دانلود مقاله Optimal MultiدرCycle Cyclostationarityدرbased Spec

دانلود مقاله Optimal MultiدرCycle Cyclostationarityدرbased Spectrum Sensing for Cognitive Radio Networks فایل ورد (word) دارای 6 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است
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توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی دانلود مقاله Optimal MultiدرCycle Cyclostationarityدرbased Spectrum Sensing for Cognitive Radio Networks فایل ورد (word) ،به هیچ وجه بهم ریختگی وجود ندارد
بخشی از متن دانلود مقاله Optimal MultiدرCycle Cyclostationarityدرbased Spectrum Sensing for Cognitive Radio Networks فایل ورد (word) :
سال انتشار: 1390
محل انتشار: نوزدهمین کنفرانس مهندسی برق ایران
تعداد صفحات: 6
نویسنده(ها):
Hamed Sadeghi – Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Paeiz Azmi –
Hamid Arezumand –
چکیده:
Reliable detection of primary users (PUs) in the presence of interference and noise is a crucial problem in cognitive radio networks. To address above issue, cyclostationary feature detectors that can robustly detect weak primary signals have been proposed in the literature. Among different candidates, in this paper we focus on the method which is based on asymptotic properties of cyclic autocorrelation estimates. The objective is to establish some optimal strategies for multi-cycle cyclostationary detection method, within which the linear combination of multiple independent test statistics corresponding to different cycle frequencies is computed. The optimality criteria considered here is the deflection coefficient and modified deflection coefficient maximization. In each case, we derive analytical approximations for the distribution of proposed test statistic under null hypothesis. Also, we study the agreement between empirically estimated distribution and proposed analytical approximation. In addition, we analytically characterize the impact of channel fading on the cyclostationarity of received signals and verify our analysis via simulation. Simulation results confirm the asymptotic detection performance of proposed optimal methods, as compared with suboptimal detectors.

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