Neural Constellation Mapping for Interference-Resistant Modulation
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Solution Overview
Problem
Conventional quadrature amplitude modulation (QAM) schemes face challenges with increased susceptibility to interference, reduced noise immunity, and spectral inefficiencies, particularly in high-order constellations, leading to decreased data reliability and limited data rates.
Innovation Solution
A neural-network-based modulation and demodulation system that dynamically adjusts constellation points and employs multi-dimensional mapping functions to adapt to real-time channel conditions, using artificial intelligence to enhance spectral efficiency and robustness against interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional quadrature amplitude modulation (QAM) schemes are used, then data transmission can be achieved, but susceptibility to interference increases and noise immunity reduces
Solution Approach 1:
The patent transforms the fixed QAM constellation parameters into learnable parameters through neural networks. The encoder and decoder networks learn optimal constellation point configurations and decision boundaries that maximize reliability while minimizing interference susceptibility, rather than using conventional fixed QAM parameters.
Solution Approach 2:
The patent replaces the mechanical/mathematical QAM modulation and demodulation system with a neural network-based system. The deterministic mapping and decision-making processes are substituted with learned mappings through artificial intelligence, enabling adaptive optimization of reliability and interference resistance.
2Productivity
If high-order QAM constellations are used, then data rates can be increased, but spectral efficiency decreases and noise immunity reduces
Solution Approach 1:
The patent introduces dynamic adaptability through neural networks that can adjust constellation configurations based on channel conditions. The system transitions from static high-order QAM to dynamic, learnable constellation structures that optimize the trade-off between data rate and noise immunity in real-time.
Solution Approach 2:
The patent changes the fixed parameters of high-order QAM constellations into flexible, learnable parameters. The neural networks optimize constellation point positions and decision boundaries to achieve better noise immunity while maintaining high data rates, rather than being constrained by conventional fixed parameter designs.
3Reliability
If conventional QAM modulation is used, then communication can be established, but energy efficiency is reduced at lower signal-to-noise ratios
Solution Approach 1:
The patent replaces the energy-inefficient conventional QAM demodulation process with a neural network-based approach. The learned decoder networks can more accurately recover transmitted data from noisy signals, reducing bit error rates and improving energy efficiency, particularly in low signal-to-noise ratio conditions where conventional methods struggle.
Data Source
AI summary
A method and apparatus comprising a neural-network-based encoder, a neural-network-based decoder, or both, are provided. In accordance with one embodiment, a method comprises mapping, in a first artificial neural network, a plurality of data values to a plurality of constellation points; adding noise to the constellation points to obtain a plurality of noise-added constellation points; mapping, in a second artificial neural network, the plurality of noise-added constellation points to a plurality of data value representations; and applying the plurality of data value representations to perform a communication operation selected from a group consisting of modulating transmit data in a transmitter and demodulating a receive signal in a receiver.