17904413. DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION simplified abstract (Apple Inc.)

DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION

Organization Name

Apple Inc.

Inventor(s)

Sigen Ye of San Diego CA (US)

Chunhai Yao of Beijing (CN)

Chunxuan Ye of San Diego CA (US)

Dawei Zhang of Saratoga CA (US)

Huaning Niu of San Jose CA (US)

Oghenekome Oteri of San Diego CA (US)

Seyed Ali Akbar Fakoorian of San Diego CA (US)

Wei Zeng of Saratoga CA (US)

Yushu Zhang of Beijing (CN)

DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17904413 titled 'DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION

The present disclosure discusses the adaptation of DMRS overhead with AI-based channel estimation in a wireless device receiving downlink data transmitted using a DMRS pattern from a network device. The device performs AI-based downlink channel estimation by inputting received downlink DMRS symbols into a neural network model to obtain an estimated downlink channel and an optimal downlink DMRS pattern, which is then reported back to the network device.

• Wireless device receives downlink data with DMRS pattern from network device
• Performs AI-based downlink channel estimation using neural network model
• Inputs received downlink DMRS symbols to obtain estimated downlink channel and optimal DMRS pattern
• Reports optimal DMRS pattern back to network device

Potential Applications: - Improved wireless communication efficiency - Enhanced network performance - Advanced signal processing techniques

Problems Solved: - Efficient adaptation of DMRS overhead - Accurate downlink channel estimation - Optimal DMRS pattern selection

Benefits: - Increased data transmission reliability - Enhanced network capacity - Improved overall network performance

Commercial Applications: Title: AI-Based DMRS Overhead Adaptation for Wireless Communication This technology can be utilized in telecommunications, 5G networks, IoT devices, and other wireless communication systems to optimize channel estimation and improve data transmission efficiency.

Questions about AI-Based DMRS Overhead Adaptation for Wireless Communication: 1. How does AI-based channel estimation improve downlink data transmission?

  - AI-based channel estimation enhances the accuracy of downlink channel estimation by utilizing neural network models to optimize DMRS patterns.

2. What are the potential benefits of using AI in wireless communication systems?

  - AI can improve network performance, increase data transmission efficiency, and enhance overall reliability in wireless communication systems.

Original Abstract Submitted

The present disclosure relates to DMRS overhead adaptation with AI-based channel estimation. A wireless device may be configured to receive, from a network device, a downlink data transmitted using a DMRS pattern; perform an AI-based downlink channel estimation based on the downlink data, including: inputting one or more received downlink DMRS symbols included in the received downlink data to a neural network model for downlink channel estimation stored in the memory of the wireless device, to obtain, as outputs of the neural network model, an estimated downlink channel corresponding to the downlink data and an optimal downlink DMRS pattern for the estimated downlink channel; and report the optimal downlink DMRS pattern to the network device.