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Found 10 papers, 0 papers with code
Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR
Simulation results show that the proposed scheme is able to take actions while accounting for future states instead of being greedy in each subframe.
QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming.
Noise Learning Based Denoising Autoencoder
This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE).
When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applications
Artificial intelligence (AI) powered wireless networks promise to revolutionize the conventional operation and structure of current networks from network design to infrastructure management, cost reduction, and user performance improvement.
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems
To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks.
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users.
Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management
The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.
Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems
However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment.
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment.
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective
LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity.
