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Paper Abstract and Keywords
Presentation 2021-12-17 14:30
[Invited Lecture] Energy-Efficient DQN-based User Association for Sub6GHz/mmWave Networks
Megumi Kaneko, Thi Ha Ly Dinh (NII), Keisuke Wakao, Kenichi Kawamura, Takatsune Moriyama, Yasushi Takatori (NTT) NS2021-109 RCS2021-192
Abstract (in Japanese) (See Japanese page) 
(in English) This work investigates the design of an energy-efficient Deep Q-Network (DQN) implemented at the user device, whose purpose is to optimize its association to multiple access points (AP) in a Beyond 5G (B5G) Sub-6GHz and mmWave integrated
network. To better cope with dynamic mobile environments, we first propose an adaptive epsilon-greedy policy at each user DQN in order to maximize the long-term sum-rate while simultaneously satisfying the Quality of Service (QoS) constraints of different applications. We then provide the detailed analysis of the energy consumed by each user device, in particular the power for DQN processing and for data movement. The trade-off between network performance in terms of sum-rate and QoS outage probability, and energy consumption at the user side is evaluated. Numerical results not only show the effectiveness of the proposed method compared to baseline, but also reveal the tremendous energy costs required by the default user DQN, underlining the importance of such energy-efficiency aware protocol design.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Reinforcement Learning / energy efficiency / User-to-multiple access points association / Deep Q-Network / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 302, RCS2021-192, pp. 88-88, Dec. 2021.
Paper # RCS2021-192 
Date of Issue 2021-12-09 (NS, RCS) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2021-109 RCS2021-192

Conference Information
Committee RCS NS  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Nara-ken Bunka Kaikan and Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Multi-hop/Relay/Cooperation, Disaster-resistant wireless network, Sensor/Mesh network, Ad-hoc network, D2D/M2M, Wireless network coding, Handover/AP switching/Connected cell control/Load balancing among base stations/Mobile network dynamic reconfiguration, QoS/QoE assurance, Wireless VoIP, IoT, Edge computing, etc. 
Paper Information
Registration To RCS 
Conference Code 2021-12-RCS-NS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Energy-Efficient DQN-based User Association for Sub6GHz/mmWave Networks 
Sub Title (in English)  
Keyword(1) Deep Reinforcement Learning  
Keyword(2) energy efficiency  
Keyword(3) User-to-multiple access points association  
Keyword(4) Deep Q-Network  
1st Author's Name Megumi Kaneko  
1st Author's Affiliation National Institute of Informatics (NII)
2nd Author's Name Thi Ha Ly Dinh  
2nd Author's Affiliation National Institute of Informatics (NII)
3rd Author's Name Keisuke Wakao  
3rd Author's Affiliation NTT Corporation (NTT)
4th Author's Name Kenichi Kawamura  
4th Author's Affiliation NTT Corporation (NTT)
5th Author's Name Takatsune Moriyama  
5th Author's Affiliation NTT Corporation (NTT)
6th Author's Name Yasushi Takatori  
6th Author's Affiliation NTT Corporation (NTT)
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Speaker Author-1 
Date Time 2021-12-17 14:30:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # NS2021-109, RCS2021-192 
Volume (vol) vol.121 
Number (no) no.301(NS), no.302(RCS) 
Page p.65(NS), p.88(RCS) 
Date of Issue 2021-12-09 (NS, RCS) 

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