… and highly dynamic data rates of users, how to allocate backhaul resource to each user …
a deep reinforcement learning (DRL) approach to address this challenge. By learning the …

… from D to train the deep Q-learning networks. Though there are many investigations regarding
the sampling method to improve the efficiency and accuracy of the training process [26], […

… wireless backhaul topology design problem. We introduce a Deep Reinforcement Learning
(… We compare the quality of the solutions derived by our DRL approach to the optimal solution…

… approach for … and backhaul (IAB) technology to provide coverage for users in the disaster
area. With the data collected from the system-level simulation, a deep reinforcement learning …

… a combination of deep reinforcement learning and graph embedding. Our proposed approach
is … since we assume that it has an infinite capacity wired backhaul link. Assuming that each …

… To tackle these challenges, we propose a dynamic, realtime approach based on deep
reinforcement learning (DRL). Fig. 1 shows framework of the UAV placement algorithm for optimal …

… This article proposes a backhaul adaptation scheme that is controlled by the load on the …
approach due to the existence of explicit and implicit constraints. A deep reinforcement learning …

… a novel deep reinforcement learning approach in this paper. Deep reinforcement learning is
… In this paper, we consider an MIMO interference network with limited backhaul capacity and …

… a reinforcement learning approach that dynamically adjusts the sharing of radio resources
between backhaul … The metrics governing the reinforcement learning techniques are both user…

… To solve the problem, we propose to leverage emerging deep reinforcement learning (DRL),
which has been shown to deliver superior performance on solving learning tasks with …