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SOUTHEAST UNIVERSITY (20240259121). 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

Organization Name

SOUTHEAST UNIVERSITY

Inventor(s)

Chengxiang Wang of Jiangsu CN

Zheao Li of Jiangsu CN

Jie Huang of Jiangsu CN

Wenqi Zhou of Jiangsu CN

Chen Huang of Jiangsu CN

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.

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