54 citations · 112 across the 11 of their papers we have counts for
12 papers
Intent-Aware DRL-Based NOMA Uplink Dynamic Scheduler for IIoT
Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce +1
We investigate the problem of supporting Industrial Internet of Things user equipment (IIoT UEs) with intent (i.e., requested quality of service (QoS)) and random traffic arrival.…
Emergent Communication Protocol Learning for Task Offloading in Industrial Internet of Things
Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce +1
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with correspondin…
Designing Cellular Networks for UAV Corridors via Bayesian Optimization
Mohamed Benzaghta, Giovanni Geraci, David Lopez-Perez +1
As traditional cellular base stations (BSs) are optimized for 2D ground service, providing 3D connectivity to uncrewed aerial vehicles (UAVs) requires re-engineering of the existin…
Learning Random Access Schemes for Massive Machine-Type Communication with MARL
Muhammad Awais Jadoon, Adriano Pastore, Monica Navarro +1
In this paper, we explore various multi-agent reinforcement learning (MARL) techniques to design grant-free random access (RA) schemes for low-complexity, low-power battery operate…
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Yunchuan Zhang, Osvaldo Simeone, Sharu Theresa Jose +2
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each can…
Fairness Based Energy-Efficient 3D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach
Nithin Babu, Igor Donevski, Alvaro Valcarce +3
In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Mo…
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling
Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce
In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy…
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce +1
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) t…
Bayesian Optimization for Radio Resource Management: Open Loop Power Control
Lorenzo Maggi, Alvaro Valcarce Rial, Jakob Hoydis
We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resour…
Toward a 6G AI-Native Air Interface
Jakob Hoydis, Fayçal Ait Aoudia, Alvaro Valcarce +1
Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Mas…
Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access
Alvaro Valcarce, Jakob Hoydis
Communication protocols are the languages used by network nodes. Before a user equipment (UE) can exchange data with a base station (BS), it must first negotiate the conditions and…
Airborne Base Stations for Emergency and Temporary Events
Alvaro Valcarce, Tinku Rasheed, Karina Gomez +7
This paper introduces a rapidly deployable wireless network based on Low Altitude Platforms and portable land units to support disaster-relief activities, and to extend capacity du…