Zero-Padding OFDM Receiver Using Machine Learning

Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inter-symbol interference (ISI) and inter-block interference (IBI) originated from multi-path fading. By introducing some redundant symbols at the transmitter such as zero padding (ZP), spectral efficiency is reduced. The amount of redundancy is related to the channel-model order, an information carrying some uncertainty in practical situations, particularly when one is willing to increase data transmission. The recent trend of utilizing neural networks to address some communication issues sparkled the idea of exploiting machine-learning (ML) to improve the performance of ZP-OFDM transceivers whenever the channel order is not known. This work presents a novel application of ML to address ZP-OFDM physical layer issues. The simulation results show that the ML ZP-OFDM brings about performance improvements, such as reduced bit-error-rate (BER), when the amount of redundancy is insufficient and some form of nonlinearity is present at the transmitter end.