Learning algorithms for scheduling in wireless networks with unknown channel statistics

Stahlbuhk, Thomas; Shrader, Brooke; Modiano, Eytan

doi:10.1016/j.adhoc.2018.10.006

Cited by 21 publications

(9 citation statements)

References 18 publications

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“…Slightly abusing the notation, we also denote the set of links that is connected to v by N (v). The primary interference model can represent Bluetooth or FH-CDMA networks as well as capture the essential feature of wireless interference [2], [19], and has been adopted in many studies on wireless scheduling, e.g., see [2]- [7] for more detailed description. Time is slotted, which can be achieved by being equipped with high accuracy GPS.…”

Section: System Modelmentioning

confidence: 99%

“…In this work, we consider the scheduling problem, where link rates and statistics are unknown a priori. This occurs when new applications try to operate efficiently under uncertainty caused by wireless fading, interference, limited feedback, measurement error, system dynamics, etc [17]- [19]. We assume that an instance link rate is revealed when it is accessed/scheduled, and it is drawn from an unknown static distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Although these aforementioned learning algorithms are amenable to distributed implementation, they are limited to single-hop networks, such as wireless access networks, and to saturated traffic scenarios (i.e., links always have packets to send), and thus cannot accordingly respond to packet arrival dynamics. Recently, Stahlbuhk et al [19] has developed a joint learning and scheduling scheme that provides provable efficiency under packet arrival dynamics, by incorporating GMM scheduling algorithm and UCB-based learning algorithm. Albeit interesting, it achieves only 1 2 of the capacity region and has linear complexity, which makes it less attractive for large-size networks.…”

Section: Introductionmentioning

confidence: 99%

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A learning-based distributed algorithm for scheduling in multi-hop wireless networks

2022

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We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A learning-based distributed algorithm for scheduling in multi-hop wireless networks

2022

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“…There are many kinds of ANNs. In terms of learning algorithms, supervised or unsupervised learning networks [3, 4], hybrid learning networks [5], associate learning networks [6], and optimisation application networks [7] are used widely. In terms of network connectionism, feed‐forward networks [8], recurrent networks [9], and reinforcement networks [10] are used widely.…”

Section: Introductionmentioning

confidence: 99%

Influence of kernel clustering on an RBFN

Zhu

Miao

2019

CAAI Transactions on Intelligence Technology

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“…This framework has been extended and applied to network switching [38], satellite communications [39], ad-hoc networking [40], [41], packet multicasting and broadcasting [42], packet-delivery-time reduction [43], multi-user MIMO [44], energy harvesting systems [45], and age-of-information minimization [46], [47]. In the works of [48] and [49], learning algorithms were used for achieving network stability under unknown arrival and channel statistics. The methods considered in those works augmented the max-weight algorithm with a statistical learning component.…”

Section: Introductionmentioning

confidence: 99%

Learning Algorithms for Minimizing Queue Length Regret

Stahlbuhk

Shrader

Modiano

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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We consider a system consisting of a single transmitter/receiver pair and N channels over which they may communicate. Packets randomly arrive to the transmitter's queue and wait to be successfully sent to the receiver. The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue. For each channel, an attempted transmission is successful with an unknown probability. The transmitter's objective is to quickly identify the best channel to minimize the number of packets in the queue over T time slots. To analyze system performance, we introduce queue length regret, which is the expected difference between the total queue length of a learning policy and a controller that knows the rates, a priori. One approach to designing a transmission policy would be to apply algorithms from the literature that solve the closely-related stochastic multi-armed bandit problem. These policies would focus on maximizing the number of successful frame transmissions over time. However, we show that these methods have Ω(log T ) queue length regret. On the other hand, we show that there exists a set of queue-length based policies that can obtain order optimal O(1) queue length regret. We use our theoretical analysis to devise heuristic methods that are shown to perform well in simulation.

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Learning algorithms for scheduling in wireless networks with unknown channel statistics

Cited by 21 publications

References 18 publications

A learning-based distributed algorithm for scheduling in multi-hop wireless networks

A learning-based distributed algorithm for scheduling in multi-hop wireless networks

Influence of kernel clustering on an RBFN

Learning Algorithms for Minimizing Queue Length Regret

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