Semi-blind Channel Estimation Iterative Refinement
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Solution Overview
Problem
In multiple-antenna overlapped multiplexing systems, existing channel estimation methods based on training sequences are inefficient when channel parameters are unknown, requiring improved performance for accurate data transmission.
Innovation Solution
A semi-blind channel estimation method that uses minimum mean square error channel estimation and a recycling algorithm, where estimated data is used as a training sequence to iteratively refine the channel parameter matrix, reducing the need for training symbols and enhancing estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional least square channel estimation based on training sequence is used, then channel estimation can be performed, but the performance is insufficient and requires more training symbols which reduces bandwidth efficiency
Solution Approach 1:
The patent applies iterative refinement where the channel estimation process dynamically improves through multiple cycles. The initial training sequence estimate is repeatedly refined by re-estimating the training sequence itself, creating a dynamic feedback loop that progressively enhances estimation accuracy without requiring additional training symbols.
Solution Approach 2:
The patent implements a feedback mechanism where the estimated channel parameters are used to re-estimate the training sequence, which then feeds back into improved channel estimation. This closed-loop feedback process allows the system to self-improve estimation performance using the same limited training data.
2Measurement precision
If more training symbols are used to improve channel estimation accuracy, then estimation performance improves, but bandwidth efficiency decreases
Solution Approach 1:
The patent creates virtual copies of the training sequence through iterative re-estimation. By treating the initially estimated training sequence as a new training sequence for subsequent estimation cycles, the system effectively multiplies the information content of the original training symbols without requiring additional physical training symbols.
Solution Approach 2:
The patent changes the parameter of training sequence length from a fixed value to an effectively expanding value through iteration. The same physical training sequence is reused multiple times across different estimation cycles, with each cycle extracting and refining additional information, thereby changing the effective information yield from the training sequence.
3Measurement precision
If iterative refinement is performed to improve estimation accuracy, then channel parameter precision improves, but computational complexity increases
Solution Approach 1:
The patent applies partial iteration by performing a fixed number of refinement cycles (e.g., 3-5 iterations) rather than exhaustive iteration. This partial action approach achieves sufficient precision improvement while deliberately limiting the computational burden, balancing accuracy gains against complexity costs.
Data Source
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AI summary
The present invention discloses a semi-blind channel estimation method and apparatus. The semi-blind channel estimation method includes: step S1: obtaining data that includes a first training sequence and that is received by a receive end; step S2: performing minimum mean square error channel estimation based on the data and the prestored first training sequence, to obtain a channel parameter matrix; step S3: detecting the first training sequence by using a least square detection algorithm, to obtain estimated data; and step S4: using the estimated data as a second training sequence, replacing the first training sequence in step S2 with the second training sequence, and cyclically performing step S2 and step S3 on the second training sequence, until a channel parameter matrix obtained last time is the same as a channel parameter matrix obtained this time, and then stopping circulation, to estimate a final channel parameter matrix. According to the foregoing technical solution in the present invention, a random channel parameter matrix in a multiple-antenna overlapped multiplexing system can be estimated, and performance is superior to performance of least square channel estimation based on a training sequence.