Neural Network Waveform Prediction for Multi-Modal SNR Shifts
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Solution Overview
Problem
In communication systems operating in Partial Band Partial Time (PBPT) jamming radio frequency environments, the rapid changes in signal-to-noise ratio (SNR) across different frequency hops make it challenging to determine the required SNR levels for effective waveform selection and transmission power control, as traditional methods rely on consistent or single-modal SNR distributions.
Innovation Solution
A method utilizing a neural network (NN) to predict the normalized minimum SNR shift required for communication waveform operation by measuring the actual SNR distribution, generating training samples, and adjusting the communication system's transmission power or selecting an appropriate waveform based on the predicted SNR shifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SNR determination methods are used in PBPT jamming environments, then the method is simple and based on consistent SNR assumptions, but the accuracy of SNR level determination deteriorates due to rapidly changing multi-modal SNR distributions
Solution Approach 1:
The patent transforms the SNR distribution characterization from assuming a single consistent value to representing multi-modal distributions with different means and standard deviations. The neural network takes as input the mean and standard deviation of SNR distribution across multiple hops, and outputs the required SNR margin adjusted for the multi-modal characteristics. This parameter transformation enables accurate SNR determination in PBPT jamming environments where traditional single-value assumptions fail.
2Measurement precision
If neural network-based prediction is implemented to handle multi-modal SNR distributions, then SNR prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent pre-trains the neural network offline using simulated multi-modal SNR distributions from PBPT jamming scenarios. During actual operation, the pre-trained network rapidly processes real-time SNR measurements from frequency hops to predict required SNR margins. This preliminary training phase separates the computationally intensive learning process from the time-critical operational phase, enabling fast real-time predictions without sacrificing accuracy.
Solution Approach 2:
The patent uses simulated SNR distributions from trained models to create training datasets that replicate real PBPT jamming conditions. These synthetic training samples allow the neural network to learn from extensive varied scenarios without requiring exhaustive real-world measurements, significantly reducing the time and resources needed for training while maintaining generalization to actual operational conditions.
3Reliability
If SNR margin is increased to ensure reliable operation in multi-modal SNR conditions, then communication reliability improves, but transmission power efficiency deteriorates
Solution Approach 1:
The patent calculates separate SNR margins for each mode in the multi-modal distribution rather than applying a single conservative margin to all conditions. The neural network analyzes the specific characteristics of each SNR mode (mean and standard deviation) and determines the appropriate margin for that particular mode. This localized approach ensures reliable operation in each specific condition while avoiding the excessive power consumption that would result from using a uniform high margin across all modes.
Data Source
AI summary
Various embodiments of the present disclosure provide a method, a device, and a storage medium for performance prediction of a communication waveform in a communication system. The method includes measuring, by a receiver, an actual SNR distribution of a communication link between a transmitter and the receiver; further includes evaluating, by a waveform performance prediction device, a normalized minimum SNR shift required for the communication waveform to operate, where the normalized minimum SNR shift is obtained based on a normalized SNR distribution using a neural network (NN), the normalized SNR distribution corresponding to the actual SNR distribution; and further includes, according to the normalized minimum SNR shift, obtaining, by a waveform performance prediction device, an actual minimum SNR shift for the actual SNR distribution, where according to the actual minimum SNR shift, the communication system is adjusted for operation.