Integrating Sensing and Communication in Cellular Networks via NR Sidelink

• URL: http://arxiv.org/abs/2109.07253v1
• Date: Wed, 15 Sep 2021 12:41:31 GMT
• Title: Integrating Sensing and Communication in Cellular Networks via NR Sidelink
• Authors: Dariush Salami, Ramin Hasibi, Stefano Savazzi, Tom Michoel, and Stephan Sigg
• Abstract summary: We discuss a common issue related to sidelink-based RF-sensing, which is its angle and rotation dependence. We propose a graph based encoder to capture propose-temporal features of the data and four approaches for multi-angle learning.
• Score: 7.42576783544779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: RF-sensing, the analysis and interpretation of movement or environment-induced patterns in received electromagnetic signals, has been actively investigated for more than a decade. Since electromagnetic signals, through cellular communication systems, are omnipresent, RF sensing has the potential to become a universal sensing mechanism with applications in smart home, retail, localization, gesture recognition, intrusion detection, etc. Specifically, existing cellular network installations might be dual-used for both communication and sensing. Such communications and sensing convergence is envisioned for future communication networks. We propose the use of NR-sidelink direct device-to-device communication to achieve device-initiated,flexible sensing capabilities in beyond 5G cellular communication systems. In this article, we specifically investigate a common issue related to sidelink-based RF-sensing, which is its angle and rotation dependence. In particular, we discuss transformations of mmWave point-cloud data which achieve rotational invariance, as well as distributed processing based on such rotational invariant inputs, at angle and distance diverse devices. To process the distributed data, we propose a graph based encoder to capture spatio-temporal features of the data and propose four approaches for multi-angle learning. The approaches are compared on a newly recorded and openly available dataset comprising 15 subjects, performing 21 gestures which are recorded from 8 angles.

Related papers

• Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access [49.83882366499547]
  This paper introduces a deep learning approach to dynamic spectrum access, leveraging the synergy of multi-modal image and spectrum data for the identification of potential transmitters. We consider an edge device equipped with a camera that is taking images of potential objects such as vehicles that may harbor transmitters. We propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center.
  arXiv  Detail & Related papers  (2023-12-21T15:26:26Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Enabling the Wireless Metaverse via Semantic Multiverse Communication [82.47169682083806]
  Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems. We propose a novel semantic communication framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs) An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI)
  arXiv  Detail & Related papers  (2022-12-13T21:21:07Z)
• Neuromorphic Integrated Sensing and Communications [28.475406916976247]
  We introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and detect the radar target using directly the received signal.
  arXiv  Detail & Related papers  (2022-09-24T00:23:25Z)
• Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals [98.95383921866096]
  We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. We show that capturing high-resolution temporal structures (nonstationarities) leads to substantial performance gains. We propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods.
  arXiv  Detail & Related papers  (2022-09-11T14:10:37Z)
• Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks [3.509171590450989]
  NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center.
  arXiv  Detail & Related papers  (2022-04-06T18:18:42Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication [40.84033154889936]
  This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition. In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors for collaborative recognition. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98% with an improvement up to 8% compared to the benchmarks.
  arXiv  Detail & Related papers  (2022-01-21T02:05:07Z)
• 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)
• ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting [0.0]
  We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting. We focus on the problem of fingerprinting wireless IoT devices in-the-wild using Deep Learning (DL) techniques.
  arXiv  Detail & Related papers  (2021-05-08T03:03:21Z)
• 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.