FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
With the rapid development of wireless communications and the growing
complexity of digital modulation schemes, traditional manual modulation
recognition methods struggle to extract reliable signal features and meet
real-time requirements in modern scenarios. Recently, deep learning based
Automatic Modulation Recognition (AMR) approaches have greatly improved
classification accuracy. However, their large model sizes and high
computational demands hinder deployment on resource-constrained devices. Model
pruning provides a general approach to reduce model complexity, but existing
weight, channel, and layer pruning techniques each present a trade-off between
compression rate, hardware acceleration, and accuracy preservation. To this
end, in this paper, we introduce FCOS, a novel Fine-to-COarse two-Stage pruning
framework that combines channel-level pruning with layer-level collapse
diagnosis to achieve extreme compression, high performance and efficient
inference. In the first stage of FCOS, hierarchical clustering and parameter
fusion are applied to channel weights to achieve channel-level pruning. Then a
Layer Collapse Diagnosis (LaCD) module uses linear probing to identify layer
collapse and removes the collapsed layers due to high channel compression
ratio. Experiments on multiple AMR benchmarks demonstrate that FCOS outperforms
existing channel and layer pruning methods. Specifically, FCOS achieves 95.51%
FLOPs reduction and 95.31% parameter reduction while still maintaining
performance close to the original ResNet56, with only a 0.46% drop in accuracy
on Sig2019-12. Code is available at https://github.com/yaolu-zjut/FCOS.
