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.

基于深度学习的信道估计方案 (SisRafNet) 在正交频分复用（OFDM）系统中利用循环模型研究频率间信道的顺序行为，克服了传统的基于循环的方法存在的延迟和内存限制，提供了比现有深度学习信道估计技术更优异的性能，并在多个信噪比下验证了 3GPP 兼容信道场景的性能。