A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems

• URL: http://arxiv.org/abs/2304.06490v1
• Date: Sat, 25 Mar 2023 04:33:37 GMT
• Title: A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems
• Authors: Wen Liu, An-Hung Hsiao, Li-Hsiang Shen, Kai-Ten Feng
• Abstract summary: This paper proposes a novel error vector assisted learning scheme for device-free indoor localization. The proposed EVAL scheme employs deep neural networks to classify the location of a person in the indoor environment. Experimental results show that our proposed EVAL scheme outperforms conventional machine learning methods.
• Score: 7.010598383249521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel state information (CSI). To mitigate the RFO problem, an error vector spectrum (EVS) is conceived thanks to its higher resolution of signal and robustness to RFO. To address these challenges, this paper proposed a novel error vector assisted learning (EVAL) for device-free indoor localization. The proposed EVAL scheme employs deep neural networks to classify the location of a person in the indoor environment by extracting ample channel features from the physical layer signals. We conducted realistic experiments based on OpenWiFi project to extract both EVS and CSI to examine the performance of different device-free localization techniques. Experimental results show that our proposed EVAL scheme outperforms conventional machine learning methods and benchmarks utilizing either CSI amplitude or phase information. Compared to most existing CSI-based localization schemes, a new paradigm with higher positioning accuracy by adopting EVS is revealed by our proposed EVAL system.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization [3.3379026542599934]
  We propose a novel fingerprintingbased framework for indoor localization called SANGRIA. We demonstrate 42.96% lower average localization error across diverse indoor locales and heterogeneous devices.
  arXiv  Detail & Related papers  (2024-03-03T00:01:29Z)
• Improved Indoor Localization with Machine Learning Techniques for IoT applications [0.0]
  This study employs machine learning algorithms in three phases: supervised regressors, supervised classifiers, and ensemble methods for RSSI-based indoor localization. The experiment's outcomes provide insights into the effectiveness of different supervised machine learning techniques in terms of localization accuracy and robustness in indoor environments.
  arXiv  Detail & Related papers  (2024-02-18T02:55:19Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization [11.222977249913411]
  We provide a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain. We tested our proposed scheme on COST 2100 simulation data and real time frequency division multiplexing (OFDM) WiFi data collected from an office scenario.
  arXiv  Detail & Related papers  (2023-09-19T08:19:34Z)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• Addressing Gap between Training Data and Deployed Environment by On-Device Learning [1.6258710071587594]
  This article introduces a neural network based on on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment.
  arXiv  Detail & Related papers  (2022-03-02T12:59:33Z)
• CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems [0.0]
  We propose a two stages low rank (TSLR) CSI feedback scheme to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback.
  arXiv  Detail & Related papers  (2021-12-13T03:50:43Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

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.