التفاصيل البيبلوغرافية
| العنوان: |
Effective Selection and Mutation in Genetic Algorithm for PAPR Reduction of OFDM Signal. |
| المؤلفون: |
Shigei, Noritaka, Araki, Kentaro, Miyajima, Hiromi |
| المصدر: |
Soft Computing in Machine Learning; 2014, p61-73, 13p |
| مستخلص: |
Orthogonal frequency division multiplexing (OFDM) is superior in spectral efficiency and is widely used in today's digital communication. One of the drawbacks of OFDM is that the peak-to-average power ratio (PAPR) of the transmitted signal tends to be high. In order to overcome this problem, peak power reduction methods based on tone injection have been proposed. The peak power reduction problem solved with tone injection (TI) is a combinatorial problem. In this paper, we propose an improved genetic algorithm (GA) for the PAPR reduction based on TI. In order to find better solutions in a short time, improved selection and mutation schemes are proposed. The effectiveness of the GA method is demonstrated by numerical simulations in terms of PAPR, computation time and bit error rate (BER). [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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