Time-Domain Channel Estimation With Pilot Signal 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 the performance of 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, a de-noising unit, and an interpolation unit to de-noise and reconstruct messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional channel estimation techniques are used, then channel estimation can be performed, but noise removal is inefficient leading to inaccurate estimation and increased power consumption
Solution Approach 1:
The patent extracts and removes noise from pilot signals using a de-noising unit that processes received pilot signals to produce clean channel estimates. This separation of noise removal as a distinct function improves estimation accuracy without requiring excessive power consumption, directly resolving the contradiction between measurement precision and energy use.
Solution Approach 2:
The patent applies de-noising operations before channel estimation to pre-process pilot signals. By removing noise in advance through convolution-based filtering and interpolation, the system achieves more accurate channel estimates with reduced computational overhead during subsequent processing stages, thereby lowering overall power consumption.
2Measurement precision
If conventional channel estimation techniques are used, then channel estimation can be performed, but computational overhead is high
Solution Approach 1:
The patent segments the channel estimation process into distinct functional units: a pilot signal extraction unit, a de-noising unit, and an interpolation unit. This modular segmentation allows each unit to perform its specific function efficiently, reducing overall computational overhead while maintaining high estimation accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces an interpolation unit as an intermediary between de-noising and final channel estimation. This intermediary component uses linear interpolation to estimate channel responses at non-pilot subcarriers based on cleaned pilot signal estimates, reducing the computational burden compared to conventional methods that process all subcarriers equally while maintaining estimation accuracy.
3Measurement precision
If noise is not effectively removed from pilot signals, then processing is simpler, but channel estimation accuracy deteriorates
Solution Approach 1:
The patent converts the harmful effect of noise in pilot signals into a beneficial process by using the noise-corrupted pilot signals as input to a de-noising unit. Through convolution-based filtering and statistical processing, the system transforms noisy measurements into clean channel estimates, turning the initial disadvantage into an opportunity for improved accuracy through structured noise removal.
Data Source
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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.