ML Receiver for Subsea Telemetry Noise Cancellation
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Solution Overview
Problem
Subsea telemetry systems face challenges in reliable communication due to environmental noise and signal distortion, particularly in underwater environments where noise levels are significantly higher than signal power, making reliable demodulation difficult and requiring costly manual optimization of communication parameters.
Innovation Solution
The implementation of machine learning techniques, including neural networks and reinforcement learning, to adapt and tune communication systems' parameters and components, such as receivers and transmitters, to improve signal processing and noise cancellation without explicit signal propagation models, using hardware-in-the-loop and software-in-the-loop approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization of communication parameters is used, then communication reliability can be improved, but system complexity and cost increase significantly
Solution Approach 1:
The receiver automatically optimizes communication parameters using machine learning algorithms. The system self-adjusts demodulation parameters, signal processing settings, and noise cancellation techniques without requiring external manual intervention, thereby maintaining high reliability while reducing system complexity
Solution Approach 2:
The system implements a feedback loop where the receiver continuously monitors communication quality metrics and uses machine learning to adjust parameters in real-time. This closed-loop control enables automatic adaptation to changing underwater conditions, improving reliability without increasing operational complexity
2Measurement precision
If explicit signal propagation models are used for noise cancellation, then signal processing accuracy can be improved, but modeling cost and time increase
Solution Approach 1:
The patent replaces traditional explicit signal propagation models with machine learning-based signal processing. The receiver uses trained neural networks and adaptive algorithms to directly process received signals and cancel noise, substituting complex physical modeling with data-driven approaches that achieve comparable or superior accuracy without extensive modeling time
Solution Approach 2:
The machine learning models are trained offline using pre-collected training data that represents various underwater propagation conditions. This preliminary training enables the receiver to handle diverse signal conditions without requiring real-time modeling, thus achieving high processing accuracy while eliminating time-consuming on-the-fly modeling
3Device complexity
If traditional demodulation methods are used in noisy environments, then system simplicity is maintained, but communication reliability deteriorates
Solution Approach 1:
The receiver implements dynamic parameter adjustment using machine learning algorithms that adapt to changing underwater acoustic conditions. The system automatically adjusts demodulation parameters, filtering settings, and signal processing techniques in real-time, maintaining simplicity of operation while achieving high reliability through adaptive intelligence
Solution Approach 2:
The system changes key processing parameters dynamically based on received signal characteristics. Machine learning algorithms continuously optimize parameters such as integration time, threshold levels, equalization coefficients, and noise filtering settings, enabling the simple receiver structure to achieve high reliability through intelligent parameter adaptation
Data Source
AI summary
A telemetry system is provided. The telemetry system includes a transmitter configured to convert digital bits representative of oil and gas operations into an analog signal and to transmit the analog signal via a communications channel. The telemetry system further includes a receiver configured to receive the analog signal and to convert the analog signal into output digital bits via an encoder, wherein the receiver comprises one or more receiver components trained via machine learning to process the analog signals for improved communications.