… capability, deep NN-based machine learning technology is … the big challenge in wireless
communications and networks … learning, unsupervised learning, and reinforcement learning, …

… challenges and open issues for future research directions. To … of reinforcement learning
methods in different wireless IoT … In this section, we provide an overview of wireless IoT …

… Therefore, in the future, to alleviate the pressure of spectrum usage in wireless communication,
based on the characteristics of DRL, this study applies DRL to wireless communication …

… in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important
and useful technology. It is a learning … to provide an in-depth introduction of the Markov …

… Since this survey mainly focuses on deep reinforcement learning for RRAM in wireless … with
”AND/OR” combinations of them; ”deep reinforcement learning,” ”DRL,” ”resource allocation,” …

… communication, computing, caching and control (4Cs) problems. The recent advances in deep
reinforcement learning (… is accompanied by an in-depth summary and comparison of DRL …

… This section presents a holistic overview of the potential research directions and the …
forward by the use of ML algorithms in the MAC protocol design for future wireless networks. …

… techniques to optimize operations of wireless network [60]. Incorporating RL and DL into
MIMO detection has evolved as a promising method for future wireless communications [71]. …

… into machine learning frameworks holds the key to future embedded intelligent
communication systems. Applying traditional signal processing and deep learning approaches …

… Through an example of DSS in a DRS architecture, the simulation results show the
effectiveness of Deep Reinforcement Learning (DRL) to maximize users’ profit margin. It also …