Blind Channel Estimation Using Perturbation Matrices
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Solution Overview
Problem
Current blind channel estimation methods for MIMO systems face challenges in achieving accurate and efficient channel estimation results, requiring a large number of iterations which lead to high computational complexity and delay, and often rely on redundant reference signal resources.
Innovation Solution
The proposed method involves determining initial sample channel matrices based on previous channel matrices or reference signals, using perturbation matrices and random algorithms to generate additional sample matrices, and grouping data signal detection results for iterative channel estimation, thereby reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blind channel estimation is performed through joint iteration between MIMO signal detection and channel estimation algorithm, then channel estimation accuracy is improved, but computational complexity and time delay increase due to large number of iterations required
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple candidate channel matrices before the iterative detection process begins. These candidate matrices are created using perturbation of an initial channel matrix, providing ready-to-use initialization values that eliminate the need for time-consuming iterative optimization during actual operation. This preliminary preparation significantly reduces computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent uses copying by creating multiple candidate channel matrices that are copies or variations of an initial channel matrix. These candidates are generated by adding perturbation matrices to the initial matrix, providing several close-to-optimal initialization options without performing full iterative optimization. This copying approach reduces computational burden while preserving accuracy.
2Measurement precision
If blind channel estimation is performed through joint iteration between MIMO signal detection and channel estimation algorithm, then channel estimation accuracy is improved, but time delay increases due to large number of iterations required
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple candidate channel matrices before the iterative detection process begins. These candidate matrices are created using perturbation of an initial channel matrix, providing ready-to-use initialization values that eliminate the need for time-consuming iterative optimization during actual operation. This preliminary preparation significantly reduces computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent uses copying by creating multiple candidate channel matrices that are copies or variations of an initial channel matrix. These candidates are generated by adding perturbation matrices to the initial matrix, providing several close-to-optimal initialization options without performing full iterative optimization. This copying approach reduces computational burden while preserving accuracy.
3Measurement precision
If reference signal resources are used for channel estimation, then channel estimation accuracy is improved, but reference signal overhead increases
Solution Approach 1:
The patent applies self-service by enabling the system to estimate channel characteristics using existing data signals rather than requiring separate reference signal resources. The blind channel estimation method allows the data signals themselves to serve the dual purpose of both information transmission and channel estimation, eliminating the need for dedicated reference signals and reducing overhead.
Solution Approach 2:
The patent applies universality by making data signals multi-functional - they serve both as information-bearing signals and as channel estimation references. This eliminates the need for separate reference signal resources, as the data signals themselves provide the necessary information for channel estimation when combined with the pre-generated candidate matrices.
Data Source
AI summary
This disclosure provides channel estimation methods and apparatuses. One method includes: determining Ps initial sample channel matrices that indicate channel states, where the Ps initial sample channel matrices include P1 first sample channel matrices and Ps-P1 second sample channel matrices, the P1 first sample channel matrices are determined based on a previous sample channel matrix or a given reference signal, and Ps is an integer greater than 1; and determining a channel matrix based on the Ps initial sample channel matrices, and obtaining a channel estimation result. Because the P1 initial sample channel matrices in the Ps initial sample channel matrices are determined based on the previous sample channel matrix or the given reference signal, an initial channel estimation result may be provided as an iterative initial sample channel matrix.