Search Results for author: Mohammad Shehab

Found 8 papers, 0 papers with code

Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins

no code implementations13 Feb 2024 Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone, Mohammad Shehab, Hirley Alves

Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks.

Multi-agent Reinforcement Learning Q-Learning +1

Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models

no code implementations24 Jan 2024 Eslam Eldeeb, Mohammad Shehab, Hirley Alves

In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events.

Management Scheduling +1

Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach

no code implementations26 Sep 2023 Eslam Eldeeb, Mohammad Shehab, Hirley Alves

In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices.

Multi-agent Reinforcement Learning reinforcement-learning

Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge

no code implementations9 Jan 2023 Eslam Eldeeb, Jean Michel de Souza Sant'Ana, Dian Echevarría Pérez, Mohammad Shehab, Nurul Huda Mahmood, Hirley Alves

We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption.

A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks

no code implementations13 Sep 2022 Eslam Eldeeb, Dian Echevarría Pérez, Jean Michel de Souza Sant'Ana, Mohammad Shehab, Nurul Huda Mahmood, Hirley Alves, Matti Latva-aho

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion.

Trajectory Planning

A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory

no code implementations2 Aug 2021 Eslam Eldeeb, Mohammad Shehab, Hirley Alves

A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques.

Traffic Prediction

Effective Energy Efficiency of Ultra-reliable Low Latency Communication

no code implementations20 Jan 2021 Mohammad Shehab, Hirley Alves, Eduard A. Jorswieck, Endrit Dosti, Matti Latva-aho

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption.

Information Theory Information Theory

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