SOUTHEAST UNIVERSITY (20240259121). PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK simplified abstract
Contents
- 1 PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Predictive Channel Modeling
- 1.13 Original Abstract Submitted
PREDICTIVE CHANNEL MODELING METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK AND LONG SHORT-TERM MEMORY ARTIFICIAL NEURAL NETWORK
Organization Name
Inventor(s)
Chengxiang Wang 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.
