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Found 12 papers, 1 papers with code
A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems
Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications.
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms.
Generative AI for Physical Layer Communications: A Survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge
Unfortunately, this E2E learning architecture requires a prior differentiable channel model to jointly train the deep neural networks (DNNs) at the transceivers, which is hardly obtained in practice.
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
To overcome these limitations, we propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet, where each access point makes power control decisions independently based on local information.
Joint Coding and Scheduling Optimization for Distributed Learning over Wireless Edge Networks
The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Transfer Learning for Future Wireless Networks: A Comprehensive Survey
Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers
In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks.
Face Swapping
Networking and Internet Architecture
Information Theory
Signal Processing
Information Theory
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach
To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles.
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications
Bringing together the latest advances in neural network architectures and ambient backscattering communications, this work allows wireless nodes to effectively "face" the jammer by first learning its jamming strategy, then adapting the rate or transmitting information right on the jamming signal.
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks
This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants.
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy.
