论文标题
基于CNN的OFDM系统的基于CNN的时间同步，并在频率选择性褪色通道中获得初始路径的辅助
CNN-based Timing Synchronization for OFDM Systems Assisted by Initial Path Acquisition in Frequency Selective Fading Channel
论文作者
论文摘要
多路径褪色会严重影响正交频施加多路复用（OFDM）系统中时正同步（TS）的准确性。为了解决此问题，我们提出了一个基于卷积的神经网络（CNN）的TS计划，该计划在本文中得到了最初的路径获取的辅助。具体而言，首先采用了经典的互相关方法来估计粗计时偏移并捕获初始路径，从而收缩了TS搜索区域。然后，开发了一维（1-D）CNN，以优化OFDM系统的TS。由于TS的搜索范围狭窄，基于CNN的TS有效地定位了准确的TS点，并激发了我们在计算复杂性和在线运行时间方面构建轻量级网络。与基于压缩传感的TS方法和基于极端学习机器的TS方法相比，仿真结果表明，所提出的方法可以通过减少的计算复杂性和在线运行时间有效地改善TS性能。此外，提出的TS方法对多路径褪色通道的变异参数提出了鲁棒性。
Multi-path fading seriously affects the accuracy of timing synchronization (TS) in orthogonal frequency division multiplexing (OFDM) systems. To tackle this issue, we propose a convolutional neural network (CNN)-based TS scheme assisted by initial path acquisition in this paper. Specifically, the classic cross-correlation method is first employed to estimate a coarse timing offset and capture an initial path, which shrinks the TS search region. Then, a one-dimensional (1-D) CNN is developed to optimize the TS of OFDM systems. Due to the narrowed search region of TS, the CNN-based TS effectively locates the accurate TS point and inspires us to construct a lightweight network in terms of computational complexity and online running time. Compared with the compressed sensing-based TS method and extreme learning machine-based TS method, simulation results show that the proposed method can effectively improve the TS performance with the reduced computational complexity and online running time. Besides, the proposed TS method presents robustness against the variant parameters of multi-path fading channels.
