… design for mmWave MU-MISO system exploiting DRL method. We propose a deep deterministic
policy gradient (DDPG) [11]-based MADRL algorithm to learn the analog beamformers. …

… Abstract—In multi-user multi-input single-output (MU-MISO) … In this paper, a distributed
deep reinforcement learning (DRL) … optimizing the beamforming problem in MUMISO systems. …

… In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO)
mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm …

… Abstract—We introduce a novel deep reinforcement learning … multiple input single output
(MU-MISO) system to maximize the … model in RIS-aided MU-MISO systems. Our findings in this …

… Reinforcement Learning (DRL) framework to optimize the secrecy performance of a Multi-User
(MU)-Multiple-Input Single-Output (MISO… DRL algorithm, the MU-MISO precoding matrix is …

… Authors in [9] address the SE performance of an IRS-assisted multi-user multiple-input
single-output (MU-MISO) downlink communication in a scenario with non-line-of-sight (NLoS) …

… off-policy reinforcement learning (RL) technique for maximizing the sum-rate of the MU-MISO …
signal and reflections off an ARIS for MU-MISO communications. The rest of the paper is …

… multi-user multiple-input singleoutput (MU-MISO) systems. The beam selection and …
reinforcement learning (DRL) and a digital precoding network based on model-driven deep learning …

… -user multiple input single output (MU-MISO) communication systems by jointly optimizing
the … To solve the formulated non-convex problem, a piecewise-deep reinforcement learning (…

In this letter, we study the transmission design for reconfigurable intelligent surface (RIS)-aided
multiuser communication networks. Different from most of the existing contributions, we …