Carrier Frequency Offset Estimation Using Selected Training Symbols
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Solution Overview
Problem
Existing low-complexity carrier frequency offset (CFO) estimation algorithms suffer from degraded performance when using short training symbols, necessitating an improved trade-off between estimation performance and computational complexity.
Innovation Solution
A method for CFO estimation that involves selecting training symbols based on design parameters Mmin and Mmax to calculate symbol differences and phase differences, allowing for reduced complexity and improved performance through symbol-by-symbol and group-based techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing low-complexity CFO estimation algorithms are used, then computational complexity is reduced, but estimation performance degrades when short training symbols are utilized
Solution Approach 1:
The patent segments the training symbols into two distinct groups: first training symbols with larger spacing and second training symbols with smaller spacing. This segmentation allows the algorithm to leverage the advantages of both groups - the first group provides robustness against frequency offset while the second group enhances estimation accuracy, thereby resolving the contradiction between complexity and performance.
Solution Approach 2:
The patent applies different processing techniques to different segments of training symbols based on their local characteristics. First training symbols use one estimation approach optimized for robustness, while second training symbols use another approach optimized for accuracy. This local differentiation enables the system to achieve high performance with limited computational resources.
2Measurement precision
If more training symbols are used to improve estimation performance, then CFO estimation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the training symbols into two segments with different spacing characteristics. This segmentation enables the system to achieve high estimation accuracy using a limited number of symbols by strategically selecting which symbols to process and how to process them, thereby avoiding the need to use all available symbols and reducing overall computational complexity.
Solution Approach 2:
The patent applies partial action by selectively processing only the most informative training symbols (first and second training symbols with specific spacing) rather than processing all training symbols. This partial processing approach achieves sufficient estimation accuracy without the computational burden of processing the complete symbol set.
Data Source
AI summary
A communication device, including a receiver configured to receive a signal comprising a plurality of training symbols and a data symbol; and at least one processor configured to: select, from among the plurality of training symbols, a first training symbol and a second training symbol based on a first parameter and a second parameter; obtain a first symbol difference corresponding to the first training symbol, and a second symbol difference corresponding to the second training symbol; estimate a carrier frequency offset based on the first symbol difference and the second symbol difference; obtain a compensated data symbol corresponding to the data symbol based on the estimated carrier frequency offset; and demodulate and decode the compensated data symbol to obtain decoded data.