activity
20202023
most citedAI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues

157 citations · 316 across the 25 of their papers we have counts for

collaborators

28 papers

cs.NI2023

Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control

Han Zhang, Hao Zhou, Medhat Elsayed +4

Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system…

cs.NI20223 cited

Cooperate or not Cooperate: Transfer Learning with Multi-Armed Bandit for Spatial Reuse in Wi-Fi

Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici +2

The exponential increase of wireless devices with highly demanding services such as streaming video, gaming and others has imposed several challenges to Wireless Local Area Network…

cs.NI20221 cited

Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul

Nien Fang Cheng, Turgay Pamuklu, Melike Erol-Kantarci

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latenc…

cs.NI202236 cited

IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture

Turgay Pamuklu, Anne Catherine Nguyen, Aisha Syed +2

Aerial base stations (ABSs) allow smart farms to offload processing responsibility of complex tasks from internet of things (IoT) devices to ABSs. IoT devices have limited energy a…

cs.NI20221 cited

Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

Anne Catherine Nguyen, Turgay Pamuklu, Aisha Syed +2

The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligen…

eess.SY20222 cited

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…