A hybrid deep learning using reptile dragonfly search algorithm for reducing the PAPR in OFDM systems

Abstract

Abstract Orthogonal frequency division multiplexing (OFDM) is a famous multi-carrier modulation technique as it has a vast range of features like robustness against multi-path fading, higher bandwidth efficiency, and higher data rates. Though, OFDM has its own challenges. Among them, high peak power to average power ratio (PAPR) of the transmitted signal is the major problem in OFDM. In recent years, deep learning has drastically enhanced the performance of PAPR. In addition, the excessive training data and high computational complexity lead to a considerable issue in OFDM system. Thus, this paper implements a new PAPR reduction scheme in OFDM Systems through hybrid deep learning algorithms. A new optimized hybrid deep learning termed O-DNN + RNN is implemented by integrating the deep neural networks (DNN) and recurrent neural networks (RNN), where the parameters of both DNN and RNN are optimized using Hybrid Reptile Dragonfly Search Algorithm (HR-DSA). The new deep learning model is adopted for determining the constellation mapping and demapping of symbols on each subcarrier. This new optimized hybrid deep learning helps in reducing the PAPR and maximizes the performance.