Zhechen Zhu

Automatic Modulation Classification Using Combination of Genetic Programming and KNN

Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbor (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.

Feature generation using genetic programming with comparative partner selection for diabetes classification

Abstract The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov–Smirnov test, Kullback–Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), utilising the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbor and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.

Genetic algorithm optimized distribution sampling test for M-QAM modulation classification

With the classification performance and computational complexity in mind, we propose a new optimized distribution sampling test (ODST) classifier for automatic classification of M-QAM signals. In ODST, signal cumulative distributions are sampled at pre-established locations. The actual sampling process is transformed into simple counting task for reduced computational complexity. The optimization of sampling locations is based on theoretical signal models derived under various channel conditions. Genetic Algorithm (GA) is employed to optimize distance metrics using sampled distribution parameters for distribution test between signals. The final decision is made based on distances between tested signal and candidate modulations. By using multiple sampling locations on signal cumulative distributions, the classifiers robustness is enhanced for possible signal statistical variance or signal model mismatching. AWGN channel, phase offset, and frequency offset are considered to evaluate the performance of the proposed algorithm. Experimental results show that the proposed method has advantages in both classification accuracy and computational complexity over most existing classifiers.

Automatic digital modulation classification using Genetic Programming with K-Nearest Neighbor

Automatic modulation classification is an intrinsically interesting problem with various civil and military applications. A generalized digital modulation classification algorithm has been developed and presented in this paper. The proposed algorithm uses Genetic Programming (GP) with K-Nearest Neighbor (K-NN). The algorithm is used to identify BPSK, QPSK, 16QAM and 64QAM modulations. Higher order cumulants have been used as input features for the algorithm. A two-stage classification approach has been used to improve the classification accuracy. The high performance of the method is demonstrated using computer simulations and in comparisons with existing methods.

Blind Digital Modulation Classification Using Minimum Distance Centroid Estimator and Non-Parametric Likelihood Function

In this paper, we propose a blind modulation classifier that differs from most existing classifiers. A low complexity minimum distance centroid estimator is suggested to estimate the channel gain and carrier phase jointly. The estimation is achieved by minimizing a signal-to-centroid distance. A new non-parametric likelihood function is proposed for fast classification with unknown noise variance and distribution. Numerical results show that the estimator provides reliable estimation of signal centroids, enabling an accurate classification with a non-parametric likelihood function. When different channel conditions are simulated, the proposed blind classifier achieves similar classification accuracy versus non-blind state-of-the-art classifiers while being more robust and having much lower complexity.

Augmented Genetic Programming for automatic digital modulation classification

Automatic modulation classification (AMC) is used to identify automatically the modulation types of transmitted signals using the received data samples in the presence of noise. It is a very important process for a receiver that has no, or limited, knowledge of signals received. It is an intermediate step between signal detection and demodulation and has various civilian and military applications. In this paper we propose to use Genetic Programming (GP) with KNN classifier for automatic classification of digital modulation types for the first time. The method proposed here has been designed for BPSK, QPSK, 16QAM and 64QAM. The results from simulation experiments show that the proposed method is able to identify the above modulation types at SNRs of 10dB and 20dB. The performance of the proposed method has been compared with existing methods and it is found to provide the best results so far.

Blind Modulation Classification for MIMO systems using Expectation Maximization

In this paper, we propose a blind modulation classifier for multiple-input multiple-output (MIMO) systems. The assumption of unknown channel matrix and noise variance has not been considered prior to this work. For each modulation candidate, the channel parameters are jointly estimated via expectation maximization (EM). The resulting estimation is used for the likelihood evaluation of the corresponding modulation candidate. Classification decision is reached using the maximum likelihood (ML) criterion. Classification performance is validated in simulated fading channel with white Gaussian noise. The proposed classifiers achieves robust classification accuracy in most scenarios for BPSK, QPSK, and 16-QAM modulations.

Adapted Geometric Semantic Genetic programming for diabetes and breast cancer classification

In this paper, we explore new Adapted Geometric Semantic (AGS) operators in the case where Genetic programming (GP) is used as a feature generator for signal classification. Also to control the computational complexity, a devolution scheme is introduced to reduce the solution complexity without any significant impact on their fitness. Fishers criterion is employed as fitness function in GP. The proposed method is tested using diabetes and breast cancer datasets. According to the experimental results, GP with AGS operators and devolution mechanism provides better classification performance while requiring less training time as compared to standard GP.

Modulation classification in MIMO fading channels via expectation maximization with non-data-aided initialization

Non-data aided channel estimation is discussed in this paper to enable blind modulation classification in multiple-input multiple-output fading channels. The channel parameters are jointly estimated via expectation maximization under each modulation hypothesis. Instead of pilot symbols, the initialization of the channel matrix is achieved through a combination of fuzzy c-means clustering and maximum likelihood mapping. The estimated channel matrix and noise power enable the blind classification of modulations using a maximum likelihood classifier. Digital modulations are tested in simulation to validate the proposed classifier. The classifier is able to achieve excellent performance when SNR level is above 5 dB.

Robustness enhancement of distribution based binary discriminative features for modulation classification

In this paper, we propose distribution based binary discriminative features and a novel feature enhancement process for automatic modulation classification. The new features exploit the signal distribution mismatch between two modulations. Signal distributions on I-Q segments, amplitude and phase, are considered to produce a comprehensive feature set for improved robustness. Logistic regression is used to reduce feature dimension and enhance classification robustness. To accomplish multi-class classification, a class oriented feature space is created for the K-nearest neighbours classifier. The test results show that the proposed method is able to achieve excellent performance in simulated environments.