1) The document proposes cooperative spectrum sensing techniques based on blind detection methods for cognitive radio networks. It studies eigenvalue-based and covariance-based spectrum sensing algorithms that do not require prior knowledge of the primary signal or noise.
2) The algorithms analyze the sample covariance matrix of the received signal to extract test statistics for detecting primary signal presence. Thresholds for the algorithms are determined using statistical theories to achieve desired probabilities of detection and false alarm.
3) Simulations evaluate the performance of the techniques under different conditions and signal types. Results show the proposed method has higher detection probability at low signal-to-noise ratios than maximum eigenvalue detection.
OFFLINE SPIKE DETECTION USING TIME DEPENDENT ENTROPY ijbesjournal
Analysis of the neuronal activities is essential in studying nervous system mechanisms.True interpretation of such mechanisms relies on the detection of the neuronal activities, which appear as action potentials or spikes in recorded neural data. So far several algorithms have been developed for spike detection. In this paper such issue is addressed using entropy measures.Transient events like spikes affect the entropy content of a signal. Thus, a time-dependent entropy framework can be used for spike detection where the entropy of each windowed segment of neural data is computed based on a generalized form of entropy. Detection method is tested on different signal to noise ratios. The results show that the time-dependent entropy method in comparison with available methods enables us to detect spikes in their exact time of occurrence with relatively lower false alarm rate.
Spectrum Sensing Detection Techniques for Overlay UsersIJMTST Journal
Spectrum allocated Agency (FCC) is currently working on the concept of white space users “borrowing” spectrum from free license holders temporarily to improve the spectrum utilization, i.e known as dynamic spectrum access (DSA). CRN systems can utilize dispersed spectrum, and thus such approach is known as dispersed spectrum cognitive radio systems. This project provides a tradeoff between a false alarm probability (Pf) and the signal to noise ratio (SNR) value of any spectrum detector to have a certain performance. Moreover, the performance of the cyclostationary detector (CD) and the matched filter detector (MF) is better than the energy detector(ED) especially at low signal to noise ratio values. Unfortunately, the cyclostationary spectrum sensing method, performance is not satisfying when the wireless fading channels are employed. In this project we provide the best trade off for spectrum usage for over lay users.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional E...IJEACS
The accurate spectrum sensing is a predominant
aspect of any competent CR system. Efficient spectrum sensing
enables a CR terminal to detect the spectrum holes (underutilized
spectral bands) by providing high spectral resolution, thereby
accrediting opportunistic transmission in the licensed band to the
CR. In order to facilitate a good spectrum management and its
efficient use a hybrid method for the detection of the spectrum
with the purpose of detecting the presence of bands of
unoccupied frequencies is proposed. The method used are
traditional energy detection and matched filter with changing
number of secondary users using each technique and finally a
centralized cooperative spectrum sensing network which employs
hard combination at the fusion centre.
Frequency based criterion for distinguishing tonal and noisy spectral componentsCSCJournals
A frequency-based criterion for distinguishing tonal and noisy spectral components is proposed. For considered spectral local maximum two instantaneous frequency estimates are determined and the difference between them is used in order to verify whether component is noisy or tonal. Since one of the estimators was invented specially for this application its properties are deeply examined. The proposed criterion is applied to the stationary and nonstationary sinusoids in order to examine its efficiency.
Performance Analysis of Noise Uncertainty in Energy Detection Spectrum Sensin...Onyebuchi nosiri
Abstract—The Performance of Energy Detection (ED) spectrum sensing technique depends on threshold selected for deciding the presence or absence of Primary User. In practice, noise density is uncertain and can affect the performance of ED in that sometimes presence of signals is confused for their absence (noise) and vice versa. The traditional energy detection algorithm was based on fixed threshold and has been observed to be inefficient under noise uncertainty. The technique requires optimizing the threshold to be more flexible to check the noise uncertainty effects. The paper therefore proposed an algorithm relative to a unique environment which in effect considered the dynamism relatively and dependent on the environment. The results obtained demonstrated significant improvement compared to the traditional energy detection system
Performance Evaluation of Computationally Efficient Energy Detection Based Sp...IJRST Journal
The rapid growth of bandwidth demanding wireless technologies has led to the problem of spectrum scarcity. However, studies show that licensed spectrum is underutilized. Cognitive radio technology promises a solution to the problem by allowing unlicensed users, access to the licensed bands opportunistically. A prime component of the cognitive radio technology is spectrum sensing. Many spectrum sensing techniques have been developed to sense the presence or not of a licensed user. This paper evaluates the performance of the energy detection based spectrum sensing technique in noisy, fading, jamming, interference environments. Both single user detection and cooperative detection situations were investigated. Closed form solutions for the probabilities of detection and false alarm were derived. The analytical results were varied by numerical computations using Monte Carlo method with MATLAB. The performance of the computationally efficient energy detection (CE-ED) techniques were evaluated by use of Receiver Operating Characteristics (ROC) curves over additive white Gaussian noise (AWGN) and fading (Rayleigh & Nakagami-m) channels. Results show that for single user detection, the energy detection technique performs better in AWGN channel than in the fading channel models. The performance of cooperative detection is better than single user detection in fading environments.
OFFLINE SPIKE DETECTION USING TIME DEPENDENT ENTROPY ijbesjournal
Analysis of the neuronal activities is essential in studying nervous system mechanisms.True interpretation of such mechanisms relies on the detection of the neuronal activities, which appear as action potentials or spikes in recorded neural data. So far several algorithms have been developed for spike detection. In this paper such issue is addressed using entropy measures.Transient events like spikes affect the entropy content of a signal. Thus, a time-dependent entropy framework can be used for spike detection where the entropy of each windowed segment of neural data is computed based on a generalized form of entropy. Detection method is tested on different signal to noise ratios. The results show that the time-dependent entropy method in comparison with available methods enables us to detect spikes in their exact time of occurrence with relatively lower false alarm rate.
Spectrum Sensing Detection Techniques for Overlay UsersIJMTST Journal
Spectrum allocated Agency (FCC) is currently working on the concept of white space users “borrowing” spectrum from free license holders temporarily to improve the spectrum utilization, i.e known as dynamic spectrum access (DSA). CRN systems can utilize dispersed spectrum, and thus such approach is known as dispersed spectrum cognitive radio systems. This project provides a tradeoff between a false alarm probability (Pf) and the signal to noise ratio (SNR) value of any spectrum detector to have a certain performance. Moreover, the performance of the cyclostationary detector (CD) and the matched filter detector (MF) is better than the energy detector(ED) especially at low signal to noise ratio values. Unfortunately, the cyclostationary spectrum sensing method, performance is not satisfying when the wireless fading channels are employed. In this project we provide the best trade off for spectrum usage for over lay users.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional E...IJEACS
The accurate spectrum sensing is a predominant
aspect of any competent CR system. Efficient spectrum sensing
enables a CR terminal to detect the spectrum holes (underutilized
spectral bands) by providing high spectral resolution, thereby
accrediting opportunistic transmission in the licensed band to the
CR. In order to facilitate a good spectrum management and its
efficient use a hybrid method for the detection of the spectrum
with the purpose of detecting the presence of bands of
unoccupied frequencies is proposed. The method used are
traditional energy detection and matched filter with changing
number of secondary users using each technique and finally a
centralized cooperative spectrum sensing network which employs
hard combination at the fusion centre.
Frequency based criterion for distinguishing tonal and noisy spectral componentsCSCJournals
A frequency-based criterion for distinguishing tonal and noisy spectral components is proposed. For considered spectral local maximum two instantaneous frequency estimates are determined and the difference between them is used in order to verify whether component is noisy or tonal. Since one of the estimators was invented specially for this application its properties are deeply examined. The proposed criterion is applied to the stationary and nonstationary sinusoids in order to examine its efficiency.
Performance Analysis of Noise Uncertainty in Energy Detection Spectrum Sensin...Onyebuchi nosiri
Abstract—The Performance of Energy Detection (ED) spectrum sensing technique depends on threshold selected for deciding the presence or absence of Primary User. In practice, noise density is uncertain and can affect the performance of ED in that sometimes presence of signals is confused for their absence (noise) and vice versa. The traditional energy detection algorithm was based on fixed threshold and has been observed to be inefficient under noise uncertainty. The technique requires optimizing the threshold to be more flexible to check the noise uncertainty effects. The paper therefore proposed an algorithm relative to a unique environment which in effect considered the dynamism relatively and dependent on the environment. The results obtained demonstrated significant improvement compared to the traditional energy detection system
Performance Evaluation of Computationally Efficient Energy Detection Based Sp...IJRST Journal
The rapid growth of bandwidth demanding wireless technologies has led to the problem of spectrum scarcity. However, studies show that licensed spectrum is underutilized. Cognitive radio technology promises a solution to the problem by allowing unlicensed users, access to the licensed bands opportunistically. A prime component of the cognitive radio technology is spectrum sensing. Many spectrum sensing techniques have been developed to sense the presence or not of a licensed user. This paper evaluates the performance of the energy detection based spectrum sensing technique in noisy, fading, jamming, interference environments. Both single user detection and cooperative detection situations were investigated. Closed form solutions for the probabilities of detection and false alarm were derived. The analytical results were varied by numerical computations using Monte Carlo method with MATLAB. The performance of the computationally efficient energy detection (CE-ED) techniques were evaluated by use of Receiver Operating Characteristics (ROC) curves over additive white Gaussian noise (AWGN) and fading (Rayleigh & Nakagami-m) channels. Results show that for single user detection, the energy detection technique performs better in AWGN channel than in the fading channel models. The performance of cooperative detection is better than single user detection in fading environments.
Single User Eigenvalue Based Detection For Spectrum Sensing In Cognitive Rad...IJMER
Scarcity of spectrum is the issue that wireless communication technology has to deal with.
Primary user is the licensed user of the spectrum. When primary user is idle or not using the spectrum
secondary user can utilize the spectrum. This sharing of spectrum can be enabled by cognitive radio
(CR) technology. The heart of enabling this technology is spectrum sensing. Spectrum sensing involves
detection of primary signal at very low SNR (in the range of -20 dB), under noise and interference
uncertainty. This requirement makes spectrum sensing, a hard nut to crack. Another major issue in
detection is hidden node problem wherein the node in vicinity of primary transmitter also indicates
absence of the primary signal since it is hidden behind the large object. There are various algorithms
for detection viz. energy detection, matched filter detection, feature detection (cyclostationary
detection, eigen-value based detection etc.) These algorithms have limitations of complexity,
requirement of signal knowledge and detection performance. In this paper the performance of
eigenvalue based detection (EBD) method in presence of noise and low SNR of the received signal has
been evaluated for a single user.
An Approach to Spectrum Sensing in Cognitive Radio IOSR Journals
Recent research shows that more than 70% of the available spectrum is not utilized
efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient
utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or
not. The term cognitive radio refers to the adoption of radio parameters using the sensed information
of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is
room for researchers in this field to explore more sophisticated approaches. There are three major
categories of spectrum sensing techniques; transmitter detection, receiver detection and interference
temperature detection. This thesis presents a survey of techniques suggested in the literature for
spectrum sensing with a performance analysis of transmitter-based detection techniques.
A Template Matching Approach to Classification of QAM Modulation using Geneti...CSCJournals
The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real-world. In this paper modulation classification for QAM is performed by Genetic Algorithm followed by Template matching, considering the constellation of the received signal. In addition this classification finds the decision boundary of the signal which is critical information for bit detection. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.
AN ANALYSIS OF THE KALMAN, EXTENDED KALMAN, UNCENTED KALMAN AND PARTICLE FILT...sipij
Tracking the Direction of Arrival (DOA) Estimation of a multiple moving sources is a significant task
which has to be performed in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs)
etc. DOA of the moving source is estimated first, later the estimated DOA using Estimation of Signal
Parameters via Rotational Invariance Technique (ESPRIT) is used as an initial value and will be provided
to any of the Kalman filter (KF), Extended Kalman filter (EKF), Uncented Kalman filter (UKF) and
Particle filter (PF) algorithms to track the moving source based on the motion model governing the motion
of the source. ESPRIT algorithm used for the estimation of the DOA is accurate but computationally
complex. The present comparative study deals with analysis of tracking the DOA Estimation Of Noncoherent,
Narrowband moving sources under different scenarios. The KF (Kalman Filter) is used when the
linear motion model corrupted by Gaussian noise, The Extended Kalman Filter (EKF), an approximated
and non-linear version of the KF is used whenever the motion model is slightly non-linear but corrupted by
Gaussian noise. The process of linearization involves the explicit computation of Jacobian and
approximation using Taylor’s series is computationally complex and expensive. The computationally
complex and expensive procedures of EKF viz explicit computation of Jacobian and approximation using
Taylor series are disadvantageous. In order to minimize the disadvantages of EKF are overcomed by the
usage of UKF, which uses a transform technique viz Unscented Transform to linearize the non-linear
model corrupted by Gaussian noise and Particle Filter (PF) Algorithms are used when the resultant model
is highly non-linear and is corrupted by non-Gaussian noise. Further the literature is concluded with
appropriate findings based on the results of the studies of different algorithms in different scenarios carried
out.
A novel scheme to improve the spectrum sensing performanceIJCNCJournal
Due to limited availability of spectrum for license
d users only, the need for secondary access by unli
censed
users is increasing. Cognitive radio turns out to b
e helping this situation because all that is needed
is a
technique that could efficiently detect the empty s
paces and provide them to the secondary devices wit
hout
causing any interference to the primary (licensed)
users. Spectrum sensing is the foremost function of
the
cognitive radio which senses the environment for wh
ite spaces. Energy detection is one of the various
spectrum sensing techniques that are under research
. Earlier it was shown that energy detection works
better under AWGN channel as compared to Rayleigh c
hannel, however the conventional spectrum sensing
techniques have a high probability of false alarm a
nd also show a better probability of detection for
higher
values of SNR. There is a need for a new technique
that shows a reduced probability of false alarm as
well
as an increase in the probability of detection for
lower values of SNR. In the present work the conven
tional
energy detection technique has been enhanced to get
better results.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES FOR COGNITIVE RADIOijngnjournal
Cognitive radio (CR) is a new paradigm that utilizes the available spectrum band. The key characteristic of CR system is to sense the electromagnetic environment to adapt their operation and dynamically vary its radio operating parameters. The technique of dynamically accessing the unused spectrum band is known as Dynamic Spectrum Access (DSA). The dynamic spectrum access technology helps to minimize unused spectrum bands. In this paper, main functions of Cognitive Radio (CR) i.e. spectrum sensing, spectrum management, spectrum mobility and spectrum sharing are discussed. Then DSA models are discussed along with different methods of DSA such as Command and Control, Exclusive-Use, Shared Use of Primary Licensed User and Commons method. Game-theoretic approach using Bertrand game model, Markovian Queuing Model for spectrum allocation in centralized architecture and Fuzzy logic based method are also discussed and result are shown.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the odd harmonic components of the signal. The algorithm helps to determine both the time and frequency information from the harmonic frequency bands. The comparative study will be done with the input and the results attained from the wavelet transform (WT) for different conditions and Simulation results are given.
Audio Features Based Steganography Detection in WAV Fileijtsrd
Audio signals containing secret information or not is a security issue addressed in the context of steganalysis. ThRainfalle conceptual ide lies in the difference of the distribution of various statistical distance measures between the cover audio signals and stego audio signals. The aim of the propose system is to analyze the audio signal which have the presence of information hiding behavior or not. Mel frequency ceptral coefficient, zero crossing rate, spectral flux and short time energy features of audio signal are extracted, and combine these features with the features extracted from the modified version that is generated by randomly modifying with significant bits. Moreover, the extracted features are detected or classified with a support vector machine in this propose system. Experimental result show that the propose method performs well in steganalysis of the audio stegnograms that are produced by using S tools4. Khin Myo Kyi "Audio Features Based Steganography Detection in WAV File" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26807.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/26807/audio-features-based-steganography-detection-in-wav-file/khin-myo-kyi
Hybrid Spectrum Sensing Method for Cognitive Radio IJECEIAES
With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software.This paper also provides comparative study of various spectrum sensing methodologies.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An Optimized Energy Detection Scheme For Spectrum Sensing In Cognitive RadioIJERD Editor
With rapid growth of wireless devices, the Scarcity of Spectrum resources arises ,due to the improper and inefficient usage of available spectrum band. This problem can be alleviated by Cognitive radio . The major function of the cognitive radio rely on efficient sensing of available spectrum and Spectrum sensing techniques have been used to enhance the detection performance. Among these techniques, Energy detection is considered to be the implemented in practice because of less complexity. In this paper we propose an Adaptive threshold scheme which improves the detection performance under low SNR region. In this paper, noise uncertainty factor is considered wherein the Probability of error is minimized in various SNR regions.
A RESEARCH ON NON COOPERATIVE HYBRID SPECTRUM SENSING TECHNIQUEIAEME Publication
The research designed in this paper is to purpose and implement a Hybrid spectrum sensing technique. As the utilization of wireless devices has been increased, there is a great demand for the radio spectrum .Cognitive Radio is a technology which can sense the spectrum to make the efficient use of resources of spectrum. Sensing of spectrum can be done by using matched filter, energy detection, waveform based detection, cyclostationary feature. Hybrid model is implemented by taking the assumptions for the distance and the SNR value, so it does not require unnecessary time for sensing of every frequency band. Results are formulated on the bases of two parameters probability of false detection and probability of correct detection. The proposed methodology has been implemented in MATLAB and the results obtained are in the form of improvement in Throughput, Energy consumption, Accuracy and improvement in Error.
The proposed model has been found efficient when compared to the other spectrum sensing techniques. It has been proved the effective improvement in throughput is by 9.9135% .Thus the results obtained are excellent and this will definitely help researcher for the future development of Cognitive Radio.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
Localization is one of the most challenging and
important issues in wireless sensor networks (WSNs),
especially if cost effective approaches are demanded. Distance
measurement based on RSSI (Received Signal Strength
Indication) is a low cost and low complexity of the distance
measurement technique, and it is widely applied in the range-
based localization of the WSN. The RSS (Received Signal
Strength) used to estimate the distance between an unknown
node and a number of reference nodes with known co-ordinates.
Location of the target node is then determined by trilateration.
Log-normal shadowing model, can better describe the
relationship between the RSSI value and distance. Non-line
of sight and multipath transmission effects as the indoor
environment, the distance error or ranging error is large. In
this paper, experimental results that are carried out to analyze
the sensitivity of RSSI measurements in an indoor
environment for various power levels are presented. Location
error influenced by distance measure error and network
connectivity is analyzed.
Index Terms—
False Node Recovery Algorithm for a Wireless Sensor NetworkRadita Apriana
This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor
network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm
combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and
more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes
up to 8.7 times, reduces 98.8%, and reduces the rate of energy consumption by approximately 31.1%.
Fundus Image Classification Using Two Dimensional Linear Discriminant Analysi...INFOGAIN PUBLICATION
It is constructed in this study a classification system of diabetic retinopathy fundus image. The system consists of two phases: training and testing. Each stage consists of preprocessing, segmentation, feature extraction and classification. The tested image comes from the MESSIDOR dataset which has a total of 100 images. The number of classes to be classified consists of four classes with each class consists of 25 images. The classes are normal, mild, moderate and severe of Diabetic retinopathy. In this study, the level of preprocessing uses grayscales green channel, Wavelet Haar, Gaussian filter and Contrast Limited Adaptive Histogram Equalization. The level of segmentation uses masking as a process of doing the subtracting operation of between the original image and the masking image. The purpose of the masking is to split between the object and the background. The feature extraction uses Two Dimensional Linear Discriminant Analysis (2DLDA). The classification uses Support Vector Machine (SVM). The test results of some scenarios show that the highest percentage of accuration of the system is up to 90%.
Single User Eigenvalue Based Detection For Spectrum Sensing In Cognitive Rad...IJMER
Scarcity of spectrum is the issue that wireless communication technology has to deal with.
Primary user is the licensed user of the spectrum. When primary user is idle or not using the spectrum
secondary user can utilize the spectrum. This sharing of spectrum can be enabled by cognitive radio
(CR) technology. The heart of enabling this technology is spectrum sensing. Spectrum sensing involves
detection of primary signal at very low SNR (in the range of -20 dB), under noise and interference
uncertainty. This requirement makes spectrum sensing, a hard nut to crack. Another major issue in
detection is hidden node problem wherein the node in vicinity of primary transmitter also indicates
absence of the primary signal since it is hidden behind the large object. There are various algorithms
for detection viz. energy detection, matched filter detection, feature detection (cyclostationary
detection, eigen-value based detection etc.) These algorithms have limitations of complexity,
requirement of signal knowledge and detection performance. In this paper the performance of
eigenvalue based detection (EBD) method in presence of noise and low SNR of the received signal has
been evaluated for a single user.
An Approach to Spectrum Sensing in Cognitive Radio IOSR Journals
Recent research shows that more than 70% of the available spectrum is not utilized
efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient
utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or
not. The term cognitive radio refers to the adoption of radio parameters using the sensed information
of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is
room for researchers in this field to explore more sophisticated approaches. There are three major
categories of spectrum sensing techniques; transmitter detection, receiver detection and interference
temperature detection. This thesis presents a survey of techniques suggested in the literature for
spectrum sensing with a performance analysis of transmitter-based detection techniques.
A Template Matching Approach to Classification of QAM Modulation using Geneti...CSCJournals
The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real-world. In this paper modulation classification for QAM is performed by Genetic Algorithm followed by Template matching, considering the constellation of the received signal. In addition this classification finds the decision boundary of the signal which is critical information for bit detection. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.
AN ANALYSIS OF THE KALMAN, EXTENDED KALMAN, UNCENTED KALMAN AND PARTICLE FILT...sipij
Tracking the Direction of Arrival (DOA) Estimation of a multiple moving sources is a significant task
which has to be performed in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs)
etc. DOA of the moving source is estimated first, later the estimated DOA using Estimation of Signal
Parameters via Rotational Invariance Technique (ESPRIT) is used as an initial value and will be provided
to any of the Kalman filter (KF), Extended Kalman filter (EKF), Uncented Kalman filter (UKF) and
Particle filter (PF) algorithms to track the moving source based on the motion model governing the motion
of the source. ESPRIT algorithm used for the estimation of the DOA is accurate but computationally
complex. The present comparative study deals with analysis of tracking the DOA Estimation Of Noncoherent,
Narrowband moving sources under different scenarios. The KF (Kalman Filter) is used when the
linear motion model corrupted by Gaussian noise, The Extended Kalman Filter (EKF), an approximated
and non-linear version of the KF is used whenever the motion model is slightly non-linear but corrupted by
Gaussian noise. The process of linearization involves the explicit computation of Jacobian and
approximation using Taylor’s series is computationally complex and expensive. The computationally
complex and expensive procedures of EKF viz explicit computation of Jacobian and approximation using
Taylor series are disadvantageous. In order to minimize the disadvantages of EKF are overcomed by the
usage of UKF, which uses a transform technique viz Unscented Transform to linearize the non-linear
model corrupted by Gaussian noise and Particle Filter (PF) Algorithms are used when the resultant model
is highly non-linear and is corrupted by non-Gaussian noise. Further the literature is concluded with
appropriate findings based on the results of the studies of different algorithms in different scenarios carried
out.
A novel scheme to improve the spectrum sensing performanceIJCNCJournal
Due to limited availability of spectrum for license
d users only, the need for secondary access by unli
censed
users is increasing. Cognitive radio turns out to b
e helping this situation because all that is needed
is a
technique that could efficiently detect the empty s
paces and provide them to the secondary devices wit
hout
causing any interference to the primary (licensed)
users. Spectrum sensing is the foremost function of
the
cognitive radio which senses the environment for wh
ite spaces. Energy detection is one of the various
spectrum sensing techniques that are under research
. Earlier it was shown that energy detection works
better under AWGN channel as compared to Rayleigh c
hannel, however the conventional spectrum sensing
techniques have a high probability of false alarm a
nd also show a better probability of detection for
higher
values of SNR. There is a need for a new technique
that shows a reduced probability of false alarm as
well
as an increase in the probability of detection for
lower values of SNR. In the present work the conven
tional
energy detection technique has been enhanced to get
better results.
A SURVEY ON DYNAMIC SPECTRUM ACCESS TECHNIQUES FOR COGNITIVE RADIOijngnjournal
Cognitive radio (CR) is a new paradigm that utilizes the available spectrum band. The key characteristic of CR system is to sense the electromagnetic environment to adapt their operation and dynamically vary its radio operating parameters. The technique of dynamically accessing the unused spectrum band is known as Dynamic Spectrum Access (DSA). The dynamic spectrum access technology helps to minimize unused spectrum bands. In this paper, main functions of Cognitive Radio (CR) i.e. spectrum sensing, spectrum management, spectrum mobility and spectrum sharing are discussed. Then DSA models are discussed along with different methods of DSA such as Command and Control, Exclusive-Use, Shared Use of Primary Licensed User and Commons method. Game-theoretic approach using Bertrand game model, Markovian Queuing Model for spectrum allocation in centralized architecture and Fuzzy logic based method are also discussed and result are shown.
Analysis of harmonics using wavelet techniqueIJECEIAES
This paper develops an approach based on wavelet technique for the estimation of harmonic presents in power system signals. The proposed technique divides the power system signals into different frequency sub-bands corresponding to the odd harmonic components of the signal. The algorithm helps to determine both the time and frequency information from the harmonic frequency bands. The comparative study will be done with the input and the results attained from the wavelet transform (WT) for different conditions and Simulation results are given.
Audio Features Based Steganography Detection in WAV Fileijtsrd
Audio signals containing secret information or not is a security issue addressed in the context of steganalysis. ThRainfalle conceptual ide lies in the difference of the distribution of various statistical distance measures between the cover audio signals and stego audio signals. The aim of the propose system is to analyze the audio signal which have the presence of information hiding behavior or not. Mel frequency ceptral coefficient, zero crossing rate, spectral flux and short time energy features of audio signal are extracted, and combine these features with the features extracted from the modified version that is generated by randomly modifying with significant bits. Moreover, the extracted features are detected or classified with a support vector machine in this propose system. Experimental result show that the propose method performs well in steganalysis of the audio stegnograms that are produced by using S tools4. Khin Myo Kyi "Audio Features Based Steganography Detection in WAV File" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26807.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/26807/audio-features-based-steganography-detection-in-wav-file/khin-myo-kyi
Hybrid Spectrum Sensing Method for Cognitive Radio IJECEIAES
With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software.This paper also provides comparative study of various spectrum sensing methodologies.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An Optimized Energy Detection Scheme For Spectrum Sensing In Cognitive RadioIJERD Editor
With rapid growth of wireless devices, the Scarcity of Spectrum resources arises ,due to the improper and inefficient usage of available spectrum band. This problem can be alleviated by Cognitive radio . The major function of the cognitive radio rely on efficient sensing of available spectrum and Spectrum sensing techniques have been used to enhance the detection performance. Among these techniques, Energy detection is considered to be the implemented in practice because of less complexity. In this paper we propose an Adaptive threshold scheme which improves the detection performance under low SNR region. In this paper, noise uncertainty factor is considered wherein the Probability of error is minimized in various SNR regions.
A RESEARCH ON NON COOPERATIVE HYBRID SPECTRUM SENSING TECHNIQUEIAEME Publication
The research designed in this paper is to purpose and implement a Hybrid spectrum sensing technique. As the utilization of wireless devices has been increased, there is a great demand for the radio spectrum .Cognitive Radio is a technology which can sense the spectrum to make the efficient use of resources of spectrum. Sensing of spectrum can be done by using matched filter, energy detection, waveform based detection, cyclostationary feature. Hybrid model is implemented by taking the assumptions for the distance and the SNR value, so it does not require unnecessary time for sensing of every frequency band. Results are formulated on the bases of two parameters probability of false detection and probability of correct detection. The proposed methodology has been implemented in MATLAB and the results obtained are in the form of improvement in Throughput, Energy consumption, Accuracy and improvement in Error.
The proposed model has been found efficient when compared to the other spectrum sensing techniques. It has been proved the effective improvement in throughput is by 9.9135% .Thus the results obtained are excellent and this will definitely help researcher for the future development of Cognitive Radio.
A New Approach for Error Reduction in Localization for Wireless Sensor Networksidescitation
Localization is one of the most challenging and
important issues in wireless sensor networks (WSNs),
especially if cost effective approaches are demanded. Distance
measurement based on RSSI (Received Signal Strength
Indication) is a low cost and low complexity of the distance
measurement technique, and it is widely applied in the range-
based localization of the WSN. The RSS (Received Signal
Strength) used to estimate the distance between an unknown
node and a number of reference nodes with known co-ordinates.
Location of the target node is then determined by trilateration.
Log-normal shadowing model, can better describe the
relationship between the RSSI value and distance. Non-line
of sight and multipath transmission effects as the indoor
environment, the distance error or ranging error is large. In
this paper, experimental results that are carried out to analyze
the sensitivity of RSSI measurements in an indoor
environment for various power levels are presented. Location
error influenced by distance measure error and network
connectivity is analyzed.
Index Terms—
False Node Recovery Algorithm for a Wireless Sensor NetworkRadita Apriana
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combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and
more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes
up to 8.7 times, reduces 98.8%, and reduces the rate of energy consumption by approximately 31.1%.
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A developer needs to evaluate software performance metrics such as power consumption at an early stage of design phase to make a device or a software efficient especially in real-time embedded systems. Constructing performance models and evaluation techniques of a given system requires a significant effort. This paper presents a framework to bridge between a Functional Modeling Approach such as FSM, UML etc. and an Analytical (Mathematical) Modeling Approach such as Hierarchical Performance Modeling (HPM) as a technique to find the expected average power consumption for different layers of abstractions. A Hierarchical Generic FSM “HGFSM” is developed to be used in order to estimate the expected average power. A case study is presented to illustrate the concepts of how the framework is used to estimate the average power and energy produced.
Performance analysis of cooperative spectrum sensing using double dynamic thr...IAESIJAI
Increased use of wireless technologies and in turn more utilization of available spectrum is subsequently leading to the increasing demand for wireless spectrum. This research work incorporates spectrum sensing detection consisting of a double dynamic threshold followed by cooperative type spectrum sensing. The performance has been analyzed using two modulation schemes, quadrature-amplitude-modulation (QAM) and binary-phase-shift-keying (BPSK). Improved probability of detection has been witnessed using the double dynamic threshold where a comparison of average values of local decision (LD) and the observed value of energy (EO) has been considered instead of using direct values of local decisions and
energy. Further, the probability-of-detection (𝑃𝑑) is found to be better with QAM as compared to the BPSK. From the results, it has been observed that the detection of primary users is also affected by the number of samples. The simulation environment considered for this work is MATLAB and the performance of cooperative spectrum sensing for 500 and 1000 samples with -9db and -12 SNR by considering different false alarm values i. e 0.1, 0.3 and 0.5 has been analyzed. The further scope shall be to enhance the primary user detection by considering different QAM schemes and different signal to noise ratio (SNRs).
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
An Approach to Spectrum Sensing in Cognitive RadioIOSR Journals
Abstract: Recent research shows that more than 70% of the available spectrum is not utilized efficiently. The bandwidth becomes expensive due to a shortage of frequencies. Therefore for efficient utilization of spectrum, we need to sniff the spectrum to determine whether it is being used by primary user or not. The term cognitive radio refers to the adoption of radio parameters using the sensed information of the spectrum. There are various spectrum sensing techniques proposed in the literature but still there is room for researchers in this field to explore more sophisticated approaches. There are three major categories of spectrum sensing techniques; transmitter detection, receiver detection and interference temperature detection. This thesis presents a survey of techniques suggested in the literature for spectrum sensing with a performance analysis of transmitter-based detection techniques. Keywords— Include at least 5 keywords or phrases
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Spectrum Sensing Detection with Sequential Forward Search in Comparison to Kn...IJMTST Journal
FCC is currently working on the concept of white space users “borrowing” spectrum from free license
holders temporarily to improve the spectrum utilization.
This project provides a relation between a Pf and the SNR value of any spectrum detector to have a
certain performance. Previous spectrum sensing detection techniques are only suitable for Low SNR and
are based on signal information values. But these methods are purely narrow band spectrum applications
In order to overcome the above said drawbacks we propose a novel method of spectrum sensing method
and is suitable for low and high SNR values, the sensed spectrum applicable for wide band applications.
Our proposed method does not require signal information at the receiver and channel information, because
this flexibility sensing rate is very high compared to previous techniques.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
Trend removal from raman spectra with local variance estimation and cubic spl...csijjournal
Trend removal is an important problem in most communication systems. Here, we show a proposed
algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and
cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to
remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems
than other techniques that use wavelet transformation to suppress noise.
TREND REMOVAL FROM RAMAN SPECTRA WITH LOCAL VARIANCE ESTIMATION AND CUBIC SPL...csijjournal
Trend removal is an important problem in most communication systems. Here, we show a proposed algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems than other techniques that use wavelet transformation to suppress noise
Trend Removal from Raman Spectra With Local Variance Estimation and Cubic Spl...csijjournal
Abstract
Trend removal is an important problem in most communication systems. Here, we show a proposed algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems than other techniques that use wavelet transformation to suppress noise.
Keywords
Raman spectroscopy, Background correction method, Local variance, Cubic spline interpolation.
TREND REMOVAL FROM RAMAN SPECTRA WITH LOCAL VARIANCE ESTIMATION AND CUBIC SPL...csijjournal
Trend removal is an important problem in most communication systems. Here, we show a proposed algorithm for trend (background) removal from Raman Spectra by merging local variance estimation and cubic spline interpolation methods. We found that Raman spectrum does not need a smoothing process to remove trend from noisy signals. Employing this technique results in more speedy and noiseless systems than other techniques that use wavelet transformation to suppress noise.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
De-Noising Corrupted ECG Signals By Empirical Mode Decomposition (EMD) With A...IOSR Journals
The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio (SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The results show that the developed algorithm is a reasonable one to de-noise ECG signals.
Three Element Beam forming Algorithm with Reduced Interference Effect in Sign...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
A linear-Discriminant-Analysis-Based Approach to Enhance the Performance of F...CSCJournals
Spike sorting is of prime importance in neurophysiology and hence has received considerable attention. However, conventional methods suffer from the degradation of clustering results in the presence of high levels of noise contamination. This paper presents a scheme for taking advantage of automatic clustering and enhancing the feature extraction efficiency, especially for low-SNR spike data. The method employs linear discriminant analysis based on a fuzzy c-means (FCM) algorithm. Simulated spike data [1] were used as the test bed due to better a priori knowledge of the spike signals. Application to both high and low signal-to-noise ratio (SNR) data showed that the proposed method outperforms conventional principal-component analysis (PCA) and FCM algorithm. FCM failed to cluster spikes for low-SNR data. For two discriminative performance indices based on Fisher's discriminant criterion, the proposed approach was over 1.36 times the ratio of between- and within-class variation of PCA for spike data with SNR ranging from 1.5 to 4.5 dB. In conclusion, the proposed scheme is unsupervised and can enhance the performance of fuzzy c-means clustering in spike sorting with low-SNR data.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Author: Robbie Edward Sayers
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(C) 2024 Robbie E. Sayers
Cooperative Spectrum Sensing Technique Based on Blind Detection Method
1. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-10, Oct- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 1772
Cooperative Spectrum Sensing Technique Based
on Blind Detection Method
B. Vijayalakshmi1
, P. Siddaiah2
1
GVP College of Engineering for Women, Visakhapatnam, India,
2
Acharya Nagarjuna University (ANU), Guntur, India
Abstract— Spectrum sensing is a key task for cognitive
radio. Our motivation is to increase the probability of
detection of spectrum sensing in cognitive radio. The
spectrum-sensing algorithms are proposed based on the
statistical methods like EVD,CVD of a covariance matrix.
In this Two test statistics are then extracted from the sample
covariance matrix. The decision on the signal presence is
made by comparing the two test statistics.The Detection
probability and the associated threshold are found based on
the statistical theory. In this paper, we study the
collaborative sensing as a means to improve the
performance of the proposed spectrum sensing technique
and show their effect on cooperative cognitive radio
network. Simulations results and performances evaluation
are done in Matlab and the results are tabulated.
Keywords— Cooperative Spectrum, Cognitive radio,
Spectrum Sensing, Eigenvalue-based Detection.
I. INTRODUCTION
The electromagnetic spectrum comprises of frequency
spectrum with varied bandwidths. The radio frequency
spectrum involves electromagnetic radiation with
frequencies between 300 Hz to 3000 GHz. The use of
electromagnetic spectrum is licensed by governments for
wireless and communication technologies. Spectrum
scarcity is the main problem as the demand for additional
bandwidth is going to increase. Measurement studies have
shown that the licensed spectrum is relatively unused across
many time and frequency slots. The Federal
Communications Commission (FCC) published a report
prepared by Spectrum Policy Task Force (SPTF) This
report indicates that most of the allotted channels are not in
use most of the time and some are partially occupied while
others are used most of the time. One of the most important
and recommended solution for the problem of spectrum
scarcity is cognitive radio (CR) as described by Joseph
Mitola in his doctoral dissertation Cognitive radio
technology is considered as the best solution because of its
ability to rapidly and autonomously adapt operating
parameters to changing requirements and conditions Main
functions of cognitive radio are spectrum sensing, spectrum
management, spectrum mobility and spectrum sharing.
Spectrum sensing detects the unused spectrum. There are
several spectrum sensing techniques that were proposed for
cognitive radio. These techniques are mainly categorized
into two:
1) Blind sensing techniques
2) Signal specific sensing techniques
While the blind sensing techniques don’t need any prior
knowledge about the transmitted signal, signal specific
sensing techniques need some information about the
features of the signal such as carrier frequency, symbol
period, modulation type, etc. This classification leads to
decide whether one of these choices best fit the CR.The
method does not need channel and signal information as
prior knowledge. has better performance compared with
eigenvalue without noise power. The proposed method has
a higher probability of detection at low SNR compared with
Maximum eigenvalue. In this paper a new scheme of the
algorithms are implemented using random matrix theories
(RMT) which produce accurate results. The sensing based
on the concept of sample covariance matrix and eigenvalues
is proposed. The ratios of distributions and probabilities of
detection (Pd) and the probabilities of false alarm (Pfa) are
calculated for the proposed algorithms. Thresholds values
for given Pfa are also established Also several simulations
are done based on the sample covariance matrix we extract
the test statistics and compare the results..
The rest of this paper is organized as follows: The detection
algorithms and in Section II and Section III gives the
performance analysis and finds thresholds for the
algorithms. Simulation results for various types of signals
are given in Section IV. Conclusions are drawn in Section
V.
2. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-10, Oct- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 1773
II. SPECTRUM SENSING BASED ON
STATISTICAL MODEL
The system comprises of a receiver/detector with an
antenna which is connected to signal processing unit to
process the signal. The received signal is sent to the
processing unit by an antenna. For detecting the signal, we
have used hypothesis testing. There are two hypothesis
namely H0 or null hypothesis and H1 or alternate
hypothesis. H0 is the representation for signal which is not
present or the signal which only has noise. H1 is the
representation where both signal and noise are present at
same time. The probability of detection and the probability
of false alarm are important for channel sensing. Probability
of false alarm(Pfa) describes the presence of the primary
user at the hypothesis H0 where as probability detection(Pd)
describes the presence of the primary user signal at the
hypothesis H1.Blind Signal Processing is one of the
spectrum sensing approaches which do not depend on the
information of the primary signal or noise and hence they
are called as blind detection schemes. Two of the most
popular blind detection schemes are the following :
• Covariance based spectrum sensing
• Eigen value based spectrum sensing
III. COVARIANCE BASED SPECTRUM
SENSING
Let xc(t) = sc(t) + ηc(t) be the continuous-time received
signal, where sc(t) is the possible primary user’s signal and
ηc(t) is the noise. ηc(t) is assumed to be a stationary process
satisfying
E(ηc(t)) = 0, E(η2c (t)) = σ2η, and
E(ηc(t)ηc(t +τ )) = 0 for any τ _= 0.
We are interested in the frequency band with central
frequency fc and bandwidth W. We sample the received
signal at a sampling rate fs, where fs ≥ W. Let Ts = 1/fs be
the sampling period. For notation simplicity, we define x(n)
Δ= xc(nTs), s(n) Δ= sc(nTs), and η(n) Δ= ηc(nTs).
There are two hypotheses:
1) H0, i.e., the signal does not exist, and
2) H1, i.e., the signal exists.
The received signal samples under the two hypotheses are
given by
H0 : x(n) =η(n) (1)
H1 : x(n) =s(n) + η(n) (2)
respectively, where s(n) is the transmitted signal samples
that passed through a wireless channel consisting of path
loss, multi path fading, and time dispersion effects; and η(n)
is the white noise, which is having mean zero and variance
σ2η. s(n) can be the superposition of the received signals
from multiple primary users. No synchronization is needed
here. diagonal elements of Rx should be nonzeros.
3.1 Covariance Based Spectrum Sensing Algorithm
outline
Step 1 : The received signal is sampled, as described above.
Step 2 : Choose a smoothing factor L and a threshold γ1,
where γ1 should be chosen to meet the requirement for the
probability of false alarm.
Step 3: Compute the autocorrelations of the received signal
λ(l), l = 0, 1, L − 1, and form the sample covariance matrix.
Step 4; Compute rnn(Ns)) where rnm(Ns) are the elements
of the sample covariance matrix ˆrx(Ns). Step 5: Determine
the presence of the signal based on T1(Ns), T2(Ns), and
threshold γ1. That is, if T1(Ns)/T2(Ns) > γ1, the signal
exists; otherwise, the signal does not exist.
3.2 Eigen value based detection Eigen values are scalar
values called lambda (λ) of a square matrix A, if there is a
nontrivial solution of a vector x called eigen vector such
that: (A -λI) x=0 Or (A- λI) =0. The idea of Eigen values is
used in signal. Detection is to find the noise in signal
samples by finding the correlation between samples. Ideally
noise samples are uncorrelated with each other. When there
is no signal, the received signal covariance matrix become
identity matrix multiply by noise power (2I) which results
all Eigen values of the matrix become same as noise
power.The main advantage of Eigen value based technique
is that it does not require any prior information of the PU’s
signal and it outperforms Energy detection techniques,
especially in the presence of noise covariance uncertainty.
3.3 Eigen value Detection Algorithms:
3.3.1 Maximum-Minimum Eigen value (MME)
Detection:
The algorithm steps for this detection method is as follow:
Step 1: Covariance Matrix of the received signal is
calculated
Step 2: Maximum and Minimum Eigen values of the Matrix
(λmax, λmin) are computed
Step 3: Decision: If λmax/λmin >γ1 “Signal exists”else
“Signal doesn’t exit”whereγ1 Threshold value for MME
Threshold value (γ) is set using Tracy-widom distribution
which is adaptive technique to set the threshold and it is
given by
3.3.2 Energy with Minimum Eigen value (EME)
detection:
3. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol
Infogain Publication (Infogainpublication.com
www.ijaems.com
The algorithm steps for this detection method is as follow:
Step 1: Covariance Matrix of the received signal is
calculated
Step 2: Average power Pavgof received matrix is calculated
Step 3: Minimum Eigen values of the Matrix (
computed
Step 4: Decision: If Pavg/λmin >γ2 “Signal exists”
else “Signal doesn’t exit” whereγ2 Threshold value
EME
Threshold value (γ2) is set using yamma distribution which
is adaptive technique to set the threshold and it is given by
3.5Cooperative Spectrum Sensing
In data combination method, each SU make local decision
based on its observed values compared with the chosen
threshold, and then forward the local decision, denoted
Di∈{0,1},to the FC to identify the PU is present or not.
There are usually three combination method based on the
decisions come from different cooperative users, such as
OR rule, AND rule and K-out-of-N rule.For OR rule, if
there just one SU to identify that the PU is active, the FC
will declare the PU active. Thus the cooperative probability
of detection Qd and probability of false alarm
where Pd,iPf,i are the SU local probability of detection and
probability of false alarm, N is the number of cooperative
users.
Assume each SU achieves identical Pd and P
spectrum sensing (i.e., Pd=Pd,i ,Pf=Pf,i
cooperative probability of detection and probab
alarm are
The cooperative missing probability is
Where Pm is the missing probability of local sensing user.
3.5.1 AND Rule:
AND rule is just opposite to OR rule, in which the FC will
declare the PU active only when all cooperative users
International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol
Infogainpublication.com)
The algorithm steps for this detection method is as follow:
received signal is
of received matrix is calculated
Step 3: Minimum Eigen values of the Matrix (λmin) is
2 “Signal exists”
2 Threshold value for
2) is set using yamma distribution which
is adaptive technique to set the threshold and it is given by:
In data combination method, each SU make local decision
d with the chosen
threshold, and then forward the local decision, denoted
{0,1},to the FC to identify the PU is present or not.
There are usually three combination method based on the
decisions come from different cooperative users, such as
N rule.For OR rule, if
there just one SU to identify that the PU is active, the FC
will declare the PU active. Thus the cooperative probability
of detection Qd and probability of false alarm Qfare
probability of detection and
probability of false alarm, N is the number of cooperative
and Pf in the local
i=1,2,…N). The
and probability of false
is the missing probability of local sensing user.
AND rule is just opposite to OR rule, in which the FC will
declare the PU active only when all cooperative users
identify that the PU is present. Qd and Qf under AND rule
are written as follows.
3.5.2 K-out-of-N rule :
K-out-of-N rule is a trade
rule. In this rule, when more than K users show that the PU
is active, the final decision of cooperative sensing is that the
channel is occupied. So under K
Qf are
The cooperative missing probability is
Where Pm is the missing probability of local sensing user
IV. SIMULATION RESULTS
Covariance based detection
Table for covariance based detection
No of
samples
Matrix
size
T1
32 8x8 2.7297
64 2x2 2.6000
64 8x8 2.8028
512 8x8 2.8101
1024 4x4 2.7391
1024 8x8 2.8511
International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-10, Oct- 2016]
) ISSN: 2454-1311
Page | 1774
identify that the PU is present. Qd and Qf under AND rule
N rule is a trade-off between OR rule and AND
rule. In this rule, when more than K users show that the PU
is active, the final decision of cooperative sensing is that the
channel is occupied. So under K-out-of-N rule, the Qd and
The cooperative missing probability is
is the missing probability of local sensing user
SIMULATION RESULTS
ovariance based detection
Table for covariance based detection
1/T2 Threshold Signal
status
2.7297 9.5008 Signal
not
present
2.6000 2.1455 Signal
present
2.8028 4.6613 Signal
not
present
2.8101 1.6718 Signal
present
2.7391 1.3028 Signal
present
2.8511 1.4446 Signal
present
4. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-10, Oct- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 1775
EME:
Table for maximum minimum eigen value method
No of
samples
SNR
(dB )
λmax/λ
min
Thresho
ld(γ1)
Signal status
32 35 -
1.7862e
+018
1.7025 Signal not
present
1024 35 5.7361e
+015
0.2684 Signal
present
1024 200 -
2.2698e
+016
0.1157 Signal not
present
MME:
Table for energy with minimum eigen value method
Fig 7.5 MME: PD V/S PF for different SNR value
Fig 7.6: COOPERATIVE: PM V/S PF for single SNR value
Fig. 7.7: COOPERATIVE: PM V/S PF for different SNR
value
Fig.7.8: COOPERATIVE: PD V/S PF (AND, OR FUSION
TECHNIQUE
V. CONCLUSIONS
In this paper, several blind spectrum sensing methods based
on dimension analysis is explained in detail. Specifically,
collaborative sensing is considered as a solution to
problems in the presented sensing method.The sensing
detector of spectrum space create new opportunities and
challenges for this type of cooperative spectrum sensing
while it solves some of the traditional problems .We also
propose a new eigenvalue spectrum sensing algorithm based
on covariance matrix. The ratio of the minimum eigenvalue
No of
sampl
es
SNR(d
B )
λmax/λmi
n
Threshold(
γ1)
Signal status
32 35 -
1.7862e+0
18
1.7025 Signal not
present
1024 35 5.7361e+0
15
0.2684 Signal
present
1024 200 -
2.2698e+0
16
0.1157 Signal not
present
512 25 1.0317e+0
16
0.3633 Signal
present
5. International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-10, Oct- 2016]
Infogain Publication (Infogainpublication.com) ISSN: 2454-1311
www.ijaems.com Page | 1776
to noise power is used as test statistic the method need only
noise power. The proposed method is better than maximum
eigenvalue detection and the energy detection for correlated
signals. By use of several parameters we have performed
simulations investigated the detection time of the analysis.
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