18456249. AI/ML EMPOWERED HIGH ORDER MODULATION simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

AI/ML EMPOWERED HIGH ORDER MODULATION

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Pranav Madadi of Sunnyvale CA (US)

Joonyoung Cho of Portland OR (US)

Jianzhong Zhang of Dallas TX (US)

AI/ML EMPOWERED HIGH ORDER MODULATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18456249 titled 'AI/ML EMPOWERED HIGH ORDER MODULATION

Simplified Explanation

The patent application describes a two-dimensional constellation for data signals that enhances the mutual information of data points by mapping data bits to in-phase and quadrature values based on signal-to-noise ratio (SNR) and code rate.

Key Features and Innovation

• Mapping data bits to in-phase and quadrature values based on SNR improves bitwise mutual information.
• Efficiency of bitwise mutual information is adapted according to the SNR.
• Mapping is subject to constraints like quadrant symmetry Lagrangian (QSL), quadrant symmetry constraint (QSC), or rectangular structure constraint (RSC).

Potential Applications

The technology can be applied in wireless communication systems, digital signal processing, and error correction coding.

Problems Solved

• Enhances the mutual information of data points in two-dimensional constellations.
• Improves the efficiency of bitwise mutual information based on SNR.

Benefits

• Increased data transmission reliability.
• Enhanced performance in noisy communication channels.
• Improved error correction capabilities.

Commercial Applications

Enhancing Wireless Communication Systems with Improved Bitwise Mutual Information Constellations

This technology can be utilized in developing advanced wireless communication systems with enhanced data transmission reliability and performance in noisy environments, catering to industries such as telecommunications and IoT.

Prior Art

The patent application builds upon existing research in signal processing and constellation mapping techniques to optimize the efficiency of bitwise mutual information in data signals.

Frequently Updated Research

Ongoing research in the field of digital signal processing and wireless communication systems continues to explore novel methods for improving data transmission efficiency and reliability through constellation mapping techniques.

Questions about the Technology

Question 1

How does the mapping of data bits to in-phase and quadrature values based on SNR contribute to enhancing mutual information in two-dimensional constellations?

Question 2

What are the potential constraints that can be applied to the mapping process to optimize the efficiency of bitwise mutual information in data signals?

Original Abstract Submitted

A two-dimensional constellation for data signals having improved bitwise mutual information of data points is based on a signal-to-noise ratio (SNR) and a code rate where, based on the SNR, data bits are mapped to pre-defined in-phase and quadrature values. The in-phase and quadrature values denote points in the two-dimensional space such that the efficiency of bitwise mutual information is adapted based on the SNR. The mapping is preferably subject to a constraint selected from one of quadrant symmetry Lagrangian (QSL), quadrant symmetry constraint (QSC), or rectangular structure constraint (RSC).