  Previous approaches for blind identification of space-frequency block codes
(SFBC) do not perform well for short observation periods due to their
inefficient utilization of frequency-domain redundancy. This paper proposes a
hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based
algorithm for SFBC signals identification over frequency-selective fading
channels to exploit two-dimensional space-frequency domain redundancy. Based on
the central limit theorem, space-domain redundancy is exploited to construct
the cross-correlation function of the estimator and frequency-domain redundancy
is incorporated in the construction of the statistics. The difference between
the two proposed algorithms is that the HT-based algorithm constructs a
chi-square statistic and employs an HT to make the decision, while the
SVM-based algorithm constructs a non-central chi-square statistic with unknown
mean as a strongly-distinguishable statistical feature and uses an SVM to make
the decision. Both algorithms do not require knowledge of the channel
coefficients, modulation type or noise power, and the SVM-based algorithm does
not require timing synchronization. Simulation results verify the superior
performance of the proposed algorithms for short observation periods with
comparable computational complexity to conventional algorithms, as well as
their acceptable identification performance in the presence of transmission
impairments.
