… resource allocation based on real-time parameters is an attractive approach. This article
proposes a radio resource … (IAB) network using deep reinforcement learning (DRL). The …

… radio resource management (RRM). For high-dimensional RRM problems in a dynamic
environment, deep reinforcement learning … radio research. This article reviews and analyzes how …

… CONCLUSION In this article, a deep reinforcement learning (DRL) based approach has been
developed for a fog radio access network (F-RAN) to minimize the long-term system power …

… To optimally allocate and manage radio resources in such networks, we typically formulate
… network sum-rate, SE, and EE, given the available radio resources and QoS requirements …

… 1) We propose reinforcement learning as a technique for dynamic and autonomous resource
… This paper proposed reinforcement learning as a solution and devised an algorithm for …

… limited resources, we … reinforcement learning-based resources allocation algorithm that not
only considers the multiple parameters of network status but also utilizes them in the learning …

… of information (AoI)-aware radio resource management for expected long-term performance
… long short-term memory and deep reinforcement learning techniques to address the partial …

… novel radio resource scheduling … reinforcement learning algorithm, where the past scheduling
experiences are learned by the agent. The influence value of IQ-III reinforcement learning …

… the traditional resource allocation defect, they … Radio-IoT (NB-CR-IoT) resource allocation
solution with the objective of reusing vacant channels in licensed spectrum and deep learning …

… In this article, we propose a deep reinforcement learning (DRL)-based resource allocation
… in a layered fog radio access network (FRAN). We formulate the optimal resource allocation …