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Found 18 papers, 2 papers with code
Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices
To this end, we capture the first known emissions from the COTS IoT chipsets transmitting WiFi and Bluetooth under two different time frames.
Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation
Most of the existing methods solve reinforcement learning-based wireless problems for a specific pre-determined wireless network scenario.
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth
Further, the proposed Mbed-ATN showcases 16. 9x fewer FLOPs and 7. 5x lesser trainable parameters when compared to Oracle.
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work.
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning
An agent may decide to transmit a certain number of steps into the future, but this decision is not communicated to the other agents, so it the task of the individual agents to attempt to transmit at appropriate times.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio
More significantly, as the primary goal, this is the first work that has established the feasibility of deploying DRL to provide optimized distributed resource allocation for next-generation of GPU-embedded radios.
Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks
Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks.
Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control
Digital twin (DT) technologies have emerged as a solution for real-time data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks.
Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression
In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum.
A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges
Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum.
Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU
Interest in unmanned aerial system (UAS) powered solutions for 6G communication networks has grown immensely with the widespread availability of machine learning based autonomy modules and embedded graphical processing units (GPUs).
Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning
It will be shown that the utility of the defender is variant in different scenarios, based on the defender that is being used.
Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN
We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components.
Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification
The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model.
Jam-Guard: Low-Cost, Hand-held Device for First Responders to Detect and Localize Jammers
Intentional and unintentional interferences collectively referred to as Radio Frequency Interference (RFI) result in severe security threat to the public safety, first responder emergency rescue and military missions.
Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment
Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS.
Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding
In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks.
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before.
Networking and Internet Architecture
