Summary of Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling For Uavs in Utm Systems, by Sravan Reddy Chintareddy et al.
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems
by Sravan Reddy Chintareddy, Keenan Roach, Kenny Cheung, Morteza Hashemi
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel data-driven framework is proposed for collaborative wideband spectrum sensing and scheduling in networked unmanned aerial vehicles (UAVs). The framework consists of three stages: model training, collaborative spectrum inference, and spectrum scheduling. In the model training stage, a machine learning (ML) model is trained using federated learning (FL) architecture on a generated dataset from a multi-cell environment. A novel architecture integrates wireless dataset generation into the FL training process, unlike existing studies that assume datasets are readily available. The collaborative spectrum inference stage proposes a fusion strategy compatible with the unmanned aircraft system traffic management (UTM) ecosystem. In the spectrum scheduling stage, reinforcement learning (RL) solutions dynamically allocate detected spectrum holes to secondary users. To evaluate the proposed methods, a comprehensive simulation framework is established using MATLAB LTE toolbox, incorporating base-station locations and emulating primary users channel usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help flying robots use radio waves efficiently is being developed. The project involves training a machine learning model that can work with many different flying robots and devices. The model is trained on fake data that simulates how real radio waves would look in different situations. Once the model is trained, it can be used to find empty spaces in the airwaves that the flying robots can use. This is useful because there are only so many available airwaves, and the flying robots need a way to communicate with each other. The team tested their approach using a computer simulation and found that it worked well. |
Keywords
» Artificial intelligence » Federated learning » Inference » Machine learning » Reinforcement learning
