Clear Channel Assessment Using Sub-Sequence Correlation
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Solution Overview
Problem
Existing communication systems using direct sequence spread spectrum (DSSS) face challenges in efficiently detecting data signals due to interference from noise and background signals, particularly in distinguishing spread spectrum signals from constant signals like pilot tones, which can lead to false detections and collisions in wireless communication channels.
Innovation Solution
The method involves detecting the presence of a spreading sequence by evaluating the correlation of sub-sequences with phase differences in a sample sequence, using a phase differentiation circuit and a detection circuit that compares correlation levels to thresholds, and employing oversampling to reduce synchronization errors and processing time, thereby quickly identifying the presence of a spread spectrum signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correlation of entire spreading sequence is used to detect presence of data signals, then detection accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The patent divides the entire spreading sequence into multiple sub-sequences and performs correlation detection on each sub-sequence separately. This segmentation reduces the computational complexity and processing time of each individual correlation operation, while still maintaining adequate detection accuracy by combining results from multiple sub-sequences.
Solution Approach 2:
The patent uses only a portion (sub-sequences) of the complete spreading sequence for detection rather than processing the entire sequence. This partial action approach achieves sufficient detection performance with reduced processing requirements, balancing accuracy and efficiency.
2Reliability
If correlation detection is performed to distinguish spread spectrum signals from constant signals, then false detections are reduced, but detection complexity increases
Solution Approach 1:
By segmenting the spreading sequence into sub-sequences and performing multiple correlation operations, the system creates a more robust detection mechanism that can distinguish spread spectrum signals from constant signals like pilot tones, reducing false detections while managing complexity through modular processing.
Solution Approach 2:
The patent employs threshold comparison of correlation results to determine signal presence, creating a feedback-based decision mechanism that improves reliability by confirming detections through multiple correlation measurements against established criteria.
3Measurement precision
If oversampling is used to reduce synchronization errors, then detection accuracy is improved, but processing load increases
Solution Approach 1:
Oversampled data is processed by dividing it into multiple sub-sequences for parallel correlation detection. This segmentation distributes the processing load across multiple smaller operations rather than one large operation, reducing the computational burden while maintaining the synchronization benefits of oversampling.
Data Source
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AI summary
Circuits and methods concerning signal detection are disclosed. In some example embodiments, an apparatus is configured to detect presence of a spreading sequence in a sample data sequence. Phase differences between samples in a sample sequence are determined. Presence of a spreading sequence in the sample sequence is detected by evaluating correlation of reference sub-sequences, of a reference spreading sequence, to the phase differences between samples in a sample sequence. Each of the reference sub-sequences includes fewer chips than the spreading sequence to be detected.