A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

SPECTRUM SENSING AND COOPERATION IN COGNITIVE-OFDM BASED WIRELESS COMMUNICATIONS NETWORKS

The world has witnessed the development of many wireless systems and applications. In addition to the large number of existing devices, such development of new and advanced wireless systems increases rapidly the demand for more radio spectrum. The radio spectrum is a limited natural resource; however, it has been observed that it is not efficiently utilized. Consequently, different dynamic spectrum access techniques have been proposed as solutions for such an inefficient use of the spectrum. Cognitive Radio (CR) is a promising intelligent technology that can identify the unoccupied portions of spectrum and opportunistically uses those portions with satisfyingly high capacity and low interference to the primary users (i.e., licensed users). The CR can be distinguished from the classical radio systems mainly by its awareness about its surrounding radio frequency environment. The spectrum sensing task is the main key for such awareness. Due to many advantages, Orthogonal Frequency Division Multiplexing system (OFDM) has been proposed as a potential candidate for the CR‟s physical layer. Additionally, the Fast Fourier Transform (FFT) in an OFDM receiver supports the performance of a wide band spectrum analysis. Multitaper spectrum estimation method (MTM) is a non-coherent promising spectrum sensing technique. It tolerates problems related to bad biasing and large variance of power estimates. This thesis focuses, generally, on the local, multi antenna based, and global cooperative spectrum sensing techniques at physical layer in OFDM-based CR systems. It starts with an investigation on the performance of using MTM and MTM with singular value decomposition in CR networks using simulation. The Optimal MTM parameters are then found. The optimal MTM based detector theoretical formulae are derived. Different optimal and suboptimal multi antenna based spectrum sensing techniques are proposed to improve the local spectrum sensing performance. Finally, a new concept of cooperative spectrum sensing is introduced, and new strategies are proposed to optimize the hard cooperative spectrum sensing in CR networks. The MTM performance is controlled by the half time bandwidth product and number of tapers. In this thesis, such parameters have been optimized using Monte Carlo simulation. The binary hypothesis test, here, is developed to ensure that the effect of choosing optimum MTM parameters is based upon performance evaluation. The results show how these optimal parameters give the highest performance with minimum complexity when MTM is used locally at CR. The optimal MTM based detector has been derived using Neyman-Pearson criterion. That includes probabilities of detection, false alarm and misses detection approximate derivations in different wireless environments. The threshold and number of sensed samples controlling is based on this theoretical work. In order to improve the local spectrum sensing performance at each CR, in the CR network, multi antenna spectrum sensing techniques are proposed using MTM and MTM with singular value decomposition in this thesis. The statistical theoretical formulae of the proposed techniques are derived including the different probabilities. ii The proposed techniques include optimal, that requires prior information about the primary user signal, and two suboptimal multi antenna spectrum sensing techniques having similar performances with different computation complexity; these do not need prior information about the primary user signalling. The work here includes derivations for the periodogram multi antenna case. Finally, in hard cooperative spectrum sensing, the cooperation optimization is necessary to improve the overall performance, and/or minimize the number of data to be sent to the main CR-base station. In this thesis, a new optimization method based on optimizing the number of locally sensed samples at each CR is proposed with two different strategies. Furthermore, the different factors that affect the hard cooperative spectrum sensing optimization are investigated and analysed and a new cooperation scheme in spectrum sensing, the master node, is proposed.Ministry of Interior-Kingdom of Saudi Arabi

Spectrum Sensing Performance in TV Bands using the Multitaper Method

Abstract—Frequency agile radios perform opportunistic spec-trum sharing by detecting unused spectrum and dynamically tuning to the available bands. To reduce harmful interference to the primary users of the spectrum, highly sensitive detectors are required. We apply the multitaper spectral estimation method to the problem of spectrum sensing in TV bands. We compare the performance of the multitaper method with that of conventional FFT-based spectrum estimation, using real signal measurements. Our results show that the multitaper approach yields a significant increase in the number of harvested channels, while maintaining a smaller probability of false alarm. Özetçe – Frekans-atik radyolar kullanılmayan frekans kuşaklarını saptayıp dinamik olarak o kanalda iletime geçerek frekans izge paylaşımına olanak sağlarlar. Paylaşım ile frekans kanalındaki birincil kullanıcıların iletimini etkilememek icin duyarlılığı yüksek dedektörlere ihtiyac ̧ vardır. Bu çalışmada, gerçek sinyal ölçümleri kullanılarak çoklu pencereleme yöntemi ile TV bantlarında izgel güc ̧ yoğunluğu elde edilmiştir. Elde edilen sonuçlar, periodogram yöntemi sonunda elde edilen izgel güc ̧ yoğunluğu ile karşılaştırılmıştır ve çoklu pencereleme yöntemi kullanıldığında, yanlıs ̧ alarm olasılığı düşük kaldığı halde frekans paylaşımı ile kullanılabilecek kanal sayısında artış olduğu gözlenmiştir. I

A Cognitive Radio Compressive Sensing Framework

With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization. A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity. This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes. Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains

Tracking aftershock sequences using empirical matched field processing

Extensive aftershock sequences present a significant problem to seismological data centres attempting to produce near real-time comprehensive seismic event bulletins. An elevated number of events to process and poorer performance of automatic phase association algorithms can lead to large delays in processing and a greatly increased human workload. Global monitoring is often performed using seismic array stations at considerable distances from the events involved. Empirical matched field processing (EMFP) is a narrow-frequency band array signal processing technique that recognizes the inter-sensor phase and amplitude relations associated with wavefronts approaching a sensor array from a given direction. We demonstrate that EMFP, using a template obtained from the first P arrival from the main shock alone, can efficiently detect and identify P arrivals on that array from subsequent events in the aftershock zone with exceptionally few false alarms (signals from other sources). The empirical wavefield template encodes all the narrow-band phase and amplitude relations observed for the main shock signal. These relations are also often robust and repeatable characteristics of signals from nearby events. The EMFP detection statistic compares the phase and amplitude relations at a given time in the incoming data stream with those for the template and is sensitive to very short-duration signals with the required characteristics. Significant deviations from the plane-wavefront model that typically degrade the performance of standard beamforming techniques can enhance signal characterization using EMFP. Waveform correlation techniques typically perform poorly for aftershocks from large earthquakes due to the distances between hypocentres and the wide range of event magnitudes and source mechanisms. EMFP on remote seismic arrays mitigates these difficulties; the narrow-band nature of the procedure makes arrival identification less sensitive to the signals’ temporal form and spectral content. The empirical steering vectors derived for the main shock P arrival can reduce the frequency dependency of the slowness vector estimates. This property helps us to automatically screen out arrivals from outside of the aftershock zone. Standard array processing pipelines could be enhanced by including both plane-wave and empirical matched field steering vectors. This would maintain present capability for the plane-wave steering vectors and provide increased sensitivity and resolution for those sources for which we have empirical calibrations.Tracking aftershock sequences using empirical matched field processingacceptedVersio

Improved speech presence probability estimation based on wavelet denoising

A reliable estimator for speech presence probability (SPP) can significantly improve the performance of many speech enhancement algorithms. Previous work showed that a good SPP estimator can be obtained by using a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. In this paper, a wavelet based denoising algorithm is proposed for such purpose. We first apply the wavelet transform to the periodogram of a noisy speech signal to generate an oracle for indicating the locations of the noise floor in the periodogram. We then make use of that oracle to selectively remove the wavelet coefficients of the noise floor in the log multitaper spectrum (MTS) of the noisy speech. The remaining wavelet coefficients are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables a significantly improvement in the quality and intelligibility of the enhanced speeches. © 2012 IEEE.published_or_final_versio