the full rank network matrix. The network coding method, relay apparatus and selection apparatus provided in the embodiments of the present invention are used for network coding.
Abstract:
This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.
Abstract:
This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.
Abstract:
Embodiments of the present invention relate to the field of communications, and provide a network coding method, a relay apparatus and a selection apparatus, which can avoid a case that a network coding system matrix is not full rank, and improve correctness of decoding. The network coding method includes: obtaining network coding information, where the network coding information includes information of a candidate network coding vector set and a candidate transmission rate set, and transmission rates in the candidate transmission rate set are in one-to-one correspondence with network coding vectors in the candidate network coding vector set; selecting a full rank network matrix according to the network coding information; and coding received source node information according to the full rank network matrix. The network coding method, relay apparatus and selection apparatus provided in the embodiments of the present invention are used for network coding.
Abstract:
A method for estimating a block error rate and a communication device are applied to the field of communications technologies. The method for estimating a block error rate includes: decoding N received coded code blocks to obtain multiple posterior probabilities APPs, where N is a natural number greater than 1; obtaining, according to the multiple posterior probabilities APPs and a preset policy, a result indicating that the decoding of each coded code block is correct or incorrect, where the preset policy includes: when a sum of absolute values of the multiple APPs is greater than or equal to a preset threshold, the decoding is correct; and obtaining a decoding block error rate according to a result indicating whether the decoding of the N coded code blocks is correct. In this way, the estimation of a decoding block error rate is implemented.
Abstract:
This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.
Abstract:
A method for estimating a block error rate and a communication device are applied to the field of communications technologies. The method for estimating a block error rate includes: decoding N received coded code blocks to obtain multiple posterior probabilities APPs, where N is a natural number greater than 1; obtaining, according to the multiple posterior probabilities APPs and a preset policy, a result indicating that the decoding of each coded code block is correct or incorrect, where the preset policy includes: when a sum of absolute values of the multiple APPs is greater than or equal to a preset threshold, the decoding is correct; and obtaining a decoding block error rate according to a result indicating whether the decoding of the N coded code blocks is correct. In this way, the estimation of a decoding block error rate is implemented.