کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
453599 694978 2016 9 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks
ترجمه فارسی عنوان
یک روش سنجش طیفی مبتنی بر فاصله اتوکلی و فاصله اقلیدسی و مقایسه آن با تشخیص انرژی شبکه های رادیویی شناختی
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی


• Gaussian noise samples are delta correlated; ACF(t) = δ(t).
• We can do spectrum sensing with the Euclidean distance between ACF(t) and a reference line.
• The Euclidean distance method performs better than the ED and ACF(1) methods.

Spectrum sensing is an important aspect of cognitive radios. This paper describes a method for spectrum sensing based on the autocorrelation of the received samples. The proposed method was evaluated by means of experiments wherein the probabilities of detection and false alarm at different signal-to-noise ratios (SNRs) were observed. The platform used for the experiments was a set of Universal Software Radio Peripheral™ (USRP™) devices acting as radio frequency front ends in combination with GNU Radio software. Since the signal processing was performed in the software domain, Gaussian noise of different levels was emulated by changing the standard deviation of a Python random number generator. In addition, the output power of a signal generator was varied to obtain different levels of SNR. A metric called the Euclidean distance was derived to analyze the autocorrelation of the samples received by the USRP™ device in order to decide between two possible situations: only noise present or signal plus noise present. The proposed method was compared with two methods: one based on the value of the autocorrelation at the first lag and another one based on the power of the signal, known as energy detection spectrum sensing technique.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 52, May 2016, Pages 319–327
نویسندگان
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