Spectrum Partitioning Using Hilbert Transform for Frequency Scan
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Solution Overview
Problem
Current wireless communication systems face challenges in initial frequency scan due to limitations in signal-to-noise ratio, leading to poor detection performance and high computational complexity, especially in mode B UE and poor coverage scenarios, where power-based scans fail and cyclic prefix correlation-based scans are sensitive to LTE bandwidth fraction and number of EARFCNs to search.
Innovation Solution
A method involving a user equipment (UE) that accumulates samples over its maximum front-end bandwidth, splits them into non-overlapping spectrum chunks, and performs correlation-based detection on these chunks to enhance wireless communication system detection, reducing EARFCN uncertainty and improving detection probability through band partitioning techniques like binary tree splitting and Hilbert transform processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If power-based frequency scan is used, then the scan process is simple to implement, but detection performance is poor in low SNR conditions (fails below -5 dB)
Solution Approach 1:
The patent divides the frequency scan process into two stages: a coarse power-based scan to identify candidate EARFCNs, followed by a fine correlation-based scan only on those candidates. This segmentation allows the system to use the simpler power-based method where it works, while applying the more reliable correlation method only where needed, thus balancing implementation simplicity with detection performance.
2Reliability
If cyclic prefix correlation-based frequency scan is used, then detection performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the correlation-based detection into a two-stage process: first performing correlation to identify candidate EARFCNs, then performing finer detection only on those candidates. This reduces the overall computational complexity compared to performing correlation across the entire frequency range, while maintaining improved detection performance.
Solution Approach 2:
The patent performs preliminary power-based detection to identify candidate EARFCNs before performing the computationally intensive correlation-based detection. This preliminary action filters down the search space, so that correlation operations are only performed on a small subset of potential frequencies, significantly reducing total computational complexity.
3Reliability
If cyclic prefix correlation-based frequency scan is used, then detection capability is enhanced, but the number of potential EARFCNs to search increases
Solution Approach 1:
The patent segments the EARFCN search space into candidate and non-candidate regions based on the results of power-based detection. Correlation-based detection is then applied only to the candidate EARFCNs, significantly reducing the number of frequencies that need to be thoroughly searched while maintaining enhanced detection capability.
Solution Approach 2:
The patent performs preliminary power-based frequency scan to identify and filter candidate EARFCNs before applying correlation-based detection. This preliminary filtering reduces the quantity of EARFCNs that require intensive correlation processing, thus reducing the overall search burden while maintaining detection enhancement.
4Reliability
If comprehensive frequency scanning is performed to ensure system detection, then detection probability is improved, but scan time increases
Solution Approach 1:
The patent segments the frequency scan into a fast coarse scan using power-based detection followed by a targeted fine scan using correlation-based detection only on identified candidates. This segmentation maintains high detection probability by thoroughly checking candidate frequencies while reducing total scan time by avoiding exhaustive correlation scanning across all possible frequencies.
Solution Approach 2:
The patent performs preliminary power-based frequency scanning to quickly identify candidate EARFCNs before performing time-consuming correlation-based detection. This preliminary action enables the system to focus computational resources only on promising frequencies, maintaining high detection probability while significantly reducing overall scan time compared to exhaustive correlation scanning.
Data Source
AI summary
A method of performing wireless communication includes accumulating, by a user equipment (UE) during initial cell search, samples of received data over a maximum front-end bandwidth of the UE. The method also includes splitting the samples into smaller, non-overlapping spectrum chunks, and performing correlation-based detection on one or more of the smaller, non-overlapping chunks. The method further includes detecting a wireless communication system based on results of the correlation-based detection.