WIRELESS COMMUNICATION METHOD AND APPARATUS

Organization Name

HUAWEI TECHNOLOGIES CO., LTD.

Inventor(s)

Gongzheng Zhang of Hangzhou (CN)

Rong Li of Boulogne Billancourt (FR)

Yunfei Qiao of Hangzhou (CN)

Jian Wang of Hangzhou (CN)

WIRELESS COMMUNICATION METHOD AND APPARATUS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18732350 titled 'WIRELESS COMMUNICATION METHOD AND APPARATUS

Simplified Explanation

This patent application describes a method for wireless communication that involves model ensembling between network devices to improve the performance of a neural network model.

Key Features and Innovation

Terminal device obtains a first model of a network device.
Terminal device receives indication information about a second model from another network device.
Terminal device sends information about an ensemble model combining the first and second models to the second network device.

Potential Applications

This technology can be applied in various wireless communication systems where knowledge transfer between network devices is crucial for enhancing neural network model performance.

Problems Solved

This technology addresses the need for efficient knowledge transfer between network devices to improve the performance of neural network models in wireless communication systems.

Benefits

Enhanced performance of neural network models.
Improved efficiency in wireless communication systems.
Facilitates knowledge transfer between network devices.

Commercial Applications

This technology can be utilized in industries such as telecommunications, IoT, and smart devices to optimize wireless communication systems and enhance neural network model performance.

Prior Art

Readers can explore prior research on model ensembling techniques in wireless communication systems to understand the evolution of this technology.

Frequently Updated Research

Stay updated on the latest advancements in model ensembling techniques for wireless communication systems to leverage cutting-edge innovations in neural network model optimization.

Questions about Wireless Communication

How does model ensembling improve neural network performance in wireless communication systems?

Model ensembling combines the strengths of different network devices to create a more robust and accurate neural network model, leading to enhanced performance in wireless communication systems.

What are the potential challenges in implementing model ensembling between network devices?

Challenges in implementing model ensembling may include compatibility issues between different network devices, data synchronization problems, and ensuring secure communication channels for knowledge transfer.

Original Abstract Submitted

Embodiments of this application provide a wireless communication method and apparatus. The method includes the following: a terminal device obtains a first model of a first network device. The terminal device receives first model indication information from a second network device. The first model indication information indicates a second model of the second network device. The terminal device sends second model indication information to the second network device. The second model indication information indicates an ensemble model of the first model and the second model. According to the methods provided in this application, the terminal device performs model ensembling, to implement knowledge transfer between network devices, thereby improving performance of a neural network model.