RL-Based Queue Management for QoS Support in Multi-channel Multi-radio Mesh Networks

Zhou, Yu; Yun, Mira; Kim, Timothy; Arora, Amrinder; Choi, Hyeong‐Ah

doi:10.1109/nca.2009.46

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Kim et al [16] considered the fairness of customer services and improved the efficiency of the network while reducing the difference of customers' waiting time. Vučević et al [17] and Zhou et al [18] used a reinforcement learning (RL) algorithm to optimize queue management that allocates the data packets to the queues.…”

Section: Introductionmentioning

confidence: 99%

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Zhang

Ли

Yan

2018

Discrete Dynamics in Nature and Society

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We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

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Section: Introductionmentioning

confidence: 99%

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Zhang

Ли

Yan

2018

Discrete Dynamics in Nature and Society

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Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

Yau

Komisarczuk

Teal

2012

Journal of Network and Computer Applications

124

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Topology forming and optimization framework for heterogeneous wireless back-haul networks supporting unidirectional technologies

Kretschmer

Batroff

Ghinea

2013

Journal of Network and Computer Applications

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Wireless operators, in developed or emerging regions, must support triple-play service offerings as demanded by the market or mandated by regulatory bodies through so-called Universal Service Obligations (USOs). Since individual operators might face different constraints such as available spectrum licenses, technologies, cost structures or a low energy footprint, the EU FP7 CARrier grade wireless MEsh Network (CARMEN) project has developed a carrier-grade heterogeneous multi-radio back-haul architecture which may be deployed to extend, complement or even replace traditional operator equipment. To support offloading of live triple-play content to broadcast-optimized, e.g., DVB-T, overlay cells, this heterogeneous wireless back-haul architecture integrates unidirectional broadcast technologies. In order to manage the physical and logical resources of such a network, a centralized coordinator approach has been chosen, where no routing state is kept at plain WiBACK Nodes (WNs) which merely store QoS-aware MPLS forwarding state. In this paper we present our Unidirectional Technology (UDT)-aware design of the centralized Topology Management Function (TMF), which provides a framework for different topology and spectrum allocation optimization strategies and algorithms to be implemented. Following the validation of the design, we present evaluation results using a hybrid local/centralized topology optimizer showing that our TMF design supports the reliable forming of optimized topologies as well as the timely recovery from node failures

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RL-Based Queue Management for QoS Support in Multi-channel Multi-radio Mesh Networks

Cited by 4 publications

References 7 publications

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

Topology forming and optimization framework for heterogeneous wireless back-haul networks supporting unidirectional technologies

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