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Design and analysis of UW-OFDM signals
Mario Huemer1, Christian Hofbauer1, Alexander Onic1
1Johannes Kepler University Linz, Institute of Signal Processing, Altenberger Strasse 69, 4040 Linz, Austria.
International Journal of Electronics and Communications = Archiv Fur Elektronik Und Ubertragungstechnik : AEU
|July 5, 2016
Summary
This study optimizes signal generation for Unique Word-Orthogonal Frequency Division Multiplexing (UW-OFDM). We analytically prove the two-step approach is optimal and present algorithms for efficient UW-OFDM signal design.
Area of Science:
- Electrical Engineering
- Signal Processing
- Telecommunications
Background:
- Unique Word-Orthogonal Frequency Division Multiplexing (UW-OFDM) utilizes a deterministic unique word (UW) in the guard interval for improved performance.
- Previous research extensively covered UW-OFDM data estimation and bit error ratio (BER) performance compared to other methods.
Purpose of the Study:
- To investigate and optimize the generation methods for UW-OFDM signals.
- To analytically and numerically evaluate different UW-OFDM signal generation strategies.
Main Methods:
- Analytical proof of the optimality of the two-step generation approach over the direct approach in systematic UW-OFDM.
- Development of a heuristic algorithm for fast numerical optimization of redundant subcarrier positions.
- Comparison of an intuitive redundant subcarrier spreading approach with transceiver cost functions.
Main Results:
- The two-step generation method for systematic UW-OFDM is analytically proven to be optimal.
- A heuristic algorithm provides efficient numerical optimization for redundant subcarrier placement.
- The intuitive approach of spreading redundant subcarriers is practically optimal concerning transceiver cost functions.
Conclusions:
- This work provides a comprehensive analysis of UW-OFDM signal generation techniques.
- The findings offer practical insights for optimizing UW-OFDM system design and implementation.
- New approximations for symbol and redundant energy distributions are derived and validated.
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