Cross-Correlation Method for Wireless Channel Estimation
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Solution Overview
Problem
Current methods for computing cross-correlations in wireless communication systems, particularly for channel estimation and PHY-type detection, are computationally intensive and inefficient, especially when dealing with multiple PHY types, leading to significant processing requirements.
Innovation Solution
The method involves shifting and sign-adjusting received sequences to form partial cross-correlations, which are then combined efficiently using a system of shift modules, correlators, and combiners, reducing the number of necessary computations by using sign-adjusted partial correlations to determine cross-correlations between reference and received sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct cross-correlation computation is used between reference sequences and received sequences, then accurate channel estimation and PHY-type detection are achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The reference sequence is segmented into multiple sub-sequences, and the cross-correlation computation is divided into multiple partial cross-correlation computations. Each partial cross-correlation operates on a subset of the data, reducing the computational burden of the overall operation while maintaining the accuracy of the channel estimation and PHY-type detection.
Solution Approach 2:
Instead of computing the full cross-correlation directly, the method computes multiple partial cross-correlations that cover different portions of the reference and received sequences. These partial results are then combined to obtain the final cross-correlation, achieving the same accuracy with reduced computational complexity.
2Measurement precision
If direct cross-correlation computation is used between reference sequences and received sequences, then accurate channel estimation and PHY-type detection are achieved, but processing time increases
Solution Approach 1:
The computation is segmented into parallel partial cross-correlation operations that can be executed simultaneously or in an optimized sequence, reducing the total processing time required for PHY-type detection while maintaining detection accuracy.
Solution Approach 2:
The received sequence is pre-processed by shifting and sign-adjusting before the partial cross-correlation computations. This preliminary action prepares the data in an optimal form for the subsequent correlation operations, reducing the computational effort and time required during the main detection phase.
3Adaptability or versatility
If multiple PHY types are detected using direct cross-correlation, then comprehensive channel estimation is achieved, but the number of operations increases significantly
Solution Approach 1:
The method segments the detection process into multiple partial cross-correlation computations, each handling a portion of the multi-PHY-type detection task. This segmentation allows for more efficient processing of multiple PHY types by distributing the computational load across multiple smaller operations rather than one large computation.
Solution Approach 2:
The partial cross-correlation framework provides a universal approach that can detect multiple PHY types using the same computational structure. The method is adaptable to different PHY types without requiring separate dedicated computation paths, improving operations efficiency while maintaining comprehensive detection capability.
Data Source
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AI summary
The present disclosure provides for a method and apparatus for efficient cross-correlation between a reference sequence and a received sequence in a wireless communication system. The reference sequence includes a concatenation of sign-adjusted sub-sequences, the sign adjustments determined by a first sign sequence of a set of sign sequences. For example, the reference sequence may be an alternating concatenation of sign-adjusted Golay complementary pair sub-sequences. The received sequence is shifted to provide a plurality of time shifted sequences that are then cross-correlated with the sub-sequences to form a set of partial cross-correlations. The partial cross-correlations are sign-adjusted using the first sign sequence and combined to produce the cross-correlation between the reference sequence and the received sequence. The cross-correlations so produced may be used for channel signature (e.g. PHY-type) identification and/or channel impulse response estimation.