… network is considered as a promising architecture for satisfying more demands of future
wireless networks… the aid of deep reinforcement learning (DRL) in the cell-free network. Firstly, …

… dynamics of network, we model the problem as a sequential decision making process,
and apply deep reinforcement learning (DRL), which aggregates reinforcement learning (RL) …

… deep reinforcement learning as a tool to solve a distributed power control problem in a
wireless multi-cell network. … in d2d networks,” IEEE Transactions on Wireless Communications, …

… in using deep reinforcement learning (deep-RL) for solving wireless networking problems
with … /robust optimization, with their applications in wireless communications and networking. …

… For this dynamic cell-free network, we propose a successive … deep reinforcement learning
(DRL) model, namely, a deep deterministic policy gradient (DDPG)-deep double Q-network (…

… In this paper, we show the potential of deep reinforcement … , deep reinforcement learning,
machine-type communication (… -type (MTC) in future wireless networks. In slotted ALOHA RA …

… deep reinforcement learning (DRL)-based resource allocation scheme to improve content
distribution in a layered fog radio access network (… , where in-network caching is deployed and …

… In [16], a multi-UAV assisted wireless communication network is proposed but the energy
supply of ground devices is not considered. Work in [17] investigates the 3D placement of UAV …

… In this paper we have presented a deep Q-network (DQN) for cross-layer resource allocation
of a cognitive radio operating in an underlay DSA spectrum sharing setup. The presented …

… communications networks. In [7], the authors considered the data scheduling and admission
control problem of a backscatter sensor network… backscatter communications networks. The …