ELKFANet: Efficient Large Kernel Network Enabled Frame Acquisition for Low SNR Scenarios

Abstract

Frame acquisition is crucial to the entire receiving process. However, both traditional methods and existing deep learning approaches encounter performance challenges in low signal-to-noise ratio (SNR) scenarios. To address this issue, this letter proposes a novel deep learning-enabled frame acquisition scheme. We first introduce a data augmentation strategy that incorporates random time offsets and additive noise, specifically designed to mimic non-ideal reception conditions, such as capturing an incomplete preamble. Subsequently, we propose a frame acquisition neural network based on an efficient large kernel module, namely ELKFANet. This module decouples a standard large convolutional kernel into a sequence of horizontal, vertical, and pointwise convolutions, thereby capturing periodic and structural signal features through a global receptive field while maintaining computational efficiency. Experimental results show that under low SNR conditions ranging from −20 dB to 0 dB, ELKFANet achieves superior overall detection performance compared with existing methods across different channel models. Furthermore, the proposed module strikes an effective balance between computational complexity and detection performance, and exhibits strong scalability across different bandwidth scenarios.