Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Navneet  Garg; Mathini Sellathurai; Tharmalingam Ratnarajah

doi:10.1109/ICASSP.2019.8682841

Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

Original language	English
Title of host publication	ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	978-1-4799-8131-1
DOIs	https://doi.org/10.1109/ICASSP.2019.8682841
Publication status	Published - 17 Apr 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Publication series

Name	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
ISSN (Electronic)	2379-190X

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Access to Document

10.1109/ICASSP.2019.8682841

A Unified Multiple Access Framework for Next Generation Mobile Networks By Removing Orthogonality (MANGO)
Ratnarajah, T. & Thompson, J.
EPSRC
1/05/17 → 31/10/20
Project: Research

Cite this

Garg, N., Sellathurai, M., & Ratnarajah, T. (2019). Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2019.8682841

Garg, Navneet ; Sellathurai, Mathini ; Ratnarajah, Tharmalingam. / Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers, 2019. (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)).

@inproceedings{c0de4a7da5134f21b0c46ee84f9aab41,

title = "Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks",

abstract = "To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.",

author = "Navneet Garg and Mathini Sellathurai and Tharmalingam Ratnarajah",

note = "Acceptance date set to exclude from REF OA Policy; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

year = "2019",

month = apr,

day = "17",

doi = "10.1109/ICASSP.2019.8682841",

language = "English",

series = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "Institute of Electrical and Electronics Engineers",

booktitle = "ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

address = "United States",

}

Garg, N, Sellathurai, M & Ratnarajah, T 2019, Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks. in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682841

Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks. / Garg, Navneet ; Sellathurai, Mathini; Ratnarajah, Tharmalingam.
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers, 2019. (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

AU - Garg, Navneet

AU - Sellathurai, Mathini

AU - Ratnarajah, Tharmalingam

N1 - Acceptance date set to exclude from REF OA Policy

PY - 2019/4/17

Y1 - 2019/4/17

N2 - To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

AB - To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

U2 - 10.1109/ICASSP.2019.8682841

DO - 10.1109/ICASSP.2019.8682841

M3 - Conference contribution

T3 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

BT - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - Institute of Electrical and Electronics Engineers

T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019

Y2 - 12 May 2019 through 17 May 2019

ER -

Garg N, Sellathurai M, Ratnarajah T. Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers. 2019. (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)). doi: 10.1109/ICASSP.2019.8682841

Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Abstract

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Projects

A Unified Multiple Access Framework for Next Generation Mobile Networks By Removing Orthogonality (MANGO)

Cite this