Research On Automatic Modulation Recognition Of Digital Communication Signals

Automatic modulation recognition is one of the key technologies, and also a hard nut to crack in the Software Radio and uncooperative receiver field. In recent years, due to rapid development of modern communication and signal processing technologies the communication systems and the corresponding modulation types are becoming more and more complicated and diverse. Besides, the channel environment is also becoming more and more deteriorated. All of above changes make modulation recognition become more and more difficult.The primary difficult problems the automatic modulation recognition must face, especially in the uncooperative environment, are summarized as follows: blind separation of single-carrier and multi-carrier signals in multipath channels; simultaneous high accuracy and wide scope of parameter estimation; dependency of estimation performance on some prior information of the signals that is not easy to obtain; sensitivity of instantaneous parameters to noise and multipath fading; uncertainty of Carrier frequency offset and initial phase which are critical in baseband recognition.This paper focuses on the key technologies of automatic digital modulation recognition including signal detection, parameter estimation, feature extraction and selection and classifier designing while laying emphasis on the following problems: recognition methods less depending on the prior information; parameter estimation independent of the modulation type; feature extraction insensitive to noise, multipath fading and frequency offset. The work finished in this paper is a part of a key engineering project of the army undertaken by the laboratory the author works with. The main work and innovative achievements obtained in this paper are summarized as follows.(1)Targeting at the problem that the time-domain-based signal detection algorithm is sensitive to noise, an adaptive signal detection algorithm based on the autocorrelation function is proposed. The algorithm set the adaptive threshold by reordering the autocorrelation function values and determining the quotient, thus is of less computational complexity and independent of modulation type, carrier offset and initial phase. Simulation results have proved the improved performances over the conventional energy-based detection algorithms under low SNR. Test on practical signals shows that the method can meet the needs caused by the diversity of the signals in uncooperative environment.(2)Targeting at the problem that the most currently available separation methods of single-carrier and multi-carrier signals (OFDM) can only be used for baseband signals, a method based on the Shintaro_K feature is proposed in this paper for IF single-carrier and OFDM signals. Besides, another method based on VAR (|| WT ||)for the same purpose is proposed by the use of the differences between the twice wavelet transform results of single-carrier and OFDM signals. Experiment results have verified the feasibility and show that the method, compared with the conventional methods in additive white Gaussian noise channel and multipath fading channel, does not need carrier offset estimation any longer and is of less computational complexity, insensitive to Gaussian white noise, however poorer anti-multipath performance.(3)With regard to parameter estimation, an improved blind SNR estimation algorithm based on subspace decomposition is proposed by the use of MDL criterion for determination of the subspace dimension. Simulation results show that the performance of the above algorithm is significantly better than the conventional ones, does not need threshold setting and without the need of prior information. To widen the estimation scope of the conventional maximum likelihood estimation algorithm, an improved algorithm based on the maximum likelihood estimation is proposed for expanding the estimation scope by roughly estimating the carrier frequency of the original signal in advance. A frequency offset estimation algorithm with enhanced accuracy is proposed based on segmented FFT. Targeting at the problem that the conventional wavelet-based symbol rate estimation methods are not suitable to the pulse shaping baseband signals and need too high SRN, a new blind symbol rate estimation algorithm based on sorting the waveforms of the wavelet transform is proposed. Because the algorithm uses the MAC spectrum line enhancement algorithm now only in a narrower search range, not only the computational complexity is remarkably reduced but also the influence of the noise pulse on the MAC spectrum is also reduced. Simulation results show that the new symbol rate estimation algorithm is of wide estimation range, high precision and moderate computational complexity.(4)As regards spectrum peak detection, an approach to define the characteristics of discrete spectral lines Pl ( n), N l( n) and peak number N p( n) along with a corresponding extraction approach are provided. Firstly, the method determines the range of spectral detection and spectral peak search, and then an enhanced spectral line detection algorithm based on MAC algorithm is employed in line feature extraction, remarkably reducing the interference of color noise and spectral fluctuation on the spectral line detection. Secondly, a spectral peak search algorithm based on binary clipping is proposed which can improve the accuracy of FSK order decision. The proposed spectral line enhancement algorithm and spectral peak search algorithm are of wider applicability and based on above algorithms a comprehensive modulation recognition scheme based on the features extracted from time domain, frequency domain and constellation Radon transform are provided. This method in fact employs less features and the decision process is also simplified.(5)Targeting at the problem of uncertainty in scale selection of the conventional recognition algorithms based on Haar wavelet transform which are not suitable to pulse shaping baseband signals a modulation recognition method based on optimal scale Haar wavelet transform is proposed. The method determines the optimal zoom level by using the maximum SNR gain before and after the transform. Simulation results show that the amplitude jumping and the ladder shapes become clearer if the optimal scale is used, thus the performances of the extracted features are remarkably improved. A modulation recognition method based on the Morlet wavelet transform is also proposed based on the derivation of Morlet wavelet transform of FSK, PSK and QAM signals. Fast convolution calculation of wavelet transform is used to speed up the Morlet wavelet transform. Simulation results show that, compared with the Haar wavelet, Morlet wavelet transform is of simplicity of scale selection, better noise immunity and less sensitivity on roll-off factor.(6)Targeting at the problem of the lack of prior knowledge in non-cooperation receiving, the feasibility of independent component analysis (ICA) towards communication signals is analyzed first, and then a modulation recognition method based on ICA is proposed. The method does ICA to signals first, then selects effective feature subset, and finally uses RBF neural network for modulation recognition. Simulation results show that the method is feasible and feature selection is effective. To speed up the algorithm and to guarantee the convergence of ICA, the FastICA algorithm, some of its key iterative steps are optimized in this paper, is applied to the modulation feature extraction and. Besides, an accelerated M-FastICA algorithm to speed up the convergence without affecting the recognition performance is also provided.(7)Targeting at the problem that the ICA-based modulation recognition method requires higher SNR, a method based on ICA in the wavelet domain is proposed. The method does ICA to the wavelet coefficients of communication signals, and the resulted separation matrix is also the separation matrix of the original communication signal. Simulation results show that signal wavelet coefficients are of more super-Gaussian compared with the original signal, and this modulation feature extraction method based on WTICA has the advantages of enhanced anti-noise performance and fast convergence.