Denoising Circuit for Wireless Channel Estimation
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Solution Overview
Problem
In 5G-NR wireless communication systems, the strong path loss of mmWave frequencies necessitates accurate channel state information (CSI) for data detection and MU precoding, but received pilot signals are often distorted by noise, leading to channel estimation errors, which existing denoising methods with high computational complexity cannot effectively address.
Innovation Solution
A denoising circuit that converts noisy complex channel vectors into beamspace-domain vectors and determines an optimal denoising parameter using a lower-complexity algorithm, such as the Stein's unbiased risk estimate (SURE) method, to generate denoised beamspace-domain vectors, which are then converted back to spatial domain for improved channel estimation, reducing computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing denoising methods are used to reduce channel estimation errors, then measurement precision is improved, but device complexity increases due to high computational complexity
Solution Approach 1:
The patent segments the channel estimation process into distinct stages: receiving pilot signals, performing denoising operations on received signals, and completing channel estimation. By isolating the denoising step as a separate processing stage with specific algorithms, the system reduces overall computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent applies parameter changes by using denoising algorithms that modify signal parameters (such as applying filters or transformations to received pilot signals) to suppress noise components. This allows the system to improve channel estimation accuracy by changing the parameter representation of received signals without requiring computationally intensive processing.
2Measurement precision
If existing denoising methods are used to suppress noise in pilot signals, then measurement precision is improved, but use of energy increases due to high computational complexity
Solution Approach 1:
By segmenting the signal processing into distinct stages with the denoising operation as a separate step, the patent enables more efficient energy utilization. The denoising algorithms process only the necessary signal components at specific stages, avoiding redundant computations and reducing overall power consumption while maintaining noise suppression effectiveness.
Solution Approach 2:
The patent uses parameter changes through denoising algorithms that transform received pilot signals into denoised versions by modifying signal parameters. This approach suppresses noise effectively while requiring less computational energy compared to more intensive processing methods, as the algorithms operate on signal characteristics rather than performing exhaustive computations.
3Measurement precision
If existing denoising methods are used to denoise pilot signals, then measurement precision is improved, but productivity decreases due to high computational complexity
Solution Approach 1:
The patent segments channel estimation into distinct processing stages with denoising as a dedicated step. This segmentation allows the system to perform denoising operations efficiently on specific signal components without requiring intensive processing of the entire signal, thereby improving processing efficiency while maintaining accurate channel estimation.
Solution Approach 2:
By applying parameter changes through denoising algorithms that transform received signals into denoised signals, the patent achieves effective noise suppression with reduced computational burden. This approach improves processing efficiency by operating on signal parameters rather than requiring exhaustive computations, enabling faster channel estimation while maintaining accuracy.
Data Source
AI summary
A wireless communication apparatus is provided. The wireless communication apparatus includes a denoising circuit configured to receive a noisy complex channel vector(s) in a spatial domain and convert the noisy complex channel vector(s) into a noisy beamspace-domain vector(s) in a beamspace domain. The denoising circuit determines an optimal denoising parameter and denoises the noisy beamspace-domain vector(s) based on the optimal denoising parameter to generate a denoised beamspace-domain vector(s). The denoising circuit then converts the denoised beamspace-domain vector(s) to a denoised complex channel vector(s) in the spatial domain. In examples discussed herein, the denoising circuit determines the optimal denoising parameter and denoises noisy beamspace-domain vector(s) based on a lower-complexity denoising algorithm having reduced computational complexity compared to existing denoising methods, thus helping to enable more accurate channel estimation in the wireless communication apparatus with reduced cost, footprint, and/or power consumption.