# A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

arXiv: https://arxiv.org/abs/2406.07929 · HTML: https://mlsanity.com/paper/2406.07929/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 66 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://mlsanity.com/paper/1811.00250/) (2018)
- [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018)
- [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://mlsanity.com/paper/1808.06866/) (2018)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Data-Driven Sparse Structure Selection for Deep Neural Networks](https://mlsanity.com/paper/1707.01213/) (2017)
- [Discrimination-aware Channel Pruning for Deep Neural Networks](https://mlsanity.com/paper/1810.11809/) (2018)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1804.03294/) (2018)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices](https://mlsanity.com/paper/1909.05073/) (2019)
- [Pruning Filter in Filter](https://mlsanity.com/paper/2009.14410/) (2020)
- [Faster CNNs with Direct Sparse Convolutions and Guided Pruning](https://mlsanity.com/paper/1608.01409/) (2016)
- [Understanding the Dynamics of DNNs Using Graph Modularity](https://mlsanity.com/paper/2111.12485/) (2021)
- [DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration](https://mlsanity.com/paper/1912.10178/) (2019)
- [To Filter Prune, or to Layer Prune, That Is The Question](https://mlsanity.com/paper/2007.05667/) (2020)
- [SigNet: A Novel Deep Learning Framework for Radio Signal Classification](https://mlsanity.com/paper/2011.03525/) (2020)
- [Layer Folding: Neural Network Depth Reduction using Activation Linearization](https://mlsanity.com/paper/2106.09309/) (2021)
- [SR-init: An interpretable layer pruning method](https://mlsanity.com/paper/2303.07677/) (2023)
- [Graph-Based Similarity of Neural Network Representations](https://mlsanity.com/paper/2111.11165/) (2021)

## Built on by (most cited first, 18 of 18)

- [SepPrune: Structured Pruning for Efficient Deep Speech Separation](https://mlsanity.com/paper/2505.12079/) (2025)
- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [SegTrans: Transferable Adversarial Examples for Segmentation Models](https://mlsanity.com/paper/2510.08922/) (2025)
- [Enhancing Image Generation Fidelity via Progressive Prompts](https://mlsanity.com/paper/2501.07070/) (2025)
- [Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLA](https://mlsanity.com/paper/2512.22208/) (2025)
- [RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively](https://mlsanity.com/paper/2411.10507/) (2024)
- [FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition](https://mlsanity.com/paper/2505.21571/) (2025)
- [DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning](https://mlsanity.com/paper/2507.12011/) (2025)
- [Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2409.05642/) (2024)
- [Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports](https://mlsanity.com/paper/2508.15845/) (2025)
- [Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuning](https://mlsanity.com/paper/2511.07198/) (2025)
- [MDM: Advancing Multi-Domain Distribution Matching for Automatic Modulation Recognition Dataset Synthesis](https://mlsanity.com/paper/2408.02714/) (2024)
- [MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition](https://mlsanity.com/paper/2502.19071/) (2025)
- [ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices](https://mlsanity.com/paper/2506.09066/) (2025)
- [Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers](https://mlsanity.com/paper/2509.01161/) (2025)
- [A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture](https://mlsanity.com/paper/2509.01164/) (2025)
- [HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification](https://mlsanity.com/paper/2512.08983/) (2025)

## Abstract (arXiv)

With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for signal classification. Although these models yield impressive results, they often come with high computational complexity and large model sizes, which hinders their practical deployment in communication systems. To address this challenge, we propose a novel layer pruning method. Specifically, we decompose the model into several consecutive blocks, each containing consecutive layers with similar semantics. Then, we identify layers that need to be preserved within each block based on their contribution. Finally, we reassemble the pruned blocks and fine-tune the compact model. Extensive experiments on five datasets demonstrate the efficiency and effectiveness of our method over a variety of state-of-the-art baselines, including layer pruning and channel pruning methods.