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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37827

Title: Reconfigurable and traffic-aware MAC design for virtualized wireless networks via reinforcement learning
Authors: Shoaei, Atoosa Dalili
Derakhshani, Mahsa
Le-Ngoc, Tho
Issue Date: 2019
Publisher: © Institute of Electrical and Electronics Engineers (IEEE)
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, doi: 10.1109/tcomm.2019.2913413
Abstract: 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.
Description: 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.
Version: Accepted for publication
DOI: 10.1109/tcomm.2019.2913413
URI: https://dspace.lboro.ac.uk/2134/37827
Publisher Link: https://doi.org/10.1109/tcomm.2019.2913413
ISSN: 0090-6778
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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