WLAN Frequency Offset Estimation Using Weighted Phase Averaging
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Solution Overview
Problem
Existing methods for estimating frequency offsets in wireless local area networks (WLAN) systems are not precise enough due to reliance on limited training signal intervals, leading to suboptimal compensation for inter-channel interference and reduced communication performance.
Innovation Solution
A method that estimates frequency offsets by utilizing multiple training signals across various intervals, calculates phase differences, and applies weighted averages to improve precision, incorporating a processor and storage unit to manage and process the detected phases and offsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only training signals within one specific interval are utilized for frequency offset estimation, then the calculation complexity is reduced, but the precision of the estimation deteriorates
Solution Approach 1:
The patent divides the frequency offset estimation into two segments: coarse frequency offset estimation using STF and fine frequency offset estimation using LTF. This segmentation allows the system to handle different aspects of frequency offset separately, improving overall precision without overwhelming computational complexity.
Solution Approach 2:
The patent transitions from using only time-domain interval information to incorporating both time-domain and frequency-domain information. By applying FFT to the training signals and analyzing both magnitude and phase across multiple subcarriers, the system adds a frequency dimension to the estimation process, significantly improving precision.
2Measurement precision
If training signals within several intervals are utilized for frequency offset estimation, then the precision of the estimation is improved, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary coarse frequency offset estimation using STF before proceeding to fine frequency offset estimation using LTF. This preliminary action reduces the search space for the fine estimation stage, making the more precise multi-interval analysis computationally feasible.
Solution Approach 2:
The patent uses a two-stage estimation approach where coarse estimation provides a preliminary result, and fine estimation refines it further. This partial action strategy avoids the need to perform full-precision estimation across all intervals simultaneously, reducing overall computational complexity while maintaining high precision.
Data Source
AI summary
A device for estimating frequency offsets which is performed by periodically transmitting training signals from a wireless local area network system. The device includes a processor and computerized codes stored in a storage unit. The processor is configured to execute the computerized code to perform a method. The method includes receiving the plurality of training signals, selecting selected training signals by a predetermined interval from the received training signals, detecting and storing phases of the selected training signals, averaging phase differences of every pair of the detected phases of the selected training signals, calculating the frequency offsets according to an average of the phase differences for every pair of the detected phases of the selected training signals, and calculating a weighted average of the calculated frequency offsets using weighting values for each of the calculated frequency offsets.