Using the Fireworks Algorithm for ML Detection of Nonlinear OFDM

João Guerreiro, M. Beko, R. Dinis, P. Montezuma
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引用次数: 5

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.
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用Fireworks算法进行非线性OFDM的ML检测
正交频分复用(OFDM)方案具有较高的包络波动和峰值平均功率比(PAPR),很容易受到非线性失真的影响,在使用传统接收机的情况下,这将严重影响其性能。然而,最近的研究表明,对OFDM信号的强烈非线性失真效应并不一定会导致性能下降。事实上,当采用最优最大似然(ML)接收机时,非线性OFDM方案的性能优于线性OFDM方案。在本文中,我们考虑了具有强非线性失真效应的OFDM方案,并提出了一种能够接近最佳机器学习性能的低复杂度检测方案。我们的技术基于烟花算法(fireworks algorithm, FWA),可以在性能和复杂性之间进行很好的权衡。
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