6 research outputs found

    MDP-based MAC design with deterministic backoffs in virtualized 802.11 WLANs

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    This paper presents MAC protocols for a virtualized 802.11 network aiming to improve network performance and isolation among service providers (SPs). Taking into account the statistical properties of arrival traffic, a Markov Decision Process (MDP) is formulated to maximize the network throughput subject to SP reservations. By introducing the policy tree of the MDP, we present an optimal access policy. Each user can track this policy tree by carrier sensing and learn its transmission opportunity. As computational complexity of the policy tree grows exponentially with the total number of users, an efficient heuristic algorithm is proposed based on the MDP formulation where each user is assigned a deterministic backoff value. Numerical results show that performance of the proposed heuristic algorithm closely matches to the optimal policy. Moreover, both optimal and heuristic algorithms significantly improve TDMA and CSMA in terms of packet delivery ratio and isolation in unsaturated networks

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

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    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

    MDP-Based MAC Design With Deterministic Backoffs in Virtualized 802.11 WLANs

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    (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other worksThis paper presents MAC protocols for a virtualized 802.11 network aiming to improve network performance and isolation among service providers (SPs). Taking into account the statistical properties of arrival traffic, a Markov Decision Process (MDP) is formulated to maximize the network throughput subject to SP reservations. By introducing the policy tree of the MDP, we present an optimal access policy. Each user can track this policy tree by carrier sensing and learn its transmission opportunity. As computational complexity of the policy tree grows exponentially with the total number of users, an efficient heuristic algorithm is proposed based on the MDP formulation where each user is assigned a deterministic backoff value. Numerical results show that performance of the proposed heuristic algorithm closely matches to the optimal policy. Moreover, both optimal and heuristic algorithms significantly improve TDMA and CSMA in terms of packet delivery ratio and isolation in unsaturated networks
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