Self-organization in small cell networks: A reinforcement learning approach
… cell networks with minimum information required to learn an … of an algorithm, based on the
idea of simultaneous learning of … ], for achieving equilibria in small cell networks. As we shall …
idea of simultaneous learning of … ], for achieving equilibria in small cell networks. As we shall …
Cellular network traffic scheduling with deep reinforcement learning
… We focus on mobile networks, which are increasingly required to deliver a new class of appli…
learning algorithm that makes control decisions from real-time cell network measurements. …
learning algorithm that makes control decisions from real-time cell network measurements. …
An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks
… cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms
can … Nevertheless, future directions and challenges for 5G UDSC networks were concisely …
can … Nevertheless, future directions and challenges for 5G UDSC networks were concisely …
A framework for automated cellular network tuning with reinforcement learning
… In Section III, we discuss reinforcement learning and its usage in our framework for cellular
network tuning. In Section IV, we propose RL-based algorithms along with a few industry …
network tuning. In Section IV, we propose RL-based algorithms along with a few industry …
A survey of machine learning techniques applied to self-organizing cellular networks
… on the learning perspective of self-organizing networks (SON) … niques encountered in cellular
networks but also manages to … application of ML algorithms in cellular networks, and, much …
networks but also manages to … application of ML algorithms in cellular networks, and, much …
Power allocation in multi-cell networks using deep reinforcement learning
… -cell networks using DRL. We introduce a deep reinforcement learning into wireless networks
… chapter, we will introduce a deep reinforcement learning algorithm to solve this problem. …
… chapter, we will introduce a deep reinforcement learning algorithm to solve this problem. …
Power allocation in multi-user cellular networks: Deep reinforcement learning approaches
… There are 100 APs in the simulated cellular network, the time cost per execution Tc of our
proposed distributed algorithms and the centralized model-based methods are listed in Table III…
proposed distributed algorithms and the centralized model-based methods are listed in Table III…
Deep reinforcement learning for scheduling in cellular networks
… In this paper, we take deep reinforcement learning (DRL) … help with AI module in cellular
networks. A simulation platform, … Deep Q-Network (DQN) is a typical RL algorithm that uses a …
networks. A simulation platform, … Deep Q-Network (DQN) is a typical RL algorithm that uses a …
Coordinated reinforcement learning for optimizing mobile networks
… learning agents have been used in the past to address the problem of optimizing mobile
networks [6… Learning algorithms leveraging coordination can use a centralized controller [7], [13] …
networks [6… Learning algorithms leveraging coordination can use a centralized controller [7], [13] …
A reinforcement learning approach to power control and rate adaptation in cellular networks
… adapts the power budget of cells to the dynamic conditions of the network and user traffics. …
results where tuning cell powers using our algorithm offers significant improvements over …
results where tuning cell powers using our algorithm offers significant improvements over …
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