Automatic modulation classification (AMC) is an intermediate step between signal detection and demodulation, it plays a key role in various civilian and military applications such as signal confirmation, interference identification, monitoring, spectrum management, and surveillance.
The interest in modulation classification has been growing since the late eighties up to date. there is a challenging problem, especially in non-cooperative environments, where no prior knowledge on the incoming signal is available .
Modulation classification is to determine or identify the type of modulation of a communication signal, while that if we know the type of modulation we easily can restore the information from the modulated signal and finally get the original message by use demodulation at the receiver side. and recently has attracted many attention that is making possible to build Intelligent receivers which can recognize the modulation type without having any prior information from the transmitting signal. Thus intelligent transmitters- receivers appears that can select the most appropriate modulation type to transmit the information due to the environmental condition and communicative channel, and also the receiver can recognize the changes of the modulation types immediately. Therefore, in the subject of the communication transparency is developed due to the modulation's types. Recognition of the modulation type of an unknown signal provides valuable insight into its structure, origin and properties, in addition to know the modulation type, some other parameters should be estimated before successful demodulation , like general pattern recognition systems , consist of measurement, feature extraction, and decision parts. The measurement is obtained by a front-end which will receive the signal of interest and carry out some preprocessing such as filtering, down-conversion, equalization, and sampling. The feature extraction part reduces the dimensionality of the measurement by extracting the distinctive features which should be simple and fast to calculate.
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In order to extract the features from the signal without distorting the signal in a manner that would affect the information that may be of interest and this is indeed a difficult process. A wide variety of techniques and approaches, for determining the modulation type applied on the signal. These techniques extracts some parameters from modulated signal and compare them to those of different modulated signal.
There are several ways to make the decision based on the obtained features such as decision functions, distance functions, and neural networks. The received signal to be classified according to its modulation type contains much uncertainty which should be encountered by statistical tools. Therefore the known methods are based on different statistics obtained from the received signal. These statistics can be derived from continuous-time signals and they hold for sampled discrete signals which may be processed by some digital device. Some known methods are based on the higher-order statistics of the received signal but they are often very complicated and difficult to obtain .In this study, we have concentrated on the problem of the feature extraction in the modulation classifiers. The features are selected for the simulation part according to their applicability for the modulation types used in radio communication existing methods for modulation classification span four main approaches. Statistical pattern recognition, decision theoretic (Maximum Likelihood), M-th law non- linearity and filtering.
The objective of real time modulation classification is
1. Learing how to formulate the AMC Problem,
2. Reviewing the theory of AMC
3. Designing a complete AMC algorithm using MTLAB/LABVIEW
4. Real-time implementation of AMC on National Instrument Hardware
The importance of REAL TIME MODULATION CALASSIFICATION:
Signal Processing systems for communications will operate in open environments,
where it is required that signals of different typologies be processed, which come from
different sources, hence with different characteristics and for different user requirements.
Communication signals traveling in space with different modulation types
and different frequencies fall in very wide band. Usually, it is required to identify
and monitor these signals for many applications, both defense and civilian. Civilian
applications may include monitoring the non-licensed transmitters, while defense applications
may be Electronic surveillance systems.
Modulation recognition is extremely important in COMINT applications for several
reasons. Firstly, applying the signal to an improper demodulator may partially
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or completely damage the signal information content. Secondly, knowing the correct
modulation type help recognize the threat and to determine suitable jamming waveform.
The component of our project is :-
1-Good programming skils (Labview)
2-Card - hardware will be used to feed a modulated signal to the computer
3-Signal processing ( as an input )
4-A program will be written in lab view to read amodulated signal
The chapter focus on definition of the modulation in general and there types and the modulation classification or what is meant by it, then the importance of modulation classification like in which field it used and why we need AMC in many application, then how it can benefit us and the goals of using AMC, also the first chapter offer the objectives of the AMC .
the chapter focus on the definition of AMC but more specific, then mentioned about the algorithm of the modulation classification and focus on it's equations analysis .also the chapter mentioned some information and parameter about the device that we will use in AMC , and about the Lab View program that will be used to program the device.
Finally , in chapter offer some flow chart for implement of AMC as will as the steps that we will out for the application .
1.2 Modulation :
modulation is the process of varying one or more properties of a high frequency periodic waveform , called the carrier signal, with respect to a modulating signal (which typically contains information to be transmitted). This is done in a similar fashion to a musican modulating a tone (a periodic waveform) from a musical instrument by varying its volume, timing and pitch. The three key parameters of a periodic waveform are its amplitude(volume),its phase(timing) and its frequency(pitch), all of which can be modified in accordance with a low frequency signal to obtain the modulated signal. Typically a high-frequency sinusoid waveform is used as carrier signal, but a square wave pulse train may also occur. In telecommunications,modulation is the process of conveying a message signal, for example a digital bit stream or an analog audio signal, inside another signal that can be physically transmitted. Modulation of a sine waveform is used to transform a baseband message signal into apassband signal, for example low-frequency audio signal into a radio-frequency signal (RF signal). In radio communications, cable TV systems or the public switched telephone network for instance, electrical signals can only be transferred over a limited passband frequency spectrum, with specific (non-zero) lower and upper cutoff frequencies. Modulating a sine-wave carrier makes it possible to keep the frequency content of the transferred signal as close as possible to the centre frequency (typically the carrier frequency) of the passband . A device that performs modulation is known as a modulator and a device that performs the inverse operation of modulation is known as a demodulator (sometimes detector or demod). A device that can do both operations is a modem(modulator-demodulator).
: 1.2.1 Type of modulation
There are types of modulation :
In digital modulation , an analog carrier signal is modulated by a digital bit stream. Digital modulation methods can be considered as digital-to-analog conversion, and the corresponding demodualtion or detection as analog-to-digital conversion. The changes in the carrier signal are chosen from afinite number of M alternative symbols (the modulation alphabet).
http://upload.wikimedia.org/wikipedia/en/thumb/4/4a/Baud.svg/200px-Baud.svg.png A simple example: A telephone line is designed for transferring audible sounds, for example tones, and not digital bits (zeros and ones). Computers may however communicate over a telephone line by means of modems, which are representing the digital bits by tones, called symbols. If there are four alternative symbols (corresponding to a musical instrument that can generate four different tones, one at a time), the first symbol may represent the bit sequence 00, the second 01, the third 10 and the fourth 11. If the modem plays a melody consisting of 1000 tones per second, the symbol rate is 1000 symbols/second, or buad. Since each tone (i.e., symbol) represents a message consisting of two digital bits in this example, the bit rate is twice the symbol rate, i.e. 2000 bits per second. This is similar to the technique used by dialup modems as opposed to DSL modems. According to one definition of digital signal, the modulated signal is a digital signal, and according to another definition, the modulation is a form of digital to analog modulation conversion. Most textbooks would consider digital modulation schemes as a form of digital transmission synonymous to digital transmission very few would consider it as analog transmission.
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Fundamental digital modulation methods
The most fundamental digital modulation techniques are based on keying :
1. PSK (phase-shift keying), a finite number of phases are used.
2. FSK (frequency-shift keying), a finite number of frequencies are used.
3. ASK (amplitude-shift keying), a finite number of amplitudes are used.
4. QAM (quadrature amplitude modulation ), a finite number of at least two phases, and at least two amplitudes are used.
In QAM, an inphase signal (the I signal, for example a cosine waveform) and a quadrature phase signal (the Q signal, for example a sine wave) are amplitude modulated with a finite number of amplitudes, and summed. It can be seen as a two-channel system, each channel using ASK. The resulting signal is equivalent to a combination of PSK and ASK.
In all of the above methods, each of these phases, frequencies or amplitudes are assigned a unique pattern of binary bits . Usually, each phase, frequency or amplitude encodes an equal number of bits. This number of bits comprises the symbol that is represented by the particular phase, frequency or amplitude.
If the alphabet consists of M = 2N alternative symbols, each symbol represents a message consisting of N bits. If the symbol rat is fs symbols/second the data rate is Nfs bit/second.
For example, with an alphabet consisting of 16 alternative symbols, each symbol represents 4 bits. Thus, the data rate is four times the baud rate.
In the case of PSK, ASK or QAM, where the carrier frequency of the modulated signal is constant, the modulation alphabet is often conveniently represented on a constellation diagram , showing the amplitude of the I signal at the x-axis, and the amplitude of the Q signal at the y-axis, for each symbol.
2. Pulse modulation methods
Pulse modulation schemes aim at transferring a narrowband analog signal over an analog baseband channel as a two-level signal by modulating a pulse wave . Some pulse modulation schemes also allow the narrowband analog signal to be transferred as a digital signal (i.e. as a quantized discrete-time signal) with a fixed bit rate, which can be transferred over an underlying digital transmission system, for example some line code. These are not modulation schemes in the conventional sense since they are not channel coding schemes, but should be considered as source coding .
3. Analog modulation methods
In analog modulation, the modulation is applied continuously in response to the analog information signal.
http://upload.wikimedia.org/wikipedia/commons/thumb/a/a4/Amfm3-en-de.gif/200px-Amfm3-en-de.gif Common analog
modulation techniques are:
1. Amplitude modulation (AM) (here the amplitude of the carrier signal is varied in accordance to the instantaneous amplitude of the modulating signal)
2. Double-sideband modulation (DSB) ,double-sideband modulation with carrier (DSB-WC) used on the AM radio broadcasting band or double-sideband suppressed carrier (DSB-SC).
3. single-sideband modulation (SSB) ,single-sideband modulation with carrier (SSB-WC) or double-sideband suppressed carrier (DSB-SC).
4.Vestigal -sideband modulation (VSB, or VSB-AM).
5.Quadrature Amplitute modulation (QAM).
6.Angle modulation , divided into a frequency modulation (FM) ,here the frequency of the carrier signal is varied in accordance to the instantaneous amplitude of the modulating signal or phase modulation(PM) , the phase shift of the carrier signal is varied in accordance to the instantaneous amplitude of the modulating signal .
1.3 Automatic Modulation Classification :
Automatic modulation classiï¬cation (AMC) is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications. Implementation of advanced information services and systems for military applications, in a crowded electromagnetic spectrum, is a challenging task for communication engineers. Friendly signals should be securely transmitted and received, whereas hostile signals must be located, identiï¬ed and jammed. The spectrum of these signals may range from high frequency (HF) to millimeter frequency band and their format can vary from simple narrowband modulations to wideband schemes. Under such conditions, advanced techniques are required for real-time signal interception and processing, which are vital for decisions involving electronic warfare operations and other tactical actions. Furthermore, blind recognition of the modulation format of the received signal is an important problem in commercial systems, especially in software deï¬ned radio (SDR), which copes with the variety of communication systems. Usually, supplementary information is transmitted to reconï¬gure the SDR system. Blind techniques can be used with an intelligent receiver, yielding an increase in the transmission efï¬ciency by reducing the overhead. Such applications have emerged the need for ï¬‚exible intelligent communication systems, where the automatic recognition of the modulation of a detected signal is a major task. The design of a modulation classiï¬er essentially involves two steps: signal preprocessing and proper selection of the classiï¬cation algorithm. Preprocessing tasks may include, but not limited to perform some or all of, noise reduction, estimation of carrier frequency, symbol period, and signal power, equalization etc. Depending on the classiï¬cation algorithm chosen in the second step, preprocessing tasks with different levels of accuracy are required; some classiï¬cation methods require precise estimates, whereas others are less sensitive to the unknown parameters.
Regarding the second step, two general classes of AMC algorithms can be crystallised, likelihood-based (LB) and feature-based (FB) methods, respectively. The former is based on the likelihood function of the received signal and the decision is made comparing the likelihood ratio against a threshold. A solution offered by the LB algorithms is optimal in the Bayesian sense, it minimises the probability of false classiï¬cation. The optimal solution suffers from computational complexity, which in many cases of interest naturally gives rise to suboptimal classiï¬ers. In the FB approach, on the other hand, several features are usually employed and a decision is made based on their observed values. These features are normally chosen in an ad hoc way. Although an FB-based method may not be optimal, it is usually simple to implement, with near-optimal performance, when designed properly. Once the modulation format is correctly identiï¬ed, other operations, such as signal demodulation and information extraction, can be subsequently performed. In general, AMC is a challenging task, especially in a non-cooperative environment, where in addition to multipath propagation, frequency-selectivity and time-varying nature of the channel, no prior knowledge of the incoming signal is available.
In recent years, new technologies for wireless communications have emerged. The wireless industry has shown great interest in orthogonal frequency division multiplexing (OFDM) systems, due to the efï¬ciency of OFDM schemes to transmit information in frequency selective fading channels, without complex equalizers. Multiple-input multiple-output (MIMO) systems have also received considerable attention, owing to the signiï¬cant capacity increase they offer. Such emerging technologies in wireless communications have raised new challenges for the designers of signal intelligence and SDR systems, such as, discriminating between OFDM and single carrier modulations, identiï¬cation of signals transmitted from multiple antenna systems.
Research on automatic classiï¬cation of both digital and analogue modulations has been carried out for at least two decades. Partial surveys of algorithms for identifying digitally modulated signals are given in. Of course, many techniques have been developed, which are different from each other when it comes to details. However, general structures that connect a variety of apparently different techniques can be identiï¬ed. we provide a uniï¬ed comprehensive overview of what has been accomplished so far in this area, highlighting the bottlenecks and challenging issues which need to be addressed by further research. A comparison among the performance of different LB and FB algorithms is also carried out, emphasising the advantages and disadvantages of diverse techniques.
1.3.1 Importance of AMC
Modulation classiï¬cation is the process of deciding, based on observations of the received signal, what modulation is being used at the transmitter. It has long been an important component of noncooperative communications , It is also becoming increasingly important in cooperative communications, with the advent of the software-deï¬ned autonomous radio. Such a radio must conï¬gure itself including what demodulator to use, based on the incoming signal . Automatic classification of the modulation type of an unknown signal has an important role in communication systems, especially in civilian purposes and in communication intelligence and military applications, to extract useful information from the signal. It has emerged in the research of communication systems. It is necessary for a multipurpose receiver automatic choose among modulation types, in the presence of noise, in order to extract the features from the signal without distorting the signal in a manner that would affect the information that may be of interest and this is indeed a difficult process. A wide variety of techniques and approaches, for determining the modulation type applied on the signal. from a certain modulated signal and compares these parameters to those of other different modulation type of the input signal. This robust classification system can identify the modulation types with a low Signal to Noise Ratio (SNR) of 6 dB with an efficiency of 98% and 97% for analogue and digital modulations respectively and at 3 dB with an efficiency of 83% and 86.6% ..