Coherence as a feature of real HF signals

Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper was published in Noise in Communication Systems, edited by Costas N. Georghiades, Langford B. White, Proc. of SPIE Vol. 5847 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.High-frequency (HF) communications is undergoing a resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals that propagate by multiple ionospheric modes. One aspect of modulation recognition is the extraction of signal identifying features. The most common features for modulation recognition are instantaneous phase, amplitude, and frequency. Many papers present results based on synthetic data and unproven assumptions. However, this paper continues our previous work by applying the coherence function to noisy real HF groundwave signals; which removes the need for synthesized data and unrealistic assumptions.James E. Giesbrecht, Russell Clarke, and Derek Abbot

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks

Author name used in this publication: Faisal Nadeem Khan2011-2012 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Signals Classification and Identification for Cognitive Radio

From the past few decades the need for higher data rates in wireless communication has increased exponentially. The spectrum access policy has restricted the growing demand of wireless devices. Out of the total given spectrum only a small portion is given for open user and a large part is given for licensed user. But the unlicensed spectrum is used more than the licensed spectrum, which forced the FCC to design a policy so that the limited spectrum can be used efficiently. The spectral occupancy of licensed spectrum is very less as compared to the unlicensed spectrum. Cognitive radio has emerged as a solution for this inefficient utilization of licensed spectrum; it identifies the unused portion of licensed spectrum which is called white space and makes them available for unlicensed user. Before giving the white space to the secondary user for transmitting the signals, it is required to identifies and classify the signals, so that the cognitive radio can work efficiently. so to classify the incoming signals many method are used like feature extraction method and neural network method.in feature extraction method, first we have to find out the feature value from all the signals then by comparing that with the threshold value we can find out the modulation type of the signal.In neural network method, we have to give the feature value to a neural network and that network will find out the type of the signa

Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation

Cognitive analytics employs and analyses complex and heterogeneous data sources generating deeper insights that mimic the natural intelligence of the human brain. Cognitive analytics-enabled Artificial Intelligence (AI) that promotes Business Model Innovation (BMI) for the efficiency of the healthcare system is a nascent and undertheorized domain. Within the healthcare management systems, stakeholders’ engagement with AI, particularly with responsible AI, to optimize BMI and improve business performance is bounded by several caveats. Using the Technology Acceptance Model (TAM) and Social Network Theory (SNT) as our conceptual foci, we empirically examine through the Multilayer Perceptron Neural Network the extent to which responsible AI leads to Business Model Innovation (BMI) through the stakeholders’ engagement. Our contributions are novel which demonstrate that cognitive analytics-enabled responsible AI is central to innovation, and healthcare stakeholders exhibit a robust propensity to reorientate and innovate their existing BMI to achieve improved business performance. It has significant implications for innovation, AI and cognitive analytics literature

Classification of linear and nonlinear modulations using Bayesian methods

La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis. Plus précisément, nous étudions trois techniques permettant d'estimer la loi a posteriori d'une modulation au niveau du récepteur. La première technique estime les paramètres inconnus associés aux diverses imperfections affectant le récepteur à l'aide d'une approche Bayésienne couplée avec une méthode de simulation MCMC (Markov Chain Monte Carlo). Une deuxième technique utilise l'algorithme de Baum Welch qui permet d'estimer de manière récursive la loi a posteriori du signal reçu et de déterminer la modulation la plus probable parmi un catalogue donné. La dernière méthode étudiée dans cette thèse consiste à corriger les erreurs de synchronisation de phase et de fréquence avec une boucle de phase. Les algorithmes considérés dans cette thèse ont permis de reconnaître un certain nombre de modulations linéaires de types QAM (Quadrature Amplitude Modulation) et PSK (Phase Shift Keying) mais aussi des modulations non linéaires de type GMSK (Gaussian Minimum Shift Keying). ABSTRACT : This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation. The first technique estimates the unknown parameters associated with various imperfections using a Bayesian approach coupled with Markov Chain Monte Carlo (MCMC) methods. A second technique uses the Baum Welch (BW) algorithm to estimate recursively the posterior probabilities and determine the most likely modulation type from a catalogue. The last method studied in this thesis corrects synchronization errors (phase and frequency offsets) with a phase-locked loop (PLL). The classification algorithms considered in this thesis can recognize a number of linear modulations such as Quadrature Amplitude Modulation (QAM), Phase Shift Keying (PSK), and nonlinear modulations such as Gaussian Minimum Shift Keying (GMSK

Automatic modulation classification of communication signals

The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know in advance if the received communication signal is analogue modulated or digitally modulated. Furthermore, it is noted that the applications of the currently existing AMR algorithms designed for handling both analog and digital communication signals are rather restricted in practice. Motivated by this, an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals is developed in this dissertation. The proposed algorithm is able to recognize the concrete modulation type if the input is an analog communication signal and to estimate the number of modulation levels and the frequency deviation if the input is an exponentially modulated digital communication signal. For linearly modulated digital communication signals, the proposed classifier will classify them into one of several nonoverlapping sets of modulation types. In addition, in M-ary FSK (MFSK) signal classification, two classifiers have also been developed. These two classifiers are also capable of providing good estimate of the frequency deviation of a received MFSK signal. For further classification of linearly modulated digital communication signals, it is often necessary to blindly equalize the received signal before performing modulation recognition. This doing generally requires knowing the carrier frequency and symbol rate of the input signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm have been developed. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. Unlike the cyclic correlation based estimators, it does not require the transmitted symbols being non-circularly distributed. The symbol rate estimator is based on digital communication signals\u27 cyclostationarity related to the symbol rate. In order to adapt to the unknown symbol rate as well as the unknown excess bandwidth, the received signal is first filtered by using a bank of filters. Symbol rate candidates and their associated confident measurements are extracted from the fourth order cyclic moments of the filtered outputs, and the final estimate of symbol rate is made based on weighted majority voting. A thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation