Success Probability Analysis for Edge Caching in Massive MIMO Networks

Navneet Garg; Mathini Sellathurai; Faheem Khan; Tharmalingam Ratnarajah; Vimal Bhatia

doi:10.1109/ANTS47819.2019.9118050

Success Probability Analysis for Edge Caching in Massive MIMO Networks

Navneet Garg, Mathini Sellathurai, Faheem Khan, Tharmalingam Ratnarajah, Vimal Bhatia

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

Abstract

Edge caching is one of the promising solutions to fulfill the demands in future wireless networks. Considering a multi-cell multi-user massive MIMO (multiple-input-multiple-output) system, in this paper, we analyze average success probability (ASP) measure under the assumption of maximal ratio transmission (MRT) precoding. With respect to caching, ASP represents a measure of both the content placement and delivery performance. For the networks, where the base stations (BSs) and users are distributed as homogeneous Poisson point process (PPP), we show that for interference-limited systems, ASP is independent of the densities of both BS and users, concluding that the evaluation of a given caching scheme depends on the threshold chosen. Simulation results are presented to show the above relations.

Original language	English
Title of host publication	2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
Publisher	IEEE
ISBN (Electronic)	9781728137155
DOIs	https://doi.org/10.1109/ANTS47819.2019.9118050
Publication status	Published - 16 Jun 2020
Event	13th IEEE International Conference on Advanced Networks and Telecommunications Systems 2019 - Goa, India Duration: 16 Dec 2019 → 19 Dec 2019

Conference

Conference	13th IEEE International Conference on Advanced Networks and Telecommunications Systems 2019
Abbreviated title	ANTS 2019
Country/Territory	India
City	Goa
Period	16/12/19 → 19/12/19

ASJC Scopus subject areas

Computer Networks and Communications
Communication

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10.1109/ANTS47819.2019.9118050

Cite this

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title = "Success Probability Analysis for Edge Caching in Massive MIMO Networks",

abstract = "Edge caching is one of the promising solutions to fulfill the demands in future wireless networks. Considering a multi-cell multi-user massive MIMO (multiple-input-multiple-output) system, in this paper, we analyze average success probability (ASP) measure under the assumption of maximal ratio transmission (MRT) precoding. With respect to caching, ASP represents a measure of both the content placement and delivery performance. For the networks, where the base stations (BSs) and users are distributed as homogeneous Poisson point process (PPP), we show that for interference-limited systems, ASP is independent of the densities of both BS and users, concluding that the evaluation of a given caching scheme depends on the threshold chosen. Simulation results are presented to show the above relations.",

author = "Navneet Garg and Mathini Sellathurai and Faheem Khan and Tharmalingam Ratnarajah and Vimal Bhatia",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 13th IEEE International Conference on Advanced Networks and Telecommunications Systems 2019, ANTS 2019 ; Conference date: 16-12-2019 Through 19-12-2019",

year = "2020",

month = jun,

day = "16",

doi = "10.1109/ANTS47819.2019.9118050",

language = "English",

booktitle = "2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)",

publisher = "IEEE",

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}

Garg, N, Sellathurai, M, Khan, F, Ratnarajah, T & Bhatia, V 2020, Success Probability Analysis for Edge Caching in Massive MIMO Networks. in 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)., 9118050, IEEE, 13th IEEE International Conference on Advanced Networks and Telecommunications Systems 2019, Goa, India, 16/12/19. https://doi.org/10.1109/ANTS47819.2019.9118050

TY - GEN

T1 - Success Probability Analysis for Edge Caching in Massive MIMO Networks

AU - Garg, Navneet

AU - Sellathurai, Mathini

AU - Khan, Faheem

AU - Ratnarajah, Tharmalingam

AU - Bhatia, Vimal

PY - 2020/6/16

Y1 - 2020/6/16

N2 - Edge caching is one of the promising solutions to fulfill the demands in future wireless networks. Considering a multi-cell multi-user massive MIMO (multiple-input-multiple-output) system, in this paper, we analyze average success probability (ASP) measure under the assumption of maximal ratio transmission (MRT) precoding. With respect to caching, ASP represents a measure of both the content placement and delivery performance. For the networks, where the base stations (BSs) and users are distributed as homogeneous Poisson point process (PPP), we show that for interference-limited systems, ASP is independent of the densities of both BS and users, concluding that the evaluation of a given caching scheme depends on the threshold chosen. Simulation results are presented to show the above relations.

AB - Edge caching is one of the promising solutions to fulfill the demands in future wireless networks. Considering a multi-cell multi-user massive MIMO (multiple-input-multiple-output) system, in this paper, we analyze average success probability (ASP) measure under the assumption of maximal ratio transmission (MRT) precoding. With respect to caching, ASP represents a measure of both the content placement and delivery performance. For the networks, where the base stations (BSs) and users are distributed as homogeneous Poisson point process (PPP), we show that for interference-limited systems, ASP is independent of the densities of both BS and users, concluding that the evaluation of a given caching scheme depends on the threshold chosen. Simulation results are presented to show the above relations.

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DO - 10.1109/ANTS47819.2019.9118050

M3 - Conference contribution

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BT - 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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T2 - 13th IEEE International Conference on Advanced Networks and Telecommunications Systems 2019

Y2 - 16 December 2019 through 19 December 2019

ER -

Success Probability Analysis for Edge Caching in Massive MIMO Networks

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