NDLI: Reinforcement learning approach to dynamic activation of base station resources in wireless networks

Content Provider	IEEE Xplore Digital Library
Author	Peng-Yong Kong Panaitopol, D.
Copyright Year	2013
Description	Author affiliation: Khalifa Univ. of Sci., Technol. & Res. (KUSTAR), Abu Dhabi, United Arab Emirates (Peng-Yong Kong) \|\| ComTech Dept., NEC Technol. (UK), Nanterre, France (Panaitopol, D.)
Abstract	Recently, the issue of energy efficiency in wireless networks has attracted much research attention due to the growing concern on global warming and operator's profitability. We focus on energy efficiency of base stations because they account for 80% of total energy consumed in a wireless network. In this paper, we intend to reduce energy consumption of a base station by dynamically activating and deactivating the modular resources at the base station depending on the instantaneous network traffic. We propose an online reinforcement learning algorithm that will continuously adapt to the changing network traffic in deciding which action to take to maximize energy saving. As an online algorithm, the proposed scheme does not require a separate training phase and can be deployed immediately. Simulation results have confirmed that the proposed algorithm can achieve more than 50% energy saving without compromising network service quality which is measured in terms of user blocking probability.
Starting Page	3264
Ending Page	3268
File Size	259348
Page Count	5
File Format	PDF
ISBN	9781467362351
ISSN	21669570
DOI	10.1109/PIMRC.2013.6666710
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Base stations Learning (artificial intelligence) Q-factor Energy consumption Heuristic algorithms Wireless networks Dynamic scheduling online Q-Learning Green wireless networks energy efficient base station reinforcement learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Dynamic pricing for smart grid with reinforcement learning

Energy Efficient Adaptive Sensing for Dynamic Coverage in Wireless Sensor Networks

Joint caching and base station activation for green heterogeneous cellular networks

Intelligent base station management in greener traffic-aware cellular networks

A Novel Energy-Efficient Dynamic Voting Cluster Algorithm for Wireless Sensor Networks

Dynamic parallel machine scheduling using the learning agent

Centralized channel and power allocation for cognitive radio networks: A Q-learning solution

A Novel Energy Saving Scheme Based on Base Stations Dynamic Configuration in Green Cellular Networks

A heuristic evaluation PDS algorithm for energy-efficient delay constrained scheduling over wireless communication

Reinforcement learning approach to dynamic activation of base station resources in wireless networks

Similar Documents

Dynamic pricing for smart grid with reinforcement learning

Energy Efficient Adaptive Sensing for Dynamic Coverage in Wireless Sensor Networks

Joint caching and base station activation for green heterogeneous cellular networks

Intelligent base station management in greener traffic-aware cellular networks

A Novel Energy-Efficient Dynamic Voting Cluster Algorithm for Wireless Sensor Networks

Dynamic parallel machine scheduling using the learning agent

Centralized channel and power allocation for cognitive radio networks: A Q-learning solution

A Novel Energy Saving Scheme Based on Base Stations Dynamic Configuration in Green Cellular Networks

A heuristic evaluation PDS algorithm for energy-efficient delay constrained scheduling over wireless communication

Reinforcement learning approach to dynamic activation of base station resources in wireless networks