首页|Enhancing Robustness of OFDM Systems Using LSTM-Based Autoencoders
Enhancing Robustness of OFDM Systems Using LSTM-Based Autoencoders
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NETL
NSTL
Wiley
The ability of orthogonal frequency division multiplexing (OFDM) to counteract frequency-selectivefading channels has made ita popular modem technology in contemporary communication systems. But maintaining dependable signaling is still difficult,especially when the signal-to-noiseratio (SNR) is low. In order to increase the dependability of OFDM systems, this study presentsan enhanced LSTM-basedautoencoder architecture. The suggested autoencoder efficiently utilizes temporal dependenciesand reduces the impacts of channel distortion by encoding and decoding OFDM signals utilizing one-hotencoding employinglong short-termmemory (LSTM) networks. The outcomes of the simulation show notable gains in performance indicators. Theaverage block error rate (BLER) of the suggested model is 0.0150, as opposed to 0.0296 for traditional autoencoders and 0.0886 forconvolutional OFDM systems. Comparably, the average packet error rate (PER) is decreased to 0.0017, surpassing convolutionalOFDM systems' 0.2260 and traditional autoencoders' 0.0070. These outcomes highlight the LSTM-basedautoencoder's efficacyin enhancing OFDM systems' dependability, especially in demanding settings. This study lays the groundwork for employingcutting-edgedeep learning methods to create reliable and effective communication systems.
NETL
NSTL
Wiley
