Hybrid Deep Learning Framework for CSI-Based Activity Recognition in Bandwidth-Constrained Wi-Fi Sensing

• URL: http://arxiv.org/abs/2602.06983v1
• Date: Tue, 27 Jan 2026 12:42:07 GMT
• Title: Hybrid Deep Learning Framework for CSI-Based Activity Recognition in Bandwidth-Constrained Wi-Fi Sensing
• Authors: Alison M. Fernandes, Hermes I. Del Monego, Bruno S. Chang, Anelise Munaretto, Hélder M. Fontes, Rui Campos,
• Abstract summary: This paper presents a novel hybrid deep learning framework designed to enhance the robustness of CSI-based Human Activity Recognition (HAR)<n>The framework's efficacy was systematically validated using a public dataset across 20, 40, and 80 MHz bandwidth configurations.
• Score: 1.0554048699217669
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a novel hybrid deep learning framework designed to enhance the robustness of CSI-based Human Activity Recognition (HAR) within bandwidth-constrained Wi-Fi sensing environments. The core of our proposed methodology is a preliminary Doppler trace extraction stage, implemented to amplify salient motion-related signal features before classification. Subsequently, these enhanced inputs are processed by a hybrid neural architecture, which integrates Inception networks responsible for hierarchical spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks that capture temporal dependencies. A Support Vector Machine (SVM) is then utilized as the final classification layer to optimize decision boundaries. The framework's efficacy was systematically validated using a public dataset across 20, 40, and 80 MHz bandwidth configurations. The model yielded accuracies of 89.27% (20 MHz), 94.13% (40 MHz), and 95.30% (80 MHz), respectively. These results confirm a marked superiority over standalone deep learning baselines, especially in the most constrained low-bandwidth scenarios. This study underscores the utility of combining Doppler-based feature engineering with a hybrid learning architecture for reliable HAR in bandwidth-limited wireless sensing applications.

Related papers

• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments [0.0]
  Human Activity Recognition via Wi-Fi Channel State Information (CSI) presents a privacy-preserving, contactless sensing approach suitable for smart homes, healthcare monitoring, and mobile IoT systems.<n>This paper proposes STAR (Sensing Technology for Activity Recognition), an edge-AI-optimized framework that integrates a lightweight neural architecture, adaptive signal processing, and hardware-aware co-optimization.<n>With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments.
  arXiv  Detail & Related papers  (2025-10-30T05:08:25Z)
• Green Learning for STAR-RIS mmWave Systems with Implicit CSI [53.03358325565645]
  Green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) broadcasting systems.<n>Motivated by the emphasis on environmental sustainability in future 6G networks, this work adopts a transmission framework for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption.
  arXiv  Detail & Related papers  (2025-09-08T15:56:06Z)
• Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method [0.0]
  Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation.<n>This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm.
  arXiv  Detail & Related papers  (2025-07-28T17:09:48Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Onboard Terrain Classification via Stacked Intelligent Metasurface-Diffractive Deep Neural Networks from SAR Level-0 Raw Data [18.05218019915147]
  This paper introduces a novel approach for real-time onboard terrain classification from Sentinel-1 (S1) level-0 raw In-phase/Quadrature (IQ) data.<n>Our method helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.
  arXiv  Detail & Related papers  (2025-03-10T09:25:44Z)
• Reservoir Network with Structural Plasticity for Human Activity Recognition [2.355460994057843]
  Echo state network (ESN) is a class of recurrent neural networks that can be used to identify unique patterns in time-series data and predict future events.<n>In this work, a custom-design neuromorphic chip based on ESN targeting edge devices is proposed.<n>The proposed system supports various learning mechanisms, including structural plasticity and synaptic plasticity, locally on-chip.
  arXiv  Detail & Related papers  (2025-03-01T07:57:22Z)
• DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs [10.215216950059874]
  jamming attacks pose substantial risks to the 5G network.<n>This work presents a novel deep learning-based technique for detecting jammers in CAV networks.<n>Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
  arXiv  Detail & Related papers  (2024-03-05T04:29:31Z)
• Bandwidth-efficient distributed neural network architectures with application to body sensor networks [73.02174868813475]
  This paper describes a conceptual design methodology to design distributed neural network architectures. We show that the proposed framework enables up to a factor 20 in bandwidth reduction with minimal loss. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.
  arXiv  Detail & Related papers  (2022-10-14T12:35:32Z)
• A Novel Approach For Analysis of Distributed Acoustic Sensing System Based on Deep Transfer Learning [0.0]
  Convolutional neural networks are highly capable tools for extracting spatial information. Long-short term memory (LSTM) is an effective instrument for processing sequential data. VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings.
  arXiv  Detail & Related papers  (2022-06-24T19:56:01Z)
• ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks [63.91384986073851]
  We propose the autoencoder-based low-rank filter-sharing technique technique (ALF) ALF shows a reduction of 70% in network parameters, 61% in operations and 41% in execution time, with minimal loss in accuracy.
  arXiv  Detail & Related papers  (2020-07-27T09:01:22Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)

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.