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RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks

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RL-Budget : A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks. / Javed, Zunera; Yau, Kok-lim Alvin; Mohamad, Hafizal et al.

In: IEEE Access, Vol. 6, 14.02.2018, p. 1055-1072.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Javed, Z, Yau, KA, Mohamad, H, Ramli, N, Qadir, J & Ni, Q 2018, 'RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks', IEEE Access, vol. 6, pp. 1055-1072. https://doi.org/10.1109/ACCESS.2017.2777867

APA

Javed, Z., Yau, K. A., Mohamad, H., Ramli, N., Qadir, J., & Ni, Q. (2018). RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks. IEEE Access, 6, 1055-1072. https://doi.org/10.1109/ACCESS.2017.2777867

Vancouver

Javed Z, Yau KA, Mohamad H, Ramli N, Qadir J, Ni Q. RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks. IEEE Access. 2018 Feb 14;6:1055-1072. Epub 2017 Nov 27. doi: 10.1109/ACCESS.2017.2777867

Author

Javed, Zunera ; Yau, Kok-lim Alvin ; Mohamad, Hafizal et al. / RL-Budget : A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks. In: IEEE Access. 2018 ; Vol. 6. pp. 1055-1072.

Bibtex

@article{bf3563d393d44eac8a8b974cf2ad557f,
title = "RL-Budget: A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks",
abstract = "Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.",
author = "Zunera Javed and Yau, {Kok-lim Alvin} and Hafizal Mohamad and Nordin Ramli and Junaid Qadir and Qiang Ni",
year = "2018",
month = feb,
day = "14",
doi = "10.1109/ACCESS.2017.2777867",
language = "English",
volume = "6",
pages = "1055--1072",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - RL-Budget

T2 - A Learning-Based Cluster Size Adjustment Scheme for Cognitive Radio Networks

AU - Javed, Zunera

AU - Yau, Kok-lim Alvin

AU - Mohamad, Hafizal

AU - Ramli, Nordin

AU - Qadir, Junaid

AU - Ni, Qiang

PY - 2018/2/14

Y1 - 2018/2/14

N2 - Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.

AB - Cognitive radio (CR) enables unlicensed users to sense for and access underutilized licensed channels (or white spaces) owned by the licensed users in an opportunistic manner. Clustering segregates nodes in a network into logical groups called clusters. In CR networks (CRNs), larger cluster size improves network scalability thereby contributing to reduced routing overhead; however, it reduces cluster stability as the number of available common channels in a cluster reduces resulting in increased number of re-clusterings and clustering overhead. This paper presents our proposed first-of-its-kind cluster size adjustment scheme based on an artificial intelligence approach called reinforcement learning. The proposed scheme adapts the cluster size with the amount of white spaces as time goes by in order to improve network scalability and cluster stability in CRNs. Due to the lack of progress in the investigation of cluster size adjustment schemes in the literature, this paper also analyzes their attributes, and then presents such schemes investigated in various kinds of distributed wireless networks. Simulation results show that our proposed scheme improves network scalability by creating larger clusters, and improves cluster stability by reducing the number of re-clusterings (i.e., the number of cluster splits) and clustering overhead, while reducing interference between licensed and unlicensed users in CRNs.

U2 - 10.1109/ACCESS.2017.2777867

DO - 10.1109/ACCESS.2017.2777867

M3 - Journal article

VL - 6

SP - 1055

EP - 1072

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -