The (inverse) discrete Fourier transform (DFT/ IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT. The DCCN can exploit the cyclic prefix (CP) of OFDM waveform for increased SNR by replacing DFT with a learned linear transform, and has the advantage of combining CP-exploitation, channel estimation, and intersymbol interference (ISI) mitigation, with a complexity of ${\mathcal O}(N^{2})$ . Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. Furthermore, a suite of novel training techniques are developed to improve the convergence and generalizability of the trained model in fading channels. This work demonstrates the capability of deep neural networks in processing OFDM waveforms and the results suggest that the FFT processor in OFDM receivers can be replaced by a hardware AI accelerator.

中文翻译：

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深度波形：一种基于深度复值卷积网络的学习型 OFDM 接收器

（逆）离散傅立叶变换 (DFT/IDFT) 通常被认为是正交频分复用 (OFDM) 系统必不可少的。在本文中，开发了一种深度复值卷积网络 (DCCN) 来从时域 OFDM 信号中恢复比特，而无需依赖任何显式的 DFT/IDFT。DCCN 可以利用 OFDM 波形的循环前缀 (CP) 通过用学习的线性变换代替 DFT 来提高 SNR，并且具有结合 CP-利用、信道估计和符号间干扰 (ISI) 缓解的优点，复杂度为 ${\mathcal O}(N^{2})$ . 数值测试表明，在具有各种延迟扩展和移动性的瑞利衰落信道中，DCCN 接收器的性能优于基于理想和近似线性最小均方误差 (LMMSE) 估计和传统 CP 增强技术的传统信道估计器。所提出的方法受益于复值神经网络的表达性质，然而，目前缺乏流行的深度学习平台的支持。作为回应，为无线 PHY 的卷积网络的设计和分析提供了复值卷积层的精确和近似实现指南。此外，还开发了一套新颖的训练技术，以提高训练模型在衰落信道中的收敛性和泛化性。