Reduction of power envelope fluctuations in OFDM signals by using neural networks

One of the main drawbacks of Orthogonal Frequency Division Multiplexing (OFDM) are the large fluctuations of its power envelope. In this letter, a novel and efficient scheme based on Multilayer Perceptron (MLP) Neural Networks (NN) is proposed. The NN synthesizes the Active Constellation Expansion - (ACE) technique which is able to drastically reduce envelope fluctuations. This is achieved with much lower complexity, faster convergence, and better performance compared to previously available methods.This work has been partly funded by the projects MULTI-ADAPTIVE (TEC2008-06327-C03-02), COMONSENS (CSD2008-00010), and the AECI Program of Research Cooperation with Morocco.Publicad

Simplified Levenberg-Marquardt Algorithm based PAPR Reduction for OFDM System with Neural Network

In recent years, OFDM is the key transmission technique in the communication system. This is because of the high channel estimation, strong against multipath fading and increased spectral efficiency. Because of the independently modulated subcarriers, the Peak to Average Power Ratio (PAPR) is very high in OFDM systems. Previously we use a number of PAPR reduction schemes using clipping, adding windows etc. But in these methods we cannot achieve the optimum reduction or the BER performance is high or the system is very complex. On considering the BER performance and system complexity we employ a new method based on the Neural Network (NN). In this new method we achieve significant PAPR reduction with great BER improvement and complexity reduction. In the simulations we seen that the PAPR reduction and BER performance are very good. DOI: 10.17762/ijritcc2321-8169.15080