HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification

• URL: http://arxiv.org/abs/2512.08983v1
• Date: Fri, 05 Dec 2025 16:03:53 GMT
• Title: HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification
• Authors: Maoyu Wang, Yao Lu, Bo Zhou, Zhuangzhi Chen, Yun Lin, Qi Xuan, Guan Gui,
• Abstract summary: This paper introduces HSCP, a Hierarchical Spectral Clustering Pruning framework.<n>It combines layer pruning with channel pruning to achieve extreme compression, high performance, and efficient inference.<n>Experiments on the UAV-M100 benchmark demonstrate that HSCP outperforms existing channel and layer pruning methods.
• Score: 16.880773405024126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of Unmanned Aerial Vehicles (UAVs) and the increasing complexity of low-altitude security threats, traditional UAV identification methods struggle to extract reliable signal features and meet real-time requirements in complex environments. Recently, deep learning based Radio Frequency Fingerprint Identification (RFFI) approaches have greatly improved recognition accuracy. However, their large model sizes and high computational demands hinder deployment on resource-constrained edge devices. While model pruning offers a general solution for complexity reduction, existing weight, channel, and layer pruning techniques struggle to concurrently optimize compression rate, hardware acceleration, and recognition accuracy. To this end, in this paper, we introduce HSCP, a Hierarchical Spectral Clustering Pruning framework that combines layer pruning with channel pruning to achieve extreme compression, high performance, and efficient inference. In the first stage, HSCP employs spectral clustering guided by Centered Kernel Alignment (CKA) to identify and remove redundant layers. Subsequently, the same strategy is applied to the channel dimension to eliminate a finer redundancy. To ensure robustness, we further employ a noise-robust fine-tuning strategy. Experiments on the UAV-M100 benchmark demonstrate that HSCP outperforms existing channel and layer pruning methods. Specifically, HSCP achieves $86.39\%$ parameter reduction and $84.44\%$ FLOPs reduction on ResNet18 while improving accuracy by $1.49\%$ compared to the unpruned baseline, and maintains superior robustness even in low signal-to-noise ratio environments.

Related papers

• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
  We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T04:30:35Z)
• Spectral Complex Autoencoder Pruning: A Fidelity-Guided Criterion for Extreme Structured Channel Compression [7.913101398893967]
  We propose a reconstruction-based criterion that measures functional redundancy at the level of individual output channels.<n>We transform a complex interaction field to the frequency domain and train a low-capacity autoencoder to reconstruct normalized spectra.<n>We find that spectral reconstruction fidelity of complex interaction fields is an effective proxy for channel-level redundancy under aggressive compression.
  arXiv  Detail & Related papers  (2026-01-14T10:34:18Z)
• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks [73.41290017870097]
  SecDiff is a plug-and-play, diffusion-aided decoding framework.<n>It significantly enhances the security and robustness of deep J SCC under adversarial wireless environments.
  arXiv  Detail & Related papers  (2025-11-03T11:24:06Z)
• Semantic Channel Equalization Strategies for Deep Joint Source-Channel Coding [8.967618587731694]
  Deep joint source-channel coding (DeepJSCC) has emerged as a powerful paradigm for end-to-end semantic communications.<n>Existing DeepJSCC schemes assume a shared latent space at transmitter (TX) and receiver (RX)<n>This mismatch introduces "semantic noise", degrading reconstruction quality and downstream task performance.
  arXiv  Detail & Related papers  (2025-10-06T10:29:07Z)
• Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detection [18.422983137700392]
  We propose a highly lightweight framework based on robust principal component analysis (RPCA) called L-RPCANet.<n>A hierarchical bottleneck structure is constructed to reduce and increase the channel dimension in the single-channel input infrared image.<n>A noise reduction module is embedded to enhance the robustness against complex noise.
  arXiv  Detail & Related papers  (2025-09-10T00:23:32Z)
• Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation [45.347078403677216]
  Large-scale models (LSMs) can be an effective framework for semantic representation and understanding.<n>However, their direct deployment is often hindered by high computational complexity and resource requirements.<n>This paper proposes a novel knowledge distillation based semantic communication framework.
  arXiv  Detail & Related papers  (2025-08-04T07:47:18Z)
• A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer [20.17660504535571]
  We propose a novel deep unfolding network (DU-TRPCA) that enforces stage-wise alternation between two tightly integrated modules: low-rank and sparse.<n>Experiments on synthetic and real-world HSIs demonstrate that DU-TRPCA surpasses state-of-the-art methods under severe mixed noise.
  arXiv  Detail & Related papers  (2025-06-03T02:01:39Z)
• FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition [20.984813278551115]
  Fine-to-COarse two-Stage pruning framework for automatic modulation recognition.<n>Experiments on multiple AMR benchmarks demonstrate that FCOS outperforms existing channel and layer pruning methods.
  arXiv  Detail & Related papers  (2025-05-27T07:12:09Z)
• SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models [52.40011613324083]
  Joint source-channel coding systems (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission.<n>Existing methods focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality.<n>We propose SING, a novel framework that formulates the recovery of high-quality images from corrupted reconstructions as an inverse problem.
  arXiv  Detail & Related papers  (2025-03-16T12:32:11Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Structured Sparsity Learning for Efficient Video Super-Resolution [99.1632164448236]
  We develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of video super-resolution (VSR) models. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks.
  arXiv  Detail & Related papers  (2022-06-15T17:36:04Z)
• CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization [61.71504948770445]
  We propose a novel channel pruning method via Class-Aware Trace Ratio Optimization (CATRO) to reduce the computational burden and accelerate the model inference. We show that CATRO achieves higher accuracy with similar cost or lower cost with similar accuracy than other state-of-the-art channel pruning algorithms. Because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.
  arXiv  Detail & Related papers  (2021-10-21T06:26:31Z)
• Discrimination-aware Network Pruning for Deep Model Compression [79.44318503847136]
  Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. We propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power. Experiments on both image classification and face recognition demonstrate the effectiveness of our methods.
  arXiv  Detail & Related papers  (2020-01-04T07:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.