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How to increase energy efficiency in cognitive radio networks

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How to increase energy efficiency in cognitive radio networks. / Robat Mili, Mohammad ; Musavian, Leila; Ashour Hamdi, Khairi et al.
In: IEEE Transactions on Communications, Vol. 64, No. 5, 13.05.2016, p. 1829-1843.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Robat Mili, M, Musavian, L, Ashour Hamdi, K & Marvasti, F 2016, 'How to increase energy efficiency in cognitive radio networks', IEEE Transactions on Communications, vol. 64, no. 5, pp. 1829-1843. https://doi.org/10.1109/TCOMM.2016.2535371

APA

Robat Mili, M., Musavian, L., Ashour Hamdi, K., & Marvasti, F. (2016). How to increase energy efficiency in cognitive radio networks. IEEE Transactions on Communications, 64(5), 1829-1843. https://doi.org/10.1109/TCOMM.2016.2535371

Vancouver

Robat Mili M, Musavian L, Ashour Hamdi K, Marvasti F. How to increase energy efficiency in cognitive radio networks. IEEE Transactions on Communications. 2016 May 13;64(5):1829-1843. Epub 2016 Feb 26. doi: 10.1109/TCOMM.2016.2535371

Author

Robat Mili, Mohammad ; Musavian, Leila ; Ashour Hamdi, Khairi et al. / How to increase energy efficiency in cognitive radio networks. In: IEEE Transactions on Communications. 2016 ; Vol. 64, No. 5. pp. 1829-1843.

Bibtex

@article{b57416b0192446f38436ac400fa4fd6d,
title = "How to increase energy efficiency in cognitive radio networks",
abstract = "In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the ε-constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.",
author = "{Robat Mili}, Mohammad and Leila Musavian and {Ashour Hamdi}, Khairi and Farokh Marvasti",
note = "{\textcopyright}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = may,
day = "13",
doi = "10.1109/TCOMM.2016.2535371",
language = "English",
volume = "64",
pages = "1829--1843",
journal = "IEEE Transactions on Communications",
issn = "0090-6778",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - How to increase energy efficiency in cognitive radio networks

AU - Robat Mili, Mohammad

AU - Musavian, Leila

AU - Ashour Hamdi, Khairi

AU - Marvasti, Farokh

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/5/13

Y1 - 2016/5/13

N2 - In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the ε-constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.

AB - In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the ε-constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.

U2 - 10.1109/TCOMM.2016.2535371

DO - 10.1109/TCOMM.2016.2535371

M3 - Journal article

VL - 64

SP - 1829

EP - 1843

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

SN - 0090-6778

IS - 5

ER -