Time-Frequency Signal Correlation Using Segmented DFT Search
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Solution Overview
Problem
Existing communication systems face challenges in optimally detecting signals with unknown timing, phase, and frequency offsets in noisy environments, leading to suboptimal performance and high complexity or resource-intensive solutions.
Innovation Solution
A system and method that divides the unique word into segments, correlates each segment over time, and applies a discrete Fourier transform over frequencies, allowing for nearly optimal detection using a single correlator instead of a bank of correlators, thereby reducing complexity and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple correlator banks are used to search over time and frequency, then detection performance is improved, but device complexity increases
Solution Approach 1:
The patent divides the correlation process into segments by splitting the received unique word into multiple sub-portions (first sub-portion, second sub-portion, etc.). Each sub-portion is correlated with its corresponding transmitted portion separately, and the results are combined through discrete Fourier transform. This segmentation allows the system to achieve comprehensive time-frequency search without requiring multiple parallel correlator banks, thereby reducing device complexity while maintaining detection performance.
Solution Approach 2:
The patent introduces a frequency dimension by applying discrete Fourier transform to the sub-correlation values. Instead of using multiple correlator banks to cover different frequencies (which would increase complexity), the system transforms the time-domain correlation results into the frequency domain, achieving frequency search capability through mathematical transformation rather than additional hardware correlators.
2Measurement precision
If brute force correlation over all frequencies is performed, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the correlation computation by dividing the unique word into sub-portions and performing correlation only on these segments. The discrete Fourier transform is then applied to combine the results. This segmentation approach achieves comprehensive frequency search (improving detection accuracy) while reducing the total computational load compared to brute force correlation over all frequencies, as the transform operates on reduced-dimensional sub-correlation data.
Solution Approach 2:
The patent replaces the mechanical approach of using multiple parallel correlator banks (which would be required for brute force frequency search) with a mathematical substitution using discrete Fourier transform. This substitution achieves the same frequency search functionality through computational transformation rather than through additional correlation operations, thereby reducing computational complexity while maintaining detection accuracy.
Data Source
AI summary
An approach is provided for correlation of a signal over time and frequency. The signal is correlated with a bit sequence over time instances and certain frequency offsets, wherein sub-segments of the signal are correlated with sub-segments of the bit sequence to generate a correlation factor associated with each signal sub-segment. The correlation factors are coherently combined to generate a final correlation factor, wherein a respective phase shift (for each frequency offset) is applied to each correlation factor to generate a set of frequency adjusted correlation factors, and the frequency adjusted correlation factors of a respective set are combined to generate the final correlation factor over the signal sub-segments, resulting in the matrix of final correlation factors over time and frequency. A signal parameter estimation is performed, based on the matrix of final correlation factors, to determine a highest correlation value for the signal over the frequency offsets.