Energy-efficient dynamic spectrum access using no-regret learning

Yao Lu; Hao He; Jun Wang; Shaoqian Li

doi:10.1109/ICICS.2009.5397468

Energy-efficient dynamic spectrum access using no-regret learning

Yao Lu^*
, Hao He
, Jun Wang
, Shaoqian Li

^*Corresponding author for this work

University of Electronic Science and Technology of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper, we consider a cross-layer design of dynamic spectrum access in distributive cognitive radio (CR) networks. We model the licensed channel as a finite-state Markov channel (FSMC) and the CR user selects one channel to access and decides transmission rate and power in order to maximize its energy efficiency. We propose a game theoretic framework to formulate this problem and apply a learning algorithm called modified regret-matching leading to correlated equilibrium which is more practical than the regret-matching learning algorithm applied in the related work. The only thing that each user needs to know is his own realized payoffs and actions. From the simulation results, the modified learning algorithm provides impressive performance.

Original language	English
Title of host publication	ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing
DOIs	https://doi.org/10.1109/ICICS.2009.5397468
State	Published - 2009
Externally published	Yes
Event	7th International Conference on Information, Communications and Signal Processing, ICICS 2009 - Macau Fisherman's Wharf, Macao Duration: 8 Dec 2009 → 10 Dec 2009

Publication series

Name	ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing

Conference

Conference	7th International Conference on Information, Communications and Signal Processing, ICICS 2009
Country/Territory	Macao
City	Macau Fisherman's Wharf
Period	8/12/09 → 10/12/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cognitve radio
Correlated equilirium
Dynamic spectrum access
Energy-efficient
Game theory
No-regret learning

Access to Document

10.1109/ICICS.2009.5397468

Cite this

Lu, Y., He, H., Wang, J., & Li, S. (2009). Energy-efficient dynamic spectrum access using no-regret learning. In ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing Article 5397468 (ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing). https://doi.org/10.1109/ICICS.2009.5397468

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title = "Energy-efficient dynamic spectrum access using no-regret learning",

abstract = "In this paper, we consider a cross-layer design of dynamic spectrum access in distributive cognitive radio (CR) networks. We model the licensed channel as a finite-state Markov channel (FSMC) and the CR user selects one channel to access and decides transmission rate and power in order to maximize its energy efficiency. We propose a game theoretic framework to formulate this problem and apply a learning algorithm called modified regret-matching leading to correlated equilibrium which is more practical than the regret-matching learning algorithm applied in the related work. The only thing that each user needs to know is his own realized payoffs and actions. From the simulation results, the modified learning algorithm provides impressive performance.",

keywords = "Cognitve radio, Correlated equilirium, Dynamic spectrum access, Energy-efficient, Game theory, No-regret learning",

author = "Yao Lu and Hao He and Jun Wang and Shaoqian Li",

year = "2009",

doi = "10.1109/ICICS.2009.5397468",

language = "英语",

isbn = "9781424446575",

series = "ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing",

booktitle = "ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing",

note = "7th International Conference on Information, Communications and Signal Processing, ICICS 2009 ; Conference date: 08-12-2009 Through 10-12-2009",

}

Lu, Y, He, H, Wang, J & Li, S 2009, Energy-efficient dynamic spectrum access using no-regret learning. in ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing., 5397468, ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing, 7th International Conference on Information, Communications and Signal Processing, ICICS 2009, Macau Fisherman's Wharf, Macao, 8/12/09. https://doi.org/10.1109/ICICS.2009.5397468

Energy-efficient dynamic spectrum access using no-regret learning. / Lu, Yao; He, Hao; Wang, Jun et al.
ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing. 2009. 5397468 (ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Energy-efficient dynamic spectrum access using no-regret learning

AU - Lu, Yao

AU - He, Hao

AU - Wang, Jun

AU - Li, Shaoqian

PY - 2009

Y1 - 2009

N2 - In this paper, we consider a cross-layer design of dynamic spectrum access in distributive cognitive radio (CR) networks. We model the licensed channel as a finite-state Markov channel (FSMC) and the CR user selects one channel to access and decides transmission rate and power in order to maximize its energy efficiency. We propose a game theoretic framework to formulate this problem and apply a learning algorithm called modified regret-matching leading to correlated equilibrium which is more practical than the regret-matching learning algorithm applied in the related work. The only thing that each user needs to know is his own realized payoffs and actions. From the simulation results, the modified learning algorithm provides impressive performance.

AB - In this paper, we consider a cross-layer design of dynamic spectrum access in distributive cognitive radio (CR) networks. We model the licensed channel as a finite-state Markov channel (FSMC) and the CR user selects one channel to access and decides transmission rate and power in order to maximize its energy efficiency. We propose a game theoretic framework to formulate this problem and apply a learning algorithm called modified regret-matching leading to correlated equilibrium which is more practical than the regret-matching learning algorithm applied in the related work. The only thing that each user needs to know is his own realized payoffs and actions. From the simulation results, the modified learning algorithm provides impressive performance.

KW - Cognitve radio

KW - Correlated equilirium

KW - Dynamic spectrum access

KW - Energy-efficient

KW - Game theory

KW - No-regret learning

UR - https://www.scopus.com/pages/publications/77949578902

U2 - 10.1109/ICICS.2009.5397468

DO - 10.1109/ICICS.2009.5397468

M3 - 会议稿件

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SN - 9781424446575

T3 - ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing

BT - ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing

T2 - 7th International Conference on Information, Communications and Signal Processing, ICICS 2009

Y2 - 8 December 2009 through 10 December 2009

ER -

Lu Y, He H, Wang J, Li S. Energy-efficient dynamic spectrum access using no-regret learning. In ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing. 2009. 5397468. (ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing). doi: 10.1109/ICICS.2009.5397468

Energy-efficient dynamic spectrum access using no-regret learning

Abstract

Publication series

Conference

UN SDGs

Keywords

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