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

TL;DR

This paper introduces a neural network-based approach that enables CP-less and pilotless OFDM communication, significantly improving spectral efficiency and throughput without substantial BER loss.

Contribution

It presents a novel joint transmitter-receiver neural network system that eliminates the need for cyclic prefix and pilots in OFDM, enhancing efficiency.

Findings

Achieves at least 18% throughput gains over traditional pilot- and CP-based systems.

Attains at least 4% throughput improvements compared to neural receivers with pilots but no CP.

Maintains comparable bit error rates despite removing conventional OFDM overheads.

Abstract

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.