361 research outputs found
Spectrum Sensing Algorithms for Cognitive Radio Applications
Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission.
In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies
Non-uniform sampling and reconstruction of multi-band signals and its application in wideband spectrum sensing of cognitive radio
Sampling theories lie at the heart of signal processing devices and
communication systems. To accommodate high operating rates while retaining low
computational cost, efficient analog-to digital (ADC) converters must be
developed. Many of limitations encountered in current converters are due to a
traditional assumption that the sampling state needs to acquire the data at the
Nyquist rate, corresponding to twice the signal bandwidth. In this thesis a
method of sampling far below the Nyquist rate for sparse spectrum multiband
signals is investigated. The method is called periodic non-uniform sampling,
and it is useful in a variety of applications such as data converters, sensor
array imaging and image compression. Firstly, a model for the sampling system
in the frequency domain is prepared. It relates the Fourier transform of
observed compressed samples with the unknown spectrum of the signal. Next, the
reconstruction process based on the topic of compressed sensing is provided. We
show that the sampling parameters play an important role on the average sample
ratio and the quality of the reconstructed signal. The concept of condition
number and its effect on the reconstructed signal in the presence of noise is
introduced, and a feasible approach for choosing a sample pattern with a low
condition number is given. We distinguish between the cases of known spectrum
and unknown spectrum signals respectively. One of the model parameters is
determined by the signal band locations that in case of unknown spectrum
signals should be estimated from sampled data. Therefore, we applied both
subspace methods and non-linear least square methods for estimation of this
parameter. We also used the information theoretic criteria (Akaike and MDL) and
the exponential fitting test techniques for model order selection in this case
An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition
This thesis introduces an innovative signal detector algorithm in facilitating the
cognitive radio functionality for the new IEEE 802.22 Wireless Regional Area
Networks (WRAN) standard. It is a signal detector based on a Singular Value
Decomposition (SVD) technique that utilizes the eigenvalue of a received signal. The
research started with a review of the current spectrum sensing methods which the
research classifies as the specific, semiblind or blind signal detector. A blind signal detector, which is known as eigenvalue based detection, was found to be the most
desired detector for its detection capabilities, time of execution, and zero a-priori knowledge. The detection algorithm was developed analytically by applying the Signal Detection Theory (SDT) and the Random Matrix Theory (RMT). It was then simulated
using Matlab® to test its performance and compared with similar eigenvalue based
signal detector. There are several techniques in finding eigenvalues. However, this
research considered two techniques known as eigenvalue decomposition (EVD) and
SVD. The research tested the algorithm with a randomly generated signal, simulated
Digital Video Broadcasting-Terrestrial (DVB-T) standard and real captured digital
television signals based on the Advanced Television Systems Committee (ATSC)
standard. The SVD based signal detector was found to be more efficient in detecting
signals without knowing the properties of the transmitted signal. The algorithm is
suitable for the blind spectrum sensing where the properties of the signal to be detected
are unknown. This is also the advantage of the algorithm since any signal would
interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22
connection. Furthermore, the algorithm performed better in the low signal-to-noise
ratio (SNR) environment. In order to use the algorithm effectively, users need to
balance between detection accuracy and execution time. It was found that a higher
number of samples would lead to more accurate detection, but will take longer time.
In contrary, fewer numbers of samples used would result in less accuracy, but faster
execution time. The contributions of this thesis are expected to assist the IEEE
802.22 Standard Working Group, regulatory bodies, network operators and end-users
in bringing broadband access to the rural areas
New challenges in wireless and free space optical communications
AbstractThis manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced
MULTI-OBJECTIVE CHANNEL DECISION FOR ADHOC COGNITIVE RADIO NETWORK
Faithfull detection of non-utilized spectrum hole in available channel is a crucial issue for cognitive radio network. Choosing the best available channel for a secondary user transmission includes settling on decision of accessible choices of free frequency spectrum based on multiple objectives. Thus channel judgment can be demonstrated as several objective decision making (MODM) problem. An ultimate goal of this exploration is to define and execute a technique for multiple objective optimizations of multiple alternative of channel decision in Adhoc cognitive radio network. After a coarse review of an articles related to the multiple objective decision making within a process of channel selection, Multiple Objective Optimization on the basis of the Ratio Analysis (MOORA) technique is taken into consideration. Some important objectives values of non-utilized spectrum collected by a fusion center are proposed as objectives for consideration in the decision of alternatives. MOORA method is applied to a matrix of replies of each channel alternatives to channel objectives which results in set ratios. Among the set of obtained dimensionless ratios, all the channel alternatives are ranked in descending order. In MOORA, channel choices with moderate objectives can top in ranking order, which is hardly conceivable with linearly weighted objectives of the different channel by using different decision making technique
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