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.

中文翻译：

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消除OFDM带来的麻烦：采用端到端学习的无导频和无CP通信

正交频分复用（OFDM）由于其高效的实现而成为无线通信系统中的主要波形之一。然而，由于需要循环前缀（CP）来减轻符号间干扰（ISI）并且需要导频来估计信道，因此它遭受了频谱效率的损失。我们在这项工作中建议通过学习基于神经网络（NN）的接收器以及在发送器处的星座几何结构和位标记来解决这些缺点，该方法允许在OFDM之上进行无CP和无导频的通信而不会显着减少位的损失错误率（BER）。与基于飞行员和基于CP的基准相比，我们的方法可实现至少18％的吞吐量增长，与使用带有飞行员但不具有CP的神经接收器的系统相比，我们的方法可实现至少4％的吞吐量增长。