Elsayed Elsayed Azzouz

Algorithms for automatic modulation recognition of communication signals

This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB.

Automatic identification of digital modulation types

Abstract In both covert and overt operations, modulation identification plays an important role. In communication intelligence (COMINT) applications the main objective is the perfect monitoring of the intercepted signals and one of the parameters that affect the perfect monitoring is the modulation type of the intercepted signal. In this paper, a set of decision criteria for identifying different types of digital modulation is developed. Also, all the key features used in the identification algorithm are calculated using the conventional signal processing methods. Computer simulations for different types of band-limited digitally modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude, and phase of different types of digitally modulated signals are derived. Also, two software solutions for estimating the instantaneous phase in the weak segments of a signal are introduced and analyzed. Finally, it is found that all modulation types of interest have been classified with success rate ≥90% at SNR = 10 dB.

Automatic analogue modulation recognition

Abstract For several reasons, modulation recognition is extremely important in communication intelligence (COMINT). In this paper, a global procedure for recognition of analogue modulation types is developed. Computer simulations for different types of band-limited analogue modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude and phase as well as the Fourier transform of different analogue modulation types are derived and used to set up a recognition procedure. It is found that all types of analogue modulation have been classified with success rate ⩾ 90% at SNR = 10 dB.

Modulation recognition using artificial neural networks

In this chapter the artificial neural networks (ANNs) approach as another solution for the modulation recognition process is studied in some detail. Unlike in other algorithms, especially those which utilise the decision-theoretic (DT) approach (Chapters 2–4), where a suitable threshold for each key feature has to be chosen, the threshold at each node (neuron) is chosen automatically and adaptively. Furthermore, in the DT approach, many algorithms based on the same key features can be developed by applying the extracted key features in different order in the classification algorithm and they perform with different success rates at the same SNR. In the DT algorithms, it was found that only one key feature is considered at a time. As a result, the probability of correct decision about a modulation type in these algorithms is based on the time-ordering of the key features used as well as probability of correct decision derived from each key feature. On the other hand, in the ANN algorithms all the key features are considered simultaneously. So, the time order of the key features does not affect on the probability of correct decision of on the modulation type of a signal. For that reason, it is suggested that the use of the ANN approach for solving the modulation recognition process may have better performance than the DT approach.

Recognition of Analogue Modulations

The current trend in modern communication systems is the use of digital modulation types rather than the analogue ones. However, the analogue modulations are still in use especially in the third world countries. So, the aim of this chapter is to discuss some of the available analogue modulation recognisers and to introduce some quick and fast algorithms for the well known analogue modulation types. The analogue modulation types that can be classified with the developed algorithms, introduced in this chapter, are: AM (with different modulation depths (60% and 80%)), DSB, VSB, LSB, USB, FM (with different modulation indices (5 and 10)), and combined modulated signals (with different modulation depths and different modulation indices). Furthermore, a non-intelligible simulated speech signal is used as a modulating signal for simulating the different types of analogue modulations to increase the degree of realism.

Recognition of Digital Modulations

In modern communication systems, digital modulation techniques rather than analogue ones are frequently used. So, the new trend is the digital modulation recognisers. Most of the digital modulation recognisers discussed in Chapter 1 utilise the pattern recognition approach such as [4], and [14]. So, they require long signal duration and the processing time may be very long; and this leads to the use of these algorithms in off-line analysis. Furthermore, some of these recognisers such as [4] require excessive computer storage to ensure correct modulation recognition. Indeed, most of them are assigned to a subset of modulation types of interest. Also, the practical implementation for some of these recognisers such as [14], [21], and [22] is excessively complex. However, the work on some of these recognisers attempts to identify digital modulations with number of levels > 4. On the other hand, the number of samples used in the algorithms presented in this thesis to decide about the modulation type is 2048 (equivalent to 1.707 msec.) and this is likely to be suitable for on-line analysis.

Recognition of Analogue & Digital Modulations

Some of the modulation recognisers for both analogue and digital modulations discussed in Chapter 1 utilise the pattern recognition approach, which requires long signal duration and strong SNR to decide about the modulation type of an RF signal. So, these recognisers are mainly used in off-line analysis. Furthermore, none of these recognisers considered VSB and combined modulation signals. In this chapter a set of fast algorithms for the recognition of both analogue and digital modulations without any a priori information about the nature of a signal, whether it is analogue or digital, is introduced. Moreover, the AMRAs introduced in Chapter 2 and Appendix C.1 are concerned with only analogue modulated signals. Also, the DMRAs introduced in Chapter 3 and Appendix C.2 are concerned with digitally modulated signals only. So, for these algorithms, there is a priori information about the nature of the signal under consideration. Sometimes, there is no a priori information about the nature of a signal. In this case the modulation recognition algorithms introduced in Chapter 2, Chapter 3, Appendix C.1, and Appendix C.2 cannot discriminate between some types of modulations such as MASK and AM, MFSK and FM, PSK2 and DSB, and PSK4 and combined moduktted signals. It is worth noting that the only difference between such modulation types is in the nature of the modulating signal used to generate these modulation types. In the analogue modulated signals a speech signal is used as modulating signal while a digital symbol sequence is used as a modulating signal in the digitally modulated signals.

Summary and Suggestions for Future Directions

In this book, a set of fast algorithms has been introduced for on-line automatic modulation recognition of communication signals. Three groups of algorithms utilise the decision-theoretic approach in addition to another three groups of algorithms utilising the ANN approach. In each approach, one group is for the recognition of analogue modulations only, the second is for the recognition of digital modulations only, and the third is for the recognition of both analogue and digital modulations without any a priori information about the nature of the signal. A number of novel key features has been proposed to fulfil the requirement of these algorithms. Due to the simplicity of the key feature extraction (using conventional signal processing tools) and the decision rules used in the decision-theoretic approach as well as the simplicity of the network structures in the ANN approach, all the developed algorithms are seen to be suitable for on-line analysis.