CNN-aided timing synchronization in OFDM systems by exploiting lightweight cascaded mode

Abstract

In orthogonal frequency division multiplexing (OFDM) systems, the existing timing synchronization (TS) methods are challenged by high computational complexity, large processing delay, and performance degradation in wireless scenarios. To alleviate these issues, a lightweight cascaded one-dimensional convolutional neural network (1-D CNN)-based TS scheme is proposed in this paper, which is developed from the joint perspective of considering cascaded mode and network lightweight. Specifically, we meticulously design two lightweight subnetworks. The first one is implemented with only one 1-D CNN layer due to the coarse timing offset task, which aims to reduce the search range of TS for the following subnetwork. Based on the narrowed search range of TS, the second subnetwork is also light-weighted with a two-layer 1-D CNN, which refines the estimation of timing offset. By jointly considering the cascaded mode and the lightweight 1-D CNN, the proposed scheme improves the TS correctness with reduced computational complexity and processing delay compared with the existing works. Simulation results validate the effectiveness and robustness of the proposed TS scheme given variant parameters.