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DRL-Based Sequential Scheduling for IRS-Assisted MIMO Communications
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  • Dariel Pereira-Ruisánchez ,
  • Óscar Fresnedo ,
  • Darian Pérez-Adán ,
  • Luis Castedo
Dariel Pereira-Ruisánchez

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Óscar Fresnedo
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Darian Pérez-Adán
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Luis Castedo
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Efficient resource allocation strategies are pivotal in vehicular communications as connected devices steeply increase in scenarios with much more stringent requirements. In this work, we propose a deep reinforcement learning (DRL)-based sequential scheduling approach for sum-rate maximization in the uplink of intelligent reflecting surface (IRS)-assisted multiuser (MU) multiple-input multiple-output (MIMO) vehicular communications. We formulate the scheduling task as a partially observable Markov decision process (POMDP) and propose a novel stream-level sequential solution based on the proximal policy optimization (PPO) algorithm. We consider a realistic imperfect channel state information (ICSI) model and assess the proposal in several communication setups comprising both spatially uncorrelated and correlated links. Simulation results show that the proposed DRL-based sequential scheduling approach is a robust alternative to more computationally demanding benchmarks.