Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
In the last few years Compressed Sampling (CS) has been well used in the area of signal processing and image compression. Recently, CS has been earning a great interest in the area of wireless communication networks. CS exploits the sparsity of the signal processed for digital acquisition to reduce the number of measurement, which leads to reductions in the size, power consumption, processing time and processing cost. This article presents application of CS for the spectrum sensing and channel estimation in Cognitive Radio (CR) networks. Basic approach of CS is introduced first, and then scheme for spectrum sensing and channel estimation for CR is discussed. First, fast and efficient compressed spectrum sensing (CSS) scheme is proposed to detect wideband spectrum, where samples are taken at sub-Nyquist rate and signal acquisition is terminated automatically once the samples are sufficient for the best spectral recovery and then, after the spectrum sensing, in the second phase notion of multipath sparsity is formalized and a novel approach based on Orthogonal Matching Pursuit (OMP) is discussed to estimate sparse multipath channels for CR networks. The effectiveness of the proposed scheme is demonstrated through comparisons with the existing conventional spectrum sensing and channel estimation methods.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
In the last few years Compressed Sampling (CS) has been well used in the area of signal processing and image compression. Recently, CS has been earning a great interest in the area of wireless communication networks. CS exploits the sparsity of the signal processed for digital acquisition to reduce the number of measurement, which leads to reductions in the size, power consumption, processing time and processing cost. This article presents application of CS for the spectrum sensing and channel estimation in Cognitive Radio (CR) networks. Basic approach of CS is introduced first, and then scheme for spectrum sensing and channel estimation for CR is discussed. First, fast and efficient compressed spectrum sensing (CSS) scheme is proposed to detect wideband spectrum, where samples are taken at sub-Nyquist rate and signal acquisition is terminated automatically once the samples are sufficient for the best spectral recovery and then, after the spectrum sensing, in the second phase notion of multipath sparsity is formalized and a novel approach based on Orthogonal Matching Pursuit (OMP) is discussed to estimate sparse multipath channels for CR networks. The effectiveness of the proposed scheme is demonstrated through comparisons with the existing conventional spectrum sensing and channel estimation methods.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Performance of cognitive radio networks with maximal ratio combining over cor...Polytechnique Montreal
In this paper, we apply the maximal ratio combining (MRC) technique to achieve higher detection probability in cognitive radio networks over correlated Rayleigh fading channels. We present a simple approach to derive the probability of detection in closed-form expression. The numerical results reveal that the detection performance is a monotonically increasing function with respect to the number of antennas. Moreover, we provide sets of complementary receiver operating characteristic (ROC) curves to illustrate the effect of antenna correlation on the sensing performance of cognitive radio networks employing MRC schemes in some respective scenarios.
A Subspace Method for Blind Channel Estimation in CP-free OFDM SystemsCSCJournals
In this paper, a subspace method is proposed for blind channel estimation in orthogonal frequency-division multiplexing (OFDM) systems over time-dispersive channel. The proposed method does not require a cyclic prefix (CP) and thus leading to higher spectral efficiency. By exploiting the block Toeplitz structure of the channel matrix, the proposed blind estimation method performs satisfactorily with very few received OFDM blocks. Numerical simulations demonstrate the superior performance of the proposed algorithm over methods reported earlier in the literature.
A Scheme for Joint Signal Reconstruction in Wireless Multimedia Sensor Networksijma
In context aware wireless multimedia sensor networks, scenarios are usually such that
signals of multiple distributed sensors contain a common sparse component and each individual
signal owns an innovation sparse component. So distributed compressive sensing based on joint
sparsity of a signal ensemble concept exploits both these intra- and inter- signal correlation structures
and compress signals to the extent possible. This paper proposes an optimized reconstruction
scheme based on joint sparsity model which is derived from the distributed compressive sensing. In
this regard, based on distributed compressive sensing, a joint reconstruction scheme is proposed to
compress and reconstruct ensemble of signals even in large scale data transmission. Furthermore,
simulation results show the effectiveness of the proposed method in diverse compression ratios and
processing times in comparison with the joint sparsity model and individual compressive sensing
reconstruction methods.
Mimo radar detection in compound gaussian clutter using orthogonal discrete f...ijma
This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for
Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed
orthogonal waveforms are designed considering the position and angle of the transmitting antenna when
viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized
Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better
detection performance in spikier Clutter.
Matrix Padding Method for Sparse Signal ReconstructionCSCJournals
Compressive sensing has been evolved as a very useful technique for sparse reconstruction of signals that are sampled at sub-Nyquist rates. Compressive sensing helps to reconstruct the signals from few linear projections of the sparse signal. This paper presents a technique for the sparse signal reconstruction by padding the compression matrix for solving the underdetermined system of simultaneous linear equations, followed by an iterative least mean square approximation. The performance of this method has been compared with the widely used compressive sensing recovery algorithms such as l1_ls, l1-magic, YALL1, Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit, etc.. The sounds generated by 3-blade engine, music, speech, etc. have been used to validate and compare the performance of the proposed technique with the other existing compressive sensing algorithms in ideal and noisy environments. The proposed technique is found to have outperformed the l1_ls, l1-magic, YALL1, OMP, CoSaMP, etc. as elucidated in the results.
Improved Timing Estimation Using Iterative Normalization Technique for OFDM S...IJECEIAES
Conventional timing estimation schemes based on autocorrelation experience perfor- mance degradation in the multipath channel environment with high delay spread. To overcome this problem, we proposed an improvement of the timing estimation for the OFDM system based on statistical change of symmetrical correlator. The new method uses iterative normalization technique to the correlator output before the detection based on statistical change of symmetric correlator is applied. Thus, it increases the detection probability and achieves better performance than previously published methods in the multipath environment. Computer simulation shows that our method is very robust in the fading multipath channel.
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.
Reduction of Azimuth Uncertainties in SAR Images Using Selective RestorationIJTET Journal
Abstract— A framework is proposed for reduction of azimuth uncertainty space borne strip map synthetic aperture radar (SAR) images. In this paper, the azimuth uncertainty in SAR images is identified by using a local average SAR image, system parameter, and a distinct metric derived from azimuth antenna pattern. The distinct metric helps isolate targets lying at locations of uncertainty. The method for restoration of uncertainty regions is selected on the basis of the size of uncertainty regions. A compressive imaging technique is engaged to bring back isolated ambiguity regions (smaller regions of interrelated pixels), clustered regions (relatively bigger regions of interrelated pixels) are filled by using exemplar-based in-painting. The recreation results on a real Terra SAR-X data set established that the proposed method can effectively remove azimuth uncertainties and enhance SAR image quality.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
On Channel Estimation of OFDM-BPSK and -QPSK over Nakagami-m Fading ChannelsCSCJournals
This paper evaluates the performance of OFDM - BPSK & -QPSK based system with and without channel estimation over Nakagami-m fading channels. Nakagami-m variants are generated by decomposition of Nakagami random variable into orthogonal random variables with Gaussian distribution envelopes. Performance of OFDM system in Nakagami channel has been reported here. The results yield the optimum value of m based on BER and SNR. Using this optimum value of m, Channel estimation over flat fading has been reported here. It has been depicted clearly from simulated graphs that channel estimation has further reduced the BER. However, threshold value of m has played a vital role during channel estimation.
Performance of cognitive radio networks with maximal ratio combining over cor...Polytechnique Montreal
In this paper, we apply the maximal ratio combining (MRC) technique to achieve higher detection probability in cognitive radio networks over correlated Rayleigh fading channels. We present a simple approach to derive the probability of detection in closed-form expression. The numerical results reveal that the detection performance is a monotonically increasing function with respect to the number of antennas. Moreover, we provide sets of complementary receiver operating characteristic (ROC) curves to illustrate the effect of antenna correlation on the sensing performance of cognitive radio networks employing MRC schemes in some respective scenarios.
A Subspace Method for Blind Channel Estimation in CP-free OFDM SystemsCSCJournals
In this paper, a subspace method is proposed for blind channel estimation in orthogonal frequency-division multiplexing (OFDM) systems over time-dispersive channel. The proposed method does not require a cyclic prefix (CP) and thus leading to higher spectral efficiency. By exploiting the block Toeplitz structure of the channel matrix, the proposed blind estimation method performs satisfactorily with very few received OFDM blocks. Numerical simulations demonstrate the superior performance of the proposed algorithm over methods reported earlier in the literature.
A Scheme for Joint Signal Reconstruction in Wireless Multimedia Sensor Networksijma
In context aware wireless multimedia sensor networks, scenarios are usually such that
signals of multiple distributed sensors contain a common sparse component and each individual
signal owns an innovation sparse component. So distributed compressive sensing based on joint
sparsity of a signal ensemble concept exploits both these intra- and inter- signal correlation structures
and compress signals to the extent possible. This paper proposes an optimized reconstruction
scheme based on joint sparsity model which is derived from the distributed compressive sensing. In
this regard, based on distributed compressive sensing, a joint reconstruction scheme is proposed to
compress and reconstruct ensemble of signals even in large scale data transmission. Furthermore,
simulation results show the effectiveness of the proposed method in diverse compression ratios and
processing times in comparison with the joint sparsity model and individual compressive sensing
reconstruction methods.
Mimo radar detection in compound gaussian clutter using orthogonal discrete f...ijma
This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for
Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed
orthogonal waveforms are designed considering the position and angle of the transmitting antenna when
viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized
Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better
detection performance in spikier Clutter.
Matrix Padding Method for Sparse Signal ReconstructionCSCJournals
Compressive sensing has been evolved as a very useful technique for sparse reconstruction of signals that are sampled at sub-Nyquist rates. Compressive sensing helps to reconstruct the signals from few linear projections of the sparse signal. This paper presents a technique for the sparse signal reconstruction by padding the compression matrix for solving the underdetermined system of simultaneous linear equations, followed by an iterative least mean square approximation. The performance of this method has been compared with the widely used compressive sensing recovery algorithms such as l1_ls, l1-magic, YALL1, Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit, etc.. The sounds generated by 3-blade engine, music, speech, etc. have been used to validate and compare the performance of the proposed technique with the other existing compressive sensing algorithms in ideal and noisy environments. The proposed technique is found to have outperformed the l1_ls, l1-magic, YALL1, OMP, CoSaMP, etc. as elucidated in the results.
Improved Timing Estimation Using Iterative Normalization Technique for OFDM S...IJECEIAES
Conventional timing estimation schemes based on autocorrelation experience perfor- mance degradation in the multipath channel environment with high delay spread. To overcome this problem, we proposed an improvement of the timing estimation for the OFDM system based on statistical change of symmetrical correlator. The new method uses iterative normalization technique to the correlator output before the detection based on statistical change of symmetric correlator is applied. Thus, it increases the detection probability and achieves better performance than previously published methods in the multipath environment. Computer simulation shows that our method is very robust in the fading multipath channel.
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.
Reduction of Azimuth Uncertainties in SAR Images Using Selective RestorationIJTET Journal
Abstract— A framework is proposed for reduction of azimuth uncertainty space borne strip map synthetic aperture radar (SAR) images. In this paper, the azimuth uncertainty in SAR images is identified by using a local average SAR image, system parameter, and a distinct metric derived from azimuth antenna pattern. The distinct metric helps isolate targets lying at locations of uncertainty. The method for restoration of uncertainty regions is selected on the basis of the size of uncertainty regions. A compressive imaging technique is engaged to bring back isolated ambiguity regions (smaller regions of interrelated pixels), clustered regions (relatively bigger regions of interrelated pixels) are filled by using exemplar-based in-painting. The recreation results on a real Terra SAR-X data set established that the proposed method can effectively remove azimuth uncertainties and enhance SAR image quality.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
On Channel Estimation of OFDM-BPSK and -QPSK over Nakagami-m Fading ChannelsCSCJournals
This paper evaluates the performance of OFDM - BPSK & -QPSK based system with and without channel estimation over Nakagami-m fading channels. Nakagami-m variants are generated by decomposition of Nakagami random variable into orthogonal random variables with Gaussian distribution envelopes. Performance of OFDM system in Nakagami channel has been reported here. The results yield the optimum value of m based on BER and SNR. Using this optimum value of m, Channel estimation over flat fading has been reported here. It has been depicted clearly from simulated graphs that channel estimation has further reduced the BER. However, threshold value of m has played a vital role during channel estimation.
- Compressive sensing (CS) theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use
- CS relies on two principle :
sparsity: which pertains to the signal of interest
In coherence : which pertains to the sensing modality
Performance optimization of hybrid fusion cluster based cooperative spectrum ...Ayman El-Saleh
This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com
Cognitive Radio: When might it Become Economically and Technically Feasible? Jeffrey Funk
My Master's students use ideas from my (Jeff Funk) forthcoming book (Technology Change and the Rise of New Industries) to analyze the economic and technical feasibility of cognitive radio. See my other slides for details on concepts, methodology, and other new industries.
CR technology is based on the fact that the licensed systems (also named primary systems PS) are not always using their spectrum bands; CR brings new radio types—cognitive radios—that should firstly, identify the existing spectrum holes, and secondly, utilize them according to an access.
An overview of cognitive radio, comparison of cognitive radio vs. conventional radio, real-world applications for cognitive radio networks, how cognitive radios improve spectrum efficiency and address the wireless spectrum shortage.
Many algorithms have been developed to find sparse representation over redundant dictionaries or
transform. This paper presents a novel method on compressive sensing (CS)-based image compression
using sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the three levels of
wavelet transform coefficients of the input image for compressive sampling. We have used three different
measurement matrix as Gaussian matrix, Bernoulli measurement matrix and random orthogonal matrix.
The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct each level of
wavelet transform separately. Experimental results demonstrate that the proposed method given better
quality of compressed image than existing methods in terms of proposed image quality evaluation indexes
and other objective (PSNR/UIQI/SSIM) measurements.
CS Based Channel Estimation for OFDM Systems under Long Delay Channels Using ...IJERA Editor
Orthogonal frequency division multiplexing (OFDM) is a technique which are used in the next-generation wireless communication. Channel estimation in the OFDM technique is one of the big challenges, ever since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. Channel computation using superimposed pilot sequences is also a fully new area, idea for using superimposed pilot sequences has been proposed by various authors for different applications. In this paper, we are introduced a high accurate, low complexity compressive sensing (CS) based channel estimation namely Auxiliary information based Subspace Pursuit (ASP) in TFT-OFDM systems. ASP based channel estimation in TFT-OFDM system is based on two steps. First is, by exploiting the signal structure of recently proposed TDM-OFDM scheme, the supporting channel information is obtained. Second is, we propose the supporting information based subspace pursuit (SP) algorithm to use a very small amount of frequency domain pilots embedded in the OFDM block used for the exact channel estimation. Moreover, the obtained auxiliary channel information is adopted to reduce the complexity of the conventional SP algorithm. Simulation results demonstrate a important reduction of the number of pilots relative to least-squares channel estimation and supporting high-order modulations like 256 QAM.
Performance analysis of compressive sensing recovery algorithms for image pr...IJECEIAES
The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based on mean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.
METHOD FOR THE DETECTION OF MIXED QPSK SIGNALS BASED ON THE CALCULATION OF FO...sipij
In this paper we propose the method for the detection of Carrier-in-Carrier signals using QPSK modulations. The method is based on the calculation of fourth-order cumulants. In accordance with the methodology based on the Receiver Operating Characteristic (ROC) curve, a threshold value for the decision rule is established. It was found that the proposed method provides the correct detection of the sum of QPSK signals for a wide range of signal-to-noise ratios and also for the different bandwidths of mixed signals. The obtained results indicate the high efficiency of the proposed detection method. The advantage of the proposed detection method over the “radiuses” method is also shown.
Method for Converter Synchronization with RF InjectionCSCJournals
This paper presents an injection method for synchronizing analog to digital converters (ADC). This approach can eliminate the need for precision routed discrete synchronization signals of current technologies, such as JESD204. By eliminating the setup and hold time requirements at the conversion (or near conversion) clock rate, higher sample rate systems can be synchronized. Measured data from an existing multiple ADC conversion system was used to evaluate the method. Coherent beams were simulated to measure the effectiveness of the method. The results show near theoretical coherent processing gain.
In this paper, we analyzed a numerical evaluation of the performance of MIMO radio systems in the LTE network environment. Downlink physical layer of the OFDM-MIMO based radio interface is considered for system model and a theoretical analysis of the bit error rate of the two space-time codes (SFBC 2×1 and FSTD 4×2 codes are adopted by the LTE norm as a function of the signal to noise ratio. Analytical expressions are given for transmission over a Rayleigh channel without spatial correlation which is then compared with Monte-Carlo simulations. Further evaluated channel capacity and simulation results show throughput almost reaches to the capacity limit.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant. In this study, the BP algorithm is modified so as to allow for the learning rate to be variable at each iteration and this achieves a faster convergence. The proposed algorithm is used to train the MLP based decision feedback equalizer (DFE) for time dispersive ISI channels. The equalizer is tested for a random input sequence of BPSK signals and its performance analysed in terms of the Bit Error Rates and speed of convergence. Simulation results show that the proposed algorithm improves the Bit Error Rate (BER) and rate of convergence.
AN APPLICATION OF NEURAL NETWORKS TO CHANNEL ESTIMATION OF THE ISDB-TB FBMC S...ijwmn
Due to the evolution of technology and the diffusion of digital television, many researchers are studying
more efficient transmission and reception methods. This fact occurs because of the demand of transmitting
videos with better quality using new standards such 8K SUPER Hi-VISION. In this scenario, modulation
techniquessuch as Filter Bank Multi Carrier, associated with advanced coding and synchronization
methods, are being applied, aiming to achieve the desired data rate to support ultra-high definition videos.
Simultaneously, it is also important to investigate ways of channel estimation that enable a better reception
of the transmitted signal. This task is not always trivial, depending on the characteristics of the channel.
Thus, the use of artificial intelligence can contribute to estimate the channel frequency response, from the
transmitted pilots. A classical algorithm called Back-propagation Training can be applied to find the
channel equalizer coefficients, making possible the correct reception of TV signals. Therefore, this work
presents a method of channel estimation that uses neural network techniques to obtain the channel
response in the Brazilian Digital System Television, called ISDB-TB, using Filter Bank Multi Carrier.
Using the Channel State Information (CSI) at the transmitter is fundamental for the precoder
design in Multi-user Multiple Input Single Output (MU-MISO-OFDM) systems. In Frequency
Division Duplex (FDD) systems, CSI can be just available at the transmitter through a limited
feedback channel [1], where we assume that each user quantizes its channel direction with a
finite number of quantization bits. In this paper, we consider a scalar quantization (SQ) scheme
of the Channel Direction Information (CDI). Although vector quantization (VQ) schemes [2],
[3] still outperform this scalar scheme in terms of quantization error and Sum rate, the former
scheme suffers from an exponential search complexity and high storage requirements at the
receiver for high number of feedback bits.
A novel delay dictionary design for compressive sensing-based time varying ch...TELKOMNIKA JOURNAL
Compressive sensing (CS) is a new attractive technique adopted for Linear Time Varying channel estimation. orthogonal frequency division multiplexing (OFDM) was proposed to be used in 4G and 5G which supports high data rate requirements. Different pilot aided channel estimation techniques were proposed to better track the channel conditions, which consumes bandwidth, thus, considerable data rate reduced. In order to estimate the channel with minimum number of pilots, compressive sensing CS was proposed to efficiently estimate the channel variations. In this paper, a novel delay dictionary-based CS was designed and simulated to estimate the linear time varying (LTV) channel. The proposed dictionary shows the suitability of estimating the channel impulse response (CIR) with low to moderate Doppler frequency shifts with acceptable bit error rate (BER) performance.
CHANNEL ESTIMATION FOR THE ISDBT B FBMC SYSTEM USING NEURAL NETWORKS: A PROPO...csandit
Due to the evolution of technology and the diffusion of digital television, many researchers have
studied more efficient transmission and reception methods. This fact occurs because of the
demand of transmitting videos with better quality using new standards such 8K SUPER Hi-
VISION. In this scenario, modulation techniques such as Filter Bank Multi Carrier, associated
with advanced coding and synchronization methods, are being applied, aiming to achieve the
desired data rate to support ultra-high definition videos. Simultaneously, it is also important to
investigate ways of channel estimation that enable a better reception of the transmitted signal.
This task is not always trivial, depending of the characteristics of the channel. Thus, the use of
artificial intelligence can contribute to estimate the channel frequency response, from the
transmitted pilots. A classical algorithm called Back-propagation Training can be applied to
find the channel equalizer coefficients, making possible the correct reception of TV signals.
Therefore, this work presents a method of channel estimation that uses neural network
techniques to obtain the channel response in the Brazilian Digital System Television, called
ISDB-TB, using Filter Bank Multi Carrier.
Compressive spectrum sensing using two-stage scheme for cognitive radio netwo...IJECEIAES
The modern applications of communications that use wideband signals suffer the lacking since the resources of this kind of signals are limited especially for fifth generation (5G). The compressive spectrum sensing (COMPSS) techniques address such issues to reuse the detected signals in the networks and applications of 5G. However, the raw techniques of COMPSS have low compression ratio and high computational complexity rather than high level of noise variance. In this paper, a hybrid COMPSS scheme has been developed for both non-cooperative and cooperative cognitive radio networks. The proposed scheme compiles on discrete wavelet transform single resolution (DWT-SR) cascaded with discrete cosine transform (DCT). The first is constructed according to the pyramid algorithm to achieve 50% while the second performed 30% compression ratios. The simulation and analytic results reveal the significant detection performance of the proposed technique is better than that of the raw COMPSS techniques.
In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
Classical Discrete-Time Fourier TransformBased Channel Estimation for MIMO-OF...IJCSEA Journal
In this document, we look at various time domain channel estimation methods with this constraint of null carriers at spectrumborders.We showin detail howto gauge the importance of the “border effect” depending on the number of null carriers, which may vary from one system to another. Thereby we assess the limit of the technique discussed when the number of null carriers is large. Finally the DFT with the truncated singular value decomposition (SVD) technique is proposed to completely eliminate the impact of the null subcarriers whatever their number. A technique for the determination of the truncation threshold for any MIMO-OFDM system is also proposed.
Similar to Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectrum Sensing (20)
Design and Optimal Configuration of Full-Duplex MAC Protocol for Cognitive Ra...Polytechnique Montreal
In this paper, we propose an adaptive medium access control (MAC) protocol for
full-duplex (FD) cognitive radio networks in which FD secondary users (SUs) perform channel contention
followed by concurrent spectrum sensing and transmission, and transmission only with maximum power
in two different stages (called the FD sensing and transmission stages, respectively) in each contention
and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol does not require synchronization
among SUs, and it efciently utilizes the spectrum and mitigates the self-interference in the FD transceiver.
We develop a mathematical model to analyze the throughput performance of the FDC-MAC protocol, where
both half-duplex (HD) transmission and FD transmission modes are considered in the transmission stage.
Then, we study the FDC-MAC conguration optimization through adaptively controlling the spectrum
sensing duration and transmit power level in the FD sensing stage.We prove that there exists optimal sensing
time and transmit power to achieve the maximum throughput, and we develop an algorithm to congure
the proposed FDC-MAC protocol. Extensive numerical results are presented to illustrate the optimal
FDC-MAC conguration and the impacts of protocol parameters and the self-interference cancellation
quality on the throughput performance. Moreover, we demonstrate the signicant throughput gains of the
FDC-MAC protocol with respect to the existing HD MAC and single-stage FD MAC protocols
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel ...Polytechnique Montreal
In this paper, we propose a semi-distributed cooperative spectrum sen
sing (SDCSS) and channel access framework
for multi-channel cognitive radio networks (CRNs). In particular, we c
onsider a SDCSS scheme where secondary
users (SUs) perform sensing and exchange sensing outcomes with ea
ch other to locate spectrum holes. In addition,
we devise the
p
-persistent CSMA-based cognitive MAC protocol integrating the SDCSS to
enable efficient spectrum
sharing among SUs. We then perform throughput analysis and develop
an algorithm to determine the spectrum
sensing and access parameters to maximize the throughput for a given
allocation of channel sensing sets. Moreover,
we consider the spectrum sensing set optimization problem for SUs to maxim
ize the overall system throughput. We
present both exhaustive search and low-complexity greedy algorithms
to determine the sensing sets for SUs and
analyze their complexity. We also show how our design and analysis can be
extended to consider reporting errors.
Finally, extensive numerical results are presented to demonstrate the sig
nificant performance gain of our optimized
design framework with respect to non-optimized designs as well as the imp
acts of different protocol parameters on
the throughput performance.
In order to improve sensing performance when the noise variance is not known, this paper considers a so-called
blind spectrum sensing technique that is based on eigenvalue models. In this paper, we employed the spiked population
models in order to identify the miss detection probability. At first, we try to estimate the unknown noise variance
based on the blind measurements at a secondary location. We then investigate the performance of detection, in terms
of both theoretical and empirical aspects, after applying this estimated noise variance result. In addition, we study the
effects of the number of SUs and the number of samples on the spectrum sensing performance.
In this paper, we consider the joint optimal sensing
and distributed MAC protocol design for cognitive radio
networks. Specifically, we design a synchronized MAC protocol
for dynamic spectrum sharing among multiple secondary
users, which incorporates spectrum sensing for protecting active
primary users. We perform saturation throughput analysis for
the proposed MAC protocol that explicitly captures spectrum
sensing performance. Then, we find its optimal configuration
by formulating a throughput maximization problem subject to
detection probability constraints for primary users. In particular,
the optimal solution of this optimization problem returns the
required sensing time for primary users’ protection and optimal
contention window for maximizing total throughput of the
secondary network. Finally, numerical results are presented to
illustrate a significant performance gain of the optimal sensing
and protocol configuration.
In a communications system, the channel is affected by an additive white Gaussian noise (AWGN)
and a fading due to a distance between a transmitter and a receiver. Especially, there are many kinds of
channel fadings. Depending on the moving speeds of transmitters or receivers, a fading type can be a slow
fading or a fast fading (i.e., the product of 0.1 and coherence time than smaller or larger than the symbol
period of signal are corresponding to fast and slow fadings). Moreover, a channel can be referred as a
selective fading or a flat fading corresponding to the product of 0.1 and coherence bandwidth than smaller
or larger than the bandwidth of signal. These above effects can suffer received signals at a destination.
Hence the performance of received signals in term of bit-error-rate (BER) is much degraded.
In order to overcome these issues, communications systems would be carefully designed. In detail,
application systems operating over the AWGN channels would use coding schemes to combat an additive
white noise. However, if environment is affected by fading, coding techniques only solve a fast fading.
It implies that, coding schemes degrade received signals when they go through slow fading channels. In
this case, an interleaving technique would be added to a communications system. In order to overcome
the fading channels, besides, using an interleaver as above, we can exploit the diversity of multi-path. It
implies that the effects of fading can be combated by transmitting the original signals over multiple paths
(experiencing independent fading) and then combining all received signals at the receiver. There are many
kinds of diversities to mitigate this issue, such as diversity in time, frequency, and space. Correspondingly,
a lot of state-of-art methods are given, viz. diversity receiving and transmitting, OFDM, space-time block
codes, MIMO, Cooperation and etc.
In summary, the main scope of this report is modeling a communications system. First, I create a
basic communications system, where it includes the modulation/demodulation using a QPSK modulation,
a channel type is an AWGN channel. Secondly, a coder/decoder scheme is added to a transmitter/receiver to
improve received signals. Thirdly, the fading channel is considered when a receiver/transmitter is moving.
It means that the slow fading is mentioned. The performance is shown to prove that the received signal
2
is degraded whether a coding scheme is used or not. Finally, an interleaver/deinterleaver is used to solve
this problem.
Besides, the performance in terms of BER is used to verify a validity of these above techniques in a
communications system.
Capacity Performance Analysis for Decode-and-Forward OFDMDual-Hop SystemPolytechnique Montreal
In this paper, we propose an exact analytical technique to evaluate the average capacity of a dual-hop OFDM relay system with decode-and-forward protocol in an independent and identical distribution (i.i.d.) Rayleigh fading channel. Four schemes, (no) matching “and” or “or” (no) power allocation, will be considered. First, the probability density function (pdf) for the end-to-end power channel gain for each scheme is described. Then, based on these pdf functions, we will give the expressions of the average capacity. Monte Carlo simulation results will be shown to confirm the analytical results for both the pdf functions and average capacities.
Tech report: Fair Channel Allocation and Access Design for Cognitive Ad Hoc N...Polytechnique Montreal
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General analytical framework for cooperative sensing and access trade-off opt...Polytechnique Montreal
In this paper, we investigate the joint cooperative spectrum sensing and access design problem for multi-channel cognitive radio networks. A general heterogeneous setting is considered where the probabilities that different channels are available, SNRs of the signals received at secondary users (SUs) due to transmissions from primary users (PUs) for different users and channels can be different. We assume a cooperative sensing strategy with a general a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs can exploit available channels. We analyze the sensing performance and the throughput achieved by the joint sensing and access design. Based on this analysis, we develop algorithms to find optimal parameters for the sensing and access protocols and to determine channel assignment for SUs to maximize the system throughput. Finally, numerical results are presented to verify the effectiveness of our design and demonstrate the relative performance of our proposed algorithms and the optimal ones.
Channel assignment for throughput maximization in cognitive radio networks Polytechnique Montreal
In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users exploit spectrum holes on a set of channels where each secondary user can use at most one available channel for communication. We develop two channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, secondary users are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm, that can improve the throughput performance compared to the non-overlapping channel assignment algorithm. In addition, we design a distributed MAC protocol for access contention resolution and integrate the derived MAC protocol overhead into the second channel assignment algorithm. Finally, numerical results are presented to validate the theoretical results and illustrate the performance gain due to the overlapping channel assignment algorithm.
Fair channel allocation and access design for cognitive ad hoc networksPolytechnique Montreal
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Channel Assignment With Access Contention Resolution for Cognitive Radio Netw...Polytechnique Montreal
In this paper, we consider the channel assignment problem for cognitive radio networks with hardware-constrained secondary users (SUs). In particular, we assume that SUs exploit spectrum holes on a set of channels where each SU can use at most one available channel for communication. We present the optimal brute-force search algorithm to solve the corresponding nonlinear integer optimization problem and analyze its complexity. Because the optimal solution has exponential complexity with the numbers of channels and SUs, we develop two low-complexity channel assignment algorithms that can efficiently utilize the spectrum holes. In the first algorithm, SUs are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm that can improve the throughput performance compared with its nonoverlapping channel assignment counterpart. Moreover, we design a distributed medium access control (MAC) protocol for access contention resolution and integrate it into the overlapping channel assignment algorithm. We then analyze the saturation throughput and the complexity of the proposed channel assignment algorithms. We also present several potential extensions, including the development of greedy channel assignment algorithms under the max-min fairness criterion and throughput analysis, considering sensing errors. Finally, numerical results are presented to validate the developed theoretical results and illustrate the performance gains due to the proposed channel assignment algorithms.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and ...Polytechnique Montreal
In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
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2. performance of signal reconstruction.
We also compare the performance of the distributed com-
pressive spectrum sensing scheme with that of the scheme of
[5] for a single CR to show the gains accrued from spatial
diversity and exploiting the joint sparsity structure. We use
(i) the mean squared error (MSE) between the reconstructed
power spectrum density (PSD) estimate and the PSD based
on Nyquist rate sampling, and (ii) the probability of detecting
spectrum occupancy over the channels as performance mea-
sures.
The rest of this paper is organized as follows. Section II
contains the model of the wide-band analog signal compressed
sensing. A compressive spectrum sensing scheme for single
CR is presented in Section III. And an extention to collabo-
rative compressed spectrum sensing for multiple CR is shown
in Section IV. Section V demonstrates simulation results.
Finally, concluding remarks are given in Section VI.
II. SYSTEM MODEL OF WIDEBAND ANALOG
SIGNAL COMPSESSED SENSING
A. Signal model
We consider the frequency range of interest to be comprised
of maxI non-overlapping consecutive spectrum bands, a CR
network consisting of J CRs and a centralized fusion center.
Sensing is performed periodically at each CR and the results
are sent to the fusion center, where a decision is made on
whether or not there is a licensed signal present in each
channel.
B. Overview of compressed sensing
According to Donoho [3], in CS theories, an N ×1 vector of
discrete-time signal x = Ψs, where Ψ is the N × N sparsity
basis matrix and s is the N ×1 vector with K N non-zero
(and large enough) entries si, can be used to reconstruct the
signal from M measurements; especially, M depends on the
reconstruction algorithm and is usually much less than N. This
measurement can be done by projecting x on to an M × N
basis matrix Φ that is incoherent with Ψ [13]
y = Φx = ΦΨs. (1)
The reconstruction is done by solving the following l1-norm
optimization problem as
ˆs= arg min
s
s 1 s.t. y = ΦΨs. (2)
Linear programming techniques, e.g., basis pursuit [14], or
iterative greedy algorithms [15] can be used to solve (2).
C. Compressed sensing of analog signals
Because CS was proposed for discrete-time signal process-
ing, we must use ADC sampling at Nyquist rate to discreterize
the analog signal before applying the CS. After that, the
compressed sensed data are sent to DSP blocks for further
manipulation. While it is true that the data volume to be
processed by DSP blocks is reduced due to the CS, a high-
speed ADC sampling at Nyquist rate is still required when
the received signal is wideband. It is natural to think about
ways to avoid the high-speed ADC by applying CS to the
analog signal directly. A related idea was first described in
[8], where the analog signal was first demodulated with a
pseudo-random chipping sequence p(t), then passed through
an analog filter h(t), and the measurements were obtained in
serial by sampling the filtered signal at sub-Nyquist rate. The
serial sampling structure is appropriate for real-time process-
ing. However, to achieve a satisfactory signal reconstruction
quality, the order of the filter is usually higher than 10. In
addition, because the measurements are obtained by sampling
the output of the analog filter sequentially, they are no longer
independent due to the convolution in the filter, which brings
some redundancy in the measurements.
Specifically, suppose that we have an analog signal x(t)
which is K − sparse over some basis Ψ for t ∈ [0, T] as in
the following expression:
x(t) =
N
i=1
siψi(t), (3)
where x is the N × 1 vector x = Ψs, Ψ is the N × N sparsity
basis matrix Ψ = [ψ0(t), ψ1(t), . . . , ψN (t)] and s an N × 1
vector with K N non-zero elements si. It has been shown
that x can be recovered using M = KO(log N) non-adaptive
linear projection measurements on to an M × N basis matrix
Φ that is incoherent with Ψ [13]. The received signal y can
be viewed as the transmitted signal plus some additive noise
y = Φx + n = ΦΨs + n. (4)
There are several choices for the distribution of Φ such as
Gaussian, Bernoulli.
Reconstruction is achieved by solving the l1-norm opti-
mization problem as in (2). In this paper, the reconstruction
problem, that has been highly interested in solving the convex
unconstrained optimization problem, is a standard approach
consisting in minimizing an objective function which includes
a quadratic (squared l2-norm) error term combined with
a sparseness-inducing (l1-norm) regularization term. So the
problem can be given by
min
s
1
2
y − ΦΨs
2
2 + τ s 1. (5)
Basic GP is able to solve a sequence of problems (5) effi-
ciently for a sequence of values of τ. The gradient projection
algorithms for solving a quadratic programming reformulation
of a class of convex nonsmooth unconstrained optimization
problems are significantly faster (in some cases by orders
of magnitude), especially in large-scale settings. Instances of
poor performance have been observed when the regularization
parameter is small, but in such cases the gradient projection
methods can be embedded in a simple continuation heuristic
to recover their efficient practical performance. The new
algorithms are easy to implement, work well across a large
range of applications, and do not appear to require application-
specific tuning.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings
3. Analog filter
h(t)
Autocorrelation
x(t)
yk
ry
pc(t)
k/M
quantizer
AIC block
Fig. 1. CS acquisition at individual CR sensing receiver.
III. COMPRESSIVE SPECTRUM SENSING AT
SINGLE CR
We begin by describing the CS acquisition and recovery
scheme for a single CR (J = 1) case. Fig. 1 depicts the
acquisition at a single CR sensing receiver. The analog base-
band signal x(t) is sampled using an AIC. Following the
circuit implementation of the AIC system in series of previous
works [7], [8], [9], an AIC may be conceptually viewed as an
ADC operating at the Nyquist rate, followed by compressive
sampling. Denote the N × 1 stacked vector at the input of the
ADC by
xk = xkN xkN+1 ... xkN+N+1
T
k = 0, 1, 2 ...,
(6)
and the M × N compressive sampling matrix by ΦA. The
output of the AIC denoted by the M × 1 vector
yk = ykM ykM+1 ... ykM+M+1
T
k = 0, 1, 2 ...,
(7)
is given by
yk = ΦAxk. (8)
The respective N × N and M × M autocorrelation matrices
of the compressed signal and the input signal vectors in (6)
and (7) are related as follows:
Ry = E ykyH
k = ΦARxΦH
A , (9)
where subscript H denotes the Hermitian. The elements of the
matrices in (9) are given by: [Ry]ij = ry (i − j) = r∗
y (j − i),
[Rx]ij = rx (i − j) = r∗
x (j − i).
The respective 2N × 1 and 2M × 1 autocorrelation vectors
corresponding to (6) and (7) can be expressed as follows:
rx = 0 rx(−N + 1) ... rx(0) ... rx(N − 1)
T
,
(10)
ry = 0 ry(−M + 1) ... ry(0) ... ry(M − 1)
T
,
(11)
here the first zero values are artificially inserted. And these
above vectors represent the first column and row of the
respective autocorrelation matrices. To obtain the CS recovery
like the formula (5), we must to make the relation between
the autocorrelation vectors in (10) and (11). Using operations
in matrix algebra, we can derive as
ry = Φrx, (12)
note that
Φ =
¯ΦAΦ1
¯ΦAΦ2
ΦAΦ3 ΦAΦ4
. (13)
Denote that φ∗
i,j is the (i, j)-th element of ΦA, the M × N
matrix ¯ΦA has its (i, j)-th element given by
¯ΦA i,j
=
0
φM+2−i,j
i = 1, j = 1, ..., N,
i = 1, j = 1, ..., N,
(14)
and the N × N matrices Φ1, Φ2, Φ3, Φ4 are defined
as Φ1 = hankel [0N×1] , 0 φ∗
1,1 ... φ∗
1,N−1 ,
Φ2 = hankel φ∗
1,1 ... φ∗
1,N , φ∗
1,N 01×(N−1) ,
Φ3 = toeplitz [0N×1] , 0 φ1, N ... φ1, 2 ,
Φ4 = toeplitz φ1,1 ... φ1,N , φ1,1 01×(N−1) ,
where hankel(c, r) is a hankel matrix (i.e., symmetric and
constant across the anti-diagonals), note that c is the first
column and r is the last row of this matrix. toeplitz(c, r)
is a toeplitz matrix (i.e., symetric and constant across the
diagonals), note that c is the first column, and r is the first
row of this matrix. And 0a×b is the a × b zero matrix.
We also know that using the wavelet-based edge detection
in [16-17], the band boundaries (locations) can be recovered
from 2N-1 local maxima of the wavelet modulus zs and the
band number is determined by the number of local peaks; as
an experiment when N M in [5], zs can be recovered
under the sparseness constraint, and therefore there is a
linear transformation equality linking zs to the compressed
measurement vector ry. And rx has a sparse representation in
the edge spectrum domain [5], that is
rx = Gzs, (15)
where zs is the discrete 2N × 1 vector, and G = (ΓFW)
−1
.
The 2N × 2N matrices W and F represent respectively a
wavelet-based smoothing and a Fourier transform. The 2N ×
2N matrix Γ is a derivative operation given by
Γ =
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎣
1 0 · · · 0
−1
... · · · 0
0
...
...
...
0 · · · −1 1
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦
.
Combining (12) and (15), we can formulate the CS recon-
struction of the edge spectrum as a convex unconstrained
optimization problem:
min
zs
1
2
ry − ΦGzs
2
2 + τ zs 1 (16)
To solve the above problem, we use the GP approach which
is described in the Section IV for an individual CR case.
The spectrum estimate can be evaluated as a cumulative sum
of terms ˆzs = ˆzs (1) ˆzs (2) · · · ˆzs (2N)
T
. The
discrete components of the PSD estimate are given by
ˆSx (n) =
n
k=1
ˆzs (k) (17)
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings
4. 1 k,1 y,1
J k,J y,J
.
Fig. 2. Distributed compressive spectrum sensing scheme for multiple CRs.
IV. COLLABORATIVE COMPRESSED SPECTRUM
SENSING
Let xj(t) be the wide-band analog baseband signal received
at the j-th CR sensing receiver. Each CR sensing receiver pro-
cesses the received signal to obtain an 2M ×1 autocorrelation
vector ry, j of the compressed signal, as in the CS acquisition
step described in Section III, these vectors are then sent to
the fusion center. The fusion center applies a GP algorithm
to jointly reconstruct J received PSDs ˆSx,j, j = 1, . . . , J and
then obtains an average PSD. The average PSD is then used
to determine the spectrum occupancy.
A. Overview of GP approach
We now describe the GP algorithm used for reconstruction
of J PSDs. We write A = ΦG in terms of its columns
A = a1 a2 · · · a2N . (18)
At the j-th CR, we introduce vectors uj and vj and make the
substitution zs,j = uj − vj, uj ≥ 0, vj ≥ 0, j = 1, · · · , J.
Here uj (i) = (zs,j (i))
+
= max {0, zs,j (i)} and vj (i) =
(−zs,j (i))
+
= max {0, −zs,j (i)} for all i = 1, . . . , 2N. Note
that (a)
+
= max {0, a}. Therefore, we have zs,j = 1T
2N uj +
1T
2N vj, where 12N = [1, 1, . . . , 1]
T
is the vector consisting
of 2N ones. The problem (16) can be modified as
min
u,v
1
2 ry,j −A (uj − vj)
2
2 +τ1T
2N uj +τ1T
2N vj
s.t. uj ≥ 0
vj ≥ 0
(19)
Problem (19) can be written in more standard bound-
constrained quadratic programming (BCQP) form as
min
p
cT
pj + 1
2 pT
j Bpj ≡ F pj ,
s.t. pj ≥ 0
(20)
where pj =
uj
vj
, c = τ1T
4N +
−b
b
, b = AT
ry,j
and B =
AT
A −AT
A
−AT
A AT
A
. The next step is solving the
problem (20) by using a GP technique. From iterate p
(k)
j to
iterate p
(k+1)
j , we must follow the below steps,
• Step 1. Choose the scalar parameters α
(k)
j > 0.
• Step 2. Set: w
(k)
j = p
(k)
j − α
(k)
j ∇F p
(k)
j
+
.
• Step 3. Choose the second scalar λ
(k)
j ∈ [0, 1].
• Step 4. Set: p
(k+1)
j = p
(k)
j + λ
(k)
j w
(k)
j − p
(k)
j .
The following subsections represent two algorithms to solve
the above problem coresponding to two different ways of
choosing α
(k)
j and λ
(k)
j .
B. Basic Gradient Projection:The GP - Basic Algorithm
In this algorithm, we search from each iterate p
(k)
j along the
negative gradient −∇F p
(k)
j , projecting onto the nonnega-
tive orthant, and performing a backtracking line search until
sufficient decrease is attained in F. We define the vector g(k)
as
g
(k)
j,i =
∇F pj
(k)
i
, if p
(k)
j,i > 0 or ∇F pj
(k)
i
< 0
0, otherwise.
(21)
where i = 1, . . . , 2N. The procedure of this algorithm is
described as follows:
1) Input:
a) An initial p(0)
= p
(0)
1 p
(0)
2 · · · p
(0)
J .
b) A 2M × J data matrix R =
ry,1 ry,2 · · · ry,J received from J
CR sensing receivers.
c) Choose parameters β ∈ (0, 1) and μ ∈ (0, 1/2).
d) Set k = 0.
2) Output: A 2N × J reconstruction matrix Zs =
zs,1 zs,2 · · · zs,J , the average of J PSD esti-
mate vectors ˆS
(J)
x .
3) Procedure:
a) Step 1. Compute α0,j as the following expression
[12]:
α0,j =
g
(k)
j
T
g
(k)
j
g
(k)
j
T
Bg
(k)
j
. (22)
Note that α0,j is solved from the expression:
α0,j = arg min
αj
F p
(k)
j − αjg
(k)
j . (23)
Then to guarantee that α0,j is not too small or too
large, we replace α0,j by mid (αmin, α0,j, αmax).
Here mid (α1, α2, α3) is defined to be the middle
value of three scalar values.
b) Step 2. Backtracking line search: choose
α
(k)
j to be the first number in the sequence
α0,j, βα0,j, β2
α0,j, . . . and satisfy the following
inequality
F p
(k)
j −α
(k)
j ∇F p
(k)
j
+
≤ F p
(k)
j −
μ∇F p
(k)
j
T
p
(k)
j − p
(k)
j −α
(k)
j ∇F p
(k)
j
+
(24)
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings
5. and update the new set of values
p
(k+1)
j = p
(k)
j − α
(k)
j ∇F p
(k)
j
+
.
c) Step 3. Termination test:
• Condition: We now convert p
(k)
j =
u
(k)
j
T
, v
(k)
j
T T
to an approximate
solution z
(k)
s,j,GP = u
(k)
j − v
(k)
j . And scanning
through the entire J CR to check the termination
condition, i.e. the convergence gradient (CG)
iteration is terminated when satisfying
ry,j − Azs,j
2
2 ≤ εD ry,j − Azs,j,GP
2
2 ,
(25)
where εD is a small positive parameter. How-
ever, the termination iteration is also performed
when the number of CG steps reaches to
maxiterD.
• We perform the convergence test and terminate
with approximation solution p
(k+1)
j if it is satis-
fied the above conditions; otherwise we increase
k to k + 1 and go back to step 1.
d) Step 4. Store the results: Store pj =
(uj)
T
, (vj)
T
T
, and calculate the reconstruction
vector ˆzs,j = uj − vj. The j-th PSD estimate
vector is ˆSx,j (n) =
n
k=1
ˆzs,j (k). And the average
of J PSD estimate vectors is
ˆS(J)
x =
1
J
J
j=1
ˆSx,j. (26)
C. Projected Barzilai-Borwein Reconstruction Algorithm
The improvement of this algorithm is updating the step by
the following formula [18]
γ(k)
= −H−1
k ∇F p(k)
, (27)
where Hk is an approximation to the Hessian of F p(k)
.
The procedure of this algorithm is similar to the basic GP
algorithm except the following steps:
1) Step 1. Compute step γ
(k)
j for the j-th CR as the
following expression:
γ
(k)
j = p
(k)
j − α
(k)
j ∇F p
(k)
j
+
− p
(k)
j . (28)
2) Step 2. Line search: The scalar λ
(k)
j , (λ
(k)
j ∈
[0, 1]) will be found to minimize F p
(k)
j + λ
(k)
j γ
(k)
j
and update the new set of values p
(k+1)
j =
p
(k)
j − α
(k)
j ∇F p
(k)
j
+
. Because F is quadratic, the
line search parameter λ
(k)
j can be evaluated by the
following closed-form expression:
λ
(k)
j = mid
⎧
⎪⎨
⎪⎩
0,
γ
(k)
j
T
∇F p
(k)
j
γ
(k)
j
T
Bγ
(k)
j
, 1
⎫
⎪⎬
⎪⎭
.
Note that if γ
(k)
j
T
Bγ
(k)
j = 0, we choose λ
(k)
j = 1.
3) Step 3. Update α
(k)
j : Denote
ξ
(k)
j = γ
(k)
j
T
Bγ
(k)
j . (29)
If ξ
(k)
j = 0, let α
(k+1)
j = αmax, otherwise
α
(k+1)
j = mid
⎧
⎪⎨
⎪⎩
αmin,
γ
(k)
j
2
2
ξ
(k)
j
, αmax
⎫
⎪⎬
⎪⎭
.
D. Performances
1) MSE Performance: The normalized MSE of estimated
PSD is computed by
MSE(J)
= E
⎧
⎪⎨
⎪⎩
ˆS
(J)
x − Sx
(J)
2
2
Sx
(J)
2
2
⎫
⎪⎬
⎪⎭
, (30)
where ˆS
(J)
x and Sx
(J)
denote the average of the J PSD
estimate vectors based on our compressed sensing approach
and the periodogram using the signals sampled at the Nyquist
rate, respectively.
2) Detection performances: We evaluate the probability
of detection Pd based on the averaged PSD estimate ˆS
(J)
x .
The detection analysis to follow, strictly speaking, holds only
for samples collected at Nyquist rate. We however use this
as a simple way to analyze the detection performance in
the compressive sampling case as well. The decision of the
presence of licensed transmission signals in the certain channel
is made by an energy detector using the estimated frequency
response over that channel, i.e., the test statistic is
E
(J)
I =
IK
i=(I−1)K+1
ˆS(J)
x (i), I = 1, 2, . . . , maxI, (31)
where I is the channel index, maxI is the number of channels,
and K is the number of PSD samples of each channel. The
PSD estimate of the j-th CR node can be evaluated as
ˆSx,j(i) =
1
H
H
h=1
|Xh,j(i)|
2
, (32)
whereXh,j(i) is the Fourier transform of the h-th block of the
received signal xh,j(n), j representing the CR node index,
n representing the time sample index, each block containing
2N time samples, and H denoting the number of blocks.
Substituting (26) and (32) to (31), the test static can be
obtained by
E
(J)
I =
1
JH
IK
i=(I−1)K+1
J
j=1
H
h=1
|Xh,j(i)|
2
. (33)
The decision rule is chosen as
E
(J)
I
H1
>
<
H0
μ, I = 1, 2, . . . , maxI, (34)
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings
6. 0.1 0.2 0.3 0.4 0.5
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Compression rate [M/N]
Reconstructionerror
Basic GP, 5CRs
PBB, 5 CRs
Basic GP, 1 CR
PBB, 1 CR
Ref. [5], 1 CR
Fig. 3. Reconstruction error (MSE) for Basic GP and PBB approaches versus
compression rate M/N for various number of collaborating CRs (SNRs of
active channels varying from 8dB to 10dB).
0.1 0.2 0.3 0.4 0.5
0
0.05
0.1
0.4
0.5
0.6
0.7
0.8
0.9
1
Compression rate [M/N]
Performance
Pd
, 5 CRs
Pd
, 1 CR
P
d
, 1CR, Ref. [5]
Pfa
, 5 CRs
Pfa
, 1 CR
Pfa
, 1 CR, Ref. [5]
Fig. 4. Probability of detection Pd and probability of a false alarm Pfa for
PBB versus compression rate M/N for various number of collaborating CRs
(SNRs of active channels varying from 8dB to 10dB).
where H0, H1 represent the hypotheses of the absence and
presence of primary signals, respectively, and μ is the decision
threshold. Under H0, E
(J)
I / σ2
n/ (JH) ∼ χ2
2JKH has a
central χ2
distribution with 2JKH degrees of freedom. The
probability of a false alarm P
(J)
fa can be obtained by
P
(J)
fa = 1 −
Γ JH, μ
JH
Γ (JH)
, (35)
where Γ(., .) is the upper incomplete gamma function [19, Sec.
(8.350)], Γ(.) is the gamma function [19, Sec. (13.10)]. Under
H1, the probability of detection P
(J)
d is evaluated by
P
(J)
d =
1
a
Ia
I=I1
Pr E
(J)
I > μ, (36)
where Ii, i = 1, . . . , a denote the indices of a active channels.
Parameters 2k mode
Elementary period T 7/64µs
Number of carriers K 1,705
Value of carrier number Kmin 0
Value of carrier number Kmax 1,704
Duration of symbol part TU 2,048× T 224µs
Carrier spacing 1/TU 4,464 Hz
Spacing between carriers Kmin and Kmax 7.61 MHz
TABLE I
THE OFDM PARAMETERS FOR THE 2K MODE.
V. SIMULATION RESULTS
The model for simulation can be briefly described in this
section. We consider at baseband, a wide frequency band of
interest ranging from -38.05 to 38.05 MHz, containing maxI
= 10 non-overlapping channels of equal bandwidth of 7.61
MHz. Our simulations will focus in the 2k mode of the DVB-T
standard. This particular mode is intended for mobile reception
of standard definition DTV. The structure of signal is followed
an OFDM frame. Each frame has a duration of TF , and
consists of 68 OFDM symbols. Four frames constitute one
super-frame. Each symbol is constituted by a set of C = 1,705
carriers in the 2k mode and transmitted with a duration TS.
A useful part with duration TU and a guard interval with a
duration Δ (choosen to 0) compose TS. The over-sampling
factor is 2. The occupancy ratio of the total 76.1 MHz band
is 50%. The received signal is damaged by additive white
Gaussian noise (AWGN) with a variance of σ2
n = 1. The
received SNRs on the a = 5 active channels are randomly
varying from 8dB to 10dB. A Gaussian wavelet function is
used for smoothing. For compressive sampling, 2N is 4096,
the compressed rate M/N is varying from 5% to 50% and H =
160 is the number of blocks. The compressive sampling matrix
ΦA has a Gaussian distributed function with zero mean and
variance 1/M. The number of PSD samples of each channel
is K = 25. We set αmin = 10−30
, αmax = 1030
for PBB
algorithm, and use β = 0.5, μ = 0.1, and τ = 0.1 AT
ry,i
∞
for both Basic and PBB algorithms.
Fig. 3 illustrates MSE performance for Basic GP and PBB
algorithms and compares with the performance result in [5].
In comparison with [5], our proposed approach in case of
1 CR slightly decreases the MSE performance because of
the reduced mutual incoherent of Φ in (12), however, our
approach can reduce the hardware cost due to AIC acquisition
at the lower sampling rate. The results show that in comparison
with Basic GP version, the PBB algorithm achieves the same
performances while the Basic GP version takes a lot of time
to get convergence [12]. So the following results are imple-
mented by using the novel PBB algorithm. This figure also
shows the performances of signal recovery quality in which
MSE decreases when the value of compression rate M/N
increases. However, as considering the effects of multiple CRs
in spectrum sensing scheme, it is easily to observe that MSE
also decreases as the number of CRs J increases; therefore,
we can obtain the lower compression rate but not degrade the
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings
7. recovery performance by using more CRs in networks. For
example, to receive MSE at 0.37, we must compress wideband
signals at the rate 0.4 in the 1 CR case, while networks with
5 CRs can be implemented at the lower compression rate 0.3.
For detection performance, Fig. 4 depicts the probability
of detection P
(J)
d with respect to both the compression ratio
M/N and the number of CRs J =1, 5, under a fixed P
(J)
fa
of 0.01. This figure demonstrates that in order to obtain
reliable performances, the joint collaboration and compression
is necessary. Especially, collaboration among CRs can avoid
the hardware cost of each CR by reducing the compression rate
M/N while remaining the high detection performance. For
instance, the probability of detection in case of 1 CR is ≈ 1
at the compression rate M/N over 0.2, while the collaboration
among 5 CRs requires the compression rate M/N from the
lower value 0.15.
Especially, analyzing the results reveals the interesting con-
clusion, i.e., in Fig. 4, the detection performances under both
our method and the approach in [5] over the examined range
of compression rates are similar while in Fig.3, the MSE
performances of these approaches have a bit differences.
VI. CONCLUSION
In this paper, we presented a distributed compressive spec-
trum sensing scheme for CR networks. To avoid the high speed
ADC systems, the alternative converters called AICs are ex-
ploited to acquire the salient information of received signals at
sub-Nyquist rates. Moreover, the GP approach is used for joint
cooperation and compressive sensing. The major barrier of GP
method, which takes a lot of time to reach the convergence,
can be solved by modifying the backtracking line search for
updating parameters. Among new fast CS techniques, PBB
algorithm, which is used to update the step of the iterations
in the recovery stage, demonstrates its outperformance, i.e.,
it not only achieves high quality of signal recovery but also
increases the speed to quickly reach convergence.
ACKNOWLEDGMENT
This research was supported by Basic Science Research
Program through the National Research Foundation of Korea
(NRF) funded by the Ministry of Education, Science and
Technology (No. 2009-0073895)
REFERENCES
[1] Proc. 1st IEEE Int’l. Symp. New Frontiers in Dynamic Spectrum Access
Networks, Nov. 2005.
[2] http://www.ieee802.org/22/.
[3] Donoho, D.L., ”Compressed sensing,” IEEE Transactions on Informa-
tion Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[4] Candes, E.J., and Tao, T., ”Near-Optimal Signal Recovery From Random
Projections: Universal Encoding Strategies?,” IEEE Transactions on
Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006.
[5] T. Zhi, and G. B. Giannakis, ”Compressed Sensing for Wideband
Cognitive Radios”, in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), pp. IV-1357-IV-1360, 2007.
[6] M. F. Duarte, M. A. Davenport, M. B. Wakin et al., ”Sparse Signal De-
tection from Incoherent Projections”, in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), pp. III-III, 2006.
[7] S. Kirolos, T. Ragheb, J. Laska et al., ”Practical Issues in Implementing
Analog-to-Information Converters”,in The 6th International Workshop
on System-on-Chip for Real-Time Applications, pp. 141-146, 2006.
[8] J. N. Laska, S. Kirolos, M. F. Duarte et al., ”Theory and Implementation
of an Analog-to-Information Converter using Random Demodulation”,
in IEEE International Symposium on Circuits and Systems (ISCAS), pp.
1959-1962, 2007.
[9] J. L. S. Kirolos, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud,
and R. Baraniuk, ”Analog-to-information conversion via random demod-
ulation,” in Proc. of the IEEE Dallas Circuits and Systems Workshop
(DCAS), 2006.
[10] M. F. Duarte, S. Sarvotham, D. Baron et al., ”Distributed Compressed
Sensing of Jointly Sparse Signals,” in Conference Record of the Thirty-
Ninth Asilomar Conference on Signals, Systems and Computers, pp.
1537-1541, 2005.
[11] J. J. More, ”Gradient projection techniques for large-scale optimization
problems,” in Proceedings of the 28th IEEE Conference on Decision
and Control, pp. 378-381 vol.371, 1989.
[12] Y.-H. D. a. R. Fletcher, ”Projected Barzilai-Borwein methods for large-
scale box-constrained quadratic programming,” Numerische Mathematik,
vol. 100, pp. 21-47, March, 2005.
[13] E. C. a. J. Romberg, ”Sparsity and Incoherence in Compressive Sam-
pling,” Inverse Problems, 23(3), pp. 969-985, June 2007.
[14] D. L. D. S. S. Chen, and M. A. Saunders, ”Atomic decomposition by
basis pursuit,” SIAMJ Sci. Comput., vol. 20, no. 1, pp. 33-61, 1999.
[15] C. L. a. M. Do, ”Signal reconstruction using sparse tree representations,”
SPIE Wavelets XI, vol. 5914, pp. 59140W.1-59140W.11, Sept 2005.
[16] Z. Tian, and G. B. Giannakis, ”A Wavelet Approach to Wideband
Spectrum Sensing for Cognitive Radios,” in 1st International Conference
on Cognitive Radio Oriented Wireless Networks and Communications,
pp. 1-5, 2006.
[17] S. Mallat, and W. L. Hwang, ”Singularity detection and processing with
wavelets,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp.
617-643, 1992.
[18] J. Barzilai and J. M. Borwein, ”Two-point step size gradient methods,”
IMA J. Numer. Anal., vol.8, no.1, pp. 141-148, January 1, 1988.
[19] I. S. Gradshtein, I. M. Ryzhik, A. Jeffrey and D. Zwillinger, ”Table of
integrals, series and products,” 7th ed., Oxford: Academic, 2007.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE DySPAN 2010 proceedings