Article thumbnail
Location of Repository

Data Format Classification for Autonomous Software Defined Radios

By Dariush Divsalar and Marvin Simon


We present maximum-likelihood (ML) coherent and noncoherent classifiers for discriminating between NRZ and Manchester coded (biphase-L) data formats for binary phase-shift-keying (BPSK) modulation. Such classification of the data format is an essential element of so-called autonomous software defined radio (SDR) receivers (similar to so-called cognitive SDR receivers in the military application) where it is desired that the receiver perform each of its functions by extracting the appropriate knowledge from the received signal and, if possible, with as little information of the other signal parameters as possible. Small and large SNR approximations to the ML classifiers are also proposed that lead to simpler implementation with comparable performance in their respective SNR regions. Numerical performance results obtained by a combination of computer simulation and, wherever possible, theoretical analyses, are presented and comparisons are made among the various configurations based on the probability of misclassification as a performance criterion. Extensions to other modulations such as QPSK are readily accomplished using the same methods described in the paper

Topics: Computer Programming and Software
Year: 2005
OAI identifier:
Sorry, our data provider has not provided any external links therefore we are unable to provide a link to the full text.

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.