AoI-Aware Optimization in RIS-Assisted mmWave Network

In the era of next-generation wireless networks, real-time applications demand freshness in information delivery, notably in IoT, cyber-physical systems, and industrial IoT. We target the optimization of average Age of Information (AoI) in a multi-user RIS-assisted mmWave network, where the sensitivity of mmWave bands to propagation challenges is mitigated through reconfigurable intelligent surfaces (RIS). Notably, prior studies have overlooked AoI optimization in RIS-enhanced mmWave networks. Our contribution lies in devising a novel Markov Decision Process (MDP) framework and a model-free Deep Reinforcement Learning (DRL) algorithm tailored to high-dimensional action spaces. Our approach orchestrates user transmission timing, power allocation, base station beamforming, and RIS reflective coefficient adjustments. Through extensive simulations, we demonstrate the superiority of our method over existing schemes, showcasing enhanced network performance. This work represents an advancement in the optimization of AoI within the context of RIS-assisted mmWave communications, shedding light on efficient control policy learning for improved network operation.