HF Sensor Network Decoding for Uncertain Channel Error Estimation
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Solution Overview
Problem
Existing HF communication systems in sensor networks, such as those used in the Ocean of Things project, suffer from high bit error rates and uncertainty due to the non-stationary nature of HF channels, leading to unreliable data transmission and estimation of quantities of interest (QoI) with significant errors.
Innovation Solution
Implementing a machine-learning-based decoder and surrogate channel model with reduced dimensionality, trained jointly with a variational autoencoder, to reconstruct and quantify uncertainties in sensor data transmitted over HF channels, and using an information-fusion unit to combine data from multiple sensors based on uncertainty levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional HF communication systems are used for sensor network data transmission, then the system can operate with existing infrastructure, but the bit error rate increases and reliability deteriorates due to non-stationary channel characteristics
Solution Approach 1:
The patent introduces machine learning models (decoder and channel model) as intermediary components between the HF channel and the data transmission system. These intermediaries learn to compensate for channel non-stationarity and improve transmission reliability by adapting to changing channel conditions rather than relying on fixed traditional communication protocols
Solution Approach 2:
The system dynamically adapts to changing HF channel parameters by using machine learning models that learn optimal decoding strategies and channel characteristics. The decoder and channel model are trained to handle varying channel conditions, effectively changing the system's response parameters based on learned patterns rather than using fixed parameters
2Measurement precision
If machine learning models are used to decode HF signals and quantify uncertainty, then estimation accuracy improves, but computational cost increases
Solution Approach 1:
The patent applies preliminary action by training the machine learning decoder and channel model offline before actual deployment. This pre-training phase allows the models to learn optimal parameters and patterns, so that during runtime, the computational burden is significantly reduced while maintaining high estimation accuracy
Solution Approach 2:
The system uses a trained channel model as a computational copy or surrogate of the actual HF channel behavior. This copied model can be evaluated much faster than running full physical channel simulations, enabling real-time uncertainty quantification with reduced computational cost
3Reliability
If data from multiple sensors is combined to improve QoI estimation, then estimation reliability improves, but the complexity of the fusion process increases
Solution Approach 1:
The patent replaces complex manual or rule-based information fusion mechanisms with an automated machine learning-based fusion process. The system uses learned models to automatically weigh and combine sensor data based on their respective uncertainties, substituting mechanical complexity with intelligent automation that simplifies the overall fusion process
Data Source
AI summary
One embodiment can provide a method and system for estimating a remote quantity of interest (QoI). During operation, the system can receive, over a communication channel, a radio frequency (RF) signal carrying an estimate of the QoI measured by a sensor. The system can estimate probability distributions of a set of random channel parameters associated with the HF communication channel. The system can further reconstruct the estimate based on the probability distributions of the channel parameters and the received RF signal, determine a level of uncertainty associated with the reconstructed estimate, and combine reconstructed estimates from multiple sensors based on the determined level of uncertainty associated with each reconstructed estimate to output a combined estimate of the QoI.