Browsing by Author "Yang, Fan"

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Exotic Mode Suppression in Plasmonic Heterotrimer System
(2019-01-17)
Song, Hanfa
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Sun, Quan
;
Li, Jie
;
Yang, Fan
;
Yang, Jinghuan
We investigate the far- and near-field properties of plasmonic heterotrimers with small detuning of the eigenfrequency numerically, theoretically, and experimentally. In our simulations, we find that the dipolar mode of the middle nanorod in the trimer system is greatly suppressed in both far and near fields. This phenomenon is confirmed by our subsequent experiments. The far-field suppression can be interpreted by the destructive interference due to the antiphase dipolar response of the respective nanorod. To deeply understand the underlying physics, an analytical model considering both the near-field-mediated direct coupling and radiative-field-mediated indirect coupling is adopted. We determine that the radiative field coupling, which is stronger in the trimer system with a smaller detuning, plays an essential role in mode suppression in the near field. Our work will provide new thoughts on the fundamental mechanisms of plasmon coupling and plasmon-based applications.
Joint Beamforming and Time Duration Optimization for Battery-free-Multi-Antenna-Relay-Assisted WPCN
(2018-01-01)
Yang, Fan
;
Xu, Wenjun
;
Lee, Chia-Han
;
Feng, Zhiyong
;
Lin, Jiaru
This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multiantenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.
Machine Learning-Based Energy-Spectrum Two-Dimensional Cognition in Energy Harvesting CRNs
(2020-01-01)
Fan, Yongjian
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Xu, Wenjun
;
Lee, Chia-Han
;
Wu, Silei
;
Yang, Fan
Energy harvesting cognitive radio network (EH-CRN) is a promising approach to address the shortage of spectrum resources and the increase of energy consumption simultaneously in wireless networks. In this article, we propose a novel machine learning (ML)-based energy-spectrum two-dimensional (2D) cognition technology to improve the sensing accuracy as well as the network throughput in EH-CRNs, which consists of sensing, prediction and decision modules. More specifically, we first study the 2D sensing module which is achieved by a carefully constructed dynamic Bayesian network (DBN) to effectively exploit the coupling between spectrum usage and energy harvesting in EH-CRNs. Then we propose a deep neural network (DNN) based 2D transmission decision module to optimize the transmission energy of secondary users (SUs). With our proposed novel 2D cognition scheme, SUs can characterize the energy-spectrum correlation and transmit data with optimal transmission energy. The proposed ML-based 2D cognition is evaluated via extensive simulations in terms of sensing accuracy, prediction accuracy, and network throughput, and simulation results indicate that our proposed scheme significantly outperforms the conventional one-dimensional (1D) cognition scheme working in spectrum or energy dimension only.