In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.

翻译：本文提出了一种基于深度学习的正交频分复用(OFDM)系统信道估计方案。我们提出的方法——命名为单时隙频率递归网络(SisRafNet)——基于对递归模型的全新研究，旨在挖掘信道在频率维度上的序列行为。利用无线信道在频率上具有高度相关性的特性，我们在单个OFDM时隙内采用递归神经网络技术，从而克服了传统基于递归的方法通常伴随的时延与内存约束问题。与现有基于深度学习的信道估计技术相比，所提出的SisRafNet能够提供更优越的估计性能，该性能已在多个信噪比条件下、符合第三代合作伙伴计划(3GPP)标准的一系列信道场景中得到验证。