# ENERGY DETECTION IMPLEMENTATION USING USRP2 AND GNU RADIO

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CR emerged as an answer to spectrum crowding problem. Any CR‟s operation comprises of four states as shown in the Figure 2.2. First the available spectrum is sensed and analyzed to find any available spectrum holes. On the basis of spectrum analysis a decision is made to opportunistically assign the available frequency to the secondary user. Spectrum sensing is the most integral part of CR because all the remaining operations of CR rely on precise sensing of available spectrum [18].

Types of Spectrum Sensing

The most important task of spectrum sensing is transmitter detection. Spectrum sensing plays a key role in the decision making part of CR. There are several different ways to sense the spectrum. Some of the key methods used for spectrum sensing are as follows:
1. Energy Detection.
2. Cyclostationary Method.
3. Matched Filter detection.
4. Wavelet detection.
Explanation and comparison of all four methods is given below.

Energy Detection

In energy detection method we measure the energy of available radio resource and compare it against a predefined threshold level. If the measured energy falls below the defined threshold level spectrum is marked as available. When the measure energy level is above the defined threshold, it‟s considered as occupied. Energy detection method does not require any prior information of the signal. In simple words it does not care about the type of modulation used for transmission of signal, phase or any other parameter of signal. It simply tells if the radio resource is available at any given time instant or not without considering the PU and SU [10].
Hypothetically, energy detection can be considered as a method based on binary decision, which can be written as follows:
𝒙 𝒕 = 𝒏 𝒕 𝑯𝟎 (1).
𝒙 𝒕 = 𝒔 𝒕 + 𝒏 𝒕 𝑯𝟏 (2).
Where s(t) is the received signal and n(t) is the Additive White Gaussian Noise (AWGN) i.e. equally distributed all over the signal. H0 and H1 represent the two outcomes of the energy detection method [4]. The energy detection method‟s working principal can be explained with the Figure 2.3.

Cyclostationary Method

A Cyclostationary process is defined as the statistical process which repeats itself cyclically or periodically [6]. Communication signals are Cyclostationary with multiple periodicities. Mathematically Cyclostationary detection can be performed as given in equation (3): 𝑹𝒙 𝑻 = 𝑬 𝒙 𝒕 + 𝑻 𝒙∗ 𝒕 − 𝑻 e−j2απ t (3).
The equation shows the autocorrelation of the observed signal x(t) with periodicity T, E represents the expectation of the outcome and α represents the cyclic frequency [6]. After autocorrelation Discrete Fourier Transform over resulting correlation is performed to get the desired result in terms of frequency components. The peaks in the acquired data give us the information about the spectrum occupancy. The Cyclostationary detection method requires prior knowledge of periodicity of signal and it can only be used with the signal possessing Cyclostationary properties. The implementation of Cyclostationary method is shown in Figure 2.4.

Matched filter detection

In the matched filter detection method a known signal is correlated with an unknown signal captured from the available radio resource to detect the presence of pattern in the unknown signal [6]. Matched filter detection method is commonly used in Radio Detection and Ranging (RADAR) communication [10]. The use of matched filter detection is very limited as it requires the prior information about the unknown signal. For example in case of GSM, the information about the preamble is required to detect the spectrum through matched filter detection method. In case of WiMAX signal prior information about the Pseudo Noise (PN) sequence is required for detection of spectrum.

### Wavelet Detection

To detect the wideband signals, wavelet detection method offers advantage over the rest of the methods in terms of both simplicity and flexibility. It can be used for dynamic spectrum access. To identify the white spaces or spectrum holes in the available radio resource, the entire spectrum is treated as the sequence of frequency sub-bands. Each sub-band of frequency has smooth power characteristics within the sub-band but changes abruptly on the edge of next sub-band. By using the wavelet detection method the spectrum holes can be found at a given instance of time by finding the singularities in the attained result [10]. The Figure 2.5 shows the wavelet detection implementation for spectrum sensing.

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GNU Radio is an open source development platform for signal processing and communication applications focusing on implementation of SDRs with low cost external RF hardware. It contains tons of libraries with signal processing routines written in C/C++ programming language. It is widely used in the wireless communication research and real time implementation of software radio systems [16].
GNU Radio applications are mainly written and developed by using Python programming language. Python provides a user friendly frontend environment to the developer to write routines in a rapid way. The performance critical signal processing routines are written in C++ [22]. Python is a high level language; it acts as a glue to integrate the routines written in C++ and executes through python. Python uses simplified wrapper and interface grabber (SWIG) for the purpose of interfacing C++ routines with python frontend application as shown in Figure 4.1. Very high speed integrated circuits hardware description language (VHDL) is a hardware descriptive language. This part of the code is executed in the Field Programmable Gate Array (FPGA) of front end hardware which is USRP2 in our scenario.

ACKNOWLEDGMENT
ABSTRACT
1 INTRODUCTION
1.1 AIMS AND OBJECTIVES
1.2 RESEARCH QUESTIONS
1.3 THESIS OUTLINE
2 SPECTRUM SENSING
2.2 TYPES OF SPECTRUM SENSING
2.2.1 Energy Detection
2.2.2 Cyclostationary Method
2.2.3 Matched filter detection
2.2.4 Wavelet Detection
2.3 QUALITATIVE ANALYSIS OF SPECTRUM SENSING TECHNIQUES
2.5 RELATED WORK
3 RESEARCH METHODOLOGY
4.1.1 GNU Radio Flow graphs, Sources and Sinks
4.3 USRP2 ARCHITECTURE AND OVERVIEW
4.3.1 USRP2 Operation with GNU Radio
5 ENERGY DETECTION IMPLEMENTATION USING USRP2 AND GNU RADIO
5.1 PROJECT SETUP
5.2 SPECTRUM SENSING ALGORITHM IMPLEMENTATION
5.3 PROJECT LIMITATIONS
5.3.1 Hardware Limitations
5.3.2 Energy detection Algorithm limitations
6 RESULTS
7 CONCLUSION AND FUTURE WORK
7.1 FUTURE WORK
Bibliography.

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