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| Presentation | 2023-10-05 09:45 [Encouragement Talk] Performance Evaluation of D2D Caching Method Using LSTM Considering Missing Data Makoto Tsunekiyo (Fukuoka Univ.), Noriaki Kamiyama (Ritsumeikan Univ.)  NS2023-85 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | As video viewing on mobile terminals becomes more common, there is concern that the backhaul traffic load on cellular networks (CN) will increase dramatically. To reduce the backhaul load, mobile edge computing, which distributes video content from a cache at the base station, has been attracting attention, but another effective method to further reduce the load is to cache the content at the mobile terminal and distribute it via D2D（device-to-device）communication. However, since the cache capacity of the MT is limited, it is effective to preferentially cache content that is expected to be in high demand along the MT's route of travel. Therefore, we proposed content demand estimation using deep learning to D2D cache delivery. We proposed a method to select contents to be cached on MTs by estimating contents that were likely to be demanded by other MTs on the travel route using a long-short term memory (LSTM) neural network, which was one of the algorithms of deep learning. First, we generated time-series data based on the number of keyword searches (number of viewings) for 10 well-known movie titles to confirm the effectiveness of the demand estimation part of the proposed method and the generality of the learning model. Next, a cache was created by making delivery requests based on the pre-movement demand distribution using the predicted values, and the hit rate with the content in the cache was calculated when delivery requests were made based on the post-movement demand distribution. Then, we compared the total number of requests per content measured and predicted for California (CA) and New York (NY) in the U.S. with the cache hit rate of LRU and the proposed method, and we confirmed that high-demand content can be predicted at the destination where the MT moved. In this paper, to reflect a more realistic situation, we evaluate the cache hit rate when the number of contents is increased and when missing data is considered, taking into account the popularity bias of the contents used in the simulation. The effectiveness of the proposed method is confirmed. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | D2D / LSTM / cache / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 198, NS2023-85, pp. 77-82, Oct. 2023. | |
| Paper # | NS2023-85 | |
| Date of Issue | 2023-09-27 (NS) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
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 | NS2023-85 |
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| Conference Information | |
| Committee | NS |
| Conference Date | 2023-10-04 - 2023-10-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaidou University + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network architecture (5G, Local 5G, Beyond5G, Mobile networks, Ad-hoc and sensor networks, Overlay and P2P networks, Programmable networks, SDN/NFV, IoT, Network slicing), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. |
| Paper Information | |
| Registration To | NS |
| Conference Code | 2023-10-NS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance Evaluation of D2D Caching Method Using LSTM Considering Missing Data |
| Sub Title (in English) | |
| Keyword(1) | D2D |
| Keyword(2) | LSTM |
| Keyword(3) | cache |
| Keyword(4) | |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Makoto Tsunekiyo |
| 1st Author's Affiliation | Fukuoka University (Fukuoka Univ.) |
| 2nd Author's Name | Noriaki Kamiyama |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| Speaker | Author-1 |
| Date Time | 2023-10-05 09:45:00 |
| Presentation Time | 25 minutes |
| Registration for | NS |
| Paper # | NS2023-85 |
| Volume (vol) | vol.123 |
| Number (no) | no.198 |
| Page | pp.77-82 |
| #Pages | 6 |
| Date of Issue | 2023-09-27 (NS) |
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