7 citations · 14 across the 6 of their papers we have counts for
7 papers
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G
Yujie Yao, Hao Zhou, Melike Erol-Kantarci
User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors…
Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, open radio access network (O-RAN) has become a promising technology to provide an open environment for network vendors and operators. Coordinating the x-applications (xAP…
Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks
Yujie Yao, Hao Zhou, Melike Erol-Kantarci
Millimeter Wave (mmWave) is an important part of 5G new radio (NR), in which highly directional beams are adapted to compensate for the substantial propagation loss based on UE loc…
Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci +1
Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to…
Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)
Han Zhang, Hao Zhou, Melike Erol-Kantarci
Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It p…
Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci +1
A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and con…