Machine Learning Demodulation for Cognitive Interference Mitigation
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Solution Overview
Problem
Conventional communication systems face challenges in efficiently removing interference from desired signals using single receive antennas, which are resource and computationally intensive, and result in decreased Signal to Noise Ratio (SNR) due to imperfect parameter estimation.
Innovation Solution
Implementing a machine learning approach that uses trained neural networks to directly demodulate digital communications data from combined waveforms, recognizing desired signal features and rejecting interference, without estimating interference signals, by comparing waveform characteristics to machine learned models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If single receive antenna is used for interference cancellation, then hardware cost is reduced, but computational resources increase and SNR decreases due to imperfect parameter estimation
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical interference cancellation system (which requires parameter estimation and signal reconstruction) with a machine learning-based system. The neural network directly processes the received signal to identify and extract the desired signal, substituting complex computational algorithms with a trained model that makes decisions based on learned patterns, thereby reducing real-time computational burden while maintaining single-antenna hardware simplicity
Solution Approach 2:
The patent applies preliminary action by training the machine learning model offline before actual deployment. During the training phase, the system learns optimal signal characteristics and interference patterns from labeled data. This pre-computed knowledge is then applied during real-time operation, eliminating the need for complex runtime parameter estimation and signal reconstruction, thus reducing computational complexity while preserving signal quality
2Object-affected harmful factors
If traditional interference cancellation is performed, then interference removal is achieved, but SNR decreases due to residual noise from imperfect parameter estimation
Solution Approach 1:
The patent substitutes the traditional parameter-estimation-based interference cancellation mechanism with a machine learning-based signal classification and extraction mechanism. Instead of estimating interference parameters and subtracting reconstructed signals (which introduces residual noise), the neural network directly identifies and extracts the desired signal by learning its characteristic patterns, thereby avoiding the noise amplification inherent in traditional cancellation methods
Solution Approach 2:
The patent uses copying by creating trained machine learning models that capture the essential characteristics of desired signals and interference patterns from training data. These models serve as virtual copies of signal behavior that can be applied to new, unseen signals. The neural network copies the learned signal patterns and uses them to identify and extract the desired signal from mixed inputs, achieving reliable signal recovery without the noise penalties of traditional cancellation
3Reliability
If machine learned models are used for demodulation, then SNR is improved and computational resources are reduced, but system complexity in model training increases
Solution Approach 1:
The patent resolves the contradiction by performing the complex model training activity as a preliminary action during an offline setup phase. The machine learning models are trained using comprehensive datasets that include various signal conditions, interference types, and noise levels. This pre-computation creates robust models that can then be deployed with minimal runtime complexity. The heavy lifting of learning complex patterns is done beforehand, allowing the deployed system to achieve high SNR with simple forward propagation operations
Solution Approach 2:
The patent applies self-service by enabling the machine learning system to automatically adapt to different signal conditions through the training process. The model learns optimal signal characteristics and interference patterns from data without requiring manual tuning or configuration during deployment. This self-learning capability reduces the need for complex manual system configuration and maintenance, offsetting the initial training complexity with long-term operational simplicity
Data Source
AI summary
Systems and methods for operating a receiver. The methods comprise: receiving, at the receiver, a combined waveform comprising a combination of a desired signal and an interference signal; and performing, by the receiver, demodulation operations to extract the desired signal from the combined waveform. The demodulation operations comprise: obtaining, by the receiver, machine learned models for recovering the desired signal from combined waveforms; comparing, by the receiver, at least one characteristic of the received combined waveform to at least one of the machine learned models; and determining a value for at least one symbol of the desired signal based on results of the comparing.