论文标题
轻巧的1-D CNN基于CIR不确定性的OFDM系统的基于1-D CNN
Lightweight 1-D CNN-based Timing Synchronization for OFDM Systems with CIR Uncertainty
论文作者
论文摘要
在这封信中，提出了一种轻量级的一维卷积神经网络（1-D CNN）的时序同步（TS）方法，以降低计算复杂性和处理延迟，并保持正交频施加到正交频率分层多路复用（OFDM）系统中的时机精度。具体而言，首先将TS任务转换为基于深度学习（DL）的分类任务，然后简化了压缩传感（CS）的TS策略的三个迭代，以形成一个轻量级网络，其CNN层的专门设计旨在突出分类功能。此外，为了增强针对通道冲动响应（CIR）不确定性的拟议方法的概括性能，利用了传播延迟的放松限制以增强培训数据的完整性。数值结果反映出，所提出的基于1-D CNN的TS方法有效提高了TS的准确性，减少了计算复杂性和处理延迟，并具有针对CIR不确定性的良好概括性能。该方法的源代码可在https://github.com/qingchj851/cnnts上获得。
In this letter, a lightweight one-dimensional convolutional neural network (1-D CNN)-based timing synchronization (TS) method is proposed to reduce the computational complexity and processing delay and hold the timing accuracy in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS task is first transformed into a deep learning (DL)-based classification task, and then three iterations of the compressed sensing (CS)-based TS strategy are simplified to form a lightweight network, whose CNN layers are specially designed to highlight the classification features. Besides, to enhance the generalization performance of the proposed method against the channel impulse responses (CIR) uncertainty, the relaxed restriction for propagation delay is exploited to augment the completeness of training data. Numerical results reflect that the proposed 1-D CNN-based TS method effectively improves the TS accuracy, reduces the computational complexity and processing delay, and possesses a good generalization performance against the CIR uncertainty. The source codes of the proposed method are available at https://github.com/qingchj851/CNNTS.
