A cross-layer adaptation scheme for improving IEEE 802.11e QoS by learning

Chiapin Wang; Po Chiang Lin; Tsungnan Lin

doi:10.1109/TNN.2006.883014

A cross-layer adaptation scheme for improving IEEE 802.11e QoS by learning

Chiapin Wang^*
, Po Chiang Lin
, Tsungnan Lin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

Abstract

In this letter, we propose a cross-layer adaptation scheme which improves IEEE 802.11e quality of service (QoS) by online adapting multidimensional medium access control (MAC)-layer parameters depending on the application-layer QoS requirements and physical layer (PHY) channel conditions. Our solution is based on an optimization approach which utilizes neural networks (NNs) to learn the cross-layer function. Simulations results demonstrate the effectiveness of our adaptation scheme.

Original language	English
Pages (from-to)	1661-1665
Number of pages	5
Journal	IEEE Transactions on Neural Networks
Volume	17
Issue number	6
DOIs	https://doi.org/10.1109/TNN.2006.883014
Publication status	Published - 2006 Nov
Externally published	Yes

Keywords

Adaptive algorithm
IEEE 802.11e wireless local area networks (WLAN)
Neural networks (NNs)
Quality of service (QoS)

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNN.2006.883014

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title = "A cross-layer adaptation scheme for improving IEEE 802.11e QoS by learning",

abstract = "In this letter, we propose a cross-layer adaptation scheme which improves IEEE 802.11e quality of service (QoS) by online adapting multidimensional medium access control (MAC)-layer parameters depending on the application-layer QoS requirements and physical layer (PHY) channel conditions. Our solution is based on an optimization approach which utilizes neural networks (NNs) to learn the cross-layer function. Simulations results demonstrate the effectiveness of our adaptation scheme.",

keywords = "Adaptive algorithm, IEEE 802.11e wireless local area networks (WLAN), Neural networks (NNs), Quality of service (QoS)",

author = "Chiapin Wang and Lin, \{Po Chiang\} and Tsungnan Lin",

note = "Funding Information: Manuscript received May 29, 2006; revised July 1, 2006. This work was supported in part by Yulong Corporation under Grant 95-S-C01A and by Taiwan National Science Council under Grants 95-2219-E-002-018 and 95-2221-E-002-190.",

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doi = "10.1109/TNN.2006.883014",

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AU - Wang, Chiapin

AU - Lin, Po Chiang

AU - Lin, Tsungnan

N1 - Funding Information: Manuscript received May 29, 2006; revised July 1, 2006. This work was supported in part by Yulong Corporation under Grant 95-S-C01A and by Taiwan National Science Council under Grants 95-2219-E-002-018 and 95-2221-E-002-190.

PY - 2006/11

Y1 - 2006/11

N2 - In this letter, we propose a cross-layer adaptation scheme which improves IEEE 802.11e quality of service (QoS) by online adapting multidimensional medium access control (MAC)-layer parameters depending on the application-layer QoS requirements and physical layer (PHY) channel conditions. Our solution is based on an optimization approach which utilizes neural networks (NNs) to learn the cross-layer function. Simulations results demonstrate the effectiveness of our adaptation scheme.

AB - In this letter, we propose a cross-layer adaptation scheme which improves IEEE 802.11e quality of service (QoS) by online adapting multidimensional medium access control (MAC)-layer parameters depending on the application-layer QoS requirements and physical layer (PHY) channel conditions. Our solution is based on an optimization approach which utilizes neural networks (NNs) to learn the cross-layer function. Simulations results demonstrate the effectiveness of our adaptation scheme.

KW - Adaptive algorithm

KW - IEEE 802.11e wireless local area networks (WLAN)

KW - Neural networks (NNs)

KW - Quality of service (QoS)

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A cross-layer adaptation scheme for improving IEEE 802.11e QoS by learning

Abstract

Keywords

ASJC Scopus subject areas

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