View source for SOUTHEAST UNIVERSITY (20240259121). PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK simplified abstract

=PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK=


==Organization Name==

[[:Category:SOUTHEAST UNIVERSITY|SOUTHEAST UNIVERSITY]]


[[Category:SOUTHEAST UNIVERSITY]]

==Inventor(s)==

[[:Category:Chengxiang Wang of Jiangsu (CN)|Chengxiang Wang of Jiangsu (CN)]][[Category:Chengxiang Wang of Jiangsu (CN)]]

[[:Category:Zheao Li of Jiangsu (CN)|Zheao Li of Jiangsu (CN)]][[Category:Zheao Li of Jiangsu (CN)]]

[[:Category:Jie Huang of Jiangsu (CN)|Jie Huang of Jiangsu (CN)]][[Category:Jie Huang of Jiangsu (CN)]]

[[:Category:Wenqi Zhou of Jiangsu (CN)|Wenqi Zhou of Jiangsu (CN)]][[Category:Wenqi Zhou of Jiangsu (CN)]]

[[:Category:Chen Huang of Jiangsu (CN)|Chen Huang of Jiangsu (CN)]][[Category:Chen Huang of Jiangsu (CN)]]

==PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract==


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

==Simplified Explanation==
The patent application describes a method for predictive channel modeling using a generative adversarial network and a long short-term memory artificial neural network. This method can accurately predict channel data in various frequency bands and scenarios, generating large datasets for simulation experiments.

* Input existing channel measurement data for training.
* Learn true channel data using a long short-term memory artificial neural network to acquire channel time sequence features.
* Utilize a generative adversarial network for adversarial learning to eliminate redundant information and generate accurate channel data.
* Achieve a balance between generative and discriminative models during continuous iteration to output a trained predictive channel model.
* Obtain statistical channel features for predictive learning of channel distribution features.

==Key Features and Innovation==
* Predictive channel modeling using a generative adversarial network and a long short-term memory artificial neural network.
* Accurate channel data prediction in different frequency bands and scenarios.
* Generation of large channel datasets for simulation experiments.
* Adversarial learning to eliminate redundant information and improve accuracy.
* Balance between generative and discriminative models for effective channel prediction.

==Potential Applications==
The technology can be applied in:
* Wireless communication systems
* IoT networks
* Radar systems
* Satellite communication

==Problems Solved==
* Real-time and complex prediction problems in wireless communication.
* Accurate channel data prediction in different scenarios.
* Efficient generation of channel datasets for simulation experiments.

==Benefits==
* Improved accuracy in channel data prediction.
* Enhanced performance in wireless communication systems.
* Efficient simulation experiments for testing channel models.

==Commercial Applications==
Predictive channel modeling technology can be utilized in:
* Telecommunication companies for optimizing network performance.
* Defense industry for radar and satellite communication systems.
* Research institutions for developing advanced wireless communication technologies.

==Prior Art==
Readers can explore prior research on generative adversarial networks, long short-term memory artificial neural networks, and channel modeling techniques in wireless communication systems.

==Frequently Updated Research==
Stay updated on the latest advancements in predictive channel modeling, generative adversarial networks, and artificial neural networks for wireless communication systems.

==Questions about Predictive Channel Modeling==
===What are the potential limitations of using generative adversarial networks for channel prediction?===
Generative adversarial networks may face challenges in handling complex channel environments and large datasets, requiring further optimization for real-world applications.

===How can the predictive channel modeling method be adapted for different wireless communication standards and protocols?===
The method can be customized by adjusting input parameters and training data to align with specific wireless communication standards and protocols, ensuring accurate predictions in diverse scenarios.


==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.



[[Category:H04B17/391]]
[[Category:H04W24/06]]
[[Category:CPC_H04B17/3913]]