کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6889064 | 1445107 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Secrecy energy efficiency optimization for MISO SWIPT systems
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Secrecy energy efficiency optimization for MISO SWIPT systems Secrecy energy efficiency optimization for MISO SWIPT systems](/preview/png/6889064.png)
چکیده انگلیسی
In this paper, we investigate the secrecy energy efficiency (SEE) optimization of the multiple-input single-output (MISO) system. First, transmission beamforming vector is designed to achieve the SEE maximization subject to the constraints of transmission power, minimum secrecy rate threshold and harvested energy threshold. The optimization problem belongs to the category of fractional optimization, which is non-convex and is very difficult to tackle. In order to solve the optimization problem, we propose an algorithm that can obtain a near-optimal solution, which consists of outer-tier and inner-tier iterations. For the outer-tier iteration, we first employ the Dinkelbach method to convert the fractional objective function into a polynomial form, and then transform the optimization problem into a difference of concave (DC) programming. For the inner-tier iteration, we employ the first-order Taylor expansion and successive convex approximation (SCA) method to solve the DC optimization problem. Then, we analyze the computational complexity of the proposed algorithm. In addition,we prove that the rank relaxation is tight. The simulation results show that the SEE performance of our proposed algorithm is obviously superior to that of secrecy rate maximization scheme and zero-forcing scheme.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physical Communication - Volume 28, June 2018, Pages 19-27
Journal: Physical Communication - Volume 28, June 2018, Pages 19-27
نویسندگان
Xiaobo Zhou, Wenlong Cai, Riqing Chen, Linqing Gui, Feng Shu, Jinyong Lin, Shuo Zhang, Yijin Zhang,