A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

By Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jing Wang, M. C. Gursoy. 2017

243 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Deep reinforcement learning based computation offloading and resource allocation for MEC (2018, methodology): “To work out this problem, we utilize Deep Reinforcement Learning (DRL) [7] as an enhanced version of RL. Based on DRL, we propose a Deep Q Network (DQN) which can use a Deep Neural Network (DNN) to estimate the value function of Qlearning algorithm.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “can be efficiently solved by deep reinforcement learning at a low computational complexity [153], [346]–[351].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “In [153], the authors focus on dynamic resource allocation in a cloud radio access network and present a DQL-based framework to minimize the total power consumption while fulfilling mobile users’ QoS requirements.”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “As summarized in Table 8, DRL algorithms have been applied in wireless networks for mobile networking [247]–[249], scheduler design [250]–[254], resource allocation [255], [256], [258], [260]–[262], and routing [263], [264].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[374] employing deep Q learning to determine the on/off modes of remote radio heads given, the current mode and user demand.”
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “of workload for VMs on cloud has been carried out by [65] [68] [69].”
Deep Reinforcement Learning Based Resource Allocation for V2V Communications (2018, background): “In [21], a deep reinforcement learning based approach has been proposed for resource allocation in the cloud radio access network (RAN) to save power and meet the user demands.”
Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach (2020, unknown): “[24] consider the problem of radio resource”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “scheme [57], the channel-related parameters are used to calculate the achievable data rate for users.”
Deep Reinforcement Learning for Resource Management in Network Slicing (2018, background): “The well-known success of AlphaGo [14] and following exciting results to apply DRL to solve resource allocation issues in some specific fields like power control [16], green communications [17], cloud radio access networks [18], mobile edge computing and caching [19]–[21], have aroused some research”