Data Driven Environmental Awareness Using Wireless Signals

• URL: http://arxiv.org/abs/2410.13159v2
• Date: Fri, 10 Jan 2025 21:00:41 GMT
• Title: Data Driven Environmental Awareness Using Wireless Signals
• Abstract summary: Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents.<n>We propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors.
• Score: 1.4776238523338288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices.

Related papers

• Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks [100.92510794177822]
  Conventional wireless networks rely on instantaneous channel state information (CSI) and react to channel variations without explicitly modeling the physical environment.<n>We propose Physical-AI: a new architecture for environment-aware wireless networking, where radio signals enable sensing, modeling, and interaction with the environment.<n>We show that the proposed framework reduces outage probability and blockage-response latency, particularly under increasing beam-switching delays.
  arXiv  Detail & Related papers  (2026-05-12T09:02:20Z)
• Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers [64.35403680946182]
  Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising solution for elderly health monitoring.<n>Existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments.<n>We propose a robust, domain-general framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture.
  arXiv  Detail & Related papers  (2026-04-23T03:28:12Z)
• Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection [5.225254533678075]
  Ambient Radio Sensing (ARS) is an ISAC approach that addresses spectrum scarcity by repurposing over-the-air radio signals.<n>ARS operates as a standalone device that passively receives communication signals, amplifies them to illuminate surrounding objects, and captures the reflected signals.<n>We have developed a prototype of ARS and validated its effectiveness through extensive experiments using ambient 5G signals.
  arXiv  Detail & Related papers  (2026-03-03T23:18:03Z)
• A Multimodal Dataset for Indoor Radio Mapping with 3D Point Clouds and RSSI [0.6077068694253743]
  This resource is designed to facilitate research in data-driven wireless modeling.<n>It aims to advance the development of robust, high-capacity indoor communication systems.
  arXiv  Detail & Related papers  (2025-11-01T11:02:16Z)
• Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
  AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
  arXiv  Detail & Related papers  (2025-10-23T13:02:14Z)
• Indoor/Outdoor Spectrum Sharing Enabled by GNSS-based Classifiers [1.3682156035049033]
  We leverage the Global Navigation Satellite System (GNSS) signals for I/O classification.<n>Our results demonstrate that threshold-based techniques and machine learning approaches can achieve greater accuracy than approaches on wireless (Wi-Fi) data.
  arXiv  Detail & Related papers  (2025-09-30T16:43:59Z)
• Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network [13.808977762165823]
  This paper introduces a novel deep learning model for cross-domain adaptation of Wi-Fi signals, inspired by physical signal shielding. The system achieved 96% accuracy and demonstrated strong material discrimination capabilities, offering potential for use in security applications to identify concealed objects based on their composition.
  arXiv  Detail & Related papers  (2025-04-29T09:18:02Z)
• RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments [1.7074276434401858]
  We introduce a novel machine learning-empowered methodology for wireless channel modeling. Key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes.
  arXiv  Detail & Related papers  (2024-06-04T01:06:41Z)
• Spatial-Domain Wireless Jamming with Reconfigurable Intelligent Surfaces [20.406776153173176]
  We propose a novel approach that allows for environment-adaptive spatial control of wireless jamming signals. We demonstrate complete denial-of-service of a Wi-Fi device while a second device located at a distance as close as 5 mm remains unaffected.
  arXiv  Detail & Related papers  (2024-02-21T12:50:44Z)
• Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges [137.47736805685457]
  We show that interference can still be effectively mitigated even without knowing its channel information. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
  arXiv  Detail & Related papers  (2023-05-11T18:06:46Z)
• Autoencoder-based Radio Frequency Interference Mitigation For SMAP Passive Radiometer [6.5358895450258325]
  Radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. This paper proposes an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users.
  arXiv  Detail & Related papers  (2023-04-25T21:37:51Z)
• Hands-on Wireless Sensing with Wi-Fi: A Tutorial [7.8774878397748065]
  This tutorial takes Wi-Fi sensing as an example. It introduces both the theoretical principles and the code implementation of data collection, signal processing, features extraction, and model design.
  arXiv  Detail & Related papers  (2022-06-20T01:53:35Z)
• 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)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• UAV-aided RF Mapping for Sensing and Connectivity in Wireless Networks [52.14281905671453]
  The use of unmanned aerial vehicles (UAV) as flying radio access network (RAN) nodes offers a promising complement to traditional fixed terrestrial deployments. Radio mapping is one of the challenges related to this task, referred here as radio mapping. The advantages induced by radio-mapping in terms of connectivity, sensing, and localization performance are illustrated.
  arXiv  Detail & Related papers  (2022-05-06T16:16:08Z)
• Optical flow-based branch segmentation for complex orchard environments [73.11023209243326]
  We train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.
  arXiv  Detail & Related papers  (2022-02-26T03:38:20Z)
• Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification [9.05607520128194]
  We present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations.
  arXiv  Detail & Related papers  (2021-03-02T20:19:05Z)
• Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data [64.48836187884325]
  Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment.
  arXiv  Detail & Related papers  (2021-02-09T15:48:35Z)
• Blind Federated Edge Learning [93.29571175702735]
  We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model. We propose an analog over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion.
  arXiv  Detail & Related papers  (2020-10-19T16:22:28Z)

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.