Fine Timing Method for OFDM Frame Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional fine timing methods in OFDM communication systems, such as IEEE 802.11p, are prone to errors due to changing signal channel parameters, leading to incorrect identification of data frames when using pre-set thresholds, either mistaking noise as frames or missing the frame beginning.
Innovation Solution
A fine timing method and system that calculates first timing metric values based on cross-correlation between received signals and multiple training sequences, identifying the set of values with the greatest summation within a time period identified by a coarse timing process, using a pattern that multiplies cross-correlation functions to determine the data frame beginning, thereby reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a pre-set threshold is used to identify data frames based on cross-correlation, then the identification process is simple and fast, but errors occur when signal channel parameters change (noise may be mistaken as frames or frame beginning may be missed)
Solution Approach 1:
The patent applies dynamics by making the threshold adaptive rather than fixed. The threshold is dynamically adjusted based on the actual signal characteristics and channel conditions. Specifically, the threshold is determined by analyzing the distribution of timing metric values and setting it based on statistical properties (e.g., mean and standard deviation) of the signal, allowing the system to adapt to changing signal-to-noise ratios and channel parameters, thereby maintaining high reliability across varying conditions.
Solution Approach 2:
The patent changes the threshold parameter from a fixed pre-set value to a dynamically determined value based on signal statistics. By calculating the mean and standard deviation of timing metric values and setting the threshold as a function of these statistical parameters (e.g., threshold = mean + k×standard deviation), the system adapts to different signal conditions, resolving the contradiction between simple operation and reliable identification.
2Measurement precision
If cross-correlation based timing metric function is used for fine timing, then high precision is achieved, but computation complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the fine timing process into distinct stages: coarse timing identification first, then fine timing refinement only within the identified coarse time period. This segmentation allows the computationally intensive cross-correlation operations to be performed only on a limited subset of time samples rather than the entire signal, reducing overall computation complexity while maintaining high timing precision.
Solution Approach 2:
The patent uses preliminary action by performing coarse timing identification before fine timing. The coarse timing process quickly identifies the approximate time period containing the data frame, which then serves as the search space for the subsequent fine timing process. This preliminary action reduces the number of cross-correlation calculations needed, lowering computation complexity while preserving measurement precision.
Data Source
Figure 1~2
Figure 3~3A
Figure 4
AI summary
A fine timing method is provided. The method comprises: calculating values of a first timing metric function based on cross-correlation between a received signal and M training sequences within a time period, to obtain a plurality of sets of X values spaced according to a certain pattern; calculating summations for the plurality of sets of X values, respectively; and determining the beginning of a data frame based on the calculated summations.