Relevant bibliographies by topics / Software defined radio(RTL)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Software defined radio(RTL)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Software defined radio(RTL)"

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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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Rahman, Md Habibur, and Md Mamunoor Islam. "A Practical Approach to Spectrum Analyzing Unit Using RTL-SDR." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 151–59. http://dx.doi.org/10.3329/rujse.v44i0.30400.

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In the present scenario, there has been an immense advancement in the field of wireless communication in this modern engineering world. Now-a-days Software Defined Radio (SDR) technology is an indisputable emerging technology and presents new challenges for communications engineers. The advancement of SDR system has made significant progress in recent years which makes it as a serious substitute of traditional hardware radio architectures where the mathematical procedures are obligatory to decode and process radio signals using analogue circuitry. Recently, computers have turned out to be powerful enough to do the required mathematical calculations using software. So aim of this paper is to demonstrate a RTL-SDR based spectrum analyzer which can be used proficiently as an alternative of existing hardware spectrum analyzer. This approach will lessen the complexity of analogue hardware system with the higher tractability of software based filtering and demodulation techniques. As RTL-SDR devices are quite cheap (Approximately 20$) and small sized, this system also offers cost effectiveness with provision of portability. An experimental study was conducted with suitable conditions to examine the feasibility and efficiency of the proposed system. The outcome of experimental result is thoroughly examined in this paper.
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Satya Narayana, P., M. N.V.S. Syam Kumar, A. Keerthi Kishan, and K. V.R.K. Suraj. "Design approach for wideband FM receiver using RTL-SDR and raspberry PI." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 9. http://dx.doi.org/10.14419/ijet.v7i2.31.13386.

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Software defined radio replaced majority of hardware modules like mixers, filters, modulators and demodulators etc., with Software blocks in the field of radio electronics and communication. In this some or all the functionalities are Configurable using this software implemented on technologies like FPGAs, DSPs etc. Owing to lack of ease in implementing and reconfiguring huge hardware modules, we move on to implement an adaptable communication system with the help of SDR, as it can be easily configured to work with wide range of frequencies. We find various SDR transceiver modules which can be interfaced with digital computer and aided with firmware like GNU radio, SDR shark, etc., allowing us to construct blocks with the help of built in components that decode and process the received data and produce required output. In requirement of implementing a cost-effective, compact sized and portable system, we use a processing unit providing enough computational power to perform signal processing tasks which is Raspberry pi. Here we are going to implement a low cost SDR communication system that capture, process and visualize the Wide Band Frequency signal.
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Stewart, Robert W., Louise Crockett, Dale Atkinson, Kenneth Barlee, David Crawford, Iain Chalmers, Mike Mclernon, and Ethem Sozer. "A low-cost desktop software defined radio design environment using MATLAB, simulink, and the RTL-SDR." IEEE Communications Magazine 53, no. 9 (September 2015): 64–71. http://dx.doi.org/10.1109/mcom.2015.7263347.

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Rahman, Aviv Yuniar, Mamba’us Sa’adah, and Istiadi. "Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (April 19, 2020): 286–95. http://dx.doi.org/10.29207/resti.v4i2.1667.

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Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.
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Sabur, Fatmawati, and Ucok Sinaga. "Rancang Bangun Trainer Spectrum Analyzer berbasis Raspberry Phyton dan Register Transfer Level - Software Defined Radio." AIRMAN: Jurnal Teknik dan Keselamatan Transportasi 3, no. 2 (December 28, 2020): 1–8. http://dx.doi.org/10.46509/ajtk.v3i2.161.

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Spectrum Analyzer merupakan perangkat yang dapat menganalisis atau menguji kondisi suatu sistem frekuensi dalam suatu jaringan komunikasi. Kondisi yang terjadi dilapangan adalah harga beli dari sebuah Spectrum Analyzer yang tinggi menyebabkan tidak semua pihak mampu memilikinya. Salah satu solusi untuk mengatasi hal tersebut adalah dengan membuat suatu hardware yang mampu melakukan unjuk kerja yang sama seperti Spectrum Analyzer tetapi dengan harga yang relatif lebih murah. Prototype Spectrum Analyzer yang akan diimplementasikan merupakan suatu perangkat yang mampu menampilkan spektrum suatu sinyal pada range frekuensi tertentu. Rekayasa ini dilaksanakan dari bulan Juli sampai dengan Oktober 2020 di Kampus Polteknik Penerbangan Makassar dan ujicoba alat dengan alat pembanding dilaksanakan di Otban Wilayah V makassar. Teknik atau metode yang digunakan dalam pengumpulan data yaitu metode pustaka dengan cara mengumpulkan beberapa data tertulis baik dari buku, literatur, dan tutorial-tutorial di internet, sebagai bahan referensi kemudian menganalisa solusi yang dapat diambil dalam penyelesaian masalah. Dari hasil pengujian yang dilakukan dengan penggunaan RTL-SDR pada Single Board Computer (SBC) Raspberry pi dapat menampilkan spectrum frequensi baik itu dilakukan secara tunggal ataupun diterapan pada jaringan sehingga dapat digunakan sebagai media pembelajaran praktik teknologi wireless atau pun materi lain yang melakukan pengukuran frekuensi. Dari sergi performa kinerja trainer spectrum analyzer dengan pemanfaatan raspberry pisebagai perangkat untuk mengolah sinyal radio/ wireless cukup baik pada pemanfaatan dengan mode CLI (command line Interface) namun relatif lambat jika digunakan pada desktop sebagai alat portable spectrum analyzer yang dapat digunakan sebagai media pembelajaran
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Sabur, Fatmawati, and Ucok Sinaga. "Design Trainer Analysis Spectrum Analyzer Based on Raspberry Python and Register Transfer Level - Software Defined Radio." Airman: Jurnal Teknik dan Keselamatan Transportasi 3, no. 2 (February 4, 2021): 1–8. http://dx.doi.org/10.46509/ajtkt.v3i2.69.

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communication network. However, the high purchase price of a Spectrum Analyzer means that not everyone can afford it. One solution to overcome this problem is to make a piece of hardware capable of performing the same performance as a Spectrum Analyzer but at a relatively cheaper price. Prototype Spectrum Analyzer to be implemented is a device capable of displaying the spectrum of a signal in a certain frequency range. This engineering was carried out from July to October 2020 at the Makassar Aviation Polytechnic Campus and testing tools with a comparison tool was carried out at Otban Region V Makassar. The technique or method used in data collection is the library method by collecting some written data from books, literature, and tutorials on the internet, as reference material and then analyzing solutions that can be taken in solving problems. From the results of tests carried out by using RTL-SDR on the Single Board Computer (SBC), Raspberry pi can display the frequency spectrum whether it is done singly or applied to the network so that it can be used as a learning medium for wireless technology practice or other materials that measure frequency From sergi, the performance of the trainer spectrum analyzer with the use of raspberry pi as a device for processing radio / wireless signals is quite good at utilization with CLI mode (command line interface) but is relatively slow when used on the desktop as a portable spectrum analyzer that can be used as a learning medium
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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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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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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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Dissertations / Theses on the topic "Software defined radio(RTL)"

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Koch, Mick V. "An Accessible Project 25 Receiver Using Low-Cost Software Defined Radio." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1464007525.

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Fiumi, Federico. "Implementazione di un sistema di comunicazione digitale wireless mediante software-defined radio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Studio e implementazione di un sistema di comunicazione digitale a modulazione numerica QPSK. Il sistema è stato implementato tramite interfacciamento Simulink di due dispositivi software defined radio: URSP2 e NooElec RTL-SDR.
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Ardrey, David, Gregory Gimler, and Mark Pippitt. "A Software Defined Radio Based Architecture for the Reagan Test Site Telemetry Modernization (RTM) Program." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596436.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
MIT Lincoln Laboratory has developed a Software Defined Radio based architecture for the Reagan Test Site Telemetry Modernization (RTM) program, which will enhance the current operations of the ground based telemetry systems and enable new modes of operation. There are three main objectives of the RTM program; increasing overall system performance, improving reliability and maintainability, and enabling future customer needs. RTM provides a fully integrated system that can be configured and remotely controlled from a single location. This centralized command and control provides a way to automate certain functions and frees up operator resources, especially for more complex mission scenarios. Software modules, running on general-purpose computers perform signal and data processing that have been traditionally performed in special purpose hardware based components. This provides the flexibility to scale and adapt to future needs, such as spectrum change, increased need for capacity, and changes to modulation, encoding, and compression. Index Terms - Software Defined Radio (SDR), Open systems architecture, telemetry receiver architecture, Aeronautical Mobile Telemetry (AMT)
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Alhasan, Raghda. "Software defined radio." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-53361.

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Software Defined Radio (SDR) technology is used to receive and transmit radio signals. Radio signals can be received using the SDR_sharp software that can be downloaded to a personal computer and combined with the RTL-SDR dongle hardware that is connected to the computer. This report gives a brief explanation of the SDR receiver, the supported software, and some applications that can be implemented with SDR. Moreover, it is shown how to install the SDR_sharp software and the hardware Zadig. After installation, wideband FM (WFM) reception and adjustment of RF gain and frequency error is illustrated. The reception of some national, regional and community/commercial stations that can be received in Växjö Sweden is presented. Keyword: Software defined radio, FM radio.
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Nash, Christopher, and Christopher Hogstrom. "SOQPSK Software Defined Radio." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596411.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
This paper presents the results of laboratory experiments using a commercial-off-the-shelf software defined radio to demodulate SOQPSK-TG for aeronautical telemetry. Using the NI USRP N210 and Zynq™ processor, we achieved 900 kbits/s demodulation and found that the USRP N210 has a signal sensitivity of -71 dBm at a BER of 10⁻⁶.
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Antunes, Lúcia Margarida da Mata. "Software defined radio em FPGA." Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/2150.

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Mestrado em Engenharia Electrónica e Telecomunicações
Esta dissertação teve como objectivo o desenvolvimento de parte de um receptor para Digital Audio Broadcasting (DAB) recorrendo aos conceitos ditados por Software Defined Radio (SDR). O receptor de rádio inclui a conversão de digital para analógico e a subsequente desmodelação de banda- base,pelo que é possível aceder à bit stream em qualquer ponto do sistema. A dissertação foi dividida em duas fases. Na primeira, o receptor completo foi simulado em MATLAB. Na segunda, o mesmo sistema foi implementado e testado numa placa XtremeDSP Development Kit-IV, a qual contêm um Field-Programmable Gate Array (FPGA). O sistema simulado foi testado com dois tipos de amostras. As primeiras consistiram em sinais DAB gerados em MATLAB e posteriormente distorcidos por diferentes canais também simulados pelo mesmo software. Foi assim possível fazer um estudo da probabilidade de erro quando o sinal é exposto a diferentes perturbações, como ruído, desvios na frequência e no tempo. O sistema foi ainda testado com amostras DAB reais. As constelações desmodelados mostraram o correcto funcionamento do sistema. Apenas parte do receptor simulado foi implementado no FPGA. A parte já desenvolvida consiste nas funções de desmodelação: desmodelação OFDM, desmodelação diferencial, frequency deinterleaving e demapeamento QPSK. O sistema de sincronização DAB não foi implementado. O sistema já desenvolvido é assim capaz de desmodelar um sinal DAB gerado no MATLAB, desde que este não contenha qualquer distorção. ABSTRACT: The aim of this dissertation was the development of part of a Digital Audio Broadcasting (DAB) receiver by means of Software Defined Radio (SDR). This radio receiver includes the Intermediate Frequency (IF) to baseband conversion and the subsequent baseband demodulation, thus one may access the bit stream in any point of the system. This dissertation was divided in two phases. In the first one, the whole DAB system was simulated in MATLAB. In the second, the receiver was implemented and tested in an XtremeDSP Development Kit-IV platform, which includes a Field-Programmable Gate Array (FPGA). The simulated system was tested with two kinds of samples. The first ones were generated in MATLAB and subsequently distorted by different channel conditions also simulated in the same software. This well known DAB digital signal allowed us to perform a Bit Error Rate (BER) study with several channel conditions, such as noise, multipath, frequency and time offsets. Further on, real DAB samples were used for testing. The demodulated QPSK constellations showed the correct operation of the system. Only part of the simulated receiver was implemented in the FPGA. This part consists in the channel demodulation functions: OFDM demodulation, differential demodulation, frequency deinterleaving and QPSK demapper. The DAB synchronization block was not implemented. The developed system is able to recover the modulated bit stream from the digital signal produced in MATLAB, since this signal is free of noise, frequency and time offsets
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Smuts, Matthys. "Software modem for a software defined radio system." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/1985.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2007.
The use of older and slower protocols has become increasingly difficult to justify due to the rapid pace at which telecommunications are advancing. To keep up to date with the latest technologies, the communications system must be designed to accommodate the transparent insertion of new communications standards in all the stages of a system. The system should, however, also remain compatible with the older standards so as not to demand an upgrade of the older systems. The concept of a software defined radio was introduced to overcome these problems. In a software defined radio system, the functionality of the communications system is defined in software, which removes the the need for alterations to the hardware during technology upgrade. To maintain interoperatibilty, the system must be based on a standardised architecture. This would further allow for enhanced scalability and provide a plug-andplay feature for the components of the system. In this thesis, generic signal processing software components are developed to illustrate the creation of a basic software modem that can be parameterised to comply fully, or partially, to various standards.
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Paffetti, Michele. "Software Defined Radio for NB-IoT." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14315/.

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The next generation of mobile radio systems is expected to providing wireless connectivity for a wide range of new applications and services involving not only people but also machines and objects. Within few years, billions of low-cost and low-complexity devices and sensors will be connected to the Internet, forming a converged ecosystem called Internet of Things (IoT). As a result, in 2016, 3GPP standardizes NB-IoT, the new narrowband radio technology developed for the IoT market. Massive connectivity, reduced UE complexity, coverage extension and deployment flexibility are the targets for this new radio interface, which also ensures harmonious coexistence with current GSM, GPRS and LTE systems. In parallel, the rise of open-source software combined with Software Defined Radio (SDR) solutions has completely changed radio systems engineering in the late years. This thesis focuses on developing the NB-IoT’s protocol stack on the EURECOM’s open-source software platform OpenAirInterface (OAI). First part of this work aims to implement NB-IoT’s Radio Resource Control functionalities on OAI. After an introduction to the platform architecture, a new RRC layer code structure and related interfaces are defined, along with a new approach for Signalling Radio Bearers management. A deep analysis on System Information scheduling is conducted and a subframe-based transmission scheme is then proposed. The last part of this thesis addresses the implementation of a multi-vendor platform interface based on Small Cell Forum’s Functional Application Platform Interface (FAPI) standard. A configurable and dynamically loadable Interface Module (IF-Module) is designed between OAI’s MAC and PHY layers. Primitives and related code structures are presented as well as corresponding Data and Configuration’s procedures. Finally, the convergence of both NB-IoT and FAPI requirements lead to re-design PHY layer mechanisms for which a downlink transmission scheme is proposed.
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Sundquist, Thomas. "Waveform Development using Software Defined Radio." Thesis, Linköping University, Department of Science and Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6464.

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Software Defined Radio (SDR) is a conception of implementing radio functions in computer software, instead of having electronics performing the functions. This thesis aims to compare two different ways of implementing these functions, or waveforms.

The Software Communications Architecture (SCA) is an open standard developed by the United States Department of Defense. It uses a CORBA interface environment to make waveform applications interoperable and platform independent. This method of developing SDR is compared to an open-source initiative going by the name GNU Radio.

Two waveform applications are developed, one transmitter using SCA, and one receiver using GNU Radio. The analog radio interface is simulated using the sound cards of two regular PCs. The development is done using the C++ and Python programming languages.

This thesis examines pros and cons of the two SDR methods, as well as performing studies of Software Defined Radio in general.

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Dumont, Nathan. "Software defined radio for cognitive networks." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619232.

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The introduction of software radio has meant that standards for radio communication can evolve in a much more natural way, changing only a little at a time without making all of the hardware obsolete. It has become apparent that these changes may affect some systems more favourably than others so allowing the software radio to decide how to adapt can actually improve the link quality. This development is known as cognitive radio and can improve the performance of a single radio link. As an extension of this progress is being made on designing cognitive networks where the software radios which make up the network not only optimise their own link but share information about their goals and situation with other nodes in the network, using all of this data together can optimise overall end-to-end performance of the network. These advances in network design and optimisation come at a time where many parts of the world are re-structuring the television broadcast bands. These have been allocated for a long time and are a generous allocation of a valuable resource. With the power of a cognitive network it is possible to design equipment that can automatically avoid the licensed TV transmitters which only take a fraction of the total bandwidth in any one area. This allows many smaller cells to be fitted between the main transmitters. Assessing the availability of bandwidth and generating maps of available spectrum for these new cognitive networks requires a new approach to radio propagation modelling in the TV bands. Previous models use a worst case scenario to make sure that there is at least enough signal to receive the public service broadcasts in the majority of homes. Predicting where the limits of reception are and where it would be safe to broadcast on these channels requires a better, terrain dependent transmission model. In this thesis the Parabolic Equation Model is applied to the problem of predicting TV band occupancy and the results of this modelling is compared to field measurement to get an idea of how accurate the model is in practice.
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Books on the topic "Software defined radio(RTL)"

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Tuttlebee, Walter, ed. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470846003.

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Tuttlebee, Walter, ed. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470846011.

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Tuttlebee, Walter, ed. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470846003.

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Tuttlebee, Walter, ed. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470846011.

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Tuttlebee, Walter H. W., ed. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2003. http://dx.doi.org/10.1002/0470867728.

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Bard, John, and Vincent J. Kovarik. Software Defined Radio. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470865200.

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Grayver, Eugene. Implementing Software Defined Radio. New York, NY: Springer New York, 2013.

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Grayver, Eugene. Implementing Software Defined Radio. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9332-8.

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Burns, Paul. Software defined radio for 3G. Boston: Artech House, 2003.

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Software defined radio for 3G. Boston: Artech House, 2003.

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Book chapters on the topic "Software defined radio(RTL)"

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Gebali, Fayez. "Software Defined Radio." In Analysis of Computer Networks, 433–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15657-6_14.

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Gupta, Aditya. "Software Defined Radio." In The IoT Hacker's Handbook, 223–63. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4300-8_9.

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Kohno, Ryuji, Ryu Miura, Hiroshi Harada, Shinichiro Haruyama, Yukitoshi Sanada, and Lachlan Michael. "Overview of Japanese Activities in Software-defined Radio." In Software Radio, 219–25. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0343-1_19.

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Benvenuto, N., G. A. Mian, and F. Momola. "Digital Receiver Architecture for Multi-Standard Software Defined Radios." In Software Radio, 143–54. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0343-1_12.

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Fettweis, Gerhard. "Software Defined Radio: What Do We Do with It?" In Software Radio, 283–88. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0343-1_25.

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Höher, Peter Adam. "Software-Defined Radio und Cognitive Radio." In Grundlagen der digitalen Informationsübertragung, 699–705. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-8348-2214-7_30.

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König, Wolfgang, Gerd Wölfle, Christian Fischer, and Tim Hentschel. "Front End Architecture for a Software Defined Radio Base Station." In Software Radio, 315–27. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0343-1_27.

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Acampora, Anthony, Joseph Soma Reddy, Haipeng Jin, and Ralph Gholmieh. "Role of Software Defined Radio in Wireless Access to the Internet." In Software Radio, 41–54. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0343-1_4.

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Grayver, Eugene. "What is a Radio?" In Implementing Software Defined Radio, 1–3. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-9332-8_1.

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Grayver, Eugene. "Software-Centric SDR Platforms." In Implementing Software Defined Radio, 131–49. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-9332-8_8.

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Conference papers on the topic "Software defined radio(RTL)"

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Bulychev, Roman V., Dmitry E. Goncharov, and Irina F. Babalova. "Obtaining IMSI by software-defined radio (RTL-SDR)." In 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2018. http://dx.doi.org/10.1109/eiconrus.2018.8316859.

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Mishra, Madhuram, Anjali Potnis, Prashant Dwivedy, and Sunil Kumar Meena. "Software defined radio based receivers using RTL — SDR: A review." In 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE). IEEE, 2017. http://dx.doi.org/10.1109/rise.2017.8378125.

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Sergienko, Alexander B. "Software-defined radio in MATLAB Simulink with RTL-SDR hardware." In 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA). IEEE, 2014. http://dx.doi.org/10.1109/icctpea.2014.6893337.

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Meshram, Shwetali, and Nilima Kolhare. "The advent software defined radio: FM receiver with RTL SDR and GNU radio." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987588.

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Sruthi, M. B., M. Abirami, A. Manikkoth, R. Gandhiraj, and K. P. Soman. "Low cost digital transceiver design for Software Defined Radio using RTL-SDR." In 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s). IEEE, 2013. http://dx.doi.org/10.1109/imac4s.2013.6526525.

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Wickert, Mark A., and McKenna R. Lovejoy. "Hands-on software defined radio experiments with the low-cost RTL-SDR dongle." In 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE). IEEE, 2015. http://dx.doi.org/10.1109/dsp-spe.2015.7369529.

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Fanan, A., N. Riley, M. Mehdawi, M. Ammar, and M. Zolfaghari. "Comparison of spectrum occupancy measurements using software defined radio RTL-SDR with a conventional spectrum analyzer approach." In 2015 23rd Telecommunications Forum Telfor (TELFOR). IEEE, 2015. http://dx.doi.org/10.1109/telfor.2015.7377447.

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Uengtrakul, Boonyarit, and Dahmmaet Bunnjaweht. "A cost efficient software defined radio receiver for demonstrating concepts in communication and signal processing using Python and RTL-SDR." In 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP). IEEE, 2014. http://dx.doi.org/10.1109/dictap.2014.6821718.

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Harris, Frederic J. "Software defined radio." In 2008 International Conference on Signals and Electronic Systems. IEEE, 2008. http://dx.doi.org/10.1109/icses.2008.4673338.

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Park, Yongtae, JeongGil Ko, and Hyogon Kim. "Demo: software defined radio." In MobiCom'14: The 20th Annual International Conference on Mobile Computing and Networking. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2639108.2641742.

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Reports on the topic "Software defined radio(RTL)"

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Gowda, A. S. Photonic Software Defined Radio. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572630.

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Poyneer, L. Addressing qubits with a software-defined radio FPGA. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1722961.

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Channamallu, Aditya. Software Defined Radio based Modulated Scatterer Antenna Measurement. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6331.

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Weingart, Troy B., Doug Sicker, Dirk Grunwald, and Michael Neufeld. Adverbs and Adjectives: An Abstraction for Software Defined Radio. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430375.

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Shribak, Dmitry, Alexander Heifetz, and Xin Huang. Development of Software Defined Radio Protocol for Acoustic Communication on Pipes. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1480537.

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Brown, Alison K., Yan Lu, and Janet Nordlie. Design and Test Results of a Software Defined Radio for Indoor Navigation. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444317.

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Loehner, Henry, Alfonzo Orozco, and Mark Hadley. Secure Software Defined Radio Project: Secure Wireless Systems for the Energy Sector (Briefing 6). Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1772564.

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Lanoue, Matthew J. Next Generation Satellite Communications: Automated Doppler Shift Compensation of PSK-31 Via Software-Defined Radio. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada604772.

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Laguna Sánchez, Gerardo Abel, and Jacobo Sandoval Gutiérrez. Reporte de investigación: Empleo del trans-receptor AD936x y una plataforma SoC, como banco de pruebas, para el desarrollo de aplicaciones Software Defined Radio. División de Ciencias Básicas e Ingeniería, November 2020. http://dx.doi.org/10.24275/uaml.ri.0001.

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