Signal identification for emerging intelligent radios: classical problems and new challenges

Signal identification, which initially found applications in electronic warfare and spectrum monitoring and surveillance, has been recently considered for commercial communications in the context of software defined and cognitive radios. In this article, I present a snapshot of the status of signal identification algorithms, starting from a general description of maximum likelihood (ML) and feature based (FB) approaches to a more detailed discussion of a practical methodology using cyclostationarity-based features. I discuss the cyclostationarity-based features of various signals and the criteria of decision for their identification, while considering classical problems of identifying single carrier linearly digitally (SCLD) modulated signals, as well as new challenges posed by the identification of orthogonal frequency division multiplexing (OFDM), SC frequency domain equalization (SC-FDE), and multiple-transmit antenna signals. I conclude the article with remarks on practical solutions to signal identification and open research issues.