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首页> 外文会议>International Conference on Signal Image Processing and Communication >Deep CNN with Batch Renormalization based Channel Estimation Algorithms
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Deep CNN with Batch Renormalization based Channel Estimation Algorithms

机译：基于批量重整化的信道估计算法深CNN

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摘要

In recent years, mobile communication technology is developing rapidly, and people's demand for wireless high-speed data communication is increasing day by day. In order to meet the needs of people, some key techniques to enhance the spectrum and energy efficiency are proposed, among which orthogonal frequency division multiplexing (OFDM) technology is a modulation method widely used in wireless broadband systems to combat frequency selective fading in wireless channels. However, the use of higher-order modulation makes the system complex, and more advanced channel estimation techniques are needed to recover the original signal more efficiently at the receiver side. In this paper, the channel transmission matrix is treated as a natural image processing and a deep CNN based denoising network is trained. The network used in this paper has the following advantages: (1) Improving the learning ability of the denoising network by increasing the width instead of the depth. (2) Using the null convolution to expand the perceptual field enables the network to extract more contextual information and reduce the computational cost. (3) Solving the mini-batch problem under hardware resource constrained conditions by Batch renormalization. Also it can accelerate the convergence of the network training. We simulate the OFDM based communication system, and the results prove that the method has excellent performance.
机译：近年来，移动通信技术正在迅速发展，人们对无线高速数据通信的需求日益增加。为了满足人们的需求，提出了提高频谱和能效的一些关键技术，其中正交频分复用（OFDM）技术是广泛应用于无线宽带系统的调制方法，以打击无线信道中的频率选择性衰落。然而，使用高阶调制使得系统复杂，并且需要更高级的信道估计技术来在接收器侧更有效地恢复原始信号。在本文中，通道传输矩阵被视为自然图像处理，并且训练基于CNN的深度CNN的去噪网络。本文中使用的网络具有以下优点：（1）通过增加宽度而不是深度来提高去噪网络的学习能力。 （2）使用NULL卷积展开感知字段使网络能够提取更多上下文信息并降低计算成本。 （3）通过批量重整化解决硬件资源受限条件下的迷你批处理问题。它也可以加速网络训练的融合。我们模拟基于OFDM的通信系统，结果证明该方法具有出色的性能。

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