Pereira Ruisánchez, DarielFresnedo, ÓscarPérez-Adán, DarianCastedo, Luis2026-06-092026-06-092024-06D. Pereira-Ruisánchez, Ó. Fresnedo, D. Pérez-Adán and L. Castedo, "DRL-Based Sequential Scheduling for IRS-Assisted MIMO Communications," in IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8445-8459, June 2024, doi: 10.1109/TVT.2024.33591171939-9359https://hdl.handle.net/2183/48547[Abstract]: 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 multi-user (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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/SchedulingIntelligent reflecting surfacesDeep reinforcement learningPPOResource allocationjournal articleopen access10.1109/TVT.2024.3359117