lab4 - ECE4305 Software-Defined Radio Systems and Analysis...

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ECE4305: Software-Defined Radio Systems and Analysis Laboratory 4: Spectrum Sensing Techniques C-Term 2011 Objective With advanced digital communication systems such as cognitive radio being used in a growing number of wireless applications, the topic of spectrum sensing has become increasingly important. This laboratory will introduce the concept, fundamental principles, and practical applications of spectrum sensing. In the experimental part of this laboratory, you will implement two different primary signal detectors and then observe their performance during over the air transmission. This is followed by a Simulink implementation of those two detectors. Finally, the open-ended design problem will focus on a commonly-used form of wireless access scheme referred to as carrier sense multiple access with collision avoidance (CSMA/CA). Contents 1 Theoretical Preparation 3 1.1 Power Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Collecting Spectral Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Primary Signal Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.1 Energy Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.2 Cyclostationary Feature Detector . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Suggested Readings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Software Implementation 12 2.1 Spectrum Sensing using Energy Detection . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 Signal Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.2 Energy Detector Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.3 Energy Threshold Selection and Hypothesis Testing . . . . . . . . . . . . . . . 13 2.2 Understanding Cyclostationary Detectors . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 USRP2 Hardware Experimentation 15 4 Open-ended Design Problem: CSMA/CA 17 4.1 Carrier Sense Multiple Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Collision Avoidance Variant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Implementation Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.4 Hints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5 Lab Report Preparation & Submission Instructions 20 1
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References 21 2
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1 Theoretical Preparation Spectrum sensing is a radio process for determining whether a signal is present across a specified RF bandwidth. This process has many applications and usages, including dynamic spectrum access networks , which are designed to maximize spectrum efficiency and capacity within congested wireless transmission environments. Dynamic spectrum access temporarily utilizes spectral white spaces in or- der to transmit data. What this means is that if a licensed (primary) user is allocated a predetermined frequency to operate on, an unlicensed (secondary) user can temporarily “borrow” the unoccupied spectrum for transmission. In a system consisting of many primary users and secondary users, the secondary users need to be able to jump into and utilize the unused spectrum of the primary users as it becomes available. In order to accomplish this action, spectrum sensing techniques are employed to avoid spectral collisions. This laboratory discusses both energy detection and cyclostationary feature detection, and goes into detail about energy detectors. 1.1 Power Spectral Density To analyze a signal in the frequency domain, the power spectral density (PSD), S x ( f ), is often used to characterize the signal, which is obtained by taking the Fourier Transform of the autocorrelation R x ( τ ) of the signal X ( t ). The PSD and the autocorrelation of a function, R x ( τ ), are mathematically related by the Einstein-Wiener-Khinchin (EWK) relations, namely: S x ( f ) = integraldisplay -∞ R x (
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  • Fall '09
  • Signal Processing, spectral density, Stationary process, spectrum sensing, energy detection

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