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Deep learning-based edge caching for multi-cluster heterogeneous networks

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journal contribution
posted on 2019-05-24, 08:55 authored by Jiachen Yang, Jipeng Zhang, Chaofan Ma, Huihui Wang, Juping Zhang, Gan Zheng
© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Neural Computing and Applications

Volume

32

Issue

19

Pages

15317 - 15328

Citation

YANG, J. ... et al., 2020. Deep learning-based edge caching for multi-cluster heterogeneous networks. Neural Computing and Applications, 32 (19), pp.15317-15328.

Publisher

© Springer (part of Springer Nature)

Version

  • AM (Accepted Manuscript)

Publisher statement

This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s00521-019-04040-z.

Acceptance date

2019-01-14

Publication date

2019-02-18

ISSN

0941-0643

eISSN

1433-3058

Language

  • en