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Chapter 1.Introduction

In recent years there has been an enormous growth in wireless communication devices and wireless users. The ever increasing demand for higher speed and reliability made researchers think about intelligent radios like Cognitive Radios (CR). But major amount of spectrum is available for licensed users. There are various communication bodies like the International Telecommunication Union (ITU), European Conference of Postal and Telecommunications Administrations (CEPT) also European Telecommunications Standards Institute (ETSI) who works on standards of communication that defines the use of spectrum for licensed and unlicensed users.

1.1 Background

Spectrum is a valuable resource in communication. Over the past few years as the use of various wireless technology is increasing rapidly so we either need more spectrum or make efficient use of current spectrum to satisfy their needs. One way of making efficient use of spectrum is employing spectrum sharing technique. There are many spectrum sharing techniques available like energy detection, cyclostationary feature detector, and matched filter. Of the above technique matched filter and cyclostationary gives more accurate result but they are bit complex and computationally harder as compared to energy detection technique. Energy detection is the simplest of above three and computationally less complex.

1.2 Motivation

Survey has shown that at any given time only portion of spectrum is utilized. According to a report published by FCC (Federal Communications Commission - America) in 2003 has set a set of rule for efficient use of spectrum for licensed and unlicensed users. Also OFCOM (Office of Communications - United Kingdom) has noticed the underutilization of spectrum. At any given time only portion of spectrum is utilized. Even if the system says there are no frequencies available, there is still some frequency available. These available frequencies are known as spectrum holes or white spaces. Some of the bands are completely occupied by users while some bands go unutilized. And that is inefficient use of spectrum. We must have noticed that in cases of emergencies like the train bomb blast in July 2006 in Mumbai, India cellular network actually failed to support huge amount of customer at the same time, this was also the case during 9/11 in USA. If we were having cognitive radios at that time peoples would have been able to talk to their families and inform about their safety. As we are moving from 3G (Third generation) to 4G (Fourth Generation) we need to make certain changes in our wireless technologies. Below shown is the measurement of 0 GHz to 6 GHz at Berkley Wireless Research Center (BWRC).

Figure 1 Measurement of 0-6 GHz Spectrum Utilization at BWRC [20]

1.3 Aim and Objective

This thesis emphasises on understanding Cognitive radios, the importance of spectrum sensing for today's world, the issues regarding the same. A simplified Matlab code is used to support our thesis. As the thesis follows you will find more about cognitive radios and spectrum sensing with a technique to generate white space at a specific frequency.

The main objective of the thesis is to do survey on spectrum sensing and spectrum sensing techniques. Then do plan a model for the same. A model can be supported by a Matlab code. And after all this we need to analyse the model we suggested and further improvements that can be done in that.

1.4 Thesis Organisation

Our thesis is organised as follows:

Chapter.2 Literature Review

This chapter begins with a brief history about cognitive radios .Which is followed by a detail explanation of Cognitive radios and spectrum sensing and some of the spectrum sensing techniques used. At the end of this chapter a business model for spectrum sensing and multi resolution of CR is given.

Chapter.3 System Description

This chapter basically deals with sampling and its importance to us. Also you will find technique to get your spectrum at specific center frequency under “generation of White Spaces”.

Chapter.4 Simulation/Design Analysis

As the name suggests, this chapter deals with simulation model and detail explanation of the code used for spectrum sensing using energy detection. In this chapter we have shown the output of the simulation used.

Chapter.6 Future Work

This chapter suggests some of the future work to be done with this thesis which could be useful for further research in this field.

Chapter.7 Conclusion

This gives the concluding part of the thesis.

Chapter 2 - Literature Review

In this chapter we are going to discuss about the cognitive radios, like what are they and why are they so important to us. The chapter begins with a short history of cognitive radios, which is followed by a general discussion on OFDM, some of the challenges faced by cognitive radios in real environment, and also some of the applications of cognitive radios. We have tried to explain spectrum sensing in brief and the techniques used for spectrum sensing in today's world. Finally a business model for spectrum sensing is showed which is preceded by multi-resolution of Cognitive radios.

2.1 History of cognitive radios

Dr. Joseph Mitola III was the first to introduce or propose the theory of Cognitive radios in 1999. According to Dr. Joseph Mitola Cognitive radios will be the radios that are smart and intelligent enough to find the available bandwidth in a spectrum. It will also have knowledge of right information that has to be passed to the user. And user does not have to take some extra effort for that. It is supposed to do this automatically. He has also mentioned in his PhD dissertation that CR is natural extensions of software defined radios. In 2002 the FCC published a report prepared by Spectrum Policy Task Force [SPTF] which says that majority of spectrum is underutilized. And there is actually is not shortage of spectrum but rather we need to make efficient use of the current spectrum. Also in same year 2002 Professor Cave from UK presented a report which speaks of the possibility of selling bandwidth to the user depending on their requirement. But it would not be fair to give unlicensed user allow to use licensed spectrum. So in December 2002 FCC issued a Notice of Enquiry (NOI) to see TV channel bands can be made available to unlicensed users. Then in 2003 FCC forms a set of rules and proposed interference temperature model for keep track of interference. Later in the same year Notice of Proposed Rulemaking (NPRM) tried to see into issues related to cognitive radio technology where it also pointed out Cognitive radios is a advanced technology which could help efficient use of spectrum by licensed users in own network and by sharing spectrum and with unlicensed users by negotiating when required. This encouraged many researchers in the field of cognitive radios. Major progress in cognitive radios took place in the year 2004, where FCC published NRPM which showed possibility of allowing secondary users to use licensed spectrum. FCC opened three bands for unlicensed users which are 6525 to 6700 MHz, 12.75 to 13.15 GHz
and 13.2125-13.25 GHz. This allowed cognitive devices to transmit six times more. Also IEEE standards are working parallel to the FCC's. Spectrum pooling system by Professor Timo A Weiss from Karlsruhe University Germany), OFDM based Cognitive radios by professor Ian F Akyildiz et al from GIT (USA) are some of the promising works in Cognitive radios. there are many researches are done, many still going on in Europe, Asia, and America, exploring various aspects of cognitive radios. In Jan 2010 first call over a CR network was made in university of Oulu using CRAMNET (Cognitive Radio Assisted Mobile Ad Hoc Network).

2.2 Cognitive radio

With the development of wireless communication devices and technologies in WLAN and WAN spectrum is becoming scarcer. Low frequency bands which are near few GHz are very scarce and highly congested. In current wireless system we are using fixed spectrum allocation scheme. In fixed spectrum allocation scheme a part of spectrum is owned by an operator. Unlicensed users are not allowed to use that spectrum. This leads to the problem of spectrum scarcity. Survey has shown that more than 50% of the spectrum is underutilized. This is where Cognitive Radio (CR) is comes into picture. CR is introduced to solve the problem of spectrum sharing.

Cognitive Radios actually scans the spectrum and during scanning it looks for spectrum holes. The main objective of the cognitive radios is to look for opportunities or white spaces in spectrum band as quickly and as much as possible. And when we say opportunity, opportunity could be in time or frequency domain. Also when we locate this spectrum holes opportunistically we also need to vacant the occupied spectrum as soon as primary user comes back. Here primary user means the users whose spectrum we are using (licensed user) and secondary means the unlicensed users. Cognitive radio is a pattern for wireless communication technology in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. These altered parameters are associated with the active monitoring of several factors in the external and internal radio environment e.g. radio frequency spectrum, user behaviour.

Cognitive radio can be said as next generation of software defined radio (SDR). They are flexible in terms of their transmission characteristics in terms of frequency, bandwidth, ISP which makes smart decisions to configure the SDR at any point in time to achieve a particular goal. By combining these two technologies makes a radio intelligent and flexible and which helps to adapt it to the variations in the environment, user requirements as per the other radio users. Adaptation to changes and requirements should lead to highly reliable communication whenever and wherever required, while making efficient use of spectrum. Good cognitive radio uses analysis done for long period to know about the environment and also his own behaviour.

There are various parameters taken into account to decide transmission and reception changes, we can distinguish certain types of cognitive radio. The main two are as follow:

  • Full Cognitive Radio : It is also known as "Mitola radio" in which every possible parameter which can be observed by a wireless node is taken into consideration to take decision
  • Spectrum Sensing Cognitive Radio: It is the type in which only the radio frequency spectrum is considered.

And Depending on the parts of the spectrum available for cognitive radio, we can distinguish as:

  • Licensed Band Cognitive Radio: It is the type in which cognitive radio is capable of using bands assigned to licensed users, apart from unlicensed bands, such as U-NII band or ISM band.
  • Unlicensed Band Cognitive Radio: This can only utilize unlicensed parts of radio frequency spectrum only or the bands which are free to use.

2.3 About OFDM

OFDM stands for Orthogonal Frequency Division Multiplexing. It is generally a type of Frequency Division Multiplexing (FDM) rather a special case in FDM. What makes it special is its orthogonal behaviour. Now the word orthogonal basically means mutually independence. When we say A is orthogonal to B, we mean that A has no vector in direction of B and vice versa or in other words A and B is mutually independent. Or the integral of two signals over one period is 0. In OFDM a single signal is first multiplexed and modulated independently to create orthogonal signals. This means the signal is first divided into number of smaller streams and then modulated further before transmission. For example imagine a slicing of cheese and grilling or cheese. Slicing is like FDM where whole data is sent in a bunch and grilling is like OFDM where the data to be sent is first divided into smaller data and then processed to transmit further like OFDM. Figure shown below is one more way to understand the concept of OFDM. On the left hand side is a big container which carry whole bunch of data at one time and take it to the destination. And on right hand side is four smaller containers where each carry smaller portion of data and take it to the destination. These smaller containers can be assumed as sub-carriers. And in case of OFDM they are orthogonal sub-carriers. The main advantage over here is that even if some of the cheese is lost during grilling, still we have not lost all the cheese.

In OFDM input data with high data rate is first passed thorough serial to parallel converter. This parallel divided data is then modulated on individually. And parallel to serial conversion is done before transmission. This parallel divided data is our sub-carriers. These sub-carriers must be orthogonal.

2.4 Cognition cycle

Above figure shows the rough model of Cognition Cycle. It reads the surrounding environment and makes decision accordingly. When we say decision we mean cognitive radio sense the requirement or urgency that may be in terms of changing a channel or churning from one technology to another depending upon the scenario. There are different stages where it observes the environment, learns it and then plans its action make required decision and then execute its plans. It is much like a radio with a power of thinking which was not been able before. The figure below shows the cognitive radio architecture suggested by Dr. Mitola.

2.4.1 Some important terms

2.4.1.1Wireless Environment/Outside world- It refers to a communication environment that includes any communication devices and the frequency bands they are working in.

2.4.1.2 Spectrum sensing- It is the technique used by cognitive radios to sense the spectrum. This action involves finding availability of white spaces or spectrum holes in the spectrum.

2.4.1.3 Spectrum Management- It involves catching the best spectrum available so as to satisfy user communication requirements. Cognitive radios should decide on the best spectrum band so that it can meet the Quality of service required for all available frequency bands, therefore these functions are necessary for Cognitive radios.

These management functions can be classified as:

  1. Spectrum analysis
  2. Spectrum decision
  3. Spectrum Mobility: It is defined as the process when a cognitive radio device exchanges its frequency of operation. Cognitive radio networks target to use the spectrum in a dynamic manner by allowing the radio terminals to operate in the best available frequency band, maintaining seamless communication requirements during the transition to better spectrum.
  4. Spectrum Sharing: providing the fair spectrum scheduling method. One of the major challenges in open spectrum usage is the spectrum sharing. It can be regarded to be similar to generic media access control MAC problems in existing systems

2.5Cognitive Radio Challenges

Three main problems experienced by CR are as follows:

  1. Interference (Mainly because of Hidden Nodes).
  2. Security.
  3. Control.

They are described as follows:

2.5.1 Interference and the Hidden Node Problem

Ideally while designing a CR we should consider that it does not have any impact on existing radio users, but in practically some impact is expected. If a particular user have non-cognitive radios, it is essential to study and make a note that how they would be affected by the interference of CR, mainly with respect to sharing resources such as spectrum, time, space etc. CR adaptive nature could be difficult to predict and thus making it hard to control the behaviour of a CR which will concern for user who faces CR interference issue. In communication industry the main concern about CR is the hidden node problem. This scenario arises when a CR is not capable to detect an interference with any of non-cognitive radios within its range, not only because of CR's own spectrum sensing is ineffective but also due to some non-cognitive radios are hidden. For example, if a transmitting contemporary user is not in the range from the CR, its transmission power may not be strong enough at the CR's location, it may be reduce than the noise floor which makes it more difficult to get detected. As the CR might not be able to detect a transmission by a contemporary user and similarly unaware of availability of the receiving by a contemporary user. Consequently, if it is confirmed as safe to use the contemporary user's frequency and CR starts transmitting, at the contemporary receiving end it will create interference. The CR may have a limited view of spectrum provided from wide spectrum measurements which may cause interference with the receiving user. The localised spectrum view denotes that a CR should be potential to find transmitting user those are communicating below the noise level, since the strength of the received signal is very weak at the CR's location. Similarly a situation can occur where the signal attenuate by distance, thus user transmission is blocked by obstacles such as buildings, towers, hills or mountains. For example consider a CR in a valley would have a limited picture of the surrounding radio environment, as compared to that if it were located on top of a hill.

2.5.2 Security Concerns

CR may be vulnerable to malicious effect, resulting into unexpected or problematic behaviour of individual CRs or complete networks. This problem springs up from the potential to re-program CRs in an unauthorized way. Hacking or placing a vulnerable code, virus on a network might enable criminals to steal valuable information from a CR through electronically, fool a network operator into charging others for services or achieve potentially widespread denial-of-service. A considerable amount of regulatory work will require to be done to clarify who would be responsible for the various security areas of CR, software developers, manufacturers, network operators and CR users themselves may all have a role to play. The CR security issue is closely related to that of SDR, which already discussed and hence not repeated here. Instead, a brief summary of the issues is given. Downloading software updates over an air interface poses some specific problems for security. Several digital signatures will be required for each piece of downloaded software in order to meet likely regulatory requirements. Exactly who is necessary to authorize software downloads must be standardized before any large scale deployment of over-the-air updates can be realized.

2.5.3 Burden of Control and Regulatory challenges

A CR in reality will have some effect on different spectrum users the compliance of these new radios is likely to focus on a "Policy module" defined within a CR, which will determine the boundaries of CR behaviour. It is important while studying CR; to consider how users would not be affected by interference from CR devices and the exact operation and nature of a proposed CR policy system must be understood carefully. It is likely to include a detail case study of the specifications and characteristics of all the contemporary users for a specific CR or complete network of CR's may share the resources. The effort in controlling CR devices, it is necessary to ensure their behaviour is properly or not, even in the case of faulty or tampered devices, that measures are quickly implemented to intense problems. This will involve policies and standards which are created in a 'universal' digitally interpretable policy, so that all CRs can understand the same. Monitoring techniques and powerful algorithms are required to enable detection and identification of 'bad' CRs and in this way it provides traceability to find or determine who is responsible for the issue. In addition to these challenges, spectrum regulators and spectrum managers will require providing access to licensed spectrum in such a way that is traceable, transparent and highly dynamic. If CRs are allowed to cover international territory additional effort will be required, due to the necessity to provide and collaborate cooperation with other countries. Assuming that acceptable control of CR policy behaviour is technically possible and feasible, it may turn out to be such a great burden that it will be simply not economically viable and the benefits of CR are outweighed by this burden.

2.6 Important Applications for Cognitive Radio are

  • Downloading of audio and video files on mobile handsets. This application requires moderate data rates and near-ubiquitous coverage.
  • Emergency services communications: It requires interoperability and a moderate data rate with local coverage.
  • Broadband wireless networking: Very high data rate required but CR users have option to accept limited coverage, e.g. hot spots.
  • Multimedia wireless and sensor networking: Broad range of data rates may be required.

2.7 Spectrum Sensing

Spectrum sensing is the process performing measurements on the part of spectrum and on the basis of measured data making a decision related spectrum usage. As the requirement and quantity of users is getting increased day by day, it is necessary for ISPs to have large amount of spectrum in order to achieve the QOS (Quality of Service). This leads the interest in unlicensed spectrum access and spectrum sensing is vital concept of this. In a situation where there are licensed user and any unlicensed exists, licensed user (primary user) is to be protected and no unlicensed user can interfere any licensed user's operation and such cases Spectrum sensing is also useful to detect the existence or non existence of a primary user. Spectrum sensing is an important concept for exploring spectrum opportunities for the secondary spectrum usage in real-time. It detects the unused spectrum and shares it without any noticeable interference with other users. It is an important requirement of the Cognitive Radio in order to sense spectrum holes. Detecting primary users is the most efficient way to detect spectrum holes.

2.8 Spectrum Sensing Techniques Available

Spectrum sensing plays a vital role in cognitive radios. And the type of spectrum sensing techniques to choose more or less depends upon the spectrum sensing technique. A method such as energy detection proves to be one of the simplest of all, but it doesn't works well at low SNR, varying noise levels, fading. On the other hand technique such as Matched filters shows better performance, but they comes complex receiver design. We are going to discuss some of these techniques as we proceed further.

2.8.1 Matched Filter Technique

This is the technique which takes minimum amount of sensing time. In this method of spectrum detection, receiver receives a pilot signal along with the data that is sent by the transmitter. A pilot signal is a single frequency that is used for synchronisation. All the secondary's those are struggling for spectrum should have knowledge of this pilot signal. There should` be tight timing synchronisation between primary and secondary. They are also required to know about the kind of modulation being used, pulse shaping. Also secondary's must have another receiver for every primary. This kind of techniques also fails when there is frequency offset. Examples of this technique are TV signals, CDMA with pilot, also used in OFDM.

2.8.2 Energy Detection technique

This is the most simples of all techniques. In this the receiver has no knowledge of the transmitted signal. The receivers need not to have knowledge about the modulation type or any kind of pilot signal. Earlier energy detection was done with the help of a LPF (Low Pass Filter), Digital to Analog converter (D/A), and square law device that used to calculate the energy of the signal. Later it is done by making use of fast Fourier transform (FFT). This is known as periodogram method in energy detection.

2.8.2 Cyclostationary Feature Detection

Signals are modulated with sine waves or cyclic prefix as in OFDM. And they are periodic. This periodic property of a signal helps it to be cyclostationary. This technique basically uses this principle of spectral correlation to detect the spectrum. Even if signals have similar PSD (Power Spectral Density) but they have do not have similar spectral correlation.

2.9 Multi-resolution for Cognitive Radio Sensing

The concept of multi-resolution for Cognitive Radio can be applied with different methods but, the basic idea is the same. The whole spectrum is first sensed by using a coarse resolution. After this first step fine resolution sensing is done on a part of interested bands. In this way CR avoids itself from sensing the spectrum at one time and thus saving time and power. In this way, the sensing time is reduced and the power also been saved from unrequited computations. Also the multiple antenna architecture helps parallel processing and enables to reduce the sensing time. But, it increases the chip area and consumption of power which is not desirable. Also for coarse resolution sensing the mixer has to produce many frequencies and

Also it should switch to one frequency for beginning the fine resolution sensing. If the signal is low pass signal then we can use fine resolution to scan the whole spectrum. Because low pass signal has low center frequencies and its sampling is doable. But for pass band signals it is not feasible to scan the whole spectrum. Because, for example say if we have some signal with center frequency of 850 MHz, it is not practically possible to do sample that signal. As according to Nyquist theorem sampling rate should be at least twice that of center frequency. Therefore it is practically not possible to sample a signal at 1600 Mega Hertz.

2.10 Business Model for Spectrum Sensing

So far we have discussed about Cognitive radios and spectrum sensing in details. Since this is telecom, and telecom involves huge capital investment. One of the most costly things in telecom is getting the license itself and then comes the infrastructure and installations etc. Currently most of countries work on static spectrum allocation basis. For spectrum sensing to work we need some kind of regulation or set of rules that all will be ready to work with. A team from Brussels University has suggested a model for the same. The same model is discussed in brief below. This model is divided into four main categories.

i. Ownership-

Ownership simply means the ownership of license. One who has license is authorized to use particular band of spectrum. And if another licensee wants to share a spectrum then it will depends on parameters discussed below. If the operator is unlicensed then there is now issue of ownership.

ii. Exclusivity-

Exclusivity means whether or not a particular operator is exclusively assigned a band of spectrum. That will be issue of regulator to decide to exclusively assign a spectrum to a specific user. If it is assigned exclusively then nobody can access that band of frequency, and if not then those bands of frequencies will be available for sharing.

iii. Tradability-

Tradability means whether or not a terminal is allowed to switch between frequencies from different operators. If tradability is permitted frequency band or bands can be auctioned for sale or given on lease.

iv. Neutrality-

It is possible that some of frequency bands can be accessed by number of RAT's (radio Access Technology) or may be limited to a particular RAT. If frequency bands are not available to number of RAT's then that band need to address more issues, such as setting technical conditions to access the band and coordinating the cooperation between multiple technologies.

  1. Unlicensed: Unlicensed deals with the band of frequencies which are free to use, like ISM band. Common example for this is Wi-Fi which operates in 2.4GHz. This band of frequency is available to all and there is special condition to access this band.
  2. Single RAT Pool: This pool is related to a group of licensee's which are not exclusively assigned any band of frequency and using same RAT.
  3. Multi RAT Pool: It is similar to Single RAT Pool except that it has multiple RATs.
  4. Single RAT Market: In this each operator is assigned with a separate frequency, but that can be accessed by the conditionally secondary's.
  5. Multi RAT Market: In this the operator is a licensee and is exclusively assigned with a band of frequency, and also with tradability.
  6. Flexible Operator and Static Spectrum: If a particular band of spectrum is exclusively assigned to an operator and without tradability. And if there is only one RAT, then it is known as Static Spectrum else Flexible Operator.

2.11 Survey outcome

As per the literature review we concludes to use Energy detection technique for spectrum sensing as it is the fastest spectrum detection technique available and also it is simpler as compared to other techniques. There are many researches done in this field and many are still doing. Because of the survey we get knowledge about various techniques available in market. This led us to do a simulation model with one of the spectrum sensing technique. Now as far as selection of spectrum sensing technique is concerned, we selected Energy detection technique.

The same could be seen in next part of the thesis which includes its design and implementation in simulation model.

Chapter 3 - Simulation Design

3.1 Sampling and Its Importance

Sampling is a process of converting the continuous analog signal to a discreet analog signal and the samples signal is the discreet time representation of the original signal. If the message is coming from a digital source, then it is in the form to be processed by digital communication systems. But in real life not every signal is digital, message signal can be analog. In situation like these, we have to first convert the analog signal into discreet time signal, this is sampling. For this process to work well, sampling rate should be selected carefully or in other words it should satisfy Nyquist criterion. And Nyquist Criterion says that the sampling frequency should be at least twice the maximum frequency in signal.

Fs ≥ 2fmax or T=1/ fmax

Where “Fs” is sampling frequency, fmax is maximum frequency in the signal. T=Sampling period.

Sampling is like reading a signal in analog form and taking its value at that instant of time. So more the samples we take better the resolution of the signal and, signal can be recovered more accurately. But if we take less number of samples, then resolution of the signal decreases. If we go on reducing the sampling rate, then times comes when it is difficult to recover the original signal from the sampled signal or in other words original information in the signal is lost. This is also known as aliasing. Aliasing is the effect which takes place if the signal is sampled less than twice the maximum frequency.

3.2 Types of Signal

3.2.1 - Time limited signal- It is a signal which exists for only certain duration of time. Out of this duration, signal does not exist. A rectangular pulse of duration “T” seconds can be considered as time limited signal.

x(t)=A … for 0<t<T and x(t)=0 ….otherwise

3.2.2 - Band Limited signals- It is a signal which has a frequency spectrum which exists only over a certain range of frequency. The value of signal outside this range of frequency is zero.

Mod(X(f))=A ….-B<f<B and Mod(X(f)) =0 ….otherwise.

3.3 Sampling Of Low pass and Band Pass signal

Low pass sampling theorem states that-

a) A band limited signal of finite energy, which has no frequency components higher than W Hertz, is completely described by the specifying the values of the signal at instants of time separated by 1/2W seconds and

b) A band limited signal of finite energy, which has no frequency components higher than W Hertz, may be recovered from the knowledge of its samples taken at rate of 2W samples per seconds.[J.Chitodes]

Band Pass sampling theorem states that-

A Bandpass signal x(t) whose maximum bandwidth is 2W can be completely represented into and recovered from its samples if it is sampled at minimum rate of twice the bandwidth.

Figure 3.4 shows us the spectrum of low pass signal and its spectrum after sampling. Low pass signal are the signal which works well under Nyquist criterion (Fs ≥ 2fmax) without any special alteration. There is another type of sampling known as Band-pass sampling. This is the type of sampling applied to a high frequency signals or the signals which cannot pass through low pass filters. These signals have high frequency, normally in 100's of mega hertz. Here we cannot use Nyquist theorem directly. As practically it is not possible to sample a signal at such a high frequency. So what we do is, we represent the signal in terms of ‘”inphase” (I) and “quadrature” (Q) components of a signal before sampling. This I and Q component is made by multiplying I and Q with sin(2π*fc*t) and cos(2π*fc*t) respectively. We can also say that we are actually moving a window of size of the bandwidth over the center frequency of the signal.

In our Matlab code we have used low pass sampling even though the frequencies are quite high, because it is not a practical model but a simulation model. In low pass sampling we sample the whole available while in band pass signal we sample a window of bandwidths.

3.4 Design Specifications

Our design specification deals with requirement for measuring the center frequencies in the spectrum and calculating the spectrum hole or white space. Figure 3.6 shows a spectrum with two signals in it. Measurement of the center frequency is shown in “Generation of White Space”. Since we have used a low pass sampling our model will scan whole spectrum and try to locate spectrum holes within it. And these holes are located with energy detection on basis of a threshold. Our model to work well we need more than one signal in a spectrum.

3.5 Generation of White Spaces

3.5.1 What are they

White spaces are basically the bands of frequencies that go unused. They are also known as spectrum hole. From the name spectrum hole, it suggests they are the holes in the spectrum, which could be filled if they are used properly. In today's communication world, most of countries work's on static spectrum allocation basis. That means a particular operator will have a dedicated band of frequencies, no matter whether he uses it or not but it will be reserved for that operator as they have paid for getting the license for that from the government. In this case, if we think of bigger picture and scan the whole spectrum for a particular duration of time say one day. We will find that there are lot of frequency bands which are not fully utilized. These some of unused bands are known as White Spaces or Spectrum Holes.

3.5.2 Factor's responsible for creation of white spaces

White spaces get created because all the bands in the frequency bands in the spectrum are not used efficiently. Because we have different technologies working in various bands of spectrum but we do not have any practical implementation on large scale of any radio to work on whole spectrum at same time.

3.5.3 How they can be avoided

It is difficult to avoid all the white spaces. But we surely can reduce it to some extent. And for that we need a special radio which can work on whole spectrum. Radio which is smart enough that could take important decisions without any human interference. This is where cognitive radios come into picture. But besides the radio, it also needs some set of rules or some regulation to make it work. Keeping this in mind the same has been discussed in Business Model foe Cognitive Radios in Chapter 2.

3.5.4 Regarding this Thesis

As far as this Thesis is concerned we are taking help of Simulation to show spectrum sensing in Matlab. And the technique which we used for that is Energy Detection technique. Now to show a spectrum we first need multiple signals in our spectrum. Those signals are the OFDM signals. And the frequency gap in between those signals is our white spaces. To show spectrum sensing it is important to create this white space. We have tried not to use the frequencies which are too high or too low. We have tried to make complex calculation's looks simpler for easy understanding.

3.5.5 Parameters/Variables Used

Number of carriers is denoted by variable “k”. “Tu” is useful OFDM symbol period. “T” is baseband elementary period. “G” is cyclic prefix. And total symbol duration is “Ts” which is equal to delta+G, where (delta= g*Tu). As said before we are using 2N-IFFT, so IFFT/FFT length ‘FS” is 4096. ‘Rs” is the simulation period. Carrier to elementary period ratio is “q” is 10. Carrier frequency “fc” = q*(1/T). As we want to add two signals later we have kept our sampling frequency of whole system as “Sf”. Sf = 700 MHz's.

3.5.6 Calculations for OFDM signal 1

For signal 1:

Tu= 100 *106 sec.

Therefore,

T=Tu/2048 = 48.8281*10-9 sec.

fc=q/T = 204.79*106 Hz

This is the center frequency for OFDM signal1.

And, Rs=4*fc=819.199*106.

Carrier spacing = (kmax * (kmin-1))/Tu

= (1705*1704/100*10-6)

= 29036.16*106

Spacing between two carriers = 29036.16*106 / 1705

= 17.03*106 Hz.

Now, we are having total 1705 symbols that are transmitted with a rate of T/2 seconds to IFFT block.

That is, one symbol is transmitted every T/2 seconds.

As we are using 4-QAM, it means we are using 2 bits per symbol.

That is, two bits are transmitted every T/2 seconds.

Therefore, one bit every T/4 seconds.

So, the bit rate for OFDM symbol 1 = T/4 = 122.07*10-6 seconds.

3.5.7 Calculations for OFDM signal 2

For signal 2:

Tu2 = 80 *106 sec.

Therefore,

T2=Tu2/2048 = 39.06*10-9 sec.

fc2=q/T2 = 256.00 * 106 Hz

This forms the center frequency of OFDM signal2.

And, Rs2=4*fc2=1024.00*106.

Carrier spacing = (kmax * (kmin-1))/Tu2

= (1705*1704/80*10-6)

= 36295.2 * 106

Spacing between two carriers = 36295.2 * 106 * 106 / 1705

= 21.28*106 Hz.

Similarly to case 1 bit rate for OFDM signal 2 is calculated as,

One bit is transmitted every T2/4 seconds.

So, the bit rate for OFDM symbol 2 = T2/4 = 9.765*10-9 seconds.

And from our code, to change the center frequency we just need to alter “Tu” (useful OFDM symbol period) or the sampling frequency ‘Sf”. For Tu as 100 micro seconds and 115 micro seconds we are getting OFDM signals with center frequencies around 200 MHz and 265 MHZ respectively. In this way we have created a white space of around 40 MHz's.

In this chapter we found the center frequency measurement technique which is followed by results analysis in the next part of our thesis.

Chapter 4 -Simulation and Result Analysis

4.1 Introduction

So far we have seen how cognitive radios work. Cognitive radios are not only for research but it is a need of today's communication system. Moreover cognitive radios are more reliable than the traditional radios. We have also discussed the role of cognitive radios in sensing the spectrum, various methods used for spectrum sensing. In our thesis we have explained many spectrum sensing techniques like matched filter, energy detection, cyclostationary features. Cognitive radios are simple extension of software defined radios. But it is flexible characteristics of frequency and bandwidth and Intelligent Signal Processing (ISP) as an add-on. With the help of it makes smart decision at correct time. Now cognitive radios and ISP generally means with the physical layer but not compulsory, ISP can be installed at higher levels too. For CR to get the smartness it needs this ISP.

Of all the above spectrum detection techniques discussed we found that energy detection is the simplest one and one of the fastest technique available for spectrum sensing. Hence we have tried to show simulation for spectrum sensing by using energy detection technique. Simulations are done using Matlab. Matlab Code (m.file) is supported with sufficient comments when needed for better understanding. In this chapter we have tried to explain the output of the system at various points.

Orthogonal Frequency Division Multiplexing (OFDM) is well suited for cognitive radios because of its robustness. OFDM can provide large data rate even in impaired channel. For decades there has been a lot of research done in the field of OFDM. OFDM is basically a bunch of large number orthogonal waveforms which does not overlap with each other (as they are orthogonal). OFDM consist of subcarriers which are narrow-band and these subcarriers are transmitted in parallel while transmission. OFDM is good with handling multipath interference. Multipath interference gives rise to frequency selective fading and Inter Symbol Interference (ISI). Because OFDM has narrow band we get flatness in channel which helps to overcome frequency selective fading. Modulation of symbols in OFDM is done at very low rate, this makes symbol much longer than our channel impulse response, and this in turn helps to overcome ISI. That is one of the reasons why we have chosen OFDM for our simulation.

In our Matlab code we have generated two OFDM signal, both with different centre frequencies. This two signals acts like the signals transmitted by two different transmitters and they met up in air. So we have added these two signals. Now signal can be considered as the signal received at the receiver end of the system, as we have not considered noise in our system. Then we have found the energy of this added signal and calculated its energy.

4.2 Simulation Analysis of OFDM signals 1 and signal 2

We want the OFDM spectrum should be centered on frequency ‘fc'. And to get that we are using 2N-IFFT to get the spectrum centered at ‘fc'. The elementary period of the OFDM is considered to be T/2. The symbol duration Tu is considered using 2048-IFFT; hence we will be using 4096-IFFT in our code. Figure 4.1 shown below is the block diagram of a typical OFDM system.

The elementary period T is defined for baseband signal, whereas we are going to perform the simulation on pass-band signal. For this we need to relate elementary period T to the time period of “1/Rs” which should be at least double the carrier frequency.

Input data is being created using “rand” function. The numbers generated by this function is decided by the internal state of generator. This function generates a stream of random numbers which are distributed uniformly. Also note that the state of “rand” function is set to “0” (rand(‘state',0)). By setting the state of “rand” function to state zero resets the generator to a same fixed state. Hence we get same results every time. If we do not set the state to zero then we will get unique set of result every time we run the program. It would be better programming practise to set the state of rand function because Matlab reset the state at every start-up. With the help of ‘rand' function ‘Kmax+1' complex numbers are generated and stored into variable 'a'. The variable ‘info' forms our actual input data to the system. This variable has 4096 points, in which zeros are padded at the beginning and at the end 853 points. Zero padding is done to avoid symbol overlapping or aliasing.

M=Kmax+1; rand('state',0);

a=-1+2*round(rand(M,1)).' + 1i*(-1+2*round(rand(M,1))).';

info =zeros(FS,1); info((FS-((A/2)-1)):FS) = [ a(((A/2)+1):A).'];

info(1:(A/2)) = [ a(1:(A/2)).']; carriers=FS.*ifft(info,FS);

The Below figure shows the real and imaginary part of the signal carriers which forms our input signal. We can also notice that signal carriers uses time period of “T/2”.

(a) OFDM Signal 1(b) OFDM Signal 2

(a) OFDM Signal 1(b) OFDM Signal 2

The purpose of zero padding is to assure frequency domain symbols are communicated in orthogonal sub channels. The length of the cyclic prefix is to be kept at least as long as impulse response of the channel at the receiver. Due to sharp transitions OFDM signal causes spurious emission which in turn leads to attenuation and thus further lengthening the impulse response of the channel.

This is then followed by an IFFT block which performs IFFT on the signal ‘info'. At its output we get a signal ‘carriers' which is an IFFT of info with 4096 points. Signal ‘carriers' is plotted in time domain and frequency domain in figure 4.1, 4.11 and figure 4.2 and 4.12 respectively. We can notice that signal ‘carriers' has time period of T/2 and signal ‘carriers' is a baseband signal. To plot the signal in frequency domain ‘pwelch' function is used. ‘pwelch' function gives the power spectral density (PSD) of a signal by employing modified periodogram method.

The output of the IFFT block is then fed to the reconstruction filter or low pass filter. Filter filters out the frequency components out of band and only passes frequencies which are in band. The signal ‘carriers' is sent the through the filter g(t).

(a) OFDM Signal 1(b) OFDM Signal 2

Below shown is signal “carriers” after pulse shaping in time domain.

(a) OFDM Signal 1(b) OFDM Signal 2

(a) OFDM Signal 1(b) OFDM Signal 2

Signal “carriers” after pulse shaping in frequency domain.For reconstruction filter we are using bandwidth as ‘Rs' and time period as 2/T. Therefore we have (2/T=40.95 MHz) - 17 MHz = 23 MHz (for OFDM signal 1) and (2/T2=51.20 MHz) - 17 MHz = 34.20 MHz (for OFDM signal 2) as our transition bandwidth for the filter's. At the output of filter we get periodic frequency response as required by discreet time system as seen in figure 4.6 and 4.16.

Now, in our code the signal ‘carriers' is used to produce signal ‘chips' which is then convolved and pulsed shaped to form a signal ‘dummy' and ‘u'. In Matlab convolution is similar to multiplication of two signals. The signal ‘u' is plotted in time and frequency domain in figure 4.4, 4.14 and figure4.5, 4.15 respectively. The signal ‘u' is the passed through a filter.

As a filter we used Butterworth filter of order 13 with approximate cut off frequency of 1/T. Figure 6 shows the filter response.

(a) OFDM Signal 1(b) OFDM Signal 2

Response of Digital to Analog Filter. In our case we are using Butterworth filter of order 13.

And figure 4.9 (a)/(b) and 4.10 (a)/(b) shows the out of filter ‘uoft' in time and frequency domain.

(a) OFDM Signal 1(b) OFDM Signal 2

(a) OFDM Signal 1(b) OFDM Signal 2

Signal output after filtering in frequency domain.

Lastly we do Up-Conversion on the signal. Up-Conversion actually converts the baseband OFDM signal to an IF (Intermediate Frequency) signal, so that the signal is ready for transmission. It also minimizes the impulse response duration of the OFDM signal. Input to the Up converter is stream of baseband symbols. Till now we got “I' and “Q”. Now this “I” and “Q” are modulated with sin (ω) and cos (ω) and then finally added and then transmitted. Where ω = 2π*fc*t. below figure show time response of our OFDM signal.

(a) OFDM Signal 1(b) OFDM Signal 2

(a) OFDM Signal 1(b) OFDM Signal 2

These baseband symbols are sampled at baseband sampling rate. Each symbol takes one of the “M” complex values. The value of the complex number and “M” forms the basic parameter for modulation. For example, 16-QAM will have 16 different complex numbers on complex plane or signal constellation; 4-QAM will have four different complex values. Up conversion is also necessary as, for the frequencies which are in Giga Hertz (GHz) traditional signal processing devices do not perform well and transistors become unstable. And one more reason for that is, it is difficult to implement amplifiers and filters that can be tuned to different frequencies, rather it would be much easier to implement a tuneable oscillator.

This gives us the simulation analysis of OFDM signal 1.Generation of OFDM signal 2 is similar to OFDM signal 1. The only change we have done is in “Tu” (OFDM period). In first case “Tu” is 100 micro seconds, while in second signal “Tu” is 80 microseconds. This gives us OFDM signal 1 with center frequency as 42 MHz and OFDM 2 signal with center frequency 85 Mega Hertz. By creating OFDM signal 1 and OFDM signal 2 we have managed to generate two frequency band in a spectrum which we will using to testing the spectrum sensing.

These to signals are transmitted in air and at the receiver we will be receiving another signal which will be the addition of these two. To keep our code simple we are not adding noise to our system. In future we plan to study the simulation of our system with noise. So in next part of our code we add these two signals. Signal “s_tilde” from OFDM signal 1 and signal ‘s_tilde2” from OFDM signal 2 is added in time domain.

(a) OFDM Signal 1(b) OFDM Signal 2

This added signal has real as well as imaginary components. So we remove imaginary parts by taking only real part of the signal and using it for further analysis. This real part of the added signal is then plotted in time domain and frequency domain.

Next task is to sense the spectrum. For sensing the spectrum we are using Energy detection technique. In which we basically we sample the whole spectrum and take energy at each time sample. This energy which is taken at each time sample is then compared to a threshold.

(a) OFDM Signal 1(b) OFDM Signal 2

If the signal has energy beyond threshold then decision could be taken as that band of spectrum is occupied or not available, and if detected energy is less than the threshold then we could say that a particular band in a spectrum is available, which can be seen in above figure. Now setting this threshold is an important task for this technique to function better. Energy detection technique is no doubt one of the fastest spectrum sensing technique available, but we must recall that according to basic definition of cognitive radios, it should not only sense the spectrum fast but also need to be accurate. And for our technique to be more accurate we can employ some more techniques with energy detection which are discussed above. But in our code we have tried to keep it simple, so the threshold is decided by (maximum energy +minimum energy)/2. This looks simple and it is effective too.

This chapter showed us the results of our model. With this we end this chapter and take a step forward towards some future work we had in our mind while doing this thesis.

Chapter 5 - Conclusion

The main aim of this thesis was to understand Cognitive radios, spectrum sensing and to provide a basic simulation tool for future work. Spectrum sensing and Cognitive radios are discussed in details and simulation is performed using Matlab. Various spectrum sensing techniques have been discussed, and simulation is done using Energy detection Technique, because it is one of the fastest detection technique and also simplest of all. Mean value of energy is taken as the threshold for spectrum sensing. However we have not considered noise in our system for simplicity, but it cannot be ignored in real time systems. And with this we conclude our thesis, but we still need to keep in mind that more work need to do in Cognitive radios.

Chapter 5 - Future Work

This thesis will form a basic tool for any further research in the field of spectrum sensing or cognitive radios. Currently we are able to detect the spectrum roughly, in future we would like to sense the spectrum with more accurate spectrum sensing techniques. Also we intend that our receiver will sense he spectrum and will give a feedback to the transmitter. This feedback will give information about the white spaces or spectrum holes in the spectrum and then transmitter will adjust its parameters (in our code we can do that by adjusting “Tu” - Useful OFDM Period) and then try to use the spectrum available. Currently we have not taken noise into account. If we consider noise and feedback from receiver, then a complete cognitive radio system will be ready.

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