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Journal articles on the topic 'Software radio'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Simić, Igor, and Aleksa Zejak. "Software radio." Vojnotehnicki glasnik 46, no. 6 (1998): 574–82. http://dx.doi.org/10.5937/vojtehg9805574s.

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2

Kwang-Cheng Chen, R. Prasad, and H. V. Poor. "Software Radio." IEEE Personal Communications 6, no. 4 (August 1999): 12. http://dx.doi.org/10.1109/mpc.1999.788209.

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3

Mitola, J., and G. Q. Maguire. "Cognitive radio: making software radios more personal." IEEE Personal Communications 6, no. 4 (1999): 13–18. http://dx.doi.org/10.1109/98.788210.

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4

da Silva, Fabrício A. B., David F. C. Moura, and Juraci F. Galdino. "Classes of Attacks for Tactical Software Defined Radios." International Journal of Embedded and Real-Time Communication Systems 3, no. 4 (October 2012): 57–82. http://dx.doi.org/10.4018/jertcs.2012100104.

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This survey presents a classification of attacks that Software Communications Architecture (SCA) compliant Software Defined Radios (SDR) can suffer. This paper also discusses how attack mitigation strategies can impact the development of a SCA-compliant software infrastructure and identifies several research directions related to SDR security. The SCA standard was originally proposed by the Joint Tactical Radio System program (JTRS), which is a program for the development of military tactical radios sponsored by the US Department of Defense. The classification presented in this paper is based on attack results on the radio set, which can also be associated with the adversary’s objectives when planning an intrusion. The identification of classes of attacks on a radio, along with the associated threats and vulnerabilities, is the first step in engineering a secure SDR system. It precedes the identification of security requirements and the development of security mechanisms. Therefore, the identification of classes of attacks is a necessary step for the definition of realistic and relevant security requirements.
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5

Jacques, Palicot, and Hentschel Tim. "Software Radio: Implementation aspects." Annales Des Télécommunications 57, no. 7-8 (July 2002): 567–69. http://dx.doi.org/10.1007/bf02995509.

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6

Savic, Dejan, Boban Pavlovic, and Milan Sunjevaric. "Software: Based radio architecture." Vojnotehnicki glasnik 48, no. 1 (2000): 48–54. http://dx.doi.org/10.5937/vojtehg0001048s.

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7

Bing, B. "Software-Defined Radio Basics." IEEE Distributed Systems Online 6, no. 10 (October 2005): 6. http://dx.doi.org/10.1109/mdso.2005.54.

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8

Mitola, J. "The software radio architecture." IEEE Communications Magazine 33, no. 5 (May 1995): 26–38. http://dx.doi.org/10.1109/35.393001.

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9

Buracchini, E. "The software radio concept." IEEE Communications Magazine 38, no. 9 (2000): 138–43. http://dx.doi.org/10.1109/35.868153.

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10

Wolf, W. "Building the software radio." Computer 38, no. 3 (March 2005): 87–89. http://dx.doi.org/10.1109/mc.2005.82.

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11

Chu, James. ""Intelligent" Software-Defined Radio ( [Book/Software Reviews]." IEEE Microwave Magazine 17, no. 11 (November 2016): 82–98. http://dx.doi.org/10.1109/mmm.2016.2600950.

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12

Qing, Liu, Cao Kai, and Lai Ying-yong. "FPGA Software Architecture for Software Defined Radio." Procedia Engineering 29 (2012): 2133–39. http://dx.doi.org/10.1016/j.proeng.2012.01.275.

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13

Dhananjaya, Shashank, and Yuvaraju B N. "Increasing the Trust Factor in Cognitive Radio Networks Driven by Software Defined Radio." International Journal of Science and Research (IJSR) 11, no. 6 (June 5, 2022): 672–75. http://dx.doi.org/10.21275/sr22607143522.

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14

Rani, Supriya. "Software Defined Radio in Radio Frequency Identification Applications." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1887–92. http://dx.doi.org/10.22214/ijraset.2021.36778.

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RFID is an important aspect of today's age because it boosts efficiency and convenience. It is used for a lot of applications that prevent thefts of automobiles and merchandise. In current times there have been continuous transitions from analog to digital systems where software is being used to define the waveforms and analog signal processing is being replaced with digital signal processing. In this paper, we have done a thorough literature survey and understood the working of how software-defined radio is implemented in radio frequency identification for a better BER performance.
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15

Nambissan, T. Jishnu, T. V. Nikhil, and V. Vinodkumar. "A VHF Radio for Software Defined Radio Applications." Procedia Technology 24 (2016): 820–26. http://dx.doi.org/10.1016/j.protcy.2016.05.109.

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16

Sivokon, V. P., and D. V. Lapshov. "SOFTWARE DEFINED RADIO TECHNOLOGY IN THE TASKS OF RADIONOISE CONTROL." Bulletin оf Kamchatka State Technical University, no. 58 (2021): 17–28. http://dx.doi.org/10.17217/2079-0333-2021-58-17-28.

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The article is dedicated to study of the atmospheric noise properties in the range of intermediate and decameter waves in the Western Bering Sea zone, where such observations were not carried out earlier. Since it is impossible to use the radio equipment of ships for such measurements, we used devices using the technology of software-defined radio systems. The measurements were carried out along the coast of Kamchatka and made it possible to establish the characteristic temporal, spatial and frequency variations in the parameters of atmospheric noise. It was found that the radio noise intensity distributions proposed due the recommendations of the International Telecommunication Union differ significantly from the real ones. The obtained data analysis showed the possibility of realizing a decameter range of circumpolar latitudes, unfavorable for radio engineering systems – a sharp increase in the intensity of radio noise due to the coincidence in time of several thunderstorm activity foci and a simultaneous decrease in absorption in the ionosphere.
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17

Reza Amini, Mohammad, Einollah Balarastaghi, and Boroujerd Branch. "Universal Neural Network Demodulator for Software Defined Radio." International Journal of Engineering and Technology 3, no. 3 (2011): 263–68. http://dx.doi.org/10.7763/ijet.2011.v3.235.

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18

Moura, David Fernandes Cruz, Fabricio Alves Barbosa da Silva, and Juraci Ferreira Galdino. "Case Studies of Attacks over Adaptive Modulation Based Tactical Software Defined Radios." Journal of Computer Networks and Communications 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/703642.

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This paper presents case studies of attacks aimed at tactical software defined radios based on a classification with the most common sources of vulnerabilities, classes of attacks, and types of intrusions that military radio sets may suffer. Besides that, we also describe how attack mitigation strategies can impact the development of SDR infrastructures. By using such approach, we identify several possible sources of vulnerabilities, attacks, intrusions, and mitigation strategies, illustrating them onto typical tactical radio network deployment scenarios, as an initial and necessary step for the definition of realistic and relevant security requirements for military software defined radio applications.
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19

Tuttlebee, Walter HW. "Advances in software defined radio." Annales Des Télécommunications 57, no. 5-6 (May 2002): 314–37. http://dx.doi.org/10.1007/bf02995167.

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20

Kubota, Shuji. "What is Software Radio Communication." Journal of the Institute of Image Information and Television Engineers 54, no. 12 (2000): 1723–24. http://dx.doi.org/10.3169/itej.54.1723.

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21

Mitola, J., and Z. Zvonar. "Software and DSP in radio." IEEE Communications Magazine 38, no. 2 (February 2000): 138. http://dx.doi.org/10.1109/mcom.2000.819907.

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22

Cass, Stephen. "Software-defined radio, part II." IEEE Spectrum 50, no. 9 (September 2013): 24–25. http://dx.doi.org/10.1109/mspec.2013.6587181.

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23

Tuttlebee, W. H. W. "Advances in software-defined radio." Electronics Systems and Software 1, no. 1 (February 1, 2003): 26–31. http://dx.doi.org/10.1049/ess:20030105.

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24

Reichhart, S. P., B. Youmans, and R. Dygert. "The software radio development system." IEEE Personal Communications 6, no. 4 (1999): 20–24. http://dx.doi.org/10.1109/98.788211.

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25

Moessner, Klaus, Didier Bourse, Dieter Greifendorf, and Joerg Stammen. "Software radio and reconfiguration management." Computer Communications 26, no. 1 (January 2003): 26–35. http://dx.doi.org/10.1016/s1403-3664(02)00116-0.

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26

Jondral, Friedrich K., Jens Elsner, and Michael Schwall. "Software Defined Radio—Guest Editorial." Journal of Signal Processing Systems 69, no. 1 (January 13, 2012): 1–3. http://dx.doi.org/10.1007/s11265-011-0651-5.

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27

Lackey, R. I., and D. W. Upmal. "Speakeasy: the military software radio." IEEE Communications Magazine 33, no. 5 (May 1995): 56–61. http://dx.doi.org/10.1109/35.392998.

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28

Cummings, M., and S. Haruyama. "FPGA in the software radio." IEEE Communications Magazine 37, no. 2 (1999): 108–12. http://dx.doi.org/10.1109/35.747258.

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29

Shepherd, R. "Engineering the embedded software radio." IEEE Communications Magazine 37, no. 11 (1999): 70–74. http://dx.doi.org/10.1109/35.803654.

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30

Kenington, P. B. "Emerging technologies for software radio." Electronics & Communication Engineering Journal 11, no. 2 (April 1, 1999): 69–83. http://dx.doi.org/10.1049/ecej:19990203.

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31

Iancu, Daniel, John Glossner, Mihai Sima, Peter Farkas, and Michael McGuire. "Software-Defined Radio and Broadcasting." International Journal of Digital Multimedia Broadcasting 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/698402.

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32

Silva Cabral, Yngrid Keila, Paulo Ribeiro Lins Júnior, and Jerônimo Silva Rocha. "Proposta de arcabouço experimental para rede de sensoriamento espectral usando rádio definido por software." Revista Principia - Divulgação Científica e Tecnológica do IFPB 1, no. 44 (April 2, 2019): 88. http://dx.doi.org/10.18265/1517-03062015v1n44p88-99.

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<p>This paper presents an architecture proposal for a spectrum sensing network using software defined radios. The radios responsible for the sensing are implemented with SDR-RTL, a low-cost radio, capable of receiving signals from several frequency bands, such as those used in FM, DAB and DVB-T. Sensing functions are implemented using GNU Radio, the most commonly used free software for configuring software-defined radios installed in Raspberry Pi’s, which makes the sensing structure significantly compact and inexpensive when compared to other solutions. Experiments are performed to measure the probability of detection in relation to the signal noiseratio, as a metric of the efficiency of the system proposed in this work</p>
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33

Berruto, Ermanno, Pilar Diaz, and Gary Fleming. "A Radio Independent Network - the Enabler for Software Radio." European Transactions on Telecommunications 10, no. 6 (November 1999): 647–57. http://dx.doi.org/10.1002/ett.4460100609.

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34

Taylor, Fred, Evan Gattis, Lucca Trapani, Dennis Akos, Sherman Lo, Todd Walter, and Yu-Hsuan Chen. "Software Defined Radio for GNSS Radio Frequency Interference Localization." Sensors 24, no. 1 (December 22, 2023): 72. http://dx.doi.org/10.3390/s24010072.

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The use of radio direction finding techniques in order to identify and reject harmful interference has been a topic of discussion both past and present for signals in the GNSS bands. Advances in commercial off-the-shelf radio hardware have led to the development of new low-cost, compact, phase coherent receiver platforms such as the KrakenSDR from KrakenRF whose testing and characterization will be the primary focus of this paper. Although not specifically designed for GNSSs, the capabilities of this platform are well aligned with the needs of GNSSs. Testing results from both benchtop and in the field will be displayed which verify the KrakenSDR’s phase coherence and angle of arrival estimates to array dependent resolution bounds. Additionally, other outputs from the KrakenSDR such as received signal strength indicators and the angle of arrival confidence values show strong connections to angle of arrival estimate quality. Within this work the testing that will be primarily presented is at 900 MHz, with results presented from a government-sponsored event where the Kraken was tested at 1575.42 MHz. Finally, a discussion of calibration of active antenna arrays for angle of arrival is included as the introduction of active antenna elements used in GNSS signal collection can influence angle of arrival estimation.
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35

DUŞTINŢĂ, Dan, and Alexandra STANCIU. "SYSTEM ON CHIP DEVELOPMENT PLATFORM FOR SOFTWARE DEFINED RADIO." Review of the Air Force Academy 16, no. 1 (August 1, 2018): 65–70. http://dx.doi.org/10.19062/1842-9238.2018.16.1.9.

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36

Javidi, Giti, and Ehsan Sheybani. "Application of Digital Signal Processing in USRP Satellite Signal Detection." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 2 (April 2017): 16–25. http://dx.doi.org/10.4018/ijitn.2017040102.

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The Universal Software Radio Peripheral development technique is designing and implementing radio frequency based systems. The distinctiveness originates from the interchangeable daughterboard within the USRP. The system is designed around the Xilinx Vertex 3 FPGA chip. This means C++, Python, and VHDL can be used to program this device. The project consists of creating a receiver. The objective of the project is to research and comprehend the hardware functionalities of the USRP. The purpose is to create codes in C++ and Python to implement receiving capabilities of the device. The goal of this project was to design a GPS receiver that is capable of recording the L1 signal from a DirecTV satellite. The USRP is used a lot for research. This project consisted of more than just one method. We used GNU Radio Companion and Matlab/Simulink. GNU Radio is open source for building software defined radios. It is also known as GRC. While using GRC the USRP1 was the device used. This software has rapid development. It runs in Ubuntu, a Linux operating system. Within this software there are logic blocks. Each block consists of information to create a flow graph. The flow graph builds and generates the program. Simulink can be compared to GRC. They both have logic blocks that have to be connected to run. Simulink can be used to create a transmitter or a receiver for software radio development and signal processing. Software-defined radio can only be defined if its baseband operations can be completely defined by software. A SDR converts digital to analog signals. The USRP can also convert digital signals from a computer to Radio Frequency Signals (RF). This software is one way to communicate between hardware and software.
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37

Lingaiah, D. "Software radio: A modern approach to radio engineering [Book Review]." IEEE Software 20, no. 4 (July 2003): 86–95. http://dx.doi.org/10.1109/ms.2003.1207473.

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38

Vega León, Andy Fabricio, and Andrea Guamo. "Comunicación Basada en Radio Cognitiva sobre Radio Definido por Software." Revista Tecnológica - ESPOL 32, no. 2 (December 30, 2020): 43–50. http://dx.doi.org/10.37815/rte.v32n2.736.

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En la era de la tecnología inalámbrica, la escasez del espectro radioeléctrico plantea un grave dilema para los proveedores de servicios y los operadores de telecomunicaciones. La tecnología de Radio Cognitiva (RC) proporciona una solución innovadora para mejorar significativamente la utilización del espectro. El presente documento desarrolla y dispone la información necesaria para implementar un enlace de comunicación basado en Radio Cognitiva. Para realizar dicha tarea se utiliza herramientas como: software GNU Radio, en donde se implementa el sistema de comunicación que incluye el detector del espectro seleccionado y se pone en manifiesto la característica principal de la tecnología RC que debe ser consciente del entorno radioeléctrico con fines de optimizarlo y, a base del mismo, permite tomar decisiones de transmisión que no interrumpe en ningún momento la comunicación del enlace ni obstaculiza o perjudica la comunicación de un Usuario primario o licenciado. Además, se emplea herramientas de Radio definido por Software (SDR) que realizan tareas de radiocomunicación como procesamiento de banda base y las diferentes configuraciones necesarias para la radiocomunicación en la banda de Ultra Alta frecuencia (UHF), logrando así establecer un enlace de comunicación basado en Radio Cognitiva.
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39

Cwalina, Krzysztof, Piotr Rajchowski, and Jarosław Sadowski. "Wideband Radio Direction Finder Implemented in Software Defined Radio Technology." Applied Mechanics and Materials 817 (January 2016): 348–55. http://dx.doi.org/10.4028/www.scientific.net/amm.817.348.

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In the paper a wideband radio direction finder (RDF) implemented in software defined radio (SDR) technology and the results of hardware layer research, including developed antenna switching unit (ASU), are presented. The results of tests of the devices, which are the part of the software defined radio platform (SDRP), and antenna switching unit, confirmed the possibility of using selected components in the final solution.
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40

Gao, Jing. "Analysis of Military Application of Software Radio Communication Technology." MATEC Web of Conferences 267 (2019): 02017. http://dx.doi.org/10.1051/matecconf/201926702017.

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The emergence of software radio technology is the third technological revolution in the field of communication. In view of the immature status of the application of software radio communication technology in our country's military affairs, on the premise of interpreting the connotation and extension of software radio communication technology, this paper systematically analyses the principle and architecture of software radio, and puts forward that military software radio communication technology can be used in electronic warfare simulation evaluation and software enhancement. The conception of the application of wire electronic warfare system expands the theoretical breadth of military application of software radio communication technology.
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41

Tato, Anxo. "Software Defined Radio: A Brief Introduction." Proceedings 2, no. 18 (September 19, 2018): 1196. http://dx.doi.org/10.3390/proceedings2181196.

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In this short article the concept of Software Defined Radio (SDR) is introduced and compared with the traditional radio. Then, a research project of atlanTTic center which used this technology was briefly presented and lastly, we include a reference to some dissemination activities related with SDR to be developed shortly.
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42

Sheybani, Ehsan, and Giti Javidi. "Integrating Software Defined Radio with USRP." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 3 (July 2017): 1–9. http://dx.doi.org/10.4018/ijitn.2017070101.

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The USRP1 is the original Universal Software Radio Peripheral hardware (USRP) that provides entry-level RF processing capability. Its primary purpose is to provide flexible software defined radio development capability at a low price. You can control the frequency you receive and transmit by installing different daughter-boards. The authors' USRP model had been configured to receive a signal from local radio stations in the DC, Maryland metropolitan area with the BasicRX model daughterboard. The programmable USRP was running python block code implemented in the GNU Radio Companion (GRC) on Ubuntu OS. With proper parameters and sinks the authors were able to tune into the radio signal, record the signal and extract the in-phase (I) and quadrature phase (Q) data and plot the phase and magnitude of the signal. Using the terminal along with proper MATLAB and Octave code, they were able to read the I/Q data and look at the Fast Fourier Transform (FFT) plot along with the I/Q data. With the proper equations, you could determine not only the direction of arrival, but one would also be able to calculate the distance from the receiver to the exact location where the signal is being transmitted. The purpose of doing this experiment was to gain experience in signal processing and receive hands on experience with the USRP and potentially add a tracking system to the authors' model for further experiments.
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43

VULAVABETI, RAGHUNATH REDDY, and REDDY K. RAVINDRA. "SOFTWARE DEFINED RADIO BASED BEACON RECEIVER." i-manager's Journal on Communication Engineering and Systems 8, no. 3 (2019): 13. http://dx.doi.org/10.26634/jcs.8.3.16779.

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44

Jararweh, Yaser, Mahmoud Al-Ayyoub, Ahmad Doulat, Ahmad Al Abed Al Aziz, Haythem A. Bany Salameh, and Abdallah A. Khreishah. "Software Defined Cognitive Radio Network Framework." International Journal of Grid and High Performance Computing 7, no. 1 (January 2015): 15–31. http://dx.doi.org/10.4018/ijghpc.2015010102.

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Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.
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45

Mitola, J., D. Chester, S. Haruyama, T. Turletti, and W. Tuttlebee. "Globalization of Software radio [Guest Editorial]." IEEE Communications Magazine 37, no. 2 (February 1999): 82–83. http://dx.doi.org/10.1109/mcom.1999.747253.

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46

Sherman, Jeff A., and Robert Jördens. "Oscillator metrology with software defined radio." Review of Scientific Instruments 87, no. 5 (May 2016): 054711. http://dx.doi.org/10.1063/1.4950898.

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47

Frank, Thomas. "SICHERE HOCHFREQUENZFUNKTIONEN DANK SOFTWARE DEFINED RADIO." ATZelektronik 7, S7 (October 18, 2012): 54–57. http://dx.doi.org/10.1365/s35658-012-0214-y.

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48

Li, Chunxiao, Niraj K. Jha, and Anand Raghunathan. "Secure reconfiguration of software-defined radio." ACM Transactions on Embedded Computing Systems 11, no. 1 (March 2012): 1–22. http://dx.doi.org/10.1145/2146417.2146427.

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49

Scherzinger, Martin. "Software Physiognomics: Adorno's Radio Analytics Today." New German Critique 43, no. 3 129 (November 2016): 53–72. http://dx.doi.org/10.1215/0094033x-3625361.

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50

Birmingham, William, and Leah Acker. "Software-defined radio for undergraduate projects." ACM SIGCSE Bulletin 39, no. 1 (March 7, 2007): 293–97. http://dx.doi.org/10.1145/1227504.1227414.

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