… Abstract—With the explosive growth in mobile data traffic, ultra-dense network (UDN) where
… An aim of this paper is to propose a deep reinforcement learning (DRL)-based approach to …

… reinforcement learning (RL), a dynamic programming framework which solves the RA problems
optimally over varying network … explore the potential of deep reinforcement learning (DRL…

… network issues, for example in cognitive radio spectrum access [3]– [6]. Our proposed
methodology utilizes deep-reinforcement … access strategy in a distributed wireless network (DWN). …

… The recent success of deep learning supports new and powerful tools … deep reinforcement
learning should be integrated into the architecture of future wireless communication networks …

… Machine learning can be a powerful tool to analyze and process data in wireless communication
networks [22]–[25]. In this paper, we exploit the advanced deep reinforcement learning …

… We compare the deep reinforcement learning algorithm with a Q-Learning based solution
and the well-known D-ACB dynamic solution under different traffic conditions [9]. We show …

… deep reinforcement learning (DRL)-based methods to optimize beamforming at the uplink
of a cell-free network… learning) that uses the Deep Deterministic Policy Gradient algorithm (…

… His main research interests include blockchain technology, wireless communications and
networking, statistical signal and data processing. He was a recipient of the Hong Kong PhD …

… His current research interests include wireless channel measurements and … wireless
communication networks, and applying artificial intelligence to wireless communication networks. …

… scarce resource and occupies an unparalleled position in the communication network [11], …
Li, “Channel state information prediction for 5g wireless communications: A deep learning …