For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal
Masaya OHTA Keiichi MIZUTANI Katsumi YAMASHITA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2010/09/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Spread Spectrum Technologies and Applications
OFDM, peak-to-average power ratio (PAPR), hopfield neural network,
Full Text: PDF>>
In this letter, a neural network (NN) for peak power reduction of an orthogonal frequency-division multiplexing (OFDM) signal is improved in order to suppress its computational complexity. Numerical experiments show that the amount of IFFTs in the proposed NN can be reduced to half, and its computational time can be reduced by 21.5% compared with a conventional NN. In comparison with the SLM, the proposed NN is effective to achieve high PAPR reduction and it has an advantage over the SLM under the equal computational condition.