Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems

• URL: http://arxiv.org/abs/2406.01727v1
• Date: Mon, 3 Jun 2024 18:39:27 GMT
• Title: Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems
• Authors: Sravan Reddy Chintareddy, Keenan Roach, Kenny Cheung, Morteza Hashemi,
• Abstract summary: We propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for unmanned aerial vehicles. In the model training stage, we explore dataset generation in a multi-cell environment. Secondly, in the collaborative spectrum fusion stage, we propose a collaborative spectrum fusion strategy. Finally, in the spectrum scheduling stage, we leverage reinforcement learning solutions to dynamically allocate the detected spectrum holes to the secondary users.
• Score: 1.9064735174703886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users (SUs) to opportunistically utilize detected "spectrum holes". Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and training a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose a novel architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. Finally, in the spectrum scheduling stage, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users. To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station~(BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.

Related papers

• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems [2.755290959487378]
  We propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs) In the spectrum scheduling phase, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users (i.e., UAVs) This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.
  arXiv  Detail & Related papers  (2023-08-09T16:08:44Z)
• Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments [80.49514665620008]
  Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
  arXiv  Detail & Related papers  (2023-06-20T21:21:19Z)
• Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks [17.770665737751372]
  We propose a novel model-aided federated MARL algorithm to coordinate multiple UAVs on a data harvesting mission with only limited knowledge about the environment. A performance comparison with standard MARL algorithms demonstrates that our proposed model-aided FedQMIX algorithm reduces the need for real-world training experiences by around three magnitudes.
  arXiv  Detail & Related papers  (2023-06-03T07:16:17Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• 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)
• Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks [50.68446003616802]
  Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
  arXiv  Detail & Related papers  (2022-02-07T05:11:01Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO [12.487990897680422]
  We introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the base station. We design a convolutional neural network, in which the input is the channel data, yielding the analog beamformers at the output. FL is demonstrated to be more tolerant to the imperfections and corruptions in the channel data as well as having less transmission overhead than CML.
  arXiv  Detail & Related papers  (2020-05-20T11:21:07Z)
• 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.