基于RRDBNet的OFDM信道估计

Wei Gao, Meihong Yang, Wei Zhang, Libin Liu
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引用次数: 1

摘要

在当前无线通信系统中,信道估计是正交频分复用(OFDM)技术的重要组成部分。然而,由于鲁棒性差、计算量大等实际原因,目前流行的信道估计算法无法得到广泛应用。为了解决OFDM系统的问题,我们提出了一种新的信道估计方案,该方案采用了一种精心设计的深度学习模型,称为RRDBNet。将多层残差网络与密集链路相结合,可以在保持残差学习优势的同时,方便地训练RRDBNet,增加结构容量。仿真结果表明,RRDBNet优于传统的最小二乘算法和现有的基于dl的超分辨率方案,低信噪比下的范围为0.5 ~ 1dB,高信噪比下的范围为2 ~ 3dB。此外,在试点数量方面,RRDBNet也优于现有的方案和方法LMMSE。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient OFDM Channel Estimation with RRDBNet
Channel estimation is important for orthogonal frequency division multiplexing (OFDM) in current wireless communication systems. Prevalent channel estimation algorithms, however, cannot be widely deployed due to some practical reasons, such as poor robustness and high computational complexity. To solve the problems for OFDM systems, we propose a new channel estimation scheme with a fine-designed deep learning model, called RRDBNet. RRDBNet can be trained easily while maintaining the advantages of residual learning and increasing the structure capacity, by combining the multi-level residual network and dense links. Our simulation results show that RRDBNet outperforms the traditional least-square algorithm and existing DL-based super-resolution schemes, which ranges from 0.5 to 1dB at low SNR and from 2 to 3dB at high SNR. Besides, in terms of the number of pilots, RRDBNet is also superior to existing schemes and approaches LMMSE.
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