Blind Multichannel FIR Filter Identification via Iterative STLS
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Solution Overview
Problem
Existing blind channel identification techniques face challenges in accurately estimating multichannel finite impulse response (FIR) filters, especially in mobile communications where channel capacity is limited, and require long data records or higher-order statistics, which are not feasible in real-time scenarios.
Innovation Solution
An iterative structured total least-squares (STLS) technique is employed to construct a block-Hankel structured matrix from noisy symbols, iteratively re-estimate singular vectors, and generate a block-Hankel structured low-rank estimate matrix to obtain STLS channel estimates, avoiding the need for simultaneous nonlinear equation solutions and preserving the affine structure of the data matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher-order statistics (HOS) are used for blind channel identification, then measurement precision is improved, but loss of time increases due to requirement of long data records
Solution Approach 1:
The patent changes the statistical order parameter from higher-order statistics (HOS) to second-order statistics (SOS), enabling accurate channel estimation without requiring long data records. This parameter change allows the system to achieve reliable channel identification using only two received signals, thus resolving the contradiction between measurement precision and time loss.
2Measurement precision
If training sequences are used for channel equalization, then measurement precision is improved, but loss of information increases due to reduction of channel capacity
Solution Approach 1:
The patent extracts the channel identification capability from training sequences by using blind identification techniques based on second-order statistics. This extraction eliminates the need for training sequences, thereby preserving channel capacity while maintaining accurate channel estimation through mathematical processing of received signals.
3Ease of operation
If conventional blind identification methods are used, then ease of operation is improved by avoiding training sequences, but measurement precision deteriorates in ill-conditioned channels
Solution Approach 1:
The patent introduces a new dimension of analysis by formulating the blind identification problem in terms of block-Hankel structured matrices and their decompositions. This dimensional transformation allows the system to achieve accurate channel estimation in ill-conditioned channels while maintaining operational simplicity, as the matrix-based approach provides a systematic framework for processing received signals.
4Measurement precision
If simultaneous nonlinear equation solutions are required, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex nonlinear estimation problem into simpler linear subproblems through block-Hankel matrix decomposition. By dividing the problem into manageable matrix operations, the system achieves accurate channel estimation without requiring complex simultaneous nonlinear equation solutions, thus reducing device complexity while maintaining measurement precision.
Data Source
AI summary
A system for, and method of, blind identification of multichannel finite impulse response filters. In one embodiment, the system includes: (1) a decomposition generator configured to construct, from noisy symbols containing data, a block-Hankel structured matrix and a decomposition thereof, (2) an iterative matrix re-estimator coupled to the decomposition generator and configured iteratively to re-estimate singular vectors corresponding to a scalar that satisfies the block-Hankel matrix until a sufficient convergence occurs, (3) a structured low-rank estimate matrix generator coupled to the iterative matrix re-estimator and configured to generate a block-Hankel structured low-rank estimate matrix related to the block-Hankel structured matrix and (4) an STLS channel estimator coupled to the structured low-rank estimate matrix generator and configured to obtain STLS channel estimates from the block-Hankel structured low-rank estimate matrix.