9 citations · 9 across the 5 of their papers we have counts for
5 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…
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…
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
Mateus P. Mota, Daniel C. Araujo, Francisco Hugo Costa Neto +2
We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding…