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20152023
most citedLearning Resilient Radio Resource Management Policies with Graph Neural Networks

42 citations · 315 across the 66 of their papers we have counts for

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Showing 2019 · eess.SYShow all

5 papers · 2 filters

eess.SY2019★ 5 cited

A Prediction-Correction Algorithm for Real-Time Model Predictive Control

Santiago Paternain, Manfred Morari, Alejandro Ribeiro

In this work we adapt a prediction-correction algorithm for continuous time-varying convex optimization problems to solve dynamic programs arising from Model Predictive Control. In…

eess.SY2019

Model-Free Learning of Optimal Ergodic Policies in Wireless Systems

Dionysios S. Kalogerias, Mark Eisen, George J. Pappas +1

Learning optimal resource allocation policies in wireless systems can be effectively achieved by formulating finite dimensional constrained programs which depend on system configur…

eess.SY2019

Safe Policies for Reinforcement Learning via Primal-Dual Methods

Santiago Paternain, Miguel Calvo-Fullana, Luiz F. O. Chamon +1

In this paper, we study the learning of safe policies in the setting of reinforcement learning problems. This is, we aim to control a Markov Decision Process (MDP) of which we do n…

eess.SY2019★ 1 cited

Optimal Resource Allocation in Wireless Control Systems via Deep Policy Gradient

Vinicius Lima Silva, Mark Eisen, Konstantinos Gatsis +1

In wireless control systems, remote control of plants is achieved through closing of the control loop over a wireless channel. As wireless communication is noisy and subject to pac…

eess.SY2019

Optimal Power Flow Using Graph Neural Networks

Damian Owerko, Fernando Gama, Alejandro Ribeiro

Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators…