SOUTHEAST UNIVERSITY (20240259121). PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
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PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
This abstract first appeared for US patent application 20240259121 titled 'PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
Original Abstract Submitted
disclosed in the present disclosure is a predictive channel modeling method based on a generative adversarial network and a long short-term memory artificial neural network, which method effectively achieves a channel prediction function in different frequency bands and scenarios, and generates a large number of channel data sets for simulation experiments. the method comprises: firstly, inputting channel measurement data for existing frequency bands and scenarios for training; then, learning true channel data using a long short-term memory artificial neural network, and acquiring a channel time sequence feature; by means of adversarial learning of a generative adversarial network, greatly eliminating redundant information of the channel data, and on the basis of the measurement data, generating accurate channel data, and acquiring massive channel information; and finally, achieving the balance between a generative model and a discriminative model during the continuous iteration of the generative adversarial network, and then outputting a trained predictive channel model. a statistical channel feature obtained by means of prediction by a model can clearly specify the predictive learning for a channel distribution feature in the present disclosure, and real-time and complex prediction problems in wireless communication can be solved.