Cognitive Radio for Emergency Networks

In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive Radio has been proposed as a promising technology to solve todayâ?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio

Simulation of the White Dwarf -- White Dwarf galactic background in the LISA data

LISA (Laser Interferometer Space Antenna) is a proposed space mission, which will use coherent laser beams exchanged between three remote spacecraft to detect and study low-frequency cosmic gravitational radiation. In the low-part of its frequency band, the LISA strain sensitivity will be dominated by the incoherent superposition of hundreds of millions of gravitational wave signals radiated by inspiraling white-dwarf binaries present in our own galaxy. In order to estimate the magnitude of the LISA response to this background, we have simulated a synthesized population that recently appeared in the literature. We find the amplitude of the galactic white-dwarf binary background in the LISA data to be modulated in time, reaching a minimum equal to about twice that of the LISA noise for a period of about two months around the time when the Sun-LISA direction is roughly oriented towards the Autumn equinox. Since the galactic white-dwarfs background will be observed by LISA not as a stationary but rather as a cyclostationary random process with a period of one year, we summarize the theory of cyclostationary random processes and present the corresponding generalized spectral method needed to characterize such process. We find that, by measuring the generalized spectral components of the white-dwarf background, LISA will be able to infer properties of the distribution of the white-dwarfs binary systems present in our Galaxy.Comment: 14 pages and 6 figures. Submitted to Classical and Quantum Gravity (Proceedings of GWDAW9

The White Dwarf -- White Dwarf galactic background in the LISA data

LISA (Laser Interferometer Space Antenna) is a proposed space mission, which will use coherent laser beams exchanged between three remote spacecraft to detect and study low-frequency cosmic gravitational radiation. In the low-part of its frequency band, the LISA strain sensitivity will be dominated by the incoherent superposition of hundreds of millions of gravitational wave signals radiated by inspiraling white-dwarf binaries present in our own galaxy. In order to estimate the magnitude of the LISA response to this background, we have simulated a synthesized population that recently appeared in the literature. We find the amplitude of the galactic white-dwarf binary background in the LISA data to be modulated in time, reaching a minimum equal to about twice that of the LISA noise for a period of about two months around the time when the Sun-LISA direction is roughly oriented towards the Autumn equinox. Since the galactic white-dwarfs background will be observed by LISA not as a stationary but rather as a cyclostationary random process with a period of one year, we summarize the theory of cyclostationary random processes, present the corresponding generalized spectral method needed to characterize such process, and make a comparison between our analytic results and those obtained by applying our method to the simulated data. We find that, by measuring the generalized spectral components of the white-dwarf background, LISA will be able to infer properties of the distribution of the white-dwarfs binary systems present in our Galaxy.Comment: 36 pages, 15 figure

Implementation of spectrum sensing techniques for cognitive radio systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work presents a method for real-time detection of secondary users at the cognitive wireless technologies base stations. Cognitive radios may hide themselves in between the primary users to avoid being charged for spectrum usage. To deal with such scenarios, a cyclostationary Fast Fourier Transform accumulation method (FAM) has been used to develop a new strategy for recognising channel users under perfect and different noise environment conditions. Channel users are tracked according to the changes in their signal parameters, such as modulation techniques. MATLAB® Simulation tool was used to run various modulation signals on channels, and the obtained spectral correlation density function shows successful recognition between secondary and primary signals. We are unaware of previous efforts to use the FAM characteristics or other detection methods to make a distinction between channel users as presented in this thesis. A novel combination of both cognitive radio technology and ultra wideband technology is interdicted in this thesis, looking for an efficient and reliable spectrum sensing method to detect the presence of primary transmitters, and a number of spectrum-sensing techniques implemented in ultra wideband and cognitive radio component (UWB-CR) under different AWGN and fading settings environments. The sensing performance of different detectors is compared in conditions of probability of detection and miss detection curves. Simulation results show that the selection of detectors rely on the different fading scenarios, detector requirements and on a priori knowledge. Furthermore, result showed that the matched filter detection method is suitable for detecting signals through UWB-CR system under various fading channels. A general observation is that the matched filter detector outperforms the other detectors in all scenarios by an average of SNR=-20 dB in the level of probability of detection (Pd) , and the energy detector slightly outperforms the cyclostationary detector, in the level Pd at SNR=-20 dB. Furthermore, the thesis adapts novel detection models of cooperative and cluster cooperative wideband spectrum sensing in cognitive radio networks. In the proposed schemes, wavelet-based multi-resolution spectrum sensing and a proposed approach scheme are utilized for improving sensing performance of both models. On the other hand, cluster based cooperative spectrum sensing with soft combination Equal Gain Combination (EGC) scheme is proposed. The proposed detection models could achieve improvement of transmitter signal detection in terms of higher probability of detection and lower probability of false alarm. In the cooperative wideband spectrum sensing model, using traditional fusion rule, existing worst performance of false alarms by measurement is 78% of the sensing bands at an average SNR=5 dB; this compares with the proposed model, which is by measurement 19% false alarms of scanning spectrum at the same SNR for cluster cooperative wideband spectrum sensing. The proposed combining methods shows improvements of results with a high probability of detection (Pd) and low probability of false alarm (Pf) at an average SNR=-16 dB compared with other traditional fusion methods; this is illustrated through numerical results

A Framework to Analyze Energy Efficiency of Multi-Band Spectrum Sensing Algorithms

Cognitive radio (CR) is a device which can detect wireless communication channels that are not in use and adapt its parameters intelligently. Networks with CRs use the available frequency bands much more efficiently and hence have higher data rates compare to traditional radios. Spectrum sensing is the class of techniques used by CRs to understand its wireless environment. Recent research on evaluating multi-band spectrum sensing algorithms is limited to only algorithm complexity and optimization; therefore, the primary goal of the study is to devise a novel framework that analyzes a multi-band spectrum sensing algorithm in terms of energy consumption and algorithm efficiency. The proposed structure leads to a comparison and evaluation of a large class of multi-band spectrum sensing algorithms. Multi-band spectrum sensing search methods such as linear, random and binary are evaluated for energy loss and detection performance using the proposed framework

Efficient transfer entropy analysis of non-stationary neural time series

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these observations, available estimators assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that deals with the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method. We test the performance and robustness of our implementation on data from simulated stochastic processes and demonstrate the method's applicability to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscientific data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON

Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure