@phdthesis{,
    author = {Lang, Andreas},
    title = {Blind Estimation of Channel Parameters in CPM Bursts},
    editor = {},
    booktitle = {},
    series = {},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2023},
    isbn = {},
    volume = {},
    number =  {},
    pages = {},
    url = {},
    doi = {},
    keywords = {Estimation; Channel Parameters; CPM},
    abstract = {Short burst transmission is of practical relevance in e.g. low power sensor or tactical networks that deploy frequency hopping. The channel conditions can be seen as mutually uncorrelated for each burst due to their spectral and or temporal separation. Because of this time variant nature, a recurring acquisition of the impairment parameters is required for each burst. This thesis proposes a blind joint estimation of several channel parameters in a flat fading environment for continuous phase modulation bursts that is realized by the expectation maximization algorithm. The main contributions are first the determining of the true estimation performance bounds of the considered transmission case, second the formulation of the expectation and maximization steps to enable the joint computation of the maximum likelihood parameter estimates and third the analysis of the parameters' scalar likelihood functions to obtain an optimized initialization grid for the algorithm. It is shown, that the joint estimator produces unbiased estimates in the relevant receive power regions and its performance in terms of the mean squared estimation error achieves the theoretical limits and slightly outperforms a state of the art pilot based estimator. Furthermore, the effective throughput is discussed and bit and frame error rates are compared to each other and to the perfectly synchronized receiver. The proposed method provides a superior performance in these metrics because of the inherently higher spectral efficiency than the pilot based contender. Its computational complexity is quantitatively analyzed and efficient computation steps and further approaches to decrease it are outlined.},
    note = {},
    
    school = {Universität der Bundeswehr München},
    
}