A PAPR Reduction Method Based on Differential Evolution

High peak-to-average ratio power (PAPR) is a critical practical problem for an OFDM system. Many techniques, such as clipping and filtering (ICF), cognitive clipping, have been proposed to deal with the issue. However, it needs to determine the relationship between error vector magnitude (EVM) and clipping ratio (CR). In this paper, we formulate a universal PAPR optimization model with EVM constraint, and propose a differential evolution (DE) algorithm with a time complexity of 2 ( log ) O N N . The optimization parameter of the new approach is clipping noise. The EVM constraint guarantees proper receiver operation that is specified by most modern communication standards. The proposed DE algorithm employs the noise vector as the population. It adjusts three crucial control parameters to minimize cost function which is the amount of PAPR reduction. Simulation results show that our proposed method can offer good performance in PAPR and bit error rate (BER).

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