Time-Domain Channel Estimation Using Moving-Average De-Noising
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Solution Overview
Problem
Conventional channel estimation techniques for satellite communication systems are inefficient in removing additive white Gaussian noise (AWGN) from pilot signals, leading to inaccurate channel estimation and increased power consumption, which affects single and multi-antenna systems.
Innovation Solution
A low complexity channel estimation technique that reconstructs channels by minimizing noise in the time domain using a convolution-based moving average and linear interpolation, involving a noisy pilot estimation unit, de-noising unit, and interpolation unit to conserve battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel estimation techniques are used, then channel estimation can be performed, but the techniques are inefficient in removing AWGN from pilot signals, leading to inaccurate channel estimation
Solution Approach 1:
The patent segments the channel estimation process into distinct functional units: a noisy pilot estimation unit that initially estimates the channel using noisy pilot signals, a de-noising unit that processes the estimated channel to remove AWGN, and an interpolation unit that reconstructs the final channel estimate. This segmentation allows each unit to specialize in specific tasks, improving overall accuracy while managing computational complexity.
Solution Approach 2:
The patent applies preliminary action by performing de-noising operations on the channel estimates before using them for subsequent processing. The de-noising unit processes the channel estimates from the noisy pilot estimation unit to remove additive white Gaussian noise, ensuring that cleaner estimates are available for interpolation and final channel reconstruction, thereby improving measurement precision.
2Measurement precision
If conventional channel estimation techniques are used, then channel estimation can be performed, but power consumption increases
Solution Approach 1:
The patent applies partial action by implementing de-noising operations selectively rather than universally. The de-noising unit processes channel estimates based on their quality and the specific requirements of the interpolation unit, rather than applying heavy computational processing to all signals equally. This selective approach reduces overall power consumption while maintaining estimation accuracy where needed.
Solution Approach 2:
The interpolation unit leverages the output of the de-noising unit in a self-service manner, using the cleaned channel estimates to reconstruct the final channel profile without requiring additional complex processing. The system efficiently chains operations where each unit's output directly enables the next unit's function, minimizing redundant computations and reducing total power consumption.
3Measurement precision
If advanced noise removal techniques are applied, then channel estimation accuracy improves, but computational overhead increases
Solution Approach 1:
The patent extracts the de-noising function as a separate, dedicated unit that operates independently from the estimation and interpolation processes. By taking out the noise removal operation as a distinct functional block, the system can apply sophisticated de-noising techniques when needed while keeping the overall architecture modular and manageable, reducing the complexity burden on any single component.
Solution Approach 2:
The patent utilizes parameter changes in the de-noising unit to adapt the level of processing based on signal conditions. The de-noising unit can adjust its operation based on the quality of incoming channel estimates and the presence of significant noise, applying stronger de-noising when necessary and lighter processing when the signal is already clean, thereby optimizing the balance between accuracy and computational overhead.
Data Source
AI summary
Techniques are provided for channel state estimation. An example method can include processing a set of signals comprising a first noisy pilot signal, a second noisy pilot signal, and noisy message signal. The method can further include determining a first noisy channel estimate based on the first noisy pilot signal and a second noisy channel estimate based on the second noisy pilot signal. The method can further include determining a first de-noised channel estimate based on the noisy pilot signal channel estimate and the second noisy pilot signal channel estimate. The method can further include determining a de-noised message signal based on the first de-noised channel estimate.