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Reconfigurable and traffic-aware MAC design for virtualized wireless networks via reinforcement learning

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posted on 24.05.2019, 13:10 authored by Atoosa Dalili Shoaei, Mahsa DerakhshaniMahsa Derakhshani, Tho Le-Ngoc
In this paper, we present a reconfigurable MAC scheme where the partition between contention-free and contention-based regimes in each frame is adaptive to the network status leveraging reinforcement learning. In particular, to support a virtualized wireless network consisting of multiple slices, each having heterogeneous and unsaturated devices, the proposed scheme aims to configure the partition for maximizing network throughput while maintaining the slice reservations. Applying complementary geometric programming (CGP) and monomial approximations, an iterative algorithm is developed to find the optimal solution. For a large number of devices, a scalable algorithm with lower computational complexity is also proposed. The partitioning algorithm requires the knowledge of the device traffic statistics. In the absence of such knowledge, we develop a learning algorithm employing Thompson sampling to acquire packet arrival probabilities of devices. Furthermore, we model the problem as a thresholding multi-armed bandit (TMAB) and propose a threshold-based reconfigurable MAC algorithm, which is proved to achieve the optimal regret bound.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Communications

Volume

67

Issue

8

Pages

5490 - 5505

Citation

SHOAEI, A.D., DERAKHSHANI, M. and LE-NGOC, T., 2019. Reconfigurable and traffic-aware MAC design for virtualized wireless networks via reinforcement learning. IEEE Transactions on Communications, 67 (8), pp.5490-5505.

Publisher

© Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Acceptance date

16/04/2019

Publication date

2019-04-26

Copyright date

2019

Notes

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

0090-6778

eISSN

1558-0857

Language

en

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