Report

ON

“SPECTRAL OCCUPANCY MEASUREMENT AND ANALYSIS FOR COGNITIVE RADIO APPLICATION ”

Submitted to

The LNMIIT

BY

Aastha Bhardwaj

Under the guidance of

Prof. Ranjan Gangopadhyay

Department of Electronics & Communication Engineering

The LNMIIT

Jaipur

ACKNOWLEDGEMENT

I present my heart filled gratitude to Prof. R Gangopadhyay and Dr. S Debnath, Head of Department of Electronics & Communication Engineering, LNMIIT for giving me an opportunity to work under their guidance on this project of cognitive radio and encouraging me to give my best to the project.

I would like to thank respected Phd. Scholars Anirudh Agarwal and Aditya Singh Sengar from the bottom of my heart for their constant guidance and support throughout the project, solving my doubts patiently, introducing me with the equipments used and giving ideas to implement things in a much simpler way.

Aastha Bhardwaj

CONTENTS

Abstract Introduction Measurement Methodology Time Series Different time series models Working with time series modeling Working with MATLAB Conclusion References

Abstract

Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. It involves spectrum sharing which consists of spectrum sensing, spectrum decision , spectrum mobility. The 24-hour spectrum usage pattern is studied. The objectives are to find how the scarce radio spectrum allocated to different services is utilized and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization, followed by prediction. While dedicated spectrum occupancy monitoring provides vital information for frequency planning and management, it usually cannot tell the common properties in spectrum occupancy. As a complement, the models approach can be used to describe and compare the occupancy situations under similar conditions. Time series analysis has been applied for modeling the radio spectrum occupancy.

INTRODUCTION

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: · highly reliable communication whenever and wherever needed; · efficient utilization of the radio spectrum. It works on the principle of artificial intelligence. The accuracy of the decisions made by artificial intelligence method are based on the quality and quantity of the inputs to the system. These inputs comprise of transmission parameters and environmental parameters which are collectively known as operating parameters.

Wireless spectrum is carved up into chunks called frequency bands. These are licensed bands, meaning that individual companies pay a licensing fee for the exclusive right to transmit on assigned channels within that band in a given geographic area. These licensed bands gave rise to the concept of primary and secondary users. A user who has higher priority or legacy rights on the usage of a specific part of the spectrum is called primary user. A user who has a lower priority and therefore exploits the spectrum in such a way that it does not cause interference to primary users is known as secondary user. A channel can

be considered as an opportunity if it is not currently used by primary users.

The cognitive radio networks will have to respect the policies, defined by regulatory bodies which are based on central idea that cognitive radio can access and share the spectrum in an opportunistic manner with licensed users, provided that there should have no or very limited impact on licensed user communication . Such a solution can be complicated and impose unique challenges due to their coexistence with primary networks, typical dynamic behavior of primary user, interference avoidance and QoS awareness. In order to meet these challenges, cognitive radio operates on cognitive cycle which comprises of 4 main steps. These steps are: spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility.

Spectrum sensing is the fundamental requirement of cognitive system to work. A cognitive user should monitor the spectrum bands to determine the presence or absence of primary user before transmission. Spectrum sensing is done in order to minimize the impact of secondary users on primary users. Basically spectrum sensing techniques are classified into three main groups: Primary transmitter detection which includes matched filter detection, energy detection and feature detection. The other two groups include primary receiver detection and interference temperature management. Spectrum Sensing Techniques are classified according to

1. Architecture :- Centralized ,Distributed

2. Spectrum allocation behavior :- Cooperative , Non Cooperative

3. Spectrum Access Technology :- Overlay Spectrum Sharing : In this, secondary users aim to exploit temporal spectrum opportunities resulting from the burstly traffic of primary users. A typical application is the

reuse of certain TV bands that are not used for TV broadcast in a particular region.

Underlay Spectrum Sharing: In this radios coexist in the same band with primary licenses, but are regulated to cause interference below prescribed limits.

Spectrum decision: Based on information of spectrum sensing, a spectrum band is analyzed and best available spectrum is selected for transmission. This allocation is focused mainly on spectrum availability, cost of communication and quality of service requirements.

Spectrum sharing: Cognitive radio has to access and share the spectrum with multiple other secondary or cognitive users. Spectrum sharing is to distribute the spectrum among all cognitive and non-cognitive users such that there should be no collisions among them. A spectrum overlay technique is a spectrum management principle whereby a secondary user uses a channel from a primary user only when it is not occupied. Spectrum underlay technique is a spectrum management principle by which signals with a very low spectral power density can coexist as a secondary user with the primary user of the frequency band.

Spectrum Mobility: The fourth step in spectrum management and one of the most prominent features of cognitive radio networks will be the ability to switch to different portions of radio spectrum as soon as spectrum left over or spectrum holes are detected. Spectrum mobility is the technique that will enable cognitive radio networks to achieve this goal. As licensed users or primary users have the right to their spectrum slice thus cannot accept any interference thus in this direction the most important and challenging issue of spectrum mobility is to avoid interference to primary users and attain a seamless communication.

Spectrum Mobility or handoff process is carried out when channel occupied by secondary users is interrupted or reclaimed by the occurrence of primary users. As soon as the primary user appears, secondary user has to vacate the frequency channel to avoid interference to primary user and switch to other available free channel to resume and finish its ongoing transmission.

Cognitive radio also involves the concept of white space and gray space. White Space refers to the unused broadcasting frequencies in the wireless spectrum whereas gray space sharing is one in which devices are given access to spectrum that is already in use.

Measurement Methodology

For doing prediction and performing cognitive cycle operations practical data sets are needed for which discone antenna and rf explorer.

A discone antenna is a version of a biconical antenna in which one of the cones is replaced by a disc. It is usually mounted vertically, with the disc at the top and the cone beneath. Omnidirectional, vertically polarized and with gain similar to a dipole, it is exceptionally wideband, offering a frequency range ratio of up to approximately 10:1. The radiation pattern in the horizontal plane is quite narrow, making its sensitivity highest in the direction of the horizon and rather less for signals coming from relatively close by. The discone’s wideband coverage makes it attractive in commercial, military, amateur radio and radio scanner applications. Using discone antenna data sets for different places are collected. The figure below shows a discone antenna.

A spectrum analyzer is used in analyzing the collected data set. It measures the magnitude of an input signal versus frequency within the full frequency range of the instrument. The primary use is to measure the power of the spectrum of known and unknown signals. One such spectrum analyzer RF Explorer is a remarkable diagnostic tool used for monitoring and troubleshooting wireless systems and communications.

The MATLAB software is used to perform different operations on collected data set. A certain threshold is fixed than power spectral densities above this threshold is assigned 1 and below the threshold is assigned 0. This gives the information about how much spectrum is occupied. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold.

Power Spectral Density(PSD): It is a measure of a signal’s power intensity in the frequency domain. The PSD provides a useful way to characterize the amplitude versus frequency content of a random signal.

With the help of data sets collected 3 graphs were plotted

1. PSD vs Frequency 2. Waterfall(PSD vs Frequency vs Time) 3. Duty Cycle vs Frequency

Duty Cycle: It is the proportion of time during which a component, device or system is operated.

Time seriesNeed for time series modeling

Spectrum monitoring is one of four key spectrum management functions which include spectrum planning , spectrum engineering and spectrum authorization. It helps spectrum managers to plan and use frequencies , avoid incompatible usage, and identify sources of harmful interference. However detailed measurements and analyses intended to quantify the performance of a particular band and a particular measuring period usually cannot be extended directly to others and usually cannot tell the common properties in spectrum occupancy. As a complement, the models approach can be used to describe and compare the occupancy situations under similar conditions.

What is time series

A time series is a sequence of data points made

over a continuous time interval out of successive measurements across that interval using equal spacing between every two consecutive measurements with each time unit within the time interval having at most one

data point.

The purpose of time series analysis is to draw inferences from series, so, one can infer the general occupancy situation without monitoring the spectrum.

Different time series models

Autoregressive Model(AR)

In statistics and signal processing, an autoregressive (AR) model is a representation of a type of random process . The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation. the AR model is not always stationary as it may contain a unit root.

In mathematics and statistics, a stationary process (or strict(ly) stationary process or strong(ly) stationary process) is a stochastic process whose joint probability distribution does not change when shifted in time. Consequently, parameters such as the mean and variance, if they are present, also do not change over time and do not follow any trends. Stationarity is used as a tool in time series analysis, where the raw data is often transformed to become stationary.

The notation AR(p) indicates an autoregressive model of order p.

Moving-average Model(MA)

In time series analysis, the moving-average (MA) model is a common approach for modeling univariate time series. The moving-average model specifies that the output variable depends linearly on its own previous stochastic term and on a stochastic term (an imperfectly predictable term). Contrary to the AR model, the MA model is always stationary.

The notation MA(q) refers to the moving average model of order q.

Autoregressive–moving-average model

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the auto-regression and the second for the moving average. Given a time series of data (Xt), the ARMA model is a tool for understanding and, perhaps, predicting future values in this series. The model consists of two parts, an autoregressive (AR) part and a moving average (MA) part. The model is usually then referred to as the ARMA(p, q) model where p is the order of the autoregressive part and q is the order of the moving average part.

A stationary process X t is defined to be an ARMA(p, q) if for every t,

∅ (B ) X t=θ (B )Z t

where Zt~N(0,σ 2) normal distribution with zero mean and variance σ 2, B is the backward shift operator

B j X t=X t− j

and φ(B), θ(B) are the pth degree autoregressive (AR) and qth degree moving average (MA) polynomials respectively.

∅ (B )=1−∅ 1B−¿....−∅ pBp

θ (B )=1+θ1B+¿....+θqBq.

Autoregressive integrated moving average

In time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting). They are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the "integrated" part of the model) can be applied to reduce the non-stationarity.

Non-seasonal ARIMA models are generally denoted ARIMA(p,d,q) where parameters p, d, and q are non-negative integers, p is the order of the autoregressive model, d is the degree of differencing, and q is the order of the moving-average model. Seasonal ARIMA models are usually denoted ARIMA(p,d,q)(P,D,Q)m, where m refers to the number of periods in each season, and the uppercase P,D,Q refer to the autoregressive, differencing, and moving average terms for the seasonal part of the ARIMA model. Seasonal ARIMA models allow for randomness in the seasonal pattern from one cycle to the next. However it can be quite complicated.

Working with time series modeling

Time series modeling involves the use of autocorrelation and partial autocorrelation function. It starts by first finding whether the model is stationary or not. If the tendency for the autocorrelation function did not die out quickly as shown in the figure below this might suggest non-

stationarity. So the difference operation should be applied to the series.

After a time series has been stationarized by differencing, the next step in fitting the model is to determine whether AR or MA terms. If the autocorrelation function displays a sharp cutoff while the partial autocorrelation function decays more slowly like the one shown in the figure given below. It is said that the stationarized occupancy series displays a MA signature, meaning that the autocorrelation pattern can be explained more easily by adding MA terms than by adding AR terms

If the partial autocorrelation function displays a sharp cutoff while the autocorrelation function decays more slowly like the one shown in the figure given below. It is said that the stationarized occupancy series displays a AR signature, meaning that the autocorrelation pattern can be explained more easily by adding AR terms than by adding MA terms.

Typically, the goodness of fit of a statistical model to a set of data is judged by comparing the observed values with the corresponding predicted values obtained from the fitted model.

Working with MATLAB

The data set of different places which is obtained with the help of discone antenna is now operated on in MATLAB software. First all the values of different days are concatenated and then a particular threshold value is set, occupancy data is obtained. For applying time series modeling we need model order(values of p, q, d) which is calculated for all the 112 channels by using autocorrelation function and partial

autocorrelation function. After all this, prediction work starts which involves the use of econometrics toolbox and financial toolbox of MATLAB. Prediction is done for different percentage of data sets. 70% data is used for training and 30% for testing . In another case 70% data is used for training and the same is used for testing. This was also done for 50%.

CONCLUSION

For prediction and analysis several operations were performed on the collected data set. There were 112 channels in every data set and on calculating autocorrelation function and partial autocorrelation function different model orders were noticed for different channels. The data sets were grouped for different time intervals i.e values in ten minutes of interval were grouped together and occupancy data set was formed. Prediction was easily performed in the case of data set where values in one minute of time interval were grouped and training-testing was done for different data as predicted vector was obtained by running the code once for all 112 channels. On the other hand in the case of data set where values in ten or fifteen minute of time interval were grouped and training-testing was done on same values then the code was run individually for all the channels. However the first approach was more time consuming. Cognitive radio (CR) is a promising technology that can alleviate the spectrum shortage problem by enabling unlicensed users equipped with CRs to coexist with incumbent users in licensed spectrum bands while causing no interference to incumbent communications.

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