COGNITIVE RADIO technology

Higher Institute for Applied Sciences and Technology Communication dept. 4th Year seminar

COGNITIVE RADIO TECHNOLOGY

Submitted by: Abdullateef Al-Muhammad Scientific Supervisor: Dr. Wissam Altabban Linguistic Supervisor: Mrs. Nada Muhana Seminar Supervisor: Dr. Nizar Zarka 2014


“IF YOU WANT TO FIND THE SECRETS OF THE UNIVERSE, THINK IN TERMS OF ENERGY, FREQUENCY AND VIBRATIONS” NICOLA TESLA

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Contents List of figures: ............................................................................................................................................................III List of tables: .............................................................................................................................................................III Acronyms and abbreviations:.................................................................................................................................. IV Key words:................................................................................................................................................................ IV Abstract ..................................................................................................................................................................... V Acknowledgments: ....................................................................................................................................................5 Introduction: ..............................................................................................................................................................1 1. CR importance: .......................................................................................................................................................2 2. History and definitions: ..........................................................................................................................................5 2.1. History:............................................................................................................................................................5 2.2. Definitions and principles of CR: ....................................................................................................................6 3. Implementation and Architecture: ........................................................................................................................9 3.1. Software Defined Radio: ................................................................................................................................9 3.2. From SDR to CR:........................................................................................................................................... 11 4. Spectrum Sensing Algorithms in CR: ................................................................................................................... 13 4.1. Spectrum sensing dimensions: ................................................................................................................... 14 4.2. Spectrum sensing algorithms: ..................................................................................................................... 15 4.2.1. Energy detection: ........................................................................................................................ 15 4.2.2. Waveform-Based Sensing: ........................................................................................................... 18 4.2.3. Cyclostationarity-Based Sensing:.................................................................................................. 18 4.2.4. Matched-Filter (correlator) sensing: ............................................................................................. 19 4.3. Comparison:................................................................................................................................................. 20 4.4. Dynamic spectrum access: .......................................................................................................................... 20 4.4.1. Interweave method: .................................................................................................................... 20 4.4.2. Underlay: .................................................................................................................................... 21 4.4.3. Overlay: ...................................................................................................................................... 21 Conclusion and recommendations:........................................................................................................................ 22 References:.............................................................................................................................................................. 23

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List of figures: Figure 1: demand on BW per user versus available Capacity ....................................................................................2 Figure 2: A sample of the snapshot of radio spectrum utilization up to 5 GHz [2] ....................................................5 Figure 3: The cognitive radio adapts to the spectrum environment; while SDR adapts to the network [4] .............7 Figure 4: (a) analog radio, (b) SDR [7]...................................................................................................................... 10 Figure 5: cognitive radio based on software defined radio [8] ............................................................................... 11 Figure 6: Various aspects of spectrum sensing for cognitive radio [9] ................................................................... 13 Figure 7: Energy detector block diagram [10] ......................................................................................................... 15 Figure 8: effect of variance variation....................................................................................................................... 16 Figure 9: spectrum opportunities from energy detector point of view [13]........................................................... 17 Figure 10: comparison between main sensing methods in terms of their sensing accuracies and complexity [9] 20 Figure 11: various strategies of dynamic spectrum access [14] .............................................................................. 21

List of tables: Table 1: abbreviations .............................................................................................................................................. IV Table 2: various spectrum dimensions and transmission opportunities [9] ........................................................... 14

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Acronyms and abbreviations: Abbreviation AACR AMC A\D BER BW B3G CR DDC DSA DSP FCC GSM ISP OfCom PSD QoS QoI RF SCD SDR SNR UWB WSS

Description Adaptive Aware CR Automatic Modulation Classification Analog to Digital converter Bit Error Rate BandWidth Beyond third Generation Cognitive Radio Digital DownConverter Dynamic Spectrum Access Digital Signal Processor Federal Communication Commission Global Services for Mobile Intelligent Signal Processing The Office of Communications Power Spectral Density Quality of Service Quality of Information Radio Frequency Spectral Correlation Density Software Defined Radio Signal to Noise Ratio Ultra Wide Band Wide-Sense Stationary Table 1: abbreviations

Key words: SDR, CR, Advanced communications, frequency (or spectrum)-agile, AACR, Virtual hardware, spectrum crisis, spectrum sensing, dynamic spectrum access.

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Abstract Spectrum and power are the lifeblood for our wireless communications. Nowadays we can notice the increasing number of devices that need to transmit and receive data in high bitrates, but using our traditional radios we will face problems with providing the sufficient spectrum range for all of it, so what we need is intelligent radios which can share spectrum with other radios but without interfering them, this radios called Cognitive Radios. In this seminar, I’ll show the importance of this technology and present its definition with a brief history about its evolution before I move on to its implementation challenges and finally I’ll show the various algorithms in spectrum sensing in some details.

Acknowledgments: I would like to thank Dr. Wissam Altabban for all of her help and support during this work and for giving me the chance to read about such a wonderful and up to date topic. Also I would like to thank Dr. Nizar Zarka for his efforts leading me to write this seminar in a scientific and proper way, and Mrs. Nada Muhana for her linguistic supervision, finally I can’t forget Dr. Ayman Alsawah’s efforts, who helped me to understand random signal processing concepts.

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Introduction: Nowadays, the radio resources and particularly the spectrum, are considered a very precious and scars resource, not because of their unavailability but because they are used inefficiently. Due to this fact a considerable research has been conducted recently for finding suitable and efficient ways to use the spectrum. In general, traditional wireless communication systems have fixed transmission parameters. In other words, their transmission frequency is fixed and the same in every location and instant of time, determined by regulatory standards. The recent popularity of telecommunications and wireless communications, has increased the usage of radio spectrum exponentially, in order to supply all the demand and improve communication parameters and Quality of Service (QoS), so new technologies need to be developed. These technologies have to deal with radio resources efficiently, they can be considered as radio systems with high intelligence and capabilities of adaption and awareness. This radio system is called “Cognitive Radio”. Cognitive Radio is still an open novel approach that is expected to solve the limitations of current systems.

 The aim of this work is to introduce the principles of cognitive radio and provide an overview of the most common technologies and strategies that are proposed for cognitive radio.

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1. CR importance: THE recent rapid growth of wireless communications has made the problem of spectrum efficiently utilization more important. For example, demand for mobile broadband will surpass the spectrum currently available in mid-2013, as figure (1) shows, according to a paper of Peter Rysavy [1], he explains how the demand for bandwidth-consuming services used by more and more people will lead to a heavy-handed pricing and limitations on mobile applications.

Figure 1: demand on BW per user versus available Capacity

On the one hand, the increasing transmission of data types (voice, short message, Web, and multimedia) and demand of high quality-of-service (QoS) applications have resulted in overcrowding of the allocated spectrum bands, leading to significantly reduced levels of user satisfaction. The problem is particularly serious in Communication-intensive situations such as after a football match or in a massive emergency. On the other hand, major licensed bands, such as those allocated for television broadcasting, amateur radio, and paging, have been found to be grossly underutilized, resulting in spectrum wastage.

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Study by the Federal Communications Commission (FCC) shows that the spectrum utilization in the 0–6 GHz band varies from 15% to 85% [2]. For this reason the FCC managed to propose the opening of licensed bands to unlicensed users, this point was the birth of Cognitive Radio (CR). But before CR we managed to solve the problem of spectrum shortage by three main procedures that can be summed up by the following points:  Almost all legacy radio users have been moved away from the prime spectrum sectors (roughly from 800 MHz to 5 GHz bands) to make way for the newly emerging wireless services, such as mobile cellular networks.  Moving the carrier frequency to new high spectrum sectors (10-30 GHz), which have been occupied by very few radio applications, we gain by this some advantages such as high rates of data transfer, but have a lot of disadvantages. the most significant of it is that radio propagation properties in very high frequency are very sensitive to rain, dust, water, vapor, and other small particles in the air.  Overlay the new wireless applications and the existing radio services. The discussion on cognitive radio technology can best begin with the remark by Ed Thomas [3] “If you look at the entire radio frequency (RF) up to 100 GHz, and take a snapshot at any given time, you’ll see that only 5 to 10% of it is being used. So there’s 90 GHz of available bandwidth.” This shows that the usage of the radio spectrum is severely inefficient, and therefore the cognitive radio can be extremely useful to exploit the unused spectrum from time to time, as long as the vacancy appears in the spectrum.

 So the main philosophy behind CR technology is to allow unlicensed users to get access to the bands dedicated for licensed users (primary users) but without causing degradation of service upon the original license holders.

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Chapter 1 HISTORY & DEFINITIONs There are various definitions of CR, this chapter will sum them up after presenting two of them and a brief history of this technology.

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2. History and definitions: 2.1. History: Fortunately CR technology is an emerging new technology and it hasn’t been in life applications till now, so its history is still short. 2.1.1. Mitola’s work: THE concept of cognitive radio was first proposed by J. Mitola III in a seminar [15] at the Royal Institute of Technology in Stockholm in 1998 and published in an article in 1999. 2.1.2. Federal Communications Commission (FCC): IN 2002, the FCC’s Spectrum Policy Task Force Report [4] identified that most spectrum is unused most of the time, as shown in Figure(2). J. Mitola

Figure 2: A sample of the snapshot of radio spectrum utilization up to 5 GHz [2]

 It was then realized that spectrum scarcity is driven mainly by ancient systems for spectrum allocation and not by a fundamental lack of spectrum.

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2.2. Definitions and principles of CR: 2.2.1. Mitola’s definition: THE earlier definition by Joseph Mitola [4]: “The cognitive radio (CR) identifies the point at which wireless personal digital assistants (PDAs) and the related networks are sufficiently computationally intelligent on the subject of radio resources and related computer-to-computer communications to (a) detect user communications needs as a function of use context, and (b) to provide radio resources and wireless services most appropriate to those needs”.

2.2.2. FCC’s definition: FCC gave another definition on cognitive radio, as the radio that can change its transmitter parameters based on interaction with the environment in which it operates.  From the definition of Mitola we can conclude that Cognitive Radio (CR) term is a radio capable of analyzing the surrounding environment (as channels and users), learning and predicting the most suitable and efficient way of using the available spectrum and adapting all of its operation parameters.

TO SUM UP: In cognitive radio terminology, primary users can be defined as the users who have higher priority or legacy rights on the usage of a specific part of the spectrum. On the other hand, secondary users, which have lower priority, exploit this spectrum in such a way that they do not cause interference to primary users. Therefore, secondary users need to have cognitive radio capabilities, such as sensing the spectrum reliably to check whether it is being used by a primary user and to change the radio parameters to exploit the unused part of the spectrum.

From the definitions above, CR is an intelligent radio which can learn and adapt, so it’s definitely based on different type of radios not like which we know.

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The enabling technology (or radio) for CR is the Software Defined Radios (SDR). Afterword we will present the concept of SDR and the relation between it and the CR. With the aid of SDR we have the cycle closed, from the environment to the network and finally the user, figure (3):

Figure 3: The cognitive radio adapts to the spectrum environment; while SDR adapts to the network [4]

2.2.3. Ideal Cognitive radio: AN ideal CR is defined as a communication system which has the following properties [5]: Sensing: RF, audio\video, temperature, acceleration…etc.  Perception: determining what is in the scope.  Planning: identify alternative actions to take on the time line probabilities.  Orienting: assessing the situation to react immediately if necessary.  Making decisions: to do the best action.  Take action: exert effects in the environment, including RF, Human-machine and machine-machine communications.  Learning Autonomously: from the experience gained from the Precedent capabilities.

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Chapter 2 Enabling Technologies & Architecture of CR This chapter will discuss the Software Defined Radio (SDR), because all Cognitive Radios are based on it, and show the relation between SDR and CR.

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3. Implementation and Architecture: AS we showed in chapter 1, we deal with a Cognitive Radio that means some radio which can sense, take decisions and learn, so we are definitely speaking about a different type of radios that exist now (totally hardware ones), in fact we need Software Defined Radio (SDR), which we can reconfigure by software, the term SDR was firstly described also by Joseph Mitola in 1992 [6].

Here, we have to distinguish between Software controlled and software defined radios, first one, we are limited to select, by software, one of the configuration of the radio which was defined during the design, but with the SDR we can reconfigure the radio by software whenever we want, thus we aren’t limited to pre-configurations.

3.1. Software Defined Radio: ACCORDING to FCC, SDR is a transmitter in which the operating parameters can be altered by making a change in software that controls the operation of the device without changes in the hardware components that affect the radio frequency emissions. SDR is the result of an evolutionary process from purely hardware-based equipment to fully software-based equipment, this process can be described in three stages: 1. Hardware driven radios: Transmit frequencies, modulation type and other radio frequency (RF) parameters are determined by hardware and cannot be changed without hardware changes. 2. Digital radios: A digital radio performs part of the signal processing or transmission digitally, but is not programmable in the field. 3. Software Defined Radios: All functions, modes and applications can be configured and reconfigured by software.

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In Figure (4) [7], (a) shows the block diagram of a hardware radio while (b) shows the SDR block diagram where DDC refers to Digital DownConverter used to move the RF signal to the baseband.

(a)

(b) Figure 4: (a) analog radio, (b) SDR [7]

 Finally SDR is a radio that can be reconfigured (by software) to adapt no matter which band of spectrum nor which modulation type.

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3.2. From SDR to CR: AS we mentioned before, J. Mitola described SDR in 1992 then he wanted to use artificial intelligence with it so he came later in 1999 with Cognitive Radio. Full Cognitive Radios (Mitola radios) do not exist at the moment and are not likely to exist until 2030 [6] [16], when we fully implement SDR and the intelligence required to make the SDR cognitive.

Figure 5: cognitive radio based on software defined radio [8]

The figure above illustrates the relationship between CR and SDR, to move from SDR toward CR we need some sensors to sense the environment around the radio and then we analyze and classify the results to adapt and reconfigure the SDR parameters.

 Finally, it’s obvious now that SDR is the core of CR, without the fully implemented SDR we can’t ever think about CR.

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Chapter 3 Spectrum Sensing Algorithms in CR As mentioned in 3.2.2 there are seven general properties of a CR. In this chapter, we will discuss the most important property of CR which is spectrum sensing.

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4. Spectrum Sensing Algorithms in CR: THE need for higher data rates is increasing as a result of the transition from voiceonly communications to multimedia type applications. Given the limitations of the natural frequency spectrum, it becomes obvious that the current static frequency allocation strategies can’t accommodate the requirements of an increasing number of higher data rate devices. In this chapter I will discuss spectrum sensing dimensions and different types of spectrum sensing algorithms and its requirements.

Figure 6: Various aspects of spectrum sensing for cognitive radio [9]

Figure (6) illustrate the various issues related to spectrum sensing, such as the different algorithms of spectrum sensing and some challenges related to it, and some standards which use sensing.

 THE main goal of all sensing algorithms is to find the spectrum opportunity for the secondary user (Cognitive User). Spectrum opportunity is often defined as a band of frequencies that are not being used by the primary user of that band at a particular time in a particular geographic area.

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4.1. Spectrum sensing dimensions: IN general, CR can exploit three dimensions of the spectrum space: frequency, time, space, but there are other dimensions for example: code dimension, angle dimension. Various dimensions listed in table (2). Dimension

Frequency

Time

What needs to be sensed?

Comments

Opportunity in the frequency domain.

The available spectrum is divided into narrower bands. Spectrum opportunity in this dimension means that all the bands are not used simultaneously at the same time.

Opportunity of a specific band in time.

This involves the availability of a specific part of the spectrum in time. In other words, the band is not continuously used.

Illustrations

Green blocks are apportunities and blue ones are occupied by primary users.

Geographical space

Location and distance of primary users.

The spectrum can be available in some parts of the geographical area while it is occupied in some other parts at a given time. These measurements can be avoided by simply looking at the interference level. No interference means no primary user transmission in a local area.

Code

Timing information is needed so that secondary users can synchronize their transmissions with respect to primary users.

The spectrum over a wideband might be used at a given time. This does not mean that there is no availability over this band. Simultaneous transmission without interfering with primary users would be possible in code domain with an orthogonal code with respect to codes that primary users are using.

Directions of primary users’ beam (azimuth and elevation angle) and locations of primary users.

Along with the knowledge of the location/position or direction of primary users, spectrum opportunities in angle dimension can be created. For example, if a primary user is transmitting in a specific direction, the secondary user can transmit in other directions without creating interference on the primary user.

Angle

Table 2: various spectrum dimensions and transmission opportunities [9]

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Spectrum sensing should include the process of identifying occupancy in all dimensions of the spectrum space, for example: certain frequency can be occupied for a given time, but it might be empty in another time if we consider two dimensions, time and frequency.

4.2. Spectrum sensing algorithms: 4.2.1. Energy detection: IT is also known as radiometry or periodogram, the most well-known spectrum sensing technique.

Figure 7: Energy detector block diagram [10]

It is based on the principle that, at the reception, the energy of the signal to be detected is always higher than the energy of the noise. The energy detector is said to be a blind signal detector because it ignores the structure of the signal. It estimates the presence of a signal by comparing the energy (or the power spectral density PSD) received with a known threshold 𝜆𝐸 , derived from the statistics characteristics of the noise. Let the received signal (after sampling) be [11]: 𝒚(𝒏) = 𝒉 ∗ 𝑺(𝒏) + 𝒘(𝒏) Where 𝑆(𝑛) is the signal transmitted by the primary user and w(n) is (the innovation) we can consider it as a white Gaussian noise floor, assuming the channel gain h=1(lossless channel). And the decision metric is the average energy estimator of N observed samples: 𝑵

𝟏 𝑴 ≜ ∑|𝒚(𝒏)𝟐 | 𝑵 𝒏=𝟏

As the number of samples N becomes large, then by the law of the large numbers M converges to 𝑬[|𝒚(𝒌)|𝟐 ]. Then the decision on occupancy of a band can be taken by comparing M with the threshold 𝜆𝐸 , in general we choose the threshold to be the noise variance.

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We have two cases 𝐻0 and 𝐻1 : 𝑯𝟎 : 𝒚(𝒏) = 𝒘(𝒏)

: If there is no presence of primary user

𝑯𝟏 : 𝒚(𝒏) = 𝒘(𝒏) + 𝑺(𝒏) : If the signal from primary user detected The performance of the detection algorithm can be summarized by two probabilities: probability of detection 𝑷𝒅 and probability of false alarm 𝑷𝒇 . 𝑷𝒅 is the probability of detecting a signal on the considered frequency when it is truly present and it is given by the following equation [9]. 𝑷𝒅 = 𝑷𝒓 (𝑴 >

𝝀𝑬 |𝑯𝟏 )

𝑷𝒇 is the probability that the test incorrectly decides that the considered frequency is occupied when it actually is not, and it can be written as [9]: 𝑷𝒇 = 𝑷𝒓 (𝑴 >

𝝀𝑬 |𝑯𝟎 )

It’s desired to make 𝑷𝒅 as large as possible, and 𝑷𝒇 as small as possible, this can be done by choosing a proper threshold 𝝀𝑬 . In spite of the simplicity of the energy detector, it’s not a perfect solution. The approximation of signal energy M gets better as N increases. Thus, the performance of the energy detector is directly linked to the number of samples. Furthermore, the energy detector relies completely on the variance of the noise 𝜎 2 which is taken as a fixed value for a short period of time. This is generally not true in practice, where the noise floor varies. Essentially this means that the energy detector will generate errors during those variations, especially when the SNR is very low, as seen in Figure (8-b), where we see an area of uncertainty surrounding the threshold 𝝀𝑬 .

Figure 8: effect of variance variation

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As illustrated in figure (8), (a) the threshold between 𝐻0 and 𝐻1 will be clear if there is no noise or if the variance of the noise is constant, on the other hand (b) we have a noise with variable variance 𝜎 then the threshold will be variable and as variance is bigger as this algorithm will lose accuracy.

The energy detection is the optimal signal detector in a White Gaussian Noise (WGN) channel considering no prior information on the signal structure [12].

 In practice, it is better to complete wide-band spectrum sensing via two stages. In the first stage, low-complexity energy detection is applied to search for possible idle sub-bands, in the second stage, more advanced spectrum sensing techniques with a higher detection sensitivity and thereby higher complexity, such as cyclostationary detection which will be discussed later.

Figure 9: spectrum opportunities from energy detector point of view [13]

Note: the next technics of sensing assume that the transmitted signal S(n) is known for the detector.

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4.2.2. Waveform-Based Sensing: Known patterns are usually utilized in wireless systems to assist synchronization between transmitter and receiver or for other purposes. Such patterns include preambles, mid-ambles, regularly transmitted pilot patterns etc. A preamble is a known sequence transmitted before each burst and a mid-amble is transmitted in the middle of a burst. In the presence of a known pattern, sensing can be performed by correlating the received signal with a known copy of itself, this method of sensing is termed as waveform-based sensing or coherent sensing, and it is clear that this method is only applicable to systems with known signal patterns. Sensing metric can be obtained as [9]: 𝑵

𝑴 = 𝑹𝒆 [∑ 𝒚(𝒏)𝑺∗ (𝒏)] 𝒏=𝟏

Where 𝑺∗ (𝒏) is the conjugate of the transmitted signal, 𝒚(𝒏) is the received signal. 4.2.3. Cyclostationarity-Based Sensing: Cyclostationarity feature detection (or features detection) is a method for detecting primary user transmissions by exploiting the Cyclostationarity features of the received signals. There are specific features associated with the information transmission of a primary user such as periodicity. Such features are usually viewed as the cyclostationary features, based on which a detector can distinguish cyclostationary signals from stationary noise. For example, center frequencies and bandwidths extracted from energy detection can also be used as reference features for classification and determining a primary user’s presence. As in most communication systems, the transmitted signals are modulated signals using sine wave carriers, while the additive noise is generally wide-sense stationary (WSS), with no correlation, cyclostationary-based detectors can be utilized to differentiate noise from primary users’ signal.

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Instead of power spectral density (PSD), cyclic autocorrelation function (CAF) is used for detecting signals present in a given spectrum [9]. The cyclic spectral density (CSD) S(f,α) function of a received signal y(n) can be calculated as [9]: +∞

𝑺(𝒇, 𝜶) = ∑ 𝑹𝜶𝒚 (𝝉)𝒆−𝒋𝟐𝝅𝒇𝝉 𝝉=−∞

Where: 𝑹𝜶𝒚 (𝝉) = 𝑬[𝒚(𝒏 + 𝝉)𝒚∗ (𝒏 − 𝝉)𝒆𝒋𝟐𝝅𝜶𝒏 ] is the cyclic autocorrelation function, and α is the cyclic frequency. Since periodicity is a common property of wireless modulated signals, while noise is WSS, the CAF of the received signal also demonstrates periodicity when the primary signal is present, and since CSD is Fourier representation of CAF it will have peaks when the cyclic frequency α equals to the fundamental frequencies of the transmitted signal. The CSD function outputs peak values when the cyclic frequency is equal to the fundamental frequencies of transmitted signal x(n). Cyclic frequencies can be assumed to be known or they can be extracted and used as features for identifying transmitted signals. Compared to energy detectors that have high false alarm probability due to noise uncertainty and cannot detect weak signals in noise, cyclostationary detectors become good alternatives because they can differentiate noise from primary users’ signal and have better detection robustness in low SNR regime. 4.2.4. Matched-Filter (correlator) sensing: IF secondary users know information about a primary user’ signal a priori, then the optimal detection method is the matched filtering. If y(n) is a sequence of received samples at the signal detector, the decision rule can be calculated as [10]: 𝑵

𝒚𝒊𝒆𝒍𝒅𝒔 𝑯𝟎 𝒊𝒇 𝑴 < 𝝀𝑬 𝟏 𝑴 = ∑ 𝒚(𝒌)𝒔(𝒌)∗ → { 𝑯𝟏 𝒊𝒇 𝑴 > 𝝀𝑬 𝑵 𝒌=𝟏

Where y is received signal, 𝒔(𝒌)∗ is the conjugate of the transmitted signal, 𝝀𝑬 threshold. Here the threshold 𝝀𝑬 is not the noise variance as it was for the energy detector. The decision is taken depending on the fact that the matched filter maximizes the power of s(k). This means it performs well even in a low SNR levels.

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4.3. Comparison: A basic comparison of the sensing methods in terms of accuracy and complexity given in this section is presented in Figure (9).

Figure 10: comparison between main sensing methods in terms of their sensing accuracies and complexity [9]

From figure (9) we see that the energy detection method is the least accuracies because it depends on the noise variance, but the other methods (which depend on the transmitted signal itself in detecting process) have more accuracy.

4.4. Dynamic spectrum access: Using the previously discussed strategies CR collects information about spectrum occupancy, here we see the algorithms that allow CR to avoid interferences and deal with spectrum mobility.

4.4.1. Interweave method: IN this method CR is only allowed to use the spectrum when primary users are inactive, the main difficulty in the interweave scheme is that sensing and predicting the activity of the primary user in order to detect the spectrum holes (opportunities) is very difficult if primary users are highly dynamic (their spectral activity changes fast), and it requires secondary transmission equipment to be very agile in switching frequency channels [14].

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4.4.2. Underlay: THIS method is based on transmitting at very low power to ensure that the interference with the primary user does not exceed a predefined limit, so the secondary transceiver must be able to operate at very low SNR. This typically restricts underlay cognitive radio to low data rate applications or very short range applications [14].

4.4.3. Overlay: IN this method CR transmitter has knowledge of the primary user’s transmission (codebook and message), this knowledge can be exploited to cancel or mitigate the interferences at the receiver.

Figure 11: various strategies of dynamic spectrum access [14]

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Conclusion and recommendations: The research on the cognitive radio is still on its early stages. Thus, most fields of cognitive radio are possible candidates for future works. In this seminar I started by presenting the importance of cognitive radio technology, and then the definition and brief history and the most important enabling technologies for CR at the end I concentrated my work on spectrum sensing algorithms in some details.

In spite of the popularity of spectrum sensing as a study subject in cognitive radio and cognitive radio networks, there are still some open subjects in this area. Generally, studies have dealt with the sensing techniques themselves but little work has considered the implementation issues and complexity of techniques concerning the spectrum sensing. Some open issues still have to be solved, generally these issues are about optimizing the algorithms and taking implementation issues and complexity of techniques in the point of view.

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References: [1]: Rysavy Peter, research report, “Mobile Broadband Capacity Constraints and the Need for Optimization”, Feb. 16, 2010. [2]: FCC, “Spectrum Policy Task Force report”, ET Docket 02-155, Nov. 2002. [3]: Fette B., “Cognitive Radio Technology”, Elsevier Inc., 2006. [4]: Chen H., Guizani M., “Next Generation Wireless Systems and Networks”, WILEY (2006). [5]: Mitola J., “Cognitive radio architecture the engineering foundations of radio XML”, Wiley 2006. [6]: Mitola J., “software radios survey, critical evolution and future directions”, IEEE, 1992.

[7]: Hosking R., “Software Defined Radio Handbook”, Pentek 2010. [8]: web page, http://www.altera.com/technology/system-design/articles/2012/cognitive-radio.html. [9]: Yucek Tevfik and Arslan Huseyin, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications” IEEE communications surveys & tutorials, VOL. 11, NO. 1, first quarter 2009. [10]: Ma. Jun, Ye Li. Geoffrey, and Hwang Biing, “Signal Processing in Cognitive Radio”, IEEE, 2009. [11]: Wang B. and Ray K. J., “Advances in Cognitive Radio Networks: A Survey”, IEEE journal of selected topics in signal processing, VOL. 5, NO. 1, FEB. 2011. [12]: Tandra R., “Fundamental limits on detection in low SNR”, thesis, Indian Institute of Technology, 2005. [13]: Waqas Ahmed, Mike Faulkner and Jason Gao, “(Ch. 9) Opportunistic Spectrum Access in Cognitive Radio Network, Foundation of Cognitive Radio Systems”, Prof Samuel Cheng (Ed.), ISBN: 978-953-51-0268-7, InTech, (2012). [14]: Lorenza Giupponi and Christian Ibars, “Cooperative Cognitive Systems, Cognitive Radio Systems”, Wei Wang (Ed.), ISBN: 978-953-307-021-6, InTech, (2009). [15]: Mitola J., Dissertation, “Cognitive Radio an Integrated Agent Architecture for Software Defined Radio”, Royal Institute of Technology, 8 May, 2000. [16]: OfCom, study, “Cognitive Radio Technology”, 15 December 2006.

For more information: [1]: Internet site, http://www.sdrforum.org. [2]: Akyildiz I., Lee W., Vuran M., Mohanty S., “Next generation, dynamic spectrum access, cognitive radio wireless networks: A survey”, Computer Networks 50 (2006) 2127–2159. [3]: Video course, ”Theory and practice of cognitive radio”, Aalborg university Denmark, MAY 2012.

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