Multi-Resolution Signal Correlation for Peak Verification
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Solution Overview
Problem
Conventional pipelined correlation techniques in communications systems are hardware intensive, computationally inefficient, and lack the ability for post-processing verification of correlation index values, requiring full-length correlations and being unable to re-correlate signals once they pass the ideal correlation peak.
Innovation Solution
An adaptive correlation method that performs a series of low-resolution, medium-resolution, and fine-resolution correlations, allowing for verification of correlation index values by iteratively increasing the sample size and using parallel processing to improve efficiency, enabling verification of correlation peaks and reducing hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional pipelined correlation techniques are used, then real-time correlation is achieved, but hardware complexity increases and computational efficiency decreases
Solution Approach 1:
The correlation process is divided into multiple stages: a first correlation stage that processes a first set of samples, and a second correlation stage that processes a second set of samples. This segmentation allows the system to achieve real-time processing while reducing the computational burden on any single stage, thereby lowering hardware complexity requirements.
Solution Approach 2:
The first correlation is performed on a first set of samples before the second correlation is performed on a second set of samples. This preliminary action allows the system to prepare and process data in a staged manner, achieving real-time correlation without requiring all computational resources to be available simultaneously, thus reducing hardware complexity.
2Measurement precision
If full-length correlations are performed over the entire uncertainty window, then correlation accuracy is improved, but the number of arithmetic operations increases linearly
Solution Approach 1:
The uncertainty window is divided into multiple segments, with the first correlation processing a first set of samples and the second correlation processing a second set of samples. This segmentation reduces the number of arithmetic operations required at any given time while maintaining overall correlation accuracy through the staged processing approach.
Solution Approach 2:
Instead of performing a single full-length correlation over the entire uncertainty window, the system performs multiple partial correlations on different sets of samples. This partial action approach reduces the computational burden of each individual operation while achieving the same overall correlation accuracy through aggregation of results.
3Reliability
If the pipelined configuration processes all possible values, then complete correlation coverage is achieved, but the system becomes hardware intensive requiring N dedicated multipliers
Solution Approach 1:
The correlation processing is segmented into multiple passes, with each pass handling a specific set of samples. This allows the system to achieve complete correlation coverage across all possible values while using fewer hardware resources at any given time, as not all N dedicated multipliers are required simultaneously.
Solution Approach 2:
The system performs correlations continuously across multiple stages and sample sets, ensuring complete coverage of all possible values. The first correlation and second correlation are performed in sequence, maintaining continuous useful action that achieves comprehensive correlation coverage without requiring all hardware resources to be active at once.
4Speed
If the pipelined correlator generates one correlation value per clock cycle, then real-time processing is maintained, but post-processing verification is prevented
Solution Approach 1:
The first correlation is performed as a preliminary action before the second correlation. This preliminary correlation provides initial results that can be verified and validated in subsequent processing stages, enabling post-processing verification while maintaining real-time processing capabilities through the staged approach.
Solution Approach 2:
The system incorporates feedback mechanisms where the results of the first correlation can be verified and used to guide the second correlation. This feedback loop enables post-processing verification of correlation index values while maintaining real-time processing speed, as the feedback allows for validation without requiring complete re-processing of all data.
Data Source
AI summary
A method is provided for correlating samples of a received signal and samples of an internally generated/stored sample sequence (“IGSSS”). The method involves performing a first iteration of a first-resolution correlation state. The first-resolution correlation state involves: selecting a first N sets of samples from the received signal; selecting a first set of samples from the IGSSS; and concurrently comparing each of the N sets of samples with the first set of samples to determine if a correlation exists between the same. If it is determined that a correlation does not exist between one of the N sets of samples and the first set of samples, then a second iteration of the first-resolution correlation state is performed. If it is determined that a correlation exists between one of the N sets of samples and the first set of samples, then a first iteration of a second-resolution correlation state is performed.