COGNITIVE RADIO TECHNOLOGY
|
|
- Sylvia Bryan
- 6 years ago
- Views:
Transcription
1 Higher Institute for Applied Sciences and Technology Communication dept. 4 th 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
2 IF YOU WANT TO FIND THE SECRETS OF THE UNIVERSE, THINK IN TERMS OF ENERGY, FREQUENCY AND VIBRATIONS NICOLA TESLA I
3 Contents List of figures:...iii List of tables:...iii Acronyms and abbreviations:... IV Key words:... IV Abstract... V Acknowledgments:...5 Introduction: CR importance: History and definitions: History: Definitions and principles of CR: Implementation and Architecture: Software Defined Radio: From SDR to CR: Spectrum Sensing Algorithms in CR: Spectrum sensing dimensions: Spectrum sensing algorithms: Energy detection: Waveform-Based Sensing: Cyclostationarity-Based Sensing: Matched-Filter (correlator) sensing: Comparison: Dynamic spectrum access: Interweave method: Underlay: Overlay: Conclusion and recommendations: References: II
4 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] Figure 5: cognitive radio based on software defined radio [8] Figure 6: Various aspects of spectrum sensing for cognitive radio [9] Figure 7: Energy detector block diagram [10] Figure 8: effect of variance variation Figure 9: spectrum opportunities from energy detector point of view [13] 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] List of tables: Table 1: abbreviations... IV Table 2: various spectrum dimensions and transmission opportunities [9] III
5 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. IV
6 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. V
7 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. 1
8 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. 2
9 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. 3
10 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. 4
11 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 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 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. 5
12 2.2. Definitions and principles of CR: 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 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. 6
13 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] 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. 7
14 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. 8
15 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 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. 9
16 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. 10
17 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. 11
18 Chapter 3 Spectrum Sensing Algorithms in CR As mentioned in there are seven general properties of a CR. In this chapter, we will discuss the most important property of CR which is spectrum sensing. 12
19 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. 13
20 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 What needs to be sensed? Opportunity in the frequency domain. Comments 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. Illustrations 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. 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. Angle 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. Table 2: various spectrum dimensions and transmission opportunities [9] 14
21 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 Spectrum sensing algorithms: 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 λ E, 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 M 1 N y(n)2 As the number of samples N becomes large, then by the law of the large numbers M converges to E[ y(k) 2 ]. n=1 Then the decision on occupancy of a band can be taken by comparing M with the threshold λ E, in general we choose the threshold to be the noise variance. 15
22 We have two cases H 0 and H 1 : H 0 : y(n) = w(n) : If there is no presence of primary user H 1 : 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 P d and probability of false alarm P f. P d 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]. P d = P r (M > λ E H 1 ) P f 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]: P f = P r (M > λ E H 0 ) It s desired to make P d as large as possible, and P f as small as possible, this can be done by choosing a proper threshold λ E. 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 λ E. Figure 8: effect of variance variation 16
23 As illustrated in figure (8), (a) the threshold between H 0 and H 1 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. 17
24 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)] Where S (n) is the conjugate of the transmitted signal, y(n) is the received signal. n= 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. 18
25 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) = R y α (τ)e j2πfτ τ= Where: R α y (τ) = E[y(n + τ)y (n τ)e j2παn ] 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 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 M = 1 N y(k)s(k) k=1 yields { H 0 if M < λ E H 1 if M > λ E Where y is received signal, s(k) is the conjugate of the transmitted signal, λ E threshold. Here the threshold λ E 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. 19
26 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 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 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]. 20
27 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] 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] 21
28 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. ******************** 22
29 References: [1]: Rysavy Peter, research report, Mobile Broadband Capacity Constraints and the Need for Optimization, Feb. 16, [2]: FCC, Spectrum Policy Task Force report, ET Docket , Nov [3]: Fette B., Cognitive Radio Technology, Elsevier Inc., [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 [6]: Mitola J., software radios survey, critical evolution and future directions, IEEE, [7]: Hosking R., Software Defined Radio Handbook, Pentek [8]: web page, [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 [10]: Ma. Jun, Ye Li. Geoffrey, and Hwang Biing, Signal Processing in Cognitive Radio, IEEE, [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 [12]: Tandra R., Fundamental limits on detection in low SNR, thesis, Indian Institute of Technology, [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: , InTech, (2012). [14]: Lorenza Giupponi and Christian Ibars, Cooperative Cognitive Systems, Cognitive Radio Systems, Wei Wang (Ed.), ISBN: , InTech, (2009). [15]: Mitola J., Dissertation, Cognitive Radio an Integrated Agent Architecture for Software Defined Radio, Royal Institute of Technology, 8 May, [16]: OfCom, study, Cognitive Radio Technology, 15 December For more information: [1]: Internet site, [2]: Akyildiz I., Lee W., Vuran M., Mohanty S., Next generation, dynamic spectrum access, cognitive radio wireless networks: A survey, Computer Networks 50 (2006) [3]: Video course, Theory and practice of cognitive radio, Aalborg university Denmark, MAY
Overview. Cognitive Radio: Definitions. Cognitive Radio. Multidimensional Spectrum Awareness: Radio Space
Overview A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications Tevfik Yucek and Huseyin Arslan Cognitive Radio Multidimensional Spectrum Awareness Challenges Spectrum Sensing Methods
More informationPerformance Evaluation of Energy Detector for Cognitive Radio Network
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 8, Issue 5 (Nov. - Dec. 2013), PP 46-51 Performance Evaluation of Energy Detector for Cognitive
More informationCognitive Ultra Wideband Radio
Cognitive Ultra Wideband Radio Soodeh Amiri M.S student of the communication engineering The Electrical & Computer Department of Isfahan University of Technology, IUT E-Mail : s.amiridoomari@ec.iut.ac.ir
More informationIMPROVED PROBABILITY OF DETECTION AT LOW SNR IN COGNITIVE RADIOS
87 IMPROVED PROBABILITY OF DETECTION AT LOW SNR IN COGNITIVE RADIOS Parvinder Kumar 1, (parvinderkr123@gmail.com)dr. Rakesh Joon 2 (rakeshjoon11@gmail.com)and Dr. Rajender Kumar 3 (rkumar.kkr@gmail.com)
More informationReview On: Spectrum Sensing in Cognitive Radio Using Multiple Antenna
Review On: Spectrum Sensing in Cognitive Radio Using Multiple Antenna Komal Pawar 1, Dr. Tanuja Dhope 2 1 P.G. Student, Department of Electronics and Telecommunication, GHRCEM, Pune, Maharashtra, India
More informationPerformance Evaluation of Wi-Fi and WiMAX Spectrum Sensing on Rayleigh and Rician Fading Channels
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 8 (August 2014), PP.27-31 Performance Evaluation of Wi-Fi and WiMAX Spectrum
More informationCooperative Spectrum Sensing in Cognitive Radio
Cooperative Spectrum Sensing in Cognitive Radio Project of the Course : Software Defined Radio Isfahan University of Technology Spring 2010 Paria Rezaeinia Zahra Ashouri 1/54 OUTLINE Introduction Cognitive
More informationTHE NEED for higher data rates is increasing as a result
116 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 11, NO. 1, FIRST QUARTER 2009 A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications Tevfik Yücek and Hüseyin Arslan Abstract The spectrum
More informationEstimation of Spectrum Holes in Cognitive Radio using PSD
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 663-670 International Research Publications House http://www. irphouse.com /ijict.htm Estimation
More informationCognitive Radio: Smart Use of Radio Spectrum
Cognitive Radio: Smart Use of Radio Spectrum Miguel López-Benítez Department of Electrical Engineering and Electronics University of Liverpool, United Kingdom M.Lopez-Benitez@liverpool.ac.uk www.lopezbenitez.es,
More informationA Brief Review of Cognitive Radio and SEAMCAT Software Tool
163 A Brief Review of Cognitive Radio and SEAMCAT Software Tool Amandeep Singh Bhandari 1, Mandeep Singh 2, Sandeep Kaur 3 1 Department of Electronics and Communication, Punjabi university Patiala, India
More informationREVIEW ON SPECTRUM DETECTION TECHNIQUES UNDER BLIND PARAMETERS
REVIEW ON SPECTRUM DETECTION TECHNIQUES UNDER BLIND PARAMETERS Noblepreet Kaur Somal 1, Gagandeep Kaur 2 1 M.tech, Electronics and Communication Engg., Punjabi University Patiala Yadavindra College of
More informationContinuous Monitoring Techniques for a Cognitive Radio Based GSM BTS
NCC 2009, January 6-8, IIT Guwahati 204 Continuous Monitoring Techniques for a Cognitive Radio Based GSM BTS Baiju Alexander, R. David Koilpillai Department of Electrical Engineering Indian Institute of
More informationSpectrum Sensing Using Bayesian Method for Maximum Spectrum Utilization in Cognitive Radio
5 Spectrum Sensing Using Bayesian Method for Maximum Spectrum Utilization in Cognitive Radio Anurama Karumanchi, Mohan Kumar Badampudi 2 Research Scholar, 2 Assoc. Professor, Dept. of ECE, Malla Reddy
More informationCyclostationary Detection in Spectrum Pooling System of Undefined Secondary Users
Cyclostationary Detection in Spectrum Pooling System of Undefined Secondary Users Nazar Radhi 1, Kahtan Aziz 2, Rafed Sabbar Abbas 3, Hamed AL-Raweshidy 4 1,3,4 Wireless Network & Communication Centre,
More informationJournal of Asian Scientific Research DEVELOPMENT OF A COGNITIVE RADIO MODEL USING WAVELET PACKET TRANSFORM - BASED ENERGY DETECTION TECHNIQUE
Journal of Asian Scientific Research ISSN(e): 2223-1331/ISSN(p): 2226-5724 URL: www.aessweb.com DEVELOPMENT OF A COGNITIVE RADIO MODEL USING WAVELET PACKET TRANSFORM - BASED ENERGY DETECTION TECHNIQUE
More informationAnalyzing the Performance of Detection Technique to Detect Primary User in Cognitive Radio Network
Analyzing the Performance of Detection Technique to Detect Primary User in Cognitive Radio Network R Lakshman Naik 1*, K Sunil Kumar 2, J Ramchander 3 1,3K KUCE&T, Kakatiya University, Warangal, Telangana
More informationAnalysis of Different Spectrum Sensing Techniques in Cognitive Radio Network
Analysis of Different Spectrum Sensing Techniques in Cognitive Radio Network Priya Geete 1 Megha Motta 2 Ph. D, Research Scholar, Suresh Gyan Vihar University, Jaipur, India Acropolis Technical Campus,
More informationEnergy Detection Technique in Cognitive Radio System
International Journal of Engineering & Technology IJET-IJENS Vol:13 No:05 69 Energy Detection Technique in Cognitive Radio System M.H Mohamad Faculty of Electronic and Computer Engineering Universiti Teknikal
More informationCo-Operative Spectrum Sensing In Cognitive Radio Network in ISM Band
Co-Operative Spectrum Sensing In Cognitive Radio Network in ISM Band 1 D.Muthukumaran, 2 S.Omkumar 1 Research Scholar, 2 Associate Professor, ECE Department, SCSVMV University, Kanchipuram ABSTRACT One
More informationA Quality of Service aware Spectrum Decision for Cognitive Radio Networks
A Quality of Service aware Spectrum Decision for Cognitive Radio Networks 1 Gagandeep Singh, 2 Kishore V. Krishnan Corresponding author* Kishore V. Krishnan, Assistant Professor (Senior) School of Electronics
More informationCognitive Radio Techniques for GSM Band
Cognitive Radio Techniques for GSM Band Baiju Alexander, R. David Koilpillai Department of Electrical Engineering Indian Institute of Technology Madras Email: {baiju,davidk}@iitm.ac.in Abstract Cognitive
More informationSpectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey
International Journal of Research and Reviews in Wireless Sensor etworks Vol. 1, o. 1, March 011 Copyright Science Academy Publisher, United Kingdom www.sciacademypublisher.com Science Academy Publisher
More informationCOGNITIVE RADIO NETWORKS IS THE NEXT STEP IN COMMUNICATION TECHNOLOGY
Computer Modelling and New Technologies, 2012, vol. 16, no. 3, 63 67 Transport and Telecommunication Institute, Lomonosov 1, LV-1019, Riga, Latvia COGNITIVE RADIO NETWORKS IS THE NEXT STEP IN COMMUNICATION
More informationA Novel Opportunistic Spectrum Access for Applications in. Cognitive Radio
A Novel Opportunistic Spectrum Access for Applications in Cognitive Radio Partha Pratim Bhattacharya Department of Electronics and Communication Engineering, Narula Institute of Technology, Agarpara, Kolkata
More informationCOGNITIVE RADIO TECHNOLOGY. Chenyuan Wang Instructor: Dr. Lin Cai November 30, 2009
COGNITIVE RADIO TECHNOLOGY 1 Chenyuan Wang Instructor: Dr. Lin Cai November 30, 2009 OUTLINE What is Cognitive Radio (CR) Motivation Defining Cognitive Radio Types of CR Cognition cycle Cognitive Tasks
More informationSpectrum Characterization for Opportunistic Cognitive Radio Systems
1 Spectrum Characterization for Opportunistic Cognitive Radio Systems Tevfik Yücek and Hüseyin Arslan Department of Electrical Engineering, University of South Florida 4202 E. Fowler Avenue, ENB-118, Tampa,
More informationSpectrum Sensing for Wireless Communication Networks
Spectrum Sensing for Wireless Communication Networks Inderdeep Kaur Aulakh, UIET, PU, Chandigarh ikaulakh@yahoo.com Abstract: Spectrum sensing techniques are envisaged to solve the problems in wireless
More informationRESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS
Abstract of Doctorate Thesis RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS PhD Coordinator: Prof. Dr. Eng. Radu MUNTEANU Author: Radu MITRAN
More informationApplication of combined TOPSIS and AHP method for Spectrum Selection in Cognitive Radio by Channel Characteristic Evaluation
International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 10, Number 2 (2017), pp. 71 79 International Research Publication House http://www.irphouse.com Application of
More informationExperimental Study of Spectrum Sensing Based on Distribution Analysis
Experimental Study of Spectrum Sensing Based on Distribution Analysis Mohamed Ghozzi, Bassem Zayen and Aawatif Hayar Mobile Communications Group, Institut Eurecom 2229 Route des Cretes, P.O. Box 193, 06904
More informationWireless Networked Systems
Wireless Networked Systems CS 795/895 - Spring 2013 Lec #10: Medium Access Control Advanced Networking Cognitive Network, Software Defined Radio Tamer Nadeem Dept. of Computer Science Spectrum Access Page
More informationImplementation Issues in Spectrum Sensing for Cognitive Radios
Implementation Issues in Spectrum Sensing for Cognitive Radios Danijela Cabric, Shridhar Mubaraq Mishra, Robert W. Brodersen Berkeley Wireless Research Center, University of California, Berkeley Abstract-
More informationCOGNITIVE RADIO AND DYNAMIC SPECTRUM SHARING
COGNITIVE RADIO AND DYNAMIC SPECTRUM SHARING Cristian Ianculescu (Booz Allen Hamilton, McLean, VA, USA; ianculescu_cristian@bah.com); Andy Mudra (Booz Allen Hamilton, McLean, VA, USA; mudra_andy@bah.com).
More informationNoise Plus Interference Power Estimation in Adaptive OFDM Systems
Noise Plus Interference Power Estimation in Adaptive OFDM Systems Tevfik Yücek and Hüseyin Arslan Department of Electrical Engineering, University of South Florida 4202 E. Fowler Avenue, ENB-118, Tampa,
More informationDynamic Spectrum Access in Cognitive Radio Wireless Sensor Networks Using Different Spectrum Sensing Techniques
Dynamic Spectrum Access in Cognitive Radio Wireless Sensor Networks Using Different Spectrum Sensing Techniques S. Anusha M. E., Research Scholar, Sona College of Technology, Salem-636005, Tamil Nadu,
More informationVarious Sensing Techniques in Cognitive Radio Networks: A Review
, pp.145-154 http://dx.doi.org/10.14257/ijgdc.2016.9.1.15 Various Sensing Techniques in Cognitive Radio Networks: A Review Jyotshana Kanti 1 and Geetam Singh Tomar 2 1 Department of Computer Science Engineering,
More informationCooperative Spectrum Sensing and Decision Making Rules for Cognitive Radio
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference
More informationCOGNITIVE RADIO TECHNOLOGY: ARCHITECTURE, SENSING AND APPLICATIONS-A SURVEY
COGNITIVE RADIO TECHNOLOGY: ARCHITECTURE, SENSING AND APPLICATIONS-A SURVEY G. Mukesh 1, K. Santhosh Kumar 2 1 Assistant Professor, ECE Dept., Sphoorthy Engineering College, Hyderabad 2 Assistant Professor,
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION The enduring growth of wireless digital communications, as well as the increasing number of wireless users, has raised the spectrum shortage in the last decade. With this growth,
More informationZOBIA ILYAS FREQUENCY DOMAIN CORRELATION BASED COMPRESSED SPECTRUM SENSING FOR COGNITIVE RADIO
ZOBIA ILYAS FREQUENCY DOMAIN CORRELATION BASED COMPRESSED SPECTRUM SENSING FOR COGNITIVE RADIO Master of Science Thesis Examiners: Prof. Markku Renfors and Dr. Tech. Sener Dikmese. Examiners and topic
More informationComparison of Detection Techniques in Spectrum Sensing
Comparison of Detection Techniques in Spectrum Sensing Salma Ibrahim AL haj Mustafa 1, Amin Babiker A/Nabi Mustafa 2 Faculty of Engineering, Department of Communications, Al-Neelain University, Khartoum-
More informationChapter 6. Agile Transmission Techniques
Chapter 6 Agile Transmission Techniques 1 Outline Introduction Wireless Transmission for DSA Non Contiguous OFDM (NC-OFDM) NC-OFDM based CR: Challenges and Solutions Chapter 6 Summary 2 Outline Introduction
More informationUrban WiMAX response to Ofcom s Spectrum Commons Classes for licence exemption consultation
Urban WiMAX response to Ofcom s Spectrum Commons Classes for licence exemption consultation July 2008 Urban WiMAX welcomes the opportunity to respond to this consultation on Spectrum Commons Classes for
More informationDetection the Spectrum Holes in the Primary Bandwidth of the Cognitive Radio Systems in Presence Noise and Attenuation
Int. J. Communications, Network and System Sciences, 2012, 5, 684-690 http://dx.doi.org/10.4236/ijcns.2012.510071 Published Online October 2012 (http://www.scirp.org/journal/ijcns) Detection the Spectrum
More informationWAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO
WAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO S.Raghave #1, R.Saravanan *2, R.Muthaiah #3 School of Computing, SASTRA University, Thanjavur-613402, India #1 raga.vanaj@gmail.com *2
More informationSPECTRUM SENSING BY CYCLO-STATIONARY DETECTOR
SPECTRUM SENSING BY CYCLO-STATIONARY DETECTOR 1 NIYATI SOHNI, 2 ANAND MANE 1,2 Sardar Patel Institute of technology Mumbai, Sadar Patel Institute of Technology Mumbai E-mail: niyati23@gmail.com, anand_mane@spit.ac.in
More informationSpectrum Sensing Using OFDM Signal and Cyclostationary Detection Technique In Cognitive Radio
ISSN: 2319-7463, Vol. 5 Issue 4, Aril-216 Spectrum Sensing Using OFDM Signal and Cyclostationary Detection Technique In Cognitive Radio Mudasir Ah Wani 1, Gagandeep Singh 2 1 M.Tech Student, Department
More informationCognitive Cellular Systems in China Challenges, Solutions and Testbed
ITU-R SG 1/WP 1B WORKSHOP: SPECTRUM MANAGEMENT ISSUES ON THE USE OF WHITE SPACES BY COGNITIVE RADIO SYSTEMS (Geneva, 20 January 2014) Cognitive Cellular Systems in China Challenges, Solutions and Testbed
More informationCycloStationary Detection for Cognitive Radio with Multiple Receivers
CycloStationary Detection for Cognitive Radio with Multiple Receivers Rajarshi Mahapatra, Krusheel M. Satyam Computer Services Ltd. Bangalore, India rajarshim@gmail.com munnangi_krusheel@satyam.com Abstract
More informationSecond order cyclostationarity of LTE OFDM signals in practical Cognitive Radio Application Shailee yadav, Rinkoo Bhatia, Shweta Verma
Second order cyclostationarity of LTE OFDM signals in practical Cognitive Radio Application Shailee yadav, Rinkoo Bhatia, Shweta Verma Abstract-Today s wireless networks are characterized by a fixed spectrum
More informationCooperative Spectrum Sensing and Spectrum Sharing in Cognitive Radio: A Review
International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] Cooperative Spectrum Sensing and Spectrum Sharing in Cognitive Radio: A Review
More informationPopulation Adaptation for Genetic Algorithm-based Cognitive Radios
Population Adaptation for Genetic Algorithm-based Cognitive Radios Timothy R. Newman, Rakesh Rajbanshi, Alexander M. Wyglinski, Joseph B. Evans, and Gary J. Minden Information Technology and Telecommunications
More informationCOGNITIVE RADIO. Priyesh V.P.
COGNITIVE RADIO Priyesh V.P. Introduction We get kicked off the Net as computers competing for bandwidth interfere with one another. We require a rich set of digital services but present communications
More informationAdvances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications Abdelmohsen Ali, Student Member, IEEE and Walaa Hamouda, Senior Member, IEEE Abstract Due to the under-utilization problem
More informationEfficient utilization of Spectral Mask in OFDM based Cognitive Radio Networks
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 6, Ver. III (Nov - Dec. 2014), PP 94-99 Efficient utilization of Spectral Mask
More informationDYNAMIC SPECTRUM SENSING USING MATCHED FILTER METHOD AND MATLAB SIMULATION
DYNAMIC SPECTRUM SENSING USING MATCHED FILTER METHOD AND MATLAB SIMULATION Miss. Nawale Tejashree L 1, Miss. Thorat Pranali R 2 1Assistant Professor, E&TC Department, RGCOE, Ahmednagar, India 2Lecturer,
More informationDiscriminating 4G and Broadcast Signals via Cyclostationary Feature Detection
Universität des Saarlandes Max-Planck-Institut für Informatik Discriminating 4G and Broadcast Signals via Cyclostationary Feature Detection Masterarbeit im Fach Informatik Masters Thesis in Computer Science
More informationCognitive Radio: Brain-Empowered Wireless Communcations
Cognitive Radio: Brain-Empowered Wireless Communcations Simon Haykin, Life Fellow, IEEE Matt Yu, EE360 Presentation, February 15 th 2012 Overview Motivation Background Introduction Radio-scene analysis
More informationPerformance Analysis of Secondary Users in Heterogeneous Cognitive Radio Network
Georgia Southern University Digital Commons@Georgia Southern Electronic Theses & Dissertations Graduate Studies, Jack N. Averitt College of Spring 2016 Performance Analysis of Secondary Users in Heterogeneous
More informationAustralian Journal of Basic and Applied Sciences
ISSN:1991-8178 Australian Journal of Basic and Applied Sciences Journal home page: www.ajbasweb.com Detection of Malicious Secondary User Using Spectral Correlation Technique in Cognitive Radio Network
More informationOverview: Trends and Implementation Challenges for Multi-Band/Wideband Communication
Overview: Trends and Implementation Challenges for Multi-Band/Wideband Communication Mona Mostafa Hella Assistant Professor, ESCE Department Rensselaer Polytechnic Institute What is RFIC? Any integrated
More informationPower Allocation with Random Removal Scheme in Cognitive Radio System
, July 6-8, 2011, London, U.K. Power Allocation with Random Removal Scheme in Cognitive Radio System Deepti Kakkar, Arun khosla and Moin Uddin Abstract--Wireless communication services have been increasing
More informationSpectrum Policy Task Force
Spectrum Policy Task Force Findings and Recommendations February 2003 mmarcus@fcc.gov www.fcc.gov/sptf 1 Outline Introduction Spectrum Policy Reform: The Time is Now Major Findings and Recommendations
More informationZukunft der Netze 9. Fachtagung des ITG-FA 5.2 Stuttgart, 7. Oktober 2010 Cognitive Radio How Much Self-Organization is Viable at Spectrum Level?
Zukunft der Netze 9. Fachtagung des ITG-FA 5.2 Stuttgart, 7. Oktober 2010 Cognitive Radio How Much Self-Organization is Viable at Spectrum Level? Klaus-D. Kohrt (ITG-FG 5.2.4) & Erik Oswald (Fraunhofer
More informationCognitive Radio Networks
1 Cognitive Radio Networks Dr. Arie Reichman Ruppin Academic Center, IL שישי טכני-רדיו תוכנה ורדיו קוגניטיבי- 1.7.11 Agenda Human Mind Cognitive Radio Networks Standardization Dynamic Frequency Hopping
More informationInternet of Things Cognitive Radio Technologies
Internet of Things Cognitive Radio Technologies Torino, 29 aprile 2010 Roberto GARELLO, Politecnico di Torino, Italy Speaker: Roberto GARELLO, Ph.D. Associate Professor in Communication Engineering Dipartimento
More informationControl issues in cognitive networks. Marko Höyhtyä and Tao Chen CWC-VTT-Gigaseminar 4th December 2008
Control issues in cognitive networks Marko Höyhtyä and Tao Chen CWC-VTT-Gigaseminar 4th December 2008 Outline Cognitive wireless networks Cognitive mesh Topology control Frequency selection Power control
More informationSmart-Radio-Technology-Enabled Opportunistic Spectrum Utilization
Smart-Radio-Technology-Enabled Opportunistic Spectrum Utilization Xin Liu Computer Science Dept. University of California, Davis Spectrum, Spectrum Spectrum is expensive and heavily regulated 3G spectrum
More informationA review paper on Software Defined Radio
A review paper on Software Defined Radio 1 Priyanka S. Kamble, 2 Bhalchandra B. Godbole Department of Electronics Engineering K.B.P.College of Engineering, Satara, India. Abstract -In this paper, we summarize
More informationCognitive Radio: a (biased) overview
cmurthy@ece.iisc.ernet.in Dept. of ECE, IISc Apr. 10th, 2008 Outline Introduction Definition Features & Classification Some Fun 1 Introduction to Cognitive Radio What is CR? The Cognition Cycle On a Lighter
More informationWireless & Cellular Communications
Wireless & Cellular Communications Slides are adopted from Lecture notes by Professor A. Goldsmith, Stanford University. Instructor presentation materials for the book: Wireless Communications, 2nd Edition,
More informationEfficient Method of Secondary Users Selection Using Dynamic Priority Scheduling
Efficient Method of Secondary Users Selection Using Dynamic Priority Scheduling ABSTRACT Sasikumar.J.T 1, Rathika.P.D 2, Sophia.S 3 PG Scholar 1, Assistant Professor 2, Professor 3 Department of ECE, Sri
More informationDYNAMIC SPECTRUM ACCESS AND SHARING USING 5G IN COGNITIVE RADIO
DYNAMIC SPECTRUM ACCESS AND SHARING USING 5G IN COGNITIVE RADIO Ms.Sakthi Mahaalaxmi.M UG Scholar, Department of Information Technology, Ms.Sabitha Jenifer.A UG Scholar, Department of Information Technology,
More informationRecent Advances in Cognitive Radios
Page 1 of 8 Recent Advances in Cognitive Radios Harit Mehta, harit.mehta@go.wustl.edu (A paper written under the guidance of Prof. Raj Jain) Download Abstract Recent advances in the field of wireless have
More informationCognitive Radio: Fundamentals and Opportunities
San Jose State University From the SelectedWorks of Robert Henry Morelos-Zaragoza Fall August 24, 2007 Cognitive Radio: Fundamentals and Opportunities Robert H Morelos-Zaragoza, San Jose State University
More informationCognitive Radio Techniques
Cognitive Radio Techniques Spectrum Sensing, Interference Mitigation, and Localization Kandeepan Sithamparanathan Andrea Giorgetti ARTECH HOUSE BOSTON LONDON artechhouse.com Contents Preface xxi 1 Introduction
More informationSpectrum Sensing in Cognitive Radio: Use of Cyclo-Stationary Detector
Spectrum Sensing in Cognitive Radio: Use of Cyclo-Stationary Detector by Manish B Dave Roll No. : 210EC4077 A Thesis submitted for partial fulfillment for the degree of Master of Technology in Electronics
More informationCognitive Radio Technology A Smarter Approach
Cognitive Radio Technology A Smarter Approach Shaika Mukhtar, Mehboob ul Amin Abstract The insatiable desire of man to exploit the radio spectrum is increasing with the introduction newer communication
More informationProgrammable Wireless Networking Overview
Programmable Wireless Networking Overview Dr. Joseph B. Evans Program Director Computer and Network Systems Computer & Information Science & Engineering National Science Foundation NSF Programmable Wireless
More informationTechniques for Spectrum Sensing in Cognitive Radio Networks: Issues and Challenges
Volume: 03 Issue: 05 May-2016 www.irjet.net p-issn: 2395-0072 Techniques for Spectrum Sensing in Cognitive Radio Networks: Issues and Challenges Maninder Singh 1, Pradeep Kumar 2, Dr. Anusheetal 3, Sandeep
More informationCognitive Radio Enabling Opportunistic Spectrum Access (OSA): Challenges and Modelling Approaches
Cognitive Radio Enabling Opportunistic Spectrum Access (OSA): Challenges and Modelling Approaches Xavier Gelabert Grupo de Comunicaciones Móviles (GCM) Instituto de Telecomunicaciones y Aplicaciones Multimedia
More informationImperfect Monitoring in Multi-agent Opportunistic Channel Access
Imperfect Monitoring in Multi-agent Opportunistic Channel Access Ji Wang Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements
More informationAnalysis of Interference from Secondary System in TV White Space
Analysis of Interference from Secondary System in TV White Space SUNIL PURI Master of Science Thesis Stockholm, Sweden 2012 TRITA-ICT-EX-2012:280 Analysis of Interference from Secondary System in TV White
More informationBreaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective
Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective Naroa Zurutuza - EE360 Winter 2014 Introduction Cognitive Radio: Wireless communication system that intelligently
More informationTesting and Measurement of Cognitive Radio and Software Defined Radio Systems
Testing and Measurement of Cognitive Radio and Software Defined Radio Systems Hüseyin Arslan University of South Florida, Tampa, FL, USA E-mail:arslan@eng.usf.edu ABSTRACT This paper describes an overview
More informationSpectrum Sensing in Cognitive Radio under different fading environment
International Journal of Scientific and Research Publications, Volume 4, Issue 11, November 2014 1 Spectrum Sensing in Cognitive Radio under different fading environment Itilekha Podder, Monami Samajdar
More informationPerformance Optimization of Software Defined Radio (SDR) based on Spectral Covariance Method using Different Window Technique
IJSTE - International Journal of Science Technology & Engineering Volume 3 Issue 03 September 2016 ISSN (online): 2349-784X Performance Optimization of Software Defined Radio (SDR) based on Spectral Covariance
More informationCYCLOSTATIONARITY BASED SIGNAL DETECTION IN COGNITIVE RADIO NETWORKS
CYCLOSTATIONARITY BASED SIGNAL DETECTION IN COGNITIVE RADIO NETWORKS 1 ALIN ANN THOMAS, 2 SUDHA T 1 Student, M.Tech in Communication Engineering, NSS College of Engineering, Palakkad, Kerala- 678008 2
More informationEfficient Multi Stage Spectrum Sensing Technique For Cognitive Radio Networks Under Noisy Condition
Efficient Multi Stage Spectrum Sensing Technique For Cognitive Radio Networks Under Noisy Condition Gajendra Singh Rathore 1 M.Tech (Communication Engineering), SENSE VIT University, Chennai Campus Chennai,
More informationBANDWIDTH-PERFORMANCE TRADEOFFS FOR A TRANSMISSION WITH CONCURRENT SIGNALS
BANDWIDTH-PERFORMANCE TRADEOFFS FOR A TRANSMISSION WITH CONCURRENT SIGNALS Aminata A. Garba Dept. of Electrical and Computer Engineering, Carnegie Mellon University aminata@ece.cmu.edu ABSTRACT We consider
More informationABSTRACT 1. INTRODUCTION
THE APPLICATION OF SOFTWARE DEFINED RADIO IN A COOPERATIVE WIRELESS NETWORK Jesper M. Kristensen (Aalborg University, Center for Teleinfrastructure, Aalborg, Denmark; jmk@kom.aau.dk); Frank H.P. Fitzek
More informationInnovative Science and Technology Publications
Innovative Science and Technology Publications International Journal of Future Innovative Science and Technology, ISSN: 2454-194X Volume-4, Issue-2, May - 2018 RESOURCE ALLOCATION AND SCHEDULING IN COGNITIVE
More informationDESIGN AND ANALYSIS OF MULTIBAND OFDM SYSTEM OVER ULTRA WIDE BAND CHANNELS
DESIGN AND ANALYSIS OF MULTIBAND OFDM SYSTEM OVER ULTRA WIDE BAND CHANNELS G.Joselin Retna Kumar Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India joselin_su@yahoo.com K.S.Shaji Principal,
More informationSome Fundamental Limitations for Cognitive Radio
Some Fundamental Limitations for Cognitive Radio Anant Sahai Wireless Foundations, UCB EECS sahai@eecs.berkeley.edu Joint work with Niels Hoven and Rahul Tandra Work supported by the NSF ITR program Outline
More informationPerformance of OFDM-Based Cognitive Radio
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 2 Issue 4 ǁ April. 2013 ǁ PP.51-57 Performance of OFDM-Based Cognitive Radio Geethu.T.George
More informationImplementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization
www.semargroups.org, www.ijsetr.com ISSN 2319-8885 Vol.02,Issue.11, September-2013, Pages:1085-1091 Implementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization D.TARJAN
More informationPage 1. Outline : Wireless Networks Lecture 6: Final Physical Layer. Direct Sequence Spread Spectrum (DSSS) Spread Spectrum
Outline 18-759 : Wireless Networks Lecture 6: Final Physical Layer Peter Steenkiste Dina Papagiannaki Spring Semester 2009 http://www.cs.cmu.edu/~prs/wireless09/ Peter A. Steenkiste 1 RF introduction Modulation
More informationDifferent Spectrum Sensing Techniques For IEEE (WRAN)
IJSRD National Conference on Technological Advancement and Automatization in Engineering January 2016 ISSN:2321-0613 Different Spectrum Sensing Techniques For IEEE 802.22(WRAN) Niyati Sohni 1 Akansha Bhargava
More informationDYNAMIC SPECTRUM SHARING IN WIRELESS COMMUNICATION
International Journal of Engineering Sciences & Emerging Technologies, April 212. ISSN: 2231 664 DYNAMIC SPECTRUM SHARING IN WIRELESS COMMUNICATION Mugdha Rathore 1,Nipun Kumar Mishra 2,Vinay Jain 3 1&3
More information