A Phase Factor Generation using RNNs Deep Learning Algorithm-Based PTS Method for PAPR Reduction of Beyond 5G FBMC Waveform

Abstract

Filter Bank Multicarrier (FBMC) is considered one of the strong applicants for a radio system beyond the fifth generation (B5G) that improves spectral access and lowers interference. It utilizes a prototype filter for each sub-carrier, making it best for the beyond fifth generation (B5G) framework. The performance of the FBMC is hugely impacted by the high peak-to-average power ratio (PAPR), which lowers the effectiveness of the power amplifier (PA) used in the 5G-based FBMC waveform. The conventional partial transmission sequence (PTS) technique requires high computational complexity due to the need for multiple Inverse Fast Fourier Transforms (IFFTs) and phase optimization, which can increase processing time and system latency. This article proposes a hybrid method combining a partial transmission sequence and recurrent neural network (RNN) known as PTS-RNNs. RNNs improve the performance of the PTS by efficiently predicting optimal phase factors, reducing computational complexity, and lowering the PAPR of the FBMC waveform. The parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) are estimated for 256 sub-carriers under the Rayleigh and Rician channels for FBMC and orthogonal frequency division multiplexing (OFDM). The experiment results reveal that the proposed PTS-RNNs method achieves an efficient 55.45% and 67.56% power saving performance for Rayleigh and Rician channels, with enhanced PSD performance while preserving the BER compared to the traditional selective mapping (SLM) and PTS methods. It is also noticeable that by adding more sub-blocks and phase parameters, PAPR can be further optimised.