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Sponsors: search.filters.sponsors.TÜBİTAKSubject: search.filters.subject.Energy harvesting

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    PublicationOpen Access
    Deep neural network based minimum length scheduling in wireless powered communication networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Khan, Nasir; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/A
    Minimization of schedule length is key in ensuring the delay performance of wireless powered communication networks (WPCNs) demanding strict timing and reliability guarantees. Previous solution methodologies proposed for these wireless networks suffer from high run-time complexity, making it very difficult to solve the problem in real time. This paper considers a run-time efficient deep learning based approach for solving minimum length scheduling problem in a full-duplex WPCN. Leveraging upon the universal approximation capability of neural networks, a multi-output feed forward deep neural network based framework is proposed where inputs are the channel coefficients and outputs are the optimal power, transmission length and schedule of users. Simulation results indicate that the proposed deep learning based approach can very well approximate the true outputs with a percentage error below 1% for different network configurations while maintaining a very low run-time complexity.
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    PublicationOpen Access
    Effect of downlink energy transfer scheduling on SDMA and TDMA uplink transmission
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Pehlivan, İbrahim; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/A
    The high cost and power consumption of digital beamforming, as a result of the high number of RF chains, has overshadowed its performance on multi-antenna wireless powered communication networks (WPCNs). This setback forced researchers to low-cost alternatives such as hybrid beamforming, which decreases the number of expensive RF chains by utilizing cheaper phase shifters. This cost-cutting, however, comes with reduced control over beamforming weights and compromise performance. To circumvent this deficiency, scheduling of energy harvesting (SEH), utilizing the degree of freedom in the time domain, has been proposed. In SEH, the downlink slot is subdivided into multiple variable-length subslots with different beamforming weights. In this paper, we examine the effect of SEH on the optimization of minimum length scheduling for space division multiple access (SDMA) uplink transmission compared to time division multiple access (TDMA) uplink transmission. Via simulations, we demonstrate that SDMA benefits more from the additional degree of freedom provided by the usage of SEH for any number of nodes. However, SDMA yields inferior delay performance compared to TDMA as the number of nodes increases, which restricts the application of SDMA with SEH, making it impractical.