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Title: Using the Fireworks Algorithm for ML Detection of Nonlinear OFDM
Authors: Guerreiro, João
Dinis, Rui
Carvalho, Paulo Montezuma
Keywords: OFDM
ML performance
Issue Date: Sep-2017
Publisher: IEEE
Abstract: Orthogonal frequency division multiplexing (OFDM) schemes have high envelope fluctuations and peak-to-average power ratio (PAPR), making them very prone to nonlinear distortion effects, which can affect significantly the performance when conventional receivers are employed. However, it was recently shown that strong nonlinear distortion effects on OFDM signals do not necessarily lead to performance degradation. In fact, Nonlinear OFDM schemes can outperform linear ones when optimum maximum likelihood (ML) receivers are employed. In this paper, we considered OFDM schemes with strong nonlinear distortion effects and we proposed a low-complexity detection scheme able to approach the optimum ML performance. Our technique is based on the fireworks algorithm (FWA) and allows excellent trade-offs between performance and complexity
Peer Reviewed: yes
Appears in Collections:AUTONOMA TECHLAB - Comunicações em conferências

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