Frequency-Domain Channel Estimation Using Pilot Noise Extraction
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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 a time domain pilot estimate using a computing system to identify noisy pilot signals, apply a convolution-based moving average for de-noising, and perform linear interpolation to determine channel estimates, thereby conserving 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 additive white Gaussian noise (AWGN) cannot be effectively removed from pilot signals, leading to inaccurate estimation
Solution Approach 1:
The patent extracts and removes the AWGN component from pilot signals by comparing received noisy pilot signals with known transmitted pilot signals. The noise is separated and eliminated through subtraction, leaving only the clean channel response for accurate estimation.
Solution Approach 2:
The patent converts the harmful AWGN into a beneficial element by using the known structure of pilot signals. The regular pattern of pilot signals allows the system to identify and isolate noise components, transforming the noise problem into a solvable mathematical operation that improves estimation accuracy.
2Use of energy by moving object
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 performing de-noising operations only on pilot signals rather than processing the entire received signal. This selective approach reduces computational complexity and power consumption while maintaining estimation accuracy through targeted noise removal from critical reference signals.
3Productivity
If conventional channel estimation techniques are used, then channel estimation can be performed, but computational overhead increases
Solution Approach 1:
The patent segments the channel estimation process into distinct stages: pilot signal extraction, noise identification and removal, and clean channel estimation. This segmentation reduces computational overhead by handling only necessary operations at each stage rather than processing all signal components uniformly.
Data Source
AI summary
Techniques are described herein for channel estimation. An example method can include processing a set of signals comprising a first noisy pilot signal associated with a first subcarrier, a second noisy pilot signal associated with a second subcarrier, and a noisy message signal. The method can further include determining a first noisy channel estimate in a frequency domain based on the first noisy pilot signal and a second noisy channel estimate in the frequency domain based on the second noisy pilot signal. The method can further include determining a first de-noised channel estimate based on the noisy channel estimate and the second noisy channel estimate. The method can further include determining a de-noised message signal based on the first de-noised channel estimate.