Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a loss of spectral efficiency as it requires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) and pilots to estimate the channel. We propose in this work to address these drawbacks by learning a neural network (NN)-based receiver jointly with a constellation geometry and bit labeling at the transmitter, that allows CP-less and pilotless communication on top of OFDM without a significant loss in bit error rate (BER). Our approach enables at least 18% throughput gains compared to a pilot and CP-based baseline, and at least 4% gains compared to a system that uses a neural receiver with pilots but no CP.
@article{arxiv.2101.08213,
title = {Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning},
author = {Fayçal Ait Aoudia and Jakob Hoydis},
journal= {arXiv preprint arXiv:2101.08213},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2009.05261