Abstract: A centralized cooperative positioning method includes: caching measurements of nodes in multiple time slices up to the current time slice; calculating location information of the nodes in the multiple time slices to obtain trajectory of the nodes in the multiple time slices; performing initial state estimations of the nodes based on measurements of a single time slice; and using the initial state estimations as initial solution values, performing the joint time-space processing on the measurements of the nodes in the multiple time slices based on trajectory constraints, to obtain a state estimation of each node at the current time slice, in which the state estimation includes an estimated value of a location of the node.

Abstract: A multi-user uplink and downlink beam alignment method for asymmetric millimeter wave large-scale MIMO includes constructing an all-digital multi-directional beam for multiple-direction probing; performing multiple rounds of downlink beam training from the base station to the UE; controlling the UE to perform a beam decision according to the receiving signals, and determining a target downlink sending-receiving beam pair; performing data processing on the receiving signals to generate training data for predicting an uplink sending narrow beam, and performing training on a preset neural network; performing online real-time signal detection based on the trained network parameters and the receiving signals to predict a target uplink sending narrow beam; and controlling the UE to feedback an index of a target downlink sending narrow beam to the base station, and widening the target downlink sending narrow beam into a target uplink receiving beam according to the feedback index.

Abstract: A multi-user uplink and downlink beam alignment method for asymmetric millimeter wave large-scale MIMO includes constructing an all-digital multi-directional beam for multiple-direction probing; performing multiple rounds of downlink beam training from the base station to the UE; controlling the UE to perform a beam decision according to the receiving signals, and determining a target downlink sending-receiving beam pair; performing data processing on the receiving signals to generate training data for predicting an uplink sending narrow beam, and performing training on a preset neural network; performing online real-time signal detection based on the trained network parameters and the receiving signals to predict a target uplink sending narrow beam; and controlling the UE to feedback an index of a target downlink sending narrow beam to the base station, and widening the target downlink sending narrow beam into a target uplink receiving beam according to the feedback index.

Abstract: A channel decoding method includes constructing a maximum likelihood decoding problem including an objective function and a parity check constraint; converting the parity check constraint in the maximum likelihood decoding problem into a cascaded form, converting a discrete constraint into a continuous constraint, and adding a penalty term to the objective function to obtain a decoding optimization problem with the penalty term; obtaining ADMM iterations according to a specific form of the penalty term, and obtaining a channel decoder based on the ADMM with the penalty term; constructing a deep learning network according to the ADMM iterations, and converting a penalty coefficient and a coefficient contained in the penalty term into network parameters; training the deep learning network with training data offline and learning the network parameters; and loading the learned network parameters in the channel decoder based on the ADMM with the penalty term, and performing real-time channel decoding.

Abstract: A channel decoding method includes constructing a maximum likelihood decoding problem including an objective function and a parity check constraint; converting the parity check constraint in the maximum likelihood decoding problem into a cascaded form, converting a discrete constraint into a continuous constraint, and adding a penalty term to the objective function to obtain a decoding optimization problem with the penalty term; obtaining ADMM iterations according to a specific form of the penalty term, and obtaining a channel decoder based on the ADMM with the penalty term; constructing a deep learning network according to the ADMM iterations, and converting a penalty coefficient and a coefficient contained in the penalty term into network parameters; training the deep learning network with training data offline and learning the network parameters; and loading the learned network parameters in the channel decoder based on the ADMM with the penalty term, and performing real-time channel decoding.

Abstract: A centralized cooperative positioning method includes: caching measurements of nodes in multiple time slices up to the current time slice; calculating location information of the nodes in the multiple time slices to obtain trajectory of the nodes in the multiple time slices; performing initial state estimations of the nodes based on measurements of a single time slice; and using the initial state estimations as initial solution values, performing the joint time-space processing on the measurements of the nodes in the multiple time slices based on trajectory constraints, to obtain a state estimation of each node at the current time slice, in which the state estimation includes an estimated value of a location of the node.

Abstract: The present disclosure provides a method and an electronic device for obtaining location information. Available measurement of a target node and multi-hop nodes in multiple consecutive time slices is obtained. A prediction is performed on the unavailable measurement due to the transmission delay to obtain prediction information. Location information of these nodes in the multiple time slices is obtained based on state variables of the target node and multi-hop nodes in the current time slice and mobility constraints, for reconstructing the trajectory information of these nodes. State information of these nodes in the current time slice is initially estimated based on available measurement to obtain initial state estimation in the current time slice. State estimation of these nodes in the current time slice is obtained by jointly processing the available measurement and the prediction information based on the trajectory constraints and the initial state estimation.

Abstract: The present disclosure provides a method and an electronic device for obtaining location information. Available measurement of a target node and multi-hop nodes in multiple consecutive time slices is obtained. A prediction is performed on the unavailable measurement due to the transmission delay to obtain prediction information. Location information of these nodes in the multiple time slices is obtained based on state variables of the target node and multi-hop nodes in the current time slice and mobility constraints, for reconstructing the trajectory information of these nodes. State information of these nodes in the current time slice is initially estimated based on available measurement to obtain initial state estimation in the current time slice. State estimation of these nodes in the current time slice is obtained by jointly processing the available measurement and the prediction information based on the trajectory constraints and the initial state estimation.