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Orthogonal frequency division multiplexing (OFDM) is one of the dominantwaveforms in wireless communication systems due to its efficientimplementation. However, it suffers from a loss of spectral efficiency as itrequires a cyclic prefix (CP) to mitigate inter-symbol interference (ISI) andpilots to estimate the channel. We propose in this work to address thesedrawbacks by learning a neural network (NN)-based receiver jointly with aconstellation geometry and bit labeling at the transmitter, that allows CP-lessand pilotless communication on top of OFDM without a significant loss in biterror rate (BER). Our approach enables at least 18% throughput gains comparedto a pilot and CP-based baseline, and at least 4% gains compared to a systemthat uses a neural receiver with pilots but no CP.

Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning