Channel Response Estimation via First-Order Approximations
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Solution Overview
Problem
Conventional methods for calculating channel responses in active sensing applications require extensive computation resources and result in significant delays due to the need for matrix inversion, making them computationally infeasible for large matrices encountered in many scenarios.
Innovation Solution
A method that calculates channel responses using first-order channel estimates through different approximation algorithms such as circulant matrix, iterative L1 norm, iterative L2 norm, linear extrapolation, and template-based approximations, reducing computational complexity by avoiding matrix inversion and utilizing a processing unit that combines these estimates with adaptive weight factors for real-time channel response calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional matrix inversion methods are used for channel estimation, then measurement precision is improved, but device complexity and computation resources increase significantly
Solution Approach 1:
The patent segments the channel estimation problem into multiple independent first-order estimates calculated from different received signals, which are then combined through a processing unit. This avoids the need for a single complex matrix inversion operation, reducing computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent changes the estimation approach from direct matrix inversion to using first-order channel estimates with adaptive weight factors. This parameter change transforms the computational problem from O(N³) matrix inversion to a more efficient combination of simpler estimates, reducing device complexity while preserving measurement precision.
2Measurement precision
If matrix inversion is performed for channel response calculation, then measurement precision is improved, but loss of time increases due to extensive computation
Solution Approach 1:
The patent performs preliminary calculations by computing multiple first-order channel estimates from different received signals before combining them. This preliminary action avoids the time-consuming matrix inversion step, reducing processing delay while maintaining the precision needed for accurate channel response calculation.
Solution Approach 2:
The patent uses adaptive weight factors that can be dynamically adjusted based on channel conditions to combine first-order estimates. This dynamic approach allows the system to maintain measurement precision across varying conditions while avoiding the fixed, time-consuming matrix inversion process.
3Reliability
If accurate channel response calculation is achieved through conventional methods, then reliability is improved, but productivity decreases due to computational burden
Solution Approach 1:
The patent divides the channel estimation task into multiple parallel first-order estimate calculations that can be processed independently and then combined. This segmentation enables real-time processing by distributing the computational load, improving productivity while maintaining the reliability needed for accurate channel response calculation.
Solution Approach 2:
The patent changes from matrix inversion to using first-order estimates with adaptive weighting, transforming the computational approach to one that achieves reliable channel estimation with significantly reduced computational burden, thereby enabling real-time processing and improving productivity.
Data Source
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AI summary
A system and method for providing a channel response, h, of a channel of interest. The system (1) comprises a receiving unit (4) adapted to receive a channel modified signal in response to a sensing signal transmitted through said channel of interest (5). The system (1) further comprises a processing unit (3) adapted to perform different first order channel estimations on the received channel modified signal to calculate first order channel estimates, ĥ, of the channel response, h, of said channel of interest (5) and adapted to combine the calculated first order channel estimates, ĥ, to calculate the channel response, h, of said channel of interest (5).