Automatic classification of digital communication signal modulations

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Zhechen_Zhu:thesis,
  author =       "Zhechen Zhu",
  title =        "Automatic classification of digital communication
                 signal modulations",
  school =       "Dept. of Electronic and Computer Engineering, Brunel
                 University",
  year =         "2014",
  address =      "UK",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Modulation
                 classification, Channel estimation, Machine learning,
                 Signal processing, Wireless communications",
  URL =          "http://bura.brunel.ac.uk/handle/2438/9246",
  URL =          "http://bura.brunel.ac.uk/bitstream/2438/9246/1/FulltextThesis.pdf",
  size =         "175 pages",
  abstract =     "Automatic modulation classification detects the
                 modulation type of received communication signals. It
                 has important applications in military scenarios to
                 facilitate jamming, intelligence, surveillance, and
                 threat analysis. The renewed interest from civilian
                 scenes has been fuelled by the development of
                 intelligent communications systems such as cognitive
                 radio and software defined radio. More specifically, it
                 is complementary to adaptive modulation and coding
                 where a modulation can be deployed from a set of
                 candidates according to the channel condition and
                 system specification for improved spectrum efficiency
                 and link reliability. In this research, we started by
                 improving some existing methods for higher
                 classification accuracy but lower complexity. Machine
                 learning techniques such as k-nearest neighbour and
                 support vector machine have been adopted for simplified
                 decision making using known features. Logistic
                 regression, genetic algorithm and genetic programming
                 have been incorporated for improved classification
                 performance through feature selection and combination.
                 We have also developed a new distribution test based
                 classifier which is tailored for modulation
                 classification with the inspiration from
                 Kolmogorov-Smirnov test. The proposed classifier is
                 shown to have improved accuracy and robustness over the
                 standard distribution test. For blind classification in
                 imperfect channels, we developed the combination of
                 minimum distance centroid estimator and non-parametric
                 likelihood function for blind modulation classification
                 without the prior knowledge on channel noise. The
                 centroid estimator provides joint estimation of channel
                 gain and carrier phase o set where both can be
                 compensated in the following nonparametric likelihood
                 function. The non-parametric likelihood function, in
                 the meantime, provide likelihood evaluation without a
                 specifically assumed noise model. The combination has
                 shown to have higher robustness when different noise
                 types are considered. To push modulation classification
                 techniques into a more timely setting, we also
                 developed the principle for blind classification in
                 MIMO systems. The classification is achieved through
                 expectation maximization channel estimation and
                 likelihood based classification. Early results have
                 shown bright prospect for the method while more work is
                 needed to further optimize the method and to provide a
                 more thorough validation.",
  notes =        "GP-KNN classifier. AWGN channels.

                 Supervisors: A. K. Nandi and H. Meng and W. Al-Nauimy",
}

Genetic Programming entries for Zhechen Zhu

Citations