LAUSR

The problem of signal detection and modulation recognition is addressed in both the blind and nonblind contexts. Many relevant modulation recognition algorithms have been created over the past three decades. The essential engineering tradeoff that fuels the algorithm-invention process is between generality and optimality. Optimal (e.g., maximum-likelihood) algorithms can be devised for narrow subsets of signal types, whereas highly general but suboptimal feature-based algorithms can be devised for wide subsets of types. It has previously been shown that cyclostationary-based classifiers possess highly desirable properties, such as tolerance to noise and cochannel interference, but also that they are computational costly and so are resistant to real-time hardware implementation. In this paper, we present a reduced-complexity method of signal detection and modulation recognition that exploits the cyclostationarity of over-the-air observed communication signals. The key idea is to represent wideband input data as a set of narrowband, contiguous, nonoverlapping subchannels, and then to extract the desired cyclostationary features for the wideband data using only sparse subsets of the subchannels.