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.
翻译:由于无线通信系统的高效实施,矫形频率分解多氧化(OFDM)是无线通信系统的主要波形之一,但是,光谱效率也丧失,因为它需要一个圆形前缀(CP)来减少相互符号干扰(ISI)和飞行员来估计频道。我们在此工作中建议,通过学习一个神经网络接收器和发射机的星座几何和位标签来消除这些缺陷,使光谱通信系统之上的无CP-无引导通信不发生重大误差率(BER)损失。我们的方法使得与试点和CP基基线相比,至少能够取得18%的吞吐量收益,与使用有飞行员但没有CP的神经接收器的系统相比,至少能够取得4%的收益。