Optimal Buffer Resource Allocation in Wireless Caching Networks

Abstract
Wireless caching systems have been exhaustively investigated in recent years. Due to limited buffer capacity, and unbalanced arrival and service rates, the backlogs may exist in the caching node and even cause buffer overflow. In this paper, we first investigate the relationship among backlogs, buffer capacity, data arrival rate and service rate, utilizing the martingale theory which is flexible in handling any arrival and service processes. Then given a target buffer overflow probability, the minimal required buffer portion is determined. If the devoted buffer capacity can fulfill all serving users' minimal buffer requirements, an optimization problem is constructed with the objective to minimize the overall buffer overflow probability. The optimization solution is obtained by a modified waterfilling scheme. Finally, the numerical results are illustrated to demonstrate the superiority of the proposed scheme.
Main Authors
Format
Conferences Conference paper
Published
2019
Series
Subjects
Publication in research information system
Publisher
IEEE
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202001081103Use this for linking
Parent publication ISBN
978-1-5386-6528-2
Review status
Peer reviewed
ISSN
2325-3789
DOI
https://doi.org/10.1109/SPAWC.2019.8815539
Conference
International Workshop on Signal Processing Advances in Wireless Communications
Language
English
Published in
IEEE International Workshop on Signal Processing Advances in Wireless Communications
Is part of publication
SPAWC 2019 : IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications
Citation
  • Liu, T., Chang, Z., Li, J., Shu, F., Ristaniemi, T., & Han, Z. (2019). Optimal Buffer Resource Allocation in Wireless Caching Networks. In SPAWC 2019 : IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications. IEEE. IEEE International Workshop on Signal Processing Advances in Wireless Communications. https://doi.org/10.1109/SPAWC.2019.8815539
License
In CopyrightOpen Access
Additional information about funding
This work is supported in part by the national natural science foundation of China under grant No. 61702258, 61771244, 61872184, in part by natural science foundation of Hebei Province of China, in part by US MURI, US NSF CNS-1717454, CNS-1731424, CNS-1702850, CNS-1646607.
Copyright© 2019, IEEE

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