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Energy-Efficient Power Allocation Using Probabilistic Interference Model for OFDM-Based Green Cognitive Radio Networks

Author

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  • Ashok Karmokar

    (Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street,M5B 2K3, Toronto, Canada)

  • Muhammad Naeem

    (Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street,M5B 2K3, Toronto, Canada
    Department of Electrical Engineering, COMSATS Institute of IT, Wah Campus, Wah, Pakistan)

  • Alagan Anpalagan

    (Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street,M5B 2K3, Toronto, Canada)

  • Muhammad Jaseemuddin

    (Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street,M5B 2K3, Toronto, Canada)

Abstract

We study the energy-efficient power allocation techniques for OFDM-based cognitive radio (CR) networks, where a CR transmitter is communicating with CR receivers on a channel borrowed from licensed primary users (PUs). Due to non-orthogonality of the transmitted signals in the adjacent bands, both the PU and the cognitive secondary user (SU) cause mutual-interference. We assume that the statistical channel state information between the cognitive transmitter and the primary receiver is known. The secondary transmitter maintains a specified statistical mutual-interference limits for all the PUs communicating in the adjacent channels. Our goal is to allocate subcarrier power for the SU so that the energy efficiency metric is optimized as well as the mutual-interference on all the active PU bands are below specified bounds. We show that the green power loading problem is a fractional programming problem. We use Charnes-Cooper transformation technique to obtain an equivalent concave optimization problem for what the solution can be readily obtained. We also propose iterative Dinkelbach method using parametric objective function for the fractional program. Numerical results are given to show the effect of different interference parameters, rate and power thresholds, and number of PUs.

Suggested Citation

  • Ashok Karmokar & Muhammad Naeem & Alagan Anpalagan & Muhammad Jaseemuddin, 2014. "Energy-Efficient Power Allocation Using Probabilistic Interference Model for OFDM-Based Green Cognitive Radio Networks," Energies, MDPI, vol. 7(4), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:4:p:2535-2557:d:35345
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    References listed on IDEAS

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    1. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
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    Cited by:

    1. Yihang Du & Ying Xu & Lei Xue & Lijia Wang & Fan Zhang, 2019. "An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning," Energies, MDPI, vol. 12(14), pages 1-21, July.

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