288 research outputs found
Sensing of DVB-T signals for white space cognitive radio systems
In cognitive radio networks, systems operating in digital television white spaces are particularly interesting for practical applications. In this paper, we consider single- antenna and multi-antenna spectrum sensing of real DVB-T signals under different channel conditions. Some of the most important algorithms are considered and compared, including energy detection, eigenvalue based techniques and methods exploiting OFDM signal knowledge. The obtained results show the algorithm performance and hierarchy in terms of ROC and detection probability under fixed false alarm rate, for different channel profiles in case of true DVB-T signal
Sensing of DVB-T signals for white space cognitive radio systems
In cognitive radio networks, systems operating in digital television white spaces are particularly interesting for practical applications. In this paper, we consider single- antenna and multi-antenna spectrum sensing of real DVB-T signals under different channel conditions. Some of the most important algorithms are considered and compared, including energy detection, eigenvalue based techniques and methods exploiting OFDM signal knowledge. The obtained results show the algorithm performance and hierarchy in terms of ROC and detection probability under fixed false alarm rate, for different channel profiles in case of true DVB-T signals
Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system
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
Enhanced Spectrum Sensing Techniques for Cognitive Radio Systems
Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems, has received increasing attention and is considered a promising solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Spectrum sensing, which enables CRs to identify spectral holes, is a critical component in CR technology. Furthermore, improving the efficiency of the radio spectrum use through spectrum sensing and dynamic spectrum access (DSA) is one of the emerging trends. In this thesis, we focus on enhanced spectrum sensing techniques that provide performance gains with reduced computational complexity for realistic waveforms considering radio frequency (RF) impairments, such as noise uncertainty and power amplifier (PA) non-linearities.
The first area of study is efficient energy detection (ED) methods for spectrum sensing under non-flat spectral characteristics, which deals with relatively simple methods for improving the detection performance. In realistic communication scenarios, the spectrum of the primary user (PU) is non-flat due to non-ideal frequency responses of the devices and frequency selective channel conditions. Weighting process with fast Fourier transform (FFT) and analysis filter bank (AFB) based multi-band sensing techniques are proposed for overcoming the challenge of non-flat characteristics. Furthermore, a sliding window based spectrum sensing approach is addressed to detect a re-appearing PU that is absent in one time and present in other time. Finally, the area under the receiver operating characteristics curve (AUC) is considered as a single-parameter performance metric and is derived for all the considered scenarios.
The second area of study is reduced complexity energy and eigenvalue based spectrum sensing techniques utilizing frequency selectivity. More specifically, novel spectrum sensing techniques, which have relatively low computational complexity and are capable of providing accurate and robust performance in low signal-to-noise ratio (SNR) with noise uncertainty, as well as in the presence of frequency selectivity, are proposed. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due the primary signal spectrum and/or the transmission channel. The offered results indicate that the proposed methods provide quite significant saving in complexity, e.g., 78% reduction in the studied example case, whereas their detection performance is improved both in the low SNR and under noise uncertainty.
Finally, a new combined spectrum sensing and resource allocation approach for multicarrier radio systems is proposed. The main contribution of this study is the evaluation of the CR performance when using wideband spectrum sensing methods in combination with water-filling and power interference (PI) based resource allocation algorithms in realistic CR scenarios. Different waveforms, such as cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM), enhanced orthogonal frequency division multiplexing (E-OFDM) and filter bank based multicarrier (FBMC), are considered with PA nonlinearity type RF impairments to see the effects of spectral leakage on the spectrum sensing and resource allocation performance. It is shown that AFB based spectrum sensing techniques and FBMC waveforms with excellent spectral containment properties have clearly better performance compared to the traditional FFT based spectrum sensing techniques with the CP-OFDM.
Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. The proposed methods are evaluated under realistic signal models
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Spectrum Sensing in Cognitive Radio Networks
Print ISBN: 978-1-4200-7775-9Today, the creation of new radio access technologies is limited by the shortage of the available radio spectrum. These new technologies are becoming evermore bandwidth de- manding due to their higher rate requirements. Cognitive radio networks and spectrum sensing techniques are a natural way to allow these new technologies to be deployed. In this chapter we will discuss spectrum sensing for cognitive radio networks. We will begin by introducing the subject in Sec. 1.1, providing a brief background followed by a discussion of spectrum sensing motivations and characteristics. Then we will move on to the spectrum sensing problem itself in Sec. 1.2, where we explain the issues that are inherent to spectrum sensing. In Sec. 1.3 we will explore the classical non-cooperative spectrum sensing techniques which form the basis for the more elaborate, cooperative techniques presented in Sec. 1.4. Finally we will close this chapter with some conclusions and open issues
SPECTRUM SENSING AND COOPERATION IN COGNITIVE-OFDM BASED WIRELESS COMMUNICATIONS NETWORKS
The world has witnessed the development of many wireless systems and
applications. In addition to the large number of existing devices, such development of
new and advanced wireless systems increases rapidly the demand for more radio
spectrum. The radio spectrum is a limited natural resource; however, it has been
observed that it is not efficiently utilized. Consequently, different dynamic spectrum
access techniques have been proposed as solutions for such an inefficient use of the
spectrum. Cognitive Radio (CR) is a promising intelligent technology that can identify
the unoccupied portions of spectrum and opportunistically uses those portions with
satisfyingly high capacity and low interference to the primary users (i.e., licensed users).
The CR can be distinguished from the classical radio systems mainly by its awareness
about its surrounding radio frequency environment. The spectrum sensing task is the
main key for such awareness. Due to many advantages, Orthogonal Frequency Division
Multiplexing system (OFDM) has been proposed as a potential candidate for the CR‟s
physical layer. Additionally, the Fast Fourier Transform (FFT) in an OFDM receiver
supports the performance of a wide band spectrum analysis. Multitaper spectrum
estimation method (MTM) is a non-coherent promising spectrum sensing technique. It
tolerates problems related to bad biasing and large variance of power estimates.
This thesis focuses, generally, on the local, multi antenna based, and global
cooperative spectrum sensing techniques at physical layer in OFDM-based CR systems.
It starts with an investigation on the performance of using MTM and MTM with
singular value decomposition in CR networks using simulation. The Optimal MTM
parameters are then found. The optimal MTM based detector theoretical formulae are
derived. Different optimal and suboptimal multi antenna based spectrum sensing
techniques are proposed to improve the local spectrum sensing performance. Finally, a
new concept of cooperative spectrum sensing is introduced, and new strategies are
proposed to optimize the hard cooperative spectrum sensing in CR networks.
The MTM performance is controlled by the half time bandwidth product and
number of tapers. In this thesis, such parameters have been optimized using Monte
Carlo simulation. The binary hypothesis test, here, is developed to ensure that the effect
of choosing optimum MTM parameters is based upon performance evaluation. The
results show how these optimal parameters give the highest performance with minimum
complexity when MTM is used locally at CR.
The optimal MTM based detector has been derived using Neyman-Pearson
criterion. That includes probabilities of detection, false alarm and misses detection
approximate derivations in different wireless environments. The threshold and number
of sensed samples controlling is based on this theoretical work.
In order to improve the local spectrum sensing performance at each CR, in the CR
network, multi antenna spectrum sensing techniques are proposed using MTM and
MTM with singular value decomposition in this thesis. The statistical theoretical
formulae of the proposed techniques are derived including the different probabilities.
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The proposed techniques include optimal, that requires prior information about the
primary user signal, and two suboptimal multi antenna spectrum sensing techniques
having similar performances with different computation complexity; these do not need
prior information about the primary user signalling. The work here includes derivations
for the periodogram multi antenna case.
Finally, in hard cooperative spectrum sensing, the cooperation optimization is
necessary to improve the overall performance, and/or minimize the number of data to be
sent to the main CR-base station. In this thesis, a new optimization method based on
optimizing the number of locally sensed samples at each CR is proposed with two
different strategies. Furthermore, the different factors that affect the hard cooperative
spectrum sensing optimization are investigated and analysed and a new cooperation
scheme in spectrum sensing, the master node, is proposed.Ministry of Interior-Kingdom of Saudi Arabi
Spectrum Sensing in the Presence of Multiple Primary Users
We consider multi-antenna cooperative spectrum sensing in cognitive radio
networks, when there may be multiple primary users. A detector based on the
spherical test is analyzed in such a scenario. Based on the moments of the
distributions involved, simple and accurate analytical formulae for the key
performance metrics of the detector are derived. The false alarm and the
detection probabilities, as well as the detection threshold and Receiver
Operation Characteristics are available in closed form. Simulations are
provided to verify the accuracy of the derived results, and to compare with
other detectors in realistic sensing scenarios.Comment: Accepted in IEEE Transactions on Communication
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