activity
20152022
most citedA Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks

29 citations · 192 across the 49 of their papers we have counts for

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5 papers · 1 filter

cs.LG20212 cited

On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems

Amal Feriani, Amine Mezghani, Ekram Hossain

We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Lea…

cs.LG20213 cited

Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty

Yuanchao Xu, Amal Feriani, Ekram Hossain

Multi-Agent Reinforcement Learning (MARL) is a challenging subarea of Reinforcement Learning due to the non-stationarity of the environments and the large dimensionality of the com…

cs.LG2020

Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks

Mohammad Salehi, Ekram Hossain

With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, d…

cs.LG20203 cited

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

S. Hu, X. Chen, W. Ni +2

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless…

cs.LG20201 cited

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

Amal Feriani, Ekram Hossain

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains,…