2 citations · 5 across the 7 of their papers we have counts for
8 papers
Energy-Aware Two-Sided Learning for Dynamic Matching Games in Mobile Crowdsensing
Sumedh J. Dongare, Anja Klein, Andrea Ortiz
Mobile crowdsensing (MCS) is a promising enabler of Sensing-as-a-Service (SaaS) for next generation networks (NGNs), where sensing, communication, and computing are jointly conside…
Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information
Sumedh J. Dongare, Christo Kurisummoottil Thomas, Andrea Ortiz +2
Mobile crowdsensing (MCS) is a promising distributed sensing paradigm for future wireless networks, where MCS platforms (MCSPs) recruit mobile units (MUs) through monetary incentiv…
Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
Sumedh J. Dongare, Patrick Weber, Andrea Ortiz +3
Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP)…
Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing
Bernd Simon, Andrea Ortiz, Walid Saad +1
The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the avai…
SeBaSi system-level Integrated Access and Backhaul simulator for self-backhauling
Amir Ashtari Gargari, Matteo Pagin, Andrea Ortiz +3
millimeter wave (mmWave) and sub-terahertz (THz) communications have the potential of increasing mobile network throughput drastically. However, the challenging propagation conditi…
Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks
Amir Ashtari Gargari, Andrea Ortiz, Matteo Pagin +4
Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamf…
Survey Propagation: A Resource Allocation Solution for Large Wireless Networks
Andrea Ortiz, Daniel Barragan-Yani
The ever-increasing number of nodes in current and future wireless communication networks brings unprecedented challenges for the allocation of the available communication resource…
Multi-Agent Reinforcement Learning for Energy Harvesting Two-Hop Communications with a Partially Observable State
Andrea Ortiz, Hussein Al-Shatri, Tobias Weber +1
We consider an energy harvesting (EH) transmitter communicating with a receiver through an EH relay. The harvested energy is used for data transmission, including the circuit energ…