42 research outputs found
CYCLOSTATIONARY FEATURES OF PAL TV AND WIRELESS MICROPHONE FOR COGNITIVE RADIO APPLICATIONS
Frequency spectrum being a scarce resource in communication system design,
spectrum sharing seems to be the solution to an optimal utilization of frequency
spectrum. The traditional fixed frequency allocation is not suitable for futuristic
networks that demand more and more spectrum for new wireless services. Cognitive
radio is a new emerging technology based on spectrum sharing concept.
Spectrum sensing is a vital task in this emerging technology by which it is able to
scan the frequency spectrum to identify the unused spectrum bands and utilize them.
In this thesis, we discuss spectrum sensing in the context of IEEE 802.22 Wireless
Regional Area Network (WRAN). In order to do so, we develop the co-existence
scenario with three cases according to geographical positions of primary services and
secondary service. In WRAN application, the SUs utilize the unused channel in TV
spectrum, which means that the primary users are TV service and other FCC part 74
low power licensed devices. We focus on special case of Analog TV-PAL service and
wireless microphone service as part 74 devices. Before discussing the spectrum
sensing technique, we propose architecture for sensing receiver. The concept of noise
uncertainty is also introduced in this context. The cyclostationarity theory is
introduced and we explain the motivation behind using the theory for spectrum
sensing and the reason that makes the cyclostationary features detector a powerful
detection technique in cognitive radio. We obtain the cyclostationary features of these
primary signals using spectral correlation function. Based on these features, we
develop two algorithms for spectrum sensing and their performances are evaluated in
comparison with energy detector which is considered as the standard simple detector.
Given that the cyclostationary features are unique for a particular signal; these
features can be used for signals classification. In our case, we use those features to
decide if the licensed channel is used by TV service or wireless microphone service.
This provides additional information for spectrum management and power control. Implementation issue is very important in cognitive radio generally and spectrum
sensing specially, hence we discuss the implementation of cyclostationary features
detector and compare its complexity with that of energy detector
CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS
The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing.
The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive.
In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable
CYCLOSTATIONARY FEATURES BASED LOW COMPLEXITY MUTLIRESOLUTION SPECTRUM SENSING FOR COGNITVE RADIO APPLICATIONS
The demand for variety of services using wireless communication has grown remarkably in the past few many years, consequently causing an acute problem of spectrum scarcity. Today, it is one of the most challenging problems in modern wireless communication. To overcome this, the concept of cognitive radio has been proposed and this technology is fast maturing.
The first and foremost function a cognitive radio must do is to sense the spectrum as accurately as possible and do it with least complexity. Among many techniques of spectrum sensing, the Multi-resolution Spectrum Sensing (MRSS) is a popular technique in recent literature. Various multi resolution techniques are used that include wavelet based spectrum estimation and spectral hole detection, wavelet based multi-resolution in analog domain and multi-resolution multiple antenna based detection. However, the basic idea is the same - the total bandwidth is sensed using coarse resolution energy detection, then, fine sensing is applied to the portion of interest. None of these techniques, however, use multi-resolution sensing using cyclostationary features for cognitive radio applications which are more reliable but computationally expensive.
In this thesis, we suggest a cyclostationary features based low complexity multi-resolution spectrum sensing for cognitive radio applications. The proposed technique discussed in this thesis is inspired by the quickness of multi-resolution and the reliability of cyclostationary feature detection. The performance of the proposed scheme is primarily evaluated by its complexity analysis and by determining the minimum signal-to-noise ratio that gives 90% probability of correct classification. Both subjective and objective evaluation show that the proposed scheme is not only superior to the commonly used energy detection method but also to various multi-resolution sensing techniques as it relies on the robustness of cyclostationary feature detection. The results found are encouraging and the proposed algorithms are proved to be not only fast but also more robust and reliable
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
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
CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS
Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets.
Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology.
Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air.
This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation.
CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS
Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets.
Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology.
Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air.
This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation.
Application-aware Cognitive Multi-hop Wireless Networking Testbed and Experiments
In this thesis, we present a new architecture for application-aware cognitive multihop wireless networks (AC-MWN) with testbed implementations and experiments. Cognitive radio is a technique to adaptively use the spectrum so that the resource can be used more efficiently in a low cost way. Multihop wireless networks can be deployed quickly and flexibly without a fixed infrastructure. In presented new architecture, we study backbone routing schemes with network cognition, routing scheme with network coding and spectrum adaptation. A testbed is implemented to test the schemes for AC-MWN. In addition to basic measurements, we implement a video streaming application based on the AC-MWN architecture using cognitive radios. The Testbed consists of three cognitive radios and three Linux laptops equipped with GNU Radio and GStreamer, open source software development toolkit and multimedia framework respectively. Resulting experiments include a range from basic half duplex data to full duplex voice communications and audio/video streaming with spectrum sensing. This testbed is a foundation for a scalable multipurpose testbed that can be used to test such networks as AC-MWN, adhoc, MANET, VANET, and wireless sensor networks. Experiment results demonstrate that the AC-MWN is applicable and valuable for future low-cost and flexible communication networks.
Adviser: Yi Qia
CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS
Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets.
Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology.
Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air.
This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation.