Ali Taleb Zadeh Kasgari

TL;DR: In this article, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BSs) and cellular-connected drone users (Drone-UEs), is introduced.

TL;DR: A novel approach based on deep reinforcement learning is proposed, in which the BS receives the state information, consisting of the users’ channel state information feedback and the available energy reported by the RIS, and optimizes its action composed of the BS transmit power allocation and RIS phase shift configuration using a neural network.

TL;DR: In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC-6G) in the downlink of a wireless network.

TL;DR: A novel control framework for stochastic optimization is proposed based on the Lyapunov drift-plus-penalty method that enables the system to minimize power, maintain slice isolation, and provide reliable and low latency end-to-end communication for RLL slices.

TL;DR: In this article, a probabilistic model for delay perception based on the brain features of a human user is proposed for allocating radio resources to human users while minimizing the transmit power and taking into account the reliability of both machine type devices and human users.