Cognitive Radio

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

INDEX

ABSTRACT…….………………………………………………………………....................................5

1.1 Introduction to Software-Defined Radio………………………………………………6 1.2 A brief history of SDR ………………………………………………………………………….7 1.3 Role of SDR ………………………..……………………………………………………………….7 1.3.1 Problems faced by Wireless Communication Industry 1.3.2 How SDR solves the problems 1.4 Features of SDR……………………………………………………...............................9

Chapter 2 –COGNITIVE RADIO TECHNOLOGY 2.1 2.2 2.3 2.4

A vision of Cognitive Radio…………………………………………………………..……10 History Leading to Cognitive Radio……………………………………………………10 Information on Cognitive Radio…………………………………………………………11 Cognitive Radio Network Paradigms………………………………………………….13 2.4.1 Underlay Paradigm 2.4.2 Overlay Paradigm 2.4.3 Interweave Paradigm

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Chapter 1 -SOFTWARE-DEFINED RADIO

Chapter 3- IMPLEMENTATION 3.1 Algorithm…………………………………….…….…….…….…….…….…….…….…….…15 3.2 Related Theory…………….………………………….…….…….…….…….…….….…….16 3.2.1 About MATLAB & Simulink 3.2.2 DSB-SC AM Modulation 3.2.3 Power Spectral Density

1

Chapter 4- SIMULATION 4.1 Simulink Model …………………………………………………………….…..…….19 4.1.1 Block Details 4.1.2 Block Properties 4.2 Matlab Code……………………………………………………….…….……..……..26 4.3 Results and Graphs ……………………………………………….…….….….…..28 4.4 Interpretation of Results ………………………………………………..…….…33

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Chapter 5 – FUTURE SCOPE OF COGNITIVE RADIO…………………………….…….34

2

REFERENCES…..…………………………………………………………………………………….……35

This report contains basic information on Cognitive Radio and Software Defined Radio. Software Defined Radio is an emerging technique in this domain that promises easy portability and adaptability of new techniques on the same hardware. Cognitive Radio technique utilizes this Software Defined Radio to intelligently and efficiently utilize the available frequency spectrum knowing about the side information of other users sharing the same spectrum. The report talks about the Cognitive Radio technique and goes into details of its Software implementation (on Simulink and MATLAB) using Spectrum Sensing and the practicality of cognitive radio in our present scenario of communication and its actual hardware implementation on Software Defined Radio.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

ABSTRACT

3

Chapter 1 SOFTWARE DEFINED RADIO

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

1.1 Introduction to Software-Defined Radio With the exponential growth in the ways and means by which people need to communicate - data communications, voice communications, video communications, broadcast messaging, command and control communications, emergency response communications, etc. - modifying radio devices easily and costeffectively has become business critical. Software defined radio (SDR) technology brings the flexibility, cost efficiency and power to drive communications forward, with wide-reaching benefits realized by service providers and product developers through to end users. Simply put Software Defined Radio is defined as: "Radio in which some or all of the physical layer functions are software defined" Software-Defined Radio (SDR) refers to the technology wherein software modules running on a generic hardware platform consisting of DSPs and general purpose microprocessors are used to implement radio functions such as generation of transmitted signal (modulation) at transmitter and tuning/detection of received radio signal (demodulation) at receiver. SDR technology can be used to implement military, commercial and civilian radio applications. A wide range of radio applications like Bluetooth, WLAN, GPS, Radar, WCDMA, GPRS, etc. can be implemented using SDR technology. Traditional hardware based radio devices limit cross-functionality and can only be modified through physical intervention. This results in higher production costs and minimal flexibility in supporting multiple waveform standards. By contrast, software defined radio technology provides an efficient and comparatively inexpensive solution to this problem, using software upgrades. SDR defines a collection of hardware and software technologies where some or all of the radio’s operating functions are implemented through modifiable software or firmware operating on programmable processing technologies. 4

1.2 A Brief History of SDR

Today’s SDR, in contrast, is a general-purpose device in which the same radio tuner and processors are used to implement many waveforms at many frequencies. The advantage of this approach is that the equipment is more versatile and cost effective.

1.3 Role of SDR 1.3.1 Problems faced by Wireless Communication Industry  Commercial Wireless network standards are continuously evolving from 2G to 2.5G/3G and then to 4G. The difference in networks of each generation being significantly in link-layer protocol standards cause problems to subscribers, wireless network operators and equipment vendors.  Subscribers are forced to buy new handsets whenever a new generation of network standards is deployed. Wireless network operators face problems during migration of the network from one generation to next due to presence of large number of subscribers using legacy handsets that may be incompatible with newer generation network. The network operators also need to incur high equipment costs when migrating from one generation to next. Equipment vendors face problems in rolling out newer generation equipment due to short time-to-market requirements.  The air interface and link-layer protocols differ across various geographies (for e.g. European wireless networks are predominantly GSM/TDMA based while in USA the wireless networks are predominantly IS95/CDMA based).

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

An SDR is a radio in which the properties of carrier frequency, signal bandwidth, modulation, and network access are defined by software. Today’s modern SDR also implements any necessary cryptography; forward error correction (FEC) coding; and source coding of voice, video, or data in software as well. The roots of SDR design go back to 1987, when Air Force Rome Labs (AFRL) funded the development of a programmable modem as an evolutionary step beyond the architecture of the integrated communications, navigation, and identification architecture (ICNIA).

5

This problem has inhibited the deployment of global roaming facilities causing great inconvenience to subscribers who travel frequently from one continent to another. Handset vendors face problems in building viable multi-mode handsets due to high cost and bulky nature of such handsets.  Wireless network operators face deployment issues while rolling-out new services/features to realize new revenue-streams since this may require large-scale customizations on subscribers’ handsets.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

1.3.2 How SDR solves the problems  For equipment manufacturers SDR enables a family of products to be implemented using common platform architecture, allowing new products to be more quickly introduced into the market.  Software can be reused across the products, reducing development costs dramatically.  For Wireless Network Operators SDR enables new features and capabilities to be added to existing infrastructure without requiring major new capital expenditures.  Remote software downloads, through which capacity can be increased, capability upgrades can be activated and new revenue generating features can be inserted.  For subscribers SDR technology reduces costs by enabling them to communicate with whomever they need, whenever they need to and in whatever manner is appropriate. SDR technology has some drawbacks like higher power consumption, higher processing power (MIPS) requirement and higher initial costs. SDR technology may not be suitable for all kinds of radio equipment due to these factors. Hence these factors should be carefully considered before using SDR technology in place of complete hardware solution. For e.g., SDR technology may not be appropriate in pagers while it may offer great benefits when used to implement base-stations. 6

1.4 Features of SDR technology

Ubiquitous Connectivity: SDR helps in realizing global roaming facility. If the terminal is incompatible with the network technology in a particular region, an appropriate software module needs to be installed onto the handset resulting in seamless network access across various geographies. Interoperability: SDR facilitates implementation of open architecture radio systems. End-users can seamlessly use innovative third-party applications on their handsets as in a PC system. This enhances the appeal and utility of the handsets.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Reconfigurability: The SDR provides flexibility in system design. Reconfigurability provides essential mechanisms to terminals and network segments to adapt dynamically, transparently and securely to the most appropriate radio access technology via selecting pre-installed software components or via software downloading and installation. The wireless network infrastructure can reconfigure itself to subscriber's handset type or the subscriber's handset can reconfigure itself to network type.

7

Chapter 2 COGNITIVE RADIO TECHNOLOGIES

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

2.1 A vision of Cognitive Radio

8

The long term vision of cognitive radio technology is one in which handsets would automatically make use of underutilized spectrum across a broad frequency range, allowing the high bandwidth requirements of the future. If a radio were smart, it could learn services available in locally accessible wireless computer networks, and could interact with those networks in their preferred protocols, to have no confusion in finding the right wireless network for a video download or a printout. Additionally, it could use the frequencies and choose waveforms that minimize and avoid interference with existing radio communication systems. It might be like having a friend in everything that’s important to your daily life.

2.2 History Leading to Cognitive Radio The sophistication possible in a software-defined radio (SDR) has now reached the level where each radio can conceivably perform beneficial tasks that help the user, help the network, and help minimize spectral congestion. The development of digital signal processing (DSP) techniques arose due to the efforts of such leaders as Alan Oppenheim, Lawrence Rabiner, Ronald Schaefer, Ben Gold, Thomas Parks, James McClellen, James Flanagan, Fred Harris, and James Kaiser. These pioneers recognized the potential for digital filtering and DSP, and prepared the seminal textbooks, innovative papers, and breakthrough signal processing techniques to teach an entire industry how to convert analog signal processes to digital processes. Meanwhile, the semiconductor industry, continuing to follow Moore’s law, evolved to the point where analog functions implemented with large discrete components were replaced with digital functions implemented in silicon, and consequently were more producible, less expensive, more reliable, smaller, and of lower power. During this same period, researchers all over the globe explored various techniques to achieve machine learning and related methods for improved machine behavior. Among these were analog threshold logic, which lead to fuzzy logic and neural

2.3 Information on Cognitive Radio What is Cognitive Radio? There are many definitions of CR and definitions are still being developed both in academia and through standards bodies such as defined in as : A cognitive radio is a wireless communication system that intelligently utilizes any available side information about the – a) Activity, b) Channel conditions, c) Codebooks, d) Messages of other nodes with which it shares the spectrum. CR implies intelligent signal processing (ISP) at the physical layer of a wireless system, i.e. the layer that performs functions such as communications resource management, access to the communications medium, etc. Usually, it is accompanied by ISP at higher layers of the Open System Interconnection (OSI) model. If ISP is not implemented at these higher layers then a CR will be restricted in what it can do. Because a communication exchange uses all seven OSI layers, ideally all seven layers need to be flexible if the CR’s intelligence is to be fully exploited. Without optimization of all the layers, spectrum efficiency gains may not be optimized.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

networks, a field founded by Frank Rosenblatt. In networking, DARPA and industrial developers at Xerox, BBN Technologies, IBM, ATT, and Cisco each developed computer-networking techniques, which evolved into the standard Ethernet and Internet we all benefit from today. The researchers are exploring wireless networks that range from access directly via a radio access point to more advanced techniques in which intermediate radio nodes serve as repeaters to forward data packets toward their eventual destination in an ad hoc network topology. Cognitive radios are nearly always applications that sit on top of an SDR, which in turn is implemented largely from digital signal processors and general-purpose processors (GPPs) built in silicon.

9

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Fig 1. The 7 layers of OSI Model

10

The idea of cognitive radio was born because of spectrum shortage. These devices utilize advanced radio and signal-processing technology along with novel spectrumallocation policies to support new wireless users operating in the existing crowded spectrum without degrading the performance of entrenched users. A cognitive radio must collect and process information about coexisting users within its spectrum, which requires advanced sensing and signal-processing capabilities. The larger barrier is the requirement for significant changes in the way wireless spectrum is currently allocated to enable cognitive techniques. Licensed frequency bands today are the radio and television bands, cellular and satellite bands, and air traffic control bands. The main advantage of the licensing approach is that the licensee completely controls its assigned spectrum, and can thus unilaterally manage interference between its users and hence their quality of service (QoS). In addition to the licensed spectrum, in recent years spectrum has been set aside in specific frequency bands that can be used without a license by radios following a specific set of etiquette rules, such as a maximum power per hertz or a shared channel access mechanism. The purpose of these unlicensed bands is to encourage innovation without the high cost to entry associated with purchasing licensed spectrum through auctions. The unlicensed bands have proven a great vehicle for innovation, and the 2.4 GHz unlicensed band currently hosts systems such as Bluetooth, 802.11b/g/n Wifi, and cordless phones. Unfortunately, the unlicensed bands can be killed by their own success, since the more devices that occupy these bands, the more interference they cause to each other.

Spectrum allocation is not just limited to licensed and unlicensed paradigms. The licensed or unlicensed bands may accommodate many additional wireless devices if these devices can exploit advanced technology to only minimally disrupt the communications of coexisting non-cognitive devices. Cognitive radio originated in the form of various solutions to this problem that allow cognitive communication with minimal impact on non-cognitive users.

Based on the type of available network side information along with the regulatory constraints, cognitive radio systems seek to underlay, overlay, or interweave their signals with those of existing users without significantly impacting their communication. The underlay paradigm allows cognitive users to operate if the interference caused to non cognitive users is below a given threshold. In overlay systems, the cognitive radios use sophisticated signal processing and coding to maintain or improve the communication of non cognitive radios while also obtaining some additional bandwidth for their own communication. In interweave systems; the cognitive radios opportunistically exploit spectral holes to communicate without disrupting other transmissions.

2.4.1

Underlay Paradigm

The underlay paradigm encompasses techniques that allow secondary communications assuming that they have knowledge of the interference caused by its transmitter to the receivers of the primary users. Specifically, the underlay paradigm mandates that concurrent primary and secondary transmissions may occur as long as the aggregated interference generated by the secondary users is below some acceptable threshold. In the underlay paradigm, the secondary user enters the primary spectrum only when its activity will not cause considerable interference or capacity penalty to the primary user. Measure of interference requires knowledge about multiuser CQI. Measurement challenges for underlay paradigm are:

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

2.4 Cognitive Radio Network Paradigms

 Measuring interference at NC receiver  Measuring direction of NC node for beam steering  Both easy if NC receiver also transmits, else hard. Underlay typically coexists with licensed users. Licensed users paid for their spectrum so they don’t want underlay, Insist on very stringent interference

11

constraints which severely limits underlay capabilities and applications. That is the main challenge for underlay policy.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

2.4.2 Overlay Paradigm The overlay paradigm allows the coexistence of simultaneous primary and secondary communications in the same frequency channel as long as the secondary users somehow aid the primary users, for example, by means of advanced coding or cooperative techniques. In particular, in a cooperative scenario the secondary users may decide to assign part of their power to their own secondary communications and the remaining power to relay the primary users transmission. The enabling premise for overlay systems is that the cognitive transmitter has knowledge of the noncognitive users’ codebooks and its messages as well. A noncognitive user message might be obtained by decoding the message at the cognitive receiver. On the one hand, the information can be used to completely cancel the interference due to the noncognitive signals at the cognitive receiver by sophisticated techniques, like dirty paper coding (DPC). On the other hand, the cognitive users can utilize this knowledge and assign part of their power for their own communication and the remainder of the power to assist (relay) the noncognitive transmissions. By careful choice of the power split, the increase in the noncognitive user’s signal-tonoise power ratio (SNR) due to the assistance from cognitive relaying can be exactly offset by the decrease in the noncognitive user’s SNR due to the interference caused by the remainder of the cognitive user’s transmit power used for its own communication.

2.3.3 Interweave Paradigm The interweave paradigm was the original motivation for cognitive radio and is based on the idea of opportunistic communications. The interweave paradigm, where a secondary user can opportunistically enter temporary spectrum holes and white spaces existing in both licensed and unlicensed radio spectrum. Fast and reliable spectrum sensing techniques are the key to the success of interweave cognitive radios.

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Chapter 3 IMPLEMENTATION



In the Project, our primary aim was to simulate a working cognitive radio for which we were required to produce a variable source generator to replicate real life usage of the frequency channels.



After completion of the source generator, the signal in channel, if there, have to be modulated so as for making it suitable for transmission and defining the frequencies that will be used by it during transmission.



All the different modulated carrier signals will then multiplexed to give a continuous spectrum.



To detect an empty channel, we have used the power spectral density of all the signals which will provide us the power being transmitted at a particular frequency channel at the moment.



Threshold values will be set for determining whether the channel is empty or not.



Once the sensing part is over, another user will be introduced acting as secondary user and will attain the particular frequency for which the PSD is below the threshold, Thus achieving functionality of cognitive radio.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

3.1 Algorithm

13

3.2

Related Theory

3.2.1 About MATLAB & SIMULINK 3.2.1.1

MATLAB

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

MATLAB® is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions) extend the MATLAB environment to solve particular classes of problems in these application areas. MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications. Key Features       

14

High-level language for technical computing Development environment for managing code, files, and data Interactive tools for iterative exploration, design, and problem solving Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration 2-D and 3-D graphics functions for visualizing data Tools for building custom graphical user interfaces Functions for integrating MATLAB based algorithms with external applications and languages, such as C, C++, Fortran, Java, COM, and Microsoft Excel

3.2.1.2

SIMULINK

Simulink® is an environment for multidomain simulation and Model-Based Design for dynamic and embedded systems. It provides an interactive graphical environment and a customizable set of block libraries that let you design, simulate, implement, and test a variety of time-varying systems, including communications, controls, signal processing, video processing, and image processing. Simulink is integrated with MATLAB®, providing immediate access to an extensive range of tools that let you develop algorithms, analyze and visualize simulations, create batch processing scripts, customize the modeling environment, and define signal, parameter, and test data.

      

  

Extensive and expandable libraries of predefined blocks Interactive graphical editor for assembling and managing intuitive block diagrams Ability to manage complex designs by segmenting models into hierarchies of design components Model Explorer to navigate, create, configure, and search all signals, parameters, properties, and generated code associated with your model Application programming interfaces (APIs) that let you connect with other simulation programs and incorporate hand-written code MATLAB Function blocks for bringing MATLAB algorithms into Simulink and embedded system implementations Simulation modes (Normal, Accelerator, and Rapid Accelerator) for running simulations interpretively or at compiled C-code speeds using fixed- or variable-step solvers Graphical debugger and profiler to examine simulation results and then diagnose performance and unexpected behavior in your design Full access to MATLAB for analyzing and visualizing results, customizing the modeling environment, and defining signal, parameter, and test data Model analysis and diagnostics tools to ensure model consistency and identify modeling errors

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Key Features

15

3.2.2 DSB-SC AM Modulation Double-sideband suppressed-carrier transmission (DSB-SC): Transmission in which frequencies produced by amplitude modulation are symmetrically spaced above and below the carrier frequency and the carrier level is reduced to the lowest practical level, ideally completely suppressed. In the double-sideband suppressed-carrier transmission (DSB-SC) modulation, unlike AM, the wave carrier is not transmitted; thus, a great percentage of power that is dedicated to it is distributed between the sideband, which implies an increase of the cover in DSB-SC, compared to AM, for the same power used.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

This is used for RDS (Radio Data System) because it is difficult to decouple.

16

3.2.3 Power Spectral Density The power spectral density (PSD) is a positive real function of a frequency variable associated with a stationary stochastic process, or a deterministic function of time, which has dimensions of power per hertz (Hz), or energy per hertz. It is often called simply the spectrum of the signal. Intuitively, the spectral density measures the frequency content of a stochastic process and helps identify periodicities.

Chapter 4 SIMULATION

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

4.1 Simulink Model

Fig 2. Primary user model in simulink

17

4.1.1 Block Details Spectrum Scope The Spectrum Scope block computes and displays the periodogram of the input. The input can be a sample-based or frame-based vector or a frame-based matrix.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Multiplexer The Mux block combines its inputs into a single vector output. An input can be a scalar or vector signal. All inputs should be of the same data type and numeric type. The elements of the vector output signal take their order from the top to bottom, or left to right, input port signals.

Sine Wave Block The Sine Wave block provides a sinusoid. The block can operate in either time-based or sample-based mode. In Time-Based Mode the output of the Sine Wave block is determined by Y=Amplitude*sin(frequency*time+phase)+bias Time-based mode has two sub modes: continuous mode or discrete mode. The value of the Sample time parameter determines whether the block operates in continuous mode or discrete mode: • •

0 (the default) causes the block to operate in continuous mode. >0 causes the block to operate in discrete mode.

Bernoulli Binary generator

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The Bernoulli Binary Generator block generates random binary numbers using a Bernoulli distribution. The Bernoulli distribution with parameter p produces zero with probability p and one with probability 1-p. The Bernoulli distribution has mean value 1-p and variance p(1-p). The Probability of a zero parameter specifies p, and can be any real number between zero and one.

Scope

DSBSC AM Modulator Passband block The DSBSC AM Modulator Passband block modulates using double-sideband suppressed-carrier amplitude modulation. The output is a passband representation of the modulated signal. Both the input and output signals are real sample-based scalar signals. If the input is u(t) as a function of time t, then the output is where fc is the Carrier frequency parameter and ? is the Initial phase parameter. Typically, an appropriate Carrier frequency value is much higher than the highest frequency of the input signal. By the Nyquist sampling theorem, the reciprocal of the model's sample time (defined by the model's signal source) must exceed twice the Carrier frequency parameter. This block works only with real inputs of type double. This block is not suited to be placed inside a triggered subsystem.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

The Scope block displays its input with respect to simulation time. The Scope block can have multiple axes (one per port); all axes have a common time range with independent y-axes. The Scope allows you to adjust the amount of time and the range of input values displayed. You can move and resize the Scope window and you can modify the Scope's parameter values during the simulation. When you start a simulation, Simulink does not open Scope windows, although it does write data to connected Scopes. As a result, if you open a Scope after a simulation, the Scope's input signal or signals will be displayed. If the signal is continuous, the Scope produces a point-to-point plot. If the signal is discrete, the Scope produces a stairstep plot.

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SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

4.1.2 Block Properties

20

Block Type

Count

Block Names

Scope

10

Scope, Scope1, Scope2, Scope3, Scope4, Scope5, Scope6, Scope7, Scope8, Scope9

Sin

5

Sine Wave, Sine Wave1, Sine Wave2, Sine Wave3, Sine Wave4

Product

5

Product, Product1, Product2, Product3, Product4

5 DSBSC AM Modulator Passband

DSBSC AM Modulator Passband, DSBSC AM Modulator Passband1, DSBSC AM Modulator Passband2, DSBSC AM Modulator Passband3, DSBSC AM Modulator Passband4

Bernoulli Binary Generator

5

Bernoulli Binary Generator, Bernoulli Binary Generator1, Bernoulli Binary Generator2, Bernoulli Binary Generator3, Bernoulli Binary Generator4

ToWorkspace

1

To Workspace

Spectrum Scope

1

Spectrum Scope

Mux

1

Mux

Table 1. Block type count

P

Seed

Ts

Frame Based

Samp Per Frame

Orient

Out Data Type

Bernoulli Binary Generator1

0.5

61

100

Off

1

Off

double

Bernoulli Binary Generator2

0.5

61

100

Off

1

Off

double

Bernoulli Binary Generator3

0.5

61

100

Off

1

Off

double

Bernoulli Binary Generator4

0.5

61

100

Off

1

Off

double

Bernoulli Binary Generator5

0.5

61

100

Off

1

Off

double

Table 2. Bernoulli Binary Generator Block Properties

Name

Inputs

Display Option

Mux

5

bar

Table 3. Mux Block Properties

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Name

21

Name

Fc

Ph

DSBSC AM Modulator Passband1

12000

45

DSBSC AM Modulator Passband2

13000

0

DSBSC AM Modulator Passband3

14000

0

DSBSC AM Modulator Passband4

15000

0

DSBSC AM Modulator Passband5

16000

0

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Table 4. DSBSC AM Modulator Passband Block Properties

Name

Sin Type

Time Source

Ampl

Bias

Frequency

Phase

Sampl es

Offset

Sampl e Time

Sine Wave 1

Time based

Use simulation time

5

0

4340

0

20

0

2

Sine Wave 2

Time based

Use simulation time

5

0

4968

0

20

0

2

Sine Wave 3

Time based

Use simulation time

5

0

628

0

20

0

2

Sine Wave 4

Time based

Use simulation time

5

0

1256

0

20

0

2

Sine Wave 5

Time based

Use simulation time

5

0

3712

0

20

0

2

Table 5. Sin Block Properties

22

Scope Properties

Domain

On

Frequency

Use Buffer FFT Num Buffer Size length Avg On

128

1024

5

Wintype Spec Scope Hamming

Rs Beta Winsamp Spec Spec Spec Scope Scope Scope 50

5

Periodic

Name

Variable Name

Max Data Points

Decimation

Save Format

Fixpt As Fi

To Workspace

In

Inf

1

Array

Off

Table 7. To Workspace Block Properties

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Table 6. Spectrum Scope Block Properties

23

4.2 MATLAB Code

Fs=12000;

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

x1=in(:,1); x2=in(:,2); x3=in(:,3); x4=in(:,4); x5=in(:,5); y=x1+x2+x3+x4+x5; Pxx = periodogram(y); Hpsd = dspdata.psd(Pxx,'Fs',Fs); plot(Hpsd) figure plot(Pxx); xlabel('Frequency (KHz)') ylabel('Power/Frequency (dB/KHz)') title('Power Spectral Density via Periodogram') check1 check2 check6 check3 check4 check5

= = = = = =

Pxx(53); Pxx(105); Pxx(150); Pxx(187); Pxx(223); Pxx(239);

if(check1 < 80) disp('Assigned to channel 1 as user was not present.'); y1 = ammod(x1,1000,Fs); y = x1 + x2 + x3 + x4 + x5 + 2*y1; elseif (check2 < 80) disp('Assigned to channel 2 as user was not present.'); y2 = ammod(x2,2000,Fs); y = x1 + x2 + x3 + x4 + x5 + 2*y2; elseif(check3 < 80) disp('Assigned to channel 4 as user was not present.'); y4 = ammod(x4,4000,Fs); y= x1 + x2 + x3 + x4 + x5+ 2*y4;

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elseif(check4 < 80) disp('Assigned to channel 5 as user was not present.'); y5 = ammod(x5,5000,Fs); y = x1 + x2 + x3 + x4 + x5 + 2*y5;

elseif(check5 < 80) disp('Assigned to channel 6 as user was not present.'); y6 = ammod(x1,6000,Fs); y = x1 + x2 + x3 + x4 + x5 + 2*y6; elseif (check6 < 80) disp('Assigned to channel 3 as user was not present.'); y3 = ammod(x1,3000,12000); y = x1 + x2 + x3 + x4 + x5 + 2*y3; else disp('All user slots in use. Try again later,'); tp=1;

figure Pxx = periodogram(y); Hpsd = dspdata.psd(Pxx,'Fs',Fs); plot(Hpsd); figure plot(Pxx); xlabel('Frequency (KHz)') ylabel('Power/Frequency (dB/KHz)') title('Power Spectral Density via Periodogram')

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

end

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SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

4.3 Results And Graphs

Plot 1. Primary User 1

26 Plot 2. Primary User 2

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Plot 3. Primary User 3

27 Plot 4. Primary User 4

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Plot 5. Primary User 5

28 Plot 6. Multiplexed Signal In Time Domain

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Plot 7. Multiplexed Signal in Frequency Domain

29 Plot 8. Power Spectral Density

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Plot 9. Power Spectral Density Via Periodogram

30 Plot 10. Power Spectral Density With Secondary User

4.4 Interpretation of Results We’ve simulated the basics of a cognitive radio systems enabling dynamic spectrum access at run time. Our approach was to take the decisions on the basis of power spectral density of the channel which can be used cognitively to find out the available gaps those can be assigned to new incoming users thus improving the overall channel’s throughput. The project is still incomplete and needs a lot of modifications which will be decided with the instructors. Overall the whole project was a success though it took quite a lot of time and research in finding out some generic algorithm for simulating the cognitive radio systems, but in the end we’d to come around with our own idea and implementing it in Simulink and MATLAB. The results are quite accurate and we’re still working on improving the code for more presentable results.

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

Plot 11. Power Spectral Density With Secondary User via Periodogram

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Chapter 5

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

FUTURE SCOPE OF COGNITIVE RADIO

Cognitive radio technology is a smarter, faster, and more efficient way to transmit information to and from fixed, mobile, other wireless communication devices. Cognitive radio builds upon software-defined radio technology. A cognitive radio system is 'aware' of its operating environment and automatically adjusts itself to maintain desired communications it’s like having a trained operator inside the radio making constant adjustments for maximum performance. Operating frequency, power output, antenna orientation/beam width, modulation, and transmitter bandwidth are just a few of the operating parameters that can automatically be adjusted on the fly in a cognitive radio system The phenomenal success of the unlicensed band in accommodating a range of wireless devices and services has led the FCC to consider opening further bands for unlicensed use. In contrast, the licensed bands are underutilized due to static frequency allocation. Realizing that CR technology has the potential to exploit the inefficiently utilized licensed bands without causing interference to incumbent users; the FCC released the Notice of Proposed Rule Making to allow unlicensed radios to operate in the TV broadcast bands. The IEEE 802.22 working group formed in November/2004 is equipped with the task of defining the air interface standard for Wireless Regional Area Networks based on CR sensing for the operation of unlicensed devices in the spectrum allocated to TV service. While CR devices are built with components that have been well-established in the telecommunications and computer science disciplines, the existing approaches to provide robustness and effective security for a network of CR devices are inadequate. Due to the particular characteristics of the CR systems, new types of attack are possible and some of the well-known types increase in complexity. Therefore, new ideas are needed to make CR networks secure and robust against specific attacks, especially against those that are inherent to the CR functionality.

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There is, therefore, the need for comprehensive and energy efficient mechanisms to discourage, identify and mitigate the attacks at all phases of the cognitive cycle, in order to obtain CR systems that are trustworthy, efficient and dependable.

REFERENCES

[1] Allen B.MacKenzie , Peter Athanas , R. Michael Buehrer , Steven W. Ellingson ,Michael Hsiao , Cameron Patterson and Claudio R. C. M. da Silva, “Cognitive Radio and Networking Research at Virginia Tech”, Proceedingd of IEEE | Vol. 97, No. 4, April 2009, pp 660 688

[3] Burke, M.; Lally, B.T.; Kerans, A.J., “Virtual Occupancy in Cognitive Radio”| 2011 , pp 328 – 336 [4] A.Venkata Reddy, E.Rama Krishna and , P.Mahipal Reddy, “Sensor Networks for Cognitive Radio: Theory and System Design” | Volume: 3 , 2011 , pp 229 – 233 [5] Na Yi, YiMa, and Rahim Tafazolli, “Underlay Cognitive Radio with Full or Partial Channel Quality Information” C.C.S.R., University of Surrey, Guildford GU2 7XH, UK, Research Article | June 2010 [6] Theodore S. Rappaport, “Wireless Communications Principles and Practice” , 2nd EDITION, 1996 [7] Bruce A. Fette, “Cognitive Radio Technology”, 1st EDITION,2006

SOFTWARE DEFINED COGNITIVE RADIO USING MATLAB

[2] S. Kaneko, T. Ueda, S. Nomura, K. Sugiyama, K. Takeuchi, and S. Nomoto, “The possibility of the prediction of radio resource availability in cognitive radio”, Proc. IEICE General Conference, Nagoya| B-17-33, Japan, Mar., 2007

[8] Kamilo Feher ,”Wireless Digital Communication: Modulation and Spread Spectrum Application” , 2nd Edition

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