Enhancing Robustness of OFDM Systems Using LSTM-Based Autoencoders

Abstract

The ability of orthogonal frequency division multiplexing (OFDM) to counteract frequency-selective fading channels has made it a popular modem technology in contemporary communication systems. But maintaining dependable signaling is still difficult, especially when the signal-to-noise ratio (SNR) is low. In order to increase the dependability of OFDM systems, this study presents an enhanced LSTM-based autoencoder architecture. The suggested autoencoder efficiently utilizes temporal dependencies and reduces the impacts of channel distortion by encoding and decoding OFDM signals utilizing one-hot encoding employing long short-term memory (LSTM) networks. The outcomes of the simulation show notable gains in performance indicators. The average block error rate (BLER) of the suggested model is 0.0150, as opposed to 0.0296 for traditional autoencoders and 0.0886 for convolutional OFDM systems. Comparably, the average packet error rate (PER) is decreased to 0.0017, surpassing convolutional OFDM systems' 0.2260 and traditional autoencoders' 0.0070. These outcomes highlight the LSTM-based autoencoder's efficacy in enhancing OFDM systems' dependability, especially in demanding settings. This study lays the groundwork for employing cutting-edge deep learning methods to create reliable and effective communication systems.