Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks

• URL: http://arxiv.org/abs/2204.03027v1
• Date: Wed, 6 Apr 2022 18:18:42 GMT
• Title: Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks
• Authors: Yi Shi, Yalin E. Sagduyu, Tugba Erpek
• Abstract summary: 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.
• Score: 3.509171590450989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the DNN models from its neighbors, and aggregates them to initialize its own model for the next round of training. Without exchanging any spectrum data, this process is repeated over time such that a common DNN is built across the network while preserving the privacy associated with signals collected at different locations. Signal classification accuracy and convergence time are evaluated for different network topologies (including line, star, ring, grid, and random networks) and packet loss events. Then, the reduction of communication overhead and energy consumption is considered with random participation of sensors in model updates. The results show the feasibility of extending cooperative spectrum sensing over a general multi-hop wireless network through federated learning and indicate its robustness to wireless network effects, thereby sustaining high accuracy with low communication overhead and energy consumption.

Related papers

• 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)
• BLIS-Net: Classifying and Analyzing Signals on Graphs [20.345611294709244]
  Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. We introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. We show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
  arXiv  Detail & Related papers  (2023-10-26T17:03:14Z)
• Deep Multi-Emitter Spectrum Occupancy Mapping that is Robust to the Number of Sensors, Noise and Threshold [32.880113150521154]
  One of the primary goals in spectrum occupancy mapping is to create a system that is robust to assumptions about the number of sensors, occupancy threshold (in dBm), sensor noise, number of emitters and the propagation environment. We show that such a system may be designed with neural networks using a process of aggregation to allow a variable number of sensors during training and testing.
  arXiv  Detail & Related papers  (2022-11-27T14:08:11Z)
• 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)
• 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)
• 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)
• Integrating Sensing and Communication in Cellular Networks via NR Sidelink [7.42576783544779]
  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.
  arXiv  Detail & Related papers  (2021-09-15T12:41:31Z)
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search [58.720142291102135]
  We construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors.
  arXiv  Detail & Related papers  (2021-08-24T13:25:02Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• 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.