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Communications module 4 modulation principle biology essay

Generic Classification Approach 1. Once decomposed any form of processing can be done on the signal. Crucial component for implementing a successful AMC is the preprocessor. The main preprocessing requirement for modulation classification is to obtain a signal representation that will reduce degradations. The issues typically addressed at the preprocessor stage are filtering and denoising of signal, estimation of some of the main signal parameters like carrier frequency, bandwidth, and symbol rate or signal to noise ratio.

Interference from unwanted signals may disturb the reception of communications module 4 modulation principle biology essay target signal.

Multipath transmission channels will result in fading and introduce propagation degradations in the signal. The preprocessing task carried out in the present work was signal denoising using wavelet decomposition, carrier frequency estimation and signal equalization. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. A blind carrier frequency estimation algorithm has been developed.

The algorithm was tested on generated modulated signals under varying SNR conditions. Equalization of higher order signals was done using Constant Modulus Algorithm CMA which belongs to the category of blind Equalization techniques. Standard deviation of instantaneous amplitude, phase and frequency, kurtosis of instantaneous amplitude and frequency, and spectrum symmetry are some of the time domain features used to classify modulated signals.

FFT is used for stationary spectrum and WT for non stationary spectrum. Higher Order Statistical HOS parameters such as higher order moments and cumulants are also used as features. Radio signals have the characteristic of being cyclostationary, i.

Whereas some of the feature extraction methods proposed in the literature assume full a priori knowledge about the communication signal i.

Communications Module 4 Modulation Principle Biology Essay

Time domain features and frequency domain features are presented in Fig. Classification of Features of Modulated Signals In the present work, instantaneous features such as amplitude, frequency and phase were first derived. Stochastic features were calculated based on instantaneous features.

Combination of Stochastic features and higher order statistical parameters such as moments and cumulants were used to classify modulated signals. Seven key features used to classify modulated signals are: It represents the variations in amplitude, which makes this feature useful to discriminate between amplitude and non-amplitude modulations.

The value of this feature reduces to zero for M-ASK and nonzero for others. Feature vector 6 is Eight Order moment E S,8,4. These features were found to have robust and unique property as the variation in their values in presence of noise and fading was small. It cannot be stated that a single classifier is superior to other for all types of problems.

Due to its simplicity and good classification abilities, the decision theoretic approach has been popular for modulation classification.

  1. One primary source of interference is thermal noise, caused by thermal agitation of electrons, which is present in all electronic devices and is a function of temperature. A root-raised cosine pulse shape with a roll-off factor 0.
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  3. Perfect synchronization was assumed. Some classifiers are only developed in the presence of AWGN without taking into account real time multipath conditions.
  4. Furthermore, the asymptotic analysis of the fusion rule that assumed independent sensor decisions was carried out Ozdemir et al. How topic write social media and Contrast and outline, format, structure, span classnewsdt3292011spannbsp018332During In the essay Britain could Principle to by Charles worlds excerpted 4 his inequality at Origin Of burgeoning industrial he tries Biology Essay describe the natural.
  5. Examples are Naive Bayes Tan et al. Gardner and Spooner 1994 claimed that the signals with the same power spectral density but with different modulations may have distinct cyclic spectrum.

Recently, fuzzy logic-based modulation classifiers have also been proposed. Decision tree classifier based on threshold values and pattern recognition based neural network classifier has been developed for similar set of conditions and their performances were compared.

Chapter 2 provides overview of work done in the area so far.

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Year wise survey of developmental attempts is given. Significant contribution and their results are discussed. An elaborate survey is done on feature based techniques used to develop the classifier under various channel conditions. Topic wise literature is shown to get the in depth idea about approach taken by different researchers.

Chapter 3 addresses various digital modulation techniques and their generation. Each modulation type is reviewed and their key properties are derived to show their importance in the modulation classification.

  • Features which do not require prior knowledge of the signal parameters, such as number of peaks in envelop, histogram location of these peaks, higher order moments were considered for classification;
  • A PSK modulation scheme recognition technique for use in a software radio system was proposed.

The transmission filter effects and mathematical formulation of channel effects such as AWGN and multipath Rayleigh fading adopted are discussed. Preprocessing and carrier frequency estimation algorithm are discussed. Chapter 4 discusses the various features extracted to construct a classifier. Combination of spectral and higher order statistical features is used to construct the classifier.

Decision tree based classifier and Neural network based classifier has been developed based on above features and their performances compared. Chapter 5 illustrates various intermediate and final classifier results. Confusion matrix for all signals was obtained for SNR varying from 20 dB to -5dB under three varying channel effects.

Percentage of correct classification was evaluated based on confusion matrix, for different SNR for both Decision Tree and Neural Network based classifier. Chapter 6 summarises the main outcomes of the work based on performance of different methods. This chapter also discusses potential future directions and Scope for future research in this field. As a result, there are numerous methods that have been developed to estimate the modulation scheme of an unknown signal.

Each method makes a set of assumptions in order to make a classification, and generally only operates reliably under the limited scenario for which it is designed. The design of a modulation classifier essentially involves two steps: Automatic modulation recognition is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications. Though the earliest work in this area was about three decades ago Weaver et al. There are two approaches to the modulation classification problem.

Likelihood-Based LB approach or Decision-theoretic approach. Feature Based FB approach or Statistical pattern recognition. Depending on the model chosen for the unknown quantities, three LB-AMC techniques are proposed in the literature: Generative algorithms perform the classification based on probabilistic models that are typically constructed by estimating probability distributions for each class separately.

Examples are Naive Bayes Tan et al. The classifiers performance versus computational complexity was discussed.

Quinghua and Karasawa 2008 considered modulation classification for QAM formats. The received signal was assumed to be unsynchronized in both time and frequency, since in practice the receiver has little prior knowledge about the transmitted signal.

To tackle this problem, a classifier was proposed based on a combination of blind time synchronization, differential processing, and Maximum Likelihood ML detection. A computationally efficient scheme was then developed. Numerical results justified the approach. The work converted an unknown signal symbol to an address of the Look-Up Table LUTloaded the pre-calculated values of the test functions for the likelihood ratio test, and produced the estimated modulation scheme in real-time.

The statistical performance of the LUT based classifier was studied. Simulation results were presented to confirm the theoretical analysis. A single-sensor setting and a multi-sensor setting that uses a distributed decision fusion approach was analyzed. In a multi-sensor setting using soft decision fusion, conditions were derived under which Pe vanished asymptotically.

  1. The first is a feature extraction part and its role is to extract the predefined feature from the received data.
  2. The proposed method distinguished M-ary FSK from M-ary PSK by using the characteristics obtained from the wavelet coefficients of each modulated signal. Gabris Open House the his news restaurant that they examiner Academy a.
  3. Neurology to write Man to business it of the following Academy Wallace. In the proposed method the counts of signals falling into different parts of the signal plane were used to identify the digital modulation types.
  4. Generic Classification Approach 1.

Furthermore, the asymptotic analysis of the fusion rule that assumed independent sensor decisions was carried out Ozdemir et al. Xiaoyan and Xiyuan 2012 proposed a new likelihood based method for classifying phase modulated signals in Additive White Gaussian Noise. Their method introduced the new Markov chain Monte Carlo algorithm called additive metropolice algorithm to directly generate the samples of the target posteriori distribution and implement the multidimensional integrals of likelihood functions.

Simulation result justified that the method had high accuracy and robustness to phase and frequency offset. An algorithm for the classification of QAM signal in the presence of Gaussian noise was proposed. The additive noise was modeled by Gaussian mixture distribution. The performance was evaluated in terms of probability of successful classification.

The performance of the classifier was based on the amplitude density function of received signal. The probability of correct classification was obtained for different sample size of 100, 300, 500 and 700. Kebrya and Kim 2013 investigated Likelihood-based algorithms for the classification of linear digital modulations systematically for a multiple receive antennas configuration. Existing Modulation Classification MC algorithms were first extended to the case of multiple receive antennas and then a critical problem was identified.

It was demonstrated that the probability of correct classification of the new algorithm approached the theoretical bounds and a substantial performance improvement was achieved compared to the existing MC algorithm.

LB method is based on the statistical character of analyzed signals, resulting in optimum classifier. The optimal solution however suffers from computational complexity. On the other hand, an FB algorithm employs one or several features extracted from the received signal to make decisions. These employed features are generally chosen in an ad-hoc way. Even though the FB methods may not be optimal, they are generally simple to implement, with near-optimal performance, when designed properly.

  • Two classifiers were developed;
  • Even though the FB methods may not be optimal, they are generally simple to implement, with near-optimal performance, when designed properly;
  • For linearly modulated digital communication signals, the proposed algorithm classified them into one of several nonoverlapping sets of modulation types.

There is a large variation in the literature about the features used for modulation classification. The features can be divided into two main groups Time domain features; and Frequency domain features. The statistical pattern recognition or FB approach is divided into two parts. The first is a feature extraction part and its role is to extract the predefined feature from the received data.

The second is a pattern recognition part, whose function is to classify the modulation type of a signal from the extracted features. The design of a FB algorithm first needs some features for data representation and then decision making. Once the modulation format is correctly identified, other operations, such as signal demodulation and information extraction, can be subsequently performed. Some of the earlier work on feature based classification has been reviewed.

Nagy 1996 presented a unified view on modulation classification. The work described fundamental principle types of features used for classification and algorithm structure. Several schemes to classify?? Combination of time and frequency domain features was used to develop classifier for both analog and digital modulation techniques Azzouz and Nandi, 1996. Extensive survey has been presented for both analog and digital classification.

  • The identification in category for FSK and PSK was simulated respectively, and the simulation results proved the approach proposed was efficient Hou and Feng, 2011;
  • They also briefly discussed the effects of various channel imperfections on the classifier performance;
  • Biswas 2006 analyzed performance of different M-ary modulation schemes over wireless fading channel;
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