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

Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jing Wang, Mustafa C Gursoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

88 Scopus citations

Abstract

Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389990
DOIs
StatePublished - Jul 28 2017
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: May 21 2017May 25 2017

Other

Other2017 IEEE International Conference on Communications, ICC 2017
CountryFrance
CityParis
Period5/21/175/25/17

Keywords

  • Cloud Radio Access Network
  • Deep Reinforcement Learning
  • Green Communications
  • Resource Allocation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs'. Together they form a unique fingerprint.

Cite this