67 research outputs found

    Spectrum Sensing Techniqes in Cognitive Radio: Cyclostationary Method

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    Cognitive Radios promise to be a major shift in wireless communications based on developing a novel approach which attempt to reduce spectrum scarcity that growing up in the past and waited to increase in the future. Since formulating stages for increasing interest in wireless application proves to be extremely challenging, it is growing rapidly. Initially this growth leads to huge demand for the radio spectrum. The novelty of this approach needs to optimize the spectrum utilization and find the efficient way for sharing the radio frequencies through spectrum sensing process. Spectrum sensing is one of the most significant tasks that allow cognitive radio functionality to implement and one of the most challenging tasks. A main challenge in sensing process arises from the fact that, detecting signals with a very low SNR in back ground of noise or severely masked by interference in specific time based on high reliability. This thesis describes the fundamental cognitive radio system aspect based on design and implementation by connecting between the theoretical and practical issue. Efficient method for sensing and detecting are studied and discussed through two fast methods of computing the spectral correlation density function, the FFT Accumulation Method and the Strip Spectral Correlation Algorithm. Several simulations have been performed to show the ability and performance of studied algorithms.fi=OpinnÀytetyö kokotekstinÀ PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=LÀrdomsprov tillgÀngligt som fulltext i PDF-format

    Spectrum sensing for cognitive radio and radar systems

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    The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

    Spektrin Havainnointi Kognitiivisissa Matkaviestinlaitteissa

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    Radio spectrum is becoming a scarce resource as increasing number of wireless devices attempt to access it. As a solution to this issue, spectrum sensing based cognitive radios have been proposed. However, significant part of them are mobile, consumer-grade devices that have strict price and form-factor limitations. This works aims to address the issues related to mobile spectrum sensing by characterizing the non-idealities with a spectrum sensor prototype. Two efficient sensing algorithms and a coarse-fine controller, which aims to minimize the energy consumption and run time of an individual sensor, are implemented on an FPGA. Functionality of the implementations is verified by laboratory and field measurements. Finally, a spatial interpolation method, Kriging, is applied to the non-ideal measurement data to create a uniform radio environment map.Langattomien laitteiden yleistyminen kasvattaa radiospektrin kÀyttöastetta ylÀrajaa kohti. Ratkaisuksi ongelmaan on kehitetty spektrin havainnointiin perustuvat kognitiiviset radiot. NÀistÀ valtaosa on kuitenkin kuluttajatason matkaviestinlaitteita, joilla on tiukat rajoitteet muun muassa hinnan ja fyysisen rakenteen suhteen. TÀssÀ työssÀ perehdytÀÀn spektrin havainnoinnin haasteisiin tutkimalla havainnoinnin epÀideaalisuuksia spektrisensoriprototyypillÀ. TyössÀ on toteutettu FPGA:lle kaksi energiatehokasta havainnointialgoritmia sekÀ karkea-herkkÀ -ohjain, joka pyrkii minimoimaan yksittÀisen spektrisensorin energiakulutusta sekÀ havainnointiaikaa. Toteutettujen algoritmien toiminta ja suorituskyky verifioidaan laboratorio- sekÀ kenttÀmittauksilla. Lopuksi esitellÀÀn avaruudellinen interpolaatiomenetelmÀ, Kriging, jota sovelletaan epÀideaaliseen kenttÀmittausdataan kattavan radiopeitekartan luomiseksi

    Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

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    The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

    Energy-detection based spectrum sensing for cognitive radio on a real-time SDR platform

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    There has been an increase in wireless applications due to the technology boom; consequently raising the level of radio spectrum demand. However, spectrum is a limited resource and cannot be infinitely subdivided to accommodate every application. At the same time, emerging wireless applications require a lot of bandwidth for operation, and have seen exponential growth in their bandwidth usage in recent years. The current spectrum allocation technique, proposed by the Federal Communications Commission (FCC) is a fixed allocation technique. This is inefficient as the spectrum is vacant during times when the primary user is not using the spectrum. This strain on the current available bandwidth has revealed signs of an upcoming spectrum crunch; hence the need to find a solution that satisfies the increasing spectrum demand, without compromising the performance of the applications. This work leverages on cognitive radio technology as a potential solution to the spectrum usage challenge. Cognitive radios have the ability to sense the spectrum and determine the presence or absence of the primary user in a particular subcarrier band. When the spectrum is vacant, a cognitive radio (secondary user) can opportunistically occupy the radio spectrum, optimizing the radio frequency band. The effectiveness of the cognitive radio is determined by the performance of the sensing techniques. Known spectrum-sensing techniques are reviewed, which include energy detection, entropy detection, matched-filter detection, and cyclostationary detection. In this dissertation, the energy sensing technique is examined. A real-time energy detector is developed on the Software-Defined Radio (SDR) testbed that is built with Universal Software Radio Peripheral (USRP) devices, and on the GNU Radio software platform. The noise floor of the system is first analysed to determine the detection threshold, which is obtained using the empirical cumulative distribution method. Simulations are carried out using MATrix LABoratory (MATLAB) to set a benchmark. In both simulations and the SDR development platform, an Orthogonal Frequency Division Multiplexing (OFDM) signal with Quadrature Phase Shift Keying (QPSK) modulation is generated and used as the test signal
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