Past queue length based low-overhead link scheduling in multi-beam wireless mesh networks

Chattopadhyay, Arpan; Chockalingam, A.

doi:10.1109/spcom.2010.5560546

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Take the network in Figure 1 as an example. Assume that the arrival station for type jobs is station 1 and the destination station for type jobs is station 6 . The sets of visible stations of stations 1 ∼ 5 are…”

Section: Problem Statementmentioning

confidence: 99%

“…The first one is to decompose it into a series of single-station queueing problems or tandem queueing network problems [5]. The second kind of methods is to simplify the multihop network control problem into link scheduling problem [6] or queue management problem [7]. The main task of link scheduling is to establish a link between the stations and select the appropriate paths for job transferring.…”

Section: Introductionmentioning

confidence: 99%

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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: Problem Statementmentioning

confidence: 99%