no code implementations • 27 Mar 2024 • Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, Mehdi Bennis
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.
no code implementations • 23 Jan 2024 • Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, Mehdi Bennis
In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling.
no code implementations • 8 Jun 2022 • 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 and its signaling in a wireless multiple access scenario.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Aug 2021 • Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce, Jakob Hoydis
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) to come up with a medium access control (MAC) protocol in a multiple access scenario.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 25 Nov 2019 • Mateus P. Mota, Daniel C. Araujo, Francisco Hugo Costa Neto, Andre L. F. de Almeida, F. Rodrigo P. Cavalcanti
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 scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER).