MSc TC Thesis Presentation

Blind Signal Identification

Dennis van der Geest

The capability to efficiently find signals of interest in a very dense electromagnetic spectrum is becoming increasingly important with the continuous increase in spectrum usage. In this research project, methods are developed to identify communication signals by estimating signal features (symbol rate, modulation scheme, etc.) in the absence of a-priori knowledge, i.e. blind. By modelling the received communication signal both as a stationary and a cyclostationary process, various feature estimation methods are evaluated based on their computational complexity, their estimation accuracy and their robustness in the presence of signal contamination, such as frequency offsets. By efficiently combining various estimation methods, a signal classification algorithm is derived which is aimed to provide an optimal tradeoff between computational complexity and classification performance.

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