Abstract: A low density parity check (LDPC) channel encoding method is used in a wireless communications system. A communication device encodes an input bit sequence by using an LDPC matrix, to obtain an encoded bit sequence for transmission. The LDPC matrix is obtained based on a lifting factor Z and a base matrix. The base matrix may be one of eight exemplary designs. The encoding method can be used in various communications systems including fifth generation (5G) telecommunication systems, and can support various encoding requirements for information bit sequences with different code lengths.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: Embodiments of this application disclose provides a low density parity check (LDPC) channel encoding method for use in a wireless communications system. A communication device encodes an input bit sequence by using a LDPC matrix, to obtain an encoded bit sequence for transmission. The LDPC matrix is obtained based on a lifting factor Z and a base matrix. Embodiments of the application provide eight particular designs of the base matrix. The encoding method provided in the embodiments of the application can be used in various communications systems including the fifth generation (5G) telecommunication systems, and can support various encoding requirements for information bit sequences with different code lengths.

Abstract: A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Abstract: Embodiments provide a data transmission method and a related device. Under the method, after determining a first data packet to be sent to a receive end, a transmit end may generate a second data packet based on the first data packet, encode the second data packet, and send an encoded second data packet. A length of the second data packet is greater than a length of the first data packet. In various embodiments, when sending a data packet to the receive end, the transmit end may generate a longer data packet from a shorter data packet through combination, and send the longer data packet to the receive end after channel coding. In this way, a relatively high channel coding gain can be obtained during data transmission, and a relatively high bit error rate of short packet transmission is avoided, so that data transmission reliability can be improved.

Abstract: This application provides a polar code encoding and decoding method and apparatus and a device. An example method includes: sequentially configuring, by a sending device, information bits and first check bits on subchannels in a first subchannel set, and configuring frozen bits on subchannels in a second subchannel set, where the subchannels in the first subchannel set are sorted according to a natural order of serial numbers of the subchannels; and performing polarization encoding on bits on the subchannels to obtain an encoded sequence. In this way, encoding efficiency and decoding efficiency are improved.

Abstract: The disclosure provides an adaptive inversion method of Internet-of-things environmental parameters based on an RFID multi-feature fusion sensing model, including the following steps. Space-medium-interference is proposed as an overall concept, from the multipath propagation mechanism of electromagnetic waves, the electromagnetic wave transmission mechanism is considered. Combining with the joint characteristics of the generalized time domain, frequency domain, energy domain, and spatial domain, a global signal transfer function of RFID sensing is analyzed and derived to complete extraction of RFID sensing main features.

Abstract: Embodiments of the present invention provide a control information transmission method and apparatus. The control information transmission method includes: determining, by a network device, N pieces of to-be-jointly-coded downlink control information DCI, where N is an integer greater than or equal to 2; coding, by the network device, the N pieces of DCI in a polar coding manner, to obtain one codeword; and mapping, by the network device, the codeword to a time-frequency resource of a downlink control channel for sending. According to the embodiments of the present invention, not only a relatively high coding gain can be obtained, but a relatively low average processing latency on a decoder side can also be obtained.

Abstract: A transformer condition evaluation method combining a FAHP-DEMATEL method and a CRITIC method is provided and includes: selecting a plurality sets of condition quantities, building a hierarchical indicator system; calculating a degree of influencing and a degree of being influenced by using the DEMATEL method on a subjective level, calculating a weight of each of a plurality of indicators of an indicator layer through the FAHP-DEMATEL method, calculating the weights of the indicators of the indicator layer through the CRITIC method on an objective level; calculating an optimal weight according to the subjective weights of the indicators and the objective weights of the indicators; and calculating condition scores layer by layer, finally determining an actual health condition of each of a plurality of transformers by combining condition level classification rules. In the disclosure, the weights are calculated based on the subjective level and the objective level.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A coding method, a wireless device, and a chip are described. A coding method may include obtaining, based on a sequence whose length is 2n?, a mother code sequence used for coding, where the sequence whose length is 2n? includes 2n? sequence numbers, a length of the mother code sequence is 2n, the mother code sequence includes 2n sequence numbers, the 2n sequence numbers are some or all of the 2n? sequence numbers, n? is a natural number, and n is a natural number less than or equal to n?. The method may also include coding an input to-be-coded bit by using the mother code sequence. According to this method, a system may store one or more long sequences, and then obtain a required mother code sequence from one of the long sequences according to a coding requirement, thereby reducing storage overheads of the system.

Abstract: A time-frequency block-sparse channel estimation method based on compressed sensing includes the following steps. Step 1: A channel model is established. Step 2: According to the channel model obtained in Step 1, a sparse signal estimation value is solved by a compressed sensing method to further calculate an index set. Step 3: According to the index set obtained in Step 2, a channel matrix estimation value is solved. The method provides a generalized block adaptive gBAMP algorithm, which uses time-frequency joint block sparsity of a massive MIMO system to further optimize selection of an index set in an algorithm iteration process to improve stability of the algorithm. Then, without a specified threshold parameter, based on an F norm, an adaptive iteration stop condition is determined based on a residual, and the validity of the method is proved.

Abstract: A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Abstract: This application provides a wireless communication method and device. The method includes: obtaining downlink control information DCI and a scrambled sequence that are sent by a network device, where bits corresponding to the scrambled sequence are scrambled with each piece of configuration information of a plurality of pieces of configuration information used for configuring a terminal device by the network device, each piece of configuration information corresponds to at least one bit in the scrambled sequence, and at least one bit of the bits that correspond to each piece of configuration information does not correspond to another piece of configuration information of the plurality of pieces of configuration information; descrambling, based on a possible value of each piece of configuration information, the bits that correspond to each piece of configuration information, to obtain a descrambled sequence; and performing a check operation by using the descrambled sequence and the DCI.

Abstract: A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage.

Abstract: A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

Abstract: A method and a system for selecting important delay taps of channel impulse response are provided, which belong to the field of adaptive transmission technology of wireless communication. The method performs local predictability estimation on all delay taps in the channel impulse response in wireless communication, thereby selecting the important delay taps in the channel impulse response. The disclosure uses recurrence rates of the delay taps to estimate local predictability of the delay taps. Noise delay taps are distinguished from the important delay taps through the local predictability of the delay taps, which improves the identification rate of the important delay taps of the channel impulse response in wireless communication. The disclosure may implement the selection of the important delay taps of the channel impulse response, which provides guarantee for implementing adaptive transmission and adaptive coding of wireless communication.

Abstract: Provided are a time domain channel prediction method and a time domain channel prediction system for an OFDM wireless communication system, which relate to the technical field of adaptive transmission in wireless communication. Frequency domain channel information is converted into time domain tap information by inverse Fourier transform. With respect to each time domain tap information, tap information prediction based on an extreme learning machine is realized, and finally predicted tap information is converted into frequency domain channel information by Fourier transform. To improve a generalization ability of a channel predictor, an output weight of the extreme learning machine is punished by a combination of l2 regularization and l1/2 regularization. The disclosure may provide satisfactory prediction performance and may output a sparse output weight, which reduces the requirement for memory storage. The disclosure ensures adaptive transmission and adaptive coding of wireless communication.

Abstract: A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters.

Abstract: A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.