Apple inc. (20240235767). DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION simplified abstract

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 20240235767 titled 'DMRS OVERHEAD ADAPTATION WITH AI-BASED CHANNEL ESTIMATION

Simplified Explanation: This patent application discusses the adaptation of DMRS overhead using AI-based channel estimation in wireless devices. The technology involves receiving downlink data with a DMRS pattern, performing AI-based channel estimation on the data, and reporting the optimal DMRS pattern back to the network device.

• Neural network model used for downlink channel estimation
• AI-based estimation of optimal downlink channel and DMRS pattern
• Wireless device receives downlink data from network device
• DMRS overhead adaptation for efficient communication
• Improved channel estimation accuracy

Potential Applications: 1. 5G and future wireless communication systems 2. IoT devices requiring efficient channel estimation 3. Mobile networks for enhanced data transmission 4. Satellite communication for optimized signal processing

Problems Solved: 1. Reducing overhead in wireless communication 2. Enhancing channel estimation accuracy 3. Improving data transmission efficiency 4. Optimizing resource allocation in networks

Benefits: 1. Faster data transmission rates 2. Enhanced network performance 3. Reduced interference in wireless communication 4. Improved overall system efficiency

Commercial Applications: Optimizing DMRS overhead using AI-based channel estimation can benefit telecommunications companies, IoT device manufacturers, and satellite communication providers by improving data transmission efficiency and network performance.

Prior Art: Prior research in the field of wireless communication and AI-based channel estimation can provide insights into similar technologies and approaches used in this patent application.

Frequently Updated Research: Stay updated on the latest advancements in AI-based channel estimation and wireless communication technologies to further enhance the efficiency and accuracy of DMRS overhead adaptation.

Questions about DMRS Overhead Adaptation with AI-Based Channel Estimation: 1. How does AI-based channel estimation improve the efficiency of DMRS overhead adaptation? 2. What are the potential challenges in implementing AI-based channel estimation for DMRS overhead adaptation in wireless devices?

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.